public void ComputeTest2()
{
// XOR
double[][] inputs =
{
new double[] { 0, 0 },
new double[] { 0, 1 },
new double[] { 1, 0 },
new double[] { 1, 1 }
};
int[] labels =
{
-1,
1,
1,
-1
};
KernelSupportVectorMachine machine = new KernelSupportVectorMachine(new Gaussian(0.1), inputs[0].Length);
SequentialMinimalOptimization smo = new SequentialMinimalOptimization(machine, inputs, labels);
smo.Complexity = 1;
double error = smo.Run();
Assert.AreEqual(-1, Math.Sign(machine.Compute(inputs[0])));
Assert.AreEqual(+1, Math.Sign(machine.Compute(inputs[1])));
Assert.AreEqual(+1, Math.Sign(machine.Compute(inputs[2])));
Assert.AreEqual(-1, Math.Sign(machine.Compute(inputs[3])));
Assert.AreEqual(error, 0);
}
public void DynamicalTimeWarpingConstructorTest2()
{
// Declare some testing data
double[][] inputs =
{
// Class -1
new double[] { 0, 1, 1, 0 },
new double[] { 0, 0, 1, 0 },
new double[] { 0, 1, 1,1, 0 },
new double[] { 0, 1,0 },
// Class +1
new double[] { 1, 0, 0, 1 },
new double[] { 1, 1, 0, 1 },
new double[] { 1, 0, 0,0, 1 },
new double[] { 1, 0,1 },
new double[] { 1, 0, 0,0, 1, 1 }
};
int[] outputs =
{
-1, -1, -1,-1, // First four sequences are of class -1
1, 1, 1,1, 1 // Last five sequences are of class +1
};
// Set the parameters of the kernel
double alpha = 1.0;
int degree = 1;
int innerVectorLength = 1;
// Create the kernel. Note that the input vector will be given out automatically
DynamicTimeWarping target = new DynamicTimeWarping(innerVectorLength, alpha, degree);
// When using variable-length kernels, specify 0 as the input length.
KernelSupportVectorMachine svm = new KernelSupportVectorMachine(target, 0);
// Create the Sequential Minimal Optimization as usual
SequentialMinimalOptimization smo = new SequentialMinimalOptimization(svm, inputs, outputs);
smo.Complexity = 1.5;
double error = smo.Run();
// Check if the model has learnt the sequences correctly.
for (int i = 0; i < inputs.Length; i++)
{
int expected = outputs[i];
int actual = System.Math.Sign(svm.Compute(inputs[i]));
Assert.AreEqual(expected, actual);
}
// Testing new sequences
Assert.AreEqual(-1, System.Math.Sign(svm.Compute(new double[] { 0, 1, 1, 0, 0 })));
Assert.AreEqual(+1, System.Math.Sign(svm.Compute(new double[] { 1, 1, 0, 0, 1, 1 })));
}
public void TrainTest()
{
Accord.Math.Tools.SetupGenerator(0);
// Example regression problem. Suppose we are trying
// to model the following equation: f(x, y) = 2x + y
double[][] inputs = // (x, y)
{
new double[] { 0, 1 }, // 2*0 + 1 = 1
new double[] { 4, 3 }, // 2*4 + 3 = 11
new double[] { 8, -8 }, // 2*8 - 8 = 8
new double[] { 2, 2 }, // 2*2 + 2 = 6
new double[] { 6, 1 }, // 2*6 + 1 = 13
new double[] { 5, 4 }, // 2*5 + 4 = 14
new double[] { 9, 1 }, // 2*9 + 1 = 19
new double[] { 1, 6 }, // 2*1 + 6 = 8
};
double[] outputs = // f(x, y)
{
1, 11, 8, 6, 13, 14, 19, 8
};
// Create Kernel Support Vector Machine with a Polynomial Kernel of 2nd degree
var machine = new KernelSupportVectorMachine(new Polynomial(2), inputs: 2);
// Create the sequential minimal optimization teacher
var learn = new SequentialMinimalOptimizationRegression(machine, inputs, outputs)
{
Complexity = 100
};
// Run the learning algorithm
double error = learn.Run();
// Compute the answer for one particular example
double fxy = machine.Compute(inputs[0]); // 1.0003849827673186
// Check for correct answers
double[] answers = new double[inputs.Length];
for (int i = 0; i < answers.Length; i++)
{
answers[i] = machine.Compute(inputs[i]);
}
Assert.AreEqual(1.0, fxy, 1e-2);
for (int i = 0; i < outputs.Length; i++)
{
Assert.AreEqual(outputs[i], answers[i], 1e-2);
}
}
private void timer3_Tick(object sender, EventArgs e)
{
double[] dt = M.Current();
double sum = 0;
for (int j = 0; j < VectorSize - 2; j++)
{
sum += dt[j];
}
for (int i = 2; i < VectorSize - 2; i++)
{
dt[i] = dt[i] / 1000;
}
if (dt[0] > 2000)
{
dt[0] = 2000;
}
if (dt[1] > 2000)
{
dt[1] = 2000;
}
dt[0] = dt[0] / 2000;
dt[1] = dt[1] / 2000;
dt[VectorSize - 2] = dt[VectorSize - 2] / 100;
dt[VectorSize - 1] = dt[VectorSize - 1] / 100;
double d = svm.Compute(dt);
textBox12.Text = d.ToString();
if (Math.Abs(d * 15) < 60)
{
if (Points < pictureBox1.Width - 1)
{
points.Add(new PointF(Points, 60 + (int)(15 * d)));
Points++;
}
else
{
points.RemoveAt(0);
for (int i = 0; i < points.Count; i++)
{
points[i] = new PointF(points[i].X - 1, points[i].Y);
}
points.Add(new PointF(Points, 60 + (int)(15 * d)));
}
}
// File.AppendAllText("Res.txt", d.ToString()+"\r\n");
pictureBox1.Refresh();
}
public void SVMTestData()
{
List <DataSet> dataset = new List <DataSet>();
FillOnes(dataset);
FillZeros(dataset);
KernelSupportVectorMachine machine = new KernelSupportVectorMachine(
new Polynomial(2), 25);
var learn = new SequentialMinimalOptimization(machine, dataset.ToArray());
double[] error = learn.Run();
double[] output = machine.Compute(dataset.ToArray());
double[] expected = new double[dataset.Count];
for (int i = 0; i < dataset.Count; i++)
{
output[i] = Math.Round(output[i]);
expected[i] = dataset[i].Expected;
}
CollectionAssert.AreEqual(expected, output);
}
public void testSVM()
{
if (_svm == null)
{
return;
}
int[] output = new int[_outputs.Length];
// Compute the machine outputs
for (int i = 0; i < _outputs.Length; i++)
{
double actual = _outputs[i];
double predicted = _svm.Compute(_inputs[i]);
// System.Console.WriteLine(Math.Sign(actual) + " " + _names[i] + " => " + predicted + " => " + Math.Sign(predicted));
output[i] = System.Math.Sign(predicted);
}
// Use confusion matrix to compute some performance metrics
ConfusionMatrix confusionMatrix = new ConfusionMatrix(output, _outputs, 1, -1);
FormDataView <double> f = new FormDataView <double>(new[] { confusionMatrix });
f.Show();
//dgvPerformance.DataSource = new[] { confusionMatrix };
}
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 sequential minimal optimization teacher
SequentialMinimalOptimization learn = new SequentialMinimalOptimization(machine, inputs, xor);
// 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()
{
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]));
}
}
private void btnTestingRun_Click(object sender, EventArgs e)
{
if (svm == null || dgvTestingSource.DataSource == null)
{
MessageBox.Show("Please create a machine first.");
return;
}
// Creates a matrix from the source data table
double[,] sourceMatrix = (dgvTestingSource.DataSource as DataTable).ToMatrix();
// Extract inputs
double[][] inputs = new double[sourceMatrix.GetLength(0)][];
for (int i = 0; i < inputs.Length; i++)
{
inputs[i] = new double[] { sourceMatrix[i, 0], sourceMatrix[i, 1] }
}
;
// Get only the label outputs
int[] expected = new int[sourceMatrix.GetLength(0)];
for (int i = 0; i < expected.Length; i++)
{
expected[i] = (int)sourceMatrix[i, 2];
}
// Compute the machine outputs
int[] output = new int[expected.Length];
for (int i = 0; i < expected.Length; i++)
{
output[i] = System.Math.Sign(svm.Compute(inputs[i]));
}
double[] expectedd = new double[expected.Length];
double[] outputd = new double[expected.Length];
for (int i = 0; i < expected.Length; i++)
{
expectedd[i] = expected[i];
outputd[i] = output[i];
}
// Use confusion matrix to compute some statistics.
ConfusionMatrix confusionMatrix = new ConfusionMatrix(output, expected, 1, -1);
dgvPerformance.DataSource = new List <ConfusionMatrix> {
confusionMatrix
};
foreach (DataGridViewColumn col in dgvPerformance.Columns)
{
col.Visible = true;
}
Column1.Visible = Column2.Visible = false;
// Create performance scatterplot
CreateResultScatterplot(zedGraphControl1, inputs, expectedd, outputd);
}
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 ComputeTest()
{
// Example AND problem
double[][] inputs =
{
new double[] { 0, 0 }, // 0 and 0: 0 (label -1)
new double[] { 0, 1 }, // 0 and 1: 0 (label -1)
new double[] { 1, 0 }, // 1 and 0: 0 (label -1)
new double[] { 1, 1 } // 1 and 1: 1 (label +1)
};
// Dichotomy SVM outputs should be given as [-1;+1]
int[] labels =
{
// 0, 0, 0, 1
-1, -1, -1, 1
};
// Create a Support Vector Machine for the given inputs
KernelSupportVectorMachine machine = new KernelSupportVectorMachine(new Gaussian(0.1), inputs[0].Length);
// Instantiate a new learning algorithm for SVMs
SequentialMinimalOptimization smo = new SequentialMinimalOptimization(machine, inputs, labels);
// Set up the learning algorithm
smo.Complexity = 1.0;
// Run
double error = smo.Run();
Assert.AreEqual(-1, Math.Sign(machine.Compute(inputs[0])));
Assert.AreEqual(-1, Math.Sign(machine.Compute(inputs[1])));
Assert.AreEqual(-1, Math.Sign(machine.Compute(inputs[2])));
Assert.AreEqual(+1, Math.Sign(machine.Compute(inputs[3])));
Assert.AreEqual(error, 0);
Assert.AreEqual(-0.6640625, machine.Threshold);
Assert.AreEqual(1, machine.Weights[0]);
Assert.AreEqual(-0.34375, machine.Weights[1]);
Assert.AreEqual(-0.328125, machine.Weights[2]);
Assert.AreEqual(-0.328125, machine.Weights[3]);
}
public void FixedWeightsTest()
{
var dataset = KernelSupportVectorMachineTest.training;
var inputs = dataset.Submatrix(null, 0, 3);
var labels = Accord.Math.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 RunTest3()
{
// Example XOR problem
double[][] inputs =
{
new double[] { 0, 0 }, // 0 xor 0: 1 (label +1)
new double[] { 0, 1 }, // 0 xor 1: 0 (label -1)
new double[] { 1, 0 }, // 1 xor 0: 0 (label -1)
new double[] { 1, 1 } // 1 xor 1: 1 (label +1)
};
// Dichotomy SVM outputs should be given as [-1;+1]
int[] labels =
{
1, -1, -1, 1
};
// Create a Kernel Support Vector Machine for the given inputs
KernelSupportVectorMachine svm = new KernelSupportVectorMachine(new Gaussian(0.1), inputs[0].Length);
// Instantiate a new learning algorithm for SVMs
SequentialMinimalOptimization smo = new SequentialMinimalOptimization(svm, inputs, labels);
// Set up the learning algorithm
smo.Complexity = 1.0;
// Run the learning algorithm
double error = smo.Run();
Assert.IsFalse(svm.IsProbabilistic);
// Instantiate the probabilistic learning calibration
var calibration = new ProbabilisticOutputCalibration(svm, inputs, labels);
// Run the calibration algorithm
double loglikelihood = calibration.Run();
Assert.IsTrue(svm.IsProbabilistic);
// Compute the decision output for one of the input vectors,
// while also retrieving the probability of the answer
double probability;
int decision = svm.Compute(inputs[0], out probability);
// At this point, decision is +1 with a probability of 75%
Assert.AreEqual(1, decision);
Assert.AreEqual(0.74999975815069375, probability, 1e-10);
}
public void TransformTest()
{
var inputs = yinyang.Submatrix(null, 0, 1).ToJagged();
var labels = yinyang.GetColumn(2).ToInt32();
ConfusionMatrix actual, expected;
SequentialMinimalOptimization a, b;
var kernel = new Polynomial(2, 0);
{
var machine = new KernelSupportVectorMachine(kernel, inputs[0].Length);
a = new SequentialMinimalOptimization(machine, inputs, labels);
a.UseComplexityHeuristic = true;
a.Run();
int[] values = new int[labels.Length];
for (int i = 0; i < values.Length; i++)
{
values[i] = Math.Sign(machine.Compute(inputs[i]));
}
expected = new ConfusionMatrix(values, labels);
}
{
var projection = inputs.Apply(kernel.Transform);
var machine = new SupportVectorMachine(projection[0].Length);
b = new SequentialMinimalOptimization(machine, projection, labels);
b.UseComplexityHeuristic = true;
b.Run();
int[] values = new int[labels.Length];
for (int i = 0; i < values.Length; i++)
{
values[i] = Math.Sign(machine.Compute(projection[i]));
}
actual = new ConfusionMatrix(values, labels);
}
Assert.AreEqual(a.Complexity, b.Complexity, 1e-15);
Assert.AreEqual(expected.TrueNegatives, actual.TrueNegatives);
Assert.AreEqual(expected.TruePositives, actual.TruePositives);
Assert.AreEqual(expected.FalseNegatives, actual.FalseNegatives);
Assert.AreEqual(expected.FalsePositives, actual.FalsePositives);
}
private static void testWeights(double[][] inputs, int[] labels, IKernel kernel)
{
{
var machine = new KernelSupportVectorMachine(kernel, inputs[0].Length);
var smo = new SequentialMinimalOptimization(machine, inputs, labels);
smo.PositiveWeight = 100;
smo.NegativeWeight = 1;
double error = smo.Run();
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(50, matrix.TruePositives); // has more importance
Assert.AreEqual(0, matrix.FalseNegatives); // has more importance
}
{
var machine = new KernelSupportVectorMachine(kernel, inputs[0].Length);
var smo = new SequentialMinimalOptimization(machine, inputs, labels);
smo.PositiveWeight = 1;
smo.NegativeWeight = 100;
double error = smo.Run();
int[] actual = new int[labels.Length];
for (int i = 0; i < actual.Length; i++)
{
actual[i] = Math.Sign(machine.Compute(inputs[i]));
}
var matrix = new ConfusionMatrix(actual, labels);
Assert.AreEqual(50, matrix.TrueNegatives); // has more importance
Assert.AreEqual(0, matrix.FalsePositives); // has more importance
}
}
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);
}
private void btnTestingRun_Click(object sender, EventArgs e)
{
if (svm == null || dgvTestingSource.DataSource == null)
{
MessageBox.Show("Please create a machine first.");
return;
}
// Creates a matrix from the source data table
double[,] table = (dgvTestingSource.DataSource as DataTable).ToMatrix();
// Extract the first columns (X)
double[][] inputs = table.GetColumns(0).ToArray();
// Extract the expected output values
double[] expected = table.GetColumn(1);
// Compute the actual machine outputs
var output = new double[expected.Length];
for (int i = 0; i < expected.Length; i++)
{
output[i] = svm.Compute(inputs[i]);
}
// Compute R² and Sum-of-squares error
double rSquared = Accord.Statistics.Tools.Determination(output, expected);
double error = Elementwise.Pow(expected.Subtract(output), 2).Sum() / output.Length;
// Anonymous magic! :D
var r = new { RSquared = rSquared, Error = error };
dgvPerformance.DataSource = (new[] { r }).ToList();
// Create performance scatter plot
CreateResultScatterplot(zedGraphControl1, inputs, expected, output);
}
public void SVMTestXOR()
{
List <DataSet> dataset = new List <DataSet>();
dataset.Add(new DataSet(-1, new double[] { -1, -1 }));
dataset.Add(new DataSet(1, new double[] { -1, 1 }));
dataset.Add(new DataSet(1, new double[] { 1, -1 }));
dataset.Add(new DataSet(-1, new double[] { 1, 1 }));
KernelSupportVectorMachine machine = new KernelSupportVectorMachine(
new Polynomial(2), 2);
var learn = new SequentialMinimalOptimization(machine, dataset.ToArray());
double[] error = learn.Run();
double[] output = machine.Compute(dataset.ToArray());
double[] expected = { -1, 1, 1, -1 };
CollectionAssert.AreEqual(expected, output);
}
/// <summary>
/// Tests the previously created machine into a new set of data.
/// </summary>
///
private void btnTestingRun_Click(object sender, EventArgs e)
{
if (svm == null || dgvTestingSource.DataSource == null)
{
MessageBox.Show("Please create a machine first.");
return;
}
// Creates a matrix from the source data table
double[,] table = (dgvTestingSource.DataSource as DataTable).ToMatrix();
// Extract the first and second columns (X and Y)
double[][] inputs = table.GetColumns(0, 1).ToArray();
// Extract the expected output labels
int[] expected = table.GetColumn(2).ToInt32();
int[] output = new int[expected.Length];
// Compute the actual machine outputs
for (int i = 0; i < expected.Length; i++)
{
output[i] = System.Math.Sign(svm.Compute(inputs[i]));
}
// Use confusion matrix to compute some performance metrics
ConfusionMatrix confusionMatrix = new ConfusionMatrix(output, expected, 1, -1);
dgvPerformance.DataSource = new [] { confusionMatrix };
// Create performance scatter plot
CreateResultScatterplot(zedGraphControl1, inputs, expected.ToDouble(), output.ToDouble());
}
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 UseClassProportionsTest()
{
var dataset = KernelSupportVectorMachineTest.training;
var inputs = dataset.Submatrix(null, 0, 3);
var labels = Accord.Math.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 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 ComputeTest5()
{
var dataset = yinyang;
double[][] inputs = dataset.Submatrix(null, 0, 1).ToArray();
int[] labels = dataset.GetColumn(2).ToInt32();
{
Linear kernel = new Linear();
var machine = new KernelSupportVectorMachine(kernel, inputs[0].Length);
var smo = new SequentialMinimalOptimization(machine, inputs, labels);
smo.Complexity = 1.0;
double error = smo.Run();
Assert.AreEqual(1.0, smo.Complexity);
Assert.AreEqual(1.0, smo.WeightRatio);
Assert.AreEqual(1.0, smo.NegativeWeight);
Assert.AreEqual(1.0, smo.PositiveWeight);
Assert.AreEqual(0.14, error);
Assert.AreEqual(30, machine.SupportVectors.Length);
double[] actualWeights = machine.Weights;
double[] expectedWeights = { -1, -1, 1, -1, -1, 1, 1, -1, 1, -1, 1, 1, -1, 0.337065120144639, -1, 1, -0.337065120144639, -1, 1, 1, -1, 1, 1, -1, -1, 1, 1, -1, -1, 1 };
Assert.IsTrue(expectedWeights.IsEqual(actualWeights, 1e-10));
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(7, matrix.FalsePositives);
Assert.AreEqual(43, matrix.TruePositives);
Assert.AreEqual(43, matrix.TrueNegatives);
}
{
Linear kernel = new Linear();
var machine = new KernelSupportVectorMachine(kernel, inputs[0].Length);
var smo = new SequentialMinimalOptimization(machine, inputs, labels);
smo.Complexity = 1.0;
smo.PositiveWeight = 0.3;
smo.NegativeWeight = 1.0;
double error = smo.Run();
Assert.AreEqual(1.0, smo.Complexity);
Assert.AreEqual(0.3 / 1.0, smo.WeightRatio);
Assert.AreEqual(1.0, smo.NegativeWeight);
Assert.AreEqual(0.3, smo.PositiveWeight);
Assert.AreEqual(0.21, error);
Assert.AreEqual(24, machine.SupportVectors.Length);
double[] actualWeights = machine.Weights;
//string str = actualWeights.ToString(Accord.Math.Formats.CSharpArrayFormatProvider.InvariantCulture);
double[] expectedWeights = { -0.771026323762095, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -0.928973676237905, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1 };
Assert.IsTrue(expectedWeights.IsEqual(actualWeights, 1e-10));
int[] actual = new int[labels.Length];
for (int i = 0; i < actual.Length; i++)
{
actual[i] = (int)machine.Compute(inputs[i]);
}
ConfusionMatrix matrix = new ConfusionMatrix(actual, labels);
Assert.AreEqual(50, matrix.FalseNegatives);
Assert.AreEqual(0, matrix.FalsePositives);
Assert.AreEqual(0, matrix.TruePositives);
Assert.AreEqual(50, matrix.TrueNegatives);
}
{
Linear kernel = new Linear();
var machine = new KernelSupportVectorMachine(kernel, inputs[0].Length);
var smo = new SequentialMinimalOptimization(machine, inputs, labels);
smo.Complexity = 1.0;
smo.PositiveWeight = 1.0;
smo.NegativeWeight = 0.3;
double error = smo.Run();
Assert.AreEqual(1.0, smo.Complexity);
Assert.AreEqual(1.0 / 0.3, smo.WeightRatio);
Assert.AreEqual(0.3, smo.NegativeWeight);
Assert.AreEqual(1.0, smo.PositiveWeight);
Assert.AreEqual(0.15, error);
Assert.AreEqual(19, machine.SupportVectors.Length);
double[] actualWeights = machine.Weights;
double[] expectedWeights = new double[] { 1, 1, -0.3, 1, -0.3, 1, 1, -0.3, 1, 1, 1, 1, 1, 1, 1, 1, 0.129080057278249, 1, 0.737797469918795 };
Assert.IsTrue(expectedWeights.IsEqual(actualWeights, 1e-10));
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(0, matrix.FalseNegatives);
Assert.AreEqual(50, matrix.FalsePositives);
Assert.AreEqual(50, matrix.TruePositives);
Assert.AreEqual(0, matrix.TrueNegatives);
}
}
public void ComputeTest()
{
// Example AND problem
double[][] inputs =
{
new double[] { 0, 0 }, // 0 and 0: 0 (label -1)
new double[] { 0, 1 }, // 0 and 1: 0 (label -1)
new double[] { 1, 0 }, // 1 and 0: 0 (label -1)
new double[] { 1, 1 } // 1 and 1: 1 (label +1)
};
// Dichotomy SVM outputs should be given as [-1;+1]
int[] labels =
{
// 0, 0, 0, 1
-1, -1, -1, 1
};
// Create a Support Vector Machine for the given inputs
KernelSupportVectorMachine machine = new KernelSupportVectorMachine(new Linear(0), inputs[0].Length);
// Instantiate a new learning algorithm for SVMs
SequentialMinimalOptimization smo = new SequentialMinimalOptimization(machine, inputs, labels);
// Set up the learning algorithm
smo.Complexity = 100.0;
// Run
double error = smo.Run();
Assert.AreEqual(0, error);
Assert.AreEqual(-1, Math.Sign(machine.Compute(inputs[0])));
Assert.AreEqual(-1, Math.Sign(machine.Compute(inputs[1])));
Assert.AreEqual(-1, Math.Sign(machine.Compute(inputs[2])));
Assert.AreEqual(+1, Math.Sign(machine.Compute(inputs[3])));
// At this point we have the weighted support vectors
// w sv b
// (+4) * (1,1) -3
// (-2) * (1,0)
// (-2) * (0,1)
//
// However, it can be seen that the last SV can be written
// as a linear combination of the two first vectors:
//
// (0,1) = (1,1) - (1,0)
//
// Since we have a linear space (we are using a linear kernel)
// this vector could be removed from the support vector set.
//
// f(x) = sum(alpha_i * x * x_i) + b
// = 4*(1,1)*x - 2*(1,0)*x - 2*(0,1)*x - 3
// = 4*(1,1)*x - 2*(1,0)*x - 2*((1,1) - (1,0))*x - 3
// = 4*(1,1)*x - 2*(1,0)*x - 2*(1,1)*x + 2*(1,0)*x - 3
// = 4*(1,1)*x - 2*(1,0)*x - 2*(1,1)*x + 2*(1,0)*x - 3
// = 2*(1,1)*x - 3
// = 2*x1 + 2*x2 - 3
//
SupportVectorReduction svr = new SupportVectorReduction(machine);
double error2 = svr.Run();
Assert.AreEqual(-1, Math.Sign(machine.Compute(inputs[0])));
Assert.AreEqual(-1, Math.Sign(machine.Compute(inputs[1])));
Assert.AreEqual(-1, Math.Sign(machine.Compute(inputs[2])));
Assert.AreEqual(+1, Math.Sign(machine.Compute(inputs[3])));
}
public void DynamicalTimeWarpingConstructorTest()
{
double[][] sequences =
{
new double[] // -1
{
0, 0, 0,
1, 1, 1,
2, 2, 2,
},
new double[] // -1
{
0, 1, 0,
0, 2, 0,
0, 3, 0
},
new double[] // +1
{
1, 1, 0,
1, 2, 0,
2, 1, 0,
},
new double[] // +1
{
0, 0, 1,
0, 0, 2,
0, 1, 3,
},
};
int[] outputs = { -1, -1, +1, +1 };
// Set the parameters of the kernel
double alpha = 0.85;
int innerVectorLength = 3;
// Create the kernel. Note that the input vector will be given out automatically
DynamicTimeWarping target = new DynamicTimeWarping(innerVectorLength, alpha);
// When using variable-length kernels, specify 0 as the input length.
KernelSupportVectorMachine svm = new KernelSupportVectorMachine(target, 0);
// Create the Sequential Minimal Optimization as usual
SequentialMinimalOptimization smo = new SequentialMinimalOptimization(svm, sequences, outputs);
smo.Complexity = 1.5;
double error = smo.Run();
// Computing the training values
var a0 = svm.Compute(sequences[0]);
var a1 = svm.Compute(sequences[1]);
var a2 = svm.Compute(sequences[2]);
var a3 = svm.Compute(sequences[3]);
Assert.AreEqual(-1, System.Math.Sign(a0));
Assert.AreEqual(-1, System.Math.Sign(a1));
Assert.AreEqual(+1, System.Math.Sign(a2));
Assert.AreEqual(+1, System.Math.Sign(a3));
// Computing a new testing value
double[] test =
{
1, 0, 1,
0, 0, 2,
0, 1, 3,
};
var a4 = svm.Compute(test);
}
public void RunTest1()
{
double[][] inputs =
{
new double[] { -1, -1 },
new double[] { -1, 1 },
new double[] { 1, -1 },
new double[] { 1, 1 }
};
int[] outputs =
{
-1,
1,
1,
-1
};
KernelSupportVectorMachine svm = new KernelSupportVectorMachine(new Gaussian(3.6), 2);
var smo = new SequentialMinimalOptimization(svm, inputs, outputs);
double error1 = smo.Run();
Assert.AreEqual(0, error1);
double[] distances = new double[outputs.Length];
for (int i = 0; i < outputs.Length; i++)
{
int y = svm.Compute(inputs[i], out distances[i]);
Assert.AreEqual(outputs[i], y);
}
var target = new ProbabilisticOutputCalibration(svm, inputs, outputs);
double ll0 = target.LogLikelihood(inputs, outputs);
double ll1 = target.Run();
double ll2 = target.LogLikelihood(inputs, outputs);
Assert.AreEqual(5.5451735748694571, ll1);
Assert.AreEqual(ll1, ll2);
Assert.IsTrue(ll1 > ll0);
double[] newdistances = new double[outputs.Length];
for (int i = 0; i < outputs.Length; i++)
{
int y = svm.Compute(inputs[i], out newdistances[i]);
Assert.AreEqual(outputs[i], y);
}
double[] probs = new double[outputs.Length];
for (int i = 0; i < outputs.Length; i++)
{
int y;
probs[i] = svm.ToMulticlass().Probability(inputs[i], out y);
Assert.AreEqual(outputs[i], y == 1 ? 1 : -1);
}
Assert.AreEqual(0.25, probs[0], 1e-5);
Assert.AreEqual(0.75, probs[1], 1e-5);
Assert.AreEqual(0.75, probs[2], 1e-5);
Assert.AreEqual(0.25, probs[3], 1e-5);
foreach (var p in probs)
{
Assert.IsFalse(Double.IsNaN(p));
}
}
public void weight_test_inhomogeneous_linear_kernel()
{
var dataset = yinyang;
double[][] inputs = dataset.Submatrix(null, 0, 1).ToJagged();
int[] labels = dataset.GetColumn(2).ToInt32();
Accord.Math.Tools.SetupGenerator(0);
var kernel = new Linear(1);
Assert.AreEqual(kernel.Constant, 1);
{
var machine = new KernelSupportVectorMachine(kernel, inputs[0].Length);
var smo = new SequentialMinimalOptimization(machine, inputs, labels);
smo.Complexity = 1.0;
smo.PositiveWeight = 1;
smo.NegativeWeight = 1;
smo.Tolerance = 0.001;
double error = smo.Run();
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(43, matrix.TruePositives); // both classes are
Assert.AreEqual(43, matrix.TrueNegatives); // well equilibrated
Assert.AreEqual(7, matrix.FalseNegatives);
Assert.AreEqual(7, matrix.FalsePositives);
Assert.AreEqual(1.0, smo.Complexity);
Assert.AreEqual(1.0, smo.WeightRatio);
Assert.AreEqual(1.0, smo.NegativeWeight);
Assert.AreEqual(1.0, smo.PositiveWeight);
Assert.AreEqual(0.14, error);
Assert.AreEqual(0.001, smo.Tolerance);
Assert.AreEqual(31, machine.SupportVectors.Length);
machine.Compress();
Assert.AreEqual(1, machine.Weights[0]);
Assert.AreEqual(1, machine.SupportVectors.Length);
Assert.AreEqual(-1.3107402300323954, machine.SupportVectors[0][0], 1e-3);
Assert.AreEqual(-0.5779471529948812, machine.SupportVectors[0][1], 1e-3);
Assert.AreEqual(-1.5338510320418068, machine.Threshold, 1e-3);
for (int i = 0; i < actual.Length; i++)
{
int expected = actual[i];
int y = Math.Sign(machine.Compute(inputs[i]));
Assert.AreEqual(expected, y);
}
}
{
var machine = new KernelSupportVectorMachine(kernel, inputs[0].Length);
var smo = new SequentialMinimalOptimization(machine, inputs, labels);
smo.Complexity = 1;
smo.PositiveWeight = 100;
smo.NegativeWeight = 1;
smo.Tolerance = 0.001;
double error = smo.Run();
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(50, matrix.TruePositives); // has more importance
Assert.AreEqual(23, matrix.TrueNegatives);
Assert.AreEqual(0, matrix.FalseNegatives); // has more importance
Assert.AreEqual(27, matrix.FalsePositives);
Assert.AreEqual(1.0, smo.Complexity);
Assert.AreEqual(100, smo.WeightRatio);
Assert.AreEqual(1.0, smo.NegativeWeight);
Assert.AreEqual(100, smo.PositiveWeight);
Assert.AreEqual(0.001, smo.Tolerance);
Assert.AreEqual(0.27, error);
Assert.AreEqual(41, machine.SupportVectors.Length);
}
{
var machine = new KernelSupportVectorMachine(kernel, inputs[0].Length);
var smo = new SequentialMinimalOptimization(machine, inputs, labels);
smo.Complexity = 1;
smo.PositiveWeight = 1;
smo.NegativeWeight = 100;
smo.Tolerance = 0.001;
double error = smo.Run();
int[] actual = new int[labels.Length];
for (int i = 0; i < actual.Length; i++)
{
actual[i] = Math.Sign(machine.Compute(inputs[i]));
}
var matrix = new ConfusionMatrix(actual, labels);
Assert.AreEqual(25, matrix.TruePositives);
Assert.AreEqual(50, matrix.TrueNegatives); // has more importance
Assert.AreEqual(25, matrix.FalseNegatives);
Assert.AreEqual(0, matrix.FalsePositives); // has more importance
Assert.AreEqual(1.0, smo.Complexity);
Assert.AreEqual(0.01, smo.WeightRatio);
Assert.AreEqual(100, smo.NegativeWeight);
Assert.AreEqual(1.0, smo.PositiveWeight);
Assert.AreEqual(0.25, error);
Assert.AreEqual(0.001, smo.Tolerance);
Assert.AreEqual(40, machine.SupportVectors.Length);
}
}
public void WeightRatioTest()
{
var dataset = KernelSupportVectorMachineTest.training;
var inputs = dataset.Submatrix(null, 0, 3);
var labels = Accord.Math.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 DynamicalTimeWarpingConstructorTest3()
{
// Suppose you have sequences of multivariate observations, and that
// those sequences could be of arbitrary length. On the other hand,
// each observation have a fixed, delimited number of dimensions.
// In this example, we have sequences of 3-dimensional observations.
// Each sequence can have an arbitrary length, but each observation
// will always have length 3:
double[][][] sequences =
{
new double[][] // first sequence
{
new double[] { 1, 1, 1 }, // first observation of the first sequence
new double[] { 1, 2, 1 }, // second observation of the first sequence
new double[] { 1, 4, 2 }, // third observation of the first sequence
new double[] { 2, 2, 2 }, // fourth observation of the first sequence
},
new double[][] // second sequence (note that this sequence has a different length)
{
new double[] { 1, 1, 1 }, // first observation of the second sequence
new double[] { 1, 5, 6 }, // second observation of the second sequence
new double[] { 2, 7, 1 }, // third observation of the second sequence
},
new double[][] // third sequence
{
new double[] { 8, 2, 1 }, // first observation of the third sequence
},
new double[][] // fourth sequence
{
new double[] { 8, 2, 5 }, // first observation of the fourth sequence
new double[] { 1, 5, 4 }, // second observation of the fourth sequence
}
};
// Now, we will also have different class labels associated which each
// sequence. We will assign -1 to sequences whose observations start
// with { 1, 1, 1 } and +1 to those that do not:
int[] outputs =
{
-1, -1, // First two sequences are of class -1 (those start with {1,1,1})
1, 1, // Last two sequences are of class +1 (don't start with {1,1,1})
};
// At this point, we will have to "flat" out the input sequences from double[][][]
// to a double[][] so they can be properly understood by the SVMs. The problem is
// that, normally, SVMs usually expect the data to be comprised of fixed-length
// input vectors and associated class labels. But in this case, we will be feeding
// them arbitrary-length sequences of input vectors and class labels associated with
// each sequence, instead of each vector.
double[][] inputs = new double[sequences.Length][];
for (int i = 0; i < sequences.Length; i++)
{
inputs[i] = Matrix.Concatenate(sequences[i]);
}
// Now we have to setup the Dynamic Time Warping kernel. We will have to
// inform the length of the fixed-length observations contained in each
// arbitrary-length sequence:
//
DynamicTimeWarping kernel = new DynamicTimeWarping(length: 3);
// Now we can create the machine. When using variable-length
// kernels, we will need to pass zero as the input length:
var svm = new KernelSupportVectorMachine(kernel, inputs: 0);
// Create the Sequential Minimal Optimization learning algorithm
var smo = new SequentialMinimalOptimization(svm, inputs, outputs)
{
Complexity = 1.5
};
// And start learning it!
double error = smo.Run(); // error will be 0.0
// At this point, we should have obtained an useful machine. Let's
// see if it can understand a few examples it hasn't seem before:
double[][] a =
{
new double[] { 1, 1, 1 },
new double[] { 7, 2, 5 },
new double[] { 2, 5, 1 },
};
double[][] b =
{
new double[] { 7, 5, 2 },
new double[] { 4, 2, 5 },
new double[] { 1, 1, 1 },
};
// Following the aforementioned logic, sequence (a) should be
// classified as -1, and sequence (b) should be classified as +1.
int resultA = System.Math.Sign(svm.Compute(Matrix.Concatenate(a))); // -1
int resultB = System.Math.Sign(svm.Compute(Matrix.Concatenate(b))); // +1
Assert.AreEqual(0, error);
Assert.AreEqual(-1, resultA);
Assert.AreEqual(+1, resultB);
}
public void WeightsTest1()
{
var dataset = yinyang;
double[][] inputs = dataset.Submatrix(null, 0, 1).ToArray();
int[] labels = dataset.GetColumn(2).ToInt32();
Accord.Math.Tools.SetupGenerator(0);
var kernel = new Linear(1);
{
var machine = new KernelSupportVectorMachine(kernel, inputs[0].Length);
var smo = new SequentialMinimalOptimization(machine, inputs, labels);
smo.Complexity = 1.0;
smo.PositiveWeight = 1;
smo.NegativeWeight = 1;
smo.Tolerance = 0.001;
double error = smo.Run();
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(43, matrix.TruePositives); // both classes are
Assert.AreEqual(43, matrix.TrueNegatives); // well equilibrated
Assert.AreEqual(7, matrix.FalseNegatives);
Assert.AreEqual(7, matrix.FalsePositives);
Assert.AreEqual(1.0, smo.Complexity);
Assert.AreEqual(1.0, smo.WeightRatio);
Assert.AreEqual(1.0, smo.NegativeWeight);
Assert.AreEqual(1.0, smo.PositiveWeight);
Assert.AreEqual(0.14, error);
Assert.AreEqual(0.001, smo.Tolerance);
Assert.AreEqual(31, machine.SupportVectors.Length);
}
{
var machine = new KernelSupportVectorMachine(kernel, inputs[0].Length);
var smo = new SequentialMinimalOptimization(machine, inputs, labels);
smo.Complexity = 1;
smo.PositiveWeight = 100;
smo.NegativeWeight = 1;
smo.Tolerance = 0.001;
double error = smo.Run();
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(50, matrix.TruePositives); // has more importance
Assert.AreEqual(23, matrix.TrueNegatives);
Assert.AreEqual(0, matrix.FalseNegatives); // has more importance
Assert.AreEqual(27, matrix.FalsePositives);
Assert.AreEqual(1.0, smo.Complexity);
Assert.AreEqual(100, smo.WeightRatio);
Assert.AreEqual(1.0, smo.NegativeWeight);
Assert.AreEqual(100, smo.PositiveWeight);
Assert.AreEqual(0.001, smo.Tolerance);
Assert.AreEqual(0.27, error);
Assert.AreEqual(41, machine.SupportVectors.Length);
}
{
var machine = new KernelSupportVectorMachine(kernel, inputs[0].Length);
var smo = new SequentialMinimalOptimization(machine, inputs, labels);
smo.Complexity = 1;
smo.PositiveWeight = 1;
smo.NegativeWeight = 100;
smo.Tolerance = 0.001;
double error = smo.Run();
int[] actual = new int[labels.Length];
for (int i = 0; i < actual.Length; i++)
{
actual[i] = Math.Sign(machine.Compute(inputs[i]));
}
var matrix = new ConfusionMatrix(actual, labels);
Assert.AreEqual(25, matrix.TruePositives);
Assert.AreEqual(50, matrix.TrueNegatives); // has more importance
Assert.AreEqual(25, matrix.FalseNegatives);
Assert.AreEqual(0, matrix.FalsePositives); // has more importance
Assert.AreEqual(1.0, smo.Complexity);
Assert.AreEqual(0.01, smo.WeightRatio);
Assert.AreEqual(100, smo.NegativeWeight);
Assert.AreEqual(1.0, smo.PositiveWeight);
Assert.AreEqual(0.25, error);
Assert.AreEqual(0.001, smo.Tolerance);
Assert.AreEqual(40, machine.SupportVectors.Length);
}
}
