/// <summary>Generate Logistic Regression model based on a set of examples.</summary>
/// <param name="x">The Matrix to process.</param>
/// <param name="y">The Vector to process.</param>
/// <returns>Model.</returns>
public override IModel Generate(Matrix x, Vector y)
{
// create initial theta
Matrix copy = x.Copy();
copy = PreProcessing.FeatureDimensions.IncreaseDimensions(copy, this.PolynomialFeatures);
// add intercept term
copy = copy.Insert(Vector.Ones(copy.Rows), 0, VectorType.Col);
Vector theta = Vector.Ones(copy.Cols);
var run = Math.Optimization.GradientDescent.Run(theta, copy, y, this.MaxIterations, this.LearningRate, new Math.Functions.Cost.LogisticCostFunction(),
this.Lambda, new Math.Functions.Regularization.Regularization());
LogisticRegressionModel model = new LogisticRegressionModel()
{
Descriptor = this.Descriptor,
Theta = run.Item2,
LogisticFunction = new Math.Functions.Logistic(),
PolynomialFeatures = this.PolynomialFeatures
};
return(model);
}
/// <summary>Generate Logistic Regression model based on a set of examples.</summary>
/// <param name="x">The Matrix to process.</param>
/// <param name="y">The Vector to process.</param>
/// <returns>Model.</returns>
public override IModel Generate(Matrix x, Vector y)
{
// create initial theta
Matrix copy = x.Copy();
copy = PreProcessing.FeatureDimensions.IncreaseDimensions(copy, PolynomialFeatures);
// add intercept term
copy = copy.Insert(Vector.Ones(copy.Rows), 0, VectorType.Col);
Vector theta = Vector.Ones(copy.Cols);
var run = numl.Math.Optimization.GradientDescent.Run(theta, copy, y, this.MaxIterations, this.LearningRate, new numl.Math.Functions.Cost.LogisticCostFunction(),
this.Lambda, new numl.Math.Functions.Regularization.Regularization());
LogisticRegressionModel model = new LogisticRegressionModel()
{
Descriptor = this.Descriptor,
Theta = run.Item2,
LogisticFunction = new Math.Functions.Logistic(),
PolynomialFeatures = this.PolynomialFeatures
};
return model;
}
public void Logistic_Regression_Test_Prediction()
{
Matrix m = new[,]
{{ 8, 1, 6 },
{ 3, 5 ,7 },
{ 4, 9, 2 }};
var model = new LogisticRegressionModel() {
LogisticFunction = new Math.Functions.Logistic(),
Theta = new Vector(new double[] { 1, 2, 1, -9 })
};
var p = model.Predict(new Vector(new double[] { 8, 1, 6 }));
Assert.AreEqual(1d, p);
}
/// <summary>Generate Logistic Regression model based on a set of examples.</summary>
/// <param name="X">The Matrix to process.</param>
/// <param name="y">The Vector to process.</param>
/// <returns>Model.</returns>
public override IModel Generate(Matrix X, Vector y)
{
X = IncreaseDimensions(X, PolynomialFeatures);
Preprocess(X);
// guarantee 1/0 based label vector
y = y.ToBinary(f => f == 1d, falseValue: 0d);
// add intercept term
X = X.Insert(Vector.Ones(X.Rows), 0, VectorType.Col, false);
var theta = Vector.Rand(X.Cols);
// run gradient descent
var optimizer = new Optimizer(theta, MaxIterations, LearningRate)
{
CostFunction = new LogisticCostFunction
{
X = X,
Y = y,
Lambda = Lambda,
Regularizer = new L2Regularizer(),
LogisticFunction = LogisticFunction
}
};
optimizer.Run();
var model = new LogisticRegressionModel
{
Descriptor = Descriptor,
NormalizeFeatures = NormalizeFeatures,
FeatureNormalizer = FeatureNormalizer,
FeatureProperties = FeatureProperties,
Theta = optimizer.Properties.Theta,
LogisticFunction = LogisticFunction,
PolynomialFeatures = PolynomialFeatures
};
return(model);
}
/// <summary>Generate Logistic Regression model based on a set of examples.</summary>
/// <param name="X">The Matrix to process.</param>
/// <param name="y">The Vector to process.</param>
/// <returns>Model.</returns>
public override IModel Generate(Matrix X, Vector y)
{
X = IncreaseDimensions(X, this.PolynomialFeatures);
this.Preprocess(X);
// guarantee 1/0 based label vector
y = y.ToBinary(f => f == 1d, falseValue: 0d);
// add intercept term
X = X.Insert(Vector.Ones(X.Rows), 0, VectorType.Col, false);
Vector theta = Vector.Rand(X.Cols);
// run gradient descent
var optimizer = new numl.Math.Optimization.Optimizer(theta, this.MaxIterations, this.LearningRate)
{
CostFunction = new numl.Math.Functions.Cost.LogisticCostFunction()
{
X = X,
Y = y,
Lambda = this.Lambda,
Regularizer = new numl.Math.Functions.Regularization.L2Regularizer(),
LogisticFunction = this.LogisticFunction
}
};
optimizer.Run();
LogisticRegressionModel model = new LogisticRegressionModel()
{
Descriptor = this.Descriptor,
NormalizeFeatures = base.NormalizeFeatures,
FeatureNormalizer = base.FeatureNormalizer,
FeatureProperties = base.FeatureProperties,
Theta = optimizer.Properties.Theta,
LogisticFunction = this.LogisticFunction,
PolynomialFeatures = this.PolynomialFeatures
};
return model;
}