private void ProcessPoints()
{
switch (m_mode)
{
case ExampleMode.Recognize:
PropagateResult result = PartyRecognitionManager.Instance.Recognize(m_points.ToArray());
//string highestPattern;
//float highestScore = result.GetHighestScore(out highestPattern);
//Debug.Log("Result: " + (highestScore > PartyRecognitionManager.Instance.SuccessThresholdPercent) + "Score: " + result.GetHighestScore(out highestPattern) + " PatternName: "+ result.HighestPattern);
result.PrettyPrint();
break;
case ExampleMode.SavePattern:
PRPatternDefinition newPattern = new PRPatternDefinition(m_points, (int)PartyRecognitionManager.Instance.DefaultNeuronNumberInput, m_exampleUI.GetPatternName());
newPattern.NormalizePoints();
PartyRecognitionManager.Instance.AddPattern(newPattern);
break;
}
m_points.Clear();
for (int i = 0; i < m_instantiatedObjects.Count; i++)
{
Destroy(m_instantiatedObjects[i]);
}
}
public PropagateResult Propagate(List <float> angles, float successThreshold)
{
float sum2 = 0f;
if (angles.Count != m_neuronsNumberInp)
{
throw new Exception("Angles doesn't match with the Neuron Input dimension");
}
for (int i = 0; i < m_neuronsNumberInp; i++)
{
m_inpA[i] = angles[i];
}
for (int i = 0; i < m_neuronsNumberHid; i++)
{
for (int n = 0; n < m_neuronsNumberInp; n++)
{
sum2 += m_hidW[i][n] * m_inpA[n];
}
m_hidA[i] = Sigmoid(sum2);
}
PropagateResult result = new PropagateResult();
for (int i = 0; i < m_neuronsNumberOut; i++)
{
sum2 = 0f;
for (int n = 0; n < m_neuronsNumberHid; n++)
{
sum2 += m_outW[i][n] * m_hidA[n];
}
m_outA[i] = Sigmoid(sum2);
result.AddScore(m_patternId[i], m_outA[i]);
}
return(result);
}
public bool Recognize(Vector2[] points, string patternId, float threshold)
{
PropagateResult result = Recognize(points);
return(result.GetScore(patternId) >= threshold);
}
