public void Save_And_Load_LogisticRegression()
{
Matrix m = new[,] {
{ 0.0512670, 0.6995600 },
{ -0.0927420, 0.6849400 },
{ -0.2137100, 0.6922500 },
{ -0.3750000, 0.5021900 },
{ -0.5132500, 0.4656400 },
{ -0.5247700, 0.2098000 },
{ -0.3980400, 0.0343570 },
{ -0.3058800, -0.1922500 },
{ 0.0167050, -0.4042400 },
{ 0.1319100, -0.5138900 },
{ -0.6111800, -0.0679820 },
{ -0.6630200, -0.2141800 },
{ -0.5996500, -0.4188600 },
{ -0.7263800, -0.0826020 },
{ -0.8300700, 0.3121300 },
{ -0.7206200, 0.5387400 },
{ -0.5938900, 0.4948800 },
{ -0.4844500, 0.9992700 },
{ -0.0063364, 0.9992700 },
{ 0.6326500, -0.0306120 },
};
Vector y = new Vector(new double[] {
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0
});
var generator = new LogisticRegressionGenerator() { Lambda = 1, LearningRate = 0.01, PolynomialFeatures = 6, MaxIterations = 400 };
var model = generator.Generate(m, y) as LogisticRegressionModel;
Serialize(model);
var lmodel = Deserialize<LogisticRegressionModel>();
Assert.AreEqual(model.Theta, lmodel.Theta);
Assert.AreEqual(model.PolynomialFeatures, lmodel.PolynomialFeatures);
Assert.AreEqual(model.LogisticFunction.GetType(), lmodel.LogisticFunction.GetType());
}
public void Logistic_Regression_Test_Generator()
{
Matrix m = new[,] {
{ 0.0512670, 0.6995600 },
{ -0.0927420, 0.6849400 },
{ -0.2137100, 0.6922500 },
{ -0.3750000, 0.5021900 },
{ -0.5132500, 0.4656400 },
{ -0.5247700, 0.2098000 },
{ -0.3980400, 0.0343570 },
{ -0.3058800, -0.1922500 },
{ 0.0167050, -0.4042400 },
{ 0.1319100, -0.5138900 },
{ -0.6111800, -0.0679820 },
{ -0.6630200, -0.2141800 },
{ -0.5996500, -0.4188600 },
{ -0.7263800, -0.0826020 },
{ -0.8300700, 0.3121300 },
{ -0.7206200, 0.5387400 },
{ -0.5938900, 0.4948800 },
{ -0.4844500, 0.9992700 },
{ -0.0063364, 0.9992700 },
{ 0.6326500, -0.0306120 },
};
Vector y = new Vector(new double[] {
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0
});
Vector test = new Vector(new double[] { 0.1319100, -0.513890
});
var generator = new LogisticRegressionGenerator() { Lambda = 1, LearningRate = 0.01, PolynomialFeatures = 6, MaxIterations = 400 };
var model2 = generator.Generate(m, y);
double p = model2.Predict(test);
Assert.AreEqual(1d, p);
}
public void Logistic_Regression_XOR_Learner()
{
var xor = new[]
{
new { a = false, b = false, c = false },
new { a = false, b = true, c = true },
new { a = true, b = false, c = true },
new { a = true, b = true, c = false },
new { a = false, b = false, c = false },
new { a = false, b = true, c = true },
new { a = true, b = false, c = true },
new { a = true, b = true, c = false },
new { a = false, b = false, c = false },
new { a = false, b = true, c = true },
new { a = true, b = false, c = true },
new { a = true, b = true, c = false },
new { a = false, b = false, c = false },
new { a = false, b = true, c = true },
new { a = true, b = false, c = true },
new { a = true, b = true, c = false },
new { a = false, b = false, c = false },
new { a = false, b = true, c = true },
new { a = true, b = false, c = true },
new { a = true, b = true, c = false },
};
var d = Descriptor.New("XOR")
.With("a").As(typeof(bool))
.With("b").As(typeof(bool))
.Learn("c").As(typeof(bool));
var generator = new LogisticRegressionGenerator() { Descriptor = d, PolynomialFeatures = 3, LearningRate = 0.01, Lambda = 0 };
var model = Learner.Learn(xor, 0.9, 5, generator).Model;
Matrix x = new[,]
{{ -1, -1 }, // false, false -> -
{ -1, 1 }, // false, true -> +
{ 1, -1 }, // true, false -> +
{ 1, 1 }}; // true, true -> -
Vector y_actual = new Vector(new[] { 0d, 1d, 1d, 0d });
Vector y = new Vector(x.Rows);
for (int i = 0; i < x.Rows; i++)
y[i] = model.Predict(x[i, VectorType.Row]);
var scored = Supervised.Score.ScorePredictions(y, y_actual);
Assert.True(scored.Accuracy >= 0.75);
Console.WriteLine($"LR Model Score: { scored }");
}
