public void TestLinearRegression()
{
// Control.LinearAlgebraProvider = new MklLinearAlgebraProvider();
// a simple dataset
var x = DenseMatrix.OfArray(new double[, ] {
{ 1 }, { 2 }
});
var y = DenseVector.OfEnumerable(new double[] { 1, 2 });
var clf = new LinearRegression();
clf.Fit(x, y);
Assert.AreEqual(1.0, clf.Coef.Column(0)[0], 1E-5);
//Assert.AreEqual(0.0, clf.Intercept[0]);
Assert.IsTrue(DenseVector.OfEnumerable(new double[] { 1, 2 }).AlmostEquals(clf.Predict(x).Column(0)));
// test it also for degenerate input
x = DenseMatrix.OfArray(new double[, ] {
{ 1 }
});
y = DenseVector.OfEnumerable(new double[] { 0 });
clf = new LinearRegression(fitIntercept: false);
clf.Fit(x, y);
Assert.AreEqual(0.0, clf.Coef.Column(0)[0]);
//assert_array_almost_equal(clf.intercept_, [0])
Assert.AreEqual(0.0, clf.Predict(x).Column(0)[0]);
}
public void PredictTest()
{
double[,] features = { { 1d, 0d }, { 1d, 1d }, { 1d, 2d }, { 1d, 3d }, { 1d, 4d } };
double[] theta = { 1d, -1d };
double[] labels = { 1d, 0d, -1d, -2d, -3d };
double[] predictions = LinearRegression.Predict(features, theta);
CollectionAssert.AreEqual(labels, predictions);
}
private void TestLinearRegression()
{
LinearRegression linearRegression = new LinearRegression(trainingEpochs, learningRate);
linearRegression.Train(dataX, dataY);
(float[] results, float optimalWeights, float optimalBias) = linearRegression.Predict(testX);
//analytical solution coefficientVector=(X'*X)*X'*Y where X is the inputX with ones in the first column and Y is inputY: bias=0.7988 and weight=0.2516
Assert.Equal(0.309068531, optimalWeights, 6);
Assert.Equal(0.373608261, optimalBias, 6);
}
protected override double GetFileEstimation(ProjectFile file)
{
double numberOfBugsReal = NumberOfFixedDefectsForFile(file.ID);
double numberOfBugsEstimation = regression.Predict(
GetPredictorValuesFor(context.SetFiles(e => e.IdIs(file.ID)))[0]
);
double numberOfBugs = numberOfBugsEstimation - numberOfBugsReal;
return(numberOfBugs > 0 ? numberOfBugs : 0);
}
public void TestLinearRegressionSparseMultipleOutcome()
{
var random = new Random(0);
var r = SampleGenerator.MakeSparseUncorrelated(random: random);
Matrix x = SparseMatrix.OfMatrix(r.X);
Vector <double> y = r.Y.Column(0);
Matrix y1 = DenseMatrix.OfColumns(y.Count, 2, new[] { y, y });
int nFeatures = x.ColumnCount;
var ols = new LinearRegression();
ols.Fit(x, y1);
Assert.AreEqual(Tuple.Create(2, nFeatures), ols.Coef.Shape());
Assert.AreEqual(Tuple.Create(2, nFeatures), ols.Coef.Shape());
Matrix <double> yPred = ols.Predict(x);
ols.Fit(x, y);
Matrix <double> yPred1 = ols.Predict(x);
Assert.IsTrue(yPred1.Column(0).AlmostEquals(yPred.Column(0)));
Assert.IsTrue(yPred1.Column(0).AlmostEquals(yPred.Column(1)));
}
public void TestLinearRegressionMultipleOutcome()
{
var result = SampleGenerator.MakeRegression(shuffle: false, random: new Random(0));
Matrix y = DenseMatrix.OfColumns(
result.Y.RowCount,
2,
new[] { result.Y.Column(0), result.Y.Column(0) });
var numFeatures = result.X.RowCount;
var clf = new LinearRegression(fitIntercept: true);
clf.Fit(result.X, y);
Assert.AreEqual(Tuple.Create(2, numFeatures), clf.Coef.Shape());
Matrix <double> yPred = clf.Predict(result.X);
clf.Fit(result.X, result.Y);
Matrix <double> yPred1 = clf.Predict(result.X);
Assert.IsTrue(yPred1.Column(0).AlmostEquals(yPred.Column(0)));
Assert.IsTrue(yPred1.Column(0).AlmostEquals(yPred.Column(1)));
}