public void ComputeTest5()
{
var dataset = SequentialMinimalOptimizationTest.GetYingYang();
double[][] inputs = dataset.Submatrix(null, 0, 1).ToJagged();
int[] labels = dataset.GetColumn(2).ToInt32();
var kernel = new Polynomial(2, 1);
Accord.Math.Tools.SetupGenerator(0);
var projection = inputs.Apply(kernel.Transform);
var machine = new SupportVectorMachine(projection[0].Length);
var smo = new LinearCoordinateDescent(machine, projection, labels)
{
Complexity = 1000000,
Tolerance = 1e-15
};
double error = smo.Run();
Assert.AreEqual(1000000.0, smo.Complexity, 1e-15);
int[] actual = new int[labels.Length];
for (int i = 0; i < actual.Length; i++)
{
actual[i] = Math.Sign(machine.Compute(projection[i]));
}
ConfusionMatrix matrix = new ConfusionMatrix(actual, labels);
Assert.AreEqual(6, matrix.FalseNegatives);
Assert.AreEqual(7, matrix.FalsePositives);
Assert.AreEqual(44, matrix.TruePositives);
Assert.AreEqual(43, matrix.TrueNegatives);
}
public void RunTest2()
{
var dataset = SequentialMinimalOptimizationTest.GetYingYang();
double[][] inputs = dataset.Submatrix(null, 0, 1).ToJagged();
int[] labels = dataset.GetColumn(2).ToInt32();
var svm = new SupportVectorMachine(inputs: 2);
var teacher = new ProbabilisticCoordinateDescent(svm, inputs, labels);
teacher.Tolerance = 1e-10;
teacher.Complexity = 1e+10;
double error = teacher.Run();
double[] weights = svm.ToWeights();
Assert.AreEqual(0.11, error);
Assert.AreEqual(3, weights.Length);
Assert.AreEqual(-1.3231203367770932, weights[0], 1e-8);
Assert.AreEqual(-3.0227742288788493, weights[1], 1e-8);
Assert.AreEqual(-0.73074823290553259, weights[2], 1e-8);
Assert.AreEqual(svm.Threshold, weights[0]);
}
public void RunTest()
{
var dataset = SequentialMinimalOptimizationTest.GetYingYang();
double[][] inputs = dataset.Submatrix(null, 0, 1).ToJagged();
int[] labels = dataset.GetColumn(2).ToInt32();
Accord.Math.Random.Generator.Seed = 0;
var svm = new SupportVectorMachine(inputs: 2);
var teacher = new ProbabilisticDualCoordinateDescent(svm, inputs, labels);
teacher.Tolerance = 1e-10;
teacher.UseComplexityHeuristic = true;
Assert.IsFalse(svm.IsProbabilistic);
double error = teacher.Run();
Assert.IsTrue(svm.IsProbabilistic);
double[] weights = svm.ToWeights();
Assert.AreEqual(0.13, error);
Assert.AreEqual(3, weights.Length);
Assert.AreEqual(-0.52913278486359605, weights[0], 1e-4);
Assert.AreEqual(-1.6426069611746976, weights[1], 1e-4);
Assert.AreEqual(-0.77766953652287762, weights[2], 1e-4);
Assert.AreEqual(svm.Threshold, weights[0]);
}
public void ComputeTest5()
{
var dataset = SequentialMinimalOptimizationTest.GetYingYang();
var inputs = dataset.Submatrix(null, 0, 1).ToJagged();
var labels = dataset.GetColumn(2).ToInt32();
var kernel = new Polynomial(2, 0);
{
var machine = new KernelSupportVectorMachine(kernel, inputs[0].Length);
var smo = new SequentialMinimalOptimization(machine, inputs, labels);
smo.UseComplexityHeuristic = true;
double error = smo.Run();
Assert.AreEqual(0.2, error);
Assert.AreEqual(0.11714451552090824, smo.Complexity);
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(20, matrix.FalseNegatives);
Assert.AreEqual(0, matrix.FalsePositives);
Assert.AreEqual(30, matrix.TruePositives);
Assert.AreEqual(50, matrix.TrueNegatives);
}
{
Accord.Math.Tools.SetupGenerator(0);
var projection = inputs.Apply(kernel.Transform);
var machine = new SupportVectorMachine(projection[0].Length);
var smo = new LinearNewtonMethod(machine, projection, labels);
smo.UseComplexityHeuristic = true;
double error = smo.Run();
Assert.AreEqual(0.18, error);
Assert.AreEqual(0.11714451552090821, smo.Complexity, 1e-15);
int[] actual = new int[labels.Length];
for (int i = 0; i < actual.Length; i++)
{
actual[i] = Math.Sign(machine.Compute(projection[i]));
}
ConfusionMatrix matrix = new ConfusionMatrix(actual, labels);
Assert.AreEqual(17, matrix.FalseNegatives);
Assert.AreEqual(1, matrix.FalsePositives);
Assert.AreEqual(33, matrix.TruePositives);
Assert.AreEqual(49, matrix.TrueNegatives);
}
}
public void multithread_test()
{
Accord.Math.Random.Generator.Seed = 0;
var dataset = SequentialMinimalOptimizationTest.GetYingYang();
double[][] inputs = dataset.Submatrix(null, 0, 1).ToJagged();
int[] labels = dataset.GetColumn(2).ToInt32();
var kernel = new Polynomial(2, 1);
var projection = kernel.Transform(inputs);
var t = new Thread[100];
var svms = new SupportVectorMachine <Linear> [t.Length];
for (int i = 0; i < t.Length; i++)
{
t[i] = new Thread((object obj) =>
{
int j = (int)obj;
var teacher = new LinearCoordinateDescent <Linear>()
{
Complexity = 1000000,
Tolerance = 1e-15
};
svms[j] = teacher.Learn(projection, labels);
});
t[i].Start(i);
}
for (int i = 0; i < t.Length; i++)
{
t[i].Join();
}
double[][] sv = svms[0].SupportVectors;
double[] w = svms[0].Weights;
for (int i = 0; i < svms.Length; i++)
{
Assert.IsTrue(sv.IsEqual(svms[i].SupportVectors));
Assert.IsTrue(w.IsEqual(svms[i].Weights));
int[] actual = svms[i].Decide(projection).ToMinusOnePlusOne();
var matrix = new ConfusionMatrix(actual, labels);
Assert.AreEqual(6, matrix.FalseNegatives);
Assert.AreEqual(7, matrix.FalsePositives);
Assert.AreEqual(44, matrix.TruePositives);
Assert.AreEqual(43, matrix.TrueNegatives);
}
}
public void KernelTest2()
{
var dataset = SequentialMinimalOptimizationTest.GetYingYang();
var inputs = dataset.Submatrix(null, 0, 1).ToJagged();
var labels = dataset.GetColumn(2).ToInt32();
var svm = new KernelSupportVectorMachine(new Linear(1), inputs: 2);
var p = new ProbabilisticCoordinateDescent(svm, inputs, labels);
Assert.NotNull(p);
}
public void KernelTest1()
{
var dataset = SequentialMinimalOptimizationTest.GetYingYang();
double[][] inputs = dataset.Submatrix(null, 0, 1).ToJagged();
int[] labels = dataset.GetColumn(2).ToInt32();
double e1, e2;
double[] w1, w2;
{
Accord.Math.Random.Generator.Seed = 0;
var svm = new SupportVectorMachine(inputs: 2);
var teacher = new ProbabilisticCoordinateDescent(svm, inputs, labels);
teacher.Tolerance = 1e-10;
teacher.Complexity = 1e+10;
e1 = teacher.Run();
w1 = svm.ToWeights();
}
{
Accord.Math.Random.Generator.Seed = 0;
var svm = new KernelSupportVectorMachine(new Linear(0), inputs: 2);
var teacher = new ProbabilisticCoordinateDescent(svm, inputs, labels);
teacher.Tolerance = 1e-10;
teacher.Complexity = 1e+10;
e2 = teacher.Run();
w2 = svm.ToWeights();
}
Assert.AreEqual(e1, e2);
Assert.AreEqual(w1.Length, w2.Length);
Assert.AreEqual(w1[0], w2[0], 1e-8);
Assert.AreEqual(w1[1], w2[1], 1e-8);
Assert.AreEqual(w1[2], w2[2], 1e-8);
}
public void different_lengths_same_error()
{
// GH-191: Different accuracy by specifying KernelSupportVectorMachine
// input length https://github.com/accord-net/framework/issues/191
var dataset = SequentialMinimalOptimizationTest.GetYingYang();
double[][] inputs = dataset.Submatrix(null, 0, 1).ToJagged();
int[] outputs = dataset.GetColumn(2).ToInt32();
var machine1 = new KernelSupportVectorMachine(new Linear(), inputs[0].Length);
var teacher1 = new LinearDualCoordinateDescent(machine1, inputs, outputs);
var error1 = teacher1.Run(true);
var machine2 = new KernelSupportVectorMachine(new Linear(), 0);
var teacher2 = new LinearDualCoordinateDescent(machine2, inputs, outputs);
var error2 = teacher2.Run(true);
Assert.AreEqual(error1, error2);
}
