/// <summary>
/// Test class weights.
/// </summary>
public void TestClassWeights()
{
Matrix x = DenseMatrix.OfArray(new[, ]
{
{ -1.0, -1.0 }, { -1.0, 0 }, { -.8, -1.0 },
{ 1.0, 1.0 }, { 1.0, 0.0 }
});
var y = new[] { 1, 1, 1, -1, -1 };
var clf = new RidgeClassifier <int>(classWeightEstimator: null);
clf.Fit(x, y);
Assert.AreEqual(
clf.Predict(DenseMatrix.OfArray(new[, ] {
{ 0.2, -1.0 }
})),
new DenseMatrix(1, 1, new double[] { 1 }));
// we give a small weights to class 1
clf = new RidgeClassifier <int>(
classWeightEstimator: ClassWeightEstimator <int> .Explicit(new Dictionary <int, double> {
{ 1, 0.001 }
}));
clf.Fit(x, y);
// now the hyperplane should rotate clock-wise and
// the prediction on this point should shift
Assert.AreEqual(
clf.Predict(DenseMatrix.OfArray(new[, ] {
{ 0.2, -1.0 }
})),
new DenseMatrix(1, 1, new double[] { -1 }));
}
public void RidgeClassifierSample()
{
var clf = new RidgeClassifier<string>(alpha: 0.5);
clf.Fit(new[,] { { 0.0, 0.0 }, { 0.0, 0.0 }, { 1.0, 1.0 } }, new[] { "a", "b", "c" });
Console.WriteLine(clf.Coef);
Console.WriteLine(clf.Intercept);
var prediction = clf.Predict(new[] { 5.0, 6.0 });
Console.WriteLine(prediction);
}
public void RidgeClassifierSample()
{
var clf = new RidgeClassifier <string>(alpha: 0.5);
clf.Fit(new[, ] {
{ 0.0, 0.0 }, { 0.0, 0.0 }, { 1.0, 1.0 }
}, new[] { "a", "b", "c" });
Console.WriteLine(clf.Coef);
Console.WriteLine(clf.Intercept);
var prediction = clf.Predict(new[] { 5.0, 6.0 });
Console.WriteLine(prediction);
}
public double?TestRidgeClassifiers(Func <Matrix <double>, Matrix <double> > filter)
{
int nClasses = yIris.Distinct().Count();
int nFeatures = xIris.ColumnCount;
var clf = new RidgeClassifier <int>();
clf.Fit(filter(xIris), yIris);
Assert.AreEqual(clf.Coef.Shape(), Tuple.Create(nClasses, nFeatures));
int[] yPred = clf.Predict(filter(xIris));
var result = yIris.Zip(yPred, Tuple.Create).Sum(t => t.Item1 == t.Item2 ? 1.0 : 0.0) / yPred.Length;
Assert.IsTrue(result >= .79);
return(null);
}
/// <summary>
/// Test class weights.
/// </summary>
public void TestClassWeights()
{
Matrix x = DenseMatrix.OfArray(new[,]
{
{-1.0, -1.0}, {-1.0, 0}, {-.8, -1.0},
{1.0, 1.0}, {1.0, 0.0}
});
var y = new[] { 1, 1, 1, -1, -1 };
var clf = new RidgeClassifier<int>(classWeightEstimator: null);
clf.Fit(x, y);
Assert.AreEqual(
clf.Predict(DenseMatrix.OfArray(new[,] { { 0.2, -1.0 } })),
new DenseMatrix(1, 1, new double[] { 1 }));
// we give a small weights to class 1
clf = new RidgeClassifier<int>(
classWeightEstimator: ClassWeightEstimator<int>.Explicit(new Dictionary<int, double> { { 1, 0.001 } }));
clf.Fit(x, y);
// now the hyperplane should rotate clock-wise and
// the prediction on this point should shift
Assert.AreEqual(
clf.Predict(DenseMatrix.OfArray(new[,] { { 0.2, -1.0 } })),
new DenseMatrix(1, 1, new double[] { -1 }));
}
public double? TestRidgeClassifiers(Func<Matrix<double>, Matrix<double>> filter)
{
int nClasses = yIris.Distinct().Count();
int nFeatures = xIris.ColumnCount;
var clf = new RidgeClassifier<int>();
clf.Fit(filter(xIris), yIris);
Assert.AreEqual(clf.Coef.Shape(), Tuple.Create(nClasses, nFeatures));
int[] yPred = clf.Predict(filter(xIris));
var result = yIris.Zip(yPred, Tuple.Create).Sum(t => t.Item1 == t.Item2 ? 1.0 : 0.0) / yPred.Length;
Assert.IsTrue(result >= .79);
return null;
}
