double Evaluate(INeuralNet network)
{
double[] fitnessTests = new double[tests];
for (int i = 0; i < fitnessTests.Length; i++)
{
double[] inputs = testInputs[i];
double[] outs = network.Calculate(inputs);
double expected = inputs.Sum() % 2;
fitnessTests[i] = Fitness(outs[0], expected);
}
return(fitnessTests.Average());
}
double Evaluate(INeuralNet network)
{
double[] fitnessTests = new double[testInputs.Count];
for (int i = 0; i < fitnessTests.Length; i++)
{
double[] inputs = testInputs[i];
double[] outs = network.Calculate(inputs);
for (int i2 = 0; i2 < outs.Length; i2++)
{
outs[i2] = Fitness(testOutputs[i][i2], outs[i2]);
}
fitnessTests[i] = outs.Average();
}
return(fitnessTests.Average());
}