public void TestLogisticRegressionTest()
{
var inputs = new double[][] {
// age, smokes?
new double[] { 55, 0 },
new double[] { 28, 0 },
new double[] { 65, 1 },
new double[] { 46, 0 },
new double[] { 86, 1 },
new double[] { 56, 1 },
new double[] { 85, 0 },
new double[] { 33, 0 },
new double[] { 21, 1 },
new double[] { 42, 1 },
};
var outputs = new double[][] {
// Whether each patient had lung cancer or not
new double[] { 1, 0 },
new double[] { 1, 0 },
new double[] { 1, 0 },
new double[] { 0, 1 },
new double[] { 0, 1 },
new double[] { 0, 1 },
new double[] { 1, 0 },
new double[] { 1, 0 },
new double[] { 1, 0 },
new double[] { 0, 1 }
};
// Accord.NET
var analysis = new MultinomialLogisticRegressionAnalysis();
analysis.Learn(inputs, outputs);
var expected = analysis.Regression.Probabilities(inputs);
// Estimator
var x = DataMap.FromJagged(inputs, new string[] { "age", "smokes" });
var y = DataMap.FromJagged(outputs, new string[] { "no_cancer", "cancer" });
var est = new LogisticRegressionEstimator();
est.Fit(x, y);
var model = est.Model;
Assert.Equal(analysis.Coefficients.Count, model.Coefficients.Count);
Assert.Equal(analysis.Coefficients[0].Value, model.Coefficients[0].Value);
Assert.Equal(analysis.Coefficients[1].Value, model.Coefficients[1].Value);
Assert.Equal(analysis.Coefficients[2].Value, model.Coefficients[2].Value);
var predicted = est.Predict(x).ToJagged <double>();
Assert.Equal(expected[0], predicted[0]);
Assert.Equal(expected[1], predicted[1]);
Assert.Equal(expected[5], predicted[5]);
Assert.Equal(expected[9], predicted[9]);
}
public void TestPlattCalibratorTest()
{
var inputs = DataMap.FromJagged(new double[][]
{
new double[] { 0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5 }
}, null, true);
var outputs = DataMap.FromJagged(new double[][]
{
new double[] { 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1 }
}, null, true);
var valid = DataMap.FromJagged(new double[][]
{
new double[] { 0, 1, 2, 3, 4, 5 }
}, null, true);
var est = new SigmoidCalibrator();
est.Fit(inputs, outputs);
var pred = est.Predict(valid).First;
Assert.True((double)pred[0] < (double)pred[1]);
Assert.True((double)pred[1] < (double)pred[2]);
Assert.True((double)pred[2] < (double)pred[3]);
Assert.True((double)pred[3] < (double)pred[4]);
Assert.True((double)pred[4] < (double)pred[5]);
}
public void TestLinearRegressionTest()
{
var inputs = new double[][] {
// age, smokes?
new double[] { 55, 0 },
new double[] { 28, 0 },
new double[] { 65, 1 },
new double[] { 46, 0 },
new double[] { 86, 1 },
new double[] { 56, 1 },
new double[] { 85, 0 },
new double[] { 33, 0 },
new double[] { 21, 1 },
new double[] { 42, 1 },
};
var output = new double[] {
// Whether each patient had lung cancer or not
0, 0, 0, 1, 1, 1, 0, 0, 0, 1
};
// Accord.NET
var analysis = new MultipleLinearRegressionAnalysis();
analysis.OrdinaryLeastSquares.UseIntercept = true;
analysis.OrdinaryLeastSquares.IsRobust = false;
analysis.Learn(inputs, output);
// Estimator
var x = DataMap.FromJagged(inputs, new string[] { "age", "smokes" });
var y = new DataMap();
y.Add("lung_cancer", output);
var est = new LinearRegressionEstimator();
est.Fit(x, y);
var model = est.Model;
Assert.Equal(analysis.Coefficients.Count, model.Coefficients.Count);
Assert.Equal(analysis.Coefficients[0].Value, model.Coefficients[0].Value);
Assert.Equal(analysis.Coefficients[1].Value, model.Coefficients[1].Value);
Assert.Equal(analysis.Coefficients[2].Value, model.Coefficients[2].Value);
}
