/*
 * Licensed to the Apache Software Foundation (ASF) under one or more
 * contributor license agreements.  See the NOTICE file distributed with
 * this work for additional information regarding copyright ownership.
 * The ASF licenses this file to You under the Apache License, Version 2.0
 * (the "License"); you may not use this file except in compliance with
 * the License.  You may obtain a copy of the License at
 *
 *      http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
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package org.apache.commons.math3.analysis.interpolation;

import org.apache.commons.math3.exception.DimensionMismatchException;
import org.apache.commons.math3.exception.NoDataException;
import org.apache.commons.math3.exception.NonMonotonicSequenceException;
import org.apache.commons.math3.exception.NotPositiveException;
import org.apache.commons.math3.exception.NullArgumentException;
import org.apache.commons.math3.util.MathArrays;
import org.apache.commons.math3.util.Precision;
import org.apache.commons.math3.optim.nonlinear.vector.jacobian.GaussNewtonOptimizer;
import org.apache.commons.math3.fitting.PolynomialFitter;
import org.apache.commons.math3.analysis.polynomials.PolynomialFunction;
import org.apache.commons.math3.optim.SimpleVectorValueChecker;

Generates a bicubic interpolation function.
Prior to generating the interpolating function, the input is smoothed using
polynomial fitting.
Since:
2.2
Deprecated:
To be removed in 4.0 (see MATH-1166)./**
 * Generates a bicubic interpolation function.
 * Prior to generating the interpolating function, the input is smoothed using
 * polynomial fitting.
 *
 * @since 2.2
 * @deprecated To be removed in 4.0 (see MATH-1166).
 */
@Deprecated
public class SmoothingPolynomialBicubicSplineInterpolator
    extends BicubicSplineInterpolator {
    
Fitter for x. /** Fitter for x. */
    private final PolynomialFitter xFitter;
    
Degree of the fitting polynomial. /** Degree of the fitting polynomial. */
    private final int xDegree;
    
Fitter for y. /** Fitter for y. */
    private final PolynomialFitter yFitter;
    
Degree of the fitting polynomial. /** Degree of the fitting polynomial. */
    private final int yDegree;

    
Default constructor. The degree of the fitting polynomials is set to 3.
/**
     * Default constructor. The degree of the fitting polynomials is set to 3.
     */
    public SmoothingPolynomialBicubicSplineInterpolator() {
        this(3);
    }

    
Params:
degree – Degree of the polynomial fitting functions.
Throws:
NotPositiveException – if degree is not positive/**
     * @param degree Degree of the polynomial fitting functions.
     * @exception NotPositiveException if degree is not positive
     */
    public SmoothingPolynomialBicubicSplineInterpolator(int degree)
        throws NotPositiveException {
        this(degree, degree);
    }

    
Params:
xDegree – Degree of the polynomial fitting functions along the
x-dimension.
yDegree – Degree of the polynomial fitting functions along the
y-dimension.
Throws:
NotPositiveException – if degrees are not positive/**
     * @param xDegree Degree of the polynomial fitting functions along the
     * x-dimension.
     * @param yDegree Degree of the polynomial fitting functions along the
     * y-dimension.
     * @exception NotPositiveException if degrees are not positive
     */
    public SmoothingPolynomialBicubicSplineInterpolator(int xDegree, int yDegree)
        throws NotPositiveException {
        if (xDegree < 0) {
            throw new NotPositiveException(xDegree);
        }
        if (yDegree < 0) {
            throw new NotPositiveException(yDegree);
        }
        this.xDegree = xDegree;
        this.yDegree = yDegree;

        final double safeFactor = 1e2;
        final SimpleVectorValueChecker checker
            = new SimpleVectorValueChecker(safeFactor * Precision.EPSILON,
                                           safeFactor * Precision.SAFE_MIN);
        xFitter = new PolynomialFitter(new GaussNewtonOptimizer(false, checker));
        yFitter = new PolynomialFitter(new GaussNewtonOptimizer(false, checker));
    }

    
{@inheritDoc}
/**
     * {@inheritDoc}
     */
    @Override
    public BicubicSplineInterpolatingFunction interpolate(final double[] xval,
                                                          final double[] yval,
                                                          final double[][] fval)
        throws NoDataException, NullArgumentException,
               DimensionMismatchException, NonMonotonicSequenceException {
        if (xval.length == 0 || yval.length == 0 || fval.length == 0) {
            throw new NoDataException();
        }
        if (xval.length != fval.length) {
            throw new DimensionMismatchException(xval.length, fval.length);
        }

        final int xLen = xval.length;
        final int yLen = yval.length;

        for (int i = 0; i < xLen; i++) {
            if (fval[i].length != yLen) {
                throw new DimensionMismatchException(fval[i].length, yLen);
            }
        }

        MathArrays.checkOrder(xval);
        MathArrays.checkOrder(yval);

        // For each line y[j] (0 <= j < yLen), construct a polynomial, with
        // respect to variable x, fitting array fval[][j]
        final PolynomialFunction[] yPolyX = new PolynomialFunction[yLen];
        for (int j = 0; j < yLen; j++) {
            xFitter.clearObservations();
            for (int i = 0; i < xLen; i++) {
                xFitter.addObservedPoint(1, xval[i], fval[i][j]);
            }

            // Initial guess for the fit is zero for each coefficients (of which
            // there are "xDegree" + 1).
            yPolyX[j] = new PolynomialFunction(xFitter.fit(new double[xDegree + 1]));
        }

        // For every knot (xval[i], yval[j]) of the grid, calculate corrected
        // values fval_1
        final double[][] fval_1 = new double[xLen][yLen];
        for (int j = 0; j < yLen; j++) {
            final PolynomialFunction f = yPolyX[j];
            for (int i = 0; i < xLen; i++) {
                fval_1[i][j] = f.value(xval[i]);
            }
        }

        // For each line x[i] (0 <= i < xLen), construct a polynomial, with
        // respect to variable y, fitting array fval_1[i][]
        final PolynomialFunction[] xPolyY = new PolynomialFunction[xLen];
        for (int i = 0; i < xLen; i++) {
            yFitter.clearObservations();
            for (int j = 0; j < yLen; j++) {
                yFitter.addObservedPoint(1, yval[j], fval_1[i][j]);
            }

            // Initial guess for the fit is zero for each coefficients (of which
            // there are "yDegree" + 1).
            xPolyY[i] = new PolynomialFunction(yFitter.fit(new double[yDegree + 1]));
        }

        // For every knot (xval[i], yval[j]) of the grid, calculate corrected
        // values fval_2
        final double[][] fval_2 = new double[xLen][yLen];
        for (int i = 0; i < xLen; i++) {
            final PolynomialFunction f = xPolyY[i];
            for (int j = 0; j < yLen; j++) {
                fval_2[i][j] = f.value(yval[j]);
            }
        }

        return super.interpolate(xval, yval, fval_2);
    }
}