public void FixedWeightsTest()
{
var dataset = KernelSupportVectorMachineTest.training;
var inputs = dataset.Submatrix(null, 0, 3);
var labels = Tools.Scale(0, 1, -1, 1, dataset.GetColumn(4)).ToInt32();
var machine = new KernelSupportVectorMachine(
Gaussian.Estimate(inputs), inputs[0].Length);
var smo = new SequentialMinimalOptimization(machine, inputs, labels);
smo.Complexity = 10;
double error = smo.Run();
Assert.AreEqual(0.19047619047619047, error);
Assert.AreEqual(265.78327637381551, ((Gaussian)machine.Kernel).Sigma);
Assert.AreEqual(29, machine.SupportVectors.Length);
double[] expectedWeights =
{
1.65717694716503, 1.20005456611466, -5.70824245415995, 10,
10, -2.38755497916487, 10, -8.15723436363058, 10, -10, 10,
10, -0.188634936781317, -5.4354281009458, -8.48341139483265,
-5.91105702760141, -5.71489190049223, 10, -2.37289205235858,
-3.33031262413522, -1.97545116517677, 10, -10, -9.563186799279,
-3.917941544845, -0.532584110773336, 4.81951847548326, 0.343668292727091,
-4.34159482731336
};
//Assert.IsTrue(expectedWeights.IsEqual(machine.Weights, 1e-5));
int[] actual = new int[labels.Length];
for (int i = 0; i < actual.Length; i++)
{
actual[i] = Math.Sign(machine.Compute(inputs[i]));
}
ConfusionMatrix matrix = new ConfusionMatrix(actual, labels);
Assert.AreEqual(8, matrix.FalseNegatives);
Assert.AreEqual(0, matrix.FalsePositives);
Assert.AreEqual(4, matrix.TruePositives);
Assert.AreEqual(30, matrix.TrueNegatives);
Assert.AreEqual(1 / 3.0, matrix.Sensitivity);
Assert.AreEqual(1, matrix.Specificity);
Assert.AreEqual(0.5, matrix.FScore);
Assert.AreEqual(0.5129891760425771, matrix.MatthewsCorrelationCoefficient);
}
public void ComputeTest5()
{
double[][] inputs = training.Submatrix(null, 0, 3);
int[] labels = Tools.Scale(0, 1, -1, 1, training.GetColumn(4)).ToInt32();
Gaussian kernel = Gaussian.Estimate(inputs);
var machine = new KernelSupportVectorMachine(kernel, inputs[0].Length);
var smo = new SequentialMinimalOptimization(machine, inputs, labels);
smo.Complexity = 1.0;
smo.UseClassProportions = true;
double error = smo.Run();
Assert.AreEqual(1, smo.Complexity);
Assert.AreEqual(0.4, smo.PositiveWeight);
Assert.AreEqual(1.0, smo.NegativeWeight);
Assert.AreEqual(0.4, smo.WeightRatio, 1e-10);
Assert.AreEqual(0.38095238095238093, error);
Assert.AreEqual(265.78327637381551, (machine.Kernel as Gaussian).Sigma);
Assert.AreEqual(32, machine.SupportVectors.Length);
int[] actual = new int[labels.Length];
for (int i = 0; i < actual.Length; i++)
{
actual[i] = Math.Sign(machine.Compute(inputs[i]));
}
ConfusionMatrix matrix = new ConfusionMatrix(actual, labels);
Assert.AreEqual(7, matrix.FalseNegatives);
Assert.AreEqual(9, matrix.FalsePositives);
Assert.AreEqual(5, matrix.TruePositives);
Assert.AreEqual(21, matrix.TrueNegatives);
Assert.AreEqual(0.41666666666666669, matrix.Sensitivity);
Assert.AreEqual(0.7, matrix.Specificity);
}
public void UseClassProportionsTest()
{
var dataset = KernelSupportVectorMachineTest.training;
var inputs = dataset.Submatrix(null, 0, 3);
var labels = Tools.Scale(0, 1, -1, 1, dataset.GetColumn(4)).ToInt32();
Gaussian kernel = Gaussian.Estimate(inputs);
var machine = new KernelSupportVectorMachine(kernel, inputs[0].Length);
var smo = new SequentialMinimalOptimization(machine, inputs, labels);
smo.Complexity = 1.0;
smo.UseClassProportions = true;
double error = smo.Run();
Assert.AreEqual(1, smo.Complexity);
Assert.AreEqual(0.4, smo.PositiveWeight);
Assert.AreEqual(1.0, smo.NegativeWeight);
Assert.AreEqual(0.4, smo.WeightRatio, 1e-10);
Assert.AreEqual(0.2857142857142857, error);
Assert.AreEqual(265.78327637381551, ((Gaussian)machine.Kernel).Sigma);
Assert.AreEqual(26, machine.SupportVectors.Length);
int[] actual = new int[labels.Length];
for (int i = 0; i < actual.Length; i++)
{
actual[i] = Math.Sign(machine.Compute(inputs[i]));
}
ConfusionMatrix matrix = new ConfusionMatrix(actual, labels);
Assert.AreEqual(12, matrix.FalseNegatives);
Assert.AreEqual(0, matrix.FalsePositives);
Assert.AreEqual(0, matrix.TruePositives);
Assert.AreEqual(30, matrix.TrueNegatives);
}
public void WeightRatioTest()
{
var dataset = KernelSupportVectorMachineTest.training;
var inputs = dataset.Submatrix(null, 0, 3);
var labels = Tools.Scale(0, 1, -1, 1, dataset.GetColumn(4)).ToInt32();
Gaussian kernel = Gaussian.Estimate(inputs);
{
var machine = new KernelSupportVectorMachine(kernel, inputs[0].Length);
var smo = new SequentialMinimalOptimization(machine, inputs, labels);
smo.Complexity = 1.0;
smo.WeightRatio = 10;
double error = smo.Run();
Assert.AreEqual(1.0, smo.PositiveWeight);
Assert.AreEqual(0.1, smo.NegativeWeight);
Assert.AreEqual(0.7142857142857143, error);
Assert.AreEqual(265.78327637381551, ((Gaussian)machine.Kernel).Sigma);
Assert.AreEqual(39, machine.SupportVectors.Length);
int[] actual = new int[labels.Length];
for (int i = 0; i < actual.Length; i++)
{
actual[i] = Math.Sign(machine.Compute(inputs[i]));
}
ConfusionMatrix matrix = new ConfusionMatrix(actual, labels);
Assert.AreEqual(12, matrix.TruePositives); // has more importance
Assert.AreEqual(0, matrix.FalseNegatives); // has more importance
Assert.AreEqual(30, matrix.FalsePositives);
Assert.AreEqual(0, matrix.TrueNegatives);
Assert.AreEqual(1.0, matrix.Sensitivity);
Assert.AreEqual(0.0, matrix.Specificity);
Assert.AreEqual(0.44444444444444448, matrix.FScore);
Assert.AreEqual(0.0, matrix.MatthewsCorrelationCoefficient);
}
{
var machine = new KernelSupportVectorMachine(kernel, inputs[0].Length);
var smo = new SequentialMinimalOptimization(machine, inputs, labels);
smo.Complexity = 1.0;
smo.WeightRatio = 0.1;
double error = smo.Run();
Assert.AreEqual(0.1, smo.PositiveWeight);
Assert.AreEqual(1.0, smo.NegativeWeight);
Assert.AreEqual(0.21428571428571427, error);
Assert.AreEqual(265.78327637381551, ((Gaussian)machine.Kernel).Sigma);
Assert.AreEqual(18, machine.SupportVectors.Length);
int[] actual = new int[labels.Length];
for (int i = 0; i < actual.Length; i++)
{
actual[i] = Math.Sign(machine.Compute(inputs[i]));
}
ConfusionMatrix matrix = new ConfusionMatrix(actual, labels);
Assert.AreEqual(8, matrix.FalseNegatives);
Assert.AreEqual(1, matrix.FalsePositives); // has more importance
Assert.AreEqual(4, matrix.TruePositives);
Assert.AreEqual(29, matrix.TrueNegatives); // has more importance
Assert.AreEqual(0.33333333333333331, matrix.Sensitivity);
Assert.AreEqual(0.96666666666666667, matrix.Specificity);
Assert.AreEqual(0.47058823529411764, matrix.FScore);
Assert.AreEqual(0.41849149947774944, matrix.MatthewsCorrelationCoefficient);
}
}
public void ComputeTest6()
{
double[][] inputs = training.Submatrix(null, 0, 3);
int[] labels = Tools.Scale(0, 1, -1, 1, training.GetColumn(4)).ToInt32();
Gaussian kernel = Gaussian.Estimate(inputs);
{
var machine = new KernelSupportVectorMachine(kernel, inputs[0].Length);
var smo = new SequentialMinimalOptimization(machine, inputs, labels);
smo.Complexity = 1.0;
smo.WeightRatio = 30 / 12.0;
double error = smo.Run();
Assert.AreEqual(1, smo.PositiveWeight);
Assert.AreEqual(0.4, smo.NegativeWeight);
Assert.AreEqual(0.21428571428571427, error);
Assert.AreEqual(265.78327637381551, (machine.Kernel as Gaussian).Sigma);
Assert.AreEqual(34, machine.SupportVectors.Length);
int[] actual = new int[labels.Length];
for (int i = 0; i < actual.Length; i++)
{
actual[i] = Math.Sign(machine.Compute(inputs[i]));
}
ConfusionMatrix matrix = new ConfusionMatrix(actual, labels);
Assert.AreEqual(9, matrix.FalseNegatives);
Assert.AreEqual(0, matrix.FalsePositives);
Assert.AreEqual(3, matrix.TruePositives);
Assert.AreEqual(30, matrix.TrueNegatives);
Assert.AreEqual(0.25, matrix.Sensitivity);
Assert.AreEqual(1.0, matrix.Specificity);
Assert.AreEqual(0.4, matrix.FScore);
Assert.AreEqual(0.4385290096535146, matrix.MatthewsCorrelationCoefficient);
}
{
var machine = new KernelSupportVectorMachine(kernel, inputs[0].Length);
var smo = new SequentialMinimalOptimization(machine, inputs, labels);
smo.Complexity = 1.0;
smo.WeightRatio = 12 / 30.0;
double error = smo.Run();
Assert.AreEqual(0.4, smo.PositiveWeight);
Assert.AreEqual(1.0, smo.NegativeWeight);
Assert.AreEqual(0.38095238095238093, error);
Assert.AreEqual(265.78327637381551, (machine.Kernel as Gaussian).Sigma);
Assert.AreEqual(32, machine.SupportVectors.Length);
int[] actual = new int[labels.Length];
for (int i = 0; i < actual.Length; i++)
{
actual[i] = Math.Sign(machine.Compute(inputs[i]));
}
ConfusionMatrix matrix = new ConfusionMatrix(actual, labels);
Assert.AreEqual(7, matrix.FalseNegatives);
Assert.AreEqual(9, matrix.FalsePositives);
Assert.AreEqual(5, matrix.TruePositives);
Assert.AreEqual(21, matrix.TrueNegatives);
Assert.AreEqual(0.41666666666666669, matrix.Sensitivity);
Assert.AreEqual(0.7, matrix.Specificity);
Assert.AreEqual(0.38461538461538458, matrix.FScore);
Assert.AreEqual(0.11180339887498948, matrix.MatthewsCorrelationCoefficient);
}
}
