private static void sparseLogistic(Sparse <double>[] inputs, double[] doubleOutputs)
{
// The dataset has output labels as 4 and 2. We have to convert them
// into negative and positive labels so they can be properly processed.
//
bool[] outputs = doubleOutputs.Apply(x => x == 2.0 ? false : true);
// Create a probabilistic SVM that can output probabilities besides a decision
var teacher = new ProbabilisticDualCoordinateDescent <Linear, Sparse <double> >()
{
Complexity = 1000,
Tolerance = 1e-5
};
// Use the learning algorithm to Learn
var svm = teacher.Learn(inputs, outputs);
// Transform the machine into a dense logistic regression:
var lr = LogisticRegression.FromWeights(svm.ToWeights());
// Compute the machine's answers
bool[] svmAnswers = svm.Decide(inputs);
// Compute the machine probability estimates:
double[] svmProbability = svm.Probability(inputs);
// Compute the logistic regression's answers:
bool[] lrAnswers = lr.Decide(inputs.ToDense());
// Compute the logistic regression probability estimates:
double[] lrProbability = lr.Probability(inputs.ToDense());
// They should be equal for both the SVM and the LR
}
public void RunTest()
{
double[][] input =
{
new double[] { 55, 0 }, // 0 - no cancer
new double[] { 28, 0 }, // 0
new double[] { 65, 1 }, // 0
new double[] { 46, 0 }, // 1 - have cancer
new double[] { 86, 1 }, // 1
new double[] { 56, 1 }, // 1
new double[] { 85, 0 }, // 0
new double[] { 33, 0 }, // 0
new double[] { 21, 1 }, // 0
new double[] { 42, 1 }, // 1
};
double[] output =
{
0, 0, 0, 1, 1, 1, 0, 0, 0, 1
};
int[] labels = output.Apply(x => x > 0 ? +1 : -1);
var svm = new SupportVectorMachine(inputs: 2);
var teacher = new ProbabilisticNewtonMethod(svm, input, labels);
teacher.Tolerance = 1e-10;
teacher.Complexity = 1e+10;
Assert.IsFalse(svm.IsProbabilistic);
double error = teacher.Run();
Assert.IsTrue(svm.IsProbabilistic);
Assert.AreEqual(0.2, error);
Assert.AreEqual(0.02064511826338301, svm.SupportVectors[0][0], 1e-10);
Assert.AreEqual(1.767889310996118, svm.SupportVectors[0][1], 1e-10);
Assert.AreEqual(-2.4577464317497455, svm.Threshold, 1e-10);
var regression = LogisticRegression.FromWeights(svm.ToWeights());
double[] actual = new double[output.Length];
for (int i = 0; i < actual.Length; i++)
{
actual[i] = regression.Compute(input[i]);
}
double ageOdds = regression.GetOddsRatio(1); // 1.0208597028836701
double smokeOdds = regression.GetOddsRatio(2); // 5.8584748789881331
Assert.AreEqual(1.0208597028836701, ageOdds, 1e-4);
Assert.AreEqual(5.8584748789881331, smokeOdds, 1e-4);
Assert.AreEqual(-2.4577464307294092, regression.Intercept, 1e-8);
Assert.AreEqual(-2.4577464307294092, regression.Coefficients[0], 1e-8);
Assert.AreEqual(0.020645118265359252, regression.Coefficients[1], 1e-8);
Assert.AreEqual(1.7678893101571855, regression.Coefficients[2], 1e-8);
}
