private void TrainPersonNeuralNetwork(
Dictionary<Person, List<double[]>[]> trainingMfccs,
Dictionary<Person, List<double[]>[]> testMfccs,
double[] input, double[][] answer, Person person,
PerceptronWrapper network)
{
GnuPlot plot = new GnuPlot(
person.FullName,
@"D:\GIT\nnetwork_mini_pw\charts\plot_" + person.FullName.Replace(' ', '_') + ".png"
);
plot.BeginPlot();
List<double[]>[] personMfccs = null;
int iterations = 0;
while (iterations < MaxIterationCounts) {
double expectedAnswer;
if (Random.NextDouble() < algorithmParams.TCoef) {
expectedAnswer = 0.0d;
Person otherPerson = SelectOtherPerson(person);
personMfccs = trainingMfccs[otherPerson];
}
else {
expectedAnswer = 1.0d;
personMfccs = trainingMfccs[person];
}
DrawInputData(input, personMfccs);
double scale = Math.Sqrt(1.0 + iterations);
double newLearningRate = algorithmParams.LearningRate / scale;
double newMomentum = algorithmParams.Momentum / scale;
answer[0][0] = expectedAnswer;
network.Train(newLearningRate, 1, newMomentum,
new[] { input },
answer);
iterations++;
if (iterations % 1000 == 0) {
double testResult = TestPersonNeuralNetwork(person, testMfccs, input);
double learnResult = TestPersonNeuralNetwork(person, trainingMfccs, input);
AddPlotPoint(plot, iterations, learnResult, testResult);
if (testResult <= TestSetMaxError)
{
break;
}
}
}
plot.AddAlgorithmParamsToTitle(algorithmParams);
plot.End2Plot();
}
