| public Fit ( double observations, double weights ) : IDistribution | ||
| observations | double | /// The array of observations to fit the model against. /// |
| weights | double | /// The weight vector containing the weight for each of the samples. /// |
| return | IDistribution | |
public void FitTest2()
{
double[] expected = { 0.50, 0.00, 0.25, 0.25 };
GeneralDiscreteDistribution target;
double[] values = { 0.00, 2.00, 3.00 };
double[] weights = { 0.50, 0.25, 0.25 };
target = new GeneralDiscreteDistribution(4);
target.Fit(values, weights);
double[] actual = target.Frequencies;
Assert.IsTrue(Matrix.IsEqual(expected, actual));
// --
double[] values2 = { 0.00, 0.00, 2.00, 3.00 };
double[] weights2 = { 0.25, 0.25, 0.25, 0.25 };
target = new GeneralDiscreteDistribution(4);
target.Fit(values2, weights2);
double[] actual2 = target.Frequencies;
Assert.IsTrue(Matrix.IsEqual(expected, actual2));
}
public void FitTest_vector_inputs()
{
double[] expected = { 0.50, 0.00, 0.25, 0.25 };
GeneralDiscreteDistribution target;
double[] values = { 0.00, 2.00, 3.00 };
double[] weights = { 0.50, 0.25, 0.25 };
target = new GeneralDiscreteDistribution(4);
target.Fit(values, weights);
double[] actual = target.Frequencies;
Assert.IsTrue(Matrix.IsEqual(expected, actual));
// --
double[][] values2 =
{
new[] { 1.00, 0.00, 0.00, 0.00 },
new[] { 0.00, 0.00, 0.00, 0.00 },
new[] { 0.00, 0.00, 1.00, 0.00 },
new[] { 0.00, 0.00, 0.00, 1.00 },
};
double[] weights2 = { 0.50, 0.00, 0.25, 0.25 };
target = new GeneralDiscreteDistribution(4);
target.Fit(values2, weights2);
double[] actual2 = target.Frequencies;
Assert.IsTrue(Matrix.IsEqual(expected, actual2));
double[][] values3 =
{
new[] { 1.00, 0.00, 0.00, 0.00 },
new[] { 0.00, 1.00, 0.00, 0.00 },
new[] { 0.00, 0.00, 1.00, 0.00 },
new[] { 0.00, 0.00, 0.00, 1.00 },
};
double[] weights3 = { 0.50, 0.00, 0.25, 0.25 };
target = new GeneralDiscreteDistribution(4);
target.Fit(values3, weights3);
double[] actual3 = target.Frequencies;
Assert.IsTrue(Matrix.IsEqual(expected, actual3));
double[][] values4 =
{
new[] { 0.50, 0.00, 0.00, 0.00 },
new[] { 0.00, 0.00, 0.00, 0.00 },
new[] { 0.00, 0.00, 0.25, 0.00 },
new[] { 0.00, 0.00, 0.00, 0.25 },
};
target = new GeneralDiscreteDistribution(4);
target.Fit(values4);
double[] actual4 = target.Frequencies;
Assert.IsTrue(Matrix.IsEqual(expected, actual4));
}
public void GenerateTest()
{
double[] transProbRow = { 0.2, 0.5, 0.3 };
var gdd = new GeneralDiscreteDistribution(transProbRow);
int n = 100000;
{
int[] samples = gdd.Generate(n);
var target = new GeneralDiscreteDistribution(3);
target.Fit(samples);
Assert.AreEqual(0.2, target.Frequencies[0], 0.01);
Assert.AreEqual(0.5, target.Frequencies[1], 0.01);
Assert.AreEqual(0.3, target.Frequencies[2], 0.01);
}
{
int[] samples = new int[n].Apply((x) => gdd.Generate());
var target = new GeneralDiscreteDistribution(3);
target.Fit(samples);
Assert.AreEqual(0.2, target.Frequencies[0], 0.01);
Assert.AreEqual(0.5, target.Frequencies[1], 0.01);
Assert.AreEqual(0.3, target.Frequencies[2], 0.01);
}
{
double[] samples = gdd.Generate(n).ToDouble();
var target = new GeneralDiscreteDistribution(3);
target.Fit(samples);
Assert.AreEqual(0.2, target.Frequencies[0], 0.01);
Assert.AreEqual(0.5, target.Frequencies[1], 0.01);
Assert.AreEqual(0.3, target.Frequencies[2], 0.01);
}
{
double[] samples = new int[n].Apply((x) => (double)gdd.Generate());
var target = new GeneralDiscreteDistribution(3);
target.Fit(samples);
Assert.AreEqual(0.2, target.Frequencies[0], 0.01);
Assert.AreEqual(0.5, target.Frequencies[1], 0.01);
Assert.AreEqual(0.3, target.Frequencies[2], 0.01);
}
}
public void FitTest3()
{
GeneralDiscreteDistribution target = new GeneralDiscreteDistribution(-1, 4);
double[] values = { 0.00, 1.00, 2.00, 3.00 };
double[] weights = { 0.25, 0.25, 0.25, 0.25 };
target.Fit(values.Subtract(1), weights);
double[] expected = { 0.25, 0.25, 0.25, 0.25 };
double[] actual = target.Frequencies;
Assert.IsTrue(Matrix.IsEqual(expected, actual));
}
