public override void Init()
{
base.Init();
m_patternDefinitionSet = new List <PRPatternDefinition>();
m_patternsDefinitionsDict = new Dictionary <string, PRPatternDefinition>();
LoadDefinitionSet();
if (!LoadNeuronalNet())
{
m_neuronalNetwork = new PartyBPN(m_defaultNeuronNumberInput - 1, Mathf.RoundToInt((m_defaultNeuronNumberInput + 5) / 2), 5, m_defaultLearningRate, m_defaultTheta, m_defaultSigmoidElastic, m_defaultMomentum);
}
}
private bool LoadNeuronalNet()
{
#if UNITY_ANDROID
string json = m_neuronalNetworkTextAsset.text;
#else
string json = File.ReadAllText(Application.dataPath + "/" + m_pathToNeuronalNetworkFile);
#endif
//string json = File.ReadAllText(Application.dataPath+"/"+m_pathToFile);
m_neuronalNetwork = new PartyBPN();
if (!string.IsNullOrEmpty(json))
{
Dictionary <string, object> data = (Dictionary <string, object>)MiniJSON.Json.Deserialize(json);
m_neuronalNetwork.Deserialize(data);
return(true);
}
else
{
return(false);
}
}
/// <summary>
/// Try to train the current BPN with a list of patterns;
/// </summary>
/// <param name="points"></param>
/// <param name="name"></param>
public IEnumerator StartBPNTraining(List <PRPatternGroup> patternDefinitions, int numberOfEpochs)
{
m_neuronalNetwork = new PartyBPN(m_defaultNeuronNumberInput - 1, Mathf.RoundToInt((patternDefinitions.Count + m_defaultNeuronNumberInput) / 2), patternDefinitions.Count, m_defaultLearningRate, m_defaultTheta, m_defaultSigmoidElastic, m_defaultMomentum);
return(m_neuronalNetwork.StartTraining(patternDefinitions, numberOfEpochs));
}
