public override PatternResult Train(List <Pattern> patterns, int iterations)
{
double error = 0.0;
if (patterns.Count == 0)
{
return(null);
}
foreach (string c in charsSet)
{
DoubleVectorList inputs = new DoubleVectorList();
DoubleVectorList outputs = new DoubleVectorList();
foreach (Pattern p in patterns)
{
// Add every item. The solution is either "yes this is a letter X" or "no this is not a letter X"
inputs.Add(p.Inputs.Scale());
if (GetOutputCharacter(p.Outputs) == c)
{
DoubleVector v = new DoubleVector(1);
v[0] = 0.99;
outputs.Add(v);
}
else
{
DoubleVector v = new DoubleVector(1);
v[0] = 0.01;
outputs.Add(v);
}
}
// Train only if we have patters for this letter in our character set
if (inputs.Count > 0)
{
// Get the specialized neural network for this specific letter
error += sann.Where(nn => nn.Solution == c).ToList()[0].NeuralNet.Train(inputs, outputs, iterations);
}
}
return(new PatternResult(error));
}
public override PatternResult Train(List <Pattern> patterns, int iterations)
{
if (patterns.Count == 0)
{
return(null);
}
DoubleVectorList inputs = new DoubleVectorList();
DoubleVectorList outputs = new DoubleVectorList();
foreach (Pattern p in patterns)
{
inputs.Add(p.Inputs.Scale(0.02, 0.98));
outputs.Add(p.Outputs);
}
double error = sann.Train(inputs, outputs, iterations);
return(new PatternResult(error));
}
public override PatternResult Train(List<Pattern> patterns, int iterations)
{
double error = 0.0;
if (patterns.Count == 0)
{
return null;
}
foreach (string c in charsSet)
{
DoubleVectorList inputs = new DoubleVectorList();
DoubleVectorList outputs = new DoubleVectorList();
foreach (Pattern p in patterns)
{
// Add every item. The solution is either "yes this is a letter X" or "no this is not a letter X"
inputs.Add(p.Inputs.Scale());
if (GetOutputCharacter(p.Outputs) == c)
{
DoubleVector v = new DoubleVector(1);
v[0] = 0.99;
outputs.Add(v);
}
else
{
DoubleVector v = new DoubleVector(1);
v[0] = 0.01;
outputs.Add(v);
}
}
// Train only if we have patters for this letter in our character set
if (inputs.Count > 0)
{
// Get the specialized neural network for this specific letter
error += sann.Where(nn => nn.Solution == c).ToList()[0].NeuralNet.Train(inputs, outputs, iterations);
}
}
return new PatternResult(error);
}
public override PatternResult Train(List<Pattern> patterns, int iterations)
{
if (patterns.Count == 0)
{
return null;
}
DoubleVectorList inputs = new DoubleVectorList();
DoubleVectorList outputs = new DoubleVectorList();
foreach (Pattern p in patterns)
{
inputs.Add(p.Inputs.Scale(0.02, 0.98));
outputs.Add(p.Outputs);
}
double error = sann.Train(inputs, outputs, iterations);
return new PatternResult(error);
}
