diff --git a/.gitattributes b/.gitattributes
new file mode 100644
index 0000000..69d52a8
--- /dev/null
+++ b/.gitattributes
@@ -0,0 +1,10 @@
+*.blend filter=lfs diff=lfs merge=lfs -text
+*.fbx filter=lfs diff=lfs merge=lfs -text
+*.glb filter=lfs diff=lfs merge=lfs -text
+*.flp filter=lfs diff=lfs merge=lfs -text
+*.wav filter=lfs diff=lfs merge=lfs -text
+*.kra filter=lfs diff=lfs merge=lfs -text
+*.uasset filter=lfs diff=lfs merge=lfs -text
+*.png filter=lfs diff=lfs merge=lfs -text
+*.vcxproj filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
\ No newline at end of file
diff --git a/ai_fighter/.gitignore b/ai_fighter/.gitignore
new file mode 100644
index 0000000..6582eaf
--- /dev/null
+++ b/ai_fighter/.gitignore
@@ -0,0 +1,74 @@
+# Visual Studio 2015 user specific files
+.vs/
+
+# Compiled Object files
+*.slo
+*.lo
+*.o
+*.obj
+
+# Precompiled Headers
+*.gch
+*.pch
+
+# Compiled Dynamic libraries
+*.so
+*.dylib
+*.dll
+
+# Fortran module files
+*.mod
+
+# Compiled Static libraries
+*.lai
+*.la
+*.a
+*.lib
+
+# Executables
+*.exe
+*.out
+*.app
+*.ipa
+
+# These project files can be generated by the engine
+*.xcodeproj
+*.xcworkspace
+*.sln
+*.suo
+*.opensdf
+*.sdf
+*.VC.db
+*.VC.opendb
+
+# Precompiled Assets
+SourceArt/**/*.png
+SourceArt/**/*.tga
+
+# Binary Files
+Binaries/*
+Plugins/*/Binaries/*
+
+# Builds
+Build/*
+
+# Whitelist PakBlacklist-<BuildConfiguration>.txt files
+!Build/*/
+Build/*/**
+!Build/*/PakBlacklist*.txt
+
+# Don't ignore icon files in Build
+!Build/**/*.ico
+
+# Built data for maps
+*_BuiltData.uasset
+
+# Configuration files generated by the Editor
+Saved/*
+
+# Compiled source files for the engine to use
+Intermediate/*
+Plugins/*/Intermediate/*
+
+# Cache files for the editor to use
+DerivedDataCache/*
diff --git a/ai_fighter/AI_Fight_Sim/.gitignore b/ai_fighter/AI_Fight_Sim/.gitignore
new file mode 100644
index 0000000..6582eaf
--- /dev/null
+++ b/ai_fighter/AI_Fight_Sim/.gitignore
@@ -0,0 +1,74 @@
+# Visual Studio 2015 user specific files
+.vs/
+
+# Compiled Object files
+*.slo
+*.lo
+*.o
+*.obj
+
+# Precompiled Headers
+*.gch
+*.pch
+
+# Compiled Dynamic libraries
+*.so
+*.dylib
+*.dll
+
+# Fortran module files
+*.mod
+
+# Compiled Static libraries
+*.lai
+*.la
+*.a
+*.lib
+
+# Executables
+*.exe
+*.out
+*.app
+*.ipa
+
+# These project files can be generated by the engine
+*.xcodeproj
+*.xcworkspace
+*.sln
+*.suo
+*.opensdf
+*.sdf
+*.VC.db
+*.VC.opendb
+
+# Precompiled Assets
+SourceArt/**/*.png
+SourceArt/**/*.tga
+
+# Binary Files
+Binaries/*
+Plugins/*/Binaries/*
+
+# Builds
+Build/*
+
+# Whitelist PakBlacklist-<BuildConfiguration>.txt files
+!Build/*/
+Build/*/**
+!Build/*/PakBlacklist*.txt
+
+# Don't ignore icon files in Build
+!Build/**/*.ico
+
+# Built data for maps
+*_BuiltData.uasset
+
+# Configuration files generated by the Editor
+Saved/*
+
+# Compiled source files for the engine to use
+Intermediate/*
+Plugins/*/Intermediate/*
+
+# Cache files for the editor to use
+DerivedDataCache/*
diff --git a/ai_fighter/AI_Fight_Sim/.vsconfig b/ai_fighter/AI_Fight_Sim/.vsconfig
new file mode 100644
index 0000000..0b1fc2f
--- /dev/null
+++ b/ai_fighter/AI_Fight_Sim/.vsconfig
@@ -0,0 +1,14 @@
+{
+  "version": "1.0",
+  "components": [
+    "Microsoft.Net.Component.4.6.2.TargetingPack",
+    "Microsoft.VisualStudio.Component.VC.14.34.17.4.x86.x64",
+    "Microsoft.VisualStudio.Component.VC.Tools.x86.x64",
+    "Microsoft.VisualStudio.Component.Windows10SDK",
+    "Microsoft.VisualStudio.Workload.CoreEditor",
+    "Microsoft.VisualStudio.Workload.ManagedDesktop",
+    "Microsoft.VisualStudio.Workload.NativeCrossPlat",
+    "Microsoft.VisualStudio.Workload.NativeDesktop",
+    "Microsoft.VisualStudio.Workload.NativeGame"
+  ]
+}
diff --git a/ai_fighter/AI_Fight_Sim/AI_Fight_Sim.uproject b/ai_fighter/AI_Fight_Sim/AI_Fight_Sim.uproject
new file mode 100644
index 0000000..c9a63be
--- /dev/null
+++ b/ai_fighter/AI_Fight_Sim/AI_Fight_Sim.uproject
@@ -0,0 +1,33 @@
+{
+	"FileVersion": 3,
+	"EngineAssociation": "5.2",
+	"Category": "",
+	"Description": "",
+	"Modules": [
+		{
+			"Name": "AI_Fight_Sim",
+			"Type": "Runtime",
+			"LoadingPhase": "Default",
+			"AdditionalDependencies": [
+				"Engine"
+			]
+		}
+	],
+	"Plugins": [
+		{
+			"Name": "ModelingToolsEditorMode",
+			"Enabled": true,
+			"TargetAllowList": [
+				"Editor"
+			]
+		},
+		{
+			"Name": "VisualStudioTools",
+			"Enabled": true,
+			"MarketplaceURL": "com.epicgames.launcher://ue/marketplace/product/362651520df94e4fa65492dbcba44ae2",
+			"SupportedTargetPlatforms": [
+				"Win64"
+			]
+		}
+	]
+}
\ No newline at end of file
diff --git a/ai_fighter/AI_Fight_Sim/Config/DefaultEditor.ini b/ai_fighter/AI_Fight_Sim/Config/DefaultEditor.ini
new file mode 100644
index 0000000..42106a6
--- /dev/null
+++ b/ai_fighter/AI_Fight_Sim/Config/DefaultEditor.ini
@@ -0,0 +1,11 @@
+[UnrealEd.SimpleMap]
+SimpleMapName=/Game/TP_ThirdPerson/Maps/ThirdPersonExampleMap
+
+[EditoronlyBP]
+bAllowClassAndBlueprintPinMatching=true
+bReplaceBlueprintWithClass= true
+bDontLoadBlueprintOutsideEditor= true
+bBlueprintIsNotBlueprintType= true
+
+[/Script/AdvancedPreviewScene.SharedProfiles]
+
diff --git a/ai_fighter/AI_Fight_Sim/Config/DefaultEditorPerProjectUserSettings.ini b/ai_fighter/AI_Fight_Sim/Config/DefaultEditorPerProjectUserSettings.ini
new file mode 100644
index 0000000..d3d6cc0
--- /dev/null
+++ b/ai_fighter/AI_Fight_Sim/Config/DefaultEditorPerProjectUserSettings.ini
@@ -0,0 +1,2 @@
+[ContentBrowser]
+ContentBrowserTab1.SelectedPaths=/Game/ThirdPersonBP
\ No newline at end of file
diff --git a/ai_fighter/AI_Fight_Sim/Config/DefaultEngine.ini b/ai_fighter/AI_Fight_Sim/Config/DefaultEngine.ini
new file mode 100644
index 0000000..440c4c2
--- /dev/null
+++ b/ai_fighter/AI_Fight_Sim/Config/DefaultEngine.ini
@@ -0,0 +1,298 @@
+[URL]
+GameName=AI_Fight_Sim
+
+[/Script/EngineSettings.GameMapsSettings]
+EditorStartupMap=/Game/ThirdPerson/Maps/Main.Main
+GameDefaultMap=/Game/ThirdPerson/Maps/Main.Main
+TransitionMap=
+bUseSplitscreen=True
+TwoPlayerSplitscreenLayout=Horizontal
+ThreePlayerSplitscreenLayout=FavorTop
+GlobalDefaultGameMode=/Game/ThirdPerson/Blueprints/BP_ThirdPersonGameMode.BP_ThirdPersonGameMode_C
+GlobalDefaultServerGameMode=None
+GameInstanceClass=/Script/AI_Fight_Sim.NeuralNetworkCommandLineParsing
+
+[/Script/Engine.RendererSettings]
+r.Mobile.ShadingPath=0
+r.Mobile.SupportGPUScene=False
+r.Mobile.AntiAliasing=1
+r.Mobile.FloatPrecisionMode=0
+r.Mobile.AllowDitheredLODTransition=False
+r.Mobile.VirtualTextures=False
+r.DiscardUnusedQuality=False
+r.Shaders.CompressionFormat=2
+r.AllowOcclusionQueries=True
+r.MinScreenRadiusForLights=0.030000
+r.MinScreenRadiusForDepthPrepass=0.030000
+r.MinScreenRadiusForCSMDepth=0.010000
+r.PrecomputedVisibilityWarning=False
+r.TextureStreaming=True
+Compat.UseDXT5NormalMaps=False
+r.VirtualTextures=False
+r.VT.EnableAutoImport=True
+r.VirtualTexturedLightmaps=False
+r.VT.AnisotropicFiltering=False
+bEnableVirtualTextureOpacityMask=False
+r.VT.TileSize=128
+r.VT.TileBorderSize=4
+r.vt.FeedbackFactor=16
+WorkingColorSpaceChoice=sRGB
+RedChromaticityCoordinate=(X=0.640000,Y=0.330000)
+GreenChromaticityCoordinate=(X=0.300000,Y=0.600000)
+BlueChromaticityCoordinate=(X=0.150000,Y=0.060000)
+WhiteChromaticityCoordinate=(X=0.312700,Y=0.329000)
+r.ClearCoatNormal=False
+r.DynamicGlobalIlluminationMethod=1
+r.ReflectionMethod=1
+r.ReflectionCaptureResolution=128
+r.ReflectionEnvironmentLightmapMixBasedOnRoughness=True
+r.Lumen.HardwareRayTracing=False
+r.Lumen.HardwareRayTracing.LightingMode=0
+r.Lumen.TranslucencyReflections.FrontLayer.EnableForProject=False
+r.Lumen.TraceMeshSDFs=0
+r.Shadow.Virtual.Enable=0
+r.RayTracing=False
+r.RayTracing.Shadows=False
+r.RayTracing.Skylight=False
+r.RayTracing.UseTextureLod=False
+r.PathTracing=True
+r.GenerateMeshDistanceFields=True
+r.DistanceFields.DefaultVoxelDensity=0.200000
+r.Nanite.ProjectEnabled=True
+r.AllowStaticLighting=True
+r.NormalMapsForStaticLighting=False
+r.ForwardShading=False
+r.VertexFoggingForOpaque=True
+r.SeparateTranslucency=True
+r.TranslucentSortPolicy=0
+TranslucentSortAxis=(X=0.000000,Y=-1.000000,Z=0.000000)
+vr.VRS.HMDFixedFoveationLevel=0
+vr.VRS.HMDFixedFoveationDynamic=False
+r.CustomDepth=1
+r.CustomDepthTemporalAAJitter=True
+r.PostProcessing.PropagateAlpha=0
+r.DefaultFeature.Bloom=True
+r.DefaultFeature.AmbientOcclusion=True
+r.DefaultFeature.AmbientOcclusionStaticFraction=True
+r.DefaultFeature.AutoExposure=False
+r.DefaultFeature.AutoExposure.Method=0
+r.DefaultFeature.AutoExposure.Bias=1.000000
+r.DefaultFeature.AutoExposure.ExtendDefaultLuminanceRange=True
+r.DefaultFeature.MotionBlur=False
+r.DefaultFeature.LensFlare=False
+r.TemporalAA.Upsampling=True
+r.AntiAliasingMethod=0
+r.MSAACount=4
+r.DefaultFeature.LightUnits=1
+r.DefaultBackBufferPixelFormat=4
+r.Shadow.UnbuiltPreviewInGame=True
+r.StencilForLODDither=False
+r.EarlyZPass=3
+r.EarlyZPassOnlyMaterialMasking=False
+r.Shadow.CSMCaching=False
+r.DBuffer=True
+r.ClearSceneMethod=1
+r.VelocityOutputPass=1
+r.Velocity.EnableVertexDeformation=2
+r.SelectiveBasePassOutputs=False
+bDefaultParticleCutouts=False
+fx.GPUSimulationTextureSizeX=1024
+fx.GPUSimulationTextureSizeY=1024
+r.AllowGlobalClipPlane=False
+r.GBufferFormat=1
+r.MorphTarget.Mode=True
+r.GPUCrashDebugging=False
+vr.InstancedStereo=False
+r.MobileHDR=True
+vr.MobileMultiView=False
+r.Mobile.UseHWsRGBEncoding=False
+vr.RoundRobinOcclusion=False
+r.MeshStreaming=False
+r.HeterogeneousVolumes=True
+r.WireframeCullThreshold=5.000000
+r.SupportStationarySkylight=True
+r.SupportLowQualityLightmaps=True
+r.SupportPointLightWholeSceneShadows=True
+r.SupportSkyAtmosphere=True
+r.SupportSkyAtmosphereAffectsHeightFog=True
+r.SupportCloudShadowOnForwardLitTranslucent=False
+r.Shadow.TranslucentPerObject.ProjectEnabled=False
+r.Water.SingleLayerWater.SupportCloudShadow=False
+r.Substrate=False
+r.Substrate.OpaqueMaterialRoughRefraction=False
+r.Substrate.Debug.AdvancedVisualizationShaders=False
+r.Material.RoughDiffuse=False
+r.Material.EnergyConservation=False
+r.OIT.SortedPixels=False
+r.SkinCache.CompileShaders=False
+r.SkinCache.SkipCompilingGPUSkinVF=False
+r.SkinCache.DefaultBehavior=1
+r.SkinCache.SceneMemoryLimitInMB=128.000000
+r.Mobile.EnableStaticAndCSMShadowReceivers=True
+r.Mobile.EnableMovableLightCSMShaderCulling=True
+r.Mobile.Forward.EnableLocalLights=True
+r.Mobile.Forward.EnableClusteredReflections=False
+r.Mobile.EnableNoPrecomputedLightingCSMShader=True
+r.Mobile.AllowDistanceFieldShadows=True
+r.Mobile.AllowMovableDirectionalLights=True
+r.Mobile.EnableMovableSpotlightsShadow=False
+r.GPUSkin.Support16BitBoneIndex=False
+r.GPUSkin.Limit2BoneInfluences=False
+r.SupportDepthOnlyIndexBuffers=True
+r.SupportReversedIndexBuffers=True
+r.Mobile.AmbientOcclusion=False
+r.GPUSkin.UnlimitedBoneInfluences=False
+r.GPUSkin.UnlimitedBoneInfluencesThreshold=8
+DefaultBoneInfluenceLimit=(Default=0,PerPlatform=())
+MaxSkinBones=(Default=65536,PerPlatform=(("Mobile", 256)))
+r.Mobile.PlanarReflectionMode=0
+r.Mobile.SupportsGen4TAA=True
+bStreamSkeletalMeshLODs=(Default=False,PerPlatform=())
+bDiscardSkeletalMeshOptionalLODs=(Default=False,PerPlatform=())
+VisualizeCalibrationColorMaterialPath=/Engine/EngineMaterials/PPM_DefaultCalibrationColor.PPM_DefaultCalibrationColor
+VisualizeCalibrationCustomMaterialPath=None
+VisualizeCalibrationGrayscaleMaterialPath=/Engine/EngineMaterials/PPM_DefaultCalibrationGrayscale.PPM_DefaultCalibrationGrayscale
+
+[/Script/WindowsTargetPlatform.WindowsTargetSettings]
+DefaultGraphicsRHI=DefaultGraphicsRHI_DX12
+-D3D12TargetedShaderFormats=PCD3D_SM5
++D3D12TargetedShaderFormats=PCD3D_SM6
+-D3D11TargetedShaderFormats=PCD3D_SM5
++D3D11TargetedShaderFormats=PCD3D_SM5
+Compiler=Default
+AudioSampleRate=48000
+AudioCallbackBufferFrameSize=1024
+AudioNumBuffersToEnqueue=1
+AudioMaxChannels=0
+AudioNumSourceWorkers=4
+SpatializationPlugin=
+SourceDataOverridePlugin=
+ReverbPlugin=
+OcclusionPlugin=
+CompressionOverrides=(bOverrideCompressionTimes=False,DurationThreshold=5.000000,MaxNumRandomBranches=0,SoundCueQualityIndex=0)
+CacheSizeKB=65536
+MaxChunkSizeOverrideKB=0
+bResampleForDevice=False
+MaxSampleRate=48000.000000
+HighSampleRate=32000.000000
+MedSampleRate=24000.000000
+LowSampleRate=12000.000000
+MinSampleRate=8000.000000
+CompressionQualityModifier=1.000000
+AutoStreamingThreshold=0.000000
+SoundCueCookQualityIndex=-1
+
+[/Script/HardwareTargeting.HardwareTargetingSettings]
+TargetedHardwareClass=Desktop
+AppliedTargetedHardwareClass=Desktop
+DefaultGraphicsPerformance=Scalable
+AppliedDefaultGraphicsPerformance=Scalable
+
+[/Script/Engine.Engine]
++ActiveGameNameRedirects=(OldGameName="TP_ThirdPersonBP",NewGameName="/Script/AI_Fight_Sim")
++ActiveGameNameRedirects=(OldGameName="/Script/TP_ThirdPersonBP",NewGameName="/Script/AI_Fight_Sim")
+bUseFixedFrameRate=True
+
+[/Script/AndroidFileServerEditor.AndroidFileServerRuntimeSettings]
+bEnablePlugin=True
+bAllowNetworkConnection=True
+SecurityToken=E929BDA04CF25111B6C69A96B207217C
+bIncludeInShipping=False
+bAllowExternalStartInShipping=False
+bCompileAFSProject=False
+bUseCompression=False
+bLogFiles=False
+bReportStats=False
+ConnectionType=USBOnly
+bUseManualIPAddress=False
+ManualIPAddress=
+
+[/Script/Engine.CollisionProfile]
+-Profiles=(Name="NoCollision",CollisionEnabled=NoCollision,ObjectTypeName="WorldStatic",CustomResponses=((Channel="Visibility",Response=ECR_Ignore),(Channel="Camera",Response=ECR_Ignore)),HelpMessage="No collision",bCanModify=False)
+-Profiles=(Name="BlockAll",CollisionEnabled=QueryAndPhysics,ObjectTypeName="WorldStatic",CustomResponses=,HelpMessage="WorldStatic object that blocks all actors by default. All new custom channels will use its own default response. ",bCanModify=False)
+-Profiles=(Name="OverlapAll",CollisionEnabled=QueryOnly,ObjectTypeName="WorldStatic",CustomResponses=((Channel="WorldStatic",Response=ECR_Overlap),(Channel="Pawn",Response=ECR_Overlap),(Channel="Visibility",Response=ECR_Overlap),(Channel="WorldDynamic",Response=ECR_Overlap),(Channel="Camera",Response=ECR_Overlap),(Channel="PhysicsBody",Response=ECR_Overlap),(Channel="Vehicle",Response=ECR_Overlap),(Channel="Destructible",Response=ECR_Overlap)),HelpMessage="WorldStatic object that overlaps all actors by default. All new custom channels will use its own default response. ",bCanModify=False)
+-Profiles=(Name="BlockAllDynamic",CollisionEnabled=QueryAndPhysics,ObjectTypeName="WorldDynamic",CustomResponses=,HelpMessage="WorldDynamic object that blocks all actors by default. All new custom channels will use its own default response. ",bCanModify=False)
+-Profiles=(Name="OverlapAllDynamic",CollisionEnabled=QueryOnly,ObjectTypeName="WorldDynamic",CustomResponses=((Channel="WorldStatic",Response=ECR_Overlap),(Channel="Pawn",Response=ECR_Overlap),(Channel="Visibility",Response=ECR_Overlap),(Channel="WorldDynamic",Response=ECR_Overlap),(Channel="Camera",Response=ECR_Overlap),(Channel="PhysicsBody",Response=ECR_Overlap),(Channel="Vehicle",Response=ECR_Overlap),(Channel="Destructible",Response=ECR_Overlap)),HelpMessage="WorldDynamic object that overlaps all actors by default. All new custom channels will use its own default response. ",bCanModify=False)
+-Profiles=(Name="IgnoreOnlyPawn",CollisionEnabled=QueryOnly,ObjectTypeName="WorldDynamic",CustomResponses=((Channel="Pawn",Response=ECR_Ignore),(Channel="Vehicle",Response=ECR_Ignore)),HelpMessage="WorldDynamic object that ignores Pawn and Vehicle. All other channels will be set to default.",bCanModify=False)
+-Profiles=(Name="OverlapOnlyPawn",CollisionEnabled=QueryOnly,ObjectTypeName="WorldDynamic",CustomResponses=((Channel="Pawn",Response=ECR_Overlap),(Channel="Vehicle",Response=ECR_Overlap),(Channel="Camera",Response=ECR_Ignore)),HelpMessage="WorldDynamic object that overlaps Pawn, Camera, and Vehicle. All other channels will be set to default. ",bCanModify=False)
+-Profiles=(Name="Pawn",CollisionEnabled=QueryAndPhysics,ObjectTypeName="Pawn",CustomResponses=((Channel="Visibility",Response=ECR_Ignore)),HelpMessage="Pawn object. Can be used for capsule of any playerable character or AI. ",bCanModify=False)
+-Profiles=(Name="Spectator",CollisionEnabled=QueryOnly,ObjectTypeName="Pawn",CustomResponses=((Channel="WorldStatic",Response=ECR_Block),(Channel="Pawn",Response=ECR_Ignore),(Channel="Visibility",Response=ECR_Ignore),(Channel="WorldDynamic",Response=ECR_Ignore),(Channel="Camera",Response=ECR_Ignore),(Channel="PhysicsBody",Response=ECR_Ignore),(Channel="Vehicle",Response=ECR_Ignore),(Channel="Destructible",Response=ECR_Ignore)),HelpMessage="Pawn object that ignores all other actors except WorldStatic.",bCanModify=False)
+-Profiles=(Name="CharacterMesh",CollisionEnabled=QueryOnly,ObjectTypeName="Pawn",CustomResponses=((Channel="Pawn",Response=ECR_Ignore),(Channel="Vehicle",Response=ECR_Ignore),(Channel="Visibility",Response=ECR_Ignore)),HelpMessage="Pawn object that is used for Character Mesh. All other channels will be set to default.",bCanModify=False)
+-Profiles=(Name="PhysicsActor",CollisionEnabled=QueryAndPhysics,ObjectTypeName="PhysicsBody",CustomResponses=,HelpMessage="Simulating actors",bCanModify=False)
+-Profiles=(Name="Destructible",CollisionEnabled=QueryAndPhysics,ObjectTypeName="Destructible",CustomResponses=,HelpMessage="Destructible actors",bCanModify=False)
+-Profiles=(Name="InvisibleWall",CollisionEnabled=QueryAndPhysics,ObjectTypeName="WorldStatic",CustomResponses=((Channel="Visibility",Response=ECR_Ignore)),HelpMessage="WorldStatic object that is invisible.",bCanModify=False)
+-Profiles=(Name="InvisibleWallDynamic",CollisionEnabled=QueryAndPhysics,ObjectTypeName="WorldDynamic",CustomResponses=((Channel="Visibility",Response=ECR_Ignore)),HelpMessage="WorldDynamic object that is invisible.",bCanModify=False)
+-Profiles=(Name="Trigger",CollisionEnabled=QueryOnly,ObjectTypeName="WorldDynamic",CustomResponses=((Channel="WorldStatic",Response=ECR_Overlap),(Channel="Pawn",Response=ECR_Overlap),(Channel="Visibility",Response=ECR_Ignore),(Channel="WorldDynamic",Response=ECR_Overlap),(Channel="Camera",Response=ECR_Overlap),(Channel="PhysicsBody",Response=ECR_Overlap),(Channel="Vehicle",Response=ECR_Overlap),(Channel="Destructible",Response=ECR_Overlap)),HelpMessage="WorldDynamic object that is used for trigger. All other channels will be set to default.",bCanModify=False)
+-Profiles=(Name="Ragdoll",CollisionEnabled=QueryAndPhysics,ObjectTypeName="PhysicsBody",CustomResponses=((Channel="Pawn",Response=ECR_Ignore),(Channel="Visibility",Response=ECR_Ignore)),HelpMessage="Simulating Skeletal Mesh Component. All other channels will be set to default.",bCanModify=False)
+-Profiles=(Name="Vehicle",CollisionEnabled=QueryAndPhysics,ObjectTypeName="Vehicle",CustomResponses=,HelpMessage="Vehicle object that blocks Vehicle, WorldStatic, and WorldDynamic. All other channels will be set to default.",bCanModify=False)
+-Profiles=(Name="UI",CollisionEnabled=QueryOnly,ObjectTypeName="WorldDynamic",CustomResponses=((Channel="WorldStatic",Response=ECR_Overlap),(Channel="Pawn",Response=ECR_Overlap),(Channel="Visibility",Response=ECR_Block),(Channel="WorldDynamic",Response=ECR_Overlap),(Channel="Camera",Response=ECR_Overlap),(Channel="PhysicsBody",Response=ECR_Overlap),(Channel="Vehicle",Response=ECR_Overlap),(Channel="Destructible",Response=ECR_Overlap)),HelpMessage="WorldStatic object that overlaps all actors by default. All new custom channels will use its own default response. ",bCanModify=False)
++Profiles=(Name="NoCollision",CollisionEnabled=NoCollision,bCanModify=False,ObjectTypeName="WorldStatic",CustomResponses=((Channel="Visibility",Response=ECR_Ignore),(Channel="Camera",Response=ECR_Ignore)),HelpMessage="No collision")
++Profiles=(Name="BlockAll",CollisionEnabled=QueryAndPhysics,bCanModify=False,ObjectTypeName="WorldStatic",CustomResponses=,HelpMessage="WorldStatic object that blocks all actors by default. All new custom channels will use its own default response. ")
++Profiles=(Name="OverlapAll",CollisionEnabled=QueryOnly,bCanModify=False,ObjectTypeName="WorldStatic",CustomResponses=((Channel="WorldStatic",Response=ECR_Overlap),(Channel="Pawn",Response=ECR_Overlap),(Channel="Visibility",Response=ECR_Overlap),(Channel="WorldDynamic",Response=ECR_Overlap),(Channel="Camera",Response=ECR_Overlap),(Channel="PhysicsBody",Response=ECR_Overlap),(Channel="Vehicle",Response=ECR_Overlap),(Channel="Destructible",Response=ECR_Overlap)),HelpMessage="WorldStatic object that overlaps all actors by default. All new custom channels will use its own default response. ")
++Profiles=(Name="BlockAllDynamic",CollisionEnabled=QueryAndPhysics,bCanModify=False,ObjectTypeName="WorldDynamic",CustomResponses=,HelpMessage="WorldDynamic object that blocks all actors by default. All new custom channels will use its own default response. ")
++Profiles=(Name="OverlapAllDynamic",CollisionEnabled=QueryOnly,bCanModify=False,ObjectTypeName="WorldDynamic",CustomResponses=((Channel="WorldStatic",Response=ECR_Overlap),(Channel="Pawn",Response=ECR_Overlap),(Channel="Visibility",Response=ECR_Overlap),(Channel="WorldDynamic",Response=ECR_Overlap),(Channel="Camera",Response=ECR_Overlap),(Channel="PhysicsBody",Response=ECR_Overlap),(Channel="Vehicle",Response=ECR_Overlap),(Channel="Destructible",Response=ECR_Overlap)),HelpMessage="WorldDynamic object that overlaps all actors by default. All new custom channels will use its own default response. ")
++Profiles=(Name="IgnoreOnlyPawn",CollisionEnabled=QueryOnly,bCanModify=False,ObjectTypeName="WorldDynamic",CustomResponses=((Channel="Pawn",Response=ECR_Ignore),(Channel="Vehicle",Response=ECR_Ignore)),HelpMessage="WorldDynamic object that ignores Pawn and Vehicle. All other channels will be set to default.")
++Profiles=(Name="OverlapOnlyPawn",CollisionEnabled=QueryOnly,bCanModify=False,ObjectTypeName="WorldDynamic",CustomResponses=((Channel="Pawn",Response=ECR_Overlap),(Channel="Vehicle",Response=ECR_Overlap),(Channel="Camera",Response=ECR_Ignore)),HelpMessage="WorldDynamic object that overlaps Pawn, Camera, and Vehicle. All other channels will be set to default. ")
++Profiles=(Name="Pawn",CollisionEnabled=QueryAndPhysics,bCanModify=False,ObjectTypeName="Pawn",CustomResponses=((Channel="Visibility",Response=ECR_Ignore)),HelpMessage="Pawn object. Can be used for capsule of any playerable character or AI. ")
++Profiles=(Name="Spectator",CollisionEnabled=QueryOnly,bCanModify=False,ObjectTypeName="Pawn",CustomResponses=((Channel="WorldStatic"),(Channel="Pawn",Response=ECR_Ignore),(Channel="Visibility",Response=ECR_Ignore),(Channel="WorldDynamic",Response=ECR_Ignore),(Channel="Camera",Response=ECR_Ignore),(Channel="PhysicsBody",Response=ECR_Ignore),(Channel="Vehicle",Response=ECR_Ignore),(Channel="Destructible",Response=ECR_Ignore)),HelpMessage="Pawn object that ignores all other actors except WorldStatic.")
++Profiles=(Name="CharacterMesh",CollisionEnabled=QueryOnly,bCanModify=False,ObjectTypeName="Pawn",CustomResponses=((Channel="Pawn",Response=ECR_Ignore),(Channel="Vehicle",Response=ECR_Ignore),(Channel="Visibility",Response=ECR_Ignore)),HelpMessage="Pawn object that is used for Character Mesh. All other channels will be set to default.")
++Profiles=(Name="PhysicsActor",CollisionEnabled=QueryAndPhysics,bCanModify=False,ObjectTypeName="PhysicsBody",CustomResponses=,HelpMessage="Simulating actors")
++Profiles=(Name="Destructible",CollisionEnabled=QueryAndPhysics,bCanModify=False,ObjectTypeName="Destructible",CustomResponses=,HelpMessage="Destructible actors")
++Profiles=(Name="InvisibleWall",CollisionEnabled=QueryAndPhysics,bCanModify=False,ObjectTypeName="WorldStatic",CustomResponses=((Channel="Visibility",Response=ECR_Ignore)),HelpMessage="WorldStatic object that is invisible.")
++Profiles=(Name="InvisibleWallDynamic",CollisionEnabled=QueryAndPhysics,bCanModify=False,ObjectTypeName="WorldDynamic",CustomResponses=((Channel="Visibility",Response=ECR_Ignore)),HelpMessage="WorldDynamic object that is invisible.")
++Profiles=(Name="Trigger",CollisionEnabled=QueryOnly,bCanModify=False,ObjectTypeName="WorldDynamic",CustomResponses=((Channel="WorldStatic",Response=ECR_Overlap),(Channel="Pawn",Response=ECR_Overlap),(Channel="Visibility",Response=ECR_Ignore),(Channel="WorldDynamic",Response=ECR_Overlap),(Channel="Camera",Response=ECR_Overlap),(Channel="PhysicsBody",Response=ECR_Overlap),(Channel="Vehicle",Response=ECR_Overlap),(Channel="Destructible",Response=ECR_Overlap)),HelpMessage="WorldDynamic object that is used for trigger. All other channels will be set to default.")
++Profiles=(Name="Ragdoll",CollisionEnabled=QueryAndPhysics,bCanModify=False,ObjectTypeName="PhysicsBody",CustomResponses=((Channel="Pawn",Response=ECR_Ignore),(Channel="Visibility",Response=ECR_Ignore)),HelpMessage="Simulating Skeletal Mesh Component. All other channels will be set to default.")
++Profiles=(Name="Vehicle",CollisionEnabled=QueryAndPhysics,bCanModify=False,ObjectTypeName="Vehicle",CustomResponses=,HelpMessage="Vehicle object that blocks Vehicle, WorldStatic, and WorldDynamic. All other channels will be set to default.")
++Profiles=(Name="UI",CollisionEnabled=QueryOnly,bCanModify=False,ObjectTypeName="WorldDynamic",CustomResponses=((Channel="WorldStatic",Response=ECR_Overlap),(Channel="Pawn",Response=ECR_Overlap),(Channel="Visibility"),(Channel="WorldDynamic",Response=ECR_Overlap),(Channel="Camera",Response=ECR_Overlap),(Channel="PhysicsBody",Response=ECR_Overlap),(Channel="Vehicle",Response=ECR_Overlap),(Channel="Destructible",Response=ECR_Overlap)),HelpMessage="WorldStatic object that overlaps all actors by default. All new custom channels will use its own default response. ")
++Profiles=(Name="DamageDealer",CollisionEnabled=QueryOnly,bCanModify=True,ObjectTypeName="DamageDealer",CustomResponses=((Channel="WorldStatic",Response=ECR_Ignore),(Channel="WorldDynamic",Response=ECR_Ignore),(Channel="Pawn",Response=ECR_Ignore),(Channel="Visibility",Response=ECR_Ignore),(Channel="Camera",Response=ECR_Ignore),(Channel="PhysicsBody",Response=ECR_Ignore),(Channel="Vehicle",Response=ECR_Ignore),(Channel="Destructible",Response=ECR_Ignore),(Channel="DamageDealer",Response=ECR_Ignore)),HelpMessage="Needs description")
++Profiles=(Name="DamageReceiver",CollisionEnabled=QueryOnly,bCanModify=True,ObjectTypeName="DamageReceiver",CustomResponses=((Channel="WorldStatic",Response=ECR_Ignore),(Channel="WorldDynamic",Response=ECR_Ignore),(Channel="Pawn",Response=ECR_Ignore),(Channel="Visibility",Response=ECR_Ignore),(Channel="Camera",Response=ECR_Ignore),(Channel="PhysicsBody",Response=ECR_Ignore),(Channel="Vehicle",Response=ECR_Ignore),(Channel="Destructible",Response=ECR_Ignore),(Channel="DamageReceiver",Response=ECR_Ignore)),HelpMessage="Needs description")
++DefaultChannelResponses=(Channel=ECC_GameTraceChannel1,DefaultResponse=ECR_Overlap,bTraceType=False,bStaticObject=False,Name="DamageDealer")
++DefaultChannelResponses=(Channel=ECC_GameTraceChannel2,DefaultResponse=ECR_Overlap,bTraceType=False,bStaticObject=False,Name="DamageReceiver")
++EditProfiles=(Name="NoCollision",CustomResponses=((Channel="DamageDealer",Response=ECR_Ignore),(Channel="DamageReceiver",Response=ECR_Ignore)))
++EditProfiles=(Name="CharacterMesh",CustomResponses=((Channel="DamageDealer",Response=ECR_Ignore),(Channel="DamageReceiver",Response=ECR_Ignore),(Channel="Pawn"),(Channel="Camera",Response=ECR_Overlap)))
+-ProfileRedirects=(OldName="BlockingVolume",NewName="InvisibleWall")
+-ProfileRedirects=(OldName="InterpActor",NewName="IgnoreOnlyPawn")
+-ProfileRedirects=(OldName="StaticMeshComponent",NewName="BlockAllDynamic")
+-ProfileRedirects=(OldName="SkeletalMeshActor",NewName="PhysicsActor")
+-ProfileRedirects=(OldName="InvisibleActor",NewName="InvisibleWallDynamic")
++ProfileRedirects=(OldName="BlockingVolume",NewName="InvisibleWall")
++ProfileRedirects=(OldName="InterpActor",NewName="IgnoreOnlyPawn")
++ProfileRedirects=(OldName="StaticMeshComponent",NewName="BlockAllDynamic")
++ProfileRedirects=(OldName="SkeletalMeshActor",NewName="PhysicsActor")
++ProfileRedirects=(OldName="InvisibleActor",NewName="InvisibleWallDynamic")
+-CollisionChannelRedirects=(OldName="Static",NewName="WorldStatic")
+-CollisionChannelRedirects=(OldName="Dynamic",NewName="WorldDynamic")
+-CollisionChannelRedirects=(OldName="VehicleMovement",NewName="Vehicle")
+-CollisionChannelRedirects=(OldName="PawnMovement",NewName="Pawn")
++CollisionChannelRedirects=(OldName="Static",NewName="WorldStatic")
++CollisionChannelRedirects=(OldName="Dynamic",NewName="WorldDynamic")
++CollisionChannelRedirects=(OldName="VehicleMovement",NewName="Vehicle")
++CollisionChannelRedirects=(OldName="PawnMovement",NewName="Pawn")
+
+[Core.Log]
+LogTemp=Warning
+LogBlueprint=Warning
+LogActor=Warning
+LogAI=Warning
+LogBlueprintUserMessages=Warning
+LogRHI=Error
+LogPluginManager=Error
+LogCsvProfiler=Error
+LogPackageName=Error
+LogWindows=Error
+LogInit=Error
+LogOnline=Error
+LogDevObjectVersion=Error
+LogConfig=Error
+LogDeviceProfileManager=Error
+LogAudio=Error
+LogAudioMixer=Error
+LogSlate=Error
+LogPakFile=Error
+LogD3D12RHI=Error
+LogShaderLibrary=Error
+LogUObjectArray=Error
+LogWorld=Error
+LogUObjectHash=Error
+LogLoad=Error
+LogMemory=Error
\ No newline at end of file
diff --git a/ai_fighter/AI_Fight_Sim/Config/DefaultGame.ini b/ai_fighter/AI_Fight_Sim/Config/DefaultGame.ini
new file mode 100644
index 0000000..bae13af
--- /dev/null
+++ b/ai_fighter/AI_Fight_Sim/Config/DefaultGame.ini
@@ -0,0 +1,7 @@
+[/Script/EngineSettings.GeneralProjectSettings]
+ProjectID=63B401E74DB269637F1E79A85E18A4FA
+ProjectName=Third Person BP Game Template
+
+[StartupActions]
+bAddPacks=True
+InsertPack=(PackSource="StarterContent.upack",PackName="StarterContent")
diff --git a/ai_fighter/AI_Fight_Sim/Config/DefaultGameUserSettings.ini b/ai_fighter/AI_Fight_Sim/Config/DefaultGameUserSettings.ini
new file mode 100644
index 0000000..823e253
--- /dev/null
+++ b/ai_fighter/AI_Fight_Sim/Config/DefaultGameUserSettings.ini
@@ -0,0 +1,14 @@
+[/Script/Engine.GameUserSettings]
+bUseVSync=False
+ResolutionSizeX=480
+ResolutionSizeY=480
+LastUserConfirmedResolutionSizeX=480
+LastUserConfirmedResolutionSizeY=480
+WindowPosX=-1
+WindowPosY=-1
+bUseDesktopResolutionForFullscreen=False
+FullscreenMode=1
+LastConfirmedFullscreenMode=1
+PreferredFullscreenMode=1
+Version=5
+
diff --git a/ai_fighter/AI_Fight_Sim/Config/DefaultInput.ini b/ai_fighter/AI_Fight_Sim/Config/DefaultInput.ini
new file mode 100644
index 0000000..f57b41d
--- /dev/null
+++ b/ai_fighter/AI_Fight_Sim/Config/DefaultInput.ini
@@ -0,0 +1,88 @@
+
+
+[/Script/Engine.InputSettings]
+-AxisConfig=(AxisKeyName="Gamepad_LeftX",AxisProperties=(DeadZone=0.25,Exponent=1.f,Sensitivity=1.f))
+-AxisConfig=(AxisKeyName="Gamepad_LeftY",AxisProperties=(DeadZone=0.25,Exponent=1.f,Sensitivity=1.f))
+-AxisConfig=(AxisKeyName="Gamepad_RightX",AxisProperties=(DeadZone=0.25,Exponent=1.f,Sensitivity=1.f))
+-AxisConfig=(AxisKeyName="Gamepad_RightY",AxisProperties=(DeadZone=0.25,Exponent=1.f,Sensitivity=1.f))
+-AxisConfig=(AxisKeyName="MouseX",AxisProperties=(DeadZone=0.f,Exponent=1.f,Sensitivity=0.07f))
+-AxisConfig=(AxisKeyName="MouseY",AxisProperties=(DeadZone=0.f,Exponent=1.f,Sensitivity=0.07f))
+-AxisConfig=(AxisKeyName="Mouse2D",AxisProperties=(DeadZone=0.f,Exponent=1.f,Sensitivity=0.07f))
++AxisConfig=(AxisKeyName="MouseY",AxisProperties=(DeadZone=0.000000,Sensitivity=0.070000,Exponent=1.000000,bInvert=False))
++AxisConfig=(AxisKeyName="MouseWheelAxis",AxisProperties=(DeadZone=0.000000,Sensitivity=1.000000,Exponent=1.000000,bInvert=False))
++AxisConfig=(AxisKeyName="Gamepad_LeftTriggerAxis",AxisProperties=(DeadZone=0.000000,Sensitivity=1.000000,Exponent=1.000000,bInvert=False))
++AxisConfig=(AxisKeyName="Gamepad_RightTriggerAxis",AxisProperties=(DeadZone=0.000000,Sensitivity=1.000000,Exponent=1.000000,bInvert=False))
++AxisConfig=(AxisKeyName="Gamepad_Special_Left_X",AxisProperties=(DeadZone=0.000000,Sensitivity=1.000000,Exponent=1.000000,bInvert=False))
++AxisConfig=(AxisKeyName="Gamepad_Special_Left_Y",AxisProperties=(DeadZone=0.000000,Sensitivity=1.000000,Exponent=1.000000,bInvert=False))
++AxisConfig=(AxisKeyName="Mouse2D",AxisProperties=(DeadZone=0.000000,Sensitivity=0.070000,Exponent=1.000000,bInvert=False))
++AxisConfig=(AxisKeyName="Gamepad_LeftX",AxisProperties=(DeadZone=0.250000,Sensitivity=1.000000,Exponent=1.000000,bInvert=False))
++AxisConfig=(AxisKeyName="Gamepad_LeftY",AxisProperties=(DeadZone=0.250000,Sensitivity=1.000000,Exponent=1.000000,bInvert=False))
++AxisConfig=(AxisKeyName="Gamepad_RightX",AxisProperties=(DeadZone=0.250000,Sensitivity=1.000000,Exponent=1.000000,bInvert=False))
++AxisConfig=(AxisKeyName="Gamepad_RightY",AxisProperties=(DeadZone=0.250000,Sensitivity=1.000000,Exponent=1.000000,bInvert=False))
++AxisConfig=(AxisKeyName="MouseX",AxisProperties=(DeadZone=0.000000,Sensitivity=0.070000,Exponent=1.000000,bInvert=False))
++AxisConfig=(AxisKeyName="Vive_Left_Trigger_Axis",AxisProperties=(DeadZone=0.000000,Sensitivity=1.000000,Exponent=1.000000,bInvert=False))
++AxisConfig=(AxisKeyName="Vive_Left_Trackpad_X",AxisProperties=(DeadZone=0.000000,Sensitivity=1.000000,Exponent=1.000000,bInvert=False))
++AxisConfig=(AxisKeyName="Vive_Left_Trackpad_Y",AxisProperties=(DeadZone=0.000000,Sensitivity=1.000000,Exponent=1.000000,bInvert=False))
++AxisConfig=(AxisKeyName="Vive_Right_Trigger_Axis",AxisProperties=(DeadZone=0.000000,Sensitivity=1.000000,Exponent=1.000000,bInvert=False))
++AxisConfig=(AxisKeyName="Vive_Right_Trackpad_X",AxisProperties=(DeadZone=0.000000,Sensitivity=1.000000,Exponent=1.000000,bInvert=False))
++AxisConfig=(AxisKeyName="Vive_Right_Trackpad_Y",AxisProperties=(DeadZone=0.000000,Sensitivity=1.000000,Exponent=1.000000,bInvert=False))
++AxisConfig=(AxisKeyName="MixedReality_Left_Trigger_Axis",AxisProperties=(DeadZone=0.000000,Sensitivity=1.000000,Exponent=1.000000,bInvert=False))
++AxisConfig=(AxisKeyName="MixedReality_Left_Thumbstick_X",AxisProperties=(DeadZone=0.000000,Sensitivity=1.000000,Exponent=1.000000,bInvert=False))
++AxisConfig=(AxisKeyName="MixedReality_Left_Thumbstick_Y",AxisProperties=(DeadZone=0.000000,Sensitivity=1.000000,Exponent=1.000000,bInvert=False))
++AxisConfig=(AxisKeyName="MixedReality_Left_Trackpad_X",AxisProperties=(DeadZone=0.000000,Sensitivity=1.000000,Exponent=1.000000,bInvert=False))
++AxisConfig=(AxisKeyName="MixedReality_Left_Trackpad_Y",AxisProperties=(DeadZone=0.000000,Sensitivity=1.000000,Exponent=1.000000,bInvert=False))
++AxisConfig=(AxisKeyName="MixedReality_Right_Trigger_Axis",AxisProperties=(DeadZone=0.000000,Sensitivity=1.000000,Exponent=1.000000,bInvert=False))
++AxisConfig=(AxisKeyName="MixedReality_Right_Thumbstick_X",AxisProperties=(DeadZone=0.000000,Sensitivity=1.000000,Exponent=1.000000,bInvert=False))
++AxisConfig=(AxisKeyName="MixedReality_Right_Thumbstick_Y",AxisProperties=(DeadZone=0.000000,Sensitivity=1.000000,Exponent=1.000000,bInvert=False))
++AxisConfig=(AxisKeyName="MixedReality_Right_Trackpad_X",AxisProperties=(DeadZone=0.000000,Sensitivity=1.000000,Exponent=1.000000,bInvert=False))
++AxisConfig=(AxisKeyName="MixedReality_Right_Trackpad_Y",AxisProperties=(DeadZone=0.000000,Sensitivity=1.000000,Exponent=1.000000,bInvert=False))
++AxisConfig=(AxisKeyName="OculusTouch_Left_Grip_Axis",AxisProperties=(DeadZone=0.000000,Sensitivity=1.000000,Exponent=1.000000,bInvert=False))
++AxisConfig=(AxisKeyName="OculusTouch_Left_Trigger_Axis",AxisProperties=(DeadZone=0.000000,Sensitivity=1.000000,Exponent=1.000000,bInvert=False))
++AxisConfig=(AxisKeyName="OculusTouch_Left_Thumbstick_X",AxisProperties=(DeadZone=0.000000,Sensitivity=1.000000,Exponent=1.000000,bInvert=False))
++AxisConfig=(AxisKeyName="OculusTouch_Left_Thumbstick_Y",AxisProperties=(DeadZone=0.000000,Sensitivity=1.000000,Exponent=1.000000,bInvert=False))
++AxisConfig=(AxisKeyName="OculusTouch_Right_Grip_Axis",AxisProperties=(DeadZone=0.000000,Sensitivity=1.000000,Exponent=1.000000,bInvert=False))
++AxisConfig=(AxisKeyName="OculusTouch_Right_Trigger_Axis",AxisProperties=(DeadZone=0.000000,Sensitivity=1.000000,Exponent=1.000000,bInvert=False))
++AxisConfig=(AxisKeyName="OculusTouch_Right_Thumbstick_X",AxisProperties=(DeadZone=0.000000,Sensitivity=1.000000,Exponent=1.000000,bInvert=False))
++AxisConfig=(AxisKeyName="OculusTouch_Right_Thumbstick_Y",AxisProperties=(DeadZone=0.000000,Sensitivity=1.000000,Exponent=1.000000,bInvert=False))
++AxisConfig=(AxisKeyName="ValveIndex_Left_Grip_Axis",AxisProperties=(DeadZone=0.000000,Sensitivity=1.000000,Exponent=1.000000,bInvert=False))
++AxisConfig=(AxisKeyName="ValveIndex_Left_Grip_Force",AxisProperties=(DeadZone=0.000000,Sensitivity=1.000000,Exponent=1.000000,bInvert=False))
++AxisConfig=(AxisKeyName="ValveIndex_Left_Trigger_Axis",AxisProperties=(DeadZone=0.000000,Sensitivity=1.000000,Exponent=1.000000,bInvert=False))
++AxisConfig=(AxisKeyName="ValveIndex_Left_Thumbstick_X",AxisProperties=(DeadZone=0.000000,Sensitivity=1.000000,Exponent=1.000000,bInvert=False))
++AxisConfig=(AxisKeyName="ValveIndex_Left_Thumbstick_Y",AxisProperties=(DeadZone=0.000000,Sensitivity=1.000000,Exponent=1.000000,bInvert=False))
++AxisConfig=(AxisKeyName="ValveIndex_Left_Trackpad_X",AxisProperties=(DeadZone=0.000000,Sensitivity=1.000000,Exponent=1.000000,bInvert=False))
++AxisConfig=(AxisKeyName="ValveIndex_Left_Trackpad_Y",AxisProperties=(DeadZone=0.000000,Sensitivity=1.000000,Exponent=1.000000,bInvert=False))
++AxisConfig=(AxisKeyName="ValveIndex_Left_Trackpad_Force",AxisProperties=(DeadZone=0.000000,Sensitivity=1.000000,Exponent=1.000000,bInvert=False))
++AxisConfig=(AxisKeyName="ValveIndex_Left_Trackpad_Touch",AxisProperties=(DeadZone=0.000000,Sensitivity=1.000000,Exponent=1.000000,bInvert=False))
++AxisConfig=(AxisKeyName="ValveIndex_Right_Grip_Axis",AxisProperties=(DeadZone=0.000000,Sensitivity=1.000000,Exponent=1.000000,bInvert=False))
++AxisConfig=(AxisKeyName="ValveIndex_Right_Grip_Force",AxisProperties=(DeadZone=0.000000,Sensitivity=1.000000,Exponent=1.000000,bInvert=False))
++AxisConfig=(AxisKeyName="ValveIndex_Right_Trigger_Axis",AxisProperties=(DeadZone=0.000000,Sensitivity=1.000000,Exponent=1.000000,bInvert=False))
++AxisConfig=(AxisKeyName="ValveIndex_Right_Thumbstick_X",AxisProperties=(DeadZone=0.000000,Sensitivity=1.000000,Exponent=1.000000,bInvert=False))
++AxisConfig=(AxisKeyName="ValveIndex_Right_Thumbstick_Y",AxisProperties=(DeadZone=0.000000,Sensitivity=1.000000,Exponent=1.000000,bInvert=False))
++AxisConfig=(AxisKeyName="ValveIndex_Right_Trackpad_X",AxisProperties=(DeadZone=0.000000,Sensitivity=1.000000,Exponent=1.000000,bInvert=False))
++AxisConfig=(AxisKeyName="ValveIndex_Right_Trackpad_Y",AxisProperties=(DeadZone=0.000000,Sensitivity=1.000000,Exponent=1.000000,bInvert=False))
++AxisConfig=(AxisKeyName="ValveIndex_Right_Trackpad_Force",AxisProperties=(DeadZone=0.000000,Sensitivity=1.000000,Exponent=1.000000,bInvert=False))
+bAltEnterTogglesFullscreen=True
+bF11TogglesFullscreen=True
+bUseMouseForTouch=False
+bEnableMouseSmoothing=True
+bEnableFOVScaling=True
+bCaptureMouseOnLaunch=True
+bEnableLegacyInputScales=True
+bEnableMotionControls=True
+bFilterInputByPlatformUser=False
+bShouldFlushPressedKeysOnViewportFocusLost=True
+bAlwaysShowTouchInterface=False
+bShowConsoleOnFourFingerTap=True
+bEnableGestureRecognizer=False
+bUseAutocorrect=False
+DefaultViewportMouseCaptureMode=CapturePermanently
+DefaultViewportMouseLockMode=LockOnCapture
+FOVScale=0.011110
+DoubleClickTime=0.200000
+DefaultPlayerInputClass=/Script/EnhancedInput.EnhancedPlayerInput
+DefaultInputComponentClass=/Script/EnhancedInput.EnhancedInputComponent
+DefaultTouchInterface=/Engine/MobileResources/HUD/DefaultVirtualJoysticks.DefaultVirtualJoysticks
+-ConsoleKeys=Tilde
++ConsoleKeys=Tilde
++ConsoleKeys=Caret
+
diff --git a/ai_fighter/AI_Fight_Sim/Config/Scalability.ini b/ai_fighter/AI_Fight_Sim/Config/Scalability.ini
new file mode 100644
index 0000000..470b4f8
--- /dev/null
+++ b/ai_fighter/AI_Fight_Sim/Config/Scalability.ini
@@ -0,0 +1,10 @@
+[ScalabilitySettings]
+; Set all quality settings to the lowest
+sg.ResolutionQuality=10  ; Adjust as necessary, 100 is full quality
+sg.ViewDistanceQuality=0
+sg.AntiAliasingQuality=0
+sg.ShadowQuality=0
+sg.PostProcessQuality=0
+sg.TextureQuality=0
+sg.EffectsQuality=0
+sg.FoliageQuality=0
diff --git a/ai_fighter/AI_Fight_Sim/Content/Characters/Fighter/AIFighter_Albedo.uasset b/ai_fighter/AI_Fight_Sim/Content/Characters/Fighter/AIFighter_Albedo.uasset
new file mode 100644
index 0000000..80557e3
--- /dev/null
+++ b/ai_fighter/AI_Fight_Sim/Content/Characters/Fighter/AIFighter_Albedo.uasset
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b6c1d204e6c285c471eb0e64e7269ff8263006ebe7ae4b64e847471fb589de68
+size 18727
diff --git a/ai_fighter/AI_Fight_Sim/Content/Characters/Fighter/AIFighter_Roughness.uasset b/ai_fighter/AI_Fight_Sim/Content/Characters/Fighter/AIFighter_Roughness.uasset
new file mode 100644
index 0000000..060c872
--- /dev/null
+++ b/ai_fighter/AI_Fight_Sim/Content/Characters/Fighter/AIFighter_Roughness.uasset
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:998be1dc88940b2971ff7a0956ec3e9066cf08bfd9aa389c76e1cbcddcb781aa
+size 22586
diff --git a/ai_fighter/AI_Fight_Sim/Content/Characters/Fighter/ExtractRootMotion.uasset b/ai_fighter/AI_Fight_Sim/Content/Characters/Fighter/ExtractRootMotion.uasset
new file mode 100644
index 0000000..dc27e4e
--- /dev/null
+++ b/ai_fighter/AI_Fight_Sim/Content/Characters/Fighter/ExtractRootMotion.uasset
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:56f4385fa662beb882f241d3c5d6cca129a35b48bf12e479cdd6e7d9de55f7c7
+size 137743
diff --git a/ai_fighter/AI_Fight_Sim/Content/Characters/Fighter/Fighter.uasset b/ai_fighter/AI_Fight_Sim/Content/Characters/Fighter/Fighter.uasset
new file mode 100644
index 0000000..66cb18e
--- /dev/null
+++ b/ai_fighter/AI_Fight_Sim/Content/Characters/Fighter/Fighter.uasset
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0f0ab3c328755e07cdd817fb312df419e3ed13ea99d71155d701b1703d2fee01
+size 10008
diff --git a/ai_fighter/AI_Fight_Sim/Content/Characters/Fighter/FighterAction.uasset b/ai_fighter/AI_Fight_Sim/Content/Characters/Fighter/FighterAction.uasset
new file mode 100644
index 0000000..6608d97
--- /dev/null
+++ b/ai_fighter/AI_Fight_Sim/Content/Characters/Fighter/FighterAction.uasset
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ab10f3ca08c9927e546a1533504b577e30c2c9e122d9505f198960fb324b6029
+size 3436
diff --git a/ai_fighter/AI_Fight_Sim/Content/Characters/Fighter/FighterAnimation.uasset b/ai_fighter/AI_Fight_Sim/Content/Characters/Fighter/FighterAnimation.uasset
new file mode 100644
index 0000000..7d79731
--- /dev/null
+++ b/ai_fighter/AI_Fight_Sim/Content/Characters/Fighter/FighterAnimation.uasset
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ce41d533f66ad240a6cbc0a09976f81224131404e0c9a6e5c25d19f7a3fd15d7
+size 999696
diff --git a/ai_fighter/AI_Fight_Sim/Content/Characters/Fighter/Moonlight.uasset b/ai_fighter/AI_Fight_Sim/Content/Characters/Fighter/Moonlight.uasset
new file mode 100644
index 0000000..d34dda4
--- /dev/null
+++ b/ai_fighter/AI_Fight_Sim/Content/Characters/Fighter/Moonlight.uasset
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6b427bf0ec008ee556f25afd33cde03ee53ba08103c83be8313c18a6954f10e2
+size 110878
diff --git a/ai_fighter/AI_Fight_Sim/Content/Characters/Fighter/MoonlightSword.uasset b/ai_fighter/AI_Fight_Sim/Content/Characters/Fighter/MoonlightSword.uasset
new file mode 100644
index 0000000..21571ab
--- /dev/null
+++ b/ai_fighter/AI_Fight_Sim/Content/Characters/Fighter/MoonlightSword.uasset
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b09955b67c35bdfead6ff823f93baa02d8c9909a339b126a14bb4a53f219fc2a
+size 11864
diff --git a/ai_fighter/AI_Fight_Sim/Content/Characters/Fighter/MoonlightWave.uasset b/ai_fighter/AI_Fight_Sim/Content/Characters/Fighter/MoonlightWave.uasset
new file mode 100644
index 0000000..9efd98a
--- /dev/null
+++ b/ai_fighter/AI_Fight_Sim/Content/Characters/Fighter/MoonlightWave.uasset
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c9ed4e42e4567aa046d9db2989c2767d75ea4ee7a7c101967d4a0ae7a151851e
+size 70020
diff --git a/ai_fighter/AI_Fight_Sim/Content/Characters/Fighter/SK_AIFighter.uasset b/ai_fighter/AI_Fight_Sim/Content/Characters/Fighter/SK_AIFighter.uasset
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+MinimumPlatformVersion=10.0.18362.0
+MaximumPlatformVersionTested=10.0.19041.0
+MaxTrianglesPerCubicMeter=500.000000
+SpatialMeshingVolumeSize=20.000000
+CompilerVersion=Default
+Windows10SDKVersion=10.0.18362.0
++CapabilityList=internetClientServer
++CapabilityList=privateNetworkClientServer
++Uap2CapabilityList=spatialPerception
+bSetDefaultCapabilities=False
+SpatializationPlugin=
+SourceDataOverridePlugin=
+ReverbPlugin=
+OcclusionPlugin=
+SoundCueCookQualityIndex=-1
+
diff --git a/ai_fighter/AI_Fight_Sim/Plugins/NeuralNetworkAIController/NeuralNetworkAIController.uplugin b/ai_fighter/AI_Fight_Sim/Plugins/NeuralNetworkAIController/NeuralNetworkAIController.uplugin
new file mode 100644
index 0000000..27cdcf5
--- /dev/null
+++ b/ai_fighter/AI_Fight_Sim/Plugins/NeuralNetworkAIController/NeuralNetworkAIController.uplugin
@@ -0,0 +1,24 @@
+{
+	"FileVersion": 3,
+	"Version": 1,
+	"VersionName": "1.0",
+	"FriendlyName": "NeuralNetworkAIController",
+	"Description": "",
+	"Category": "Other",
+	"CreatedBy": "Jacob VanDomelen",
+	"CreatedByURL": "",
+	"DocsURL": "",
+	"MarketplaceURL": "",
+	"SupportURL": "",
+	"CanContainContent": true,
+	"IsBetaVersion": false,
+	"IsExperimentalVersion": false,
+	"Installed": false,
+	"Modules": [
+		{
+			"Name": "NeuralNetworkAIController",
+			"Type": "Runtime",
+			"LoadingPhase": "PostEngineInit"
+		}
+	]
+}
\ No newline at end of file
diff --git a/ai_fighter/AI_Fight_Sim/Plugins/NeuralNetworkAIController/Resources/Icon128.png b/ai_fighter/AI_Fight_Sim/Plugins/NeuralNetworkAIController/Resources/Icon128.png
new file mode 100644
index 0000000..26245f6
--- /dev/null
+++ b/ai_fighter/AI_Fight_Sim/Plugins/NeuralNetworkAIController/Resources/Icon128.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f7239efaeefbd82de33ebe18518e50de075ea4188a468a9e4991396433d2275f
+size 12699
diff --git a/ai_fighter/AI_Fight_Sim/Plugins/NeuralNetworkAIController/Source/NeuralNetworkAIController/NeuralNetworkAIController.Build.cs b/ai_fighter/AI_Fight_Sim/Plugins/NeuralNetworkAIController/Source/NeuralNetworkAIController/NeuralNetworkAIController.Build.cs
new file mode 100644
index 0000000..9a5dd92
--- /dev/null
+++ b/ai_fighter/AI_Fight_Sim/Plugins/NeuralNetworkAIController/Source/NeuralNetworkAIController/NeuralNetworkAIController.Build.cs
@@ -0,0 +1,59 @@
+// Copyright Epic Games, Inc. All Rights Reserved.
+
+using System.IO;
+using UnrealBuildTool;
+
+public class NeuralNetworkAIController : ModuleRules
+{
+	public NeuralNetworkAIController(ReadOnlyTargetRules Target) : base(Target)
+	{
+		PCHUsage = ModuleRules.PCHUsageMode.UseExplicitOrSharedPCHs;
+		
+		PublicIncludePaths.AddRange(
+			new string[] {
+				// ... add public include paths required here ...
+				Path.Combine(ModuleDirectory, "ThirdParty", "FANN", "include")
+            }
+			);
+		PublicSystemLibraryPaths.Add(Path.Combine(ModuleDirectory, "ThirdParty", "FANN", "include"));
+				
+		
+		PrivateIncludePaths.AddRange(
+			new string[] {
+				// ... add other private include paths required here ...
+			}
+			);
+			
+		
+		PublicDependencyModuleNames.AddRange(
+			new string[]
+			{
+				"Core",
+				// ... add other public dependencies that you statically link with here ...
+			}
+			);
+			
+		
+		PrivateDependencyModuleNames.AddRange(
+			new string[]
+			{
+				"CoreUObject",
+				"Engine",
+				"Slate",
+				"SlateCore",
+				// ... add private dependencies that you statically link with here ...	
+			}
+			);
+		
+		
+		DynamicallyLoadedModuleNames.AddRange(
+			new string[]
+			{
+				// ... add any modules that your module loads dynamically here ...
+				//Path.Combine(ModuleDirectory, "ThirdParty", "FANN", "bin", "fann.dll")
+			}
+			);
+
+		PublicAdditionalLibraries.Add(Path.Combine(ModuleDirectory, "ThirdParty", "FANN", "lib", "fann.lib"));
+    }
+}
diff --git a/ai_fighter/AI_Fight_Sim/Plugins/NeuralNetworkAIController/Source/NeuralNetworkAIController/Private/FannAIController.cpp b/ai_fighter/AI_Fight_Sim/Plugins/NeuralNetworkAIController/Source/NeuralNetworkAIController/Private/FannAIController.cpp
new file mode 100644
index 0000000..a678121
--- /dev/null
+++ b/ai_fighter/AI_Fight_Sim/Plugins/NeuralNetworkAIController/Source/NeuralNetworkAIController/Private/FannAIController.cpp
@@ -0,0 +1,91 @@
+// Fill out your copyright notice in the Description page of Project Settings.
+
+
+#include "FannAIController.h"
+#include "string"
+#include "fann.h"
+
+UFannAIController::UFannAIController() : NeuralNetwork(nullptr)
+{
+}
+
+UFannAIController::~UFannAIController()
+{
+    if (NeuralNetwork != nullptr)
+    {
+        // Clean up the neural network when the handler is destroyed
+        fann_destroy(NeuralNetwork);
+    }
+}
+
+void UFannAIController::LoadNeuralNetworkFromFile(const FString& FilePath)
+{
+    std::string FilePathSTD = TCHAR_TO_UTF8(*FilePath);
+
+    // Attempt to load the neural network
+    NeuralNetwork = fann_create_from_file(FilePathSTD.c_str());
+
+    if (NeuralNetwork) {
+        // Neural network loaded successfully, print a message
+        UE_LOG(LogTemp, Display, TEXT("Neural network loaded successfully."))
+    }
+    else {
+        // Failed to load neural network, print an error message
+        UE_LOG(LogTemp, Error, TEXT("Failed to load neural network."))
+    }
+}
+
+FNeuralNetworkOutput UFannAIController::RunNeuralNetwork(const FNeuralNetworkInput& Input)
+{
+    FNeuralNetworkOutput Output;
+
+    if (NeuralNetwork == nullptr)
+    {
+        UE_LOG(LogTemp, Warning, TEXT("NeuralNetwork is not loaded."));
+        return Output; // Return empty output if neural network is not loaded
+    }
+
+    // Prepare the input array for the neural network based on the input struct
+    float inputArray[] = {
+        Input.Distance,
+        Input.EnemyOrientationVertical,
+        Input.EnemyOrientationHorizontal,
+        Input.EnemyCharacterWorldOrientation,
+        Input.CameraWorldOrientation,
+        Input.CharacterWorldOrientation,
+        Input.EnemyPerformingAction,
+        Input.EnemyRolling,
+        Input.EnemyKnockback,
+        Input.EnemyLightAttack,
+        Input.EnemyHeavyAttack,
+        Input.EnemySpecialAttack,
+        Input.PlayerRolling,
+        Input.PlayerKnockback,
+        Input.PlayerLightAttack,
+        Input.PlayerHeavyAttack,
+        Input.PlayerSpecialAttack,
+        Input.PlayerStamina,
+        Input.PlayerLightAttackLimit,
+        Input.PlayerHeavyAttackLimit,
+        Input.PlayerSpecialAttackLimit,
+        Input.Time
+    };
+
+    // Assuming fann_run returns a pointer to the output array from the neural network
+    float* outputArray = fann_run(NeuralNetwork, inputArray);
+
+    // Map the neural network's output back to the FNeuralNetworkOutput struct
+    // Ensure the size of outputArray matches the expected output size
+    Output.VerticalMovement = outputArray[0];
+    Output.HorizontalMovement = outputArray[1];
+    Output.RotationYaw = outputArray[2];
+    Output.RotationPitch = outputArray[3];
+    Output.LightAttack = outputArray[4];
+    Output.HeavyAttack = outputArray[5];
+    Output.SpecialAttack = outputArray[6];
+    Output.Roll = outputArray[7];
+
+    return Output;
+}
+
+
diff --git a/ai_fighter/AI_Fight_Sim/Plugins/NeuralNetworkAIController/Source/NeuralNetworkAIController/Private/NeuralNetworkAIController.cpp b/ai_fighter/AI_Fight_Sim/Plugins/NeuralNetworkAIController/Source/NeuralNetworkAIController/Private/NeuralNetworkAIController.cpp
new file mode 100644
index 0000000..20db679
--- /dev/null
+++ b/ai_fighter/AI_Fight_Sim/Plugins/NeuralNetworkAIController/Source/NeuralNetworkAIController/Private/NeuralNetworkAIController.cpp
@@ -0,0 +1,18 @@
+#include "NeuralNetworkAIController.h"
+#include "Modules/ModuleManager.h"
+
+#define LOCTEXT_NAMESPACE "FNeuralNetworkAIControllerModule"
+
+void FNeuralNetworkAIControllerModule::StartupModule()
+{
+    // Initialization logic here
+}
+
+void FNeuralNetworkAIControllerModule::ShutdownModule()
+{
+    // Cleanup logic here
+}
+
+#undef LOCTEXT_NAMESPACE
+
+IMPLEMENT_MODULE(FNeuralNetworkAIControllerModule, NeuralNetworkAIController)
\ No newline at end of file
diff --git a/ai_fighter/AI_Fight_Sim/Plugins/NeuralNetworkAIController/Source/NeuralNetworkAIController/Public/FannAIController.h b/ai_fighter/AI_Fight_Sim/Plugins/NeuralNetworkAIController/Source/NeuralNetworkAIController/Public/FannAIController.h
new file mode 100644
index 0000000..74bf275
--- /dev/null
+++ b/ai_fighter/AI_Fight_Sim/Plugins/NeuralNetworkAIController/Source/NeuralNetworkAIController/Public/FannAIController.h
@@ -0,0 +1,142 @@
+// Fill out your copyright notice in the Description page of Project Settings.
+
+#pragma once
+
+#include "CoreMinimal.h"
+#include "UObject/NoExportTypes.h"
+#include "fann.h"
+#include "FannAIController.generated.h"
+
+USTRUCT(BlueprintType)
+struct FNeuralNetworkInput
+{
+    GENERATED_BODY()
+
+public:
+    UPROPERTY(EditAnywhere, BlueprintReadWrite, Category = "Neural Network")
+    float Distance;
+
+    UPROPERTY(EditAnywhere, BlueprintReadWrite, Category = "Neural Network")
+    float EnemyOrientationVertical;
+
+    UPROPERTY(EditAnywhere, BlueprintReadWrite, Category = "Neural Network")
+    float EnemyOrientationHorizontal;
+
+    UPROPERTY(EditAnywhere, BlueprintReadWrite, Category = "Neural Network")
+    float EnemyCharacterWorldOrientation;
+
+    UPROPERTY(EditAnywhere, BlueprintReadWrite, Category = "Neural Network")
+    float CameraWorldOrientation;
+
+    UPROPERTY(EditAnywhere, BlueprintReadWrite, Category = "Neural Network")
+    float CharacterWorldOrientation;
+
+    UPROPERTY(EditAnywhere, BlueprintReadWrite, Category = "Neural Network")
+    float EnemyPerformingAction;
+
+    UPROPERTY(EditAnywhere, BlueprintReadWrite, Category = "Neural Network")
+    float EnemyRolling;
+
+    UPROPERTY(EditAnywhere, BlueprintReadWrite, Category = "Neural Network")
+    float EnemyKnockback;
+
+    UPROPERTY(EditAnywhere, BlueprintReadWrite, Category = "Neural Network")
+    float EnemyLightAttack;
+
+    UPROPERTY(EditAnywhere, BlueprintReadWrite, Category = "Neural Network")
+    float EnemyHeavyAttack;
+
+    UPROPERTY(EditAnywhere, BlueprintReadWrite, Category = "Neural Network")
+    float EnemySpecialAttack;
+
+    UPROPERTY(EditAnywhere, BlueprintReadWrite, Category = "Neural Network")
+    float PlayerRolling;
+
+    UPROPERTY(EditAnywhere, BlueprintReadWrite, Category = "Neural Network")
+    float PlayerKnockback;
+
+    UPROPERTY(EditAnywhere, BlueprintReadWrite, Category = "Neural Network")
+    float PlayerLightAttack;
+
+    UPROPERTY(EditAnywhere, BlueprintReadWrite, Category = "Neural Network")
+    float PlayerHeavyAttack;
+
+    UPROPERTY(EditAnywhere, BlueprintReadWrite, Category = "Neural Network")
+    float PlayerSpecialAttack;
+
+    UPROPERTY(EditAnywhere, BlueprintReadWrite, Category = "Neural Network")
+    float PlayerStamina;
+
+    UPROPERTY(EditAnywhere, BlueprintReadWrite, Category = "Neural Network")
+    float PlayerLightAttackLimit;
+
+    UPROPERTY(EditAnywhere, BlueprintReadWrite, Category = "Neural Network")
+    float PlayerHeavyAttackLimit;
+
+    UPROPERTY(EditAnywhere, BlueprintReadWrite, Category = "Neural Network")
+    float PlayerSpecialAttackLimit;
+
+    UPROPERTY(EditAnywhere, BlueprintReadWrite, Category = "Neural Network")
+    float Time;
+
+    // Constructor for easy initialization
+    FNeuralNetworkInput() {}
+};
+
+USTRUCT(BlueprintType)
+struct FNeuralNetworkOutput
+{
+    GENERATED_BODY()
+
+public:
+    UPROPERTY(EditAnywhere, BlueprintReadWrite, Category = "Neural Network")
+    float VerticalMovement;
+
+    UPROPERTY(EditAnywhere, BlueprintReadWrite, Category = "Neural Network")
+    float HorizontalMovement;
+
+    UPROPERTY(EditAnywhere, BlueprintReadWrite, Category = "Neural Network")
+    float RotationYaw;
+
+    UPROPERTY(EditAnywhere, BlueprintReadWrite, Category = "Neural Network")
+    float RotationPitch;
+
+    UPROPERTY(EditAnywhere, BlueprintReadWrite, Category = "Neural Network")
+    float LightAttack;
+
+    UPROPERTY(EditAnywhere, BlueprintReadWrite, Category = "Neural Network")
+    float HeavyAttack;
+
+    UPROPERTY(EditAnywhere, BlueprintReadWrite, Category = "Neural Network")
+    float SpecialAttack;
+
+    UPROPERTY(EditAnywhere, BlueprintReadWrite, Category = "Neural Network")
+    float Roll;
+
+    // Constructor for easy initialization
+    FNeuralNetworkOutput() {}
+};
+
+/**
+ * 
+ */
+UCLASS(Blueprintable)
+class NEURALNETWORKAICONTROLLER_API UFannAIController : public UObject
+{
+    GENERATED_BODY()
+
+public:
+    UFannAIController();
+    virtual ~UFannAIController();
+
+    // Load a neural network from a file
+    UFUNCTION(BlueprintCallable, Category="Neural Network")
+    void LoadNeuralNetworkFromFile(const FString& FilePath);
+
+    UFUNCTION(BlueprintCallable, Category = "Neural Network")
+    FNeuralNetworkOutput RunNeuralNetwork(const FNeuralNetworkInput& Input);
+
+private:
+    struct fann* NeuralNetwork;
+	
+};
diff --git a/ai_fighter/AI_Fight_Sim/Plugins/NeuralNetworkAIController/Source/NeuralNetworkAIController/Public/NeuralNetworkAIController.h b/ai_fighter/AI_Fight_Sim/Plugins/NeuralNetworkAIController/Source/NeuralNetworkAIController/Public/NeuralNetworkAIController.h
new file mode 100644
index 0000000..0251b0f
--- /dev/null
+++ b/ai_fighter/AI_Fight_Sim/Plugins/NeuralNetworkAIController/Source/NeuralNetworkAIController/Public/NeuralNetworkAIController.h
@@ -0,0 +1,10 @@
+#pragma once
+
+#include "Modules/ModuleManager.h"
+
+class FNeuralNetworkAIControllerModule : public IModuleInterface
+{
+public:
+    virtual void StartupModule() override;
+    virtual void ShutdownModule() override;
+};
diff --git a/ai_fighter/AI_Fight_Sim/Plugins/NeuralNetworkAIController/Source/NeuralNetworkAIController/ThirdParty/FANN/include/doublefann.h b/ai_fighter/AI_Fight_Sim/Plugins/NeuralNetworkAIController/Source/NeuralNetworkAIController/ThirdParty/FANN/include/doublefann.h
new file mode 100644
index 0000000..247779f
--- /dev/null
+++ b/ai_fighter/AI_Fight_Sim/Plugins/NeuralNetworkAIController/Source/NeuralNetworkAIController/ThirdParty/FANN/include/doublefann.h
@@ -0,0 +1,33 @@
+/*
+Fast Artificial Neural Network Library (fann)
+Copyright (C) 2003-2016 Steffen Nissen (steffen.fann@gmail.com)
+
+This library is free software; you can redistribute it and/or
+modify it under the terms of the GNU Lesser General Public
+License as published by the Free Software Foundation; either
+version 2.1 of the License, or (at your option) any later version.
+
+This library is distributed in the hope that it will be useful,
+but WITHOUT ANY WARRANTY; without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
+Lesser General Public License for more details.
+
+You should have received a copy of the GNU Lesser General Public
+License along with this library; if not, write to the Free Software
+Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA  02111-1307  USA
+*/
+
+#ifndef __doublefann_h__
+#define __doublefann_h__
+
+typedef double fann_type;
+
+#undef DOUBLEFANN
+#define DOUBLEFANN
+#define FANNPRINTF "%.20e"
+#define FANNSCANF "%le"
+
+#define FANN_INCLUDE
+#include "fann.h"
+
+#endif
diff --git a/ai_fighter/AI_Fight_Sim/Plugins/NeuralNetworkAIController/Source/NeuralNetworkAIController/ThirdParty/FANN/include/fann.h b/ai_fighter/AI_Fight_Sim/Plugins/NeuralNetworkAIController/Source/NeuralNetworkAIController/ThirdParty/FANN/include/fann.h
new file mode 100644
index 0000000..5d4c0b7
--- /dev/null
+++ b/ai_fighter/AI_Fight_Sim/Plugins/NeuralNetworkAIController/Source/NeuralNetworkAIController/ThirdParty/FANN/include/fann.h
@@ -0,0 +1,661 @@
+/*
+Fast Artificial Neural Network Library (fann)
+Copyright (C) 2003-2016 Steffen Nissen (steffen.fann@gmail.com)
+
+This library is free software; you can redistribute it and/or
+modify it under the terms of the GNU Lesser General Public
+License as published by the Free Software Foundation; either
+version 2.1 of the License, or (at your option) any later version.
+
+This library is distributed in the hope that it will be useful,
+but WITHOUT ANY WARRANTY; without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
+Lesser General Public License for more details.
+
+You should have received a copy of the GNU Lesser General Public
+License along with this library; if not, write to the Free Software
+Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA  02111-1307  USA
+*/
+
+/* This file defines the user interface to the fann library.
+   It is included from fixedfann.h, floatfann.h and doublefann.h and should
+   NOT be included directly. If included directly it will react as if
+   floatfann.h was included.
+*/
+
+/* Section: FANN Creation/Execution
+
+   The FANN library is designed to be very easy to use.
+   A feedforward ann can be created by a simple <fann_create_standard> function, while
+   other ANNs can be created just as easily. The ANNs can be trained by <fann_train_on_file>
+   and executed by <fann_run>.
+
+   All of this can be done without much knowledge of the internals of ANNs, although the ANNs
+   created will still be powerful and effective. If you have more knowledge about ANNs, and desire
+   more control, almost every part of the ANNs can be parametrized to create specialized and highly
+   optimal ANNs.
+ */
+/* Group: Creation, Destruction & Execution */
+
+#ifndef FANN_INCLUDE
+/* just to allow for inclusion of fann.h in normal stuations where only floats are needed */
+#ifdef FIXEDFANN
+#include "fixedfann.h"
+#else
+#include "floatfann.h"
+#endif /* FIXEDFANN  */
+
+#else
+
+/* COMPAT_TIME REPLACEMENT */
+#ifndef _WIN32
+#include <sys/time.h>
+#else /* _WIN32 */
+#if !defined(_MSC_EXTENSIONS) && !defined(_INC_WINDOWS)
+extern unsigned long __stdcall GetTickCount(void);
+
+#else /* _MSC_EXTENSIONS */
+#define WIN32_LEAN_AND_MEAN
+#include <windows.h>
+#endif /* _MSC_EXTENSIONS */
+#endif /* _WIN32 */
+
+#ifndef __fann_h__
+#define __fann_h__
+
+#ifdef __cplusplus
+extern "C" {
+
+#ifndef __cplusplus
+} /* to fool automatic indention engines */
+#endif
+#endif /* __cplusplus */
+
+#ifndef NULL
+#define NULL 0
+#endif /* NULL */
+
+/* ----- Macros used to define DLL external entrypoints ----- */
+/*
+ DLL Export, import and calling convention for Windows.
+ Only defined for Microsoft VC++ FANN_EXTERNAL indicates
+ that a function will be exported/imported from a dll
+ FANN_API ensures that the DLL calling convention
+ will be used for  a function regardless of the calling convention
+ used when compiling.
+
+ For a function to be exported from a DLL its prototype and
+ declaration must be like this:
+    FANN_EXTERNAL void FANN_API function(char *argument)
+
+ The following ifdef block is a way of creating macros which
+ make exporting from a DLL simple. All files within a DLL are
+ compiled with the FANN_DLL_EXPORTS symbol defined on the
+ command line. This symbol should not be defined on any project
+ that uses this DLL. This way any other project whose source
+ files include this file see FANN_EXTERNAL functions as being imported
+ from a DLL, whereas a DLL sees symbols defined with this
+ macro as being exported which makes calls more efficient.
+ The __stdcall calling convention is used for functions in a
+ windows DLL.
+
+ The callback functions for fann_set_callback must be declared as FANN_API
+ so the DLL and the application program both use the same
+ calling convention.
+*/
+
+/*
+ The following sets the default for MSVC++ 2003 or later to use
+ the fann dll's. To use a lib or fixedfann.c, floatfann.c or doublefann.c
+ with those compilers FANN_NO_DLL has to be defined before
+ including the fann headers.
+ The default for previous MSVC compilers such as VC++ 6 is not
+ to use dll's. To use dll's FANN_USE_DLL has to be defined before
+ including the fann headers.
+*/
+#if defined(_MSC_VER) && (_MSC_VER > 1300)
+#ifndef FANN_NO_DLL
+#define FANN_USE_DLL
+#endif /* FANN_USE_LIB */
+#endif /* _MSC_VER */
+#if defined(_MSC_VER) && (defined(FANN_USE_DLL) || defined(FANN_DLL_EXPORTS))
+#ifdef FANN_DLL_EXPORTS
+#define FANN_EXTERNAL __declspec(dllexport)
+#else /*  */
+#define FANN_EXTERNAL __declspec(dllimport)
+#endif /* FANN_DLL_EXPORTS*/
+#define FANN_API __stdcall
+#else /*  */
+#define FANN_EXTERNAL
+#define FANN_API
+#endif /* _MSC_VER */
+/* ----- End of macros used to define DLL external entrypoints ----- */
+
+#include "fann_error.h"
+#include "fann_activation.h"
+#include "fann_data.h"
+#include "fann_internal.h"
+#include "fann_train.h"
+#include "fann_cascade.h"
+#include "fann_io.h"
+
+/* Function: fann_create_standard
+
+        Creates a standard fully connected backpropagation neural network.
+
+        There will be a bias neuron in each layer (except the output layer),
+        and this bias neuron will be connected to all neurons in the next layer.
+        When running the network, the bias nodes always emits 1.
+
+        To destroy a <struct fann> use the <fann_destroy> function.
+
+        Parameters:
+                num_layers - The total number of layers including the input and the output layer.
+                ... - Integer values determining the number of neurons in each layer starting with
+   the input layer and ending with the output layer.
+
+        Returns:
+                A pointer to the newly created <struct fann>.
+
+        Example:
+                > // Creating an ANN with 2 input neurons, 1 output neuron,
+                > // and two hidden layers with 8 and 9 neurons
+                > struct fann *ann = fann_create_standard(4, 2, 8, 9, 1);
+
+        See also:
+                <fann_create_standard_array>, <fann_create_sparse>, <fann_create_shortcut>
+
+        This function appears in FANN >= 2.0.0.
+*/
+FANN_EXTERNAL struct fann *FANN_API fann_create_standard(unsigned int num_layers, ...);
+
+/* Function: fann_create_standard_array
+   Just like <fann_create_standard>, but with an array of layer sizes
+   instead of individual parameters.
+
+        Example:
+                > // Creating an ANN with 2 input neurons, 1 output neuron,
+                > // and two hidden layers with 8 and 9 neurons
+                > unsigned int layers[4] = {2, 8, 9, 1};
+                > struct fann *ann = fann_create_standard_array(4, layers);
+
+        See also:
+                <fann_create_standard>, <fann_create_sparse>, <fann_create_shortcut>
+
+        This function appears in FANN >= 2.0.0.
+*/
+FANN_EXTERNAL struct fann *FANN_API fann_create_standard_array(unsigned int num_layers,
+                                                               const unsigned int *layers);
+
+/* Function: fann_create_sparse
+
+        Creates a standard backpropagation neural network, which is not fully connected.
+
+        Parameters:
+                connection_rate - The connection rate controls how many connections there will be in
+   the network. If the connection rate is set to 1, the network will be fully connected, but if it
+   is set to 0.5 only half of the connections will be set. A connection rate of 1 will yield the
+   same result as <fann_create_standard> num_layers - The total number of layers including the input
+   and the output layer.
+                ... - Integer values determining the number of neurons in each layer starting with
+   the input layer and ending with the output layer.
+
+        Returns:
+                A pointer to the newly created <struct fann>.
+
+        See also:
+                <fann_create_sparse_array>, <fann_create_standard>, <fann_create_shortcut>
+
+        This function appears in FANN >= 2.0.0.
+*/
+FANN_EXTERNAL struct fann *FANN_API fann_create_sparse(float connection_rate,
+                                                       unsigned int num_layers, ...);
+
+/* Function: fann_create_sparse_array
+   Just like <fann_create_sparse>, but with an array of layer sizes
+   instead of individual parameters.
+
+        See <fann_create_standard_array> for a description of the parameters.
+
+        See also:
+                <fann_create_sparse>, <fann_create_standard>, <fann_create_shortcut>
+
+        This function appears in FANN >= 2.0.0.
+*/
+FANN_EXTERNAL struct fann *FANN_API fann_create_sparse_array(float connection_rate,
+                                                             unsigned int num_layers,
+                                                             const unsigned int *layers);
+
+/* Function: fann_create_shortcut
+
+        Creates a standard backpropagation neural network, which is fully connected and which
+        also has shortcut connections.
+
+        Shortcut connections are connections that skip layers. A fully connected network with
+   shortcut connections is a network where all neurons are connected to all neurons in later layers.
+        Including direct connections from the input layer to the output layer.
+
+        See <fann_create_standard> for a description of the parameters.
+
+        See also:
+                <fann_create_shortcut_array>, <fann_create_standard>, <fann_create_sparse>,
+
+        This function appears in FANN >= 2.0.0.
+*/
+FANN_EXTERNAL struct fann *FANN_API fann_create_shortcut(unsigned int num_layers, ...);
+
+/* Function: fann_create_shortcut_array
+   Just like <fann_create_shortcut>, but with an array of layer sizes
+   instead of individual parameters.
+
+        See <fann_create_standard_array> for a description of the parameters.
+
+        See also:
+                <fann_create_shortcut>, <fann_create_standard>, <fann_create_sparse>
+
+        This function appears in FANN >= 2.0.0.
+*/
+FANN_EXTERNAL struct fann *FANN_API fann_create_shortcut_array(unsigned int num_layers,
+                                                               const unsigned int *layers);
+/* Function: fann_destroy
+   Destroys the entire network, properly freeing all the associated memory.
+
+        This function appears in FANN >= 1.0.0.
+*/
+FANN_EXTERNAL void FANN_API fann_destroy(struct fann *ann);
+
+/* Function: fann_copy
+   Creates a copy of a fann structure.
+
+   Data in the user data <fann_set_user_data> is not copied, but the user data pointer is copied.
+
+        This function appears in FANN >= 2.2.0.
+*/
+FANN_EXTERNAL struct fann *FANN_API fann_copy(struct fann *ann);
+
+/* Function: fann_run
+        Will run input through the neural network, returning an array of outputs, the number of
+   which being equal to the number of neurons in the output layer.
+
+        See also:
+                <fann_test>
+
+        This function appears in FANN >= 1.0.0.
+*/
+FANN_EXTERNAL fann_type *FANN_API fann_run(struct fann *ann, fann_type *input);
+
+/* Function: fann_randomize_weights
+        Give each connection a random weight between *min_weight* and *max_weight*
+
+        From the beginning the weights are random between -0.1 and 0.1.
+
+        See also:
+                <fann_init_weights>
+
+        This function appears in FANN >= 1.0.0.
+*/
+FANN_EXTERNAL void FANN_API fann_randomize_weights(struct fann *ann, fann_type min_weight,
+                                                   fann_type max_weight);
+
+/* Function: fann_init_weights
+        Initialize the weights using Widrow + Nguyen's algorithm.
+
+        This function behaves similarly to fann_randomize_weights. It will use the algorithm
+   developed by Derrick Nguyen and Bernard Widrow to set the weights in such a way as to speed up
+   training. This technique is not always successful, and in some cases can be less efficient than a
+   purely random initialization.
+
+        The algorithm requires access to the range of the input data (ie, largest and smallest
+   input), and therefore accepts a second argument, data, which is the training data that will be
+   used to train the network.
+
+        See also:
+                <fann_randomize_weights>, <fann_read_train_from_file>
+
+        This function appears in FANN >= 1.1.0.
+*/
+FANN_EXTERNAL void FANN_API fann_init_weights(struct fann *ann, struct fann_train_data *train_data);
+
+/* Function: fann_print_connections
+        Will print the connections of the ann in a compact matrix, for easy viewing of the internals
+        of the ann.
+
+        The output from fann_print_connections on a small (2 2 1) network trained on the xor problem
+        >Layer / Neuron 012345
+        >L   1 / N    3 BBa...
+        >L   1 / N    4 BBA...
+        >L   1 / N    5 ......
+        >L   2 / N    6 ...BBA
+        >L   2 / N    7 ......
+
+        This network has five real neurons and two bias neurons. This gives a total of seven neurons
+        named from 0 to 6. The connections between these neurons can be seen in the matrix. "." is a
+        place where there is no connection, while a character tells how strong the connection is on
+   a scale from a-z. The two real neurons in the hidden layer (neuron 3 and 4 in layer 1) have
+        connections from the three neurons in the previous layer as is visible in the first two
+   lines. The output neuron (6) has connections from the three neurons in the hidden layer 3 - 5 as
+   is visible in the fourth line.
+
+        To simplify the matrix output neurons are not visible as neurons that connections can come
+   from, and input and bias neurons are not visible as neurons that connections can go to.
+
+        This function appears in FANN >= 1.2.0.
+*/
+FANN_EXTERNAL void FANN_API fann_print_connections(struct fann *ann);
+
+/* Group: Parameters */
+/* Function: fann_print_parameters
+
+        Prints all of the parameters and options of the ANN
+
+        This function appears in FANN >= 1.2.0.
+*/
+FANN_EXTERNAL void FANN_API fann_print_parameters(struct fann *ann);
+
+/* Function: fann_get_num_input
+
+   Get the number of input neurons.
+
+        This function appears in FANN >= 1.0.0.
+*/
+FANN_EXTERNAL unsigned int FANN_API fann_get_num_input(struct fann *ann);
+
+/* Function: fann_get_num_output
+
+   Get the number of output neurons.
+
+        This function appears in FANN >= 1.0.0.
+*/
+FANN_EXTERNAL unsigned int FANN_API fann_get_num_output(struct fann *ann);
+
+/* Function: fann_get_total_neurons
+
+   Get the total number of neurons in the entire network. This number does also include the
+        bias neurons, so a 2-4-2 network has 2+4+2 +2(bias) = 10 neurons.
+
+        This function appears in FANN >= 1.0.0.
+*/
+FANN_EXTERNAL unsigned int FANN_API fann_get_total_neurons(struct fann *ann);
+
+/* Function: fann_get_total_connections
+
+   Get the total number of connections in the entire network.
+
+        This function appears in FANN >= 1.0.0.
+*/
+FANN_EXTERNAL unsigned int FANN_API fann_get_total_connections(struct fann *ann);
+
+/* Function: fann_get_network_type
+
+    Get the type of neural network it was created as.
+
+    Parameters:
+                ann - A previously created neural network structure of
+            type <struct fann> pointer.
+
+        Returns:
+        The neural network type from enum <fann_network_type_enum>
+
+    See Also:
+        <fann_network_type_enum>
+
+   This function appears in FANN >= 2.1.0
+*/
+FANN_EXTERNAL enum fann_nettype_enum FANN_API fann_get_network_type(struct fann *ann);
+
+/* Function: fann_get_connection_rate
+
+    Get the connection rate used when the network was created
+
+    Parameters:
+                ann - A previously created neural network structure of
+            type <struct fann> pointer.
+
+        Returns:
+        The connection rate
+
+   This function appears in FANN >= 2.1.0
+*/
+FANN_EXTERNAL float FANN_API fann_get_connection_rate(struct fann *ann);
+
+/* Function: fann_get_num_layers
+
+    Get the number of layers in the network
+
+    Parameters:
+                ann - A previously created neural network structure of
+            type <struct fann> pointer.
+
+        Returns:
+                The number of layers in the neural network
+
+        Example:
+                > // Obtain the number of layers in a neural network
+                > struct fann *ann = fann_create_standard(4, 2, 8, 9, 1);
+        > unsigned int num_layers = fann_get_num_layers(ann);
+
+   This function appears in FANN >= 2.1.0
+*/
+FANN_EXTERNAL unsigned int FANN_API fann_get_num_layers(struct fann *ann);
+
+/*Function: fann_get_layer_array
+
+    Get the number of neurons in each layer in the network.
+
+    Bias is not included so the layers match the fann_create functions.
+
+    Parameters:
+                ann - A previously created neural network structure of
+            type <struct fann> pointer.
+
+    The layers array must be preallocated to at least
+    sizeof(unsigned int) * fann_num_layers() long.
+
+   This function appears in FANN >= 2.1.0
+*/
+FANN_EXTERNAL void FANN_API fann_get_layer_array(struct fann *ann, unsigned int *layers);
+
+/* Function: fann_get_bias_array
+
+    Get the number of bias in each layer in the network.
+
+    Parameters:
+                ann - A previously created neural network structure of
+            type <struct fann> pointer.
+
+    The bias array must be preallocated to at least
+    sizeof(unsigned int) * fann_num_layers() long.
+
+   This function appears in FANN >= 2.1.0
+*/
+FANN_EXTERNAL void FANN_API fann_get_bias_array(struct fann *ann, unsigned int *bias);
+
+/* Function: fann_get_connection_array
+
+    Get the connections in the network.
+
+    Parameters:
+                ann - A previously created neural network structure of
+            type <struct fann> pointer.
+
+    The connections array must be preallocated to at least
+    sizeof(struct fann_connection) * fann_get_total_connections() long.
+
+   This function appears in FANN >= 2.1.0
+*/
+FANN_EXTERNAL void FANN_API fann_get_connection_array(struct fann *ann,
+                                                      struct fann_connection *connections);
+
+/* Function: fann_set_weight_array
+
+    Set connections in the network.
+
+    Parameters:
+                ann - A previously created neural network structure of
+            type <struct fann> pointer.
+
+    Only the weights can be changed, connections and weights are ignored
+    if they do not already exist in the network.
+
+    The array must have sizeof(struct fann_connection) * num_connections size.
+
+   This function appears in FANN >= 2.1.0
+*/
+FANN_EXTERNAL void FANN_API fann_set_weight_array(struct fann *ann,
+                                                  struct fann_connection *connections,
+                                                  unsigned int num_connections);
+
+/* Function: fann_set_weight
+
+    Set a connection in the network.
+
+    Parameters:
+                ann - A previously created neural network structure of
+            type <struct fann> pointer.
+
+    Only the weights can be changed. The connection/weight is
+    ignored if it does not already exist in the network.
+
+   This function appears in FANN >= 2.1.0
+*/
+FANN_EXTERNAL void FANN_API fann_set_weight(struct fann *ann, unsigned int from_neuron,
+                                            unsigned int to_neuron, fann_type weight);
+
+/* Function: fann_get_weights
+
+    Get all the network weights.
+
+    Parameters:
+                ann - A previously created neural network structure of
+            type <struct fann> pointer.
+                weights - A fann_type pointer to user data. It is the responsibility
+                        of the user to allocate sufficient space to store all the weights.
+
+   This function appears in FANN >= x.y.z
+*/
+FANN_EXTERNAL void FANN_API fann_get_weights(struct fann *ann, fann_type *weights);
+
+/* Function: fann_set_weights
+
+    Set network weights.
+
+    Parameters:
+                ann - A previously created neural network structure of
+            type <struct fann> pointer.
+                weights - A fann_type pointer to user data. It is the responsibility
+                        of the user to make the weights array sufficient long
+                        to store all the weights.
+
+   This function appears in FANN >= x.y.z
+*/
+FANN_EXTERNAL void FANN_API fann_set_weights(struct fann *ann, fann_type *weights);
+
+/* Function: fann_set_user_data
+
+    Store a pointer to user defined data. The pointer can be
+    retrieved with <fann_get_user_data> for example in a
+    callback. It is the user's responsibility to allocate and
+    deallocate any data that the pointer might point to.
+
+    Parameters:
+                ann - A previously created neural network structure of
+            type <struct fann> pointer.
+                user_data - A void pointer to user defined data.
+
+   This function appears in FANN >= 2.1.0
+*/
+FANN_EXTERNAL void FANN_API fann_set_user_data(struct fann *ann, void *user_data);
+
+/* Function: fann_get_user_data
+
+    Get a pointer to user defined data that was previously set
+    with <fann_set_user_data>. It is the user's responsibility to
+    allocate and deallocate any data that the pointer might point to.
+
+    Parameters:
+                ann - A previously created neural network structure of
+            type <struct fann> pointer.
+
+    Returns:
+        A void pointer to user defined data.
+
+   This function appears in FANN >= 2.1.0
+*/
+FANN_EXTERNAL void *FANN_API fann_get_user_data(struct fann *ann);
+
+/* Function: fann_disable_seed_rand
+
+   Disables the automatic random generator seeding that happens in FANN.
+
+   Per default FANN will always seed the random generator when creating a new network,
+   unless FANN_NO_SEED is defined during compilation of the library. This method can
+   disable this at runtime.
+
+   This function appears in FANN >= 2.3.0
+*/
+FANN_EXTERNAL void FANN_API fann_disable_seed_rand();
+
+/* Function: fann_enable_seed_rand
+
+   Enables the automatic random generator seeding that happens in FANN.
+
+   Per default FANN will always seed the random generator when creating a new network,
+   unless FANN_NO_SEED is defined during compilation of the library. This method can
+   disable this at runtime.
+
+   This function appears in FANN >= 2.3.0
+*/
+FANN_EXTERNAL void FANN_API fann_enable_seed_rand();
+
+#ifdef FIXEDFANN
+
+/* Function: fann_get_decimal_point
+
+        Returns the position of the decimal point in the ann.
+
+        This function is only available when the ANN is in fixed point mode.
+
+        The decimal point is described in greater detail in the tutorial <Fixed Point Usage>.
+
+        See also:
+                <Fixed Point Usage>, <fann_get_multiplier>, <fann_save_to_fixed>,
+   <fann_save_train_to_fixed>
+
+        This function appears in FANN >= 1.0.0.
+*/
+FANN_EXTERNAL unsigned int FANN_API fann_get_decimal_point(struct fann *ann);
+
+/* Function: fann_get_multiplier
+
+    returns the multiplier that fix point data is multiplied with.
+
+        This function is only available when the ANN is in fixed point mode.
+
+        The multiplier is the used to convert between floating point and fixed point notation.
+        A floating point number is multiplied with the multiplier in order to get the fixed point
+        number and visa versa.
+
+        The multiplier is described in greater detail in the tutorial <Fixed Point Usage>.
+
+        See also:
+                <Fixed Point Usage>, <fann_get_decimal_point>, <fann_save_to_fixed>,
+   <fann_save_train_to_fixed>
+
+        This function appears in FANN >= 1.0.0.
+*/
+FANN_EXTERNAL unsigned int FANN_API fann_get_multiplier(struct fann *ann);
+
+#endif /* FIXEDFANN */
+
+#ifdef __cplusplus
+#ifndef __cplusplus
+/* to fool automatic indention engines */
+{
+
+#endif
+}
+#endif /* __cplusplus */
+
+#endif /* __fann_h__ */
+
+#endif /* NOT FANN_INCLUDE */
diff --git a/ai_fighter/AI_Fight_Sim/Plugins/NeuralNetworkAIController/Source/NeuralNetworkAIController/ThirdParty/FANN/include/fann_activation.h b/ai_fighter/AI_Fight_Sim/Plugins/NeuralNetworkAIController/Source/NeuralNetworkAIController/ThirdParty/FANN/include/fann_activation.h
new file mode 100644
index 0000000..9c634ee
--- /dev/null
+++ b/ai_fighter/AI_Fight_Sim/Plugins/NeuralNetworkAIController/Source/NeuralNetworkAIController/ThirdParty/FANN/include/fann_activation.h
@@ -0,0 +1,183 @@
+/*
+Fast Artificial Neural Network Library (fann)
+Copyright (C) 2003-2016 Steffen Nissen (steffen.fann@gmail.com)
+
+This library is free software; you can redistribute it and/or
+modify it under the terms of the GNU Lesser General Public
+License as published by the Free Software Foundation; either
+version 2.1 of the License, or (at your option) any later version.
+
+This library is distributed in the hope that it will be useful,
+but WITHOUT ANY WARRANTY; without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
+Lesser General Public License for more details.
+
+You should have received a copy of the GNU Lesser General Public
+License along with this library; if not, write to the Free Software
+Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA  02111-1307  USA
+*/
+
+#ifndef __fann_activation_h__
+#define __fann_activation_h__
+/* internal include file, not to be included directly
+ */
+
+/* Implementation of the activation functions
+ */
+
+/* stepwise linear functions used for some of the activation functions */
+
+/* defines used for the stepwise linear functions */
+
+/* Uses the single precision version of math.h functions if
+__doublefann_h__ is not defined
+*/
+#ifndef __doublefann_h__
+#define FANN_EXP(x) expf(x)
+#define FANN_SIN(x) sinf(x)
+#define FANN_COS(x) cosf(x)
+#else
+#define FANN_EXP(x) exp(x)
+#define FANN_SIN(x) sin(x)
+#define FANN_COS(x) cos(x)
+#endif
+
+#define fann_linear_func(v1, r1, v2, r2, sum) \
+  (((((r2) - (r1)) * ((sum) - (v1))) / ((v2) - (v1))) + (r1))
+#define fann_stepwise(v1, v2, v3, v4, v5, v6, r1, r2, r3, r4, r5, r6, min, max, sum)           \
+  (sum < v5 ? (sum < v3 ? (sum < v2 ? (sum < v1 ? min : fann_linear_func(v1, r1, v2, r2, sum)) \
+                                    : fann_linear_func(v2, r2, v3, r3, sum))                   \
+                        : (sum < v4 ? fann_linear_func(v3, r3, v4, r4, sum)                    \
+                                    : fann_linear_func(v4, r4, v5, r5, sum)))                  \
+            : (sum < v6 ? fann_linear_func(v5, r5, v6, r6, sum) : max))
+
+/* FANN_LINEAR */
+/* #define fann_linear(steepness, sum) fann_mult(steepness, sum) */
+#define fann_linear_derive(steepness, value) (steepness)
+
+/* FANN_SIGMOID */
+/* #define fann_sigmoid(steepness, sum) (1.0f/(1.0f + exp(-2.0f * steepness * sum))) */
+#define fann_sigmoid_real(sum) (1.0f / (1.0f + FANN_EXP(-2.0f * sum)))
+#define fann_sigmoid_derive(steepness, value) (2.0f * steepness * value * (1.0f - value))
+
+/* FANN_SIGMOID_SYMMETRIC */
+/* #define fann_sigmoid_symmetric(steepness, sum) (2.0f/(1.0f + exp(-2.0f * steepness * sum))
+ * - 1.0f) */
+#define fann_sigmoid_symmetric_real(sum) (2.0f / (1.0f + FANN_EXP(-2.0f * sum)) - 1.0f)
+#define fann_sigmoid_symmetric_derive(steepness, value) steepness*(1.0f - (value * value))
+
+/* FANN_GAUSSIAN */
+/* #define fann_gaussian(steepness, sum) (exp(-sum * steepness * sum * steepness)) */
+#define fann_gaussian_real(sum) (FANN_EXP(-sum * sum))
+#define fann_gaussian_derive(steepness, value, sum) (-2.0f * sum * value * steepness * steepness)
+
+/* FANN_GAUSSIAN_SYMMETRIC */
+/* #define fann_gaussian_symmetric(steepness, sum) ((exp(-sum * steepness * sum *
+ * steepness)*2.0)-1.0) */
+#define fann_gaussian_symmetric_real(sum) ((FANN_EXP(-sum * sum) * 2.0f) - 1.0f)
+#define fann_gaussian_symmetric_derive(steepness, value, sum) \
+  (-2.0f * sum * (value + 1.0f) * steepness * steepness)
+
+/* FANN_ELLIOT */
+/* #define fann_elliot(steepness, sum) (((sum * steepness) / 2.0f) / (1.0f + fann_abs(sum *
+ * steepness)) + 0.5f) */
+#define fann_elliot_real(sum) (((sum) / 2.0f) / (1.0f + fann_abs(sum)) + 0.5f)
+#define fann_elliot_derive(steepness, value, sum) \
+  (steepness * 1.0f / (2.0f * (1.0f + fann_abs(sum)) * (1.0f + fann_abs(sum))))
+
+/* FANN_ELLIOT_SYMMETRIC */
+/* #define fann_elliot_symmetric(steepness, sum) ((sum * steepness) / (1.0f + fann_abs(sum *
+ * steepness)))*/
+#define fann_elliot_symmetric_real(sum) ((sum) / (1.0f + fann_abs(sum)))
+#define fann_elliot_symmetric_derive(steepness, value, sum) \
+  (steepness * 1.0f / ((1.0f + fann_abs(sum)) * (1.0f + fann_abs(sum))))
+
+/* FANN_SIN_SYMMETRIC */
+#define fann_sin_symmetric_real(sum) (FANN_SIN(sum))
+#define fann_sin_symmetric_derive(steepness, sum) (steepness * cos(steepness * sum))
+
+/* FANN_COS_SYMMETRIC */
+#define fann_cos_symmetric_real(sum) (FANN_COS(sum))
+#define fann_cos_symmetric_derive(steepness, sum) (steepness * -sin(steepness * sum))
+
+/* FANN_SIN */
+#define fann_sin_real(sum) (FANN_SIN(sum) / 2.0f + 0.5f)
+#define fann_sin_derive(steepness, sum) (steepness * cos(steepness * sum) / 2.0f)
+
+/* FANN_COS */
+#define fann_cos_real(sum) (FANN_COS(sum) / 2.0f + 0.5f)
+#define fann_cos_derive(steepness, sum) (steepness * -sin(steepness * sum) / 2.0f)
+
+#define fann_activation_switch(activation_function, value, result)                                 \
+  switch (activation_function) {                                                                   \
+    case FANN_LINEAR:                                                                              \
+      result = (fann_type)value;                                                                   \
+      break;                                                                                       \
+    case FANN_LINEAR_PIECE:                                                                        \
+      result = (fann_type)((value < 0) ? 0 : (value > 1) ? 1 : value);                             \
+      break;                                                                                       \
+    case FANN_LINEAR_PIECE_SYMMETRIC:                                                              \
+      result = (fann_type)((value < -1) ? -1 : (value > 1) ? 1 : value);                           \
+      break;                                                                                       \
+    case FANN_SIGMOID:                                                                             \
+      result = (fann_type)fann_sigmoid_real(value);                                                \
+      break;                                                                                       \
+    case FANN_SIGMOID_SYMMETRIC:                                                                   \
+      result = (fann_type)fann_sigmoid_symmetric_real(value);                                      \
+      break;                                                                                       \
+    case FANN_SIGMOID_SYMMETRIC_STEPWISE:                                                          \
+      result = (fann_type)fann_stepwise(                                                           \
+          ((fann_type)-2.64665293693542480469e+00), ((fann_type)-1.47221934795379638672e+00),      \
+          ((fann_type)-5.49306154251098632812e-01), ((fann_type)5.49306154251098632812e-01),       \
+          ((fann_type)1.47221934795379638672e+00), ((fann_type)2.64665293693542480469e+00),        \
+          ((fann_type)-9.90000009536743164062e-01), ((fann_type)-8.99999976158142089844e-01),      \
+          ((fann_type)-5.00000000000000000000e-01), ((fann_type)5.00000000000000000000e-01),       \
+          ((fann_type)8.99999976158142089844e-01), ((fann_type)9.90000009536743164062e-01), -1, 1, \
+          value);                                                                                  \
+      break;                                                                                       \
+    case FANN_SIGMOID_STEPWISE:                                                                    \
+      result = (fann_type)fann_stepwise(                                                           \
+          ((fann_type)-2.64665246009826660156e+00), ((fann_type)-1.47221946716308593750e+00),      \
+          ((fann_type)-5.49306154251098632812e-01), ((fann_type)5.49306154251098632812e-01),       \
+          ((fann_type)1.47221934795379638672e+00), ((fann_type)2.64665293693542480469e+00),        \
+          ((fann_type)4.99999988824129104614e-03), ((fann_type)5.00000007450580596924e-02),        \
+          ((fann_type)2.50000000000000000000e-01), ((fann_type)7.50000000000000000000e-01),        \
+          ((fann_type)9.49999988079071044922e-01), ((fann_type)9.95000004768371582031e-01), 0, 1,  \
+          value);                                                                                  \
+      break;                                                                                       \
+    case FANN_THRESHOLD:                                                                           \
+      result = (fann_type)((value < 0) ? 0 : 1);                                                   \
+      break;                                                                                       \
+    case FANN_THRESHOLD_SYMMETRIC:                                                                 \
+      result = (fann_type)((value < 0) ? -1 : 1);                                                  \
+      break;                                                                                       \
+    case FANN_GAUSSIAN:                                                                            \
+      result = (fann_type)fann_gaussian_real(value);                                               \
+      break;                                                                                       \
+    case FANN_GAUSSIAN_SYMMETRIC:                                                                  \
+      result = (fann_type)fann_gaussian_symmetric_real(value);                                     \
+      break;                                                                                       \
+    case FANN_ELLIOT:                                                                              \
+      result = (fann_type)fann_elliot_real(value);                                                 \
+      break;                                                                                       \
+    case FANN_ELLIOT_SYMMETRIC:                                                                    \
+      result = (fann_type)fann_elliot_symmetric_real(value);                                       \
+      break;                                                                                       \
+    case FANN_SIN_SYMMETRIC:                                                                       \
+      result = (fann_type)fann_sin_symmetric_real(value);                                          \
+      break;                                                                                       \
+    case FANN_COS_SYMMETRIC:                                                                       \
+      result = (fann_type)fann_cos_symmetric_real(value);                                          \
+      break;                                                                                       \
+    case FANN_SIN:                                                                                 \
+      result = (fann_type)fann_sin_real(value);                                                    \
+      break;                                                                                       \
+    case FANN_COS:                                                                                 \
+      result = (fann_type)fann_cos_real(value);                                                    \
+      break;                                                                                       \
+    case FANN_GAUSSIAN_STEPWISE:                                                                   \
+      result = 0;                                                                                  \
+      break;                                                                                       \
+  }
+
+#endif
diff --git a/ai_fighter/AI_Fight_Sim/Plugins/NeuralNetworkAIController/Source/NeuralNetworkAIController/ThirdParty/FANN/include/fann_cascade.h b/ai_fighter/AI_Fight_Sim/Plugins/NeuralNetworkAIController/Source/NeuralNetworkAIController/ThirdParty/FANN/include/fann_cascade.h
new file mode 100644
index 0000000..fd56367
--- /dev/null
+++ b/ai_fighter/AI_Fight_Sim/Plugins/NeuralNetworkAIController/Source/NeuralNetworkAIController/ThirdParty/FANN/include/fann_cascade.h
@@ -0,0 +1,550 @@
+/*
+Fast Artificial Neural Network Library (fann)
+Copyright (C) 2003-2016 Steffen Nissen (steffen.fann@gmail.com)
+
+This library is free software; you can redistribute it and/or
+modify it under the terms of the GNU Lesser General Public
+License as published by the Free Software Foundation; either
+version 2.1 of the License, or (at your option) any later version.
+
+This library is distributed in the hope that it will be useful,
+but WITHOUT ANY WARRANTY; without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
+Lesser General Public License for more details.
+
+You should have received a copy of the GNU Lesser General Public
+License along with this library; if not, write to the Free Software
+Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA  02111-1307  USA
+*/
+
+#ifndef __fann_cascade_h__
+#define __fann_cascade_h__
+
+/* Section: FANN Cascade Training
+   Cascade training differs from ordinary training in the sense that it starts with an empty neural
+   network and then adds neurons one by one, while it trains the neural network. The main benefit of
+   this approach is that you do not have to guess the number of hidden layers and neurons prior to
+   training, but cascade training has also proved better at solving some problems.
+
+   The basic idea of cascade training is that a number of candidate neurons are trained separate
+   from the real network, then the most promising of these candidate neurons is inserted into the
+   neural network. Then the output connections are trained and new candidate neurons are prepared.
+   The candidate neurons are created as shortcut connected neurons in a new hidden layer, which
+   means that the final neural network will consist of a number of hidden layers with one shortcut
+   connected neuron in each.
+*/
+
+/* Group: Cascade Training */
+
+#ifndef FIXEDFANN
+/* Function: fann_cascadetrain_on_data
+
+   Trains on an entire dataset, for a period of time using the Cascade2 training algorithm.
+   This algorithm adds neurons to the neural network while training, which means that it
+   needs to start with an ANN without any hidden layers. The neural network should also use
+   shortcut connections, so <fann_create_shortcut> should be used to create the ANN like this:
+   >struct fann *ann = fann_create_shortcut(2, fann_num_input_train_data(train_data),
+   fann_num_output_train_data(train_data));
+
+   This training uses the parameters set using the fann_set_cascade_..., but it also uses another
+   training algorithm as it's internal training algorithm. This algorithm can be set to either
+   FANN_TRAIN_RPROP or FANN_TRAIN_QUICKPROP by <fann_set_training_algorithm>, and the parameters
+   set for these training algorithms will also affect the cascade training.
+
+   Parameters:
+                ann - The neural network
+                data - The data, which should be used during training
+                max_neuron - The maximum number of neurons to be added to neural network
+                neurons_between_reports - The number of neurons between printing a status report to
+   stdout. A value of zero means no reports should be printed. desired_error - The desired
+   <fann_get_MSE> or <fann_get_bit_fail>, depending on which stop function is chosen by
+   <fann_set_train_stop_function>.
+
+        Instead of printing out reports every neurons_between_reports, a callback function can be
+   called (see <fann_set_callback>).
+
+        See also:
+                <fann_train_on_data>, <fann_cascadetrain_on_file>, <Parameters>
+
+        This function appears in FANN >= 2.0.0.
+*/
+FANN_EXTERNAL void FANN_API fann_cascadetrain_on_data(struct fann *ann,
+                                                      struct fann_train_data *data,
+                                                      unsigned int max_neurons,
+                                                      unsigned int neurons_between_reports,
+                                                      float desired_error);
+
+/* Function: fann_cascadetrain_on_file
+
+   Does the same as <fann_cascadetrain_on_data>, but reads the training data directly from a file.
+
+   See also:
+                <fann_cascadetrain_on_data>
+
+        This function appears in FANN >= 2.0.0.
+*/
+FANN_EXTERNAL void FANN_API fann_cascadetrain_on_file(struct fann *ann, const char *filename,
+                                                      unsigned int max_neurons,
+                                                      unsigned int neurons_between_reports,
+                                                      float desired_error);
+
+/* Group: Parameters */
+
+/* Function: fann_get_cascade_output_change_fraction
+
+   The cascade output change fraction is a number between 0 and 1 determining how large a fraction
+   the <fann_get_MSE> value should change within <fann_get_cascade_output_stagnation_epochs> during
+   training of the output connections, in order for the training not to stagnate. If the training
+   stagnates, the training of the output connections will be ended and new candidates will be
+   prepared.
+
+   This means:
+   If the MSE does not change by a fraction of <fann_get_cascade_output_change_fraction> during a
+   period of <fann_get_cascade_output_stagnation_epochs>, the training of the output connections
+   is stopped because the training has stagnated.
+
+   If the cascade output change fraction is low, the output connections will be trained more and if
+   the fraction is high they will be trained less.
+
+   The default cascade output change fraction is 0.01, which is equivalent to a 1% change in MSE.
+
+   See also:
+                <fann_set_cascade_output_change_fraction>, <fann_get_MSE>,
+   <fann_get_cascade_output_stagnation_epochs>
+
+        This function appears in FANN >= 2.0.0.
+ */
+FANN_EXTERNAL float FANN_API fann_get_cascade_output_change_fraction(struct fann *ann);
+
+/* Function: fann_set_cascade_output_change_fraction
+
+   Sets the cascade output change fraction.
+
+   See also:
+                <fann_get_cascade_output_change_fraction>
+
+        This function appears in FANN >= 2.0.0.
+ */
+FANN_EXTERNAL void FANN_API
+fann_set_cascade_output_change_fraction(struct fann *ann, float cascade_output_change_fraction);
+
+/* Function: fann_get_cascade_output_stagnation_epochs
+
+   The number of cascade output stagnation epochs determines the number of epochs training is
+   allowed to continue without changing the MSE by a fraction of
+   <fann_get_cascade_output_change_fraction>.
+
+   See more info about this parameter in <fann_get_cascade_output_change_fraction>.
+
+   The default number of cascade output stagnation epochs is 12.
+
+   See also:
+                <fann_set_cascade_output_stagnation_epochs>,
+   <fann_get_cascade_output_change_fraction>
+
+        This function appears in FANN >= 2.0.0.
+ */
+FANN_EXTERNAL unsigned int FANN_API fann_get_cascade_output_stagnation_epochs(struct fann *ann);
+
+/* Function: fann_set_cascade_output_stagnation_epochs
+
+   Sets the number of cascade output stagnation epochs.
+
+   See also:
+                <fann_get_cascade_output_stagnation_epochs>
+
+        This function appears in FANN >= 2.0.0.
+ */
+FANN_EXTERNAL void FANN_API fann_set_cascade_output_stagnation_epochs(
+    struct fann *ann, unsigned int cascade_output_stagnation_epochs);
+
+/* Function: fann_get_cascade_candidate_change_fraction
+
+   The cascade candidate change fraction is a number between 0 and 1 determining how large a
+   fraction the <fann_get_MSE> value should change within
+   <fann_get_cascade_candidate_stagnation_epochs> during training of the candidate neurons, in order
+   for the training not to stagnate. If the training stagnates, the training of the candidate
+   neurons will be ended and the best candidate will be selected.
+
+   This means:
+   If the MSE does not change by a fraction of <fann_get_cascade_candidate_change_fraction> during a
+   period of <fann_get_cascade_candidate_stagnation_epochs>, the training of the candidate neurons
+   is stopped because the training has stagnated.
+
+   If the cascade candidate change fraction is low, the candidate neurons will be trained more and
+   if the fraction is high they will be trained less.
+
+   The default cascade candidate change fraction is 0.01, which is equivalent to a 1% change in MSE.
+
+   See also:
+                <fann_set_cascade_candidate_change_fraction>, <fann_get_MSE>,
+   <fann_get_cascade_candidate_stagnation_epochs>
+
+        This function appears in FANN >= 2.0.0.
+ */
+FANN_EXTERNAL float FANN_API fann_get_cascade_candidate_change_fraction(struct fann *ann);
+
+/* Function: fann_set_cascade_candidate_change_fraction
+
+   Sets the cascade candidate change fraction.
+
+   See also:
+                <fann_get_cascade_candidate_change_fraction>
+
+        This function appears in FANN >= 2.0.0.
+ */
+FANN_EXTERNAL void FANN_API fann_set_cascade_candidate_change_fraction(
+    struct fann *ann, float cascade_candidate_change_fraction);
+
+/* Function: fann_get_cascade_candidate_stagnation_epochs
+
+   The number of cascade candidate stagnation epochs determines the number of epochs training is
+   allowed to continue without changing the MSE by a fraction of
+   <fann_get_cascade_candidate_change_fraction>.
+
+   See more info about this parameter in <fann_get_cascade_candidate_change_fraction>.
+
+   The default number of cascade candidate stagnation epochs is 12.
+
+   See also:
+                <fann_set_cascade_candidate_stagnation_epochs>,
+   <fann_get_cascade_candidate_change_fraction>
+
+        This function appears in FANN >= 2.0.0.
+ */
+FANN_EXTERNAL unsigned int FANN_API fann_get_cascade_candidate_stagnation_epochs(struct fann *ann);
+
+/* Function: fann_set_cascade_candidate_stagnation_epochs
+
+   Sets the number of cascade candidate stagnation epochs.
+
+   See also:
+                <fann_get_cascade_candidate_stagnation_epochs>
+
+        This function appears in FANN >= 2.0.0.
+ */
+FANN_EXTERNAL void FANN_API fann_set_cascade_candidate_stagnation_epochs(
+    struct fann *ann, unsigned int cascade_candidate_stagnation_epochs);
+
+/* Function: fann_get_cascade_weight_multiplier
+
+   The weight multiplier is a parameter which is used to multiply the weights from the candidate
+   neuron before adding the neuron to the neural network. This parameter is usually between 0 and 1,
+   and is used to make the training a bit less aggressive.
+
+   The default weight multiplier is 0.4
+
+   See also:
+                <fann_set_cascade_weight_multiplier>
+
+        This function appears in FANN >= 2.0.0.
+ */
+FANN_EXTERNAL fann_type FANN_API fann_get_cascade_weight_multiplier(struct fann *ann);
+
+/* Function: fann_set_cascade_weight_multiplier
+
+   Sets the weight multiplier.
+
+   See also:
+                <fann_get_cascade_weight_multiplier>
+
+        This function appears in FANN >= 2.0.0.
+ */
+FANN_EXTERNAL void FANN_API fann_set_cascade_weight_multiplier(struct fann *ann,
+                                                               fann_type cascade_weight_multiplier);
+
+/* Function: fann_get_cascade_candidate_limit
+
+   The candidate limit is a limit for how much the candidate neuron may be trained.
+   The limit is a limit on the proportion between the MSE and candidate score.
+
+   Set this to a lower value to avoid overfitting and to a higher if overfitting is
+   not a problem.
+
+   The default candidate limit is 1000.0
+
+   See also:
+                <fann_set_cascade_candidate_limit>
+
+        This function appears in FANN >= 2.0.0.
+ */
+FANN_EXTERNAL fann_type FANN_API fann_get_cascade_candidate_limit(struct fann *ann);
+
+/* Function: fann_set_cascade_candidate_limit
+
+   Sets the candidate limit.
+
+   See also:
+                <fann_get_cascade_candidate_limit>
+
+        This function appears in FANN >= 2.0.0.
+ */
+FANN_EXTERNAL void FANN_API fann_set_cascade_candidate_limit(struct fann *ann,
+                                                             fann_type cascade_candidate_limit);
+
+/* Function: fann_get_cascade_max_out_epochs
+
+   The maximum out epochs determines the maximum number of epochs the output connections
+   may be trained after adding a new candidate neuron.
+
+   The default max out epochs is 150
+
+   See also:
+                <fann_set_cascade_max_out_epochs>
+
+        This function appears in FANN >= 2.0.0.
+ */
+FANN_EXTERNAL unsigned int FANN_API fann_get_cascade_max_out_epochs(struct fann *ann);
+
+/* Function: fann_set_cascade_max_out_epochs
+
+   Sets the maximum out epochs.
+
+   See also:
+                <fann_get_cascade_max_out_epochs>
+
+        This function appears in FANN >= 2.0.0.
+ */
+FANN_EXTERNAL void FANN_API fann_set_cascade_max_out_epochs(struct fann *ann,
+                                                            unsigned int cascade_max_out_epochs);
+
+/* Function: fann_get_cascade_min_out_epochs
+
+   The minimum out epochs determines the minimum number of epochs the output connections
+   must be trained after adding a new candidate neuron.
+
+   The default min out epochs is 50
+
+   See also:
+                <fann_set_cascade_min_out_epochs>
+
+        This function appears in FANN >= 2.2.0.
+ */
+FANN_EXTERNAL unsigned int FANN_API fann_get_cascade_min_out_epochs(struct fann *ann);
+
+/* Function: fann_set_cascade_min_out_epochs
+
+   Sets the minimum out epochs.
+
+   See also:
+                <fann_get_cascade_min_out_epochs>
+
+        This function appears in FANN >= 2.2.0.
+ */
+FANN_EXTERNAL void FANN_API fann_set_cascade_min_out_epochs(struct fann *ann,
+                                                            unsigned int cascade_min_out_epochs);
+
+/* Function: fann_get_cascade_max_cand_epochs
+
+   The maximum candidate epochs determines the maximum number of epochs the input
+   connections to the candidates may be trained before adding a new candidate neuron.
+
+   The default max candidate epochs is 150
+
+   See also:
+                <fann_set_cascade_max_cand_epochs>
+
+        This function appears in FANN >= 2.0.0.
+ */
+FANN_EXTERNAL unsigned int FANN_API fann_get_cascade_max_cand_epochs(struct fann *ann);
+
+/* Function: fann_set_cascade_max_cand_epochs
+
+   Sets the max candidate epochs.
+
+   See also:
+                <fann_get_cascade_max_cand_epochs>
+
+        This function appears in FANN >= 2.0.0.
+ */
+FANN_EXTERNAL void FANN_API fann_set_cascade_max_cand_epochs(struct fann *ann,
+                                                             unsigned int cascade_max_cand_epochs);
+
+/* Function: fann_get_cascade_min_cand_epochs
+
+   The minimum candidate epochs determines the minimum number of epochs the input
+   connections to the candidates may be trained before adding a new candidate neuron.
+
+   The default min candidate epochs is 50
+
+   See also:
+                <fann_set_cascade_min_cand_epochs>
+
+        This function appears in FANN >= 2.2.0.
+ */
+FANN_EXTERNAL unsigned int FANN_API fann_get_cascade_min_cand_epochs(struct fann *ann);
+
+/* Function: fann_set_cascade_min_cand_epochs
+
+   Sets the min candidate epochs.
+
+   See also:
+                <fann_get_cascade_min_cand_epochs>
+
+        This function appears in FANN >= 2.2.0.
+ */
+FANN_EXTERNAL void FANN_API fann_set_cascade_min_cand_epochs(struct fann *ann,
+                                                             unsigned int cascade_min_cand_epochs);
+
+/* Function: fann_get_cascade_num_candidates
+
+   The number of candidates used during training (calculated by multiplying
+   <fann_get_cascade_activation_functions_count>, <fann_get_cascade_activation_steepnesses_count>
+   and <fann_get_cascade_num_candidate_groups>).
+
+   The actual candidates is defined by the <fann_get_cascade_activation_functions> and
+   <fann_get_cascade_activation_steepnesses> arrays. These arrays define the activation functions
+   and activation steepnesses used for the candidate neurons. If there are 2 activation functions
+   in the activation function array and 3 steepnesses in the steepness array, then there will be
+   2x3=6 different candidates which will be trained. These 6 different candidates can be copied into
+   several candidate groups, where the only difference between these groups is the initial weights.
+   If the number of groups is set to 2, then the number of candidate neurons will be 2x3x2=12. The
+   number of candidate groups is defined by <fann_set_cascade_num_candidate_groups>.
+
+   The default number of candidates is 6x4x2 = 48
+
+   See also:
+                <fann_get_cascade_activation_functions>,
+   <fann_get_cascade_activation_functions_count>, <fann_get_cascade_activation_steepnesses>,
+   <fann_get_cascade_activation_steepnesses_count>, <fann_get_cascade_num_candidate_groups>
+
+        This function appears in FANN >= 2.0.0.
+ */
+FANN_EXTERNAL unsigned int FANN_API fann_get_cascade_num_candidates(struct fann *ann);
+
+/* Function: fann_get_cascade_activation_functions_count
+
+   The number of activation functions in the <fann_get_cascade_activation_functions> array.
+
+   The default number of activation functions is 10.
+
+   See also:
+                <fann_get_cascade_activation_functions>, <fann_set_cascade_activation_functions>
+
+        This function appears in FANN >= 2.0.0.
+ */
+FANN_EXTERNAL unsigned int FANN_API fann_get_cascade_activation_functions_count(struct fann *ann);
+
+/* Function: fann_get_cascade_activation_functions
+
+   The cascade activation functions array is an array of the different activation functions used by
+   the candidates.
+
+   See <fann_get_cascade_num_candidates> for a description of which candidate neurons will be
+   generated by this array.
+
+   The default activation functions are {FANN_SIGMOID, FANN_SIGMOID_SYMMETRIC, FANN_GAUSSIAN,
+   FANN_GAUSSIAN_SYMMETRIC, FANN_ELLIOT, FANN_ELLIOT_SYMMETRIC, FANN_SIN_SYMMETRIC,
+   FANN_COS_SYMMETRIC, FANN_SIN, FANN_COS}
+
+   See also:
+                <fann_get_cascade_activation_functions_count>,
+   <fann_set_cascade_activation_functions>, <fann_activationfunc_enum>
+
+        This function appears in FANN >= 2.0.0.
+ */
+FANN_EXTERNAL enum fann_activationfunc_enum *FANN_API
+fann_get_cascade_activation_functions(struct fann *ann);
+
+/* Function: fann_set_cascade_activation_functions
+
+   Sets the array of cascade candidate activation functions. The array must be just as long
+   as defined by the count.
+
+   See <fann_get_cascade_num_candidates> for a description of which candidate neurons will be
+   generated by this array.
+
+   See also:
+                <fann_get_cascade_activation_steepnesses_count>,
+   <fann_get_cascade_activation_steepnesses>
+
+        This function appears in FANN >= 2.0.0.
+ */
+FANN_EXTERNAL void FANN_API fann_set_cascade_activation_functions(
+    struct fann *ann, enum fann_activationfunc_enum *cascade_activation_functions,
+    unsigned int cascade_activation_functions_count);
+
+/* Function: fann_get_cascade_activation_steepnesses_count
+
+   The number of activation steepnesses in the <fann_get_cascade_activation_functions> array.
+
+   The default number of activation steepnesses is 4.
+
+   See also:
+                <fann_get_cascade_activation_steepnesses>, <fann_set_cascade_activation_functions>
+
+        This function appears in FANN >= 2.0.0.
+ */
+FANN_EXTERNAL unsigned int FANN_API fann_get_cascade_activation_steepnesses_count(struct fann *ann);
+
+/* Function: fann_get_cascade_activation_steepnesses
+
+   The cascade activation steepnesses array is an array of the different activation functions used
+   by the candidates.
+
+   See <fann_get_cascade_num_candidates> for a description of which candidate neurons will be
+   generated by this array.
+
+   The default activation steepnesses is {0.25, 0.50, 0.75, 1.00}
+
+   See also:
+                <fann_set_cascade_activation_steepnesses>,
+   <fann_get_cascade_activation_steepnesses_count>
+
+        This function appears in FANN >= 2.0.0.
+ */
+FANN_EXTERNAL fann_type *FANN_API fann_get_cascade_activation_steepnesses(struct fann *ann);
+
+/* Function: fann_set_cascade_activation_steepnesses
+
+   Sets the array of cascade candidate activation steepnesses. The array must be just as long
+   as defined by the count.
+
+   See <fann_get_cascade_num_candidates> for a description of which candidate neurons will be
+   generated by this array.
+
+   See also:
+                <fann_get_cascade_activation_steepnesses>,
+   <fann_get_cascade_activation_steepnesses_count>
+
+        This function appears in FANN >= 2.0.0.
+ */
+FANN_EXTERNAL void FANN_API
+fann_set_cascade_activation_steepnesses(struct fann *ann, fann_type *cascade_activation_steepnesses,
+                                        unsigned int cascade_activation_steepnesses_count);
+
+/* Function: fann_get_cascade_num_candidate_groups
+
+   The number of candidate groups is the number of groups of identical candidates which will be used
+   during training.
+
+   This number can be used to have more candidates without having to define new parameters for the
+   candidates.
+
+   See <fann_get_cascade_num_candidates> for a description of which candidate neurons will be
+   generated by this parameter.
+
+   The default number of candidate groups is 2
+
+   See also:
+                <fann_set_cascade_num_candidate_groups>
+
+        This function appears in FANN >= 2.0.0.
+ */
+FANN_EXTERNAL unsigned int FANN_API fann_get_cascade_num_candidate_groups(struct fann *ann);
+
+/* Function: fann_set_cascade_num_candidate_groups
+
+   Sets the number of candidate groups.
+
+   See also:
+                <fann_get_cascade_num_candidate_groups>
+
+        This function appears in FANN >= 2.0.0.
+ */
+FANN_EXTERNAL void FANN_API
+fann_set_cascade_num_candidate_groups(struct fann *ann, unsigned int cascade_num_candidate_groups);
+
+#endif /* FIXEDFANN */
+
+#endif
diff --git a/ai_fighter/AI_Fight_Sim/Plugins/NeuralNetworkAIController/Source/NeuralNetworkAIController/ThirdParty/FANN/include/fann_cpp.h b/ai_fighter/AI_Fight_Sim/Plugins/NeuralNetworkAIController/Source/NeuralNetworkAIController/ThirdParty/FANN/include/fann_cpp.h
new file mode 100644
index 0000000..412b752
--- /dev/null
+++ b/ai_fighter/AI_Fight_Sim/Plugins/NeuralNetworkAIController/Source/NeuralNetworkAIController/ThirdParty/FANN/include/fann_cpp.h
@@ -0,0 +1,2968 @@
+#ifndef FANN_CPP_H_INCLUDED
+#define FANN_CPP_H_INCLUDED
+
+#include <memory>
+#include <iterator>
+/*
+ *  Fast Artificial Neural Network (fann) C++ Wrapper
+ *  Copyright (C) 2004-2006 created by freegoldbar (at) yahoo dot com
+ *
+ *  This wrapper is free software; you can redistribute it and/or
+ *  modify it under the terms of the GNU Lesser General Public
+ *  License as published by the Free Software Foundation; either
+ *  version 2.1 of the License, or (at your option) any later version.
+ *
+ *  This wrapper is distributed in the hope that it will be useful,
+ *  but WITHOUT ANY WARRANTY; without even the implied warranty of
+ *  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
+ *  Lesser General Public License for more details.
+ *
+ *  You should have received a copy of the GNU Lesser General Public
+ *  License along with this library; if not, write to the Free Software
+ *  Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA  02111-1307  USA
+ *
+ */
+
+/*
+ *  Title: FANN C++ Wrapper
+ *
+ *  Overview:
+ *
+ *  The Fann Wrapper for C++ provides two classes: <neural_net>
+ *  and <training_data>. To use the wrapper include
+ *  doublefann.h, floatfann.h or fixedfann.h before the
+ *  fann_cpp.h header file.
+ *
+ *  To get started see xor_sample.cpp in the examples directory.
+ *  The license is LGPL. Copyright (C) 2004-2006 created by <freegoldbar@yahoo.com>.
+ *
+ *
+ *  Note:  Notes and differences from C API
+ *
+ *  -  The Fann Wrapper for C++ is a minimal wrapper without use of
+ *       templates or exception handling for efficient use in any environment.
+ *       Benefits include stricter type checking, simpler memory
+ *       management and possibly code completion in program editor.
+ *  -  Method names are the same as the function names in the C
+ *       API except the fann_ prefix has been removed. Enums in the
+ *       namespace are similarly defined without the FANN_ prefix.
+ *  -  The arguments to the methods are the same as the C API
+ *       except that the struct fann *ann/struct fann_train_data *data
+ *       arguments are encapsulated so they are not present in the
+ *       method signatures or are translated into class references.
+ *  -  C++ style constructors have been implemented to make network creation
+ *       easier
+ *  -  The neural network and training data is automatically cleaned
+ *       up in the destructors
+ *  -  To make the destructors virtual define USE_VIRTUAL_DESTRUCTOR
+ *       before including the header file.
+ *  -  Additional methods are available on the training_data class to
+ *       give access to the underlying training data. They are get_input,
+ *       get_output and set_train_data. Finally fann_duplicate_train_data
+ *       has been replaced by a copy constructor.
+ *
+ */
+
+#include <assert.h>
+#include <stdarg.h>
+#include <string>
+#include "fann_data_cpp.h"
+#include "fann_training_data_cpp.h"
+
+/* Namespace: FANN
+    The FANN namespace groups the C++ wrapper definitions */
+namespace FANN {
+/* Class: neural_net
+    <neural_net> is the main neural network class used for both training and execution
+
+    Encapsulation of a neural network <struct fann> and
+    associated C API functions.
+*/
+class neural_net {
+ public:
+  /* Constructor: neural_net(network_type_enum net_type, unsigned int num_layers, const unsigned int
+     *layers)
+
+      Creates a neural network of the desired <network_type_enum> net_type, based on array of
+     layers.
+
+      Parameters:
+          net_type - The desired network type of the neural network
+          num_layers - The total number of layers including the input and the output layer.
+          layers - array of the layer sizes
+
+      NOTE: if layers does not have the same size as num_layers, the result is undefined
+
+      Example:
+        >FANN::neural_net net(LAYER, 3, (unsigned int[]) {2, 3, 1});
+
+      This function appears in FANN >= 2.3.0.
+   */
+  neural_net(network_type_enum net_type, unsigned int num_layers, const unsigned int *layers) {
+    switch (net_type) {
+      case LAYER:
+        ann = fann_create_standard_array(num_layers, layers);
+        break;
+      case SHORTCUT:
+        ann = fann_create_shortcut_array(num_layers, layers);
+        break;
+    }
+    assert(ann != NULL);
+  }
+
+  /* Constructor: neural_net(network_type_enum net_type, InputIterator layersBeginIterator,
+     InputIterator layersEndIterator)
+
+      Creates a neural network of the desired <network_type_enum> net_type, based on iterator to the
+     layers.
+
+      Parameters:
+          net_type - The desired network type of the neural network
+          layersBeginIterator - begin iterator to the collection of unsigned int layers
+          layersEndIterator - end iterator to the collection of unsigned int layers
+
+      Example:
+        >vector<unsigned int> layers{2, 3, 4, 5};
+        >neural_net net(LAYER, layers.begin(), layers.end());
+
+      This function appears in FANN >= 2.3.0.
+   */
+  template <class InputIterator>
+  neural_net(network_type_enum net_type, InputIterator layersBeginIterator,
+             InputIterator layersEndIterator) {
+    unsigned int num_layers =
+        static_cast<unsigned int>(std::distance(layersBeginIterator, layersEndIterator));
+    unsigned int *layers = new unsigned int[num_layers];
+    std::copy(layersBeginIterator, layersEndIterator, layers);
+
+    switch (net_type) {
+      case LAYER:
+        ann = fann_create_standard_array(num_layers, layers);
+        break;
+      case SHORTCUT:
+        ann = fann_create_shortcut_array(num_layers, layers);
+        break;
+    }
+    delete[] layers;
+    assert(ann != NULL);
+  }
+
+  /* Constructor: neural_net(network_type_enum net_type, unsigned int num_layers, ...)
+
+          Creates a neural network of the desired <network_type_enum> net_type.
+
+          Parameters:
+                  num_layers - The total number of layers including the input and the output layer.
+                  ... - Integer values determining the number of neurons in each layer starting with
+     the input layer and ending with the output layer.
+
+      Example:
+          >const unsigned int num_layers = 3;
+          >const unsigned int num_input = 2;
+          >const unsigned int num_hidden = 3;
+          >const unsigned int num_output = 1;
+          >
+          >FANN::neural_net net(num_layers, num_input, num_hidden, num_output);
+
+          This function appears in FANN >= 2.3.0.
+  */
+  neural_net(network_type_enum net_type, unsigned int num_layers, ...) {
+    std::unique_ptr<unsigned int[]> data(new unsigned int[num_layers]);
+
+    va_list layers;
+    va_start(layers, num_layers);
+    for (unsigned int i = 0; i < num_layers; i++) data.get()[i] = va_arg(layers, unsigned int);
+    va_end(layers);
+
+    switch (net_type) {
+      case LAYER:
+        ann = fann_create_standard_array(num_layers, data.get());
+        break;
+      case SHORTCUT:
+        ann = fann_create_shortcut_array(num_layers, data.get());
+        break;
+    }
+    assert(ann != NULL);
+  }
+
+  /* Constructor: neural_net(float connection_rate, unsigned int num_layers, ...)
+
+          Creates a standard backpropagation neural network, which is sparsely connected, this will
+     default the <network_type_enum> to <LAYER>
+
+          Parameters:
+                  connection_rate - The connection rate controls how many connections there will be
+     in the network. If the connection rate is set to 1, the network will be fully connected, but if
+     it is set to 0.5 only half of the connections will be set. A connection rate of 1 will yield
+     the same result as <fann_create_standard> num_layers - The total number of layers including the
+     input and the output layer.
+                  ... - Integer values determining the number of neurons in each layer starting with
+     the input layer and ending with the output layer.
+
+          This function appears in FANN >= 2.3.0.
+  */
+  neural_net(float connection_rate, unsigned int num_layers, ...) {
+    std::unique_ptr<unsigned int[]> data(new unsigned int[num_layers]);
+
+    va_list layers;
+    va_start(layers, num_layers);
+    for (unsigned int i = 0; i < num_layers; i++) data.get()[i] = va_arg(layers, unsigned int);
+    va_end(layers);
+
+    ann = fann_create_sparse_array(connection_rate, num_layers, data.get());
+    assert(ann != NULL);
+  }
+
+  /* Constructor: neural_net(float connection_rate, unsigned int num_layers, const unsigned int
+     *layers)
+
+          Creates a standard backpropagation neural network, which is sparsely connected, this will
+     default the <network_type_enum> to <LAYER>
+
+          Parameters:
+                  connection_rate - The connection rate controls how many connections there will be
+     in the network. If the connection rate is set to 1, the network will be fully connected, but if
+     it is set to 0.5 only half of the connections will be set. A connection rate of 1 will yield
+     the same result as <fann_create_standard> num_layers - The total number of layers including the
+     input and the output layer. layers - Integer values determining the number of neurons in each
+     layer starting with the input layer and ending with the output layer.
+
+          This function appears in FANN >= 2.3.0.
+  */
+  neural_net(float connection_rate, unsigned int num_layers, const unsigned int *layers) {
+    ann = fann_create_sparse_array(connection_rate, num_layers, layers);
+    assert(ann != NULL);
+  }
+
+  /* Constructor: neural_net(const std::string &configuration_file)
+
+     Constructs a backpropagation neural network from a configuration file,
+     which have been saved by <save>.
+
+     See also:
+          <save>, <save_to_fixed>
+
+     This function appears in FANN >= 2.3.0.
+   */
+  neural_net(const std::string &configuration_file) {
+    ann = fann_create_from_file(configuration_file.c_str());
+    assert(ann != NULL);
+  }
+
+  /* Constructor neural_net(const neural_net &other)
+
+      Creates a copy the other neural_net.
+      */
+  neural_net(const neural_net &other) : ann(NULL) { copy_from_struct_fann(other.ann); }
+
+  /* Constructor: neural_net(struct fann *other)
+
+     Creates a copy the other neural_net.
+      */
+  neural_net(struct fann *other) { copy_from_struct_fann(other); }
+
+  /* Constructor: neural_net() - DEPRECATED
+
+      Creates an empty neural net.
+      Use one of the create functions to create the neural network.
+
+     NOTE: As of version 2.3.0 it recommended to create neural networks using the constructors
+     instead of creating an empty network and setting the internal structure of that. This method is
+     hence discouraged and will be deprecated later on.
+
+      See also:
+                  <create_standard>, <create_sparse>, <create_shortcut>,
+                  <create_standard_array>, <create_sparse_array>, <create_shortcut_array>
+  */
+  neural_net() : ann(NULL) {}
+
+  /* Method: copy_from_struct_fann - DEPRECATED
+
+     Set the internal fann struct to a copy of other
+
+     NOTE: As of version 2.3.0 it recommended to create neural networks using the constructors
+     instead of creating an empty network and setting the internal structure of that. This method is
+     hence discouraged and will be deprecated later on.
+  */
+  void copy_from_struct_fann(struct fann *other) {
+    destroy();
+    if (other != NULL) ann = fann_copy(other);
+  }
+
+  /* Destructor: ~neural_net
+
+      Provides automatic cleanup of data.
+      Define USE_VIRTUAL_DESTRUCTOR if you need the destructor to be virtual.
+
+      See also:
+          <destroy>
+  */
+#ifdef USE_VIRTUAL_DESTRUCTOR
+  virtual
+#endif
+
+      ~neural_net() {
+    destroy();
+  }
+
+  /* Method: destroy
+
+      Destructs the entire network. Called automatically by the destructor.
+
+      See also:
+          <~neural_net>
+  */
+  void destroy() {
+    if (ann != NULL) {
+      user_context *user_data = static_cast<user_context *>(fann_get_user_data(ann));
+      if (user_data != NULL) delete user_data;
+
+      fann_destroy(ann);
+      ann = NULL;
+    }
+  }
+
+  /* Method: create_standard - DEPRECATED
+
+          Creates a standard fully connected backpropagation neural network.
+
+          There will be a bias neuron in each layer (except the output layer),
+          and this bias neuron will be connected to all neurons in the next layer.
+          When running the network, the bias nodes always emits 1.
+
+          Parameters:
+                  num_layers - The total number of layers including the input and the output layer.
+                  ... - Integer values determining the number of neurons in each layer starting with
+     the input layer and ending with the output layer.
+
+          Returns:
+                  Boolean true if the network was created, false otherwise.
+
+      Example:
+          >const unsigned int num_layers = 3;
+          >const unsigned int num_input = 2;
+          >const unsigned int num_hidden = 3;
+          >const unsigned int num_output = 1;
+          >
+          >FANN::neural_net net;
+          >net.create_standard(num_layers, num_input, num_hidden, num_output);
+
+     NOTE: As of version 2.3.0 it recommended to create neural networks using the constructors
+     instead of creating an empty network and setting the internal structure of that. This method is
+     hence discouraged and will be deprecated later on.
+
+          See also:
+                  <create_standard_array>, <create_sparse>, <create_shortcut>,
+                  <fann_create_standard_array>
+
+          This function appears in FANN >= 2.0.0.
+  */
+  bool create_standard(unsigned int num_layers, ...) {
+    std::unique_ptr<unsigned int[]> data(new unsigned int[num_layers]);
+
+    va_list layers;
+    va_start(layers, num_layers);
+    for (unsigned int i = 0; i < num_layers; i++) data.get()[i] = va_arg(layers, unsigned int);
+    va_end(layers);
+
+    bool status = create_standard_array(num_layers, data.get());
+    return status;
+  }
+
+  /* Method: create_standard_array - DEPRECATED
+
+     Just like <create_standard>, but with an array of layer sizes
+     instead of individual parameters.
+
+     NOTE: As of version 2.3.0 it recommended to create neural networks using the constructors
+     instead of creating an empty network and setting the internal structure of that. This method is
+     hence discouraged and will be deprecated later on.
+
+          See also:
+                  <create_standard>, <create_sparse>, <create_shortcut>,
+                  <fann_create_standard>
+
+          This function appears in FANN >= 2.0.0.
+  */
+  bool create_standard_array(unsigned int num_layers, const unsigned int *layers) {
+    destroy();
+    ann = fann_create_standard_array(num_layers, layers);
+    return (ann != NULL);
+  }
+
+  /* Method: create_sparse - DEPRECATED
+
+          Creates a standard backpropagation neural network, which is not fully connected.
+
+          Parameters:
+                  connection_rate - The connection rate controls how many connections there will be
+     in the network. If the connection rate is set to 1, the network will be fully connected, but if
+     it is set to 0.5 only half of the connections will be set. A connection rate of 1 will yield
+     the same result as <fann_create_standard> num_layers - The total number of layers including the
+     input and the output layer.
+                  ... - Integer values determining the number of neurons in each layer starting with
+     the input layer and ending with the output layer.
+
+          Returns:
+                  Boolean true if the network was created, false otherwise.
+
+     NOTE: As of version 2.3.0 it recommended to create neural networks using the constructors
+     instead of creating an empty network and setting the internal structure of that. This method is
+     hence discouraged and will be deprecated later on.
+
+          See also:
+                  <create_standard>, <create_sparse_array>, <create_shortcut>,
+                  <fann_create_sparse>
+
+          This function appears in FANN >= 2.0.0.
+  */
+  bool create_sparse(float connection_rate, unsigned int num_layers, ...) {
+    std::unique_ptr<unsigned int[]> data(new unsigned int[num_layers]);
+
+    va_list layers;
+    va_start(layers, num_layers);
+    for (unsigned int i = 0; i < num_layers; i++) data.get()[i] = va_arg(layers, unsigned int);
+    va_end(layers);
+
+    bool status = create_sparse_array(connection_rate, num_layers, data.get());
+    return status;
+  }
+
+  /* Method: create_sparse_array - DEPRECATED
+     Just like <create_sparse>, but with an array of layer sizes
+     instead of individual parameters.
+
+     See <create_sparse> for a description of the parameters.
+
+     NOTE: As of version 2.3.0 it recommended to create neural networks using the constructors
+     instead of creating an empty network and setting the internal structure of that. This method is
+     hence discouraged and will be deprecated later on.
+
+     See also:
+                  <create_standard>, <create_sparse>, <create_shortcut>,
+                  <fann_create_sparse_array>
+
+          This function appears in FANN >= 2.0.0.
+  */
+  bool create_sparse_array(float connection_rate, unsigned int num_layers,
+                           const unsigned int *layers) {
+    destroy();
+    ann = fann_create_sparse_array(connection_rate, num_layers, layers);
+    return (ann != NULL);
+  }
+
+  /* Method: create_shortcut - DEPRECATED
+
+          Creates a standard backpropagation neural network, which is fully connected and which
+          also has shortcut connections.
+
+          Shortcut connections are connections that skip layers. A fully connected network with
+     shortcut connections, is a network where all neurons are connected to all neurons in later
+     layers. Including direct connections from the input layer to the output layer.
+
+          See <create_standard> for a description of the parameters.
+
+      NOTE: As of version 2.3.0 it recommended to create neural networks using the constructors
+     instead of creating an empty network and setting the internal structure of that. This method is
+     hence discouraged and will be deprecated later on.
+
+          See also:
+                  <create_standard>, <create_sparse>, <create_shortcut_array>,
+                  <fann_create_shortcut>
+
+          This function appears in FANN >= 2.0.0.
+  */
+  bool create_shortcut(unsigned int num_layers, ...) {
+    std::unique_ptr<unsigned int[]> data(new unsigned int[num_layers]);
+
+    va_list layers;
+    va_start(layers, num_layers);
+    for (unsigned int i = 0; i < num_layers; i++) data.get()[i] = va_arg(layers, unsigned int);
+    va_end(layers);
+
+    bool status = create_shortcut_array(num_layers, data.get());
+    return status;
+  }
+
+  /* Method: create_shortcut_array - DEPRECATED
+
+     Just like <create_shortcut>, but with an array of layer sizes
+     instead of individual parameters.
+
+          See <create_standard_array> for a description of the parameters.
+
+      NOTE: As of version 2.3.0 it recommended to create neural networks using the constructors
+     instead of creating an empty network and setting the internal structure of that. This method is
+     hence discouraged and will be deprecated later on.
+
+          See also:
+                  <create_standard>, <create_sparse>, <create_shortcut>,
+                  <fann_create_shortcut_array>
+
+          This function appears in FANN >= 2.0.0.
+  */
+  bool create_shortcut_array(unsigned int num_layers, const unsigned int *layers) {
+    destroy();
+    ann = fann_create_shortcut_array(num_layers, layers);
+    return (ann != NULL);
+  }
+
+  /* Method: run
+
+          Will run input through the neural network, returning an array of outputs, the number of
+     which being equal to the number of neurons in the output layer.
+
+          See also:
+                  <test>, <fann_run>
+
+          This function appears in FANN >= 1.0.0.
+  */
+  fann_type *run(fann_type *input) {
+    if (ann == NULL) {
+      return NULL;
+    }
+    return fann_run(ann, input);
+  }
+
+  /* Method: randomize_weights
+
+          Give each connection a random weight between *min_weight* and *max_weight*
+
+          From the beginning the weights are random between -0.1 and 0.1.
+
+          See also:
+                  <init_weights>, <fann_randomize_weights>
+
+          This function appears in FANN >= 1.0.0.
+  */
+  void randomize_weights(fann_type min_weight, fann_type max_weight) {
+    if (ann != NULL) {
+      fann_randomize_weights(ann, min_weight, max_weight);
+    }
+  }
+
+  /* Method: init_weights
+
+          Initialize the weights using Widrow + Nguyen's algorithm.
+
+          This function behaves similarly to fann_randomize_weights. It will use the algorithm
+     developed by Derrick Nguyen and Bernard Widrow to set the weights in such a way as to speed up
+     training. This technique is not always successful, and in some cases can be less efficient than
+     a purely random initialization.
+
+          The algorithm requires access to the range of the input data (ie, largest and smallest
+     input), and therefore accepts a second argument, data, which is the training data that will be
+     used to train the network.
+
+          See also:
+                  <randomize_weights>, <training_data::read_train_from_file>,
+          <fann_init_weights>
+
+          This function appears in FANN >= 1.1.0.
+  */
+  void init_weights(const training_data &data) {
+    if ((ann != NULL) && (data.train_data != NULL)) {
+      fann_init_weights(ann, data.train_data);
+    }
+  }
+
+  /* Method: print_connections
+
+          Will print the connections of the ann in a compact matrix, for easy viewing of the
+     internals of the ann.
+
+          The output from fann_print_connections on a small (2 2 1) network trained on the xor
+     problem >Layer / Neuron 012345 >L   1 / N    3 BBa... >L   1 / N    4 BBA... >L   1 / N    5
+     ...... >L   2 / N    6 ...BBA >L   2 / N    7 ......
+
+          This network have five real neurons and two bias neurons. This gives a total of seven
+     neurons named from 0 to 6. The connections between these neurons can be seen in the matrix. "."
+     is a place where there is no connection, while a character tells how strong the connection is
+     on a scale from a-z. The two real neurons in the hidden layer (neuron 3 and 4 in layer 1) has
+          connection from the three neurons in the previous layer as is visible in the first two
+     lines. The output neuron (6) has connections form the three neurons in the hidden layer 3 - 5
+     as is visible in the fourth line.
+
+          To simplify the matrix output neurons is not visible as neurons that connections can come
+     from, and input and bias neurons are not visible as neurons that connections can go to.
+
+          This function appears in FANN >= 1.2.0.
+  */
+  void print_connections() {
+    if (ann != NULL) {
+      fann_print_connections(ann);
+    }
+  }
+
+  /* Method: create_from_file - DEPRECATED
+
+     Constructs a backpropagation neural network from a configuration file,
+     which have been saved by <save>.
+
+     NOTE: As of version 2.3.0 it recommended to create neural networks using the constructors
+     instead of creating an empty network and setting the internal structure of that. This method is
+     hence discouraged and will be deprecated later on.
+
+     See also:
+          <save>, <save_to_fixed>, <fann_create_from_file>
+
+     This function appears in FANN >= 1.0.0.
+   */
+  bool create_from_file(const std::string &configuration_file) {
+    destroy();
+    ann = fann_create_from_file(configuration_file.c_str());
+    return (ann != NULL);
+  }
+
+  /* Method: save
+
+     Save the entire network to a configuration file.
+
+     The configuration file contains all information about the neural network and enables
+     <create_from_file> to create an exact copy of the neural network and all of the
+     parameters associated with the neural network.
+
+     These two parameters (<set_callback>, <set_error_log>) are *NOT* saved
+     to the file because they cannot safely be ported to a different location. Also temporary
+     parameters generated during training like <get_MSE> is not saved.
+
+     Return:
+     The function returns 0 on success and -1 on failure.
+
+     See also:
+      <create_from_file>, <save_to_fixed>, <fann_save>
+
+     This function appears in FANN >= 1.0.0.
+   */
+  bool save(const std::string &configuration_file) {
+    if (ann == NULL) {
+      return false;
+    }
+    if (fann_save(ann, configuration_file.c_str()) == -1) {
+      return false;
+    }
+    return true;
+  }
+
+  /* Method: save_to_fixed
+
+     Saves the entire network to a configuration file.
+     But it is saved in fixed point format no matter which
+     format it is currently in.
+
+     This is useful for training a network in floating points,
+     and then later executing it in fixed point.
+
+     The function returns the bit position of the fix point, which
+     can be used to find out how accurate the fixed point network will be.
+     A high value indicates high precision, and a low value indicates low
+     precision.
+
+     A negative value indicates very low precision, and a very
+     strong possibility for overflow.
+     (the actual fix point will be set to 0, since a negative
+     fix point does not make sence).
+
+     Generally, a fix point lower than 6 is bad, and should be avoided.
+     The best way to avoid this, is to have less connections to each neuron,
+     or just less neurons in each layer.
+
+     The fixed point use of this network is only intended for use on machines that
+     have no floating point processor, like an iPAQ. On normal computers the floating
+     point version is actually faster.
+
+     See also:
+      <create_from_file>, <save>, <fann_save_to_fixed>
+
+     This function appears in FANN >= 1.0.0.
+  */
+  int save_to_fixed(const std::string &configuration_file) {
+    int fixpoint = 0;
+    if (ann != NULL) {
+      fixpoint = fann_save_to_fixed(ann, configuration_file.c_str());
+    }
+    return fixpoint;
+  }
+
+#ifndef FIXEDFANN
+
+  /* Method: train
+
+     Train one iteration with a set of inputs, and a set of desired outputs.
+     This training is always incremental training (see <FANN::training_algorithm_enum>),
+     since only one pattern is presented.
+
+     Parameters:
+          ann - The neural network structure
+          input - an array of inputs. This array must be exactly <fann_get_num_input> long.
+          desired_output - an array of desired outputs. This array must be exactly
+     <fann_get_num_output> long.
+
+          See also:
+                  <train_on_data>, <train_epoch>, <fann_train>
+
+          This function appears in FANN >= 1.0.0.
+   */
+  void train(fann_type *input, fann_type *desired_output) {
+    if (ann != NULL) {
+      fann_train(ann, input, desired_output);
+    }
+  }
+
+  /* Method: train_epoch
+      Train one epoch with a set of training data.
+
+      Train one epoch with the training data stored in data. One epoch is where all of
+      the training data is considered exactly once.
+
+          This function returns the MSE error as it is calculated either before or during
+          the actual training. This is not the actual MSE after the training epoch, but since
+          calculating this will require to go through the entire training set once more, it is
+          more than adequate to use this value during training.
+
+          The training algorithm used by this function is chosen by the
+     <fann_set_training_algorithm> function.
+
+          See also:
+                  <train_on_data>, <test_data>, <fann_train_epoch>
+
+          This function appears in FANN >= 1.2.0.
+   */
+  float train_epoch(const training_data &data) {
+    float mse = 0.0f;
+    if ((ann != NULL) && (data.train_data != NULL)) {
+      mse = fann_train_epoch(ann, data.train_data);
+    }
+    return mse;
+  }
+
+  /* Method: train_on_data
+
+     Trains on an entire dataset, for a period of time.
+
+     This training uses the training algorithm chosen by <set_training_algorithm>,
+     and the parameters set for these training algorithms.
+
+     Parameters:
+                  ann - The neural network
+                  data - The data, which should be used during training
+                  max_epochs - The maximum number of epochs the training should continue
+                  epochs_between_reports - The number of epochs between printing a status report to
+     stdout. A value of zero means no reports should be printed. desired_error - The desired
+     <get_MSE> or <get_bit_fail>, depending on which stop function is chosen by
+     <set_train_stop_function>.
+
+          Instead of printing out reports every epochs_between_reports, a callback function can be
+     called (see <set_callback>).
+
+          See also:
+                  <train_on_file>, <train_epoch>, <fann_train_on_data>
+
+          This function appears in FANN >= 1.0.0.
+  */
+  void train_on_data(const training_data &data, unsigned int max_epochs,
+                     unsigned int epochs_between_reports, float desired_error) {
+    if ((ann != NULL) && (data.train_data != NULL)) {
+      fann_train_on_data(ann, data.train_data, max_epochs, epochs_between_reports, desired_error);
+    }
+  }
+
+  /* Method: train_on_file
+
+     Does the same as <train_on_data>, but reads the training data directly from a file.
+
+     See also:
+                  <train_on_data>, <fann_train_on_file>
+
+          This function appears in FANN >= 1.0.0.
+  */
+  void train_on_file(const std::string &filename, unsigned int max_epochs,
+                     unsigned int epochs_between_reports, float desired_error) {
+    if (ann != NULL) {
+      fann_train_on_file(ann, filename.c_str(), max_epochs, epochs_between_reports, desired_error);
+    }
+  }
+
+#endif /* NOT FIXEDFANN */
+
+  /* Method: test
+
+     Test with a set of inputs, and a set of desired outputs.
+     This operation updates the mean square error, but does not
+     change the network in any way.
+
+     See also:
+                  <test_data>, <train>, <fann_test>
+
+     This function appears in FANN >= 1.0.0.
+  */
+  fann_type *test(fann_type *input, fann_type *desired_output) {
+    fann_type *output = NULL;
+    if (ann != NULL) {
+      output = fann_test(ann, input, desired_output);
+    }
+    return output;
+  }
+
+  /* Method: test_data
+
+     Test a set of training data and calculates the MSE for the training data.
+
+     This function updates the MSE and the bit fail values.
+
+     See also:
+          <test>, <get_MSE>, <get_bit_fail>, <fann_test_data>
+
+          This function appears in FANN >= 1.2.0.
+   */
+  float test_data(const training_data &data) {
+    float mse = 0.0f;
+    if ((ann != NULL) && (data.train_data != NULL)) {
+      mse = fann_test_data(ann, data.train_data);
+    }
+    return mse;
+  }
+
+  /* Method: get_MSE
+     Reads the mean square error from the network.
+
+     Reads the mean square error from the network. This value is calculated during
+     training or testing, and can therefore sometimes be a bit off if the weights
+     have been changed since the last calculation of the value.
+
+     See also:
+          <test_data>, <fann_get_MSE>
+
+          This function appears in FANN >= 1.1.0.
+   */
+  float get_MSE() {
+    float mse = 0.0f;
+    if (ann != NULL) {
+      mse = fann_get_MSE(ann);
+    }
+    return mse;
+  }
+
+  /* Method: reset_MSE
+
+     Resets the mean square error from the network.
+
+     This function also resets the number of bits that fail.
+
+     See also:
+          <get_MSE>, <get_bit_fail_limit>, <fann_reset_MSE>
+
+      This function appears in FANN >= 1.1.0
+   */
+  void reset_MSE() {
+    if (ann != NULL) {
+      fann_reset_MSE(ann);
+    }
+  }
+
+#ifndef FIXEDFANN
+
+  /* Method: set_callback
+
+     Sets the callback function for use during training. The user_data is passed to
+     the callback. It can point to arbitrary data that the callback might require and
+     can be NULL if it is not used.
+
+     See <FANN::callback_type> for more information about the callback function.
+
+     The default callback function simply prints out some status information.
+
+     This function appears in FANN >= 2.0.0.
+   */
+  void set_callback(callback_type callback, void *user_data) {
+    if (ann != NULL) {
+      // Allocated data is also deleted in the destroy method called by the destructor
+      user_context *user_instance = static_cast<user_context *>(fann_get_user_data(ann));
+      if (user_instance != NULL) delete user_instance;
+
+      user_instance = new user_context();
+      user_instance->user_callback = callback;
+      user_instance->user_data = user_data;
+      user_instance->net = this;
+      fann_set_user_data(ann, user_instance);
+
+      if (callback != NULL)
+        fann_set_callback(ann, &FANN::neural_net::internal_callback);
+      else
+        fann_set_callback(ann, NULL);
+    }
+  }
+
+#endif /* NOT FIXEDFANN */
+
+  /* Method: print_parameters
+
+          Prints all of the parameters and options of the neural network
+
+      See also:
+          <fann_print_parameters>
+
+          This function appears in FANN >= 1.2.0.
+  */
+  void print_parameters() {
+    if (ann != NULL) {
+      fann_print_parameters(ann);
+    }
+  }
+
+  /* Method: get_training_algorithm
+
+     Return the training algorithm as described by <FANN::training_algorithm_enum>.
+     This training algorithm is used by <train_on_data> and associated functions.
+
+     Note that this algorithm is also used during <cascadetrain_on_data>, although only
+     FANN::TRAIN_RPROP and FANN::TRAIN_QUICKPROP is allowed during cascade training.
+
+     The default training algorithm is FANN::TRAIN_RPROP.
+
+     See also:
+      <set_training_algorithm>, <FANN::training_algorithm_enum>,
+      <fann_get_training_algorithm>
+
+     This function appears in FANN >= 1.0.0.
+   */
+  training_algorithm_enum get_training_algorithm() {
+    fann_train_enum training_algorithm = FANN_TRAIN_INCREMENTAL;
+    if (ann != NULL) {
+      training_algorithm = fann_get_training_algorithm(ann);
+    }
+    return static_cast<training_algorithm_enum>(training_algorithm);
+  }
+
+  /* Method: set_training_algorithm
+
+     Set the training algorithm.
+
+     More info available in <get_training_algorithm>
+
+     This function appears in FANN >= 1.0.0.
+   */
+  void set_training_algorithm(training_algorithm_enum training_algorithm) {
+    if (ann != NULL) {
+      fann_set_training_algorithm(ann, static_cast<fann_train_enum>(training_algorithm));
+    }
+  }
+
+  /* Method: get_learning_rate
+
+     Return the learning rate.
+
+     The learning rate is used to determine how aggressive training should be for some of the
+     training algorithms (FANN::TRAIN_INCREMENTAL, FANN::TRAIN_BATCH, FANN::TRAIN_QUICKPROP).
+     Do however note that it is not used in FANN::TRAIN_RPROP.
+
+     The default learning rate is 0.7.
+
+     See also:
+          <set_learning_rate>, <set_training_algorithm>,
+      <fann_get_learning_rate>
+
+     This function appears in FANN >= 1.0.0.
+   */
+  float get_learning_rate() {
+    float learning_rate = 0.0f;
+    if (ann != NULL) {
+      learning_rate = fann_get_learning_rate(ann);
+    }
+    return learning_rate;
+  }
+
+  /* Method: set_learning_rate
+
+     Set the learning rate.
+
+     More info available in <get_learning_rate>
+
+     This function appears in FANN >= 1.0.0.
+   */
+  void set_learning_rate(float learning_rate) {
+    if (ann != NULL) {
+      fann_set_learning_rate(ann, learning_rate);
+    }
+  }
+
+  /*************************************************************************************************************/
+
+  /* Method: get_activation_function
+
+     Get the activation function for neuron number *neuron* in layer number *layer*,
+     counting the input layer as layer 0.
+
+     It is not possible to get activation functions for the neurons in the input layer.
+
+     Information about the individual activation functions is available at
+     <FANN::activation_function_enum>.
+
+     Returns:
+      The activation function for the neuron or -1 if the neuron is not defined in the neural
+     network.
+
+     See also:
+          <set_activation_function_layer>, <set_activation_function_hidden>,
+          <set_activation_function_output>, <set_activation_steepness>,
+      <set_activation_function>, <fann_get_activation_function>
+
+     This function appears in FANN >= 2.1.0
+   */
+  activation_function_enum get_activation_function(int layer, int neuron) {
+    unsigned int activation_function = 0;
+    if (ann != NULL) {
+      activation_function = fann_get_activation_function(ann, layer, neuron);
+    }
+    return static_cast<activation_function_enum>(activation_function);
+  }
+
+  /* Method: set_activation_function
+
+     Set the activation function for neuron number *neuron* in layer number *layer*,
+     counting the input layer as layer 0.
+
+     It is not possible to set activation functions for the neurons in the input layer.
+
+     When choosing an activation function it is important to note that the activation
+     functions have different range. FANN::SIGMOID is e.g. in the 0 - 1 range while
+     FANN::SIGMOID_SYMMETRIC is in the -1 - 1 range and FANN::LINEAR is unbounded.
+
+     Information about the individual activation functions is available at
+     <FANN::activation_function_enum>.
+
+     The default activation function is FANN::SIGMOID_STEPWISE.
+
+     See also:
+          <set_activation_function_layer>, <set_activation_function_hidden>,
+          <set_activation_function_output>, <set_activation_steepness>,
+      <get_activation_function>, <fann_set_activation_function>
+
+     This function appears in FANN >= 2.0.0.
+   */
+  void set_activation_function(activation_function_enum activation_function, int layer,
+                               int neuron) {
+    if (ann != NULL) {
+      fann_set_activation_function(ann, static_cast<fann_activationfunc_enum>(activation_function),
+                                   layer, neuron);
+    }
+  }
+
+  /* Method: set_activation_function_layer
+
+     Set the activation function for all the neurons in the layer number *layer*,
+     counting the input layer as layer 0.
+
+     It is not possible to set activation functions for the neurons in the input layer.
+
+     See also:
+          <set_activation_function>, <set_activation_function_hidden>,
+          <set_activation_function_output>, <set_activation_steepness_layer>,
+      <fann_set_activation_function_layer>
+
+     This function appears in FANN >= 2.0.0.
+   */
+  void set_activation_function_layer(activation_function_enum activation_function, int layer) {
+    if (ann != NULL) {
+      fann_set_activation_function_layer(
+          ann, static_cast<fann_activationfunc_enum>(activation_function), layer);
+    }
+  }
+
+  /* Method: set_activation_function_hidden
+
+     Set the activation function for all of the hidden layers.
+
+     See also:
+          <set_activation_function>, <set_activation_function_layer>,
+          <set_activation_function_output>, <set_activation_steepness_hidden>,
+      <fann_set_activation_function_hidden>
+
+     This function appears in FANN >= 1.0.0.
+   */
+  void set_activation_function_hidden(activation_function_enum activation_function) {
+    if (ann != NULL) {
+      fann_set_activation_function_hidden(
+          ann, static_cast<fann_activationfunc_enum>(activation_function));
+    }
+  }
+
+  /* Method: set_activation_function_output
+
+     Set the activation function for the output layer.
+
+     See also:
+          <set_activation_function>, <set_activation_function_layer>,
+          <set_activation_function_hidden>, <set_activation_steepness_output>,
+      <fann_set_activation_function_output>
+
+     This function appears in FANN >= 1.0.0.
+   */
+  void set_activation_function_output(activation_function_enum activation_function) {
+    if (ann != NULL) {
+      fann_set_activation_function_output(
+          ann, static_cast<fann_activationfunc_enum>(activation_function));
+    }
+  }
+
+  /* Method: get_activation_steepness
+
+     Get the activation steepness for neuron number *neuron* in layer number *layer*,
+     counting the input layer as layer 0.
+
+     It is not possible to get activation steepness for the neurons in the input layer.
+
+     The steepness of an activation function says something about how fast the activation function
+     goes from the minimum to the maximum. A high value for the activation function will also
+     give a more aggressive training.
+
+     When training neural networks where the output values should be at the extremes (usually 0 and
+     1, depending on the activation function), a steep activation function can be used (e.g. 1.0).
+
+     The default activation steepness is 0.5.
+
+     Returns:
+      The activation steepness for the neuron or -1 if the neuron is not defined in the neural
+     network.
+
+     See also:
+          <set_activation_steepness_layer>, <set_activation_steepness_hidden>,
+          <set_activation_steepness_output>, <set_activation_function>,
+      <set_activation_steepness>, <fann_get_activation_steepness>
+
+     This function appears in FANN >= 2.1.0
+   */
+  fann_type get_activation_steepness(int layer, int neuron) {
+    fann_type activation_steepness = 0;
+    if (ann != NULL) {
+      activation_steepness = fann_get_activation_steepness(ann, layer, neuron);
+    }
+    return activation_steepness;
+  }
+
+  /* Method: set_activation_steepness
+
+     Set the activation steepness for neuron number *neuron* in layer number *layer*,
+     counting the input layer as layer 0.
+
+     It is not possible to set activation steepness for the neurons in the input layer.
+
+     The steepness of an activation function says something about how fast the activation function
+     goes from the minimum to the maximum. A high value for the activation function will also
+     give a more aggressive training.
+
+     When training neural networks where the output values should be at the extremes (usually 0 and
+     1, depending on the activation function), a steep activation function can be used (e.g. 1.0).
+
+     The default activation steepness is 0.5.
+
+     See also:
+          <set_activation_steepness_layer>, <set_activation_steepness_hidden>,
+          <set_activation_steepness_output>, <set_activation_function>,
+      <get_activation_steepness>, <fann_set_activation_steepness>
+
+     This function appears in FANN >= 2.0.0.
+   */
+  void set_activation_steepness(fann_type steepness, int layer, int neuron) {
+    if (ann != NULL) {
+      fann_set_activation_steepness(ann, steepness, layer, neuron);
+    }
+  }
+
+  /* Method: set_activation_steepness_layer
+
+     Set the activation steepness all of the neurons in layer number *layer*,
+     counting the input layer as layer 0.
+
+     It is not possible to set activation steepness for the neurons in the input layer.
+
+     See also:
+          <set_activation_steepness>, <set_activation_steepness_hidden>,
+          <set_activation_steepness_output>, <set_activation_function_layer>,
+      <fann_set_activation_steepness_layer>
+
+     This function appears in FANN >= 2.0.0.
+   */
+  void set_activation_steepness_layer(fann_type steepness, int layer) {
+    if (ann != NULL) {
+      fann_set_activation_steepness_layer(ann, steepness, layer);
+    }
+  }
+
+  /* Method: set_activation_steepness_hidden
+
+     Set the steepness of the activation steepness in all of the hidden layers.
+
+     See also:
+          <set_activation_steepness>, <set_activation_steepness_layer>,
+          <set_activation_steepness_output>, <set_activation_function_hidden>,
+      <fann_set_activation_steepness_hidden>
+
+     This function appears in FANN >= 1.2.0.
+   */
+  void set_activation_steepness_hidden(fann_type steepness) {
+    if (ann != NULL) {
+      fann_set_activation_steepness_hidden(ann, steepness);
+    }
+  }
+
+  /* Method: set_activation_steepness_output
+
+     Set the steepness of the activation steepness in the output layer.
+
+     See also:
+          <set_activation_steepness>, <set_activation_steepness_layer>,
+          <set_activation_steepness_hidden>, <set_activation_function_output>,
+      <fann_set_activation_steepness_output>
+
+     This function appears in FANN >= 1.2.0.
+   */
+  void set_activation_steepness_output(fann_type steepness) {
+    if (ann != NULL) {
+      fann_set_activation_steepness_output(ann, steepness);
+    }
+  }
+
+  /*************************************************************************************************************/
+
+  /* Method: get_train_error_function
+
+     Returns the error function used during training.
+
+     The error functions is described further in <FANN::error_function_enum>
+
+     The default error function is FANN::ERRORFUNC_TANH
+
+     See also:
+          <set_train_error_function>, <fann_get_train_error_function>
+
+     This function appears in FANN >= 1.2.0.
+    */
+  error_function_enum get_train_error_function() {
+    fann_errorfunc_enum train_error_function = FANN_ERRORFUNC_LINEAR;
+    if (ann != NULL) {
+      train_error_function = fann_get_train_error_function(ann);
+    }
+    return static_cast<error_function_enum>(train_error_function);
+  }
+
+  /* Method: set_train_error_function
+
+     Set the error function used during training.
+
+     The error functions is described further in <FANN::error_function_enum>
+
+     See also:
+          <get_train_error_function>, <fann_set_train_error_function>
+
+     This function appears in FANN >= 1.2.0.
+   */
+  void set_train_error_function(error_function_enum train_error_function) {
+    if (ann != NULL) {
+      fann_set_train_error_function(ann, static_cast<fann_errorfunc_enum>(train_error_function));
+    }
+  }
+
+  /* Method: get_quickprop_decay
+
+     The decay is a small negative valued number which is the factor that the weights
+     should become smaller in each iteration during quickprop training. This is used
+     to make sure that the weights do not become too high during training.
+
+     The default decay is -0.0001.
+
+     See also:
+          <set_quickprop_decay>, <fann_get_quickprop_decay>
+
+     This function appears in FANN >= 1.2.0.
+   */
+  float get_quickprop_decay() {
+    float quickprop_decay = 0.0f;
+    if (ann != NULL) {
+      quickprop_decay = fann_get_quickprop_decay(ann);
+    }
+    return quickprop_decay;
+  }
+
+  /* Method: set_quickprop_decay
+
+     Sets the quickprop decay factor.
+
+     See also:
+          <get_quickprop_decay>, <fann_set_quickprop_decay>
+
+     This function appears in FANN >= 1.2.0.
+  */
+  void set_quickprop_decay(float quickprop_decay) {
+    if (ann != NULL) {
+      fann_set_quickprop_decay(ann, quickprop_decay);
+    }
+  }
+
+  /* Method: get_quickprop_mu
+
+     The mu factor is used to increase and decrease the step-size during quickprop training.
+     The mu factor should always be above 1, since it would otherwise decrease the step-size
+     when it was suppose to increase it.
+
+     The default mu factor is 1.75.
+
+     See also:
+          <set_quickprop_mu>, <fann_get_quickprop_mu>
+
+     This function appears in FANN >= 1.2.0.
+  */
+  float get_quickprop_mu() {
+    float quickprop_mu = 0.0f;
+    if (ann != NULL) {
+      quickprop_mu = fann_get_quickprop_mu(ann);
+    }
+    return quickprop_mu;
+  }
+
+  /* Method: set_quickprop_mu
+
+      Sets the quickprop mu factor.
+
+     See also:
+          <get_quickprop_mu>, <fann_set_quickprop_mu>
+
+     This function appears in FANN >= 1.2.0.
+  */
+  void set_quickprop_mu(float quickprop_mu) {
+    if (ann != NULL) {
+      fann_set_quickprop_mu(ann, quickprop_mu);
+    }
+  }
+
+  /* Method: get_rprop_increase_factor
+
+     The increase factor is a value larger than 1, which is used to
+     increase the step-size during RPROP training.
+
+     The default increase factor is 1.2.
+
+     See also:
+          <set_rprop_increase_factor>, <fann_get_rprop_increase_factor>
+
+     This function appears in FANN >= 1.2.0.
+  */
+  float get_rprop_increase_factor() {
+    float factor = 0.0f;
+    if (ann != NULL) {
+      factor = fann_get_rprop_increase_factor(ann);
+    }
+    return factor;
+  }
+
+  /* Method: set_rprop_increase_factor
+
+     The increase factor used during RPROP training.
+
+     See also:
+          <get_rprop_increase_factor>, <fann_set_rprop_increase_factor>
+
+     This function appears in FANN >= 1.2.0.
+  */
+  void set_rprop_increase_factor(float rprop_increase_factor) {
+    if (ann != NULL) {
+      fann_set_rprop_increase_factor(ann, rprop_increase_factor);
+    }
+  }
+
+  /* Method: get_rprop_decrease_factor
+
+     The decrease factor is a value smaller than 1, which is used to decrease the step-size during
+     RPROP training.
+
+     The default decrease factor is 0.5.
+
+     See also:
+      <set_rprop_decrease_factor>, <fann_get_rprop_decrease_factor>
+
+     This function appears in FANN >= 1.2.0.
+  */
+  float get_rprop_decrease_factor() {
+    float factor = 0.0f;
+    if (ann != NULL) {
+      factor = fann_get_rprop_decrease_factor(ann);
+    }
+    return factor;
+  }
+
+  /* Method: set_rprop_decrease_factor
+
+     The decrease factor is a value smaller than 1, which is used to decrease the step-size during
+     RPROP training.
+
+     See also:
+      <get_rprop_decrease_factor>, <fann_set_rprop_decrease_factor>
+
+     This function appears in FANN >= 1.2.0.
+  */
+  void set_rprop_decrease_factor(float rprop_decrease_factor) {
+    if (ann != NULL) {
+      fann_set_rprop_decrease_factor(ann, rprop_decrease_factor);
+    }
+  }
+
+  /* Method: get_rprop_delta_zero
+
+     The initial step-size is a small positive number determining how small the initial step-size
+     may be.
+
+     The default value delta zero is 0.1.
+
+     See also:
+          <set_rprop_delta_zero>, <fann_get_rprop_delta_zero>
+
+     This function appears in FANN >= 2.1.0.
+  */
+  float get_rprop_delta_zero() {
+    float delta = 0.0f;
+    if (ann != NULL) {
+      delta = fann_get_rprop_delta_zero(ann);
+    }
+    return delta;
+  }
+
+  /* Method: set_rprop_delta_zero
+
+     The initial step-size is a small positive number determining how small the initial step-size
+     may be.
+
+     See also:
+          <get_rprop_delta_zero>, <fann_set_rprop_delta_zero>
+
+     This function appears in FANN >= 2.1.0.
+  */
+  void set_rprop_delta_zero(float rprop_delta_zero) {
+    if (ann != NULL) {
+      fann_set_rprop_delta_zero(ann, rprop_delta_zero);
+    }
+  }
+
+  /* Method: get_rprop_delta_min
+
+     The minimum step-size is a small positive number determining how small the minimum step-size
+     may be.
+
+     The default value delta min is 0.0.
+
+     See also:
+          <set_rprop_delta_min>, <fann_get_rprop_delta_min>
+
+     This function appears in FANN >= 1.2.0.
+  */
+  float get_rprop_delta_min() {
+    float delta = 0.0f;
+    if (ann != NULL) {
+      delta = fann_get_rprop_delta_min(ann);
+    }
+    return delta;
+  }
+
+  /* Method: set_rprop_delta_min
+
+     The minimum step-size is a small positive number determining how small the minimum step-size
+     may be.
+
+     See also:
+          <get_rprop_delta_min>, <fann_set_rprop_delta_min>
+
+     This function appears in FANN >= 1.2.0.
+  */
+  void set_rprop_delta_min(float rprop_delta_min) {
+    if (ann != NULL) {
+      fann_set_rprop_delta_min(ann, rprop_delta_min);
+    }
+  }
+
+  /* Method: get_rprop_delta_max
+
+     The maximum step-size is a positive number determining how large the maximum step-size may be.
+
+     The default delta max is 50.0.
+
+     See also:
+          <set_rprop_delta_max>, <get_rprop_delta_min>, <fann_get_rprop_delta_max>
+
+     This function appears in FANN >= 1.2.0.
+  */
+  float get_rprop_delta_max() {
+    float delta = 0.0f;
+    if (ann != NULL) {
+      delta = fann_get_rprop_delta_max(ann);
+    }
+    return delta;
+  }
+
+  /* Method: set_rprop_delta_max
+
+     The maximum step-size is a positive number determining how large the maximum step-size may be.
+
+     See also:
+          <get_rprop_delta_max>, <get_rprop_delta_min>, <fann_set_rprop_delta_max>
+
+     This function appears in FANN >= 1.2.0.
+  */
+  void set_rprop_delta_max(float rprop_delta_max) {
+    if (ann != NULL) {
+      fann_set_rprop_delta_max(ann, rprop_delta_max);
+    }
+  }
+
+  /* Method: get_sarprop_weight_decay_shift
+
+     The sarprop weight decay shift.
+
+     The default delta max is -6.644.
+
+     See also:
+          <set_sarprop_weight_decay_shift>, <fann get_sarprop_weight_decay_shift>
+
+     This function appears in FANN >= 2.1.0.
+  */
+  float get_sarprop_weight_decay_shift() {
+    float res = 0.0f;
+    if (ann != NULL) {
+      res = fann_get_rprop_delta_max(ann);
+    }
+    return res;
+  }
+
+  /* Method: set_sarprop_weight_decay_shift
+
+     Set the sarprop weight decay shift.
+
+          This function appears in FANN >= 2.1.0.
+
+      See also:
+          <get_sarprop_weight_decay_shift>, <fann_set_sarprop_weight_decay_shift>
+  */
+  void set_sarprop_weight_decay_shift(float sarprop_weight_decay_shift) {
+    if (ann != NULL) {
+      fann_set_sarprop_weight_decay_shift(ann, sarprop_weight_decay_shift);
+    }
+  }
+
+  /* Method: get_sarprop_step_error_threshold_factor
+
+     The sarprop step error threshold factor.
+
+     The default delta max is 0.1.
+
+     See also:
+          <set_sarprop_step_error_threshold_factor>, <fann get_sarprop_step_error_threshold_factor>
+
+     This function appears in FANN >= 2.1.0.
+  */
+  float get_sarprop_step_error_threshold_factor() {
+    float res = 0.0f;
+    if (ann != NULL) {
+      res = fann_get_rprop_delta_max(ann);
+    }
+    return res;
+  }
+
+  /* Method: set_sarprop_step_error_threshold_factor
+
+     Set the sarprop step error threshold factor.
+
+          This function appears in FANN >= 2.1.0.
+
+      See also:
+          <get_sarprop_step_error_threshold_factor>, <fann_set_sarprop_step_error_threshold_factor>
+  */
+  void set_sarprop_step_error_threshold_factor(float sarprop_step_error_threshold_factor) {
+    if (ann != NULL) {
+      fann_set_sarprop_step_error_threshold_factor(ann, sarprop_step_error_threshold_factor);
+    }
+  }
+
+  /* Method: get_sarprop_step_error_shift
+
+     The get sarprop step error shift.
+
+     The default delta max is 1.385.
+
+     See also:
+          <set_sarprop_step_error_shift>, <fann get_sarprop_step_error_shift>
+
+     This function appears in FANN >= 2.1.0.
+  */
+  float get_sarprop_step_error_shift() {
+    float res = 0.0f;
+    if (ann != NULL) {
+      res = fann_get_rprop_delta_max(ann);
+    }
+    return res;
+  }
+
+  /* Method: set_sarprop_step_error_shift
+
+     Set the sarprop step error shift.
+
+          This function appears in FANN >= 2.1.0.
+
+      See also:
+          <get_sarprop_step_error_shift>, <fann_set_sarprop_step_error_shift>
+  */
+  void set_sarprop_step_error_shift(float sarprop_step_error_shift) {
+    if (ann != NULL) {
+      fann_set_sarprop_step_error_shift(ann, sarprop_step_error_shift);
+    }
+  }
+
+  /* Method: get_sarprop_temperature
+
+         The sarprop weight decay shift.
+
+         The default delta max is 0.015.
+
+         See also:
+             <set_sarprop_temperature>, <fann get_sarprop_temperature>
+
+         This function appears in FANN >= 2.1.0.
+      */
+  float get_sarprop_temperature() {
+    float res = 0.0f;
+    if (ann != NULL) {
+      res = fann_get_rprop_delta_max(ann);
+    }
+    return res;
+  }
+
+  /* Method: set_sarprop_temperature
+
+     Set the sarprop_temperature.
+
+          This function appears in FANN >= 2.1.0.
+
+      See also:
+          <get_sarprop_temperature>, <fann_set_sarprop_temperature>
+  */
+  void set_sarprop_temperature(float sarprop_temperature) {
+    if (ann != NULL) {
+      fann_set_sarprop_temperature(ann, sarprop_temperature);
+    }
+  }
+
+  /* Method: get_num_input
+
+     Get the number of input neurons.
+
+          This function appears in FANN >= 1.0.0.
+  */
+  unsigned int get_num_input() {
+    unsigned int num_input = 0;
+    if (ann != NULL) {
+      num_input = fann_get_num_input(ann);
+    }
+    return num_input;
+  }
+
+  /* Method: get_num_output
+
+     Get the number of output neurons.
+
+          This function appears in FANN >= 1.0.0.
+  */
+  unsigned int get_num_output() {
+    unsigned int num_output = 0;
+    if (ann != NULL) {
+      num_output = fann_get_num_output(ann);
+    }
+    return num_output;
+  }
+
+  /* Method: get_total_neurons
+
+     Get the total number of neurons in the entire network. This number does also include the
+          bias neurons, so a 2-4-2 network has 2+4+2 +2(bias) = 10 neurons.
+
+          This function appears in FANN >= 1.0.0.
+  */
+  unsigned int get_total_neurons() {
+    if (ann == NULL) {
+      return 0;
+    }
+    return fann_get_total_neurons(ann);
+  }
+
+  /* Method: get_total_connections
+
+     Get the total number of connections in the entire network.
+
+          This function appears in FANN >= 1.0.0.
+  */
+  unsigned int get_total_connections() {
+    if (ann == NULL) {
+      return 0;
+    }
+    return fann_get_total_connections(ann);
+  }
+
+#ifdef FIXEDFANN
+  /* Method: get_decimal_point
+
+      Returns the position of the decimal point in the ann.
+
+      This function is only available when the ANN is in fixed point mode.
+
+      The decimal point is described in greater detail in the tutorial <Fixed Point Usage>.
+
+      See also:
+          <Fixed Point Usage>, <get_multiplier>, <save_to_fixed>,
+          <training_data::save_train_to_fixed>, <fann_get_decimal_point>
+
+      This function appears in FANN >= 1.0.0.
+  */
+  unsigned int get_decimal_point() {
+    if (ann == NULL) {
+      return 0;
+    }
+    return fann_get_decimal_point(ann);
+  }
+
+  /* Method: get_multiplier
+
+      Returns the multiplier that fix point data is multiplied with.
+
+      This function is only available when the ANN is in fixed point mode.
+
+      The multiplier is the used to convert between floating point and fixed point notation.
+      A floating point number is multiplied with the multiplier in order to get the fixed point
+      number and visa versa.
+
+      The multiplier is described in greater detail in the tutorial <Fixed Point Usage>.
+
+      See also:
+          <Fixed Point Usage>, <get_decimal_point>, <save_to_fixed>,
+          <training_data::save_train_to_fixed>, <fann_get_multiplier>
+
+      This function appears in FANN >= 1.0.0.
+  */
+  unsigned int get_multiplier() {
+    if (ann == NULL) {
+      return 0;
+    }
+    return fann_get_multiplier(ann);
+  }
+#endif /* FIXEDFANN */
+
+  /*********************************************************************/
+
+  /* Method: get_network_type
+
+      Get the type of neural network it was created as.
+
+          Returns:
+          The neural network type from enum <FANN::network_type_enum>
+
+      See Also:
+          <fann_get_network_type>
+
+     This function appears in FANN >= 2.1.0
+  */
+  network_type_enum get_network_type() {
+    fann_nettype_enum network_type = FANN_NETTYPE_LAYER;
+    if (ann != NULL) {
+      network_type = fann_get_network_type(ann);
+    }
+    return static_cast<network_type_enum>(network_type);
+  }
+
+  /* Method: get_connection_rate
+
+      Get the connection rate used when the network was created
+
+          Returns:
+          The connection rate
+
+      See also:
+          <fann_get_connection_rate>
+
+     This function appears in FANN >= 2.1.0
+  */
+  float get_connection_rate() {
+    if (ann == NULL) {
+      return 0;
+    }
+    return fann_get_connection_rate(ann);
+  }
+
+  /* Method: get_num_layers
+
+      Get the number of layers in the network
+
+          Returns:
+                  The number of layers in the neural network
+
+      See also:
+          <fann_get_num_layers>
+
+     This function appears in FANN >= 2.1.0
+  */
+  unsigned int get_num_layers() {
+    if (ann == NULL) {
+      return 0;
+    }
+    return fann_get_num_layers(ann);
+  }
+
+  /* Method: get_layer_array
+
+      Get the number of neurons in each layer in the network.
+
+      Bias is not included so the layers match the create methods.
+
+      The layers array must be preallocated to at least
+      sizeof(unsigned int) * get_num_layers() long.
+
+      See also:
+          <fann_get_layer_array>
+
+     This function appears in FANN >= 2.1.0
+  */
+  void get_layer_array(unsigned int *layers) {
+    if (ann != NULL) {
+      fann_get_layer_array(ann, layers);
+    }
+  }
+
+  /* Method: get_bias_array
+
+      Get the number of bias in each layer in the network.
+
+      The bias array must be preallocated to at least
+      sizeof(unsigned int) * get_num_layers() long.
+
+      See also:
+          <fann_get_bias_array>
+
+      This function appears in FANN >= 2.1.0
+  */
+  void get_bias_array(unsigned int *bias) {
+    if (ann != NULL) {
+      fann_get_bias_array(ann, bias);
+    }
+  }
+
+  /* Method: get_connection_array
+
+      Get the connections in the network.
+
+      The connections array must be preallocated to at least
+      sizeof(struct fann_connection) * get_total_connections() long.
+
+      See also:
+          <fann_get_connection_array>
+
+     This function appears in FANN >= 2.1.0
+  */
+  void get_connection_array(connection *connections) {
+    if (ann != NULL) {
+      fann_get_connection_array(ann, connections);
+    }
+  }
+
+  /* Method: set_weight_array
+
+      Set connections in the network.
+
+      Only the weights can be changed, connections and weights are ignored
+      if they do not already exist in the network.
+
+      The array must have sizeof(struct fann_connection) * num_connections size.
+
+      See also:
+          <fann_set_weight_array>
+
+     This function appears in FANN >= 2.1.0
+  */
+  void set_weight_array(connection *connections, unsigned int num_connections) {
+    if (ann != NULL) {
+      fann_set_weight_array(ann, connections, num_connections);
+    }
+  }
+
+  /* Method: set_weight
+
+      Set a connection in the network.
+
+      Only the weights can be changed. The connection/weight is
+      ignored if it does not already exist in the network.
+
+      See also:
+          <fann_set_weight>
+
+     This function appears in FANN >= 2.1.0
+  */
+  void set_weight(unsigned int from_neuron, unsigned int to_neuron, fann_type weight) {
+    if (ann != NULL) {
+      fann_set_weight(ann, from_neuron, to_neuron, weight);
+    }
+  }
+
+  /* Method: get_weights
+
+      Get all the network weights.
+
+      See also:
+          <fann_get_weights>
+
+      This function appears in FANN >= x.y.z
+  */
+  void get_weights(fann_type *weights) {
+    if (ann != NULL) {
+      fann_get_weights(ann, weights);
+    }
+  }
+
+  /* Method: set_weights
+
+      Set network weights.
+
+      See also:
+          <fann_set_weights>
+
+      This function appears in FANN >= x.y.z
+  */
+  void set_weights(fann_type *weights) {
+    if (ann != NULL) {
+      fann_set_weights(ann, weights);
+    }
+  }
+
+  /*********************************************************************/
+
+  /* Method: get_learning_momentum
+
+     Get the learning momentum.
+
+     The learning momentum can be used to speed up FANN::TRAIN_INCREMENTAL training.
+     A too high momentum will however not benefit training. Setting momentum to 0 will
+     be the same as not using the momentum parameter. The recommended value of this parameter
+     is between 0.0 and 1.0.
+
+     The default momentum is 0.
+
+     See also:
+     <set_learning_momentum>, <set_training_algorithm>
+
+     This function appears in FANN >= 2.0.0.
+   */
+  float get_learning_momentum() {
+    float learning_momentum = 0.0f;
+    if (ann != NULL) {
+      learning_momentum = fann_get_learning_momentum(ann);
+    }
+    return learning_momentum;
+  }
+
+  /* Method: set_learning_momentum
+
+     Set the learning momentum.
+
+     More info available in <get_learning_momentum>
+
+     This function appears in FANN >= 2.0.0.
+   */
+  void set_learning_momentum(float learning_momentum) {
+    if (ann != NULL) {
+      fann_set_learning_momentum(ann, learning_momentum);
+    }
+  }
+
+  /* Method: get_train_stop_function
+
+     Returns the the stop function used during training.
+
+     The stop function is described further in <FANN::stop_function_enum>
+
+     The default stop function is FANN::STOPFUNC_MSE
+
+     See also:
+          <get_train_stop_function>, <get_bit_fail_limit>
+
+     This function appears in FANN >= 2.0.0.
+   */
+  stop_function_enum get_train_stop_function() {
+    enum fann_stopfunc_enum stopfunc = FANN_STOPFUNC_MSE;
+    if (ann != NULL) {
+      stopfunc = fann_get_train_stop_function(ann);
+    }
+    return static_cast<stop_function_enum>(stopfunc);
+  }
+
+  /* Method: set_train_stop_function
+
+     Set the stop function used during training.
+
+     The stop function is described further in <FANN::stop_function_enum>
+
+     See also:
+          <get_train_stop_function>
+
+     This function appears in FANN >= 2.0.0.
+   */
+  void set_train_stop_function(stop_function_enum train_stop_function) {
+    if (ann != NULL) {
+      fann_set_train_stop_function(ann, static_cast<enum fann_stopfunc_enum>(train_stop_function));
+    }
+  }
+
+  /* Method: get_bit_fail_limit
+
+     Returns the bit fail limit used during training.
+
+     The bit fail limit is used during training when the <FANN::stop_function_enum> is set to
+     FANN_STOPFUNC_BIT.
+
+     The limit is the maximum accepted difference between the desired output and the actual output
+     during training. Each output that diverges more than this limit is counted as an error bit.
+     This difference is divided by two when dealing with symmetric activation functions,
+     so that symmetric and not symmetric activation functions can use the same limit.
+
+     The default bit fail limit is 0.35.
+
+     See also:
+          <set_bit_fail_limit>
+
+     This function appears in FANN >= 2.0.0.
+   */
+  fann_type get_bit_fail_limit() {
+    fann_type bit_fail_limit = 0.0f;
+
+    if (ann != NULL) {
+      bit_fail_limit = fann_get_bit_fail_limit(ann);
+    }
+    return bit_fail_limit;
+  }
+
+  /* Method: set_bit_fail_limit
+
+     Set the bit fail limit used during training.
+
+     See also:
+          <get_bit_fail_limit>
+
+     This function appears in FANN >= 2.0.0.
+   */
+  void set_bit_fail_limit(fann_type bit_fail_limit) {
+    if (ann != NULL) {
+      fann_set_bit_fail_limit(ann, bit_fail_limit);
+    }
+  }
+
+  /* Method: get_bit_fail
+
+          The number of fail bits; means the number of output neurons which differ more
+          than the bit fail limit (see <get_bit_fail_limit>, <set_bit_fail_limit>).
+          The bits are counted in all of the training data, so this number can be higher than
+          the number of training data.
+
+          This value is reset by <reset_MSE> and updated by all the same functions which also
+          updates the MSE value (e.g. <test_data>, <train_epoch>)
+
+          See also:
+                  <FANN::stop_function_enum>, <get_MSE>
+
+          This function appears in FANN >= 2.0.0
+  */
+  unsigned int get_bit_fail() {
+    unsigned int bit_fail = 0;
+    if (ann != NULL) {
+      bit_fail = fann_get_bit_fail(ann);
+    }
+    return bit_fail;
+  }
+
+#ifndef FIXEDFANN
+  /*********************************************************************/
+
+  /* Method: cascadetrain_on_data
+
+     Trains on an entire dataset, for a period of time using the Cascade2 training algorithm.
+     This algorithm adds neurons to the neural network while training, which means that it
+     needs to start with an ANN without any hidden layers. The neural network should also use
+     shortcut connections, so <create_shortcut> should be used to create the ANN like this:
+     >net.create_shortcut(2, train_data.num_input_train_data(), train_data.num_output_train_data());
+
+     This training uses the parameters set using the set_cascade_..., but it also uses another
+     training algorithm as it's internal training algorithm. This algorithm can be set to either
+     FANN::TRAIN_RPROP or FANN::TRAIN_QUICKPROP by <set_training_algorithm>, and the parameters
+     set for these training algorithms will also affect the cascade training.
+
+     Parameters:
+                  data - The data, which should be used during training
+                  max_neuron - The maximum number of neurons to be added to neural network
+                  neurons_between_reports - The number of neurons between printing a status report
+     to stdout. A value of zero means no reports should be printed. desired_error - The desired
+     <fann_get_MSE> or <fann_get_bit_fail>, depending on which stop function is chosen by
+     <fann_set_train_stop_function>.
+
+          Instead of printing out reports every neurons_between_reports, a callback function can be
+     called (see <set_callback>).
+
+          See also:
+                  <train_on_data>, <cascadetrain_on_file>, <fann_cascadetrain_on_data>
+
+          This function appears in FANN >= 2.0.0.
+  */
+  void cascadetrain_on_data(const training_data &data, unsigned int max_neurons,
+                            unsigned int neurons_between_reports, float desired_error) {
+    if ((ann != NULL) && (data.train_data != NULL)) {
+      fann_cascadetrain_on_data(ann, data.train_data, max_neurons, neurons_between_reports,
+                                desired_error);
+    }
+  }
+
+  /* Method: cascadetrain_on_file
+
+     Does the same as <cascadetrain_on_data>, but reads the training data directly from a file.
+
+     See also:
+                  <fann_cascadetrain_on_data>, <fann_cascadetrain_on_file>
+
+          This function appears in FANN >= 2.0.0.
+  */
+  void cascadetrain_on_file(const std::string &filename, unsigned int max_neurons,
+                            unsigned int neurons_between_reports, float desired_error) {
+    if (ann != NULL) {
+      fann_cascadetrain_on_file(ann, filename.c_str(), max_neurons, neurons_between_reports,
+                                desired_error);
+    }
+  }
+
+  /* Method: get_cascade_output_change_fraction
+
+     The cascade output change fraction is a number between 0 and 1 determining how large a fraction
+     the <get_MSE> value should change within <get_cascade_output_stagnation_epochs> during
+     training of the output connections, in order for the training not to stagnate. If the training
+     stagnates, the training of the output connections will be ended and new candidates will be
+     prepared.
+
+     This means:
+     If the MSE does not change by a fraction of <get_cascade_output_change_fraction> during a
+     period of <get_cascade_output_stagnation_epochs>, the training of the output connections
+     is stopped because the training has stagnated.
+
+     If the cascade output change fraction is low, the output connections will be trained more and
+     if the fraction is high they will be trained less.
+
+     The default cascade output change fraction is 0.01, which is equalent to a 1% change in MSE.
+
+     See also:
+                  <set_cascade_output_change_fraction>, <get_MSE>,
+          <get_cascade_output_stagnation_epochs>, <fann_get_cascade_output_change_fraction>
+
+          This function appears in FANN >= 2.0.0.
+   */
+  float get_cascade_output_change_fraction() {
+    float change_fraction = 0.0f;
+    if (ann != NULL) {
+      change_fraction = fann_get_cascade_output_change_fraction(ann);
+    }
+    return change_fraction;
+  }
+
+  /* Method: set_cascade_output_change_fraction
+
+     Sets the cascade output change fraction.
+
+     See also:
+                  <get_cascade_output_change_fraction>, <fann_set_cascade_output_change_fraction>
+
+          This function appears in FANN >= 2.0.0.
+   */
+  void set_cascade_output_change_fraction(float cascade_output_change_fraction) {
+    if (ann != NULL) {
+      fann_set_cascade_output_change_fraction(ann, cascade_output_change_fraction);
+    }
+  }
+
+  /* Method: get_cascade_output_stagnation_epochs
+
+     The number of cascade output stagnation epochs determines the number of epochs training is
+     allowed to continue without changing the MSE by a fraction of
+     <get_cascade_output_change_fraction>.
+
+     See more info about this parameter in <get_cascade_output_change_fraction>.
+
+     The default number of cascade output stagnation epochs is 12.
+
+     See also:
+                  <set_cascade_output_stagnation_epochs>, <get_cascade_output_change_fraction>,
+          <fann_get_cascade_output_stagnation_epochs>
+
+          This function appears in FANN >= 2.0.0.
+   */
+  unsigned int get_cascade_output_stagnation_epochs() {
+    unsigned int stagnation_epochs = 0;
+    if (ann != NULL) {
+      stagnation_epochs = fann_get_cascade_output_stagnation_epochs(ann);
+    }
+    return stagnation_epochs;
+  }
+
+  /* Method: set_cascade_output_stagnation_epochs
+
+     Sets the number of cascade output stagnation epochs.
+
+     See also:
+                  <get_cascade_output_stagnation_epochs>,
+     <fann_set_cascade_output_stagnation_epochs>
+
+          This function appears in FANN >= 2.0.0.
+   */
+  void set_cascade_output_stagnation_epochs(unsigned int cascade_output_stagnation_epochs) {
+    if (ann != NULL) {
+      fann_set_cascade_output_stagnation_epochs(ann, cascade_output_stagnation_epochs);
+    }
+  }
+
+  /* Method: get_cascade_candidate_change_fraction
+
+     The cascade candidate change fraction is a number between 0 and 1 determining how large a
+     fraction the <get_MSE> value should change within <get_cascade_candidate_stagnation_epochs>
+     during training of the candidate neurons, in order for the training not to stagnate. If the
+     training stagnates, the training of the candidate neurons will be ended and the best candidate
+     will be selected.
+
+     This means:
+     If the MSE does not change by a fraction of <get_cascade_candidate_change_fraction> during a
+     period of <get_cascade_candidate_stagnation_epochs>, the training of the candidate neurons
+     is stopped because the training has stagnated.
+
+     If the cascade candidate change fraction is low, the candidate neurons will be trained more and
+     if the fraction is high they will be trained less.
+
+     The default cascade candidate change fraction is 0.01, which is equalent to a 1% change in MSE.
+
+     See also:
+                  <set_cascade_candidate_change_fraction>, <get_MSE>,
+          <get_cascade_candidate_stagnation_epochs>, <fann_get_cascade_candidate_change_fraction>
+
+          This function appears in FANN >= 2.0.0.
+   */
+  float get_cascade_candidate_change_fraction() {
+    float change_fraction = 0.0f;
+    if (ann != NULL) {
+      change_fraction = fann_get_cascade_candidate_change_fraction(ann);
+    }
+    return change_fraction;
+  }
+
+  /* Method: set_cascade_candidate_change_fraction
+
+     Sets the cascade candidate change fraction.
+
+     See also:
+                  <get_cascade_candidate_change_fraction>,
+          <fann_set_cascade_candidate_change_fraction>
+
+          This function appears in FANN >= 2.0.0.
+   */
+  void set_cascade_candidate_change_fraction(float cascade_candidate_change_fraction) {
+    if (ann != NULL) {
+      fann_set_cascade_candidate_change_fraction(ann, cascade_candidate_change_fraction);
+    }
+  }
+
+  /* Method: get_cascade_candidate_stagnation_epochs
+
+     The number of cascade candidate stagnation epochs determines the number of epochs training is
+     allowed to continue without changing the MSE by a fraction of
+     <get_cascade_candidate_change_fraction>.
+
+     See more info about this parameter in <get_cascade_candidate_change_fraction>.
+
+     The default number of cascade candidate stagnation epochs is 12.
+
+     See also:
+                  <set_cascade_candidate_stagnation_epochs>,
+     <get_cascade_candidate_change_fraction>, <fann_get_cascade_candidate_stagnation_epochs>
+
+          This function appears in FANN >= 2.0.0.
+   */
+  unsigned int get_cascade_candidate_stagnation_epochs() {
+    unsigned int stagnation_epochs = 0;
+    if (ann != NULL) {
+      stagnation_epochs = fann_get_cascade_candidate_stagnation_epochs(ann);
+    }
+    return stagnation_epochs;
+  }
+
+  /* Method: set_cascade_candidate_stagnation_epochs
+
+     Sets the number of cascade candidate stagnation epochs.
+
+     See also:
+                  <get_cascade_candidate_stagnation_epochs>,
+          <fann_set_cascade_candidate_stagnation_epochs>
+
+          This function appears in FANN >= 2.0.0.
+   */
+  void set_cascade_candidate_stagnation_epochs(unsigned int cascade_candidate_stagnation_epochs) {
+    if (ann != NULL) {
+      fann_set_cascade_candidate_stagnation_epochs(ann, cascade_candidate_stagnation_epochs);
+    }
+  }
+
+  /* Method: get_cascade_weight_multiplier
+
+     The weight multiplier is a parameter which is used to multiply the weights from the candidate
+     neuron before adding the neuron to the neural network. This parameter is usually between 0 and
+     1, and is used to make the training a bit less aggressive.
+
+     The default weight multiplier is 0.4
+
+     See also:
+                  <set_cascade_weight_multiplier>, <fann_get_cascade_weight_multiplier>
+
+          This function appears in FANN >= 2.0.0.
+   */
+  fann_type get_cascade_weight_multiplier() {
+    fann_type weight_multiplier = 0;
+    if (ann != NULL) {
+      weight_multiplier = fann_get_cascade_weight_multiplier(ann);
+    }
+    return weight_multiplier;
+  }
+
+  /* Method: set_cascade_weight_multiplier
+
+     Sets the weight multiplier.
+
+     See also:
+                  <get_cascade_weight_multiplier>, <fann_set_cascade_weight_multiplier>
+
+          This function appears in FANN >= 2.0.0.
+   */
+  void set_cascade_weight_multiplier(fann_type cascade_weight_multiplier) {
+    if (ann != NULL) {
+      fann_set_cascade_weight_multiplier(ann, cascade_weight_multiplier);
+    }
+  }
+
+  /* Method: get_cascade_candidate_limit
+
+     The candidate limit is a limit for how much the candidate neuron may be trained.
+     The limit is a limit on the proportion between the MSE and candidate score.
+
+     Set this to a lower value to avoid overfitting and to a higher if overfitting is
+     not a problem.
+
+     The default candidate limit is 1000.0
+
+     See also:
+                  <set_cascade_candidate_limit>, <fann_get_cascade_candidate_limit>
+
+          This function appears in FANN >= 2.0.0.
+   */
+  fann_type get_cascade_candidate_limit() {
+    fann_type candidate_limit = 0;
+    if (ann != NULL) {
+      candidate_limit = fann_get_cascade_candidate_limit(ann);
+    }
+    return candidate_limit;
+  }
+
+  /* Method: set_cascade_candidate_limit
+
+     Sets the candidate limit.
+
+     See also:
+                  <get_cascade_candidate_limit>, <fann_set_cascade_candidate_limit>
+
+          This function appears in FANN >= 2.0.0.
+   */
+  void set_cascade_candidate_limit(fann_type cascade_candidate_limit) {
+    if (ann != NULL) {
+      fann_set_cascade_candidate_limit(ann, cascade_candidate_limit);
+    }
+  }
+
+  /* Method: get_cascade_max_out_epochs
+
+     The maximum out epochs determines the maximum number of epochs the output connections
+     may be trained after adding a new candidate neuron.
+
+     The default max out epochs is 150
+
+     See also:
+                  <set_cascade_max_out_epochs>, <fann_get_cascade_max_out_epochs>
+
+          This function appears in FANN >= 2.0.0.
+   */
+  unsigned int get_cascade_max_out_epochs() {
+    unsigned int max_out_epochs = 0;
+    if (ann != NULL) {
+      max_out_epochs = fann_get_cascade_max_out_epochs(ann);
+    }
+    return max_out_epochs;
+  }
+
+  /* Method: set_cascade_max_out_epochs
+
+     Sets the maximum out epochs.
+
+     See also:
+                  <get_cascade_max_out_epochs>, <fann_set_cascade_max_out_epochs>
+
+          This function appears in FANN >= 2.0.0.
+   */
+  void set_cascade_max_out_epochs(unsigned int cascade_max_out_epochs) {
+    if (ann != NULL) {
+      fann_set_cascade_max_out_epochs(ann, cascade_max_out_epochs);
+    }
+  }
+
+  /* Method: get_cascade_max_cand_epochs
+
+     The maximum candidate epochs determines the maximum number of epochs the input
+     connections to the candidates may be trained before adding a new candidate neuron.
+
+     The default max candidate epochs is 150
+
+     See also:
+                  <set_cascade_max_cand_epochs>, <fann_get_cascade_max_cand_epochs>
+
+          This function appears in FANN >= 2.0.0.
+   */
+  unsigned int get_cascade_max_cand_epochs() {
+    unsigned int max_cand_epochs = 0;
+    if (ann != NULL) {
+      max_cand_epochs = fann_get_cascade_max_cand_epochs(ann);
+    }
+    return max_cand_epochs;
+  }
+
+  /* Method: set_cascade_max_cand_epochs
+
+     Sets the max candidate epochs.
+
+     See also:
+                  <get_cascade_max_cand_epochs>, <fann_set_cascade_max_cand_epochs>
+
+          This function appears in FANN >= 2.0.0.
+   */
+  void set_cascade_max_cand_epochs(unsigned int cascade_max_cand_epochs) {
+    if (ann != NULL) {
+      fann_set_cascade_max_cand_epochs(ann, cascade_max_cand_epochs);
+    }
+  }
+
+  /* Method: get_cascade_num_candidates
+
+     The number of candidates used during training (calculated by multiplying
+     <get_cascade_activation_functions_count>, <get_cascade_activation_steepnesses_count> and
+     <get_cascade_num_candidate_groups>).
+
+     The actual candidates is defined by the <get_cascade_activation_functions> and
+     <get_cascade_activation_steepnesses> arrays. These arrays define the activation functions
+     and activation steepnesses used for the candidate neurons. If there are 2 activation functions
+     in the activation function array and 3 steepnesses in the steepness array, then there will be
+     2x3=6 different candidates which will be trained. These 6 different candidates can be copied
+     into several candidate groups, where the only difference between these groups is the initial
+     weights. If the number of groups is set to 2, then the number of candidate neurons will be
+     2x3x2=12. The number of candidate groups is defined by <set_cascade_num_candidate_groups>.
+
+     The default number of candidates is 6x4x2 = 48
+
+     See also:
+                  <get_cascade_activation_functions>, <get_cascade_activation_functions_count>,
+                  <get_cascade_activation_steepnesses>, <get_cascade_activation_steepnesses_count>,
+                  <get_cascade_num_candidate_groups>, <fann_get_cascade_num_candidates>
+
+          This function appears in FANN >= 2.0.0.
+   */
+  unsigned int get_cascade_num_candidates() {
+    unsigned int num_candidates = 0;
+    if (ann != NULL) {
+      num_candidates = fann_get_cascade_num_candidates(ann);
+    }
+    return num_candidates;
+  }
+
+  /* Method: get_cascade_activation_functions_count
+
+     The number of activation functions in the <get_cascade_activation_functions> array.
+
+     The default number of activation functions is 10.
+
+     See also:
+                  <get_cascade_activation_functions>, <set_cascade_activation_functions>,
+          <fann_get_cascade_activation_functions_count>
+
+          This function appears in FANN >= 2.0.0.
+   */
+  unsigned int get_cascade_activation_functions_count() {
+    unsigned int activation_functions_count = 0;
+    if (ann != NULL) {
+      activation_functions_count = fann_get_cascade_activation_functions_count(ann);
+    }
+    return activation_functions_count;
+  }
+
+  /* Method: get_cascade_activation_functions
+
+     The cascade activation functions array is an array of the different activation functions used
+     by the candidates.
+
+     See <get_cascade_num_candidates> for a description of which candidate neurons will be
+     generated by this array.
+
+     See also:
+                  <get_cascade_activation_functions_count>, <set_cascade_activation_functions>,
+                  <FANN::activation_function_enum>
+
+          This function appears in FANN >= 2.0.0.
+   */
+  activation_function_enum *get_cascade_activation_functions() {
+    enum fann_activationfunc_enum *activation_functions = NULL;
+    if (ann != NULL) {
+      activation_functions = fann_get_cascade_activation_functions(ann);
+    }
+    return reinterpret_cast<activation_function_enum *>(activation_functions);
+  }
+
+  /* Method: set_cascade_activation_functions
+
+     Sets the array of cascade candidate activation functions. The array must be just as long
+     as defined by the count.
+
+     See <get_cascade_num_candidates> for a description of which candidate neurons will be
+     generated by this array.
+
+     See also:
+                  <get_cascade_activation_steepnesses_count>, <get_cascade_activation_steepnesses>,
+          <fann_set_cascade_activation_functions>
+
+          This function appears in FANN >= 2.0.0.
+   */
+  void set_cascade_activation_functions(activation_function_enum *cascade_activation_functions,
+                                        unsigned int cascade_activation_functions_count) {
+    if (ann != NULL) {
+      fann_set_cascade_activation_functions(
+          ann, reinterpret_cast<enum fann_activationfunc_enum *>(cascade_activation_functions),
+          cascade_activation_functions_count);
+    }
+  }
+
+  /* Method: get_cascade_activation_steepnesses_count
+
+     The number of activation steepnesses in the <get_cascade_activation_functions> array.
+
+     The default number of activation steepnesses is 4.
+
+     See also:
+                  <get_cascade_activation_steepnesses>, <set_cascade_activation_functions>,
+          <fann_get_cascade_activation_steepnesses_count>
+
+          This function appears in FANN >= 2.0.0.
+   */
+  unsigned int get_cascade_activation_steepnesses_count() {
+    unsigned int activation_steepness_count = 0;
+    if (ann != NULL) {
+      activation_steepness_count = fann_get_cascade_activation_steepnesses_count(ann);
+    }
+    return activation_steepness_count;
+  }
+
+  /* Method: get_cascade_activation_steepnesses
+
+     The cascade activation steepnesses array is an array of the different activation functions used
+     by the candidates.
+
+     See <get_cascade_num_candidates> for a description of which candidate neurons will be
+     generated by this array.
+
+     The default activation steepnesses is {0.25, 0.50, 0.75, 1.00}
+
+     See also:
+                  <set_cascade_activation_steepnesses>, <get_cascade_activation_steepnesses_count>,
+          <fann_get_cascade_activation_steepnesses>
+
+          This function appears in FANN >= 2.0.0.
+   */
+  fann_type *get_cascade_activation_steepnesses() {
+    fann_type *activation_steepnesses = NULL;
+    if (ann != NULL) {
+      activation_steepnesses = fann_get_cascade_activation_steepnesses(ann);
+    }
+    return activation_steepnesses;
+  }
+
+  /* Method: set_cascade_activation_steepnesses
+
+     Sets the array of cascade candidate activation steepnesses. The array must be just as long
+     as defined by the count.
+
+     See <get_cascade_num_candidates> for a description of which candidate neurons will be
+     generated by this array.
+
+     See also:
+                  <get_cascade_activation_steepnesses>, <get_cascade_activation_steepnesses_count>,
+          <fann_set_cascade_activation_steepnesses>
+
+          This function appears in FANN >= 2.0.0.
+   */
+  void set_cascade_activation_steepnesses(fann_type *cascade_activation_steepnesses,
+                                          unsigned int cascade_activation_steepnesses_count) {
+    if (ann != NULL) {
+      fann_set_cascade_activation_steepnesses(ann, cascade_activation_steepnesses,
+                                              cascade_activation_steepnesses_count);
+    }
+  }
+
+  /* Method: get_cascade_num_candidate_groups
+
+     The number of candidate groups is the number of groups of identical candidates which will be
+     used during training.
+
+     This number can be used to have more candidates without having to define new parameters for the
+     candidates.
+
+     See <get_cascade_num_candidates> for a description of which candidate neurons will be
+     generated by this parameter.
+
+     The default number of candidate groups is 2
+
+     See also:
+                  <set_cascade_num_candidate_groups>, <fann_get_cascade_num_candidate_groups>
+
+          This function appears in FANN >= 2.0.0.
+   */
+  unsigned int get_cascade_num_candidate_groups() {
+    unsigned int num_candidate_groups = 0;
+    if (ann != NULL) {
+      num_candidate_groups = fann_get_cascade_num_candidate_groups(ann);
+    }
+    return num_candidate_groups;
+  }
+
+  /* Method: set_cascade_num_candidate_groups
+
+     Sets the number of candidate groups.
+
+     See also:
+                  <get_cascade_num_candidate_groups>, <fann_set_cascade_num_candidate_groups>
+
+          This function appears in FANN >= 2.0.0.
+   */
+  void set_cascade_num_candidate_groups(unsigned int cascade_num_candidate_groups) {
+    if (ann != NULL) {
+      fann_set_cascade_num_candidate_groups(ann, cascade_num_candidate_groups);
+    }
+  }
+
+  /*********************************************************************/
+
+  /* Method: scale_train
+
+     Scale input and output data based on previously calculated parameters.
+
+     See also:
+                  <descale_train>, <fann_scale_train>
+
+          This function appears in FANN >= 2.1.0.
+   */
+  void scale_train(training_data &data) {
+    if (ann != NULL) {
+      fann_scale_train(ann, data.train_data);
+    }
+  }
+
+  /* Method: descale_train
+
+     Descale input and output data based on previously calculated parameters.
+
+     See also:
+                  <scale_train>, <fann_descale_train>
+
+          This function appears in FANN >= 2.1.0.
+   */
+  void descale_train(training_data &data) {
+    if (ann != NULL) {
+      fann_descale_train(ann, data.train_data);
+    }
+  }
+
+  /* Method: set_input_scaling_params
+
+     Calculate scaling parameters for future use based on training data.
+
+     See also:
+                  <set_output_scaling_params>, <fann_set_input_scaling_params>
+
+          This function appears in FANN >= 2.1.0.
+   */
+  bool set_input_scaling_params(const training_data &data, float new_input_min,
+                                float new_input_max) {
+    bool status = false;
+    if (ann != NULL) {
+      status =
+          (fann_set_input_scaling_params(ann, data.train_data, new_input_min, new_input_max) != -1);
+    }
+    return status;
+  }
+
+  /* Method: set_output_scaling_params
+
+     Calculate scaling parameters for future use based on training data.
+
+     See also:
+                  <set_input_scaling_params>, <fann_set_output_scaling_params>
+
+          This function appears in FANN >= 2.1.0.
+   */
+  bool set_output_scaling_params(const training_data &data, float new_output_min,
+                                 float new_output_max) {
+    bool status = false;
+    if (ann != NULL) {
+      status = (fann_set_output_scaling_params(ann, data.train_data, new_output_min,
+                                               new_output_max) != -1);
+    }
+    return status;
+  }
+
+  /* Method: set_scaling_params
+
+     Calculate scaling parameters for future use based on training data.
+
+     See also:
+                  <clear_scaling_params>, <fann_set_scaling_params>
+
+          This function appears in FANN >= 2.1.0.
+   */
+  bool set_scaling_params(const training_data &data, float new_input_min, float new_input_max,
+                          float new_output_min, float new_output_max) {
+    bool status = false;
+    if (ann != NULL) {
+      status = (fann_set_scaling_params(ann, data.train_data, new_input_min, new_input_max,
+                                        new_output_min, new_output_max) != -1);
+    }
+    return status;
+  }
+
+  /* Method: clear_scaling_params
+
+     Clears scaling parameters.
+
+     See also:
+                  <set_scaling_params>, <fann_clear_scaling_params>
+
+          This function appears in FANN >= 2.1.0.
+   */
+  bool clear_scaling_params() {
+    bool status = false;
+    if (ann != NULL) {
+      status = (fann_clear_scaling_params(ann) != -1);
+    }
+    return status;
+  }
+
+  /* Method: scale_input
+
+     Scale data in input vector before feed it to ann based on previously calculated parameters.
+
+     See also:
+                  <descale_input>, <scale_output>, <fann_scale_input>
+
+          This function appears in FANN >= 2.1.0.
+   */
+  void scale_input(fann_type *input_vector) {
+    if (ann != NULL) {
+      fann_scale_input(ann, input_vector);
+    }
+  }
+
+  /* Method: scale_output
+
+     Scale data in output vector before feed it to ann based on previously calculated parameters.
+
+     See also:
+                  <descale_output>, <scale_input>, <fann_scale_output>
+
+          This function appears in FANN >= 2.1.0.
+   */
+  void scale_output(fann_type *output_vector) {
+    if (ann != NULL) {
+      fann_scale_output(ann, output_vector);
+    }
+  }
+
+  /* Method: descale_input
+
+     Scale data in input vector after get it from ann based on previously calculated parameters.
+
+     See also:
+                  <scale_input>, <descale_output>, <fann_descale_input>
+
+          This function appears in FANN >= 2.1.0.
+   */
+  void descale_input(fann_type *input_vector) {
+    if (ann != NULL) {
+      fann_descale_input(ann, input_vector);
+    }
+  }
+
+  /* Method: descale_output
+
+     Scale data in output vector after get it from ann based on previously calculated parameters.
+
+     See also:
+                  <scale_output>, <descale_input>, <fann_descale_output>
+
+          This function appears in FANN >= 2.1.0.
+   */
+  void descale_output(fann_type *output_vector) {
+    if (ann != NULL) {
+      fann_descale_output(ann, output_vector);
+    }
+  }
+
+#endif /* FIXEDFANN */
+
+  /*********************************************************************/
+
+  /* Method: set_error_log
+
+     Change where errors are logged to.
+
+     If log_file is NULL, no errors will be printed.
+
+     If neural_net is empty i.e. ann is NULL, the default log will be set.
+     The default log is the log used when creating a neural_net.
+     This default log will also be the default for all new structs
+     that are created.
+
+     The default behavior is to log them to stderr.
+
+     See also:
+          <struct fann_error>, <fann_set_error_log>
+
+     This function appears in FANN >= 1.1.0.
+   */
+  void set_error_log(FILE *log_file) {
+    fann_set_error_log(reinterpret_cast<struct fann_error *>(ann), log_file);
+  }
+
+  /* Method: get_errno
+
+     Returns the last error number.
+
+     See also:
+      <fann_errno_enum>, <fann_reset_errno>, <fann_get_errno>
+
+     This function appears in FANN >= 1.1.0.
+   */
+  unsigned int get_errno() { return fann_get_errno(reinterpret_cast<struct fann_error *>(ann)); }
+
+  /* Method: reset_errno
+
+     Resets the last error number.
+
+     This function appears in FANN >= 1.1.0.
+   */
+  void reset_errno() { fann_reset_errno(reinterpret_cast<struct fann_error *>(ann)); }
+
+  /* Method: reset_errstr
+
+     Resets the last error string.
+
+     This function appears in FANN >= 1.1.0.
+   */
+  void reset_errstr() { fann_reset_errstr(reinterpret_cast<struct fann_error *>(ann)); }
+
+  /* Method: get_errstr
+
+     Returns the last errstr.
+
+     This function calls <fann_reset_errno> and <fann_reset_errstr>
+
+     This function appears in FANN >= 1.1.0.
+   */
+  std::string get_errstr() {
+    return std::string(fann_get_errstr(reinterpret_cast<struct fann_error *>(ann)));
+  }
+
+  /* Method: print_error
+
+     Prints the last error to stderr.
+
+     This function appears in FANN >= 1.1.0.
+   */
+  void print_error() { fann_print_error(reinterpret_cast<struct fann_error *>(ann)); }
+
+  /* Function: disable_seed_rand
+
+     Disables the automatic random generator seeding that happens in FANN.
+
+     Per default FANN will always seed the random generator when creating a new network,
+     unless FANN_NO_SEED is defined during compilation of the library. This method can
+     disable this at runtime.
+
+     This function appears in FANN >= 2.3.0
+  */
+  void disable_seed_rand() { fann_disable_seed_rand(); }
+
+  /* Function: enable_seed_rand
+
+     Enables the automatic random generator seeding that happens in FANN.
+
+     Per default FANN will always seed the random generator when creating a new network,
+     unless FANN_NO_SEED is defined during compilation of the library. This method can
+     disable this at runtime.
+
+     This function appears in FANN >= 2.3.0
+  */
+  void enable_seed_rand() { fann_enable_seed_rand(); }
+
+  /*********************************************************************/
+
+ private:
+  // Structure used by set_callback to hold information about a user callback
+  typedef struct user_context_type {
+    callback_type user_callback;  // Pointer to user callback function
+    void *user_data;              // Arbitrary data pointer passed to the callback
+    neural_net *net;              // This pointer for the neural network
+  } user_context;
+
+#ifndef FIXEDFANN
+
+  // Internal callback used to convert from pointers to class references
+  static int FANN_API internal_callback(struct fann *ann, struct fann_train_data *train,
+                                        unsigned int max_epochs,
+                                        unsigned int epochs_between_reports, float desired_error,
+                                        unsigned int epochs) {
+    user_context *user_data = static_cast<user_context *>(fann_get_user_data(ann));
+    if (user_data != NULL) {
+      FANN::training_data data;
+      data.train_data = train;
+
+      int result =
+          (*user_data->user_callback)(*user_data->net, data, max_epochs, epochs_between_reports,
+                                      desired_error, epochs, user_data->user_data);
+
+      data.train_data = NULL;  // Prevent automatic cleanup
+      return result;
+    } else {
+      return -1;  // This should not occur except if out of memory
+    }
+  }
+
+#endif /* NOT FIXEDFANN */
+
+ protected:
+  // Pointer the encapsulated fann neural net structure
+  struct fann *ann;
+};
+
+/*************************************************************************/
+}  // namespace FANN
+
+#endif /* FANN_CPP_H_INCLUDED */
diff --git a/ai_fighter/AI_Fight_Sim/Plugins/NeuralNetworkAIController/Source/NeuralNetworkAIController/ThirdParty/FANN/include/fann_data.h b/ai_fighter/AI_Fight_Sim/Plugins/NeuralNetworkAIController/Source/NeuralNetworkAIController/ThirdParty/FANN/include/fann_data.h
new file mode 100644
index 0000000..1425305
--- /dev/null
+++ b/ai_fighter/AI_Fight_Sim/Plugins/NeuralNetworkAIController/Source/NeuralNetworkAIController/ThirdParty/FANN/include/fann_data.h
@@ -0,0 +1,800 @@
+/*
+Fast Artificial Neural Network Library (fann)
+Copyright (C) 2003-2016 Steffen Nissen (steffen.fann@gmail.com)
+
+This library is free software; you can redistribute it and/or
+modify it under the terms of the GNU Lesser General Public
+License as published by the Free Software Foundation; either
+version 2.1 of the License, or (at your option) any later version.
+
+This library is distributed in the hope that it will be useful,
+but WITHOUT ANY WARRANTY; without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
+Lesser General Public License for more details.
+
+You should have received a copy of the GNU Lesser General Public
+License along with this library; if not, write to the Free Software
+Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA  02111-1307  USA
+*/
+
+#ifndef __fann_data_h__
+#define __fann_data_h__
+
+#include <stdio.h>
+
+/* Section: FANN Datatypes
+
+   The two main datatypes used in the fann library are <struct fann>,
+   which represents an artificial neural network, and <struct fann_train_data>,
+   which represents training data.
+ */
+
+/* Type: fann_type
+   fann_type is the type used for the weights, inputs and outputs of the neural network.
+
+        fann_type is defined as a:
+        float - if you include fann.h or floatfann.h
+        double - if you include doublefann.h
+        int - if you include fixedfann.h (please be aware that fixed point usage is
+                        only to be used during execution, and not during training).
+*/
+
+/* Enum: fann_train_enum
+        The Training algorithms used when training on <struct fann_train_data> with functions like
+        <fann_train_on_data> or <fann_train_on_file>. The incremental training alters the weights
+        after each time it is presented an input pattern, while batch only alters the weights once
+   after it has been presented to all the patterns.
+
+        FANN_TRAIN_INCREMENTAL -  Standard backpropagation algorithm, where the weights are
+                updated after each training pattern. This means that the weights are updated many
+                times during a single epoch. For this reason some problems will train very fast with
+                this algorithm, while other more advanced problems will not train very well.
+        FANN_TRAIN_BATCH -  Standard backpropagation algorithm, where the weights are updated after
+                calculating the mean square error for the whole training set. This means that the
+   weights are only updated once during an epoch. For this reason some problems will train slower
+   with this algorithm. But since the mean square error is calculated more correctly than in
+                incremental training, some problems will reach better solutions with this algorithm.
+        FANN_TRAIN_RPROP - A more advanced batch training algorithm which achieves good results
+                for many problems. The RPROP training algorithm is adaptive, and does therefore not
+                use the learning_rate. Some other parameters can however be set to change the way
+   the RPROP algorithm works, but it is only recommended for users with insight in how the RPROP
+                training algorithm works. The RPROP training algorithm is described by
+                [Riedmiller and Braun, 1993], but the actual learning algorithm used here is the
+                iRPROP- training algorithm which is described by [Igel and Husken, 2000] which
+                is a variant of the standard RPROP training algorithm.
+        FANN_TRAIN_QUICKPROP - A more advanced batch training algorithm which achieves good results
+                for many problems. The quickprop training algorithm uses the learning_rate parameter
+                along with other more advanced parameters, but it is only recommended to change
+   these advanced parameters, for users with insight in how the quickprop training algorithm works.
+                The quickprop training algorithm is described by [Fahlman, 1988].
+        FANN_TRAIN_SARPROP - THE SARPROP ALGORITHM: A SIMULATED ANNEALING ENHANCEMENT TO RESILIENT
+   BACK PROPAGATION
+    http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.47.8197&rep=rep1&type=pdf
+
+        See also:
+                <fann_set_training_algorithm>, <fann_get_training_algorithm>
+*/
+enum fann_train_enum {
+  FANN_TRAIN_INCREMENTAL = 0,
+  FANN_TRAIN_BATCH,
+  FANN_TRAIN_RPROP,
+  FANN_TRAIN_QUICKPROP,
+  FANN_TRAIN_SARPROP
+};
+
+/* Constant: FANN_TRAIN_NAMES
+
+   Constant array consisting of the names for the training algorithms, so that the name of an
+   training function can be received by:
+   (code)
+   char *name = FANN_TRAIN_NAMES[train_function];
+   (end)
+
+   See Also:
+      <fann_train_enum>
+*/
+static char const *const FANN_TRAIN_NAMES[] = {"FANN_TRAIN_INCREMENTAL", "FANN_TRAIN_BATCH",
+                                               "FANN_TRAIN_RPROP", "FANN_TRAIN_QUICKPROP",
+                                               "FANN_TRAIN_SARPROP"};
+
+/* Enums: fann_activationfunc_enum
+
+        The activation functions used for the neurons during training. The activation functions
+        can either be defined for a group of neurons by <fann_set_activation_function_hidden> and
+        <fann_set_activation_function_output> or it can be defined for a single neuron by
+   <fann_set_activation_function>.
+
+        The steepness of an activation function is defined in the same way by
+        <fann_set_activation_steepness_hidden>, <fann_set_activation_steepness_output> and
+   <fann_set_activation_steepness>.
+
+   The functions are described with functions where:
+   * x is the input to the activation function,
+   * y is the output,
+   * s is the steepness and
+   * d is the derivation.
+
+   FANN_LINEAR - Linear activation function.
+     * span: -inf < y < inf
+         * y = x*s, d = 1*s
+         * Can NOT be used in fixed point.
+
+   FANN_THRESHOLD - Threshold activation function.
+         * x < 0 -> y = 0, x >= 0 -> y = 1
+         * Can NOT be used during training.
+
+   FANN_THRESHOLD_SYMMETRIC - Threshold activation function.
+         * x < 0 -> y = -1, x >= 0 -> y = 1
+         * Can NOT be used during training.
+
+   FANN_SIGMOID - Sigmoid activation function.
+         * One of the most used activation functions.
+         * span: 0 < y < 1
+         * y = 1/(1 + exp(-2*s*x))
+         * d = 2*s*y*(1 - y)
+
+   FANN_SIGMOID_STEPWISE - Stepwise linear approximation to sigmoid.
+         * Faster than sigmoid but a bit less precise.
+
+   FANN_SIGMOID_SYMMETRIC - Symmetric sigmoid activation function, aka. tanh.
+         * One of the most used activation functions.
+         * span: -1 < y < 1
+         * y = tanh(s*x) = 2/(1 + exp(-2*s*x)) - 1
+         * d = s*(1-(y*y))
+
+   FANN_SIGMOID_SYMMETRIC_STEPWISE - Stepwise linear approximation to symmetric sigmoid.
+         * Faster than symmetric sigmoid but a bit less precise.
+
+   FANN_GAUSSIAN - Gaussian activation function.
+         * 0 when x = -inf, 1 when x = 0 and 0 when x = inf
+         * span: 0 < y < 1
+         * y = exp(-x*s*x*s)
+         * d = -2*x*s*y*s
+
+   FANN_GAUSSIAN_SYMMETRIC - Symmetric gaussian activation function.
+         * -1 when x = -inf, 1 when x = 0 and 0 when x = inf
+         * span: -1 < y < 1
+         * y = exp(-x*s*x*s)*2-1
+         * d = -2*x*s*(y+1)*s
+
+   FANN_ELLIOT - Fast (sigmoid like) activation function defined by David Elliott
+         * span: 0 < y < 1
+         * y = ((x*s) / 2) / (1 + |x*s|) + 0.5
+         * d = s*1/(2*(1+|x*s|)*(1+|x*s|))
+
+   FANN_ELLIOT_SYMMETRIC - Fast (symmetric sigmoid like) activation function defined by David
+   Elliott
+         * span: -1 < y < 1
+         * y = (x*s) / (1 + |x*s|)
+         * d = s*1/((1+|x*s|)*(1+|x*s|))
+
+        FANN_LINEAR_PIECE - Bounded linear activation function.
+         * span: 0 <= y <= 1
+         * y = x*s, d = 1*s
+
+        FANN_LINEAR_PIECE_SYMMETRIC - Bounded linear activation function.
+         * span: -1 <= y <= 1
+         * y = x*s, d = 1*s
+
+        FANN_SIN_SYMMETRIC - Periodical sinus activation function.
+         * span: -1 <= y <= 1
+         * y = sin(x*s)
+         * d = s*cos(x*s)
+
+        FANN_COS_SYMMETRIC - Periodical cosinus activation function.
+         * span: -1 <= y <= 1
+         * y = cos(x*s)
+         * d = s*-sin(x*s)
+
+        FANN_SIN - Periodical sinus activation function.
+         * span: 0 <= y <= 1
+         * y = sin(x*s)/2+0.5
+         * d = s*cos(x*s)/2
+
+        FANN_COS - Periodical cosinus activation function.
+         * span: 0 <= y <= 1
+         * y = cos(x*s)/2+0.5
+         * d = s*-sin(x*s)/2
+
+        See also:
+        <fann_set_activation_function_layer>, <fann_set_activation_function_hidden>,
+        <fann_set_activation_function_output>, <fann_set_activation_steepness>,
+    <fann_set_activation_function>
+*/
+enum fann_activationfunc_enum {
+  FANN_LINEAR = 0,
+  FANN_THRESHOLD,
+  FANN_THRESHOLD_SYMMETRIC,
+  FANN_SIGMOID,
+  FANN_SIGMOID_STEPWISE,
+  FANN_SIGMOID_SYMMETRIC,
+  FANN_SIGMOID_SYMMETRIC_STEPWISE,
+  FANN_GAUSSIAN,
+  FANN_GAUSSIAN_SYMMETRIC,
+  /* Stepwise linear approximation to gaussian.
+   * Faster than gaussian but a bit less precise.
+   * NOT implemented yet.
+   */
+  FANN_GAUSSIAN_STEPWISE,
+  FANN_ELLIOT,
+  FANN_ELLIOT_SYMMETRIC,
+  FANN_LINEAR_PIECE,
+  FANN_LINEAR_PIECE_SYMMETRIC,
+  FANN_SIN_SYMMETRIC,
+  FANN_COS_SYMMETRIC,
+  FANN_SIN,
+  FANN_COS
+};
+
+/* Constant: FANN_ACTIVATIONFUNC_NAMES
+
+   Constant array consisting of the names for the activation function, so that the name of an
+   activation function can be received by:
+   (code)
+   char *name = FANN_ACTIVATIONFUNC_NAMES[activation_function];
+   (end)
+
+   See Also:
+      <fann_activationfunc_enum>
+*/
+static char const *const FANN_ACTIVATIONFUNC_NAMES[] = {"FANN_LINEAR",
+                                                        "FANN_THRESHOLD",
+                                                        "FANN_THRESHOLD_SYMMETRIC",
+                                                        "FANN_SIGMOID",
+                                                        "FANN_SIGMOID_STEPWISE",
+                                                        "FANN_SIGMOID_SYMMETRIC",
+                                                        "FANN_SIGMOID_SYMMETRIC_STEPWISE",
+                                                        "FANN_GAUSSIAN",
+                                                        "FANN_GAUSSIAN_SYMMETRIC",
+                                                        "FANN_GAUSSIAN_STEPWISE",
+                                                        "FANN_ELLIOT",
+                                                        "FANN_ELLIOT_SYMMETRIC",
+                                                        "FANN_LINEAR_PIECE",
+                                                        "FANN_LINEAR_PIECE_SYMMETRIC",
+                                                        "FANN_SIN_SYMMETRIC",
+                                                        "FANN_COS_SYMMETRIC",
+                                                        "FANN_SIN",
+                                                        "FANN_COS"};
+
+/* Enum: fann_errorfunc_enum
+        Error function used during training.
+
+        FANN_ERRORFUNC_LINEAR - Standard linear error function.
+        FANN_ERRORFUNC_TANH - Tanh error function, usually better
+                but can require a lower learning rate. This error function aggressively targets
+   outputs that differ much from the desired, while not targeting outputs that only differ a little
+   that much. This activation function is not recommended for cascade training and incremental
+   training.
+
+        See also:
+                <fann_set_train_error_function>, <fann_get_train_error_function>
+*/
+enum fann_errorfunc_enum { FANN_ERRORFUNC_LINEAR = 0, FANN_ERRORFUNC_TANH };
+
+/* Constant: FANN_ERRORFUNC_NAMES
+
+   Constant array consisting of the names for the training error functions, so that the name of an
+   error function can be received by:
+   (code)
+   char *name = FANN_ERRORFUNC_NAMES[error_function];
+   (end)
+
+   See Also:
+      <fann_errorfunc_enum>
+*/
+static char const *const FANN_ERRORFUNC_NAMES[] = {"FANN_ERRORFUNC_LINEAR", "FANN_ERRORFUNC_TANH"};
+
+/* Enum: fann_stopfunc_enum
+        Stop criteria used during training.
+
+        FANN_STOPFUNC_MSE - Stop criterion is Mean Square Error (MSE) value.
+        FANN_STOPFUNC_BIT - Stop criterion is number of bits that fail. The number of bits; means
+   the number of output neurons which differ more than the bit fail limit (see
+   <fann_get_bit_fail_limit>, <fann_set_bit_fail_limit>). The bits are counted in all of the
+   training data, so this number can be higher than the number of training data.
+
+        See also:
+                <fann_set_train_stop_function>, <fann_get_train_stop_function>
+*/
+enum fann_stopfunc_enum { FANN_STOPFUNC_MSE = 0, FANN_STOPFUNC_BIT };
+
+/* Constant: FANN_STOPFUNC_NAMES
+
+   Constant array consisting of the names for the training stop functions, so that the name of a
+   stop function can be received by:
+   (code)
+   char *name = FANN_STOPFUNC_NAMES[stop_function];
+   (end)
+
+   See Also:
+      <fann_stopfunc_enum>
+*/
+static char const *const FANN_STOPFUNC_NAMES[] = {"FANN_STOPFUNC_MSE", "FANN_STOPFUNC_BIT"};
+
+/* Enum: fann_network_type_enum
+
+    Definition of network types used by <fann_get_network_type>
+
+    FANN_NETTYPE_LAYER - Each layer only has connections to the next layer
+    FANN_NETTYPE_SHORTCUT - Each layer has connections to all following layers
+
+   See Also:
+      <fann_get_network_type>
+
+   This enumeration appears in FANN >= 2.1.0
+*/
+enum fann_nettype_enum {
+  FANN_NETTYPE_LAYER = 0, /* Each layer only has connections to the next layer */
+  FANN_NETTYPE_SHORTCUT   /* Each layer has connections to all following layers */
+};
+
+/* Constant: FANN_NETWORK_TYPE_NAMES
+
+   Constant array consisting of the names for the network types, so that the name of an
+   network type can be received by:
+   (code)
+   char *network_type_name = FANN_NETWORK_TYPE_NAMES[fann_get_network_type(ann)];
+   (end)
+
+   See Also:
+      <fann_get_network_type>
+
+   This constant appears in FANN >= 2.1.0
+*/
+static char const *const FANN_NETTYPE_NAMES[] = {"FANN_NETTYPE_LAYER", "FANN_NETTYPE_SHORTCUT"};
+
+/* forward declarations for use with the callback */
+struct fann;
+struct fann_train_data;
+/* Type: fann_callback_type
+   This callback function can be called during training when using <fann_train_on_data>,
+   <fann_train_on_file> or <fann_cascadetrain_on_data>.
+
+        >typedef int (FANN_API * fann_callback_type) (struct fann *ann, struct fann_train_data
+   *train,
+        > unsigned int max_epochs, >                                             unsigned int
+   epochs_between_reports, >                                             float desired_error,
+   unsigned int epochs);
+
+        The callback can be set by using <fann_set_callback> and is very useful for doing custom
+        things during training. It is recommended to use this function when implementing custom
+        training procedures, or when visualizing the training in a GUI etc. The parameters which the
+        callback function takes are the parameters given to <fann_train_on_data>, plus an epochs
+        parameter which tells how many epochs the training has taken so far.
+
+        The callback function should return an integer, if the callback function returns -1, the
+   training will terminate.
+
+        Example of a callback function:
+                >int FANN_API test_callback(struct fann *ann, struct fann_train_data *train,
+                >				            unsigned int max_epochs, unsigned int
+   epochs_between_reports,
+                >				            float desired_error, unsigned int
+   epochs)
+                >{
+                >	printf("Epochs     %8d. MSE: %.5f. Desired-MSE: %.5f\n", epochs,
+   fann_get_MSE(ann), desired_error); >	return 0;
+                >}
+
+        See also:
+                <fann_set_callback>, <fann_train_on_data>
+ */
+FANN_EXTERNAL typedef int(FANN_API *fann_callback_type)(struct fann *ann,
+                                                        struct fann_train_data *train,
+                                                        unsigned int max_epochs,
+                                                        unsigned int epochs_between_reports,
+                                                        float desired_error, unsigned int epochs);
+
+/* ----- Data structures -----
+ * No data within these structures should be altered directly by the user.
+ */
+
+struct fann_neuron {
+  /* Index to the first and last connection
+   * (actually the last is a past end index)
+   */
+  unsigned int first_con;
+  unsigned int last_con;
+  /* The sum of the inputs multiplied with the weights */
+  fann_type sum;
+  /* The value of the activation function applied to the sum */
+  fann_type value;
+  /* The steepness of the activation function */
+  fann_type activation_steepness;
+  /* Used to choose which activation function to use */
+  enum fann_activationfunc_enum activation_function;
+#ifdef __GNUC__
+} __attribute__((packed));
+#else
+};
+#endif
+
+/* A single layer in the neural network.
+ */
+struct fann_layer {
+  /* A pointer to the first neuron in the layer
+   * When allocated, all the neurons in all the layers are actually
+   * in one long array, this is because we want to easily clear all
+   * the neurons at once.
+   */
+  struct fann_neuron *first_neuron;
+
+  /* A pointer to the neuron past the last neuron in the layer */
+  /* the number of neurons is last_neuron - first_neuron */
+  struct fann_neuron *last_neuron;
+};
+
+/* Struct: struct fann_error
+
+        Structure used to store error-related information, both
+        <struct fann> and <struct fann_train_data> can be casted to this type.
+
+        See also:
+                <fann_set_error_log>, <fann_get_errno>
+*/
+struct fann_error {
+  enum fann_errno_enum errno_f;
+  FILE *error_log;
+  char *errstr;
+};
+
+/* 	Struct: struct fann
+        The fast artificial neural network (fann) structure.
+
+        Data within this structure should never be accessed directly, but only by using the
+        *fann_get_...* and *fann_set_...* functions.
+
+        The fann structure is created using one of the *fann_create_...* functions and each of
+        the functions which operates on the structure takes *struct fann * ann* as the first
+   parameter.
+
+        See also:
+                <fann_create_standard>, <fann_destroy>
+ */
+struct fann {
+  /* The type of error that last occured. */
+  enum fann_errno_enum errno_f;
+
+  /* Where to log error messages. */
+  FILE *error_log;
+
+  /* A string representation of the last error. */
+  char *errstr;
+
+  /* the learning rate of the network */
+  float learning_rate;
+
+  /* The learning momentum used for backpropagation algorithm. */
+  float learning_momentum;
+
+  /* the connection rate of the network
+   * between 0 and 1, 1 meaning fully connected
+   */
+  float connection_rate;
+
+  /* is 1 if shortcut connections are used in the ann otherwise 0
+   * Shortcut connections are connections that skip layers.
+   * A fully connected ann with shortcut connections are a ann where
+   * neurons have connections to all neurons in all later layers.
+   */
+  enum fann_nettype_enum network_type;
+
+  /* pointer to the first layer (input layer) in an array af all the layers,
+   * including the input and outputlayers
+   */
+  struct fann_layer *first_layer;
+
+  /* pointer to the layer past the last layer in an array af all the layers,
+   * including the input and outputlayers
+   */
+  struct fann_layer *last_layer;
+
+  /* Total number of neurons.
+   * very useful, because the actual neurons are allocated in one long array
+   */
+  unsigned int total_neurons;
+
+  /* Number of input neurons (not calculating bias) */
+  unsigned int num_input;
+
+  /* Number of output neurons (not calculating bias) */
+  unsigned int num_output;
+
+  /* The weight array */
+  fann_type *weights;
+
+  /* The connection array */
+  struct fann_neuron **connections;
+
+  /* Used to contain the errors used during training
+   * Is allocated during first training session,
+   * which means that if we do not train, it is never allocated.
+   */
+  fann_type *train_errors;
+
+  /* Training algorithm used when calling fann_train_on_..
+   */
+  enum fann_train_enum training_algorithm;
+
+#ifdef FIXEDFANN
+  /* the decimal_point, used for shifting the fix point
+   * in fixed point integer operatons.
+   */
+  unsigned int decimal_point;
+
+  /* the multiplier, used for multiplying the fix point
+   * in fixed point integer operatons.
+   * Only used in special cases, since the decimal_point is much faster.
+   */
+  unsigned int multiplier;
+
+  /* When in choosen (or in fixed point), the sigmoid function is
+   * calculated as a stepwise linear function. In the
+   * activation_results array, the result is saved, and in the
+   * two values arrays, the values that gives the results are saved.
+   */
+  fann_type sigmoid_results[6];
+  fann_type sigmoid_values[6];
+  fann_type sigmoid_symmetric_results[6];
+  fann_type sigmoid_symmetric_values[6];
+#endif
+
+  /* Total number of connections.
+   * very useful, because the actual connections
+   * are allocated in one long array
+   */
+  unsigned int total_connections;
+
+  /* used to store outputs in */
+  fann_type *output;
+
+  /* the number of data used to calculate the mean square error.
+   */
+  unsigned int num_MSE;
+
+  /* the total error value.
+   * the real mean square error is MSE_value/num_MSE
+   */
+  float MSE_value;
+
+  /* The number of outputs which would fail (only valid for classification problems)
+   */
+  unsigned int num_bit_fail;
+
+  /* The maximum difference between the actual output and the expected output
+   * which is accepted when counting the bit fails.
+   * This difference is multiplied by two when dealing with symmetric activation functions,
+   * so that symmetric and not symmetric activation functions can use the same limit.
+   */
+  fann_type bit_fail_limit;
+
+  /* The error function used during training. (default FANN_ERRORFUNC_TANH)
+   */
+  enum fann_errorfunc_enum train_error_function;
+
+  /* The stop function used during training. (default FANN_STOPFUNC_MSE)
+   */
+  enum fann_stopfunc_enum train_stop_function;
+
+  /* The callback function used during training. (default NULL)
+   */
+  fann_callback_type callback;
+
+  /* A pointer to user defined data. (default NULL)
+   */
+  void *user_data;
+
+  /* Variables for use with Cascade Correlation */
+
+  /* The error must change by at least this
+   * fraction of its old value to count as a
+   * significant change.
+   */
+  float cascade_output_change_fraction;
+
+  /* No change in this number of epochs will cause
+   * stagnation.
+   */
+  unsigned int cascade_output_stagnation_epochs;
+
+  /* The error must change by at least this
+   * fraction of its old value to count as a
+   * significant change.
+   */
+  float cascade_candidate_change_fraction;
+
+  /* No change in this number of epochs will cause
+   * stagnation.
+   */
+  unsigned int cascade_candidate_stagnation_epochs;
+
+  /* The current best candidate, which will be installed.
+   */
+  unsigned int cascade_best_candidate;
+
+  /* The upper limit for a candidate score
+   */
+  fann_type cascade_candidate_limit;
+
+  /* Scale of copied candidate output weights
+   */
+  fann_type cascade_weight_multiplier;
+
+  /* Maximum epochs to train the output neurons during cascade training
+   */
+  unsigned int cascade_max_out_epochs;
+
+  /* Maximum epochs to train the candidate neurons during cascade training
+   */
+  unsigned int cascade_max_cand_epochs;
+
+  /* Minimum epochs to train the output neurons during cascade training
+   */
+  unsigned int cascade_min_out_epochs;
+
+  /* Minimum epochs to train the candidate neurons during cascade training
+   */
+  unsigned int cascade_min_cand_epochs;
+
+  /* An array consisting of the activation functions used when doing
+   * cascade training.
+   */
+  enum fann_activationfunc_enum *cascade_activation_functions;
+
+  /* The number of elements in the cascade_activation_functions array.
+   */
+  unsigned int cascade_activation_functions_count;
+
+  /* An array consisting of the steepnesses used during cascade training.
+   */
+  fann_type *cascade_activation_steepnesses;
+
+  /* The number of elements in the cascade_activation_steepnesses array.
+   */
+  unsigned int cascade_activation_steepnesses_count;
+
+  /* The number of candidates of each type that will be present.
+   * The actual number of candidates is then
+   * cascade_activation_functions_count *
+   * cascade_activation_steepnesses_count *
+   * cascade_num_candidate_groups
+   */
+  unsigned int cascade_num_candidate_groups;
+
+  /* An array consisting of the score of the individual candidates,
+   * which is used to decide which candidate is the best
+   */
+  fann_type *cascade_candidate_scores;
+
+  /* The number of allocated neurons during cascade correlation algorithms.
+   * This number might be higher than the actual number of neurons to avoid
+   * allocating new space too often.
+   */
+  unsigned int total_neurons_allocated;
+
+  /* The number of allocated connections during cascade correlation algorithms.
+   * This number might be higher than the actual number of neurons to avoid
+   * allocating new space too often.
+   */
+  unsigned int total_connections_allocated;
+
+  /* Variables for use with Quickprop training */
+
+  /* Decay is used to make the weights not go so high */
+  float quickprop_decay;
+
+  /* Mu is a factor used to increase and decrease the stepsize */
+  float quickprop_mu;
+
+  /* Variables for use with with RPROP training */
+
+  /* Tells how much the stepsize should increase during learning */
+  float rprop_increase_factor;
+
+  /* Tells how much the stepsize should decrease during learning */
+  float rprop_decrease_factor;
+
+  /* The minimum stepsize */
+  float rprop_delta_min;
+
+  /* The maximum stepsize */
+  float rprop_delta_max;
+
+  /* The initial stepsize */
+  float rprop_delta_zero;
+
+  /* Defines how much the weights are constrained to smaller values at the beginning */
+  float sarprop_weight_decay_shift;
+
+  /* Decides if the stepsize is too big with regard to the error */
+  float sarprop_step_error_threshold_factor;
+
+  /* Defines how much the stepsize is influenced by the error */
+  float sarprop_step_error_shift;
+
+  /* Defines how much the epoch influences weight decay and noise */
+  float sarprop_temperature;
+
+  /* Current training epoch */
+  unsigned int sarprop_epoch;
+
+  /* Used to contain the slope errors used during batch training
+   * Is allocated during first training session,
+   * which means that if we do not train, it is never allocated.
+   */
+  fann_type *train_slopes;
+
+  /* The previous step taken by the quickprop/rprop procedures.
+   * Not allocated if not used.
+   */
+  fann_type *prev_steps;
+
+  /* The slope values used by the quickprop/rprop procedures.
+   * Not allocated if not used.
+   */
+  fann_type *prev_train_slopes;
+
+  /* The last delta applied to a connection weight.
+   * This is used for the momentum term in the backpropagation algorithm.
+   * Not allocated if not used.
+   */
+  fann_type *prev_weights_deltas;
+
+#ifndef FIXEDFANN
+  /* Arithmetic mean used to remove steady component in input data.  */
+  float *scale_mean_in;
+
+  /* Standart deviation used to normalize input data (mostly to [-1;1]). */
+  float *scale_deviation_in;
+
+  /* User-defined new minimum for input data.
+   * Resulting data values may be less than user-defined minimum.
+   */
+  float *scale_new_min_in;
+
+  /* Used to scale data to user-defined new maximum for input data.
+   * Resulting data values may be greater than user-defined maximum.
+   */
+  float *scale_factor_in;
+
+  /* Arithmetic mean used to remove steady component in output data.  */
+  float *scale_mean_out;
+
+  /* Standart deviation used to normalize output data (mostly to [-1;1]). */
+  float *scale_deviation_out;
+
+  /* User-defined new minimum for output data.
+   * Resulting data values may be less than user-defined minimum.
+   */
+  float *scale_new_min_out;
+
+  /* Used to scale data to user-defined new maximum for output data.
+   * Resulting data values may be greater than user-defined maximum.
+   */
+  float *scale_factor_out;
+#endif
+};
+
+/* Type: fann_connection
+
+    Describes a connection between two neurons and its weight
+
+    from_neuron - Unique number used to identify source neuron
+    to_neuron - Unique number used to identify destination neuron
+    weight - The numerical value of the weight
+
+    See Also:
+        <fann_get_connection_array>, <fann_set_weight_array>
+
+   This structure appears in FANN >= 2.1.0
+*/
+struct fann_connection {
+  /* Unique number used to identify source neuron */
+  unsigned int from_neuron;
+  /* Unique number used to identify destination neuron */
+  unsigned int to_neuron;
+  /* The numerical value of the weight */
+  fann_type weight;
+};
+
+#endif
diff --git a/ai_fighter/AI_Fight_Sim/Plugins/NeuralNetworkAIController/Source/NeuralNetworkAIController/ThirdParty/FANN/include/fann_data_cpp.h b/ai_fighter/AI_Fight_Sim/Plugins/NeuralNetworkAIController/Source/NeuralNetworkAIController/ThirdParty/FANN/include/fann_data_cpp.h
new file mode 100644
index 0000000..62ebf34
--- /dev/null
+++ b/ai_fighter/AI_Fight_Sim/Plugins/NeuralNetworkAIController/Source/NeuralNetworkAIController/ThirdParty/FANN/include/fann_data_cpp.h
@@ -0,0 +1,295 @@
+/*
+Fast Artificial Neural Network Library (fann)
+Copyright (C) 2003-2016 Steffen Nissen (steffen.fann@gmail.com)
+
+This library is free software; you can redistribute it and/or
+modify it under the terms of the GNU Lesser General Public
+License as published by the Free Software Foundation; either
+version 2.1 of the License, or (at your option) any later version.
+
+This library is distributed in the hope that it will be useful,
+but WITHOUT ANY WARRANTY; without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
+Lesser General Public License for more details.
+
+You should have received a copy of the GNU Lesser General Public
+License along with this library; if not, write to the Free Software
+Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA  02111-1307  USA
+*/
+
+#ifndef FANN_FANN_DATA_CPP_H_H
+#define FANN_FANN_DATA_CPP_H_H
+
+#include <stdarg.h>
+#include <string>
+
+/* Section: FANN C++ Datatypes
+   This section includes enums and helper data types used by the two main classes <neural_net> and
+   <training_data>
+ */
+
+/* Type: fann_type
+   fann_type is the type used for the weights, inputs and outputs of the neural network.
+
+        fann_type is defined as a:
+        float - if you include fann.h or floatfann.h
+        double - if you include doublefann.h
+        int - if you include fixedfann.h (please be aware that fixed point usage is
+                        only to be used during execution, and not during training).
+*/
+
+namespace FANN {
+/* Enum: error_function_enum
+        Error function used during training.
+
+        ERRORFUNC_LINEAR - Standard linear error function.
+        ERRORFUNC_TANH - Tanh error function, usually better
+                but can require a lower learning rate. This error function aggressively targets
+   outputs that differ much from the desired, while not targeting outputs that only differ a little
+   that much. This activation function is not recommended for cascade training and incremental
+   training.
+
+        See also:
+                <neural_net::set_train_error_function>, <neural_net::get_train_error_function>
+*/
+enum error_function_enum { ERRORFUNC_LINEAR = FANN_ERRORFUNC_LINEAR, ERRORFUNC_TANH };
+
+/* Enum: stop_function_enum
+        Stop criteria used during training.
+
+        STOPFUNC_MSE - Stop criteria is Mean Square Error (MSE) value.
+        STOPFUNC_BIT - Stop criteria is number of bits that fail. The number of bits; means the
+                number of output neurons which differ more than the bit fail limit
+                (see <neural_net::get_bit_fail_limit>, <neural_net::set_bit_fail_limit>).
+                The bits are counted in all of the training data, so this number can be higher than
+                the number of training data.
+
+        See also:
+                <neural_net::set_train_stop_function>, <neural_net::get_train_stop_function>
+*/
+enum stop_function_enum { STOPFUNC_MSE = FANN_STOPFUNC_MSE, STOPFUNC_BIT };
+
+/* Enum: training_algorithm_enum
+        The Training algorithms used when training on <training_data> with functions like
+        <neural_net::train_on_data> or <neural_net::train_on_file>. The incremental training
+    looks alters the weights after each time it is presented an input pattern, while batch
+    only alters the weights once after it has been presented to all the patterns.
+
+        TRAIN_INCREMENTAL -  Standard backpropagation algorithm, where the weights are
+                updated after each training pattern. This means that the weights are updated many
+                times during a single epoch. For this reason some problems, will train very fast
+   with this algorithm, while other more advanced problems will not train very well. TRAIN_BATCH -
+   Standard backpropagation algorithm, where the weights are updated after calculating the mean
+   square error for the whole training set. This means that the weights are only updated once during
+   an epoch. For this reason some problems, will train slower with this algorithm. But since the
+   mean square error is calculated more correctly than in incremental training, some problems will
+   reach a better solutions with this algorithm. TRAIN_RPROP - A more advanced batch training
+   algorithm which achieves good results for many problems. The RPROP training algorithm is
+   adaptive, and does therefore not use the learning_rate. Some other parameters can however be set
+   to change the way the RPROP algorithm works, but it is only recommended for users with insight in
+   how the RPROP training algorithm works. The RPROP training algorithm is described by [Riedmiller
+   and Braun, 1993], but the actual learning algorithm used here is the iRPROP- training algorithm
+   which is described by [Igel and Husken, 2000] which is a variant of the standard RPROP training
+   algorithm. TRAIN_QUICKPROP - A more advanced batch training algorithm which achieves good results
+                for many problems. The quickprop training algorithm uses the learning_rate parameter
+                along with other more advanced parameters, but it is only recommended to change
+   these advanced parameters, for users with insight in how the quickprop training algorithm works.
+                The quickprop training algorithm is described by [Fahlman, 1988].
+            FANN_TRAIN_SARPROP - THE SARPROP ALGORITHM: A SIMULATED ANNEALING ENHANCEMENT TO
+   RESILIENT BACK PROPAGATION
+        http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.47.8197&rep=rep1&type=pdf
+
+
+        See also:
+                <neural_net::set_training_algorithm>, <neural_net::get_training_algorithm>
+*/
+enum training_algorithm_enum {
+  TRAIN_INCREMENTAL = FANN_TRAIN_INCREMENTAL,
+  TRAIN_BATCH,
+  TRAIN_RPROP,
+  TRAIN_QUICKPROP,
+  TRAIN_SARPROP
+};
+
+/* Enum: activation_function_enum
+
+        The activation functions used for the neurons during training. The activation functions
+        can either be defined for a group of neurons by <neural_net::set_activation_function_hidden>
+    and <neural_net::set_activation_function_output> or it can be defined for a single neuron by
+    <neural_net::set_activation_function>.
+
+        The steepness of an activation function is defined in the same way by
+        <neural_net::set_activation_steepness_hidden>, <neural_net::set_activation_steepness_output>
+    and <neural_net::set_activation_steepness>.
+
+   The functions are described with functions where:
+   * x is the input to the activation function,
+   * y is the output,
+   * s is the steepness and
+   * d is the derivation.
+
+   FANN_LINEAR - Linear activation function.
+     * span: -inf < y < inf
+         * y = x*s, d = 1*s
+         * Can NOT be used in fixed point.
+
+   FANN_THRESHOLD - Threshold activation function.
+         * x < 0 -> y = 0, x >= 0 -> y = 1
+         * Can NOT be used during training.
+
+   FANN_THRESHOLD_SYMMETRIC - Threshold activation function.
+         * x < 0 -> y = 0, x >= 0 -> y = 1
+         * Can NOT be used during training.
+
+   FANN_SIGMOID - Sigmoid activation function.
+         * One of the most used activation functions.
+         * span: 0 < y < 1
+         * y = 1/(1 + exp(-2*s*x))
+         * d = 2*s*y*(1 - y)
+
+   FANN_SIGMOID_STEPWISE - Stepwise linear approximation to sigmoid.
+         * Faster than sigmoid but a bit less precise.
+
+   FANN_SIGMOID_SYMMETRIC - Symmetric sigmoid activation function, aka. tanh.
+         * One of the most used activation functions.
+         * span: -1 < y < 1
+         * y = tanh(s*x) = 2/(1 + exp(-2*s*x)) - 1
+         * d = s*(1-(y*y))
+
+   FANN_SIGMOID_SYMMETRIC - Stepwise linear approximation to symmetric sigmoid.
+         * Faster than symmetric sigmoid but a bit less precise.
+
+   FANN_GAUSSIAN - Gaussian activation function.
+         * 0 when x = -inf, 1 when x = 0 and 0 when x = inf
+         * span: 0 < y < 1
+         * y = exp(-x*s*x*s)
+         * d = -2*x*s*y*s
+
+   FANN_GAUSSIAN_SYMMETRIC - Symmetric gaussian activation function.
+         * -1 when x = -inf, 1 when x = 0 and 0 when x = inf
+         * span: -1 < y < 1
+         * y = exp(-x*s*x*s)*2-1
+         * d = -2*x*s*(y+1)*s
+
+   FANN_ELLIOT - Fast (sigmoid like) activation function defined by David Elliott
+         * span: 0 < y < 1
+         * y = ((x*s) / 2) / (1 + |x*s|) + 0.5
+         * d = s*1/(2*(1+|x*s|)*(1+|x*s|))
+
+   FANN_ELLIOT_SYMMETRIC - Fast (symmetric sigmoid like) activation function defined by David
+   Elliott
+         * span: -1 < y < 1
+         * y = (x*s) / (1 + |x*s|)
+         * d = s*1/((1+|x*s|)*(1+|x*s|))
+
+        FANN_LINEAR_PIECE - Bounded linear activation function.
+         * span: 0 < y < 1
+         * y = x*s, d = 1*s
+
+        FANN_LINEAR_PIECE_SYMMETRIC - Bounded Linear activation function.
+         * span: -1 < y < 1
+         * y = x*s, d = 1*s
+
+    FANN_SIN_SYMMETRIC - Periodical sinus activation function.
+     * span: -1 <= y <= 1
+     * y = sin(x*s)
+     * d = s*cos(x*s)
+
+    FANN_COS_SYMMETRIC - Periodical cosinus activation function.
+     * span: -1 <= y <= 1
+     * y = cos(x*s)
+     * d = s*-sin(x*s)
+
+        See also:
+                <neural_net::set_activation_function_hidden>,
+                <neural_net::set_activation_function_output>
+*/
+enum activation_function_enum {
+  LINEAR = FANN_LINEAR,
+  THRESHOLD,
+  THRESHOLD_SYMMETRIC,
+  SIGMOID,
+  SIGMOID_STEPWISE,
+  SIGMOID_SYMMETRIC,
+  SIGMOID_SYMMETRIC_STEPWISE,
+  GAUSSIAN,
+  GAUSSIAN_SYMMETRIC,
+  GAUSSIAN_STEPWISE,
+  ELLIOT,
+  ELLIOT_SYMMETRIC,
+  LINEAR_PIECE,
+  LINEAR_PIECE_SYMMETRIC,
+  SIN_SYMMETRIC,
+  COS_SYMMETRIC
+};
+
+/* Enum: network_type_enum
+
+    Definition of network types used by <neural_net::get_network_type>
+
+    LAYER - Each layer only has connections to the next layer
+    SHORTCUT - Each layer has connections to all following layers
+
+   See Also:
+      <neural_net::get_network_type>, <fann_get_network_type>
+
+   This enumeration appears in FANN >= 2.1.0
+*/
+enum network_type_enum { LAYER = FANN_NETTYPE_LAYER, SHORTCUT };
+
+/* Type: connection
+
+    Describes a connection between two neurons and its weight
+
+    from_neuron - Unique number used to identify source neuron
+    to_neuron - Unique number used to identify destination neuron
+    weight - The numerical value of the weight
+
+    See Also:
+        <neural_net::get_connection_array>, <neural_net::set_weight_array>
+
+   This structure appears in FANN >= 2.1.0
+*/
+typedef struct fann_connection connection;
+
+/* Forward declaration of class neural_net and training_data */
+class neural_net;
+
+class training_data;
+
+/* Type: callback_type
+   This callback function can be called during training when using <neural_net::train_on_data>,
+   <neural_net::train_on_file> or <neural_net::cascadetrain_on_data>.
+
+    >typedef int (*callback_type) (neural_net &net, training_data &train,
+    >    unsigned int max_epochs, unsigned int epochs_between_reports,
+    >    float desired_error, unsigned int epochs, void *user_data);
+
+        The callback can be set by using <neural_net::set_callback> and is very useful for doing
+   custom things during training. It is recommended to use this function when implementing custom
+        training procedures, or when visualizing the training in a GUI etc. The parameters which the
+        callback function takes is the parameters given to the <neural_net::train_on_data>, plus an
+   epochs parameter which tells how many epochs the training have taken so far.
+
+        The callback function should return an integer, if the callback function returns -1, the
+   training will terminate.
+
+        Example of a callback function that prints information to cout:
+        >int print_callback(FANN::neural_net &net, FANN::training_data &train,
+        >    unsigned int max_epochs, unsigned int epochs_between_reports,
+        >    float desired_error, unsigned int epochs, void *user_data)
+        >{
+        >    cout << "Epochs     " << setw(8) << epochs << ". "
+        >         << "Current Error: " << left << net.get_MSE() << right << endl;
+        >    return 0;
+        >}
+
+        See also:
+                <neural_net::set_callback>, <fann_callback_type>
+ */
+typedef int (*callback_type)(neural_net &net, training_data &train, unsigned int max_epochs,
+                             unsigned int epochs_between_reports, float desired_error,
+                             unsigned int epochs, void *user_data);
+}  // namespace FANN
+
+#endif  // FANN_FANN_DATA_CPP_H_H
diff --git a/ai_fighter/AI_Fight_Sim/Plugins/NeuralNetworkAIController/Source/NeuralNetworkAIController/ThirdParty/FANN/include/fann_error.h b/ai_fighter/AI_Fight_Sim/Plugins/NeuralNetworkAIController/Source/NeuralNetworkAIController/ThirdParty/FANN/include/fann_error.h
new file mode 100644
index 0000000..9c79c0c
--- /dev/null
+++ b/ai_fighter/AI_Fight_Sim/Plugins/NeuralNetworkAIController/Source/NeuralNetworkAIController/ThirdParty/FANN/include/fann_error.h
@@ -0,0 +1,162 @@
+/*
+Fast Artificial Neural Network Library (fann)
+Copyright (C) 2003-2016 Steffen Nissen (steffen.fann@gmail.com)
+
+This library is free software; you can redistribute it and/or
+modify it under the terms of the GNU Lesser General Public
+License as published by the Free Software Foundation; either
+version 2.1 of the License, or (at your option) any later version.
+
+This library is distributed in the hope that it will be useful,
+but WITHOUT ANY WARRANTY; without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
+Lesser General Public License for more details.
+
+You should have received a copy of the GNU Lesser General Public
+License along with this library; if not, write to the Free Software
+Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA  02111-1307  USA
+*/
+
+#ifndef __fann_error_h__
+#define __fann_error_h__
+
+#include <stdio.h>
+
+#define FANN_ERRSTR_MAX 128
+struct fann_error;
+
+/* Section: FANN Error Handling
+
+   Errors from the fann library are usually reported on stderr.
+   It is however possible to redirect these error messages to a file,
+   or completely ignore them by the <fann_set_error_log> function.
+
+   It is also possible to inspect the last error message by using the
+   <fann_get_errno> and <fann_get_errstr> functions.
+ */
+
+/* Enum: fann_errno_enum
+        Used to define error events on <struct fann> and <struct fann_train_data>.
+
+        See also:
+                <fann_get_errno>, <fann_reset_errno>, <fann_get_errstr>
+
+        FANN_E_NO_ERROR - No error
+        FANN_E_CANT_OPEN_CONFIG_R - Unable to open configuration file for reading
+        FANN_E_CANT_OPEN_CONFIG_W - Unable to open configuration file for writing
+        FANN_E_WRONG_CONFIG_VERSION - Wrong version of configuration file
+        FANN_E_CANT_READ_CONFIG - Error reading info from configuration file
+        FANN_E_CANT_READ_NEURON - Error reading neuron info from configuration file
+        FANN_E_CANT_READ_CONNECTIONS - Error reading connections from configuration file
+        FANN_E_WRONG_NUM_CONNECTIONS - Number of connections not equal to the number expected
+        FANN_E_CANT_OPEN_TD_W - Unable to open train data file for writing
+        FANN_E_CANT_OPEN_TD_R - Unable to open train data file for reading
+        FANN_E_CANT_READ_TD - Error reading training data from file
+        FANN_E_CANT_ALLOCATE_MEM - Unable to allocate memory
+        FANN_E_CANT_TRAIN_ACTIVATION - Unable to train with the selected activation function
+        FANN_E_CANT_USE_ACTIVATION - Unable to use the selected activation function
+        FANN_E_TRAIN_DATA_MISMATCH - Irreconcilable differences between two <struct fann_train_data>
+   structures FANN_E_CANT_USE_TRAIN_ALG - Unable to use the selected training algorithm
+        FANN_E_TRAIN_DATA_SUBSET - Trying to take subset which is not within the training set
+        FANN_E_INDEX_OUT_OF_BOUND - Index is out of bound
+        FANN_E_SCALE_NOT_PRESENT - Scaling parameters not present
+    FANN_E_INPUT_NO_MATCH - The number of input neurons in the ann and data don't match
+    FANN_E_OUTPUT_NO_MATCH - The number of output neurons in the ann and data don't match
+        FANN_E_WRONG_PARAMETERS_FOR_CREATE - The parameters for create_standard are wrong, either
+   too few parameters provided or a negative/very high value provided
+*/
+enum fann_errno_enum {
+  FANN_E_NO_ERROR = 0,
+  FANN_E_CANT_OPEN_CONFIG_R,
+  FANN_E_CANT_OPEN_CONFIG_W,
+  FANN_E_WRONG_CONFIG_VERSION,
+  FANN_E_CANT_READ_CONFIG,
+  FANN_E_CANT_READ_NEURON,
+  FANN_E_CANT_READ_CONNECTIONS,
+  FANN_E_WRONG_NUM_CONNECTIONS,
+  FANN_E_CANT_OPEN_TD_W,
+  FANN_E_CANT_OPEN_TD_R,
+  FANN_E_CANT_READ_TD,
+  FANN_E_CANT_ALLOCATE_MEM,
+  FANN_E_CANT_TRAIN_ACTIVATION,
+  FANN_E_CANT_USE_ACTIVATION,
+  FANN_E_TRAIN_DATA_MISMATCH,
+  FANN_E_CANT_USE_TRAIN_ALG,
+  FANN_E_TRAIN_DATA_SUBSET,
+  FANN_E_INDEX_OUT_OF_BOUND,
+  FANN_E_SCALE_NOT_PRESENT,
+  FANN_E_INPUT_NO_MATCH,
+  FANN_E_OUTPUT_NO_MATCH,
+  FANN_E_WRONG_PARAMETERS_FOR_CREATE
+};
+
+/* Group: Error Handling */
+
+/* Function: fann_set_error_log
+
+   Change where errors are logged to. Both <struct fann> and <struct fann_data> can be
+   casted to <struct fann_error>, so this function can be used to set either of these.
+
+   If log_file is NULL, no errors will be printed.
+
+   If errdat is NULL, the default log will be set. The default log is the log used when creating
+   <struct fann> and <struct fann_data>. This default log will also be the default for all new
+   structs that are created.
+
+   The default behavior is to log them to stderr.
+
+   See also:
+    <struct fann_error>
+
+   This function appears in FANN >= 1.1.0.
+ */
+FANN_EXTERNAL void FANN_API fann_set_error_log(struct fann_error *errdat, FILE *log_file);
+
+/* Function: fann_get_errno
+
+   Returns the last error number.
+
+   See also:
+    <fann_errno_enum>, <fann_reset_errno>
+
+   This function appears in FANN >= 1.1.0.
+ */
+FANN_EXTERNAL enum fann_errno_enum FANN_API fann_get_errno(struct fann_error *errdat);
+
+/* Function: fann_reset_errno
+
+   Resets the last error number.
+
+   This function appears in FANN >= 1.1.0.
+ */
+FANN_EXTERNAL void FANN_API fann_reset_errno(struct fann_error *errdat);
+
+/* Function: fann_reset_errstr
+
+   Resets the last error string.
+
+   This function appears in FANN >= 1.1.0.
+ */
+FANN_EXTERNAL void FANN_API fann_reset_errstr(struct fann_error *errdat);
+
+/* Function: fann_get_errstr
+
+   Returns the last errstr.
+
+   This function calls <fann_reset_errno> and <fann_reset_errstr>
+
+   This function appears in FANN >= 1.1.0.
+ */
+FANN_EXTERNAL char *FANN_API fann_get_errstr(struct fann_error *errdat);
+
+/* Function: fann_print_error
+
+   Prints the last error to stderr.
+
+   This function appears in FANN >= 1.1.0.
+ */
+FANN_EXTERNAL void FANN_API fann_print_error(struct fann_error *errdat);
+
+FANN_EXTERNAL extern FILE *fann_default_error_log;
+
+#endif
diff --git a/ai_fighter/AI_Fight_Sim/Plugins/NeuralNetworkAIController/Source/NeuralNetworkAIController/ThirdParty/FANN/include/fann_internal.h b/ai_fighter/AI_Fight_Sim/Plugins/NeuralNetworkAIController/Source/NeuralNetworkAIController/ThirdParty/FANN/include/fann_internal.h
new file mode 100644
index 0000000..4aac070
--- /dev/null
+++ b/ai_fighter/AI_Fight_Sim/Plugins/NeuralNetworkAIController/Source/NeuralNetworkAIController/ThirdParty/FANN/include/fann_internal.h
@@ -0,0 +1,158 @@
+/*
+Fast Artificial Neural Network Library (fann)
+Copyright (C) 2003-2016 Steffen Nissen (steffen.fann@gmail.com)
+
+This library is free software; you can redistribute it and/or
+modify it under the terms of the GNU Lesser General Public
+License as published by the Free Software Foundation; either
+version 2.1 of the License, or (at your option) any later version.
+
+This library is distributed in the hope that it will be useful,
+but WITHOUT ANY WARRANTY; without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
+Lesser General Public License for more details.
+
+You should have received a copy of the GNU Lesser General Public
+License along with this library; if not, write to the Free Software
+Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA  02111-1307  USA
+*/
+
+#ifndef __fann_internal_h__
+#define __fann_internal_h__
+/* internal include file, not to be included directly
+ */
+
+#include <math.h>
+#include <stdio.h>
+#include <stdlib.h>
+#include "fann_data.h"
+
+#define FANN_FIX_VERSION "FANN_FIX_2.0"
+#define FANN_FLO_VERSION "FANN_FLO_2.1"
+
+#ifdef FIXEDFANN
+#define FANN_CONF_VERSION FANN_FIX_VERSION
+#else
+#define FANN_CONF_VERSION FANN_FLO_VERSION
+#endif
+
+#define FANN_GET(type, name) \
+  FANN_EXTERNAL type FANN_API fann_get_##name(struct fann *ann) { return ann->name; }
+
+#define FANN_SET(type, name) \
+  FANN_EXTERNAL void FANN_API fann_set_##name(struct fann *ann, type value) { ann->name = value; }
+
+#define FANN_GET_SET(type, name) \
+  FANN_GET(type, name)           \
+  FANN_SET(type, name)
+
+struct fann_train_data;
+
+struct fann *fann_allocate_structure(unsigned int num_layers);
+void fann_allocate_neurons(struct fann *ann);
+
+void fann_allocate_connections(struct fann *ann);
+
+int fann_save_internal(struct fann *ann, const char *configuration_file,
+                       unsigned int save_as_fixed);
+int fann_save_internal_fd(struct fann *ann, FILE *conf, const char *configuration_file,
+                          unsigned int save_as_fixed);
+int fann_save_train_internal(struct fann_train_data *data, const char *filename,
+                             unsigned int save_as_fixed, unsigned int decimal_point);
+int fann_save_train_internal_fd(struct fann_train_data *data, FILE *file, const char *filename,
+                                unsigned int save_as_fixed, unsigned int decimal_point);
+
+void fann_update_stepwise(struct fann *ann);
+void fann_seed_rand();
+
+void fann_error(struct fann_error *errdat, const enum fann_errno_enum errno_f, ...);
+void fann_init_error_data(struct fann_error *errdat);
+
+struct fann *fann_create_from_fd(FILE *conf, const char *configuration_file);
+struct fann_train_data *fann_read_train_from_fd(FILE *file, const char *filename);
+
+void fann_compute_MSE(struct fann *ann, fann_type *desired_output);
+void fann_update_output_weights(struct fann *ann);
+void fann_backpropagate_MSE(struct fann *ann);
+void fann_update_weights(struct fann *ann);
+void fann_update_slopes_batch(struct fann *ann, struct fann_layer *layer_begin,
+                              struct fann_layer *layer_end);
+void fann_update_weights_quickprop(struct fann *ann, unsigned int num_data,
+                                   unsigned int first_weight, unsigned int past_end);
+void fann_update_weights_batch(struct fann *ann, unsigned int num_data, unsigned int first_weight,
+                               unsigned int past_end);
+void fann_update_weights_irpropm(struct fann *ann, unsigned int first_weight,
+                                 unsigned int past_end);
+void fann_update_weights_sarprop(struct fann *ann, unsigned int epoch, unsigned int first_weight,
+                                 unsigned int past_end);
+
+void fann_clear_train_arrays(struct fann *ann);
+
+fann_type fann_activation(struct fann *ann, unsigned int activation_function, fann_type steepness,
+                          fann_type value);
+
+fann_type fann_activation_derived(unsigned int activation_function, fann_type steepness,
+                                  fann_type value, fann_type sum);
+
+int fann_desired_error_reached(struct fann *ann, float desired_error);
+
+/* Some functions for cascade */
+int fann_train_outputs(struct fann *ann, struct fann_train_data *data, float desired_error);
+
+float fann_train_outputs_epoch(struct fann *ann, struct fann_train_data *data);
+
+int fann_train_candidates(struct fann *ann, struct fann_train_data *data);
+
+fann_type fann_train_candidates_epoch(struct fann *ann, struct fann_train_data *data);
+
+void fann_install_candidate(struct fann *ann);
+int fann_check_input_output_sizes(struct fann *ann, struct fann_train_data *data);
+
+int fann_initialize_candidates(struct fann *ann);
+
+void fann_set_shortcut_connections(struct fann *ann);
+
+int fann_allocate_scale(struct fann *ann);
+
+FANN_EXTERNAL void FANN_API fann_scale_data_to_range(fann_type **data, unsigned int num_data,
+                                                     unsigned int num_elem, fann_type old_min,
+                                                     fann_type old_max, fann_type new_min,
+                                                     fann_type new_max);
+
+/* called fann_max, in order to not interferre with predefined versions of max */
+#define fann_max(x, y) (((x) > (y)) ? (x) : (y))
+#define fann_min(x, y) (((x) < (y)) ? (x) : (y))
+#define fann_safe_free(x) \
+  {                       \
+    if (x) {              \
+      free(x);            \
+      x = NULL;           \
+    }                     \
+  }
+#define fann_clip(x, lo, hi) (((x) < (lo)) ? (lo) : (((x) > (hi)) ? (hi) : (x)))
+#define fann_exp2(x) exp(0.69314718055994530942 * (x))
+/*#define fann_clip(x, lo, hi) (x)*/
+
+#define fann_rand(min_value, max_value) \
+  (((float)(min_value)) +               \
+   (((float)(max_value) - ((float)(min_value))) * rand() / (RAND_MAX + 1.0f)))
+
+#define fann_abs(value) (((value) > 0) ? (value) : -(value))
+
+#ifdef FIXEDFANN
+
+#define fann_mult(x, y) ((x * y) >> decimal_point)
+#define fann_div(x, y) (((x) << decimal_point) / y)
+#define fann_random_weight() (fann_type)(fann_rand(0, multiplier / 10))
+#define fann_random_bias_weight() (fann_type)(fann_rand((0 - multiplier) / 10, multiplier / 10))
+
+#else
+
+#define fann_mult(x, y) (x * y)
+#define fann_div(x, y) (x / y)
+#define fann_random_weight() (fann_rand(-0.1f, 0.1f))
+#define fann_random_bias_weight() (fann_rand(-0.1f, 0.1f))
+
+#endif
+
+#endif
diff --git a/ai_fighter/AI_Fight_Sim/Plugins/NeuralNetworkAIController/Source/NeuralNetworkAIController/ThirdParty/FANN/include/fann_io.h b/ai_fighter/AI_Fight_Sim/Plugins/NeuralNetworkAIController/Source/NeuralNetworkAIController/ThirdParty/FANN/include/fann_io.h
new file mode 100644
index 0000000..c72e072
--- /dev/null
+++ b/ai_fighter/AI_Fight_Sim/Plugins/NeuralNetworkAIController/Source/NeuralNetworkAIController/ThirdParty/FANN/include/fann_io.h
@@ -0,0 +1,100 @@
+/*
+Fast Artificial Neural Network Library (fann)
+Copyright (C) 2003-2016 Steffen Nissen (steffen.fann@gmail.com)
+
+This library is free software; you can redistribute it and/or
+modify it under the terms of the GNU Lesser General Public
+License as published by the Free Software Foundation; either
+version 2.1 of the License, or (at your option) any later version.
+
+This library is distributed in the hope that it will be useful,
+but WITHOUT ANY WARRANTY; without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
+Lesser General Public License for more details.
+
+You should have received a copy of the GNU Lesser General Public
+License along with this library; if not, write to the Free Software
+Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA  02111-1307  USA
+*/
+
+#ifndef __fann_io_h__
+#define __fann_io_h__
+
+/* Section: FANN File Input/Output
+
+   It is possible to save an entire ann to a file with <fann_save> for future loading with
+   <fann_create_from_file>.
+ */
+
+/* Group: File Input and Output */
+
+/* Function: fann_create_from_file
+
+   Constructs a backpropagation neural network from a configuration file, which has been saved by
+   <fann_save>.
+
+   See also:
+        <fann_save>, <fann_save_to_fixed>
+
+   This function appears in FANN >= 1.0.0.
+ */
+FANN_EXTERNAL struct fann *FANN_API fann_create_from_file(const char *configuration_file);
+
+/* Function: fann_save
+
+   Save the entire network to a configuration file.
+
+   The configuration file contains all information about the neural network and enables
+   <fann_create_from_file> to create an exact copy of the neural network and all of the
+   parameters associated with the neural network.
+
+   These three parameters (<fann_set_callback>, <fann_set_error_log>,
+   <fann_set_user_data>) are *NOT* saved to the file because they cannot safely be
+   ported to a different location. Also temporary parameters generated during training
+   like <fann_get_MSE> are not saved.
+
+   Return:
+   The function returns 0 on success and -1 on failure.
+
+   See also:
+    <fann_create_from_file>, <fann_save_to_fixed>
+
+   This function appears in FANN >= 1.0.0.
+ */
+FANN_EXTERNAL int FANN_API fann_save(struct fann *ann, const char *configuration_file);
+
+/* Function: fann_save_to_fixed
+
+   Saves the entire network to a configuration file.
+   But it is saved in fixed point format no matter which
+   format it is currently in.
+
+   This is useful for training a network in floating points,
+   and then later executing it in fixed point.
+
+   The function returns the bit position of the fix point, which
+   can be used to find out how accurate the fixed point network will be.
+   A high value indicates high precision, and a low value indicates low
+   precision.
+
+   A negative value indicates very low precision, and a very
+   strong possibility for overflow.
+   (the actual fix point will be set to 0, since a negative
+   fix point does not make sense).
+
+   Generally, a fix point lower than 6 is bad, and should be avoided.
+   The best way to avoid this, is to have less connections to each neuron,
+   or just less neurons in each layer.
+
+   The fixed point use of this network is only intended for use on machines that
+   have no floating point processor, like an iPAQ. On normal computers the floating
+   point version is actually faster.
+
+   See also:
+    <fann_create_from_file>, <fann_save>
+
+   This function appears in FANN >= 1.0.0.
+*/
+FANN_EXTERNAL int FANN_API fann_save_to_fixed(struct fann *ann, const char *configuration_file);
+
+#endif
diff --git a/ai_fighter/AI_Fight_Sim/Plugins/NeuralNetworkAIController/Source/NeuralNetworkAIController/ThirdParty/FANN/include/fann_train.h b/ai_fighter/AI_Fight_Sim/Plugins/NeuralNetworkAIController/Source/NeuralNetworkAIController/ThirdParty/FANN/include/fann_train.h
new file mode 100644
index 0000000..18f0486
--- /dev/null
+++ b/ai_fighter/AI_Fight_Sim/Plugins/NeuralNetworkAIController/Source/NeuralNetworkAIController/ThirdParty/FANN/include/fann_train.h
@@ -0,0 +1,1391 @@
+/*
+Fast Artificial Neural Network Library (fann)
+Copyright (C) 2003-2016 Steffen Nissen (steffen.fann@gmail.com)
+
+This library is free software; you can redistribute it and/or
+modify it under the terms of the GNU Lesser General Public
+License as published by the Free Software Foundation; either
+version 2.1 of the License, or (at your option) any later version.
+
+This library is distributed in the hope that it will be useful,
+but WITHOUT ANY WARRANTY; without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
+Lesser General Public License for more details.
+
+You should have received a copy of the GNU Lesser General Public
+License along with this library; if not, write to the Free Software
+Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA  02111-1307  USA
+*/
+
+#ifndef __fann_train_h__
+#define __fann_train_h__
+
+/* Section: FANN Training
+
+        There are many different ways of training neural networks and the FANN library supports
+        a number of different approaches.
+
+        Two fundamentally different approaches are the most commonly used:
+
+                Fixed topology training - The size and topology of the ANN is determined in advance
+                        and the training alters the weights in order to minimize the difference
+   between the desired output values and the actual output values. This kind of training is
+                        supported by <fann_train_on_data>.
+
+                Evolving topology training - The training start out with an empty ANN, only
+   consisting of input and output neurons. Hidden neurons and connections are added during training,
+                        in order to reach the same goal as for fixed topology training. This kind of
+   training is supported by <FANN Cascade Training>.
+ */
+
+/* Struct: struct fann_train_data
+        Structure used to store data, for use with training.
+
+        The data inside this structure should never be manipulated directly, but should use some
+        of the supplied functions in <Training Data Manipulation>.
+
+        The training data structure is very usefull for storing data during training and testing of
+   a neural network.
+
+        See also:
+        <fann_read_train_from_file>, <fann_train_on_data>, <fann_destroy_train>
+*/
+struct fann_train_data {
+  enum fann_errno_enum errno_f;
+  FILE *error_log;
+  char *errstr;
+
+  unsigned int num_data;
+  unsigned int num_input;
+  unsigned int num_output;
+  fann_type **input;
+  fann_type **output;
+};
+
+/* Section: FANN Training */
+
+/* Group: Training */
+
+#ifndef FIXEDFANN
+/* Function: fann_train
+
+   Train one iteration with a set of inputs, and a set of desired outputs.
+   This training is always incremental training (see <fann_train_enum>), since
+   only one pattern is presented.
+
+   Parameters:
+        ann - The neural network structure
+        input - an array of inputs. This array must be exactly <fann_get_num_input> long.
+        desired_output - an array of desired outputs. This array must be exactly
+   <fann_get_num_output> long.
+
+        See also:
+                <fann_train_on_data>, <fann_train_epoch>
+
+        This function appears in FANN >= 1.0.0.
+ */
+FANN_EXTERNAL void FANN_API fann_train(struct fann *ann, fann_type *input,
+                                       fann_type *desired_output);
+
+#endif /* NOT FIXEDFANN */
+
+/* Function: fann_test
+   Test with a set of inputs, and a set of desired outputs.
+   This operation updates the mean square error, but does not
+   change the network in any way.
+
+   See also:
+                <fann_test_data>, <fann_train>
+
+   This function appears in FANN >= 1.0.0.
+*/
+FANN_EXTERNAL fann_type *FANN_API fann_test(struct fann *ann, fann_type *input,
+                                            fann_type *desired_output);
+
+/* Function: fann_get_MSE
+   Reads the mean square error from the network.
+
+   Reads the mean square error from the network. This value is calculated during
+   training or testing, and can therefore sometimes be a bit off if the weights
+   have been changed since the last calculation of the value.
+
+   See also:
+        <fann_test_data>
+
+        This function appears in FANN >= 1.1.0.
+ */
+FANN_EXTERNAL float FANN_API fann_get_MSE(struct fann *ann);
+
+/* Function: fann_get_bit_fail
+
+        The number of fail bits; means the number of output neurons which differ more
+        than the bit fail limit (see <fann_get_bit_fail_limit>, <fann_set_bit_fail_limit>).
+        The bits are counted in all of the training data, so this number can be higher than
+        the number of training data.
+
+        This value is reset by <fann_reset_MSE> and updated by all the same functions which also
+        update the MSE value (e.g. <fann_test_data>, <fann_train_epoch>)
+
+        See also:
+                <fann_stopfunc_enum>, <fann_get_MSE>
+
+        This function appears in FANN >= 2.0.0
+*/
+FANN_EXTERNAL unsigned int FANN_API fann_get_bit_fail(struct fann *ann);
+
+/* Function: fann_reset_MSE
+   Resets the mean square error from the network.
+
+   This function also resets the number of bits that fail.
+
+   See also:
+        <fann_get_MSE>, <fann_get_bit_fail_limit>
+
+    This function appears in FANN >= 1.1.0
+ */
+FANN_EXTERNAL void FANN_API fann_reset_MSE(struct fann *ann);
+
+/* Group: Training Data Training */
+
+#ifndef FIXEDFANN
+
+/* Function: fann_train_on_data
+
+   Trains on an entire dataset, for a period of time.
+
+   This training uses the training algorithm chosen by <fann_set_training_algorithm>,
+   and the parameters set for these training algorithms.
+
+   Parameters:
+                ann - The neural network
+                data - The data, which should be used during training
+                max_epochs - The maximum number of epochs the training should continue
+                epochs_between_reports - The number of epochs between printing a status report to
+   stdout. A value of zero means no reports should be printed. desired_error - The desired
+   <fann_get_MSE> or <fann_get_bit_fail>, depending on which stop function is chosen by
+   <fann_set_train_stop_function>.
+
+        Instead of printing out reports every epochs_between_reports, a callback function can be
+   called (see <fann_set_callback>).
+
+        See also:
+                <fann_train_on_file>, <fann_train_epoch>, <Parameters>
+
+        This function appears in FANN >= 1.0.0.
+*/
+FANN_EXTERNAL void FANN_API fann_train_on_data(struct fann *ann, struct fann_train_data *data,
+                                               unsigned int max_epochs,
+                                               unsigned int epochs_between_reports,
+                                               float desired_error);
+
+/* Function: fann_train_on_file
+
+   Does the same as <fann_train_on_data>, but reads the training data directly from a file.
+
+   See also:
+                <fann_train_on_data>
+
+        This function appears in FANN >= 1.0.0.
+*/
+FANN_EXTERNAL void FANN_API fann_train_on_file(struct fann *ann, const char *filename,
+                                               unsigned int max_epochs,
+                                               unsigned int epochs_between_reports,
+                                               float desired_error);
+
+/* Function: fann_train_epoch
+   Train one epoch with a set of training data.
+
+    Train one epoch with the training data stored in data. One epoch is where all of
+    the training data is considered exactly once.
+
+        This function returns the MSE error as it is calculated either before or during
+        the actual training. This is not the actual MSE after the training epoch, but since
+        calculating this will require to go through the entire training set once more, it is
+        more than adequate to use this value during training.
+
+        The training algorithm used by this function is chosen by the <fann_set_training_algorithm>
+        function.
+
+        See also:
+                <fann_train_on_data>, <fann_test_data>
+
+        This function appears in FANN >= 1.2.0.
+ */
+FANN_EXTERNAL float FANN_API fann_train_epoch(struct fann *ann, struct fann_train_data *data);
+#endif /* NOT FIXEDFANN */
+
+/* Function: fann_test_data
+
+   Test a set of training data and calculates the MSE for the training data.
+
+   This function updates the MSE and the bit fail values.
+
+   See also:
+        <fann_test>, <fann_get_MSE>, <fann_get_bit_fail>
+
+        This function appears in FANN >= 1.2.0.
+ */
+FANN_EXTERNAL float FANN_API fann_test_data(struct fann *ann, struct fann_train_data *data);
+
+/* Group: Training Data Manipulation */
+
+/* Function: fann_read_train_from_file
+   Reads a file that stores training data.
+
+   The file must be formatted like:
+   >num_train_data num_input num_output
+   >inputdata separated by space
+   >outputdata separated by space
+   >
+   >.
+   >.
+   >.
+   >
+   >inputdata separated by space
+   >outputdata separated by space
+
+   See also:
+        <fann_train_on_data>, <fann_destroy_train>, <fann_save_train>
+
+    This function appears in FANN >= 1.0.0
+*/
+FANN_EXTERNAL struct fann_train_data *FANN_API fann_read_train_from_file(const char *filename);
+
+/* Function: fann_create_train
+   Creates an empty training data struct.
+
+   See also:
+     <fann_read_train_from_file>, <fann_train_on_data>, <fann_destroy_train>,
+     <fann_save_train>, <fann_create_train_array>
+
+    This function appears in FANN >= 2.2.0
+*/
+FANN_EXTERNAL struct fann_train_data *FANN_API fann_create_train(unsigned int num_data,
+                                                                 unsigned int num_input,
+                                                                 unsigned int num_output);
+
+/* Function: fann_create_train_pointer_array
+   Creates an training data struct and fills it with data from provided arrays of pointer.
+
+   A copy of the data is made so there are no restrictions on the
+   allocation of the input/output data and the caller is responsible
+   for the deallocation of the data pointed to by input and output.
+
+   See also:
+     <fann_read_train_from_file>, <fann_train_on_data>, <fann_destroy_train>,
+     <fann_save_train>, <fann_create_train>, <fann_create_train_array>
+
+    This function appears in FANN >= 2.3.0
+*/
+FANN_EXTERNAL struct fann_train_data *FANN_API
+fann_create_train_pointer_array(unsigned int num_data, unsigned int num_input, fann_type **input,
+                                unsigned int num_output, fann_type **output);
+
+/* Function: fann_create_train_array
+   Creates an training data struct and fills it with data from provided arrays, where the arrays
+   must have the dimensions: input[num_data*num_input] output[num_data*num_output]
+
+   A copy of the data is made so there are no restrictions on the
+   allocation of the input/output data and the caller is responsible
+   for the deallocation of the data pointed to by input and output.
+
+   See also:
+     <fann_read_train_from_file>, <fann_train_on_data>, <fann_destroy_train>,
+     <fann_save_train>, <fann_create_train>, <fann_create_train_pointer_array>
+
+    This function appears in FANN >= 2.3.0
+*/
+FANN_EXTERNAL struct fann_train_data *FANN_API fann_create_train_array(unsigned int num_data,
+                                                                       unsigned int num_input,
+                                                                       fann_type *input,
+                                                                       unsigned int num_output,
+                                                                       fann_type *output);
+
+/* Function: fann_create_train_from_callback
+   Creates the training data struct from a user supplied function.
+   As the training data are numerable (data 1, data 2...), the user must write
+   a function that receives the number of the training data set (input,output)
+   and returns the set.  fann_create_train_from_callback will call the user
+   supplied function 'num_data' times, one input-output pair each time. Each
+   time the user supplied function is called, the time of the call will be passed
+   as the 'num' parameter and the user supplied function must write the input
+   and output to the corresponding parameters.
+
+
+   Parameters:
+     num_data      - The number of training data
+     num_input     - The number of inputs per training data
+     num_output    - The number of ouputs per training data
+     user_function - The user supplied function
+
+   Parameters for the user function:
+     num        - The number of the training data set
+     num_input  - The number of inputs per training data
+     num_output - The number of ouputs per training data
+     input      - The set of inputs
+     output     - The set of desired outputs
+
+   See also:
+     <fann_read_train_from_file>, <fann_train_on_data>, <fann_destroy_train>,
+     <fann_save_train>
+
+    This function appears in FANN >= 2.1.0
+*/
+FANN_EXTERNAL struct fann_train_data *FANN_API fann_create_train_from_callback(
+    unsigned int num_data, unsigned int num_input, unsigned int num_output,
+    void(FANN_API *user_function)(unsigned int, unsigned int, unsigned int, fann_type *,
+                                  fann_type *));
+
+/* Function: fann_destroy_train
+   Destructs the training data and properly deallocates all of the associated data.
+   Be sure to call this function when finished using the training data.
+
+    This function appears in FANN >= 1.0.0
+ */
+FANN_EXTERNAL void FANN_API fann_destroy_train(struct fann_train_data *train_data);
+
+/* Function: fann_get_train_input
+   Gets the training input data at the given position
+
+   See also:
+     <fann_get_train_output>
+
+   This function appears in FANN >= 2.3.0
+ */
+FANN_EXTERNAL fann_type *FANN_API fann_get_train_input(struct fann_train_data *data,
+                                                       unsigned int position);
+
+/* Function: fann_get_train_output
+   Gets the training output data at the given position
+
+   See also:
+     <fann_get_train_output>
+
+   This function appears in FANN >= 2.3.0
+ */
+FANN_EXTERNAL fann_type *FANN_API fann_get_train_output(struct fann_train_data *data,
+                                                        unsigned int position);
+
+/* Function: fann_shuffle_train_data
+
+   Shuffles training data, randomizing the order.
+   This is recommended for incremental training, while it has no influence during batch training.
+
+   This function appears in FANN >= 1.1.0.
+ */
+FANN_EXTERNAL void FANN_API fann_shuffle_train_data(struct fann_train_data *train_data);
+
+#ifndef FIXEDFANN
+
+/* Function: fann_get_min_train_input
+
+   Get the minimum value of all in the input data
+
+   This function appears in FANN >= 2.3.0
+*/
+FANN_EXTERNAL fann_type FANN_API fann_get_min_train_input(struct fann_train_data *train_data);
+
+/* Function: fann_get_max_train_input
+
+   Get the maximum value of all in the input data
+
+   This function appears in FANN >= 2.3.0
+*/
+FANN_EXTERNAL fann_type FANN_API fann_get_max_train_input(struct fann_train_data *train_data);
+
+/* Function: fann_get_min_train_output
+
+   Get the minimum value of all in the output data
+
+   This function appears in FANN >= 2.3.0
+*/
+FANN_EXTERNAL fann_type FANN_API fann_get_min_train_output(struct fann_train_data *train_data);
+
+/* Function: fann_get_max_train_output
+
+   Get the maximum value of all in the output data
+
+   This function appears in FANN >= 2.3.0
+*/
+FANN_EXTERNAL fann_type FANN_API fann_get_max_train_output(struct fann_train_data *train_data);
+
+/* Function: fann_scale_train
+
+   Scale input and output data based on previously calculated parameters.
+
+   Parameters:
+     ann      - ann for which trained parameters were calculated before
+     data     - training data that needs to be scaled
+
+   See also:
+        <fann_descale_train>, <fann_set_scaling_params>
+
+    This function appears in FANN >= 2.1.0
+*/
+FANN_EXTERNAL void FANN_API fann_scale_train(struct fann *ann, struct fann_train_data *data);
+
+/* Function: fann_descale_train
+
+   Descale input and output data based on previously calculated parameters.
+
+   Parameters:
+     ann      - ann for which trained parameters were calculated before
+     data     - training data that needs to be descaled
+
+   See also:
+        <fann_scale_train>, <fann_set_scaling_params>
+
+    This function appears in FANN >= 2.1.0
+ */
+FANN_EXTERNAL void FANN_API fann_descale_train(struct fann *ann, struct fann_train_data *data);
+
+/* Function: fann_set_input_scaling_params
+
+   Calculate input scaling parameters for future use based on training data.
+
+   Parameters:
+         ann           - ann for which parameters need to be calculated
+         data          - training data that will be used to calculate scaling parameters
+         new_input_min - desired lower bound in input data after scaling (not strictly followed)
+         new_input_max - desired upper bound in input data after scaling (not strictly followed)
+
+   See also:
+         <fann_set_output_scaling_params>
+
+    This function appears in FANN >= 2.1.0
+ */
+FANN_EXTERNAL int FANN_API fann_set_input_scaling_params(struct fann *ann,
+                                                         const struct fann_train_data *data,
+                                                         float new_input_min, float new_input_max);
+
+/* Function: fann_set_output_scaling_params
+
+   Calculate output scaling parameters for future use based on training data.
+
+   Parameters:
+         ann            - ann for which parameters need to be calculated
+         data           - training data that will be used to calculate scaling parameters
+         new_output_min - desired lower bound in output data after scaling (not strictly followed)
+         new_output_max - desired upper bound in output data after scaling (not strictly followed)
+
+   See also:
+         <fann_set_input_scaling_params>
+
+    This function appears in FANN >= 2.1.0
+ */
+FANN_EXTERNAL int FANN_API fann_set_output_scaling_params(struct fann *ann,
+                                                          const struct fann_train_data *data,
+                                                          float new_output_min,
+                                                          float new_output_max);
+
+/* Function: fann_set_scaling_params
+
+   Calculate input and output scaling parameters for future use based on training data.
+
+   Parameters:
+         ann            - ann for which parameters need to be calculated
+         data           - training data that will be used to calculate scaling parameters
+         new_input_min  - desired lower bound in input data after scaling (not strictly followed)
+         new_input_max  - desired upper bound in input data after scaling (not strictly followed)
+         new_output_min - desired lower bound in output data after scaling (not strictly followed)
+         new_output_max - desired upper bound in output data after scaling (not strictly followed)
+
+   See also:
+         <fann_set_input_scaling_params>, <fann_set_output_scaling_params>
+
+    This function appears in FANN >= 2.1.0
+ */
+FANN_EXTERNAL int FANN_API fann_set_scaling_params(struct fann *ann,
+                                                   const struct fann_train_data *data,
+                                                   float new_input_min, float new_input_max,
+                                                   float new_output_min, float new_output_max);
+
+/* Function: fann_clear_scaling_params
+
+   Clears scaling parameters.
+
+   Parameters:
+     ann - ann for which to clear scaling parameters
+
+    This function appears in FANN >= 2.1.0
+ */
+FANN_EXTERNAL int FANN_API fann_clear_scaling_params(struct fann *ann);
+
+/* Function: fann_scale_input
+
+   Scale data in input vector before feeding it to ann based on previously calculated parameters.
+
+   Parameters:
+     ann          - for which scaling parameters were calculated
+     input_vector - input vector that will be scaled
+
+   See also:
+     <fann_descale_input>, <fann_scale_output>
+
+    This function appears in FANN >= 2.1.0
+*/
+FANN_EXTERNAL void FANN_API fann_scale_input(struct fann *ann, fann_type *input_vector);
+
+/* Function: fann_scale_output
+
+   Scale data in output vector before feeding it to ann based on previously calculated parameters.
+
+   Parameters:
+     ann           - for which scaling parameters were calculated
+     output_vector - output vector that will be scaled
+
+   See also:
+     <fann_descale_output>, <fann_scale_input>
+
+    This function appears in FANN >= 2.1.0
+ */
+FANN_EXTERNAL void FANN_API fann_scale_output(struct fann *ann, fann_type *output_vector);
+
+/* Function: fann_descale_input
+
+   Scale data in input vector after getting it from ann based on previously calculated parameters.
+
+   Parameters:
+     ann          - for which scaling parameters were calculated
+     input_vector - input vector that will be descaled
+
+   See also:
+     <fann_scale_input>, <fann_descale_output>
+
+    This function appears in FANN >= 2.1.0
+ */
+FANN_EXTERNAL void FANN_API fann_descale_input(struct fann *ann, fann_type *input_vector);
+
+/* Function: fann_descale_output
+
+   Scale data in output vector after getting it from ann based on previously calculated parameters.
+
+   Parameters:
+     ann           - for which scaling parameters were calculated
+     output_vector - output vector that will be descaled
+
+   See also:
+     <fann_scale_output>, <fann_descale_input>
+
+    This function appears in FANN >= 2.1.0
+ */
+FANN_EXTERNAL void FANN_API fann_descale_output(struct fann *ann, fann_type *output_vector);
+
+#endif
+
+/* Function: fann_scale_input_train_data
+
+   Scales the inputs in the training data to the specified range.
+
+   A simplified scaling method, which is mostly useful in examples where it's known that all the
+   data will be in one range and it should be transformed to another range.
+
+   It is not recommended to use this on subsets of data as the complete input range might not be
+   available in that subset.
+
+   For more powerful scaling, please consider <fann_scale_train>
+
+   See also:
+        <fann_scale_output_train_data>, <fann_scale_train_data>, <fann_scala_input>
+
+   This function appears in FANN >= 2.0.0.
+ */
+FANN_EXTERNAL void FANN_API fann_scale_input_train_data(struct fann_train_data *train_data,
+                                                        fann_type new_min, fann_type new_max);
+
+/* Function: fann_scale_output_train_data
+
+   Scales the outputs in the training data to the specified range.
+
+   A simplified scaling method, which is mostly useful in examples where it's known that all the
+   data will be in one range and it should be transformed to another range.
+
+   It is not recommended to use this on subsets of data as the complete input range might not be
+   available in that subset.
+
+   For more powerful scaling, please consider <fann_scale_train>
+
+   See also:
+        <fann_scale_input_train_data>, <fann_scale_train_data>
+
+   This function appears in FANN >= 2.0.0.
+ */
+FANN_EXTERNAL void FANN_API fann_scale_output_train_data(struct fann_train_data *train_data,
+                                                         fann_type new_min, fann_type new_max);
+
+/* Function: fann_scale_train_data
+
+   Scales the inputs and outputs in the training data to the specified range.
+
+   A simplified scaling method, which is mostly useful in examples where it's known that all the
+   data will be in one range and it should be transformed to another range.
+
+   It is not recommended to use this on subsets of data as the complete input range might not be
+   available in that subset.
+
+   For more powerful scaling, please consider <fann_scale_train>
+
+   See also:
+        <fann_scale_output_train_data>, <fann_scale_input_train_data>
+
+   This function appears in FANN >= 2.0.0.
+ */
+FANN_EXTERNAL void FANN_API fann_scale_train_data(struct fann_train_data *train_data,
+                                                  fann_type new_min, fann_type new_max);
+
+/* Function: fann_merge_train_data
+
+   Merges the data from *data1* and *data2* into a new <struct fann_train_data>.
+
+   This function appears in FANN >= 1.1.0.
+ */
+FANN_EXTERNAL struct fann_train_data *FANN_API fann_merge_train_data(struct fann_train_data *data1,
+                                                                     struct fann_train_data *data2);
+
+/* Function: fann_duplicate_train_data
+
+   Returns an exact copy of a <struct fann_train_data>.
+
+   This function appears in FANN >= 1.1.0.
+ */
+FANN_EXTERNAL struct fann_train_data *FANN_API
+fann_duplicate_train_data(struct fann_train_data *data);
+
+/* Function: fann_subset_train_data
+
+   Returns an copy of a subset of the <struct fann_train_data>, starting at position *pos*
+   and *length* elements forward.
+
+   >fann_subset_train_data(train_data, 0, fann_length_train_data(train_data))
+
+   Will do the same as <fann_duplicate_train_data>.
+
+   See also:
+        <fann_length_train_data>
+
+   This function appears in FANN >= 2.0.0.
+ */
+FANN_EXTERNAL struct fann_train_data *FANN_API fann_subset_train_data(struct fann_train_data *data,
+                                                                      unsigned int pos,
+                                                                      unsigned int length);
+
+/* Function: fann_length_train_data
+
+   Returns the number of training patterns in the <struct fann_train_data>.
+
+   This function appears in FANN >= 2.0.0.
+ */
+FANN_EXTERNAL unsigned int FANN_API fann_length_train_data(struct fann_train_data *data);
+
+/* Function: fann_num_input_train_data
+
+   Returns the number of inputs in each of the training patterns in the <struct fann_train_data>.
+
+   See also:
+        <fann_num_train_data>, <fann_num_output_train_data>
+
+   This function appears in FANN >= 2.0.0.
+ */
+FANN_EXTERNAL unsigned int FANN_API fann_num_input_train_data(struct fann_train_data *data);
+
+/* Function: fann_num_output_train_data
+
+   Returns the number of outputs in each of the training patterns in the <struct fann_train_data>.
+
+   See also:
+        <fann_num_train_data>, <fann_num_input_train_data>
+
+   This function appears in FANN >= 2.0.0.
+ */
+FANN_EXTERNAL unsigned int FANN_API fann_num_output_train_data(struct fann_train_data *data);
+
+/* Function: fann_save_train
+
+   Save the training structure to a file, with the format as specified in
+   <fann_read_train_from_file>
+
+   Return:
+   The function returns 0 on success and -1 on failure.
+
+   See also:
+        <fann_read_train_from_file>, <fann_save_train_to_fixed>
+
+   This function appears in FANN >= 1.0.0.
+ */
+FANN_EXTERNAL int FANN_API fann_save_train(struct fann_train_data *data, const char *filename);
+
+/* Function: fann_save_train_to_fixed
+
+   Saves the training structure to a fixed point data file.
+
+   This function is very useful for testing the quality of a fixed point network.
+
+   Return:
+   The function returns 0 on success and -1 on failure.
+
+   See also:
+        <fann_save_train>
+
+   This function appears in FANN >= 1.0.0.
+ */
+FANN_EXTERNAL int FANN_API fann_save_train_to_fixed(struct fann_train_data *data,
+                                                    const char *filename,
+                                                    unsigned int decimal_point);
+
+/* Group: Parameters */
+
+/* Function: fann_get_training_algorithm
+
+   Return the training algorithm as described by <fann_train_enum>. This training algorithm
+   is used by <fann_train_on_data> and associated functions.
+
+   Note that this algorithm is also used during <fann_cascadetrain_on_data>, although only
+   FANN_TRAIN_RPROP and FANN_TRAIN_QUICKPROP is allowed during cascade training.
+
+   The default training algorithm is FANN_TRAIN_RPROP.
+
+   See also:
+    <fann_set_training_algorithm>, <fann_train_enum>
+
+   This function appears in FANN >= 1.0.0.
+ */
+FANN_EXTERNAL enum fann_train_enum FANN_API fann_get_training_algorithm(struct fann *ann);
+
+/* Function: fann_set_training_algorithm
+
+   Set the training algorithm.
+
+   More info available in <fann_get_training_algorithm>
+
+   This function appears in FANN >= 1.0.0.
+ */
+FANN_EXTERNAL void FANN_API fann_set_training_algorithm(struct fann *ann,
+                                                        enum fann_train_enum training_algorithm);
+
+/* Function: fann_get_learning_rate
+
+   Return the learning rate.
+
+   The learning rate is used to determine how aggressive training should be for some of the
+   training algorithms (FANN_TRAIN_INCREMENTAL, FANN_TRAIN_BATCH, FANN_TRAIN_QUICKPROP).
+   Do however note that it is not used in FANN_TRAIN_RPROP.
+
+   The default learning rate is 0.7.
+
+   See also:
+        <fann_set_learning_rate>, <fann_set_training_algorithm>
+
+   This function appears in FANN >= 1.0.0.
+ */
+FANN_EXTERNAL float FANN_API fann_get_learning_rate(struct fann *ann);
+
+/* Function: fann_set_learning_rate
+
+   Set the learning rate.
+
+   More info available in <fann_get_learning_rate>
+
+   This function appears in FANN >= 1.0.0.
+ */
+FANN_EXTERNAL void FANN_API fann_set_learning_rate(struct fann *ann, float learning_rate);
+
+/* Function: fann_get_learning_momentum
+
+   Get the learning momentum.
+
+   The learning momentum can be used to speed up FANN_TRAIN_INCREMENTAL training.
+   A too high momentum will however not benefit training. Setting momentum to 0 will
+   be the same as not using the momentum parameter. The recommended value of this parameter
+   is between 0.0 and 1.0.
+
+   The default momentum is 0.
+
+   See also:
+   <fann_set_learning_momentum>, <fann_set_training_algorithm>
+
+   This function appears in FANN >= 2.0.0.
+ */
+FANN_EXTERNAL float FANN_API fann_get_learning_momentum(struct fann *ann);
+
+/* Function: fann_set_learning_momentum
+
+   Set the learning momentum.
+
+   More info available in <fann_get_learning_momentum>
+
+   This function appears in FANN >= 2.0.0.
+ */
+FANN_EXTERNAL void FANN_API fann_set_learning_momentum(struct fann *ann, float learning_momentum);
+
+/* Function: fann_get_activation_function
+
+   Get the activation function for neuron number *neuron* in layer number *layer*,
+   counting the input layer as layer 0.
+
+   It is not possible to get activation functions for the neurons in the input layer.
+
+   Information about the individual activation functions is available at <fann_activationfunc_enum>.
+
+   Returns:
+    The activation function for the neuron or -1 if the neuron is not defined in the neural network.
+
+   See also:
+        <fann_set_activation_function_layer>, <fann_set_activation_function_hidden>,
+        <fann_set_activation_function_output>, <fann_set_activation_steepness>,
+    <fann_set_activation_function>
+
+   This function appears in FANN >= 2.1.0
+ */
+FANN_EXTERNAL enum fann_activationfunc_enum FANN_API fann_get_activation_function(struct fann *ann,
+                                                                                  int layer,
+                                                                                  int neuron);
+
+/* Function: fann_set_activation_function
+
+   Set the activation function for neuron number *neuron* in layer number *layer*,
+   counting the input layer as layer 0.
+
+   It is not possible to set activation functions for the neurons in the input layer.
+
+   When choosing an activation function it is important to note that the activation
+   functions have different range. FANN_SIGMOID is e.g. in the 0 - 1 range while
+   FANN_SIGMOID_SYMMETRIC is in the -1 - 1 range and FANN_LINEAR is unbounded.
+
+   Information about the individual activation functions is available at <fann_activationfunc_enum>.
+
+   The default activation function is FANN_SIGMOID_STEPWISE.
+
+   See also:
+        <fann_set_activation_function_layer>, <fann_set_activation_function_hidden>,
+        <fann_set_activation_function_output>, <fann_set_activation_steepness>,
+    <fann_get_activation_function>
+
+   This function appears in FANN >= 2.0.0.
+ */
+FANN_EXTERNAL void FANN_API fann_set_activation_function(
+    struct fann *ann, enum fann_activationfunc_enum activation_function, int layer, int neuron);
+
+/* Function: fann_set_activation_function_layer
+
+   Set the activation function for all the neurons in the layer number *layer*,
+   counting the input layer as layer 0.
+
+   It is not possible to set activation functions for the neurons in the input layer.
+
+   See also:
+        <fann_set_activation_function>, <fann_set_activation_function_hidden>,
+        <fann_set_activation_function_output>, <fann_set_activation_steepness_layer>
+
+   This function appears in FANN >= 2.0.0.
+ */
+FANN_EXTERNAL void FANN_API fann_set_activation_function_layer(
+    struct fann *ann, enum fann_activationfunc_enum activation_function, int layer);
+
+/* Function: fann_set_activation_function_hidden
+
+   Set the activation function for all of the hidden layers.
+
+   See also:
+        <fann_set_activation_function>, <fann_set_activation_function_layer>,
+        <fann_set_activation_function_output>, <fann_set_activation_steepness_hidden>
+
+   This function appears in FANN >= 1.0.0.
+ */
+FANN_EXTERNAL void FANN_API fann_set_activation_function_hidden(
+    struct fann *ann, enum fann_activationfunc_enum activation_function);
+
+/* Function: fann_set_activation_function_output
+
+   Set the activation function for the output layer.
+
+   See also:
+        <fann_set_activation_function>, <fann_set_activation_function_layer>,
+        <fann_set_activation_function_hidden>, <fann_set_activation_steepness_output>
+
+   This function appears in FANN >= 1.0.0.
+ */
+FANN_EXTERNAL void FANN_API fann_set_activation_function_output(
+    struct fann *ann, enum fann_activationfunc_enum activation_function);
+
+/* Function: fann_get_activation_steepness
+
+   Get the activation steepness for neuron number *neuron* in layer number *layer*,
+   counting the input layer as layer 0.
+
+   It is not possible to get activation steepness for the neurons in the input layer.
+
+   The steepness of an activation function says something about how fast the activation function
+   goes from the minimum to the maximum. A high value for the activation function will also
+   give a more aggressive training.
+
+   When training neural networks where the output values should be at the extremes (usually 0 and 1,
+   depending on the activation function), a steep activation function can be used (e.g. 1.0).
+
+   The default activation steepness is 0.5.
+
+   Returns:
+    The activation steepness for the neuron or -1 if the neuron is not defined in the neural
+   network.
+
+   See also:
+        <fann_set_activation_steepness_layer>, <fann_set_activation_steepness_hidden>,
+        <fann_set_activation_steepness_output>, <fann_set_activation_function>,
+    <fann_set_activation_steepness>
+
+   This function appears in FANN >= 2.1.0
+ */
+FANN_EXTERNAL fann_type FANN_API fann_get_activation_steepness(struct fann *ann, int layer,
+                                                               int neuron);
+
+/* Function: fann_set_activation_steepness
+
+   Set the activation steepness for neuron number *neuron* in layer number *layer*,
+   counting the input layer as layer 0.
+
+   It is not possible to set activation steepness for the neurons in the input layer.
+
+   The steepness of an activation function says something about how fast the activation function
+   goes from the minimum to the maximum. A high value for the activation function will also
+   give a more aggressive training.
+
+   When training neural networks where the output values should be at the extremes (usually 0 and 1,
+   depending on the activation function), a steep activation function can be used (e.g. 1.0).
+
+   The default activation steepness is 0.5.
+
+   See also:
+        <fann_set_activation_steepness_layer>, <fann_set_activation_steepness_hidden>,
+        <fann_set_activation_steepness_output>, <fann_set_activation_function>,
+    <fann_get_activation_steepness>
+
+   This function appears in FANN >= 2.0.0.
+ */
+FANN_EXTERNAL void FANN_API fann_set_activation_steepness(struct fann *ann, fann_type steepness,
+                                                          int layer, int neuron);
+
+/* Function: fann_set_activation_steepness_layer
+
+   Set the activation steepness for all of the neurons in layer number *layer*,
+   counting the input layer as layer 0.
+
+   It is not possible to set activation steepness for the neurons in the input layer.
+
+   See also:
+        <fann_set_activation_steepness>, <fann_set_activation_steepness_hidden>,
+        <fann_set_activation_steepness_output>, <fann_set_activation_function_layer>
+
+   This function appears in FANN >= 2.0.0.
+ */
+FANN_EXTERNAL void FANN_API fann_set_activation_steepness_layer(struct fann *ann,
+                                                                fann_type steepness, int layer);
+
+/* Function: fann_set_activation_steepness_hidden
+
+   Set the steepness of the activation steepness in all of the hidden layers.
+
+   See also:
+        <fann_set_activation_steepness>, <fann_set_activation_steepness_layer>,
+        <fann_set_activation_steepness_output>, <fann_set_activation_function_hidden>
+
+   This function appears in FANN >= 1.2.0.
+ */
+FANN_EXTERNAL void FANN_API fann_set_activation_steepness_hidden(struct fann *ann,
+                                                                 fann_type steepness);
+
+/* Function: fann_set_activation_steepness_output
+
+   Set the steepness of the activation steepness in the output layer.
+
+   See also:
+        <fann_set_activation_steepness>, <fann_set_activation_steepness_layer>,
+        <fann_set_activation_steepness_hidden>, <fann_set_activation_function_output>
+
+   This function appears in FANN >= 1.2.0.
+ */
+FANN_EXTERNAL void FANN_API fann_set_activation_steepness_output(struct fann *ann,
+                                                                 fann_type steepness);
+
+/* Function: fann_get_train_error_function
+
+   Returns the error function used during training.
+
+   The error functions are described further in <fann_errorfunc_enum>
+
+   The default error function is FANN_ERRORFUNC_TANH
+
+   See also:
+        <fann_set_train_error_function>
+
+   This function appears in FANN >= 1.2.0.
+  */
+FANN_EXTERNAL enum fann_errorfunc_enum FANN_API fann_get_train_error_function(struct fann *ann);
+
+/* Function: fann_set_train_error_function
+
+   Set the error function used during training.
+
+   The error functions are described further in <fann_errorfunc_enum>
+
+   See also:
+        <fann_get_train_error_function>
+
+   This function appears in FANN >= 1.2.0.
+ */
+FANN_EXTERNAL void FANN_API
+fann_set_train_error_function(struct fann *ann, enum fann_errorfunc_enum train_error_function);
+
+/* Function: fann_get_train_stop_function
+
+   Returns the the stop function used during training.
+
+   The stop function is described further in <fann_stopfunc_enum>
+
+   The default stop function is FANN_STOPFUNC_MSE
+
+   See also:
+        <fann_get_train_stop_function>, <fann_get_bit_fail_limit>
+
+   This function appears in FANN >= 2.0.0.
+ */
+FANN_EXTERNAL enum fann_stopfunc_enum FANN_API fann_get_train_stop_function(struct fann *ann);
+
+/* Function: fann_set_train_stop_function
+
+   Set the stop function used during training.
+
+   Returns the the stop function used during training.
+
+   The stop function is described further in <fann_stopfunc_enum>
+
+   See also:
+        <fann_get_train_stop_function>
+
+   This function appears in FANN >= 2.0.0.
+ */
+FANN_EXTERNAL void FANN_API
+fann_set_train_stop_function(struct fann *ann, enum fann_stopfunc_enum train_stop_function);
+
+/* Function: fann_get_bit_fail_limit
+
+   Returns the bit fail limit used during training.
+
+   The bit fail limit is used during training where the <fann_stopfunc_enum> is set to
+   FANN_STOPFUNC_BIT.
+
+   The limit is the maximum accepted difference between the desired output and the actual output
+   during training. Each output that diverges more than this limit is counted as an error bit. This
+   difference is divided by two when dealing with symmetric activation functions, so that symmetric
+   and not symmetric activation functions can use the same limit.
+
+   The default bit fail limit is 0.35.
+
+   See also:
+        <fann_set_bit_fail_limit>
+
+   This function appears in FANN >= 2.0.0.
+ */
+FANN_EXTERNAL fann_type FANN_API fann_get_bit_fail_limit(struct fann *ann);
+
+/* Function: fann_set_bit_fail_limit
+
+   Set the bit fail limit used during training.
+
+   See also:
+        <fann_get_bit_fail_limit>
+
+   This function appears in FANN >= 2.0.0.
+ */
+FANN_EXTERNAL void FANN_API fann_set_bit_fail_limit(struct fann *ann, fann_type bit_fail_limit);
+
+/* Function: fann_set_callback
+
+   Sets the callback function for use during training.
+
+   See <fann_callback_type> for more information about the callback function.
+
+   The default callback function simply prints out some status information.
+
+   This function appears in FANN >= 2.0.0.
+ */
+FANN_EXTERNAL void FANN_API fann_set_callback(struct fann *ann, fann_callback_type callback);
+
+/* Function: fann_get_quickprop_decay
+
+   The decay is a small negative valued number which is the factor that the weights
+   should become smaller in each iteration during quickprop training. This is used
+   to make sure that the weights do not become too high during training.
+
+   The default decay is -0.0001.
+
+   See also:
+        <fann_set_quickprop_decay>
+
+   This function appears in FANN >= 1.2.0.
+ */
+FANN_EXTERNAL float FANN_API fann_get_quickprop_decay(struct fann *ann);
+
+/* Function: fann_set_quickprop_decay
+
+   Sets the quickprop decay factor.
+
+   See also:
+        <fann_get_quickprop_decay>
+
+   This function appears in FANN >= 1.2.0.
+*/
+FANN_EXTERNAL void FANN_API fann_set_quickprop_decay(struct fann *ann, float quickprop_decay);
+
+/* Function: fann_get_quickprop_mu
+
+   The mu factor is used to increase and decrease the step-size during quickprop training.
+   The mu factor should always be above 1, since it would otherwise decrease the step-size
+   when it was supposed to increase it.
+
+   The default mu factor is 1.75.
+
+   See also:
+        <fann_set_quickprop_mu>
+
+   This function appears in FANN >= 1.2.0.
+*/
+FANN_EXTERNAL float FANN_API fann_get_quickprop_mu(struct fann *ann);
+
+/* Function: fann_set_quickprop_mu
+
+    Sets the quickprop mu factor.
+
+   See also:
+        <fann_get_quickprop_mu>
+
+   This function appears in FANN >= 1.2.0.
+*/
+FANN_EXTERNAL void FANN_API fann_set_quickprop_mu(struct fann *ann, float quickprop_mu);
+
+/* Function: fann_get_rprop_increase_factor
+
+   The increase factor is a value larger than 1, which is used to
+   increase the step-size during RPROP training.
+
+   The default increase factor is 1.2.
+
+   See also:
+        <fann_set_rprop_increase_factor>
+
+   This function appears in FANN >= 1.2.0.
+*/
+FANN_EXTERNAL float FANN_API fann_get_rprop_increase_factor(struct fann *ann);
+
+/* Function: fann_set_rprop_increase_factor
+
+   The increase factor used during RPROP training.
+
+   See also:
+        <fann_get_rprop_increase_factor>
+
+   This function appears in FANN >= 1.2.0.
+*/
+FANN_EXTERNAL void FANN_API fann_set_rprop_increase_factor(struct fann *ann,
+                                                           float rprop_increase_factor);
+
+/* Function: fann_get_rprop_decrease_factor
+
+   The decrease factor is a value smaller than 1, which is used to decrease the step-size during
+   RPROP training.
+
+   The default decrease factor is 0.5.
+
+   See also:
+    <fann_set_rprop_decrease_factor>
+
+   This function appears in FANN >= 1.2.0.
+*/
+FANN_EXTERNAL float FANN_API fann_get_rprop_decrease_factor(struct fann *ann);
+
+/* Function: fann_set_rprop_decrease_factor
+
+   The decrease factor is a value smaller than 1, which is used to decrease the step-size during
+   RPROP training.
+
+   See also:
+    <fann_get_rprop_decrease_factor>
+
+   This function appears in FANN >= 1.2.0.
+*/
+FANN_EXTERNAL void FANN_API fann_set_rprop_decrease_factor(struct fann *ann,
+                                                           float rprop_decrease_factor);
+
+/* Function: fann_get_rprop_delta_min
+
+   The minimum step-size is a small positive number determining how small the minimum step-size may
+   be.
+
+   The default value delta min is 0.0.
+
+   See also:
+        <fann_set_rprop_delta_min>
+
+   This function appears in FANN >= 1.2.0.
+*/
+FANN_EXTERNAL float FANN_API fann_get_rprop_delta_min(struct fann *ann);
+
+/* Function: fann_set_rprop_delta_min
+
+   The minimum step-size is a small positive number determining how small the minimum step-size may
+   be.
+
+   See also:
+        <fann_get_rprop_delta_min>
+
+   This function appears in FANN >= 1.2.0.
+*/
+FANN_EXTERNAL void FANN_API fann_set_rprop_delta_min(struct fann *ann, float rprop_delta_min);
+
+/* Function: fann_get_rprop_delta_max
+
+   The maximum step-size is a positive number determining how large the maximum step-size may be.
+
+   The default delta max is 50.0.
+
+   See also:
+        <fann_set_rprop_delta_max>, <fann_get_rprop_delta_min>
+
+   This function appears in FANN >= 1.2.0.
+*/
+FANN_EXTERNAL float FANN_API fann_get_rprop_delta_max(struct fann *ann);
+
+/* Function: fann_set_rprop_delta_max
+
+   The maximum step-size is a positive number determining how large the maximum step-size may be.
+
+   See also:
+        <fann_get_rprop_delta_max>, <fann_get_rprop_delta_min>
+
+   This function appears in FANN >= 1.2.0.
+*/
+FANN_EXTERNAL void FANN_API fann_set_rprop_delta_max(struct fann *ann, float rprop_delta_max);
+
+/* Function: fann_get_rprop_delta_zero
+
+   The initial step-size is a positive number determining the initial step size.
+
+   The default delta zero is 0.1.
+
+   See also:
+        <fann_set_rprop_delta_zero>, <fann_get_rprop_delta_min>, <fann_get_rprop_delta_max>
+
+   This function appears in FANN >= 2.1.0.
+*/
+FANN_EXTERNAL float FANN_API fann_get_rprop_delta_zero(struct fann *ann);
+
+/* Function: fann_set_rprop_delta_zero
+
+   The initial step-size is a positive number determining the initial step size.
+
+   See also:
+        <fann_get_rprop_delta_zero>, <fann_get_rprop_delta_zero>
+
+   This function appears in FANN >= 2.1.0.
+*/
+FANN_EXTERNAL void FANN_API fann_set_rprop_delta_zero(struct fann *ann, float rprop_delta_max);
+
+/* Method: fann_get_sarprop_weight_decay_shift
+
+   The sarprop weight decay shift.
+
+   The default delta max is -6.644.
+
+   See also:
+   <fann fann_set_sarprop_weight_decay_shift>
+
+   This function appears in FANN >= 2.1.0.
+   */
+FANN_EXTERNAL float FANN_API fann_get_sarprop_weight_decay_shift(struct fann *ann);
+
+/* Method: fann_set_sarprop_weight_decay_shift
+
+   Set the sarprop weight decay shift.
+
+   This function appears in FANN >= 2.1.0.
+
+   See also:
+   <fann_set_sarprop_weight_decay_shift>
+   */
+FANN_EXTERNAL void FANN_API fann_set_sarprop_weight_decay_shift(struct fann *ann,
+                                                                float sarprop_weight_decay_shift);
+
+/* Method: fann_get_sarprop_step_error_threshold_factor
+
+   The sarprop step error threshold factor.
+
+   The default delta max is 0.1.
+
+   See also:
+   <fann fann_get_sarprop_step_error_threshold_factor>
+
+   This function appears in FANN >= 2.1.0.
+   */
+FANN_EXTERNAL float FANN_API fann_get_sarprop_step_error_threshold_factor(struct fann *ann);
+
+/* Method: fann_set_sarprop_step_error_threshold_factor
+
+   Set the sarprop step error threshold factor.
+
+   This function appears in FANN >= 2.1.0.
+
+   See also:
+   <fann_get_sarprop_step_error_threshold_factor>
+   */
+FANN_EXTERNAL void FANN_API fann_set_sarprop_step_error_threshold_factor(
+    struct fann *ann, float sarprop_step_error_threshold_factor);
+
+/* Method: fann_get_sarprop_step_error_shift
+
+   The get sarprop step error shift.
+
+   The default delta max is 1.385.
+
+   See also:
+   <fann_set_sarprop_step_error_shift>
+
+   This function appears in FANN >= 2.1.0.
+   */
+FANN_EXTERNAL float FANN_API fann_get_sarprop_step_error_shift(struct fann *ann);
+
+/* Method: fann_set_sarprop_step_error_shift
+
+   Set the sarprop step error shift.
+
+   This function appears in FANN >= 2.1.0.
+
+   See also:
+   <fann_get_sarprop_step_error_shift>
+   */
+FANN_EXTERNAL void FANN_API fann_set_sarprop_step_error_shift(struct fann *ann,
+                                                              float sarprop_step_error_shift);
+
+/* Method: fann_get_sarprop_temperature
+
+   The sarprop weight decay shift.
+
+   The default delta max is 0.015.
+
+   See also:
+   <fann_set_sarprop_temperature>
+
+   This function appears in FANN >= 2.1.0.
+   */
+FANN_EXTERNAL float FANN_API fann_get_sarprop_temperature(struct fann *ann);
+
+/* Method: fann_set_sarprop_temperature
+
+   Set the sarprop_temperature.
+
+   This function appears in FANN >= 2.1.0.
+
+   See also:
+   <fann_get_sarprop_temperature>
+   */
+FANN_EXTERNAL void FANN_API fann_set_sarprop_temperature(struct fann *ann,
+                                                         float sarprop_temperature);
+
+#endif
diff --git a/ai_fighter/AI_Fight_Sim/Plugins/NeuralNetworkAIController/Source/NeuralNetworkAIController/ThirdParty/FANN/include/fann_training_data_cpp.h b/ai_fighter/AI_Fight_Sim/Plugins/NeuralNetworkAIController/Source/NeuralNetworkAIController/ThirdParty/FANN/include/fann_training_data_cpp.h
new file mode 100644
index 0000000..04248f6
--- /dev/null
+++ b/ai_fighter/AI_Fight_Sim/Plugins/NeuralNetworkAIController/Source/NeuralNetworkAIController/ThirdParty/FANN/include/fann_training_data_cpp.h
@@ -0,0 +1,543 @@
+/*
+Fast Artificial Neural Network Library (fann)
+Copyright (C) 2003-2016 Steffen Nissen (steffen.fann@gmail.com)
+
+This library is free software; you can redistribute it and/or
+modify it under the terms of the GNU Lesser General Public
+License as published by the Free Software Foundation; either
+version 2.1 of the License, or (at your option) any later version.
+
+This library is distributed in the hope that it will be useful,
+but WITHOUT ANY WARRANTY; without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
+Lesser General Public License for more details.
+
+You should have received a copy of the GNU Lesser General Public
+License along with this library; if not, write to the Free Software
+Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA  02111-1307  USA
+*/
+
+#ifndef FANN_FANN_TRAINING_DATA_CPP_H
+#define FANN_FANN_TRAINING_DATA_CPP_H
+
+#include <stdarg.h>
+#include <string>
+
+namespace FANN {
+
+/* Section: FANN C++ Training Data
+ */
+
+/* Class: training_data
+
+<training_data> is used to create and manipulate training data used by the <neural_net>
+
+Encapsulation of a training data set <struct fann_train_data> and
+associated C API functions.
+*/
+class training_data {
+ public:
+  /* Constructor: training_data
+
+      Default constructor creates an empty training data.
+      Use <read_train_from_file>, <set_train_data> or <create_train_from_callback> to initialize.
+  */
+  training_data() : train_data(NULL) {}
+
+  /* Constructor: training_data
+
+      Copy constructor constructs a copy of the training data.
+      Corresponds to the C API <fann_duplicate_train_data> function.
+  */
+  training_data(const training_data &data) {
+    train_data = fann_duplicate_train_data(data.train_data);
+  }
+
+  /* Destructor: ~training_data
+
+      Provides automatic cleanup of data.
+      Define USE_VIRTUAL_DESTRUCTOR if you need the destructor to be virtual.
+
+      See also:
+          <destroy>
+  */
+#ifdef USE_VIRTUAL_DESTRUCTOR
+  virtual
+#endif
+
+      ~training_data() {
+    destroy_train();
+  }
+
+  /* Method: destroy
+
+      Destructs the training data. Called automatically by the destructor.
+
+      See also:
+          <~training_data>
+  */
+  void destroy_train() {
+    if (train_data != NULL) {
+      fann_destroy_train(train_data);
+      train_data = NULL;
+    }
+  }
+
+  /* Method: read_train_from_file
+     Reads a file that stores training data.
+
+     The file must be formatted like:
+     >num_train_data num_input num_output
+     >inputdata seperated by space
+     >outputdata seperated by space
+     >
+     >.
+     >.
+     >.
+     >
+     >inputdata seperated by space
+     >outputdata seperated by space
+
+     See also:
+          <neural_net::train_on_data>, <save_train>, <fann_read_train_from_file>
+
+      This function appears in FANN >= 1.0.0
+  */
+  bool read_train_from_file(const std::string &filename) {
+    destroy_train();
+    train_data = fann_read_train_from_file(filename.c_str());
+    return (train_data != NULL);
+  }
+
+  /* Method: save_train
+
+     Save the training structure to a file, with the format as specified in <read_train_from_file>
+
+     Return:
+     The function returns true on success and false on failure.
+
+     See also:
+          <read_train_from_file>, <save_train_to_fixed>, <fann_save_train>
+
+     This function appears in FANN >= 1.0.0.
+   */
+  bool save_train(const std::string &filename) {
+    if (train_data == NULL) {
+      return false;
+    }
+    if (fann_save_train(train_data, filename.c_str()) == -1) {
+      return false;
+    }
+    return true;
+  }
+
+  /* Method: save_train_to_fixed
+
+     Saves the training structure to a fixed point data file.
+
+     This function is very useful for testing the quality of a fixed point network.
+
+     Return:
+     The function returns true on success and false on failure.
+
+     See also:
+          <save_train>, <fann_save_train_to_fixed>
+
+     This function appears in FANN >= 1.0.0.
+   */
+  bool save_train_to_fixed(const std::string &filename, unsigned int decimal_point) {
+    if (train_data == NULL) {
+      return false;
+    }
+    if (fann_save_train_to_fixed(train_data, filename.c_str(), decimal_point) == -1) {
+      return false;
+    }
+    return true;
+  }
+
+  /* Method: shuffle_train_data
+
+     Shuffles training data, randomizing the order.
+     This is recommended for incremental training, while it have no influence during batch training.
+
+     This function appears in FANN >= 1.1.0.
+   */
+  void shuffle_train_data() {
+    if (train_data != NULL) {
+      fann_shuffle_train_data(train_data);
+    }
+  }
+
+  /* Method: merge_train_data
+
+     Merges the data into the data contained in the <training_data>.
+
+     This function appears in FANN >= 1.1.0.
+   */
+  void merge_train_data(const training_data &data) {
+    fann_train_data *new_data = fann_merge_train_data(train_data, data.train_data);
+    if (new_data != NULL) {
+      destroy_train();
+      train_data = new_data;
+    }
+  }
+
+  /* Method: length_train_data
+
+     Returns the number of training patterns in the <training_data>.
+
+     See also:
+     <num_input_train_data>, <num_output_train_data>, <fann_length_train_data>
+
+     This function appears in FANN >= 2.0.0.
+   */
+  unsigned int length_train_data() {
+    if (train_data == NULL) {
+      return 0;
+    } else {
+      return fann_length_train_data(train_data);
+    }
+  }
+
+  /* Method: num_input_train_data
+
+     Returns the number of inputs in each of the training patterns in the <training_data>.
+
+     See also:
+     <num_output_train_data>, <length_train_data>, <fann_num_input_train_data>
+
+     This function appears in FANN >= 2.0.0.
+   */
+  unsigned int num_input_train_data() {
+    if (train_data == NULL) {
+      return 0;
+    } else {
+      return fann_num_input_train_data(train_data);
+    }
+  }
+
+  /* Method: num_output_train_data
+
+     Returns the number of outputs in each of the training patterns in the <struct fann_train_data>.
+
+     See also:
+     <num_input_train_data>, <length_train_data>, <fann_num_output_train_data>
+
+     This function appears in FANN >= 2.0.0.
+   */
+  unsigned int num_output_train_data() {
+    if (train_data == NULL) {
+      return 0;
+    } else {
+      return fann_num_output_train_data(train_data);
+    }
+  }
+
+  /* Method: get_input
+      Grant access to the encapsulated data since many situations
+      and applications creates the data from sources other than files
+      or uses the training data for testing and related functions
+
+      Returns:
+          A pointer to the array of input training data
+
+      See also:
+          <get_output>, <set_train_data>
+
+     This function appears in FANN >= 2.0.0.
+  */
+  fann_type **get_input() {
+    if (train_data == NULL) {
+      return NULL;
+    } else {
+      return train_data->input;
+    }
+  }
+
+  /* Method: get_output
+
+      Grant access to the encapsulated data since many situations
+      and applications creates the data from sources other than files
+      or uses the training data for testing and related functions
+
+      Returns:
+          A pointer to the array of output training data
+
+      See also:
+          <get_input>, <set_train_data>
+
+     This function appears in FANN >= 2.0.0.
+  */
+  fann_type **get_output() {
+    if (train_data == NULL) {
+      return NULL;
+    } else {
+      return train_data->output;
+    }
+  }
+
+  /* Method: get_train_input
+      Gets the training input data at the given position
+
+      Returns:
+          A pointer to the array of input training data at the given position
+
+      See also:
+          <get_train_output>, <set_train_data>
+
+     This function appears in FANN >= 2.3.0.
+  */
+  fann_type *get_train_input(unsigned int position) {
+    return fann_get_train_input(train_data, position);
+  }
+
+  /* Method: get_train_output
+      Gets the training output data at the given position
+
+      Returns:
+          A pointer to the array of output training data at the given position
+
+      See also:
+          <get_train_input>
+
+     This function appears in FANN >= 2.3.0.
+  */
+  fann_type *get_train_output(unsigned int position) {
+    return fann_get_train_output(train_data, position);
+  }
+
+  /* Method: set_train_data
+
+      Set the training data to the input and output data provided.
+
+      A copy of the data is made so there are no restrictions on the
+      allocation of the input/output data and the caller is responsible
+      for the deallocation of the data pointed to by input and output.
+
+     Parameters:
+       num_data      - The number of training data
+       num_input     - The number of inputs per training data
+       num_output    - The number of ouputs per training data
+       input      - The set of inputs (a pointer to an array of pointers to arrays of floating point
+     data) output     - The set of desired outputs (a pointer to an array of pointers to arrays of
+     floating point data)
+
+      See also:
+          <get_input>, <get_output>
+  */
+  void set_train_data(unsigned int num_data, unsigned int num_input, fann_type **input,
+                      unsigned int num_output, fann_type **output) {
+    set_train_data(fann_create_train_pointer_array(num_data, num_input, input, num_output, output));
+  }
+
+  /* Method: set_train_data
+
+      Set the training data to the input and output data provided.
+
+      A copy of the data is made so there are no restrictions on the
+      allocation of the input/output data and the caller is responsible
+      for the deallocation of the data pointed to by input and output.
+
+     Parameters:
+       num_data      - The number of training data
+       num_input     - The number of inputs per training data
+       num_output    - The number of ouputs per training data
+       input      - The set of inputs (an array with the dimension num_data*num_input)
+       output     - The set of desired outputs (an array with the dimension num_data*num_output)
+
+      See also:
+          <get_input>, <get_output>
+  */
+  void set_train_data(unsigned int num_data, unsigned int num_input, fann_type *input,
+                      unsigned int num_output, fann_type *output) {
+    set_train_data(fann_create_train_array(num_data, num_input, input, num_output, output));
+  }
+
+ private:
+  /* Set the training data to the struct fann_training_data pointer.
+      The struct has to be allocated with malloc to be compatible
+      with fann_destroy. */
+  void set_train_data(struct fann_train_data *data) {
+    destroy_train();
+    train_data = data;
+  }
+
+ public:
+  /*********************************************************************/
+
+  /* Method: create_train_from_callback
+     Creates the training data struct from a user supplied function.
+     As the training data are numerable (data 1, data 2...), the user must write
+     a function that receives the number of the training data set (input,output)
+     and returns the set.
+
+     Parameters:
+       num_data      - The number of training data
+       num_input     - The number of inputs per training data
+       num_output    - The number of ouputs per training data
+       user_function - The user suplied function
+
+     Parameters for the user function:
+       num        - The number of the training data set
+       num_input  - The number of inputs per training data
+       num_output - The number of ouputs per training data
+       input      - The set of inputs
+       output     - The set of desired outputs
+
+     See also:
+       <training_data::read_train_from_file>, <neural_net::train_on_data>,
+       <fann_create_train_from_callback>
+
+      This function appears in FANN >= 2.1.0
+  */
+  void create_train_from_callback(unsigned int num_data, unsigned int num_input,
+                                  unsigned int num_output,
+                                  void(FANN_API *user_function)(unsigned int, unsigned int,
+                                                                unsigned int, fann_type *,
+                                                                fann_type *)) {
+    destroy_train();
+    train_data = fann_create_train_from_callback(num_data, num_input, num_output, user_function);
+  }
+
+#ifndef FIXEDFANN
+  /* Function: get_min_input
+
+     Get the minimum value of all in the input data
+
+     This function appears in FANN >= 2.3.0
+  */
+  fann_type get_min_input() { return fann_get_min_train_input(train_data); }
+
+  /* Function: get_max_input
+
+     Get the maximum value of all in the input data
+
+     This function appears in FANN >= 2.3.0
+  */
+  fann_type get_max_input() { return fann_get_max_train_input(train_data); }
+
+  /* Function: get_min_output
+
+     Get the minimum value of all in the output data
+
+     This function appears in FANN >= 2.3.0
+  */
+  fann_type get_min_output() { return fann_get_min_train_output(train_data); }
+
+  /* Function: get_max_output
+
+     Get the maximum value of all in the output data
+
+     This function appears in FANN >= 2.3.0
+  */
+  fann_type get_max_output() { return fann_get_max_train_output(train_data); }
+#endif /* FIXEDFANN */
+
+  /* Method: scale_input_train_data
+
+     Scales the inputs in the training data to the specified range.
+
+     A simplified scaling method, which is mostly useful in examples where it's known that all the
+     data will be in one range and it should be transformed to another range.
+
+     It is not recommended to use this on subsets of data as the complete input range might not be
+     available in that subset.
+
+     For more powerful scaling, please consider <neural_net::scale_train>
+
+     See also:
+          <scale_output_train_data>, <scale_train_data>, <fann_scale_input_train_data>
+
+     This function appears in FANN >= 2.0.0.
+   */
+  void scale_input_train_data(fann_type new_min, fann_type new_max) {
+    if (train_data != NULL) {
+      fann_scale_input_train_data(train_data, new_min, new_max);
+    }
+  }
+
+  /* Method: scale_output_train_data
+
+     Scales the outputs in the training data to the specified range.
+
+     A simplified scaling method, which is mostly useful in examples where it's known that all the
+     data will be in one range and it should be transformed to another range.
+
+     It is not recommended to use this on subsets of data as the complete input range might not be
+     available in that subset.
+
+     For more powerful scaling, please consider <neural_net::scale_train>
+
+     See also:
+          <scale_input_train_data>, <scale_train_data>, <fann_scale_output_train_data>
+
+     This function appears in FANN >= 2.0.0.
+   */
+  void scale_output_train_data(fann_type new_min, fann_type new_max) {
+    if (train_data != NULL) {
+      fann_scale_output_train_data(train_data, new_min, new_max);
+    }
+  }
+
+  /* Method: scale_train_data
+
+     Scales the inputs and outputs in the training data to the specified range.
+
+     A simplified scaling method, which is mostly useful in examples where it's known that all the
+     data will be in one range and it should be transformed to another range.
+
+     It is not recommended to use this on subsets of data as the complete input range might not be
+     available in that subset.
+
+     For more powerful scaling, please consider <neural_net::scale_train>
+
+     See also:
+          <scale_output_train_data>, <scale_input_train_data>, <fann_scale_train_data>
+
+     This function appears in FANN >= 2.0.0.
+   */
+  void scale_train_data(fann_type new_min, fann_type new_max) {
+    if (train_data != NULL) {
+      fann_scale_train_data(train_data, new_min, new_max);
+    }
+  }
+
+  /* Method: subset_train_data
+
+     Changes the training data to a subset, starting at position *pos*
+     and *length* elements forward. Use the copy constructor to work
+     on a new copy of the training data.
+
+      >FANN::training_data full_data_set;
+      >full_data_set.read_train_from_file("somefile.train");
+      >FANN::training_data *small_data_set = new FANN::training_data(full_data_set);
+      >small_data_set->subset_train_data(0, 2); // Only use first two
+      >// Use small_data_set ...
+      >delete small_data_set;
+
+     See also:
+          <fann_subset_train_data>
+
+     This function appears in FANN >= 2.0.0.
+   */
+  void subset_train_data(unsigned int pos, unsigned int length) {
+    if (train_data != NULL) {
+      struct fann_train_data *temp = fann_subset_train_data(train_data, pos, length);
+      destroy_train();
+      train_data = temp;
+    }
+  }
+
+  /*********************************************************************/
+
+ protected:
+  /* The neural_net class has direct access to the training data */
+  friend class neural_net;
+
+  /* Pointer to the encapsulated training data */
+  struct fann_train_data *train_data;
+};
+
+}  // namespace FANN
+
+#endif  // FANN_FANN_TRAINING_DATA_CPP_H
diff --git a/ai_fighter/AI_Fight_Sim/Plugins/NeuralNetworkAIController/Source/NeuralNetworkAIController/ThirdParty/FANN/include/fixedfann.h b/ai_fighter/AI_Fight_Sim/Plugins/NeuralNetworkAIController/Source/NeuralNetworkAIController/ThirdParty/FANN/include/fixedfann.h
new file mode 100644
index 0000000..df3bcd4
--- /dev/null
+++ b/ai_fighter/AI_Fight_Sim/Plugins/NeuralNetworkAIController/Source/NeuralNetworkAIController/ThirdParty/FANN/include/fixedfann.h
@@ -0,0 +1,33 @@
+/*
+Fast Artificial Neural Network Library (fann)
+Copyright (C) 2003-2016 Steffen Nissen (steffen.fann@gmail.com)
+
+This library is free software; you can redistribute it and/or
+modify it under the terms of the GNU Lesser General Public
+License as published by the Free Software Foundation; either
+version 2.1 of the License, or (at your option) any later version.
+
+This library is distributed in the hope that it will be useful,
+but WITHOUT ANY WARRANTY; without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
+Lesser General Public License for more details.
+
+You should have received a copy of the GNU Lesser General Public
+License along with this library; if not, write to the Free Software
+Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA  02111-1307  USA
+*/
+
+#ifndef __fixedfann_h__
+#define __fixedfann_h__
+
+typedef int fann_type;
+
+#undef FIXEDFANN
+#define FIXEDFANN
+#define FANNPRINTF "%d"
+#define FANNSCANF "%d"
+
+#define FANN_INCLUDE
+#include "fann.h"
+
+#endif
diff --git a/ai_fighter/AI_Fight_Sim/Plugins/NeuralNetworkAIController/Source/NeuralNetworkAIController/ThirdParty/FANN/include/floatfann.h b/ai_fighter/AI_Fight_Sim/Plugins/NeuralNetworkAIController/Source/NeuralNetworkAIController/ThirdParty/FANN/include/floatfann.h
new file mode 100644
index 0000000..ab876ca
--- /dev/null
+++ b/ai_fighter/AI_Fight_Sim/Plugins/NeuralNetworkAIController/Source/NeuralNetworkAIController/ThirdParty/FANN/include/floatfann.h
@@ -0,0 +1,33 @@
+/*
+Fast Artificial Neural Network Library (fann)
+Copyright (C) 2003-2016 Steffen Nissen (steffen.fann@gmail.com)
+
+This library is free software; you can redistribute it and/or
+modify it under the terms of the GNU Lesser General Public
+License as published by the Free Software Foundation; either
+version 2.1 of the License, or (at your option) any later version.
+
+This library is distributed in the hope that it will be useful,
+but WITHOUT ANY WARRANTY; without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
+Lesser General Public License for more details.
+
+You should have received a copy of the GNU Lesser General Public
+License along with this library; if not, write to the Free Software
+Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA  02111-1307  USA
+*/
+
+#ifndef __floatfann_h__
+#define __floatfann_h__
+
+typedef float fann_type;
+
+#undef FLOATFANN
+#define FLOATFANN
+#define FANNPRINTF "%.20e"
+#define FANNSCANF "%f"
+
+#define FANN_INCLUDE
+#include "fann.h"
+
+#endif
diff --git a/ai_fighter/AI_Fight_Sim/Plugins/NeuralNetworkAIController/Source/NeuralNetworkAIController/ThirdParty/FANN/include/parallel_fann.h b/ai_fighter/AI_Fight_Sim/Plugins/NeuralNetworkAIController/Source/NeuralNetworkAIController/ThirdParty/FANN/include/parallel_fann.h
new file mode 100644
index 0000000..28d0781
--- /dev/null
+++ b/ai_fighter/AI_Fight_Sim/Plugins/NeuralNetworkAIController/Source/NeuralNetworkAIController/ThirdParty/FANN/include/parallel_fann.h
@@ -0,0 +1,53 @@
+/*
+ * parallel_FANN.h
+ *
+ *     Author: Alessandro Pietro Bardelli
+ */
+#ifndef DISABLE_PARALLEL_FANN
+#ifndef PARALLEL_FANN_H_
+#define PARALLEL_FANN_H_
+
+#include "fann.h"
+
+#ifdef __cplusplus
+extern "C" {
+
+#ifndef __cplusplus
+} /* to fool automatic indention engines */
+#endif
+#endif /* __cplusplus */
+
+#ifndef FIXEDFANN
+FANN_EXTERNAL float FANN_API fann_train_epoch_batch_parallel(struct fann *ann,
+                                                             struct fann_train_data *data,
+                                                             const unsigned int threadnumb);
+
+FANN_EXTERNAL float FANN_API fann_train_epoch_irpropm_parallel(struct fann *ann,
+                                                               struct fann_train_data *data,
+                                                               const unsigned int threadnumb);
+
+FANN_EXTERNAL float FANN_API fann_train_epoch_quickprop_parallel(struct fann *ann,
+                                                                 struct fann_train_data *data,
+                                                                 const unsigned int threadnumb);
+
+FANN_EXTERNAL float FANN_API fann_train_epoch_sarprop_parallel(struct fann *ann,
+                                                               struct fann_train_data *data,
+                                                               const unsigned int threadnumb);
+
+FANN_EXTERNAL float FANN_API fann_train_epoch_incremental_mod(struct fann *ann,
+                                                              struct fann_train_data *data);
+
+FANN_EXTERNAL float FANN_API fann_test_data_parallel(struct fann *ann, struct fann_train_data *data,
+                                                     const unsigned int threadnumb);
+#endif /* FIXEDFANN */
+
+#ifdef __cplusplus
+#ifndef __cplusplus
+/* to fool automatic indention engines */
+{
+#endif
+}
+#endif /* __cplusplus */
+
+#endif /* PARALLEL_FANN_H_ */
+#endif /* DISABLE_PARALLEL_FANN */
diff --git a/ai_fighter/AI_Fight_Sim/Plugins/NeuralNetworkAIController/Source/NeuralNetworkAIController/ThirdParty/FANN/include/parallel_fann.hpp b/ai_fighter/AI_Fight_Sim/Plugins/NeuralNetworkAIController/Source/NeuralNetworkAIController/ThirdParty/FANN/include/parallel_fann.hpp
new file mode 100644
index 0000000..ff5ee6d
--- /dev/null
+++ b/ai_fighter/AI_Fight_Sim/Plugins/NeuralNetworkAIController/Source/NeuralNetworkAIController/ThirdParty/FANN/include/parallel_fann.hpp
@@ -0,0 +1,55 @@
+/*
+ * parallel_FANN.hpp
+ *     Author: Alessandro Pietro Bardelli
+ */
+#ifndef DISABLE_PARALLEL_FANN
+#ifndef PARALLEL_FANN_HPP_
+#define PARALLEL_FANN_HPP_
+#include <omp.h>
+#include <vector>
+#include "fann.h"
+
+#ifndef FIXEDFANN
+namespace parallel_fann {
+float train_epoch_batch_parallel(struct fann *ann, struct fann_train_data *data,
+                                 const unsigned int threadnumb);
+
+float train_epoch_irpropm_parallel(struct fann *ann, struct fann_train_data *data,
+                                   const unsigned int threadnumb);
+
+float train_epoch_quickprop_parallel(struct fann *ann, struct fann_train_data *data,
+                                     const unsigned int threadnumb);
+
+float train_epoch_sarprop_parallel(struct fann *ann, struct fann_train_data *data,
+                                   const unsigned int threadnumb);
+
+float train_epoch_incremental_mod(struct fann *ann, struct fann_train_data *data);
+
+float train_epoch_batch_parallel(struct fann *ann, struct fann_train_data *data,
+                                 const unsigned int threadnumb,
+                                 std::vector<std::vector<fann_type> > &predicted_outputs);
+
+float train_epoch_irpropm_parallel(struct fann *ann, struct fann_train_data *data,
+                                   const unsigned int threadnumb,
+                                   std::vector<std::vector<fann_type> > &predicted_outputs);
+
+float train_epoch_quickprop_parallel(struct fann *ann, struct fann_train_data *data,
+                                     const unsigned int threadnumb,
+                                     std::vector<std::vector<fann_type> > &predicted_outputs);
+
+float train_epoch_sarprop_parallel(struct fann *ann, struct fann_train_data *data,
+                                   const unsigned int threadnumb,
+                                   std::vector<std::vector<fann_type> > &predicted_outputs);
+
+float train_epoch_incremental_mod(struct fann *ann, struct fann_train_data *data,
+                                  std::vector<std::vector<fann_type> > &predicted_outputs);
+
+float test_data_parallel(struct fann *ann, struct fann_train_data *data,
+                         const unsigned int threadnumb);
+float test_data_parallel(struct fann *ann, struct fann_train_data *data,
+                         const unsigned int threadnumb,
+                         std::vector<std::vector<fann_type> > &predicted_outputs);
+}  // namespace parallel_fann
+#endif /* FIXEDFANN */
+#endif /* PARALLEL_FANN_HPP_ */
+#endif /* DISABLE_PARALLEL_FANN */
diff --git a/ai_fighter/AI_Fight_Sim/Plugins/NeuralNetworkAIController/Source/NeuralNetworkAIController/ThirdParty/FANN/lib/pkgconfig/fann.pc b/ai_fighter/AI_Fight_Sim/Plugins/NeuralNetworkAIController/Source/NeuralNetworkAIController/ThirdParty/FANN/lib/pkgconfig/fann.pc
new file mode 100644
index 0000000..9d80ccb
--- /dev/null
+++ b/ai_fighter/AI_Fight_Sim/Plugins/NeuralNetworkAIController/Source/NeuralNetworkAIController/ThirdParty/FANN/lib/pkgconfig/fann.pc
@@ -0,0 +1,11 @@
+prefix=${pcfiledir}/../..
+exec_prefix=${prefix}/bin
+libdir=${prefix}/lib
+includedir=${prefix}/include
+ 
+Name: fann
+Description: Fast Artificial Neural Network Library
+Version: 
+Libs: "-L${libdir}" -lm -lfann
+Cflags: "-I${includedir}"
+
diff --git a/ai_fighter/AI_Fight_Sim/Plugins/NeuralNetworkAIController/Source/NeuralNetworkAIController/ThirdParty/FANN/share/fann/copyright b/ai_fighter/AI_Fight_Sim/Plugins/NeuralNetworkAIController/Source/NeuralNetworkAIController/ThirdParty/FANN/share/fann/copyright
new file mode 100644
index 0000000..b1e3f5a
--- /dev/null
+++ b/ai_fighter/AI_Fight_Sim/Plugins/NeuralNetworkAIController/Source/NeuralNetworkAIController/ThirdParty/FANN/share/fann/copyright
@@ -0,0 +1,504 @@
+		  GNU LESSER GENERAL PUBLIC LICENSE
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+
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+    This library is free software; you can redistribute it and/or
+    modify it under the terms of the GNU Lesser General Public
+    License as published by the Free Software Foundation; either
+    version 2.1 of the License, or (at your option) any later version.
+
+    This library is distributed in the hope that it will be useful,
+    but WITHOUT ANY WARRANTY; without even the implied warranty of
+    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
+    Lesser General Public License for more details.
+
+    You should have received a copy of the GNU Lesser General Public
+    License along with this library; if not, write to the Free Software
+    Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA  02111-1307  USA
+
+Also add information on how to contact you by electronic and paper mail.
+
+You should also get your employer (if you work as a programmer) or your
+school, if any, to sign a "copyright disclaimer" for the library, if
+necessary.  Here is a sample; alter the names:
+
+  Yoyodyne, Inc., hereby disclaims all copyright interest in the
+  library `Frob' (a library for tweaking knobs) written by James Random Hacker.
+
+  <signature of Ty Coon>, 1 April 1990
+  Ty Coon, President of Vice
+
+That's all there is to it!
+
+
diff --git a/ai_fighter/AI_Fight_Sim/Plugins/NeuralNetworkAIController/Source/NeuralNetworkAIController/ThirdParty/FANN/share/fann/fann-config.cmake b/ai_fighter/AI_Fight_Sim/Plugins/NeuralNetworkAIController/Source/NeuralNetworkAIController/ThirdParty/FANN/share/fann/fann-config.cmake
new file mode 100644
index 0000000..97d27c2
--- /dev/null
+++ b/ai_fighter/AI_Fight_Sim/Plugins/NeuralNetworkAIController/Source/NeuralNetworkAIController/ThirdParty/FANN/share/fann/fann-config.cmake
@@ -0,0 +1,53 @@
+get_filename_component(VCPKG_IMPORT_PREFIX "${CMAKE_CURRENT_LIST_DIR}/../../" ABSOLUTE)
+#                                               -*- cmake -*-
+#
+#  fann-config.cmake(.in)
+#
+
+# Use the following variables to compile and link against FANN:
+#  FANN_FOUND              - True if FANN was found on your system
+#  FANN_USE_FILE           - The file making FANN usable
+#  FANN_DEFINITIONS        - Definitions needed to build with FANN
+#  FANN_INCLUDE_DIR        - Directory where fann.h can be found
+#  FANN_INCLUDE_DIRS       - List of directories of FANN and it's dependencies
+#  FANN_LIBRARY            - FANN library location
+#  FANN_LIBRARIES          - List of libraries to link against FANN library
+#  FANN_LIBRARY_DIRS       - List of directories containing FANN' libraries
+#  FANN_ROOT_DIR           - The base directory of FANN
+#  FANN_VERSION_STRING     - A human-readable string containing the version
+#  FANN_VERSION_MAJOR      - The major version of FANN
+#  FANN_VERSION_MINOR      - The minor version of FANN
+#  FANN_VERSION_PATCH      - The patch version of FANN
+
+
+####### Expanded from @PACKAGE_INIT@ by configure_package_config_file() #######
+####### Any changes to this file will be overwritten by the next CMake run ####
+####### The input file was fann-config.cmake.in                            ########
+
+get_filename_component(PACKAGE_PREFIX_DIR "${CMAKE_CURRENT_LIST_DIR}/../../" ABSOLUTE)
+
+macro(set_and_check _var _file)
+  set(${_var} "${_file}")
+  if(NOT EXISTS "${_file}")
+    message(FATAL_ERROR "File or directory ${_file} referenced by variable ${_var} does not exist !")
+  endif()
+endmacro()
+
+####################################################################################
+
+set ( FANN_FOUND 1 )
+set ( FANN_USE_FILE     "${VCPKG_IMPORT_PREFIX}/lib/cmake/fann/fann-use.cmake" )
+
+set ( FANN_DEFINITIONS  "" )
+set ( FANN_INCLUDE_DIR  "${VCPKG_IMPORT_PREFIX}/include" )
+set ( FANN_INCLUDE_DIRS "${VCPKG_IMPORT_PREFIX}/include" )
+set ( FANN_LIBRARY      "fann" )
+set ( FANN_LIBRARIES    "fann" )
+set ( FANN_LIBRARY_DIRS "${VCPKG_IMPORT_PREFIX}/lib" )
+set ( FANN_ROOT_DIR     "${VCPKG_IMPORT_PREFIX}" )
+
+set ( FANN_VERSION_STRING "2.2.0" )
+set ( FANN_VERSION_MAJOR  "2" )
+set ( FANN_VERSION_MINOR  "2" )
+set ( FANN_VERSION_PATCH  "0" )
+
diff --git a/ai_fighter/AI_Fight_Sim/Plugins/NeuralNetworkAIController/Source/NeuralNetworkAIController/ThirdParty/FANN/share/fann/fann-use.cmake b/ai_fighter/AI_Fight_Sim/Plugins/NeuralNetworkAIController/Source/NeuralNetworkAIController/ThirdParty/FANN/share/fann/fann-use.cmake
new file mode 100644
index 0000000..7f04255
--- /dev/null
+++ b/ai_fighter/AI_Fight_Sim/Plugins/NeuralNetworkAIController/Source/NeuralNetworkAIController/ThirdParty/FANN/share/fann/fann-use.cmake
@@ -0,0 +1,9 @@
+#                                               -*- cmake -*-
+#
+#  fann-use.cmake
+#
+
+add_definitions     ( ${FANN_DEFINITIONS} )
+include_directories ( ${FANN_INCLUDE_DIRS} )
+link_directories    ( ${FANN_LIBRARY_DIRS} )
+
diff --git a/ai_fighter/AI_Fight_Sim/Plugins/NeuralNetworkAIController/Source/NeuralNetworkAIController/ThirdParty/FANN/share/fann/vcpkg.spdx.json b/ai_fighter/AI_Fight_Sim/Plugins/NeuralNetworkAIController/Source/NeuralNetworkAIController/ThirdParty/FANN/share/fann/vcpkg.spdx.json
new file mode 100644
index 0000000..66604e0
--- /dev/null
+++ b/ai_fighter/AI_Fight_Sim/Plugins/NeuralNetworkAIController/Source/NeuralNetworkAIController/ThirdParty/FANN/share/fann/vcpkg.spdx.json
@@ -0,0 +1,198 @@
+{
+  "$schema": "https://raw.githubusercontent.com/spdx/spdx-spec/v2.2.1/schemas/spdx-schema.json",
+  "spdxVersion": "SPDX-2.2",
+  "dataLicense": "CC0-1.0",
+  "SPDXID": "SPDXRef-DOCUMENT",
+  "documentNamespace": "https://spdx.org/spdxdocs/fann-x64-windows-2023-01-26-ce8397b7-6f7b-4dbc-b19b-e00cdba7c913",
+  "name": "fann:x64-windows@2023-01-26 ec3e8660f6be2382038aa9415c905fcbf1fe448b16400cd48d2ff4d126526ba5",
+  "creationInfo": {
+    "creators": [
+      "Tool: vcpkg-8a83681f921b10d86ae626fd833c253f4f8c355b"
+    ],
+    "created": "2024-03-10T08:55:29Z"
+  },
+  "relationships": [
+    {
+      "spdxElementId": "SPDXRef-port",
+      "relationshipType": "GENERATES",
+      "relatedSpdxElement": "SPDXRef-binary"
+    },
+    {
+      "spdxElementId": "SPDXRef-port",
+      "relationshipType": "CONTAINS",
+      "relatedSpdxElement": "SPDXRef-file-0"
+    },
+    {
+      "spdxElementId": "SPDXRef-port",
+      "relationshipType": "CONTAINS",
+      "relatedSpdxElement": "SPDXRef-file-1"
+    },
+    {
+      "spdxElementId": "SPDXRef-port",
+      "relationshipType": "CONTAINS",
+      "relatedSpdxElement": "SPDXRef-file-2"
+    },
+    {
+      "spdxElementId": "SPDXRef-port",
+      "relationshipType": "CONTAINS",
+      "relatedSpdxElement": "SPDXRef-file-3"
+    },
+    {
+      "spdxElementId": "SPDXRef-port",
+      "relationshipType": "CONTAINS",
+      "relatedSpdxElement": "SPDXRef-file-4"
+    },
+    {
+      "spdxElementId": "SPDXRef-port",
+      "relationshipType": "CONTAINS",
+      "relatedSpdxElement": "SPDXRef-file-5"
+    },
+    {
+      "spdxElementId": "SPDXRef-binary",
+      "relationshipType": "GENERATED_FROM",
+      "relatedSpdxElement": "SPDXRef-port"
+    },
+    {
+      "spdxElementId": "SPDXRef-file-0",
+      "relationshipType": "CONTAINED_BY",
+      "relatedSpdxElement": "SPDXRef-port"
+    },
+    {
+      "spdxElementId": "SPDXRef-file-1",
+      "relationshipType": "CONTAINED_BY",
+      "relatedSpdxElement": "SPDXRef-port"
+    },
+    {
+      "spdxElementId": "SPDXRef-file-2",
+      "relationshipType": "CONTAINED_BY",
+      "relatedSpdxElement": "SPDXRef-port"
+    },
+    {
+      "spdxElementId": "SPDXRef-file-3",
+      "relationshipType": "CONTAINED_BY",
+      "relatedSpdxElement": "SPDXRef-port"
+    },
+    {
+      "spdxElementId": "SPDXRef-file-4",
+      "relationshipType": "CONTAINED_BY",
+      "relatedSpdxElement": "SPDXRef-port"
+    },
+    {
+      "spdxElementId": "SPDXRef-file-5",
+      "relationshipType": "CONTAINED_BY",
+      "relatedSpdxElement": "SPDXRef-port"
+    }
+  ],
+  "packages": [
+    {
+      "name": "fann",
+      "SPDXID": "SPDXRef-port",
+      "versionInfo": "2023-01-26",
+      "downloadLocation": "git+https://github.com/Microsoft/vcpkg#ports/fann",
+      "homepage": "https://github.com/libfann/fann",
+      "licenseConcluded": "LGPL-2.1-only",
+      "licenseDeclared": "NOASSERTION",
+      "copyrightText": "NOASSERTION",
+      "description": "Fast Artificial Neural Network (FANN) Library is a free open source neural network library, which implements multilayer artificial neural networks in C with support for both fully connected and sparsely connected networks.",
+      "comment": "This is the port (recipe) consumed by vcpkg."
+    },
+    {
+      "name": "fann:x64-windows",
+      "SPDXID": "SPDXRef-binary",
+      "versionInfo": "ec3e8660f6be2382038aa9415c905fcbf1fe448b16400cd48d2ff4d126526ba5",
+      "downloadLocation": "NONE",
+      "licenseConcluded": "LGPL-2.1-only",
+      "licenseDeclared": "NOASSERTION",
+      "copyrightText": "NOASSERTION",
+      "comment": "This is a binary package built by vcpkg."
+    },
+    {
+      "SPDXID": "SPDXRef-resource-1",
+      "name": "libfann/fann",
+      "downloadLocation": "git+https://github.com/libfann/fann@8409b42d308bf9428b9d3e60927595e53a797bbc",
+      "licenseConcluded": "NOASSERTION",
+      "licenseDeclared": "NOASSERTION",
+      "copyrightText": "NOASSERTION",
+      "checksums": [
+        {
+          "algorithm": "SHA512",
+          "checksumValue": "4ad66808d7c88911d4c6d63368240ece2d0cbc73d89a95d32261b95dc551c47c46b3a34cc81b8cb0e03fe3f9ea61cb304e028780357bcf332d660824b066fd1e"
+        }
+      ]
+    }
+  ],
+  "files": [
+    {
+      "fileName": "./F:/vandomej/Downloads/vcpkg/ports/fann/fix-build_type.patch",
+      "SPDXID": "SPDXRef-file-0",
+      "checksums": [
+        {
+          "algorithm": "SHA256",
+          "checksumValue": "24b138e38b90893e2e69830aa3fb9abce52c9edfbd8a8e5f3d1022e44ad28888"
+        }
+      ],
+      "licenseConcluded": "NOASSERTION",
+      "copyrightText": "NOASSERTION"
+    },
+    {
+      "fileName": "./F:/vandomej/Downloads/vcpkg/ports/fann/fix-installation.patch",
+      "SPDXID": "SPDXRef-file-1",
+      "checksums": [
+        {
+          "algorithm": "SHA256",
+          "checksumValue": "cf8fb095fe040aaba230bc20ca45918808104a1d264a7e619993c5bd16795722"
+        }
+      ],
+      "licenseConcluded": "NOASSERTION",
+      "copyrightText": "NOASSERTION"
+    },
+    {
+      "fileName": "./F:/vandomej/Downloads/vcpkg/ports/fann/fix-uwp-build.patch",
+      "SPDXID": "SPDXRef-file-2",
+      "checksums": [
+        {
+          "algorithm": "SHA256",
+          "checksumValue": "6fd094c7eab67fd8cc8b2832a0f78231b0df8f55c1af705b48f2148f1241497b"
+        }
+      ],
+      "licenseConcluded": "NOASSERTION",
+      "copyrightText": "NOASSERTION"
+    },
+    {
+      "fileName": "./F:/vandomej/Downloads/vcpkg/ports/fann/portfile.cmake",
+      "SPDXID": "SPDXRef-file-3",
+      "checksums": [
+        {
+          "algorithm": "SHA256",
+          "checksumValue": "fcf360a46b2d257f7d7de63f6a04f04fe9511cd55611cb22e6a96d11628605d6"
+        }
+      ],
+      "licenseConcluded": "NOASSERTION",
+      "copyrightText": "NOASSERTION"
+    },
+    {
+      "fileName": "./F:/vandomej/Downloads/vcpkg/ports/fann/remove-nouse-target.patch",
+      "SPDXID": "SPDXRef-file-4",
+      "checksums": [
+        {
+          "algorithm": "SHA256",
+          "checksumValue": "1e337a505a1c74f5d35d28296d8e1fa04d4acfe49e9b73e27edfc237d553496f"
+        }
+      ],
+      "licenseConcluded": "NOASSERTION",
+      "copyrightText": "NOASSERTION"
+    },
+    {
+      "fileName": "./F:/vandomej/Downloads/vcpkg/ports/fann/vcpkg.json",
+      "SPDXID": "SPDXRef-file-5",
+      "checksums": [
+        {
+          "algorithm": "SHA256",
+          "checksumValue": "62e752bacc2f5fd3b0bf3f9d58dc4d0d1606ce5456a0e1244bf3c09dbe490fbe"
+        }
+      ],
+      "licenseConcluded": "NOASSERTION",
+      "copyrightText": "NOASSERTION"
+    }
+  ]
+}
diff --git a/ai_fighter/AI_Fight_Sim/Plugins/NeuralNetworkAIController/Source/NeuralNetworkAIController/ThirdParty/FANN/share/fann/vcpkg_abi_info.txt b/ai_fighter/AI_Fight_Sim/Plugins/NeuralNetworkAIController/Source/NeuralNetworkAIController/ThirdParty/FANN/share/fann/vcpkg_abi_info.txt
new file mode 100644
index 0000000..d969670
--- /dev/null
+++ b/ai_fighter/AI_Fight_Sim/Plugins/NeuralNetworkAIController/Source/NeuralNetworkAIController/ThirdParty/FANN/share/fann/vcpkg_abi_info.txt
@@ -0,0 +1,20 @@
+cmake 3.27.1
+features core
+fix-build_type.patch 24b138e38b90893e2e69830aa3fb9abce52c9edfbd8a8e5f3d1022e44ad28888
+fix-installation.patch cf8fb095fe040aaba230bc20ca45918808104a1d264a7e619993c5bd16795722
+fix-uwp-build.patch 6fd094c7eab67fd8cc8b2832a0f78231b0df8f55c1af705b48f2148f1241497b
+portfile.cmake fcf360a46b2d257f7d7de63f6a04f04fe9511cd55611cb22e6a96d11628605d6
+ports.cmake 0500e9e2422fe0084c99bdd0c9de4c7069b76da14c8b58228a7e95ebac43058a
+post_build_checks 2
+powershell 7.2.16
+remove-nouse-target.patch 1e337a505a1c74f5d35d28296d8e1fa04d4acfe49e9b73e27edfc237d553496f
+triplet x64-windows
+triplet_abi 4556164a2cd3dd6f4742101eabb46def7e71b6e5856faa88e5d005aac12a803c-e36df1c7f50ab25f9c182fa927d06c19ae082e0d599f132b3f655784b49e4b33-37ddf335dc10d14fa90c13a22cc1ec1cdbd1efcd
+vcpkg-cmake 60e25dfd2d1ab5afe209de30eee1a8a9428fc7faa4dfb087874027758bfd9348
+vcpkg-cmake-config 22296a1b86e887eb28cbf7acb600c3e0cdf98358cb8b61f48bcb79d78e7534df
+vcpkg.json 62e752bacc2f5fd3b0bf3f9d58dc4d0d1606ce5456a0e1244bf3c09dbe490fbe
+vcpkg_copy_pdbs d57e4f196c82dc562a9968c6155073094513c31e2de475694143d3aa47954b1c
+vcpkg_fixup_pkgconfig 904e67c46ecbb67379911bc1d7222855c0cbfcf1129bf47783858bcf0cc44970
+vcpkg_from_git 96ed81968f76354c00096dd8cd4e63c6a235fa969334a11ab18d11c0c512ff58
+vcpkg_from_github b743742296a114ea1b18ae99672e02f142c4eb2bef7f57d36c038bedbfb0502f
+vcpkg_install_copyright ba6c169ab4e59fa05682e530cdeb883767de22c8391f023d4e6844a7ec5dd3d2
diff --git a/ai_fighter/AI_Fight_Sim/Source/AI_Fight_Sim.Target.cs b/ai_fighter/AI_Fight_Sim/Source/AI_Fight_Sim.Target.cs
new file mode 100644
index 0000000..d0f5e77
--- /dev/null
+++ b/ai_fighter/AI_Fight_Sim/Source/AI_Fight_Sim.Target.cs
@@ -0,0 +1,15 @@
+// Fill out your copyright notice in the Description page of Project Settings.
+
+using UnrealBuildTool;
+using System.Collections.Generic;
+
+public class AI_Fight_SimTarget : TargetRules
+{
+	public AI_Fight_SimTarget(TargetInfo Target) : base(Target)
+	{
+		Type = TargetType.Game;
+		DefaultBuildSettings = BuildSettingsVersion.V2;
+
+		ExtraModuleNames.AddRange( new string[] { "AI_Fight_Sim" } );
+	}
+}
diff --git a/ai_fighter/AI_Fight_Sim/Source/AI_Fight_Sim/AI_Fight_Sim.Build.cs b/ai_fighter/AI_Fight_Sim/Source/AI_Fight_Sim/AI_Fight_Sim.Build.cs
new file mode 100644
index 0000000..291c928
--- /dev/null
+++ b/ai_fighter/AI_Fight_Sim/Source/AI_Fight_Sim/AI_Fight_Sim.Build.cs
@@ -0,0 +1,23 @@
+// Fill out your copyright notice in the Description page of Project Settings.
+
+using UnrealBuildTool;
+
+public class AI_Fight_Sim : ModuleRules
+{
+	public AI_Fight_Sim(ReadOnlyTargetRules Target) : base(Target)
+	{
+		PCHUsage = PCHUsageMode.UseExplicitOrSharedPCHs;
+	
+		PublicDependencyModuleNames.AddRange(new string[] { "Core", "CoreUObject", "Engine", "InputCore" });
+
+		PrivateDependencyModuleNames.AddRange(new string[] {  });
+
+		// Uncomment if you are using Slate UI
+		// PrivateDependencyModuleNames.AddRange(new string[] { "Slate", "SlateCore" });
+		
+		// Uncomment if you are using online features
+		// PrivateDependencyModuleNames.Add("OnlineSubsystem");
+
+		// To include OnlineSubsystemSteam, add it to the plugins section in your uproject file with the Enabled attribute set to true
+	}
+}
diff --git a/ai_fighter/AI_Fight_Sim/Source/AI_Fight_Sim/AI_Fight_Sim.cpp b/ai_fighter/AI_Fight_Sim/Source/AI_Fight_Sim/AI_Fight_Sim.cpp
new file mode 100644
index 0000000..91413da
--- /dev/null
+++ b/ai_fighter/AI_Fight_Sim/Source/AI_Fight_Sim/AI_Fight_Sim.cpp
@@ -0,0 +1,6 @@
+// Fill out your copyright notice in the Description page of Project Settings.
+
+#include "AI_Fight_Sim.h"
+#include "Modules/ModuleManager.h"
+
+IMPLEMENT_PRIMARY_GAME_MODULE( FDefaultGameModuleImpl, AI_Fight_Sim, "AI_Fight_Sim" );
diff --git a/ai_fighter/AI_Fight_Sim/Source/AI_Fight_Sim/AI_Fight_Sim.h b/ai_fighter/AI_Fight_Sim/Source/AI_Fight_Sim/AI_Fight_Sim.h
new file mode 100644
index 0000000..90aad9e
--- /dev/null
+++ b/ai_fighter/AI_Fight_Sim/Source/AI_Fight_Sim/AI_Fight_Sim.h
@@ -0,0 +1,6 @@
+// Fill out your copyright notice in the Description page of Project Settings.
+
+#pragma once
+
+#include "CoreMinimal.h"
+
diff --git a/ai_fighter/AI_Fight_Sim/Source/AI_Fight_Sim/Private/NeuralNetworkCommandLineParsing.cpp b/ai_fighter/AI_Fight_Sim/Source/AI_Fight_Sim/Private/NeuralNetworkCommandLineParsing.cpp
new file mode 100644
index 0000000..5439f2b
--- /dev/null
+++ b/ai_fighter/AI_Fight_Sim/Source/AI_Fight_Sim/Private/NeuralNetworkCommandLineParsing.cpp
@@ -0,0 +1,36 @@
+#include "NeuralNetworkCommandLineParsing.h"
+
+void UNeuralNetworkCommandLineParsing::Init()
+{
+    Super::Init(); // Call the base class's Init function
+
+    // Initialize ConfigFilePath to an empty string to ensure it has a default value
+    NeuralNetwork1ConfigPath = TEXT("");
+    NeuralNetwork2ConfigPath = TEXT("");
+
+    // Temporary variable to hold the command line argument value
+    FString CommandLineConfigPath;
+    // Check for a "-NNConfig=" argument in the command line
+    if (FParse::Value(FCommandLine::Get(), TEXT("-NN1Config="), CommandLineConfigPath))
+    {
+        // If found, assign the command line argument to the class variable
+        NeuralNetwork1ConfigPath = CommandLineConfigPath;
+    }
+
+    // Check for a "-NNConfig=" argument in the command line
+    if (FParse::Value(FCommandLine::Get(), TEXT("-NN2Config="), CommandLineConfigPath))
+    {
+        // If found, assign the command line argument to the class variable
+        NeuralNetwork2ConfigPath = CommandLineConfigPath;
+    }
+
+    // Optional: Log the config file path for debugging purposes
+    UE_LOG(LogTemp, Warning, TEXT("Neural Network 1 Config File Path: %s"), *NeuralNetwork1ConfigPath);
+    UE_LOG(LogTemp, Warning, TEXT("Neural Network 2 Config File Path: %s"), *NeuralNetwork2ConfigPath);
+}
+
+// Implementation of WriteStringToFile
+bool UNeuralNetworkCommandLineParsing::WriteStringToFile(const FString& FilePath, const FString& Data)
+{
+    return FFileHelper::SaveStringToFile(Data, *FilePath);
+}
diff --git a/ai_fighter/AI_Fight_Sim/Source/AI_Fight_Sim/Public/NeuralNetworkCommandLineParsing.h b/ai_fighter/AI_Fight_Sim/Source/AI_Fight_Sim/Public/NeuralNetworkCommandLineParsing.h
new file mode 100644
index 0000000..c7fa58c
--- /dev/null
+++ b/ai_fighter/AI_Fight_Sim/Source/AI_Fight_Sim/Public/NeuralNetworkCommandLineParsing.h
@@ -0,0 +1,27 @@
+#include "CoreMinimal.h"
+#include "Engine/GameInstance.h"
+#include "Misc/FileHelper.h"
+#include "HAL/PlatformFilemanager.h"
+#include "NeuralNetworkCommandLineParsing.generated.h"
+
+UCLASS()
+class AI_FIGHT_SIM_API UNeuralNetworkCommandLineParsing : public UGameInstance
+{
+    GENERATED_BODY()
+
+public:
+    // Override the Init function
+    virtual void Init() override;
+
+    UPROPERTY(VisibleAnywhere, BlueprintReadOnly, Category = "Neural Network")
+    FString NeuralNetwork1ConfigPath;
+
+    UPROPERTY(VisibleAnywhere, BlueprintReadOnly, Category = "Neural Network")
+    FString NeuralNetwork2ConfigPath;
+
+    // Function to write a string to a file
+    UFUNCTION(BlueprintCallable, Category = "File Operations")
+    bool WriteStringToFile(const FString& FilePath, const FString& Data);
+};
+
+
diff --git a/ai_fighter/AI_Fight_Sim/Source/AI_Fight_SimEditor.Target.cs b/ai_fighter/AI_Fight_Sim/Source/AI_Fight_SimEditor.Target.cs
new file mode 100644
index 0000000..c2c0e4a
--- /dev/null
+++ b/ai_fighter/AI_Fight_Sim/Source/AI_Fight_SimEditor.Target.cs
@@ -0,0 +1,15 @@
+// Fill out your copyright notice in the Description page of Project Settings.
+
+using UnrealBuildTool;
+using System.Collections.Generic;
+
+public class AI_Fight_SimEditorTarget : TargetRules
+{
+	public AI_Fight_SimEditorTarget(TargetInfo Target) : base(Target)
+	{
+		Type = TargetType.Editor;
+		DefaultBuildSettings = BuildSettingsVersion.V2;
+
+		ExtraModuleNames.AddRange( new string[] { "AI_Fight_Sim" } );
+	}
+}