public void LeastSquaresConstructorTest()
{
double[][] inputs =
{
new double[] { -1, -1 },
new double[] { -1, 1 },
new double[] { 1, -1 },
new double[] { 1, 1 }
};
int[] or =
{
0,
0,
0,
+1
};
// Create Kernel Support Vector Machine with a Polynomial Kernel of 2nd degree
SupportVectorMachine machine = new SupportVectorMachine(inputs[0].Length);
bool thrown = false;
try
{
LeastSquaresLearning learn = new LeastSquaresLearning(machine, inputs, or);
}
catch (ArgumentOutOfRangeException)
{
thrown = true;
}
Assert.IsTrue(thrown);
}
public void LearnTest2()
{
var dataset = new YinYang();
double[][] inputs = dataset.Instances;
int[] outputs = dataset.ClassLabels.Apply(x => x ? 1 : -1);
// Create Kernel Support Vector Machine with a Polynomial Kernel of 2nd degree
KernelSupportVectorMachine machine = new KernelSupportVectorMachine(new Polynomial(3), inputs[0].Length);
// Create the Least Squares Support Vector Machine teacher
LeastSquaresLearning learn = new LeastSquaresLearning(machine, inputs, outputs);
learn.Complexity = 1 / 0.1;
// Run the learning algorithm
learn.Run();
int[] output = inputs.Apply(p => Math.Sign(machine.Compute(p)));
for (int i = 0; i < output.Length; i++)
{
Assert.AreEqual(System.Math.Sign(outputs[i]), System.Math.Sign(output[i]));
}
}
/// <summary>
/// <inheritdoc />
/// </summary>
public override void Train()
{
var inputs = data.GetSelectedInput(features);
var outputs = data.GetExpectedClassificationOutput();
var teacher = new LeastSquaresLearning <Gaussian, double[]>()
{
Kernel = new Gaussian(),
UseComplexityHeuristic = true,
WeightRatio = 2.0,
UseKernelEstimation = true,
};
svm = teacher.Learn(inputs, outputs);
Save();
}
public void LargeLearningTest1()
{
// Create large input vectors
int rows = 1000;
int dimension = 10000;
double[][] inputs = new double[rows][];
int[] outputs = new int[rows];
Random rnd = new Random();
for (int i = 0; i < inputs.Length; i++)
{
inputs[i] = new double[dimension];
if (i > rows / 2)
{
for (int j = 0; j < dimension; j++)
{
inputs[i][j] = rnd.NextDouble();
}
outputs[i] = -1;
}
else
{
for (int j = 0; j < dimension; j++)
{
inputs[i][j] = rnd.NextDouble() * 4.21 + 5;
}
outputs[i] = +1;
}
}
KernelSupportVectorMachine svm = new KernelSupportVectorMachine(new Polynomial(2), dimension);
LeastSquaresLearning smo = new LeastSquaresLearning(svm, inputs, outputs);
double error = smo.Run();
Assert.AreEqual(0, error);
}
public void LearnTest()
{
double[][] inputs =
{
new double[] { -1, -1 },
new double[] { -1, 1 },
new double[] { 1, -1 },
new double[] { 1, 1 }
};
int[] xor =
{
-1,
1,
1,
-1
};
// Create Kernel Support Vector Machine with a Polynomial Kernel of 2nd degree
KernelSupportVectorMachine machine = new KernelSupportVectorMachine(new Polynomial(2), inputs[0].Length);
// Create the Least Squares Support Vector Machine teacher
LeastSquaresLearning learn = new LeastSquaresLearning(machine, inputs, xor);
learn.Complexity = 10;
// Run the learning algorithm
learn.Run();
int[] output = inputs.Apply(p => Math.Sign(machine.Compute(p)));
for (int i = 0; i < output.Length; i++)
{
Assert.AreEqual(System.Math.Sign(xor[i]), System.Math.Sign(output[i]));
}
}
public void LearnTest2()
{
double[][] inputs = yinyang.Submatrix(null, 0, 1).ToJagged();
int[] outputs = yinyang.GetColumn(2).ToInt32();
// Create Kernel Support Vector Machine with a Polynomial Kernel of 2nd degree
KernelSupportVectorMachine machine = new KernelSupportVectorMachine(new Polynomial(3), inputs[0].Length);
// Create the Least Squares Support Vector Machine teacher
LeastSquaresLearning learn = new LeastSquaresLearning(machine, inputs, outputs);
learn.Complexity = 1 / 0.1;
// Run the learning algorithm
learn.Run();
int[] output = inputs.Apply(p => Math.Sign(machine.Compute(p)));
for (int i = 0; i < output.Length; i++)
{
Assert.AreEqual(System.Math.Sign(outputs[i]), System.Math.Sign(output[i]));
}
}
public void LeastSquaresConstructorTest()
{
double[][] inputs =
{
new double[] { -1, -1 },
new double[] { -1, 1 },
new double[] { 1, -1 },
new double[] { 1, 1 }
};
int[] or =
{
0,
0,
0,
+1
};
// Create Kernel Support Vector Machine with a Polynomial Kernel of 2nd degree
var machine = new SupportVectorMachine(inputs[0].Length);
var learn = new LeastSquaresLearning(machine, inputs, or);
double error = learn.Run();
Assert.AreEqual(0, error);
{
int[] iout = new int[inputs.Length];
machine.ToMulticlass().Decide(inputs, iout);
for (int i = 0; i < iout.Length; i++)
{
Assert.AreEqual(or[i], iout[i]);
}
}
{
double[] dout = new double[inputs.Length];
machine.ToMulticlass().Decide(inputs, dout);
for (int i = 0; i < dout.Length; i++)
{
Assert.AreEqual(or[i], dout[i]);
}
}
{
bool[] bout = new bool[inputs.Length];
machine.Decide(inputs, bout);
Assert.IsFalse(bout[0]);
Assert.IsFalse(bout[1]);
Assert.IsFalse(bout[2]);
Assert.IsTrue(bout[3]);
}
{
int[][] iiout = Jagged.Create <int>(inputs.Length, 2);
machine.ToMulticlass().Decide(inputs, iiout);
for (int i = 0; i < iiout.Length; i++)
{
Assert.AreEqual(or[i], iiout[i][0]);
Assert.AreEqual(or[i], iiout[i][1] == 1 ? 0 : 1);
}
}
{
bool[][] bbout = Jagged.Create <bool>(inputs.Length, 2);
machine.ToMulticlass().Decide(inputs, bbout);
for (int i = 0; i < bbout.Length; i++)
{
Assert.AreEqual(or[i], bbout[i][0] ? 1 : 0);
Assert.AreEqual(or[i], bbout[i][1] ? 0 : 1);
}
}
}
