public void ExplicitBPTimeTest()
{
int[] layers = new int[] { 4,10,1 };
MultilayerPerceptron nn = new MLPGenerator().Create(layers,1,new Sigmoid());
var result=nn.BP(new BPRequest(TestVec,1500*10));
throw new NotImplementedException("BP Total ms="+result.BPTime.TotalMilliseconds.ToString());
}
private void button2_Click(object sender,EventArgs e)
{
int cycles=int.Parse(textBox1.Text);
int hiddenNeurons=int.Parse(textBox2.Text);
double learnRate=double.Parse(textBox3.Text);
double momentum=double.Parse(textBox4.Text);
int[] layers = new int[] { 1,hiddenNeurons,1 };
MultilayerPerceptron ann=new MLPGenerator().Create(layers,1,new Sigmoid(2));
ann.Reset(-1,1);
ann.Momentum=momentum;
ann.LearnFactor=learnRate;
List<TrainingData> trainingList=new List<TrainingData>();
for(int i=0;i<Map.Count;i++) {
double[] input=new double[] { (double)Map[i].Item1/(double)pictureBox1.Width };
double[] output=new double[] { (double)Map[i].Item2/(double)pictureBox1.Height };
trainingList.Add(new TrainingData(input,output));
}
var res=ann.BP(new BPRequest(trainingList.ToArray(),cycles));
for(int i=0;i<pictureBox1.Width;i++) {
int x = i;
double yd = ann.Pulse(new double[] { (double)i/(double)pictureBox1.Width })[0];
int y=Convert.ToInt32(yd*pictureBox1.Height);
Point p= new Point(x,y);
Graph.DrawRectangle(new Pen(new SolidBrush(Color.Orange)),p.X,p.Y,1,1);
}
pictureBox1.Image=Bitmap;
}
public void BPMinCyclesTest()
{
int[] layers = new int[] { 4,5,1 };
MultilayerPerceptron nn = new MLPGenerator().Create(layers,1,new Sigmoid());
var result=nn.BP(new BPRequest(TestVec,5000,40));
Assert.IsTrue(result.Epochs.Length>=40);
}
public void BPMaxCyclesTest()
{
int[] layers = new int[] { 4,5,1 };
MultilayerPerceptron nn = new MLPGenerator().Create(layers,1,new Sigmoid());
var result=nn.BP(new BPRequest(TestVec,5));
Assert.AreEqual(result.Epochs.Length,5);
}
public void PulseManySameResultsTest()
{
MLPGenerator gen = new MLPGenerator();
int[] layers = new int[] { 4,3,2 };
MultilayerPerceptron nn = gen.Create(layers,1,new Sigmoid());
double[] inp = new double[] { -0.978, 2.34, 0.2, -0.33};
double[] res = nn.Pulse(inp);
double[] res2 = nn.Pulse(inp);
double[] res3 = nn.Pulse(inp);
Assert.AreEqual(res.Length, res2.Length);
Assert.AreEqual(res.Length, res3.Length);
for(int i=0; i<res.Length; i++) {
Assert.AreEqual(res[i], res2[i]);
Assert.AreEqual(res[i], res3[i]);
}
}
public void FullNetworkScanTest()
{
int[] layers=new int[]{2,3,1};
MultilayerPerceptron nn=new MLPGenerator().Create(layers,1,new Sigmoid());
//check input and output layer
Assert.AreEqual(nn.Structure.Input.Length,2);
Assert.AreEqual(nn.Structure.Output.Length,1);
int count=0;
foreach(var i in nn.Structure.Input) {
foreach(var j in i.Next)
CheckConnection(i,j.Next,ref count);
}
Assert.AreEqual(count,12);
//check bias
Assert.AreEqual(nn.Structure.Bias.Next.Length,4);
Assert.IsTrue(nn.Structure.Bias.Next.All(x => x.Next is Neuron));
}
public void ResetAutoTest()
{
int[] layers = new int[] { 4,5,1 };
MultilayerPerceptron nn = new MLPGenerator().Create(layers,1,new Sigmoid());
double[] randomSelected=new double[] {
nn.Structure.Input[1].Next[1].Weight.Value,
nn.Structure.Input[1].Next[2].Weight.Value,
nn.Structure.Input[1].Next[2].Next.Next[0].Weight.Value,
nn.Structure.Output[0].Previous[1].Weight.Value
};
double[] inp = new double[] { -0.978, 2.34, 0.2, -0.33};
double[] pulsed=nn.Pulse(inp);
nn.Reset(0,1);
double[] randomSelected2=new double[] {
nn.Structure.Input[1].Next[1].Weight.Value,
nn.Structure.Input[1].Next[2].Weight.Value,
nn.Structure.Input[1].Next[2].Next.Next[0].Weight.Value,
nn.Structure.Output[0].Previous[1].Weight.Value
};
for(int i=0;i<randomSelected.Length;i++) {
Assert.AreNotEqual(randomSelected[i],randomSelected2[i]);
}
double[] pulsed2=nn.Pulse(inp);
for(int i=0;i<pulsed.Length;i++) {
Assert.AreNotEqual(pulsed[i],pulsed2[i]);
}
}
public void ReasonableBPTimeTest()
{
int[] layers = new int[] { 4,5,1 };
MultilayerPerceptron nn = new MLPGenerator().Create(layers,1,new Sigmoid());
var result=nn.BP(new BPRequest(TestVec,10000));
Assert.IsTrue(result.BPTime.TotalMilliseconds<2000);
}
public void ResetManualTest()
{
int[] layers = new int[] { 4,5,1 };
MultilayerPerceptron nn = new MLPGenerator().Create(layers,1,new Sigmoid());
nn.Reset(() => 0.7);
Assert.AreEqual(nn.Structure.Input[1].Next[2].Weight.Value,0.7); // random selected
}
