public double GetLikelihoodRatio(GeneralizedLinearRegression model)
{
#pragma warning disable 612, 618
return(regression.GetLogLikelihoodRatio(inputData, outputData, model));
#pragma warning restore 612, 618
}
private void constructor(GeneralizedLinearRegression regression)
{
this.regression = regression;
this.parameterCount = regression.Coefficients.Length;
this.hessian = new double[parameterCount, parameterCount];
this.gradient = new double[parameterCount];
}
private void constructor(GeneralizedLinearRegression regression)
{
if (regression == null)
{
throw new ArgumentNullException("regression");
}
this.regression = regression;
this.parameterCount = regression.Coefficients.Length;
this.hessian = new double[parameterCount, parameterCount];
this.gradient = new double[parameterCount];
}
public void ComputeTest2()
{
double[][] input =
{
new double[] { 55, 0 }, // 0 - no cancer
new double[] { 28, 0 }, // 0
new double[] { 65, 1 }, // 0
new double[] { 46, 0 }, // 1 - have cancer
new double[] { 86, 1 }, // 1
new double[] { 56, 1 }, // 1
new double[] { 85, 0 }, // 0
new double[] { 33, 0 }, // 0
new double[] { 21, 1 }, // 0
new double[] { 42, 1 }, // 1
};
double[] output =
{
0, 0, 0, 1, 1, 1, 0, 0, 0, 1
};
var regression = new GeneralizedLinearRegression(new ProbitLinkFunction(), inputs: 2);
var teacher = new IterativeReweightedLeastSquares(regression);
double delta = 0;
do
{
// Perform an iteration
delta = teacher.Run(input, output);
} while (delta > 0.001);
Assert.AreEqual(3, regression.Coefficients.Length);
Assert.AreEqual(-1.4807594445304693, regression.Coefficients[0], 1e-10);
Assert.AreEqual(0.012417175632016827, regression.Coefficients[1], 1e-10);
Assert.AreEqual(1.072665379969842, regression.Coefficients[2], 1e-10);
Assert.IsFalse(regression.Coefficients.HasNaN());
Assert.AreEqual(3, regression.StandardErrors.Length);
Assert.AreEqual(1.6402037052797314, regression.StandardErrors[0], 1e-10);
Assert.AreEqual(0.026119425452145524, regression.StandardErrors[1], 1e-10);
Assert.AreEqual(1.1297252500874606, regression.StandardErrors[2], 1e-10);
Assert.IsFalse(regression.StandardErrors.HasNaN());
}
protected override void EndProcessing()
{
double[][] inputs;
double[] outputs;
if (ParameterSetName == "XY")
{
inputs = Converter.ToDoubleJaggedArray(X);
outputs = Converter.ToDoubleArray(Y);
}
else
{
outputs = _data.GetColumn(OutputName).ToDoubleArray();
_data.RemoveColumn(OutputName);
inputs = _data.ToDoubleJaggedArray();
}
double[] w = null;
if (Weights != null)
{
w = Converter.ToDoubleArray(Weights);
}
var model = new GeneralizedLinearRegression(LinkFunctionConvert.Get(LinkFunction))
{
NumberOfInputs = inputs[0].Length
};
var learner = new IterativeReweightedLeastSquares(model)
{
MaxIterations = 200,
Regularization = 0
};
learner.Learn(inputs, outputs, w);
WriteObject(model);
}
public void ComputeTest()
{
// Example from http://bayes.bgsu.edu/bcwr/vignettes/probit_regression.pdf
double[][] input =
{
new double[] { 525 },
new double[] { 533 },
new double[] { 545 },
new double[] { 582 },
new double[] { 581 },
new double[] { 576 },
new double[] { 572 },
new double[] { 609 },
new double[] { 559 },
new double[] { 543 },
new double[] { 576 },
new double[] { 525 },
new double[] { 574 },
new double[] { 582 },
new double[] { 574 },
new double[] { 471 },
new double[] { 595 },
new double[] { 557 },
new double[] { 557 },
new double[] { 584 },
new double[] { 599 },
new double[] { 517 },
new double[] { 649 },
new double[] { 584 },
new double[] { 463 },
new double[] { 591 },
new double[] { 488 },
new double[] { 563 },
new double[] { 553 },
new double[] { 549 }
};
double[] output =
{
0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1
};
var regression = new GeneralizedLinearRegression(new ProbitLinkFunction(), inputs: 1);
var teacher = new IterativeReweightedLeastSquares(regression);
double delta = 0;
do
{
// Perform an iteration
delta = teacher.Run(input, output);
} while (delta > 1e-6);
Assert.AreEqual(2, regression.Coefficients.Length);
Assert.AreEqual(-17.6984, regression.Coefficients[0], 1e-4);
Assert.AreEqual(0.03293, regression.Coefficients[1], 1e-4);
Assert.AreEqual(2, regression.StandardErrors.Length);
Assert.AreEqual(9.2731983954911374, regression.StandardErrors[0], 1e-6);
Assert.AreEqual(0.016768779446085, regression.StandardErrors[1], 1e-6);
}
//---------------------------------------------
#region Public Methods
/// <summary>
/// Gets the Log-Likelihood Ratio between this model and another model.
/// </summary>
///
/// <param name="model">Another logistic regression model.</param>
/// <returns>The Likelihood-Ratio between the two models.</returns>
///
public double GetLikelihoodRatio(GeneralizedLinearRegression model)
{
return(regression.GetLogLikelihoodRatio(inputData, outputData, model));
}
/// <summary>
/// Constructs a new Iterative Reweighted Least Squares.
/// </summary>
///
/// <param name="regression">The regression to estimate.</param>
///
public IterativeReweightedLeastSquares(GeneralizedLinearRegression regression)
{
constructor(regression);
}
/// <summary>
/// Constructs a new Iterative Reweighted Least Squares.
/// </summary>
///
/// <param name="regression">The regression to estimate.</param>
///
public IterativeReweightedLeastSquares(LogisticRegression regression)
{
var glm = GeneralizedLinearRegression.FromLogisticRegression(regression, makeCopy: false);
constructor(glm);
}
private void constructor(GeneralizedLinearRegression regression)
{
this.regression = regression;
this.parameterCount = regression.Coefficients.Length;
this.hessian = new double[parameterCount, parameterCount];
this.gradient = new double[parameterCount];
}
/// <summary>
/// Constructs a new Iterative Reweighted Least Squares.
/// </summary>
///
/// <param name="regression">The regression to estimate.</param>
///
public IterativeReweightedLeastSquares(GeneralizedLinearRegression regression)
{
constructor(regression);
}
private void constructor(GeneralizedLinearRegression regression)
{
if (regression == null)
throw new ArgumentNullException("regression");
this.regression = regression;
this.parameterCount = regression.Coefficients.Length;
this.hessian = new double[parameterCount, parameterCount];
this.gradient = new double[parameterCount];
}