public void XOR(int iterations, double minimumAccuracy)
{
var mlp = new MultilayerPerceptron <Tanh>(2, new int[] { 10, 10 }, 1);
double[][] inputs = new double[][] {
new double[] { 0, 0 },
new double[] { 1, 0 },
new double[] { 0, 1 },
new double[] { 1, 1 },
};
double[][] outputs = new double[][] {
new double[] { 0 },
new double[] { 1 },
new double[] { 1 },
new double[] { 0 },
};
double[] output = new double[1];
for (int i = 0; i < iterations; i++)
{
for (int j = 0; j < inputs.Length; j++)
{
mlp.Predict(inputs[j], output);
mlp.Train(outputs[j], 0.4, 0.9);
}
}
for (int j = 0; j < inputs.Length; j++)
{
mlp.Predict(inputs[j], output);
double diff = 1 - Math.Abs(output[0] - outputs[j][0]);
Assert.GreaterOrEqual(diff, minimumAccuracy);
}
}
private static void Main()
{
// The mlp takes column vectors as input and gives column vectors as output. The dlib::matrix
// object is used to represent the column vectors. So the first thing we do here is declare
// a convenient typedef for the matrix object we will be using.
// This typedef declares a matrix with 2 rows and 1 column. It will be the
// object that contains each of our 2 dimensional samples. (Note that if you wanted
// more than 2 features in this vector you can simply change the 2 to something else)
//typedef matrix<double, 2, 1 > sample_type;
// make an instance of a sample matrix so we can use it below
using (var sample = new SampleType(2, 1))
{
// Create a multi-layer perceptron network. This network has 2 nodes on the input layer
// (which means it takes column vectors of length 2 as input) and 5 nodes in the first
// hidden layer. Note that the other 4 variables in the mlp's constructor are left at
// their default values.
using (var net = new MultilayerPerceptron <Kernel1>(2, 5))
{
// Now let's put some data into our sample and train on it. We do this
// by looping over 41*41 points and labeling them according to their
// distance from the origin.
for (var i = 0; i < 1000; ++i)
{
for (var r = -20; r <= 20; ++r)
{
for (var c = -20; c <= 20; ++c)
{
sample[0] = r;
sample[1] = c;
// if this point is less than 10 from the origin
if (Math.Sqrt((double)r * r + c * c) <= 10)
{
net.Train(sample, 1);
}
else
{
net.Train(sample, 0);
}
}
}
}
// Now we have trained our mlp. Let's see how well it did.
// Note that if you run this program multiple times you will get different results. This
// is because the mlp network is randomly initialized.
// each of these statements prints out the output of the network given a particular sample.
sample[0] = 3.123;
sample[1] = 4;
using (var ret = net.Operator(sample))
Console.WriteLine($"This sample should be close to 1 and it is classified as a {ret}");
sample[0] = 13.123;
sample[1] = 9.3545;
using (var ret = net.Operator(sample))
Console.WriteLine($"This sample should be close to 0 and it is classified as a {ret}");
sample[0] = 13.123;
sample[1] = 0;
using (var ret = net.Operator(sample))
Console.WriteLine($"This sample should be close to 0 and it is classified as a {ret}");
}
}
}
public void Iris(int iterations, double minimumAccuracy)
{
string[] lines = File.ReadAllLines("./Data/iris.csv");
double[][] inputs = new double[lines.Length][];
double[][] outputs = new double[lines.Length][];
for (int i = 0; i < lines.Length; i++)
{
inputs[i] = new double[4];
outputs[i] = new double[1];
string[] values = lines[i].Split(',');
for (int j = 0; j < 4; j++)
{
inputs[i][j] = double.Parse(values[j]);
}
outputs[i][0] = Map(values[4]);
}
for (int i = 0; i < 4; i++)
{
double min = double.PositiveInfinity;
double max = double.NegativeInfinity;
for (int j = 0; j < inputs.Length; j++)
{
if (inputs[j][i] < min)
{
min = inputs[j][i];
}
if (inputs[j][i] > max)
{
max = inputs[j][i];
}
}
for (int j = 0; j < inputs.Length; j++)
{
inputs[j][i] = Normalize((inputs[j][i] - min) / (max - min));
}
}
var mlp = new MultilayerPerceptron <Tanh>(4, new int[] { 10, 10 }, 1);
double normalizedCorrect = 0;
double[] output = new double[1];
for (int i = 0; i < iterations; i++)
{
int correct = 0;
for (int j = 0; j < inputs.Length; j++)
{
mlp.Predict(inputs[j], output);
mlp.Train(outputs[j], 0.1, 0.4);
double expectedOutput = outputs[j][0];
if (Map(expectedOutput) == Map(output[0]))
{
correct++;
}
}
normalizedCorrect = (double)correct / inputs.Length;
}
Assert.GreaterOrEqual(normalizedCorrect, minimumAccuracy);
}