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removed deprecated methods
this does not belong to commons-math yet, but will probably be merged some day git-svn-id: https://svn.apache.org/repos/asf/commons/proper/math/branches/MATH_2_0@651259 13f79535-47bb-0310-9956-ffa450edef68
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Luc Maisonobe
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c29768da8d
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8c206f6225
@ -73,65 +73,6 @@ public class HarmonicFitter
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firstGuessNeeded = false;
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}
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/**
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* Simple constructor.
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* @param maxIterations maximum number of iterations allowed
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* @param convergence criterion threshold below which we do not need
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* to improve the criterion anymore
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* @param steadyStateThreshold steady state detection threshold, the
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* problem has reached a steady state (read converged) if
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* <code>Math.abs (Jn - Jn-1) < Jn * convergence</code>, where
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* <code>Jn</code> and <code>Jn-1</code> are the current and
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* preceding criterion value (square sum of the weighted residuals
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* of considered measurements).
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* @param epsilon threshold under which the matrix of the linearized
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* problem is considered singular (see {@link
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* org.spaceroots.mantissa.linalg.SquareMatrix#solve(
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* org.spaceroots.mantissa.linalg.Matrix,double) SquareMatrix.solve}).
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* @deprecated replaced by {@link #HarmonicFitter(Estimator)}
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* as of version 7.0
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*/
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public HarmonicFitter(int maxIterations, double convergence,
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double steadyStateThreshold, double epsilon) {
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this(new GaussNewtonEstimator(maxIterations, convergence,
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steadyStateThreshold, epsilon));
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}
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/**
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* Simple constructor.
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* <p>This constructor can be used when a first estimate of the
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* coefficients is already known.</p>
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* @param coefficients first estimate of the coefficients.
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* A reference to this array is hold by the newly created
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* object. Its elements will be adjusted during the fitting process
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* and they will be set to the adjusted coefficients at the end.
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* @param maxIterations maximum number of iterations allowed
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* @param convergence criterion threshold below which we do not need
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* to improve the criterion anymore
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* @param steadyStateThreshold steady state detection threshold, the
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* problem has reached a steady state (read converged) if
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* <code>Math.abs (Jn - Jn-1) < Jn * convergence</code>, where
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* <code>Jn</code> and <code>Jn-1</code> are the current and
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* preceding criterion value (square sum of the weighted residuals
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* of considered measurements).
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* @param epsilon threshold under which the matrix of the linearized
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* problem is considered singular (see {@link
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* org.spaceroots.mantissa.linalg.SquareMatrix#solve(
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* org.spaceroots.mantissa.linalg.Matrix,double) SquareMatrix.solve}).
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* @deprecated replaced by {@link #HarmonicFitter(EstimatedParameter[],
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* Estimator)} as of version 7.0
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*/
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public HarmonicFitter(EstimatedParameter[] coefficients,
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int maxIterations, double convergence,
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double steadyStateThreshold, double epsilon) {
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this(coefficients,
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new GaussNewtonEstimator(maxIterations, convergence,
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steadyStateThreshold, epsilon));
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}
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public double[] fit()
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throws EstimationException {
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if (firstGuessNeeded) {
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@ -78,78 +78,6 @@ public class PolynomialFitter
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super(coefficients, estimator);
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}
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/** Simple constructor.
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* <p>The polynomial fitter built this way are complete polynoms,
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* ie. a n-degree polynom has n+1 coefficients. In order to build
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* fitter for sparse polynoms (for example <code>a x^20 - b
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* x^30</code>, on should first build the coefficients array and
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* provide it to {@link
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* #PolynomialFitter(PolynomialCoefficient[], int, double, double,
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* double)}.</p>
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* @param degree maximal degree of the polynom
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* @param maxIterations maximum number of iterations allowed
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* @param convergence criterion threshold below which we do not need
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* to improve the criterion anymore
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* @param steadyStateThreshold steady state detection threshold, the
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* problem has reached a steady state (read converged) if
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* <code>Math.abs (Jn - Jn-1) < Jn * convergence</code>, where
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* <code>Jn</code> and <code>Jn-1</code> are the current and
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* preceding criterion value (square sum of the weighted residuals
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* of considered measurements).
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* @param epsilon threshold under which the matrix of the linearized
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* problem is considered singular (see {@link
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* org.spaceroots.mantissa.linalg.SquareMatrix#solve(
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* org.spaceroots.mantissa.linalg.Matrix,double) SquareMatrix.solve}).
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* @deprecated replaced by {@link #PolynomialFitter(int,Estimator)}
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* as of version 7.0
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*/
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public PolynomialFitter(int degree,
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int maxIterations, double convergence,
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double steadyStateThreshold, double epsilon) {
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this(degree,
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new GaussNewtonEstimator(maxIterations, steadyStateThreshold,
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convergence, epsilon));
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}
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/** Simple constructor.
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* <p>This constructor can be used either when a first estimate of
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* the coefficients is already known (which is of little interest
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* because the fit cost is the same whether a first guess is known or
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* not) or when one needs to handle sparse polynoms like <code>a
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* x^20 - b x^30</code>.</p>
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* @param coefficients first estimate of the coefficients.
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* A reference to this array is hold by the newly created
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* object. Its elements will be adjusted during the fitting process
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* and they will be set to the adjusted coefficients at the end.
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* @param maxIterations maximum number of iterations allowed
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* @param convergence criterion threshold below which we do not need
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* to improve the criterion anymore
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* @param steadyStateThreshold steady state detection threshold, the
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* problem has reached a steady state (read converged) if
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* <code>Math.abs (Jn - Jn-1) < Jn * convergence</code>, where
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* <code>Jn</code> and <code>Jn-1</code> are the current and
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* preceding criterion value (square sum of the weighted residuals
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* of considered measurements).
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* @param epsilon threshold under which the matrix of the linearized
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* problem is considered singular (see {@link
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* org.spaceroots.mantissa.linalg.SquareMatrix#solve(
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* org.spaceroots.mantissa.linalg.Matrix,double) SquareMatrix.solve}).
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* @deprecated replaced by {@link #PolynomialFitter(PolynomialCoefficient[],
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* Estimator)} as of version 7.0
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*/
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public PolynomialFitter(PolynomialCoefficient[] coefficients,
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int maxIterations, double convergence,
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double steadyStateThreshold, double epsilon) {
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this(coefficients,
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new GaussNewtonEstimator(maxIterations, steadyStateThreshold,
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convergence, epsilon));
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}
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/** Get the value of the function at x according to the current parameters value.
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* @param x abscissa at which the theoretical value is requested
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* @return theoretical value at x
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