public static void Demo()
{
PCANetwork network = PCANetwork.Create(64, 8);
var dataSet = DataGenerator.GenerateDataSet1();
var pca = new PCA(network, 0.7);
PCATrainer trainer = new PCATrainer(network, 50, pca, 0.00000005);
Normalizer3 normalizer = new Normalizer3();
//Normalizer normalizer = new Normalizer();
normalizer.Fit(dataSet.XList);
var normalizedX = normalizer.Normalize(dataSet.XList);
Utils.DisplayListList(dataSet.XList);
Console.WriteLine("--------------------------");
Utils.DisplayListList(normalizedX);
//Utils.DisplayListList(normalizer.DeNormalize(normalizedX));
trainer.Fit(normalizedX);
List <List <double> > convertedX = trainer.GetConvertedDim(normalizedX);
//convertedX=normalizer.DeNormalize(convertedX);
BPNetwork bpNetwork = BPNetwork.Create(8, new int[] { 4 /*, 4, 3*/ }, 3, ActivationFunction.SIGMOID, ActivationFunction.SIGMOID);
var bp = new BP(bpNetwork, 0.6);
BPTrainer bpTrainer = new BPTrainer(bpNetwork, 2000, bp);
bpTrainer.Fit(convertedX, dataSet.YList);
List <List <double> > predicted_ys = bpTrainer.Predict(convertedX);
CorrectCalculator correctCalculator = new CorrectCalculator();
var convertedPredictValue = NeuronOutputConverter.OrderInteger(predicted_ys);
double correct = correctCalculator.Calculate(convertedPredictValue, dataSet.YList);
Console.WriteLine("Score: {0}%", correct * 100);
}
public static void Demo()
{
//BPNetwork network = BPNetwork.Create(1, new int[] { 1/*, 4, 3*/ }, 2, ActivationFunction.SIGMOID, ActivationFunction.SIGMOID);
BPNetwork network = BPNetwork.Create(32, new int[] { 10 /*, 4, 3*/ }, 1, ActivationFunction.SIGMOID, ActivationFunction.SIGMOID);
network.Display();
//var dataSet = DataGenerator.GenerateDataSet1();
var dataSet = DataGenerator.GenerateDataSet1_2();
var bp = new BP(network, 0.6);
BPTrainer trainer = new BPTrainer(network, 500, bp);
trainer.Fit(dataSet.XList, dataSet.YList);
List <List <double> > predicted_ys = trainer.Predict(dataSet.XList);
Console.WriteLine("Network Outputs: ");
foreach (var ys in predicted_ys)
{
Console.Write(" ");
foreach (var y in ys)
{
Console.Write(y);
Console.Write(", ");
}
Console.WriteLine();
}
network.Display();
CorrectCalculator correctCalculator = new CorrectCalculator();
var convertedPredictValue = NeuronOutputConverter.OrderInteger(predicted_ys);
double correct = correctCalculator.Calculate(convertedPredictValue, dataSet.YList);
Console.WriteLine("Score: {0}%", correct * 100);
StringBuilder sb_epoch = new StringBuilder();
StringBuilder sb_errors = new StringBuilder();
int step = 0;
trainer.Epoch_Errors.ForEach(err => {
sb_epoch.Append(step + ",");
sb_errors.Append(err + ",");
step++;
});
//生成文本数据，拷贝到python中作图
StringBuilder sb = new StringBuilder();
sb.AppendLine("import matplotlib.pyplot as plt");
sb.AppendLine("x1=[" + sb_epoch.ToString().TrimEnd(",".ToCharArray()) + "]");
sb.AppendLine("y1=[" + sb_errors.ToString().TrimEnd(",".ToCharArray()) + "]");
sb.AppendLine("plt.plot(x1,y1,'b^')");
sb.AppendLine("plt.show()");
var file = System.IO.Path.Combine(AppContext.BaseDirectory, "display.py");
File.WriteAllText(file, sb.ToString());
Console.WriteLine("saved to path: " + file);
System.Diagnostics.Process.Start("C:\\ProgramData\\Anaconda3\\envs\\keras\\python.exe", "\"" + file + "\"");
}
