//Methods
public void Run()
{
MackeyGlassGeneratorSettings modSettings = new MackeyGlassGeneratorSettings(18, 0.1, 0.2);
IGenerator generator = new MackeyGlassGenerator(modSettings);
int steps = 100;
for (int i = 0; i < steps; i++)
{
Console.WriteLine(generator.Next());
}
Console.ReadLine();
generator.Reset();
for (int i = 0; i < steps; i++)
{
Console.WriteLine(generator.Next());
}
Console.ReadLine();
///*
SimpleIFSettings settings = new SimpleIFSettings(1,
new RandomValueSettings(15, 15),
new RandomValueSettings(0.05, 0.05),
new RandomValueSettings(5, 5),
new RandomValueSettings(20, 20),
0
);
IActivationFunction af = ActivationFactory.Create(settings, new Random(0));
//*/
TestActivation(af, 800, 0.15, 10, 600);
return;
}
/// <summary>
/// Creates an initialized instance.
/// </summary>
/// <param name="elem">A xml element containing the configuration data.</param>
public AnalogNeuronGroupSettings(XElement elem)
{
//Validation
XElement settingsElem = Validate(elem, XsdTypeName);
//Parsing
//Name
Name = settingsElem.Attribute("name").Value;
//Relative share
RelShare = double.Parse(settingsElem.Attribute("relShare").Value, CultureInfo.InvariantCulture);
//Activation settings
ActivationCfg = ActivationFactory.LoadSettings(settingsElem.Elements().First());
//Firing threshold
FiringThreshold = double.Parse(settingsElem.Attribute("firingThreshold").Value, CultureInfo.InvariantCulture);
ThresholdMaxRefDeepness = int.Parse(settingsElem.Attribute("thresholdMaxRefDeepness").Value, CultureInfo.InvariantCulture);
//Bias
XElement biasSettingsElem = settingsElem.Elements("bias").FirstOrDefault();
BiasCfg = biasSettingsElem == null ? null : new RandomValueSettings(biasSettingsElem);
//Retainment
XElement retainmentSettingsElem = settingsElem.Elements("retainment").FirstOrDefault();
RetainmentCfg = retainmentSettingsElem == null ? null : new RetainmentSettings(retainmentSettingsElem);
//Predictors
PredictorsCfg = new PredictorsProviderSettings(settingsElem.Elements("predictors").First());
Check();
return;
}
//Constructors
/// <summary>
/// Creates an initialized instance
/// </summary>
/// <param name="numOfNeurons">Number of hidden layer neurons</param>
/// <param name="activationCfg">Layer activation configuration</param>
public HiddenLayerSettings(int numOfNeurons, RCNetBaseSettings activationCfg)
{
NumOfNeurons = numOfNeurons;
ActivationCfg = ActivationFactory.DeepCloneActivationSettings(activationCfg);
Check();
return;
}
/// <summary>
/// The deep copy constructor
/// </summary>
/// <param name="source">Source instance</param>
public FeedForwardNetworkSettings(FeedForwardNetworkSettings source)
{
OutputLayerActivation = null;
OutputRange = null;
if (source.OutputLayerActivation != null)
{
OutputLayerActivation = ActivationFactory.DeepCloneActivationSettings(source.OutputLayerActivation);
OutputRange = source.OutputRange.DeepClone();
}
RegressionMethod = source.RegressionMethod;
HiddenLayerCollection = new List <HiddenLayerSettings>(source.HiddenLayerCollection.Count);
foreach (HiddenLayerSettings shls in source.HiddenLayerCollection)
{
HiddenLayerCollection.Add(shls.DeepClone());
}
LinRegrTrainerCfg = null;
RPropTrainerCfg = null;
if (source.LinRegrTrainerCfg != null)
{
LinRegrTrainerCfg = source.LinRegrTrainerCfg.DeepClone();
}
if (source.RPropTrainerCfg != null)
{
RPropTrainerCfg = source.RPropTrainerCfg.DeepClone();
}
return;
}
/// <summary>
/// Creates an initialized instance.
/// </summary>
/// <param name="elem">Xml element containing the initialization settings.</param>
public SpikingNeuronGroupSettings(XElement elem)
{
//Validation
XElement settingsElem = Validate(elem, XsdTypeName);
//Parsing
//Name
Name = settingsElem.Attribute("name").Value;
//Relative share
RelShare = double.Parse(settingsElem.Attribute("relShare").Value, CultureInfo.InvariantCulture);
//Activation settings
ActivationCfg = ActivationFactory.LoadSettings(settingsElem.Elements().First());
//Homogenous excitability
XElement homogenousExcitabilityElem = settingsElem.Elements("homogenousExcitability").FirstOrDefault();
HomogenousExcitabilityCfg = homogenousExcitabilityElem == null ? new HomogenousExcitabilitySettings() : new HomogenousExcitabilitySettings(homogenousExcitabilityElem);
//Bias
XElement biasSettingsElem = settingsElem.Elements("bias").FirstOrDefault();
BiasCfg = biasSettingsElem == null ? null : new RandomValueSettings(biasSettingsElem);
//Predictors
XElement predictorsSettingsElem = settingsElem.Elements("predictors").FirstOrDefault();
if (predictorsSettingsElem != null)
{
PredictorsCfg = new PredictorsSettings(predictorsSettingsElem);
}
Check();
return;
}
/// <summary>
/// Creates the instance and initialize it from given xml element.
/// This is the preferred way to instantiate reservoir settings.
/// </summary>
/// <param name="elem">
/// Xml data containing feed forward network settings.
/// Content of xml element is always validated against the xml schema.
/// </param>
public FeedForwardNetworkSettings(XElement elem)
{
//Validation
ElemValidator validator = new ElemValidator();
Assembly assemblyRCNet = Assembly.GetExecutingAssembly();
validator.AddXsdFromResources(assemblyRCNet, "RCNet.Neural.Network.FF.FeedForwardNetworkSettings.xsd");
validator.AddXsdFromResources(assemblyRCNet, "RCNet.RCNetTypes.xsd");
XElement feedForwardNetworkSettingsElem = validator.Validate(elem, "rootElem");
//Parsing
OutputLayerActivation = ActivationFactory.LoadSettings(feedForwardNetworkSettingsElem.Descendants().First());
if (!IsAllowedActivation(OutputLayerActivation, out Interval outputRange))
{
throw new ApplicationException($"Activation can't be used in FF network. Activation function has to be stateless and has to support derivative calculation.");
}
OutputRange = outputRange;
RegressionMethod = ParseTrainingMethodType(feedForwardNetworkSettingsElem.Attribute("regressionMethod").Value);
//Hidden layers
HiddenLayerCollection = new List <HiddenLayerSettings>();
XElement hiddenLayersElem = feedForwardNetworkSettingsElem.Descendants("hiddenLayers").FirstOrDefault();
if (hiddenLayersElem != null)
{
foreach (XElement layerElem in hiddenLayersElem.Descendants("layer"))
{
HiddenLayerCollection.Add(new HiddenLayerSettings(layerElem));
}
}
//Trainers
LinRegrTrainerCfg = null;
RPropTrainerCfg = null;
switch (RegressionMethod)
{
case TrainingMethodType.Linear:
XElement linRegrTrainerElem = feedForwardNetworkSettingsElem.Descendants("linRegrTrainer").FirstOrDefault();
if (linRegrTrainerElem != null)
{
LinRegrTrainerCfg = new LinRegrTrainerSettings(linRegrTrainerElem);
}
else
{
LinRegrTrainerCfg = new LinRegrTrainerSettings();
}
break;
case TrainingMethodType.Resilient:
XElement resPropTrainerElem = feedForwardNetworkSettingsElem.Descendants("resPropTrainer").FirstOrDefault();
if (resPropTrainerElem != null)
{
RPropTrainerCfg = new RPropTrainerSettings(resPropTrainerElem);
}
else
{
RPropTrainerCfg = new RPropTrainerSettings();
}
break;
}
return;
}
/// <summary>
/// Creates the instance and initialize it from given xml element.
/// </summary>
/// <param name="elem">
/// Xml data containing settings.
/// Content of xml element is always validated against the xml schema.
/// </param>
public NeuronGroupSettings(XElement elem)
{
//Validation
ElemValidator validator = new ElemValidator();
Assembly assemblyRCNet = Assembly.GetExecutingAssembly();
validator.AddXsdFromResources(assemblyRCNet, "RCNet.Neural.Network.SM.Preprocessing.PoolNeuronGroupSettings.xsd");
validator.AddXsdFromResources(assemblyRCNet, "RCNet.RCNetTypes.xsd");
XElement settingsElem = validator.Validate(elem, "rootElem");
//Parsing
//Name
Name = settingsElem.Attribute("name").Value;
//Role
Role = CommonEnums.ParseNeuronRole(settingsElem.Attribute("role").Value);
//Relative share
RelativeShare = double.Parse(settingsElem.Attribute("relShare").Value, CultureInfo.InvariantCulture);
//Augmented states
AugmentedStates = bool.Parse(settingsElem.Attribute("augmentedStates").Value);
//Activation settings
ActivationCfg = ActivationFactory.LoadSettings(settingsElem.Descendants().First());
//Bias
BiasCfg = new RandomValueSettings(settingsElem.Descendants("bias").First());
return;
}
/// <summary>
/// Creates the instance and initialize it from given xml element.
/// This is the preferred way to instantiate settings.
/// </summary>
/// <param name="elem">
/// Xml data containing settings.
/// Content of xml element is always validated against the xml schema.
/// </param>
public FeedForwardNetworkSettings(XElement elem)
{
//Validation
ElemValidator validator = new ElemValidator();
Assembly assemblyRCNet = Assembly.GetExecutingAssembly();
validator.AddXsdFromResources(assemblyRCNet, "RCNet.Neural.Network.FF.FeedForwardNetworkSettings.xsd");
validator.AddXsdFromResources(assemblyRCNet, "RCNet.RCNetTypes.xsd");
XElement settingsElem = validator.Validate(elem, "rootElem");
//Parsing
OutputLayerActivation = ActivationFactory.LoadSettings(settingsElem.Descendants().First());
if (!IsAllowedActivation(OutputLayerActivation, out Interval outputRange))
{
throw new ApplicationException($"Activation can't be used in FF network. Activation function has to be stateless and has to support derivative calculation.");
}
OutputRange = outputRange;
//Hidden layers
HiddenLayerCollection = new List <HiddenLayerSettings>();
XElement hiddenLayersElem = settingsElem.Descendants("hiddenLayers").FirstOrDefault();
if (hiddenLayersElem != null)
{
foreach (XElement layerElem in hiddenLayersElem.Descendants("layer"))
{
HiddenLayerCollection.Add(new HiddenLayerSettings(layerElem));
}
}
//Trainer configuration
TrainerCfg = null;
foreach (XElement candidate in settingsElem.Descendants())
{
if (candidate.Name.LocalName == "linRegrTrainer")
{
TrainerCfg = new LinRegrTrainerSettings(candidate);
}
else if (candidate.Name.LocalName == "qrdRegrTrainer")
{
TrainerCfg = new QRDRegrTrainerSettings(candidate);
}
else if (candidate.Name.LocalName == "ridgeRegrTrainer")
{
TrainerCfg = new RidgeRegrTrainerSettings(candidate);
}
else if (candidate.Name.LocalName == "resPropTrainer")
{
TrainerCfg = new RPropTrainerSettings(candidate);
}
if (TrainerCfg != null)
{
break;
}
}
if (TrainerCfg == null)
{
throw new Exception("Trainer settings not found.");
}
return;
}
/// <summary>
/// The deep copy constructor
/// </summary>
/// <param name="source">Source instance</param>
public FeedForwardNetworkSettings(FeedForwardNetworkSettings source)
{
OutputActivationCfg = ActivationFactory.DeepCloneActivationSettings(source.OutputActivationCfg);
OutputRange = source.OutputRange.DeepClone();
HiddenLayersCfg = (HiddenLayersSettings)source.HiddenLayersCfg.DeepClone();
TrainerCfg = source.TrainerCfg.DeepClone();
return;
}
public static DInput isHurt(ResourceFightDNCreature p_cre)
{
DActivationFunction activate = ActivationFactory.generateSigmoid(1, 1, false, true, true);
return(() =>
{
return activate(p_cre.m_health.Value / p_cre.m_health.Max);
});
}
//Static methods
/// <summary>
/// Fuction tests if specified activation can be used in FF network
/// </summary>
/// <param name="activationSettings">Activation settings</param>
/// <param name="outputRange">Returned range of the activation function</param>
public static bool IsAllowedActivation(RCNetBaseSettings activationSettings, out Interval outputRange)
{
outputRange = ActivationFactory.GetInfo(activationSettings, out bool stateless, out bool supportsDerivative);
if (!stateless || !supportsDerivative)
{
return(false);
}
return(true);
}
//Resource
public static DInput isHungry(ResourceFightDNCreature p_cre)
{
DActivationFunction activate = ActivationFactory.generateSigmoid(1, 1, false, true, true);
return(() =>
{
//Debug.Log(p_cre.m_energy.Value + " : " + activate(p_cre.m_energy.Value/p_cre.m_energy.Max));
return activate(p_cre.m_energy.Value / p_cre.m_energy.Max);
});
}
/// <summary>
/// Copy constructor
/// </summary>
/// <param name="source">Source instance</param>
public HiddenLayerSettings(HiddenLayerSettings source)
{
NumOfNeurons = source.NumOfNeurons;
Activation = null;
if (source.Activation != null)
{
Activation = ActivationFactory.DeepCloneActivationSettings(source.Activation);
}
return;
}
/// <summary>
/// Creates the instance and initializes it from given xml element.
/// </summary>
/// <param name="elem">
/// Xml data containing the settings.
/// </param>
public HiddenLayerSettings(XElement elem)
{
NumOfNeurons = int.Parse(elem.Attribute("neurons").Value);
Activation = ActivationFactory.LoadSettings(elem.Descendants().First());
if (!IsAllowedActivation(Activation, out Interval outputRange))
{
throw new ApplicationException($"Activation can't be used in FF network. Activation has to be time independent and has to support derivative.");
}
return;
}
//Methods
//Static methods
/// <summary>
/// Fuction checks if specified activation can be used in FF network
/// </summary>
/// <param name="activationSettings">Activation settings</param>
/// <returns></returns>
public static bool IsAllowedActivation(ActivationSettings activationSettings)
{
IActivationFunction af = ActivationFactory.Create(activationSettings);
if (!af.Stateless || !af.SupportsComputeDerivativeMethod)
{
return(false);
}
return(true);
}
//Methods
//Static methods
/// <summary>
/// Fuction checks if specified activation can be used in FF network
/// </summary>
/// <param name="activationSettings">Activation settings</param>
/// <param name="outputRange">Returned range of the activation function</param>
public static bool IsAllowedActivation(Object activationSettings, out Interval outputRange)
{
IActivationFunction af = ActivationFactory.Create(activationSettings, new Random());
outputRange = af.OutputRange.DeepClone();
if (!af.Stateless || !af.SupportsDerivative)
{
return(false);
}
return(true);
}
/// <summary>
/// The deep copy constructor
/// </summary>
/// <param name="source">Source instance</param>
public NeuronGroupSettings(NeuronGroupSettings source)
{
Name = source.Name;
Role = source.Role;
RelativeShare = source.RelativeShare;
AugmentedStates = source.AugmentedStates;
Count = source.Count;
ActivationCfg = ActivationFactory.DeepCloneActivationSettings(source.ActivationCfg);
BiasCfg = source.BiasCfg.DeepClone();
return;
}
public static DInput bulletsUpAhead(ResourceFightDNCreature p_cre)
{
DActivationFunction activate = ActivationFactory.generateSigmoid(5, 1, false, true, false);
return(() =>
{
GameObject[] bullets = p_cre.sense("BULLET");
// Debug.Log(bullets.Length + " : " + activate(bullets.Length));
return activate(bullets.Length);
});
}
/// <summary>
/// Creates an initialized instance from given xml element.
/// </summary>
/// <param name="elem">Xml element containing the settings.</param>
public HiddenLayerSettings(XElement elem)
{
//Validation
XElement settingsElem = Validate(elem, XsdTypeName);
//Parsing
NumOfNeurons = int.Parse(settingsElem.Attribute("neurons").Value);
ActivationCfg = ActivationFactory.LoadSettings(settingsElem.Elements().First());
Check();
return;
}
public ActivationApplicationServiceTests()
{
activationFactory = new ActivationFactory();
identityResolver = new FakeIdentityResolver();
domainNotificationHandler = new DomainNotificationHandler();
fakeUnitOfWork = new FakeUnitOfWork();
serviceBus = new FakeServiceBus();
domainBus = new FakeDomainServiceBus();
activationRepository = new FakeActivationRepository();
service = new ActivationApplicationService(activationRepository, identityResolver, domainNotificationHandler, fakeUnitOfWork, serviceBus, domainBus);
}
public void ShouldReturnAnActivation()
{
IActivationFactory factory = new ActivationFactory();
Guid companyId = Guid.NewGuid();
Guid tenantId = Guid.NewGuid();
Activation activation = factory.CreateActivation(companyId, tenantId);
Assert.IsNotNull(activation);
Assert.AreEqual(companyId, activation.Id);
}
//Constructors
/// <summary>
/// Creates an initialized instance
/// </summary>
/// <param name="outputActivationCfg">Output layer activation configuration</param>
/// <param name="hiddenLayersCfg">Hidden layers configuration. Hidden layers are optional.</param>
/// <param name="trainerCfg">Configuration of associated trainer</param>
public FeedForwardNetworkSettings(RCNetBaseSettings outputActivationCfg,
HiddenLayersSettings hiddenLayersCfg,
RCNetBaseSettings trainerCfg
)
{
OutputActivationCfg = ActivationFactory.DeepCloneActivationSettings(outputActivationCfg);
OutputRange = ActivationFactory.GetInfo(OutputActivationCfg, out _, out _);
HiddenLayersCfg = hiddenLayersCfg == null ? new HiddenLayersSettings() : (HiddenLayersSettings)hiddenLayersCfg.DeepClone();
TrainerCfg = trainerCfg.DeepClone();
Check();
return;
}
/// <summary>
/// Instantiates an initialized feed forward network
/// </summary>
/// <param name="numOfInputValues">Number of network's input values</param>
/// <param name="numOfOutputValues">Number of network's output values</param>
/// <param name="settings">Configuration parameters</param>
public FeedForwardNetwork(int numOfInputValues, int numOfOutputValues, FeedForwardNetworkSettings settings)
: this(numOfInputValues, numOfOutputValues)
{
//Initialize FF network
for (int i = 0; i < settings.HiddenLayerCollection.Count; i++)
{
AddLayer(settings.HiddenLayerCollection[i].NumOfNeurons,
ActivationFactory.Create(settings.HiddenLayerCollection[i].Activation)
);
}
FinalizeStructure(ActivationFactory.Create(settings.OutputLayerActivation));
return;
}
/// <summary>
/// Tests whether the activation function can be used as the FF network's hidden layer activation.
/// </summary>
/// <param name="activationCfg">The configuration of the activation function.</param>
public static bool IsAllowedHiddenAF(IActivationSettings activationCfg)
{
if (activationCfg.TypeOfActivation != ActivationType.Analog)
{
return(false);
}
AFAnalogBase analogAF = (AFAnalogBase)ActivationFactory.CreateAF(activationCfg, new Random(0));
if (!analogAF.SupportsDerivative || analogAF.DependsOnSorround)
{
return(false);
}
return(true);
}
public void brainAction()
{
Matrix <float> current_matrix = getInputValueMatrix() * m_weights[0];
DActivationFunction activator = ActivationFactory.generateSigmoid(2, 2, true, false, false);
MatrixCalc.activate(activator, current_matrix);
for (int i = 1; i < m_weights.Length; i++)
{
current_matrix = current_matrix * m_weights[i];
MatrixCalc.activate(activator, current_matrix);
}
performOutputs(current_matrix);
}
/// <summary>
/// Creates an initialized instance.
/// </summary>
/// <param name="numOfInputValues">The number of the network's input values.</param>
/// <param name="numOfOutputValues">The number of the network's output values.</param>
/// <param name="cfg">The configuration of the network and associated trainer.</param>
public FeedForwardNetwork(int numOfInputValues, int numOfOutputValues, FeedForwardNetworkSettings cfg)
: this(numOfInputValues, numOfOutputValues)
{
Random rand = new Random(1);
//Initialize FF network
for (int i = 0; i < cfg.HiddenLayersCfg.HiddenLayerCfgCollection.Count; i++)
{
AddLayer(cfg.HiddenLayersCfg.HiddenLayerCfgCollection[i].NumOfNeurons,
(AFAnalogBase)ActivationFactory.CreateAF(cfg.HiddenLayersCfg.HiddenLayerCfgCollection[i].ActivationCfg, rand)
);
}
FinalizeStructure((AFAnalogBase)ActivationFactory.CreateAF(cfg.OutputActivationCfg, rand));
return;
}
/// <summary>
/// Creates an initialized instance.
/// </summary>
/// <param name="elem">Xml element containing the initialization settings</param>
public FeedForwardNetworkSettings(XElement elem)
{
//Validation
XElement settingsElem = Validate(elem, XsdTypeName);
//Parsing
OutputActivationCfg = ActivationFactory.LoadSettings(settingsElem.Elements().First());
OutputRange = ActivationFactory.GetInfo(OutputActivationCfg, out _, out _);
//Hidden layers
XElement hiddenLayersElem = settingsElem.Elements("hiddenLayers").FirstOrDefault();
if (hiddenLayersElem != null)
{
HiddenLayersCfg = new HiddenLayersSettings(hiddenLayersElem);
}
else
{
HiddenLayersCfg = new HiddenLayersSettings();
}
//Trainer configuration
TrainerCfg = null;
foreach (XElement candidate in settingsElem.Elements())
{
if (candidate.Name.LocalName == "qrdRegrTrainer")
{
TrainerCfg = new QRDRegrTrainerSettings(candidate);
break;
}
else if (candidate.Name.LocalName == "ridgeRegrTrainer")
{
TrainerCfg = new RidgeRegrTrainerSettings(candidate);
break;
}
else if (candidate.Name.LocalName == "elasticRegrTrainer")
{
TrainerCfg = new ElasticRegrTrainerSettings(candidate);
break;
}
else if (candidate.Name.LocalName == "resPropTrainer")
{
TrainerCfg = new RPropTrainerSettings(candidate);
break;
}
}
Check();
return;
}
//Constructors
/// <summary>
/// The deep copy constructor
/// </summary>
/// <param name="source">Source instance</param>
public NeuronGroupSettings(NeuronGroupSettings source)
{
Name = source.Name;
Role = source.Role;
RelativeShare = source.RelativeShare;
ReadoutNeuronsDensity = source.ReadoutNeuronsDensity;
Count = source.Count;
ActivationType = source.ActivationType;
ActivationCfg = ActivationFactory.DeepCloneActivationSettings(source.ActivationCfg);
SignalingRestriction = source.SignalingRestriction;
BiasCfg = source.BiasCfg?.DeepClone();
AnalogFiringThreshold = source.AnalogFiringThreshold;
RetainmentNeuronsDensity = source.RetainmentNeuronsDensity;
RetainmentStrengthCfg = source.RetainmentStrengthCfg?.DeepClone();
PredictorsCfg = source.PredictorsCfg?.DeepClone();
return;
}
public static DInput isStronger(ResourceFightDNCreature p_cre)
{
DActivationFunction activate = ActivationFactory.generateSigmoid(1f, 1, false, true, false);
return(() =>
{
GameObject closest = p_cre.senseClosest("CREATURE");
if (closest == null)
{
return 0;
}
ResourceFightDNCreature enemy = closest.GetComponent <ResourceFightDNCreature>();
float strength_check = (p_cre.m_health.Value + (p_cre.m_damage * 5)) / (enemy.m_health.Value + (enemy.m_damage * 5));
//Debug.Log( "[" + strength_check +"] " + activate( strength_check-0.5f ) );
return activate(strength_check - 0.5f); //Your mesure of toughness/ enemy mesure of toughness set up so double enemy gives 0 and half enemy 1
});
}
//Dodging
public static DInput bulletCollisionImminent(ResourceFightDNCreature p_cre)
{
DActivationFunction prox_activate = ActivationFactory.generateSigmoid(1.5f, 2, false, true, true);
DActivationFunction angle_activate = ActivationFactory.generateSigmoid(10f, 2, false, true, true);
DActivationFunction full_activate = ActivationFactory.generateSigmoid(1, 1, false, true, false);
return(() =>
{
GameObject[] bullets = p_cre.sense("BULLET");
List <GameObject> bullets_close_and_coming_towards = new List <GameObject>();
foreach (GameObject bul in bullets)
{
if (Vector2Calc.proximity(bul.transform.position, p_cre.transform.position) < 2 && Vector2Calc.checkAngle(p_cre.transform.position - bul.transform.position, bul.GetComponent <Rigidbody2D>().velocity, 10))
{
bullets_close_and_coming_towards.Add(bul);
}
}
float activation = 0;
foreach (GameObject bul in bullets_close_and_coming_towards)
{
float bul_prox = prox_activate(Vector2Calc.proximity(bul.transform.position, p_cre.transform.position) - 0.5f);
float bul_angle = angle_activate(Mathf.Abs(Vector2Calc.getAngle(p_cre.transform.position - bul.transform.position, bul.GetComponent <Rigidbody2D>().velocity)));
float bul_active = full_activate(bul_prox * bul_angle);
if (bul_active > activation)
{
activation = bul_active;
}
}
//if(activation >0 ) Debug.Log(activation);
return activation;
});
}
