public void RelativeParameterConstructorTest()
{
var criteria = new RelativeParameterConvergence(iterations: 0, tolerance: 0.1);
int progress = 1;
double[] parameters = { 12345.6, 952.12, 1925.1 };
do
{
// Do some processing...
// Update current iteration information:
criteria.NewValues = parameters.Divide(progress++);
} while (!criteria.HasConverged);
// The method will converge after reaching the
// maximum of 11 iterations with a final value
// of { 1234.56, 95.212, 192.51 }:
int iterations = criteria.CurrentIteration; // 11
var v = criteria.OldValues; // { 1234.56, 95.212, 192.51 }
Assert.AreEqual(11, criteria.CurrentIteration);
Assert.AreEqual(1234.56, criteria.OldValues[0]);
Assert.AreEqual(95.212, criteria.OldValues[1]);
Assert.AreEqual(192.51, criteria.OldValues[2]);
}
/// <summary>
/// Constructs a new Gradient Descent algorithm.
/// </summary>
///
public LogisticGradientDescent()
{
convergence = new RelativeParameterConvergence()
{
MaxIterations = 0,
Tolerance = 1e-8
};
}
/// <summary>
/// Constructs a new Newton-Raphson learning algorithm
/// for Cox's Proportional Hazards models.
/// </summary>
///
public ProportionalHazardsNewtonRaphson()
{
this.convergence = new RelativeParameterConvergence()
{
Iterations = 0,
Tolerance = 1e-5
};
this.Estimator = HazardEstimator.BreslowNelsonAalen;
this.Ties = HazardTiesMethod.Efron;
this.Lambda = 0.1;
this.Token = new CancellationToken();
}
/// <summary>
/// Constructs a new Newton-Raphson learning algorithm
/// for Cox's Proportional Hazards models.
/// </summary>
///
/// <param name="hazards">The model to estimate.</param>
///
public ProportionalHazardsNewtonRaphson(ProportionalHazards hazards)
{
this.regression = hazards;
this.parameterCount = hazards.Coefficients.Length;
this.hessian = new double[parameterCount, parameterCount];
this.gradient = new double[parameterCount];
this.partialHessian = new double[parameterCount, parameterCount];
this.partialGradient = new double[parameterCount];
this.convergence = new RelativeParameterConvergence()
{
Iterations = 0,
Tolerance = 1e-5
};
}
/// <summary>
/// Constructs a new Newton-Raphson learning algorithm
/// for Cox's Proportional Hazards models.
/// </summary>
///
/// <param name="hazards">The model to estimate.</param>
///
public ProportionalHazardsNewtonRaphson(ProportionalHazards hazards)
{
this.regression = hazards;
this.parameterCount = hazards.Coefficients.Length;
this.hessian = new double[parameterCount, parameterCount];
this.gradient = new double[parameterCount];
this.partialHessian = new double[parameterCount, parameterCount];
this.partialGradient = new double[parameterCount];
this.convergence = new RelativeParameterConvergence()
{
Iterations = 0,
Tolerance = 1e-5
};
this.Estimator = HazardEstimator.BreslowNelsonAalen;
this.Ties = HazardTiesMethod.Efron;
this.Lambda = 0.1;
}
/// <summary>
/// Creates a new <see cref="LowerBoundNewtonRaphson"/>.
/// </summary>
///
public LowerBoundNewtonRaphson()
{
convergence = new RelativeParameterConvergence();
}
