|
public Generate ( Matrix X, |
||
| X | Matrix | The Matrix to process. |
| y | The Vector to process. | |
| 리턴 | IModel | |
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 = 0.1, PolynomialFeatures = 6, MaxIterations = 400, NormalizeFeatures = true };
var model2 = generator.Generate(m, y);
double p = model2.Predict(test);
Assert.Equal(1d, p);
}