public void test() {
// initialize input and output values
var input = new double[4][] {
new double[] { 0, 0 }, new double[] { 0, 1 },
new double[] { 1, 0 }, new double[] { 1, 1 }
};
var output = new double[4][] {
new double[] { 0 }, new double[] { 1 },
new double[] { 1 }, new double[] { 1 }
};
// create neural network
var network = new ActivationNetwork(
new SigmoidFunction(2),
2, // two inputs in the network
//2, // two neurons in the first layer
1); // one neuron in the second layer
// create teacher
var teacher =
new BackPropagationLearning(network);
// loop
while (true) {
// run epoch of learning procedure
var error = teacher.RunEpoch(input, output);
// check error value to see if we need to stop
// ...
if (error < 0.001) {
break;
}
}
Console.WriteLine(network.Compute(new double[] { 0, 0 })[0] + ","
+ network.Compute(new double[] { 0, 1 })[0] + ","
+ network.Compute(new double[] { 1, 0 })[0] + ","
+ network.Compute(new double[] { 1, 1 })[0]);
}
public void Test()
{
ActivationNetwork network = new ActivationNetwork(
new SigmoidFunction(),
2, // two inputs in the network
2, // two neurons in the first layer
1); // one neuron in the second layer
BackPropagationLearning teacher = new BackPropagationLearning(network);
double lastError = double.MaxValue;
int counter = 0;
while (true)
{
counter++;
var error = teacher.RunEpoch(input, output);
if (lastError - error < 0.0000001 && error < 0.001)
break;
lastError = error;
}
//var bla = network.Compute(input[0])[0];
//var round = Math.Round(network.Compute(input[0])[0], 2);
//var result = output[0][0];
//Assert.IsTrue(Math.Abs(round - result) < double.Epsilon);
Assert.IsTrue(Math.Abs(network.Compute(input[0])[0] - output[0][0]) < 0.03);
Assert.IsTrue(Math.Abs(network.Compute(input[1])[0] - output[1][0]) < 0.03);
Assert.IsTrue(Math.Abs(network.Compute(input[2])[0] - output[2][0]) < 0.03);
Assert.IsTrue(Math.Abs(network.Compute(input[3])[0] - output[3][0]) < 0.03);
Console.WriteLine($"Loop counter = {counter}.");
}
static void Main(string[] args)
{
// initialize input and output values
double[][] input = new double[4][] {
new double[] {0, 0}, new double[] {0, 1},
new double[] {1, 0}, new double[] {1, 1}
};
double[][] output = new double[4][] {
new double[] {0}, new double[] {1},
new double[] {1}, new double[] {0}
};
// create neural network
ActivationNetwork network = new ActivationNetwork(
new SigmoidFunction(1),
2, // two inputs in the network
2, // two neurons in the first layer
1); // one neuron in the second layer
// create teacher
BackPropagationLearning teacher =
new BackPropagationLearning(network);
// loop
for (int i = 0; i < 10000; i++)
{
// run epoch of learning procedure
double error = teacher.RunEpoch(input, output);
// check error value to see if we need to stop
// ...
Console.Out.WriteLine("#" + i + "\t" + error);
}
double[] ret1 = network.Compute(new double[] { 0, 0 });
double[] ret2 = network.Compute(new double[] { 1, 0 });
double[] ret3 = network.Compute(new double[] { 0, 1 });
double[] ret4 = network.Compute(new double[] { 1, 1 });
Console.Out.WriteLine();
Console.Out.WriteLine("Eval(0, 0) = " + ret1[0]);
Console.Out.WriteLine("Eval(1, 0) = " + ret2[0]);
Console.Out.WriteLine("Eval(0, 1) = " + ret3[0]);
Console.Out.WriteLine("Eval(1, 1) = " + ret4[0]);
Console.ReadLine();
}
public void TestGenetic()
{
ActivationNetwork network = new ActivationNetwork(new SigmoidFunction(), 2, 2, 1);
EvolutionaryLearning superTeacher = new EvolutionaryLearning(network, 10);
double lastError = double.MaxValue;
int counter = 0;
while (true)
{
counter++;
var error = superTeacher.RunEpoch(input, output);
if (lastError - error < 0.0000001 && error < 0.0001)
break;
lastError = error;
}
Assert.IsTrue(Math.Abs(network.Compute(input[0])[0] - output[0][0]) < 0.03);
Assert.IsTrue(Math.Abs(network.Compute(input[1])[0] - output[1][0]) < 0.03);
Assert.IsTrue(Math.Abs(network.Compute(input[2])[0] - output[2][0]) < 0.03);
Assert.IsTrue(Math.Abs(network.Compute(input[3])[0] - output[3][0]) < 0.03);
Console.WriteLine($"Loop counter = {counter}.");
}
public void Test()
{
var network = new ActivationNetwork(new SigmoidFunction(), inputCount, firstLayerNeurons, secondLayerNeurons, thirdLayerNeurons, lastLayerNeurons);
var teacher = new BackPropagationLearning(network);
var lastError = double.MaxValue;
var counter = 0;
while (true)
{
counter++;
var error = teacher.RunEpoch(input, output);
if ((lastError - error < 0.00001 && error < 0.01) || counter > 1200000)
break;
lastError = error;
}
var result1 = network.Compute(new double[] {1, 0, 1, 0, 1, 0, 1, 0});
Console.WriteLine($"2 + 2, 2 * 2 = {result1[0]}, {result1[1]}");
var result2 = network.Compute(new double[] {0, 1, 0, 1, 1, 0, 0, 1});
Console.WriteLine($"1 + 1, 2 * 1 = {result2[0]}, {result2[1]}");
var result3 = network.Compute(new double[] {1, 0, 1, 0, 0, 1, 0, 0});
Console.WriteLine($"2 + 2, 1 * 0 = {result3[0]}, {result3[1]}");
var result4 = network.Compute(new double[] {0, 1, 0, 0, 0, 1, 1, 0});
Console.WriteLine($"1 + 0, 1 * 2 = {result4[0]}, {result4[1]}");
}
public double Evaluate(IChromosome chromosome)
{
// Конструираме невронна мрежа и изчисляваме резултата
DoubleArrayChromosome dac = (DoubleArrayChromosome)chromosome;
ActivationNetwork Network = new ActivationNetwork(
new BipolarSigmoidFunction(sigmoidAlphaValue),
mArchitecture[0], mArchitecture[1], mArchitecture[2]);
int current = 0;
int i = 0;
// Тегла на скрит слой
for (i = 0; i < mArchitecture[1]; i++)
{
for(int j=0; j < mArchitecture[0]; j++){
Network[0][i][j] = dac.Value[current++];
}
}
// Тегла на изходен слой
for (i = 0; i < mArchitecture[2]; i++)
{
for (int j = 0; j < mArchitecture[1]; j++)
{
Network[1][i][j] = dac.Value[current++];
}
}
double Sum = 0.0;
for (int cnt = 0; cnt < mInput.Length; cnt++)
{
double[] predicted_output = Network.Compute(mInput[cnt]);
for (int l = 0; l < predicted_output.Length; l++)
{
Sum += (predicted_output[l] - mOutput[cnt][l]) * (predicted_output[l] - mOutput[cnt][l]);
}
}
return 100-Sum;
}
public void RunEpochTest1()
{
Accord.Math.Tools.SetupGenerator(0);
double[][] input =
{
new double[] { -1, -1 },
new double[] { -1, 1 },
new double[] { 1, -1 },
new double[] { 1, 1 }
};
double[][] output =
{
new double[] { -1 },
new double[] { 1 },
new double[] { 1 },
new double[] { -1 }
};
Neuron.RandGenerator = new ThreadSafeRandom(0);
ActivationNetwork network = new ActivationNetwork(
new BipolarSigmoidFunction(2), 2, 2, 1);
var teacher = new ParallelResilientBackpropagationLearning(network);
double error = 1.0;
while (error > 1e-5)
error = teacher.RunEpoch(input, output);
for (int i = 0; i < input.Length; i++)
{
double actual = network.Compute(input[i])[0];
double expected = output[i][0];
Assert.AreEqual(expected, actual, 0.01);
Assert.IsFalse(Double.IsNaN(actual));
}
}
private double ComputeCVError(ActivationNetwork network, double[][] dataIn, double[][] dataOut)
{
double error = 0;
for (int i = 0; i < dataIn.Length; i++)
{
double[] output = network.Compute(dataIn[i]);
for (int j = 0; j < output.Length; j++)
error += (output[j] - dataOut[i][j]) * (output[j] - dataOut[i][j]);
}
return error / 2 / dataIn.Length;
}
static void Main(string[] args)
{
Console.WriteLine("This is a demo application that combines Linear Discriminant Analysis (LDA) and Multilayer Perceptron(MLP).");
double[,] inputs =
{
{ 4, 1 },
{ 2, 4 },
{ 2, 3 },
{ 3, 6 },
{ 4, 4 },
{ 9, 10 },
{ 6, 8 },
{ 9, 5 },
{ 8, 7 },
{ 10, 8 }
};
int[] output =
{
1, 1, 2, 1, 1, 2, 2, 2, 1, 2
};
Console.WriteLine("\r\nProcessing sample data, pease wait...");
//1.1
var lda = new LinearDiscriminantAnalysis(inputs, output);
//1.2 Compute the analysis
lda.Compute();
//1.3
double[,] projection = lda.Transform(inputs);
//both LDA and MLP have a little bit different inputs
//e.x double[,] to double[][], etc.
//e.x. LDA needs int classes and MLP needs classes to be in the range [0..1]
#region convertions
int vector_count = projection.GetLength(0);
int dimensions = projection.GetLength(1);
//====================================================================
// conver for NN
double[][] input2 = new double[vector_count][];
double[][] output2 = new double[vector_count][];
for (int i = 0; i < input2.Length; i++)
{
input2[i] = new double[projection.GetLength(1)];
for (int j = 0; j < projection.GetLength(1); j++)
{
input2[i][j] = projection[i, j];
}
output2[i] = new double[1];
//we turn classes from ints to doubles in the range [0..1], because we use sigmoid for the NN
output2[i][0] = Convert.ToDouble(output[i]) / 10;
}
#endregion
//2.1 create neural network
ActivationNetwork network = new ActivationNetwork(
new SigmoidFunction(2),
dimensions, // inputs neurons in the network
dimensions, // neurons in the first layer
1); // one neuron in the second layer
//2.2 create teacher
BackPropagationLearning teacher = new BackPropagationLearning(network);
//2.3 loop
int p = 0;
while (true)
{
// run epoch of learning procedure
double error = teacher.RunEpoch(input2, output2);
p++;
if (p > 1000000) break;
// instead of iterations we can check error values to see if we need to stop
}
//====================================================================
//3. Classify
double[,] sample = { { 10, 8 } };
double[,] projectedSample = lda.Transform(sample);
double[] projectedSample2 = new double[2];
projectedSample2[0] = projectedSample[0, 0];
projectedSample2[1] = projectedSample[0, 1];
double[] classs = network.Compute(projectedSample2);
Console.WriteLine("========================");
//we convert back to int classes by first rounding and then multipling by 10 (because we devided to 10 before)
//if you do not get the expected result
//- rounding might be a problem
//- try more training
Console.WriteLine(Math.Round(classs[0], 1, MidpointRounding.AwayFromZero)*10);
Console.ReadLine();
}
// Worker thread
void SearchSolution()
{
// number of learning samples
int samples = data.GetLength(0);
// data transformation factor
double yFactor = 1.7 / chart.RangeY.Length;
double yMin = chart.RangeY.Min;
double xFactor = 2.0 / chart.RangeX.Length;
double xMin = chart.RangeX.Min;
// prepare learning data
double[][] input = new double[samples][];
double[][] output = new double[samples][];
for (int i = 0; i < samples; i++)
{
input[i] = new double[1];
output[i] = new double[1];
// set input
input[i][0] = (data[i, 0] - xMin) * xFactor - 1.0;
// set output
output[i][0] = (data[i, 1] - yMin) * yFactor - 0.85;
}
// create multi-layer neural network
ActivationNetwork network = new ActivationNetwork(
new BipolarSigmoidFunction(sigmoidAlphaValue),
1, neuronsInFirstLayer, 1);
// create teacher
BackPropagationLearning teacher = new BackPropagationLearning(network);
// set learning rate and momentum
teacher.LearningRate = learningRate;
teacher.Momentum = momentum;
// iterations
int iteration = 1;
// solution array
double[,] solution = new double[50, 2];
double[] networkInput = new double[1];
// calculate X values to be used with solution function
for (int j = 0; j < 50; j++)
{
solution[j, 0] = chart.RangeX.Min + (double)j * chart.RangeX.Length / 49;
}
// loop
while (!needToStop)
{
// run epoch of learning procedure
double error = teacher.RunEpoch(input, output) / samples;
// calculate solution
for (int j = 0; j < 50; j++)
{
networkInput[0] = (solution[j, 0] - xMin) * xFactor - 1.0;
solution[j, 1] = (network.Compute(networkInput)[0] + 0.85) / yFactor + yMin;
}
chart.UpdateDataSeries("solution", solution);
// calculate error
double learningError = 0.0;
for (int j = 0, k = data.GetLength(0); j < k; j++)
{
networkInput[0] = input[j][0];
learningError += Math.Abs(data[j, 1] - ((network.Compute(networkInput)[0] + 0.85) / yFactor + yMin));
}
// set current iteration's info
UpdateTextbox(currentIterationBox, iteration.ToString());
//currentIterationBox.Text = iteration.ToString();
UpdateTextbox(currentErrorBox, learningError.ToString("F3"));
//currentErrorBox.Text = learningError.ToString("F3");
// increase current iteration
iteration++;
// check if we need to stop
if ((iterations != 0) && (iteration > iterations))
break;
}
// enable settings controls
EnableControls(true);
}
static void Learn()
{
var network = new ActivationNetwork(
new SigmoidFunction(),
baseMaker.InputSize,
arguments.NeuronsCount,
baseMaker.OutputSize
);
network.Randomize();
foreach (var l in network.Layers)
foreach (var n in l.Neurons)
for (int i = 0; i < n.Weights.Length; i++)
n.Weights[i] = rnd.NextDouble() * 2 - 1;
var teacher = new BackPropagationLearning(network);
teacher.LearningRate = 1;
teacher.Momentum = 0;
while (true)
{
var watch = new Stopwatch();
watch.Start();
while (watch.ElapsedMilliseconds < 500)
{
teacher.RunEpoch(baseMaker.Inputs, baseMaker.Answers);
}
watch.Stop();
var count = 0;
percentage = new double[baseMaker.OutputSize, baseMaker.OutputSize];
for (int i = 0; i < baseMaker.OutputSize; i++)
for (int j = 0; j < baseMaker.OutputSize * 5; j++)
{
var task = baseMaker.GenerateRandom(i);
var output = network.Compute(task);
var max = output.Max();
var maxIndex = Enumerable.Range(0, output.Length).Where(z => output[z] == max).First();
percentage[i, maxIndex]++;
if (i != maxIndex) totalErrors++;
count++;
}
var maxPercentage = percentage.Cast<double>().Max();
for (int i = 0; i < baseMaker.OutputSize; i++)
for (int j = 0; j < baseMaker.OutputSize; j++)
percentage[i, j] /= maxPercentage;
totalErrors /= count;
form.BeginInvoke(new Action(Update));
}
}
public EstimationResult Estimate(IEnumerable<IDateValue> dateValues)
{
var data = dateValues.ToArray();
var samplesCount = data.Length - LayerWidth;
var factor = 1.7 / data.Length;
var yMin = data.Min(x => x.Value);
var input = new double[samplesCount][];
var output = new double[samplesCount][];
for (var i = 0; i < samplesCount; i++)
{
input[i] = new double[LayerWidth];
output[i] = new double[1];
for (var j = 0; j < LayerWidth; j++)
input[i][j] = (data[i + j].Value - yMin) * factor - 0.85;
output[i][0] = (data[i + LayerWidth].Value - yMin) * factor - 0.85;
}
var network = new ActivationNetwork(
new BipolarSigmoidFunction(SigmoidAlphaValue),
LayerWidth, LayerWidth * 2, 1);
var teacher = new BackPropagationLearning(network)
{
LearningRate = LearningRate,
Momentum = Momentum
};
var solutionSize = data.Length - LayerWidth;
var solution = new double[solutionSize, 2];
var networkInput = new double[LayerWidth];
for (var j = 0; j < solutionSize; j++)
solution[j, 0] = j + LayerWidth;
TimesLoop.Do(Iterations, () =>
{
teacher.RunEpoch(input, output);
for (int i = 0, n = data.Length - LayerWidth; i < n; i++)
{
for (var j = 0; j < LayerWidth; j++)
networkInput[j] = (data[i + j].Value - yMin) * factor - 0.85;
solution[i, 1] = (network.Compute(networkInput)[0] + 0.85) / factor + yMin;
}
});
return EstimationResult.Create(solution[0, 1], this);
}
public double[] Run(int[] numOfHiddenNeurals)
{
// validation and testing error
double[] errors = new double[2];
// number of learning samples
int samples = _data.Length - _windowSize;
int trainingSamples = samples - _predictionSize;
// prepare learning data
double[][] input = new double[samples][];
double[][] output = new double[samples][];
// sample indices
int[] indices = new int[samples];
int[] trainingIndices = new int[trainingSamples];
// normalization function
var normalizeFunc = new MinMaxNormalization(_xMax, _xMin, _data.Max(), _data.Min());
for (int i = 0; i < samples; i++)
{
input[i] = new double[_windowSize];
output[i] = new double[_outputNum];
// set input
for (int j = 0; j < _windowSize; j++)
{
input[i][j] = normalizeFunc.Compute(_data[i + j]);
}
// set output
for (int j = 0; j < _outputNum; j++)
{
output[i][j] = normalizeFunc.Compute(_data[i + _windowSize + j]);
}
indices[i] = i;
}
// randomize the sample indices
Utils.Shuffle<int>(indices);
output.Swap(indices);
input.Swap(indices);
// get training samples
double[][] trainingInput = new double[trainingSamples][];
double[][] trainingOutput = new double[trainingSamples][];
for (int i = 0; i < trainingSamples; i++)
{
trainingInput[i] = new double[_windowSize];
trainingOutput[i] = new double[_outputNum];
// set input
for (int j = 0; j < _windowSize; j++)
{
trainingInput[i][j] = input[i][j];
}
// set output
for (int j = 0; j < _outputNum; j++)
{
trainingOutput[i][j] = output[i][j];
}
trainingIndices[i] = i;
}
//// randomize the sample indices
//Utils.Shuffle<int>(trainingIndices);
//trainingOutput.Swap(trainingIndices);
//trainingInput.Swap(trainingIndices);
// create multi-layer neural network
int[] neuronsCount = numOfHiddenNeurals.Concat(new int[] { _outputNum }).ToArray();
ActivationNetwork network = new ActivationNetwork(
_function,
_windowSize, neuronsCount);
// create teacher
BackPropagationLearning teacher = new BackPropagationLearning(network);
// set learning rate and momentum
teacher.LearningRate = _learningRate;
teacher.Momentum = 0.0;
//var teacher = new ParallelResilientBackpropagationLearning(network);
//teacher.Reset(_learningRate);
// iterations
int iteration = 1;
// solution array
int solutionSize = _data.Length - _windowSize;
double[,] solution = new double[solutionSize, 1 + _outputNum];
// calculate X values to be used with solution function
for (int j = 0; j < solutionSize; j++)
{
solution[j, 0] = j + _windowSize;
}
// loop
var needToStop = false;
while (!needToStop)
{
// run epoch of learning procedure
double error = teacher.RunEpoch(trainingInput, trainingOutput) / trainingSamples;
// calculate solution and learning and prediction errors every 5
double learningError = 0.0;
double predictionError = 0.0;
if (iteration % 5 == 0)
{
// go through all the data
for (int i = 0; i < samples; i++)
{
double y = 0.0;
double o = 0.0;
double err = 0.0;
for (int j = 0; j < _outputNum; j++)
{
y = output[i][j];
o = network.Compute(input[i])[j];
err += (o - y) * (o - y) / _outputNum;
// evalue the function
solution[i, j + 1] = normalizeFunc.Inverse(o);
}
// calculate prediction error (MSE)
if (i >= trainingSamples)
{
predictionError += err;// Math.Pow((solution[i, 1] - normalizeFunc.Inverse(output[i][0])), 2.0);
}
else
{
learningError += err;
}
}
}
// Adaptive Learning - decrease the learning rate
// n(t) = n0 * a^(t/T)
// a = 1/10^x, x >= 1
teacher.LearningRate = _learningRate * Math.Pow(0.1, iteration / _iterations);
// increase iteration
iteration++;
// check if we need to stop
if ((_iterations != 0) && (iteration > _iterations))
{
errors[0] = learningError / trainingSamples;
errors[1] = predictionError / _predictionSize;
Console.WriteLine("Final Learning MSE Error: " + errors[0]);
Console.WriteLine("Final Prediction MSE Error: " + errors[1]);
Console.WriteLine("Final Learning Rate: " + teacher.LearningRate);
Console.WriteLine("Window Size: " + _windowSize + "\n"
+ "Number of Hidden Neurons: " + neuronsCount[0] + "\n"
+ "Output Size: " + _outputNum);
break;
}
}
////print result to file
//Console.WriteLine("Real Values\t\tRegression Values");
//for (int i = samples - 1; i >= trainingSamples; --i)
//{
// Console.WriteLine(normalizeFunc.Inverse(output[i][0]) + "\t\t" + solution[i, 1]);
//}
return errors;
}
public static void ExactTrainingData()
{
TrainData[] tdatas;
// AForge.Neuro.Learning.BackPropagationLearning beural=new AForge.Neuro.Learning.BackPropagationLearning(new AForge.Neuro.ActivationNetwork(
tdatas = traindatas.ToArray();
double[][] output = new double[tdatas.Length-1][];
double[][] input = new double[tdatas.Length-1][];
for (int i = 1; i < tdatas.Length; i++)
{
int spddiff = 0, voldiff = 0, occdiff = 0, u_spddifft = 0, u_voldifft = 0, u_occdifft = 0, d_spddifft = 0, d_voldifft = 0, d_occdifft = 0,level=0;
tdatas[i].getTrainData(ref voldiff, ref spddiff, ref occdiff);
u_spddifft = tdatas[i].vd1.AvgSpd - tdatas[i - 1].vd1.AvgSpd;
u_voldifft = tdatas[i].vd1.Volume - tdatas[i-1].vd1.Volume;
u_occdifft = tdatas[i].vd1.Occupancy - tdatas[i - 1].vd1.Occupancy;
d_spddifft = tdatas[i].vd2.AvgSpd - tdatas[i - 1].vd2.AvgSpd;
d_voldifft = tdatas[i].vd2.Volume - tdatas[i - 1].vd2.Volume;
d_occdifft = tdatas[i].vd2.Occupancy - tdatas[i - 1].vd2.Occupancy;
level = tdatas[i-1].Level;
output[i-1] = new double[1];
output[i-1][0] = level;
input[i-1] = new double[9];
input[i-1][0] = spddiff;
input[i-1][1] = voldiff;
input[i-1][2] = occdiff;
input[i-1][3] = u_spddifft;
input[i-1][4] = u_voldifft;
input[i-1][5] = u_occdifft;
input[i-1][6] = d_spddifft;
input[i-1][7] = d_voldifft;
input[i-1][8] = d_occdifft;
Console.WriteLine(spddiff + "," + voldiff + "," + occdiff + "," + u_spddifft + "," + u_voldifft + "," + u_occdifft + "," + d_spddifft + "," + d_voldifft + "," + d_occdifft+","+level);
}
ActivationNetwork network = new ActivationNetwork(
new BipolarSigmoidFunction(1.5),9, 25, 1 );
// create teacher
BackPropagationLearning teacher = new BackPropagationLearning(network);
teacher.Momentum = 0.1;
teacher.LearningRate = 0.01;
// loop
double err=100;
int cnt = 0;
while (err / tdatas.Length >0.00079)
{
// run epoch of learning procedure
err = teacher.RunEpoch( input, output );
Console.WriteLine(err / tdatas.Length);
cnt++;
}
for (int i = 0; i < tdatas.Length-1; i++)
{
if (System.Convert.ToInt32(output[i][0]) != System.Convert.ToInt32(network.Compute(input[i])[0]))
Console.WriteLine("fail");
// Console.WriteLine("chreck:"+Math.Abs((output[i][0] - network.Compute(input[i])[0]))>0,5?:"fail","");
}
}
// Worker thread
void SearchSolution( )
{
windowSize = _WordToID.Count ;
//windowSize = 3;
// number of learning samples
int samples = data.Length - predictionSize - windowSize;
//int samples = 157;
// data transformation factor
double factor = 1.7 / chart.RangeY.Length;
double yMin = chart.RangeY.Min;
// prepare learning data
double[][] input = new double[samples][];
double[][] output = new double[samples][];
for ( int i = 0; i < samples; i++ )
{
input[i] = new double[windowSize];
output[i] = new double[1];
// set input
for ( int j = 0; j < windowSize; j++ )
{
input[i][j] = ( data[i + j] - yMin ) * factor - 0.85;
}
// set output
output[i][0] = ( data[i + windowSize] - yMin ) * factor - 0.85;
}
// create multi-layer neural network
_network = new ActivationNetwork(
new BipolarSigmoidFunction( sigmoidAlphaValue ),
//new ThresholdFunction(),
windowSize, windowSize*2, 1);
// create teacher
BackPropagationLearning teacher = new BackPropagationLearning( _network );
// set learning rate and momentum
teacher.LearningRate = learningRate;
teacher.Momentum = momentum;
// iterations
int iteration = 1;
// solution array
int solutionSize = data.Length - windowSize;
double[,] solution = new double[solutionSize, 2];
double[] networkInput = new double[windowSize];
// calculate X values to be used with solution function
for ( int j = 0; j < solutionSize; j++ )
{
solution[j, 0] = j + windowSize;
}
// loop
while ( !needToStop )
{
// run epoch of learning procedure
double error = teacher.RunEpoch( input, output ) / samples;
// calculate solution and learning and prediction errors
double learningError = 0.0;
double predictionError = 0.0;
// go through all the data
for ( int i = 0, n = data.Length - windowSize; i < n; i++ )
{
// put values from current window as network's input
for ( int j = 0; j < windowSize; j++ )
{
networkInput[j] = ( data[i + j] - yMin ) * factor - 0.85;
}
// evalue the function
solution[i, 1] = ( _network.Compute( networkInput)[0] + 0.85 ) / factor + yMin;
if (iteration == iterations)
{
string format = string.Format("[{0}][{1}] = [{2}] + [{3}] = {4}", i, networkInput[0], _network[0].Output[0], _network[0].Output[1], solution[i, 1]);
//listBox1.Items.Add(format);
}
// calculate prediction error
if ( i >= n - predictionSize )
{
predictionError += Math.Abs( solution[i, 1] - data[windowSize + i] );
}
else
{
learningError += Math.Abs( solution[i, 1] - data[windowSize + i] );
}
}
// update solution on the chart
chart.UpdateDataSeries( "solution", solution );
// set current iteration's info
//currentIterationBox.Text = iteration.ToString( );
//currentLearningErrorBox.Text = learningError.ToString( "F3" );
//currentPredictionErrorBox.Text = predictionError.ToString( "F3" );
// increase current iteration
iteration++;
// check if we need to stop
if ( ( iterations != 0 ) && ( iteration > iterations ) )
break;
}
// show new solution
for ( int j = windowSize, k = 0, n = data.Length; j < n; j++, k++ )
{
lock (this)
{
//dataList.Items[j].SubItems.Add(solution[k, 1].ToString());
}
}
//listBox1.Items.Add(network.ToString());
// enable settings controls
//EnableControls( true );
}
private void SearchSolution()
{
MemoryStream ms = new MemoryStream();
mNetwork = new ActivationNetwork(
new BipolarSigmoidFunction(sigmoidAlphaValue),
mInput[0].Length, mHiddenNeurons, mOutput[0].Length);
// create teacher
BackPropagationLearning teacher = new BackPropagationLearning(mNetwork);
// set learning rate and momentum
teacher.LearningRate = mLearningRate;
teacher.Momentum = mMomentum;
bool needToStop = false;
int iteration = 0;
while (!needToStop)
{
double error = teacher.RunEpoch(mInput, mOutput) / mInput.Length;
mErrors[iteration] = error;
double test_error = 0.0;
for (int i = 0; i < mTestInput.Length; i++)
{
double[] test_result = mNetwork.Compute(mTestInput[i]);
test_error += ( mTestOutput[i][0] - test_result[0])*( mTestOutput[i][0] - test_result[0]);
}
mTestErrors[iteration] = Math.Sqrt(test_error);
if (min_test_error > mTestErrors[iteration])
{
min_test_error = mTestErrors[iteration];
// mTestBestNetwork = mNetwork;
ms = new MemoryStream();
mNetwork.Save(this.Id + ".txt");
}
iteration++;
if (iteration >= mIterations) needToStop = true;
}
mTestBestNetwork = (ActivationNetwork)ActivationNetwork.Load(this.Id + ".txt");
}
// Worker thread
void SearchSolution()
{
// number of learning samples
int samples = data.Length - predictionSize - windowSize;
// data transformation factor
double factor = 1.7 / chart.RangeY.Length;
double yMin = chart.RangeY.Min;
// prepare learning data
double[][] input = new double[samples][];
double[][] output = new double[samples][];
for (int i = 0; i < samples; i++)
{
input[i] = new double[windowSize];
output[i] = new double[1];
// set input
for (int j = 0; j < windowSize; j++)
{
input[i][j] = (data[i + j] - yMin) * factor - 0.85;
}
// set output
output[i][0] = (data[i + windowSize] - yMin) * factor - 0.85;
}
// create multi-layer neural network
ActivationNetwork network = new ActivationNetwork(
new BipolarSigmoidFunction(sigmoidAlphaValue),
windowSize, windowSize * 2, 1);
// create teacher
var teacher = new ParallelResilientBackpropagationLearning(network);
teacher.Reset(initialStep);
// run at least one backpropagation epoch
//teacher2.RunEpoch(input, output);
// iterations
int iteration = 1;
// solution array
int solutionSize = data.Length - windowSize;
double[,] solution = new double[solutionSize, 2];
double[] networkInput = new double[windowSize];
// calculate X values to be used with solution function
for (int j = 0; j < solutionSize; j++)
{
solution[j, 0] = j + windowSize;
}
// loop
while (!needToStop)
{
// run epoch of learning procedure
double error = teacher.RunEpoch(input, output) / samples;
// calculate solution and learning and prediction errors
double learningError = 0.0;
double predictionError = 0.0;
// go through all the data
for (int i = 0, n = data.Length - windowSize; i < n; i++)
{
// put values from current window as network's input
for (int j = 0; j < windowSize; j++)
{
networkInput[j] = (data[i + j] - yMin) * factor - 0.85;
}
// evalue the function
solution[i, 1] = (network.Compute(networkInput)[0] + 0.85) / factor + yMin;
// calculate prediction error
if (i >= n - predictionSize)
{
predictionError += Math.Abs(solution[i, 1] - data[windowSize + i]);
}
else
{
learningError += Math.Abs(solution[i, 1] - data[windowSize + i]);
}
}
// update solution on the chart
chart.UpdateDataSeries("solution", solution);
// set current iteration's info
SetText(currentIterationBox, iteration.ToString());
SetText(currentLearningErrorBox, learningError.ToString("F3"));
SetText(currentPredictionErrorBox, predictionError.ToString("F3"));
// increase current iteration
iteration++;
// check if we need to stop
if ((iterations != 0) && (iteration > iterations))
break;
}
// show new solution
for (int j = windowSize, k = 0, n = data.Length; j < n; j++, k++)
{
AddSubItem(dataList, j, solution[k, 1].ToString());
}
// enable settings controls
EnableControls(true);
}
public void Learn()
{
var network = new ActivationNetwork(new BipolarSigmoidFunction(), Constants.StoneCount, 1);
var teacher = new BackPropagationLearning(network);//new PerceptronLearning(network);
var data = LoadData("4-6-2012-04-24.know");
double error = 1.0;
int index = 0;
while (error > 0.001 && index < 100000) {
error = teacher.RunEpoch(data.Item1, data.Item2);
index++;
}
network.Save("4-6-2012-04-24.bp.net");
var text = "□○○○●○○□○●●□□●□□";
var i = ToDouble(text);//-2
var o = network.Compute(i);
var eval = o[0] * 2 * Constants.StoneCount - Constants.StoneCount;
Console.WriteLine("{0} {1}", text, eval);
}
public void LearnDemo()
{
ActivationNetwork network = new ActivationNetwork(new ThresholdFunction(), 2, 1);//Constants.StoneCount
PerceptronLearning teacher = new PerceptronLearning(network);
double[][] input = new double[4][];
double[][] output = new double[4][];
input[0] = new double[] { 0, 0 };
output[0] = new double[] { 0 };
input[1] = new double[] { 0, 1 };
output[1] = new double[] { 0 };
input[2] = new double[] { 1, 0 };
output[2] = new double[] { 0 };
input[3] = new double[] { 1, 1 };
output[3] = new double[] { 1 };
double error = 1.0;
while (error > 0.001) {
error = teacher.RunEpoch(input, output);
}
var k = network.Compute(new double[] { 0.9, 0.7 });
var o = network.Output;
network.Save("a.txt");
}
static void NeuralNetworkAccompanimentTest()
{
// initialize input and output values
double[][] input = new double[4][] {
new double[] {0, 0}, new double[] {0, 1},
new double[] {1, 0}, new double[] {1, 1}
};
double[][] output = new double[4][] {
new double[] {0}, new double[] {1},
new double[] {1}, new double[] {0}
};
SigmoidFunction sig = new SigmoidFunction();
Accord.Neuro.Networks.RestrictedBoltzmannMachine boltz = new Accord.Neuro.Networks.RestrictedBoltzmannMachine(200, 200);
// create neural network
ActivationNetwork network = new ActivationNetwork(
new SigmoidFunction(2),
200,
20,
200);
//BackPropagationLearning teacher = new BackPropagationLearning(network);
//LevenbergMarquardtLearning teacher = new LevenbergMarquardtLearning(network);
Accord.Neuro.Learning.ParallelResilientBackpropagationLearning teacher = new ParallelResilientBackpropagationLearning(network);
Accord.Neuro.Networks.DeepBeliefNetwork dpn = new Accord.Neuro.Networks.DeepBeliefNetwork(200, 20);
// teacher.IncreaseFactor = 1.01;
Composition c = Composition.LoadFromMIDI("test/other/ff7tifa.mid");
MusicPlayer player = new MusicPlayer();
//player.Play(c.Tracks[0]);
List<double[]> inputs = new List<double[]>(); List<double[]> outputs = new List<double[]>();
inputs.Add(GetDoublesFromNotes((c.Tracks[0].GetMainSequence() as MelodySequence).ToArray()));
outputs.Add(GetDoublesFromNotes((c.Tracks[1].GetMainSequence() as MelodySequence).ToArray()));
// inputs.Add(GetDoublesFromNotes((c.Tracks[1].GetMainSequence() as MelodySequence).ToArray()));
// outputs.Add(GetDoublesFromNotes((c.Tracks[2].GetMainSequence() as MelodySequence).ToArray()));
// inputs.Add(GetDoublesFromNotes((c.Tracks[0].GetMainSequence() as MelodySequence).ToArray()));
// outputs.Add(GetDoublesFromNotes((c.Tracks[3].GetMainSequence() as MelodySequence).ToArray()));
int its = 0;
while (its++ < 10000)
{
double error = teacher.RunEpoch(inputs.ToArray(), outputs.ToArray());
Console.WriteLine("{0}: Error - {1}", its, error);
}
var input_melody = (c.Tracks[0].GetMainSequence() as MelodySequence);
var new_notes = network.Compute(GetDoublesFromNotes(input_melody.ToArray()));
var new_mel = GetMelodyFromDoubles(new_notes);
player.Play(new_mel);
Console.ReadLine();
}
double[][] Compute(ActivationNetwork net, double[][] input)
{
double[][] result = new double[input.Length][];
for (int i = 0; i < input.Length; i++) {
double[] tmp = net.Compute(input[i]);
result[i] = new double[tmp.Length];
for (int j = 0; j < tmp.Length; j++) {
result[i][j] = tmp[j];
}
}
return result;
}
