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Mathematic support for Strata
/*
* Copyright (C) 2009 - present by OpenGamma Inc. and the OpenGamma group of companies
*
* Please see distribution for license.
*/
package com.opengamma.strata.math.impl.statistics.leastsquare;
import static com.opengamma.strata.math.MathUtils.pow2;
import static org.apache.commons.math3.util.CombinatoricsUtils.binomialCoefficient;
import java.util.List;
import java.util.function.Function;
import com.google.common.collect.Lists;
import com.google.common.primitives.Doubles;
import com.opengamma.strata.collect.ArgChecker;
import com.opengamma.strata.collect.DoubleArrayMath;
import com.opengamma.strata.collect.array.DoubleArray;
import com.opengamma.strata.collect.array.DoubleMatrix;
import com.opengamma.strata.math.impl.linearalgebra.SVDecompositionCommons;
import com.opengamma.strata.math.impl.matrix.CommonsMatrixAlgebra;
import com.opengamma.strata.math.impl.matrix.MatrixAlgebra;
import com.opengamma.strata.math.linearalgebra.Decomposition;
import com.opengamma.strata.math.linearalgebra.DecompositionResult;
/**
* Generalized least square method.
*/
public class GeneralizedLeastSquare {
private final Decomposition _decomposition;
private final MatrixAlgebra _algebra;
/**
* Creates an instance.
*/
public GeneralizedLeastSquare() {
_decomposition = new SVDecompositionCommons();
_algebra = new CommonsMatrixAlgebra();
}
/**
*
* @param The type of the independent variables (e.g. Double, double[], DoubleArray etc)
* @param x independent variables
* @param y dependent (scalar) variables
* @param sigma (Gaussian) measurement error on dependent variables
* @param basisFunctions set of basis functions - the fitting function is formed by these basis functions times a set of weights
* @return the results of the least square
*/
public GeneralizedLeastSquareResults solve(
T[] x, double[] y, double[] sigma, List> basisFunctions) {
return solve(x, y, sigma, basisFunctions, 0.0, 0);
}
/**
* Generalised least square with penalty on (higher-order) finite differences of weights.
* @param The type of the independent variables (e.g. Double, double[], DoubleArray etc)
* @param x independent variables
* @param y dependent (scalar) variables
* @param sigma (Gaussian) measurement error on dependent variables
* @param basisFunctions set of basis functions - the fitting function is formed by these basis functions times a set of weights
* @param lambda strength of penalty function
* @param differenceOrder difference order between weights used in penalty function
* @return the results of the least square
*/
public GeneralizedLeastSquareResults solve(
T[] x, double[] y, double[] sigma, List> basisFunctions,
double lambda, int differenceOrder) {
ArgChecker.notNull(x, "x null");
ArgChecker.notNull(y, "y null");
ArgChecker.notNull(sigma, "sigma null");
ArgChecker.notEmpty(basisFunctions, "empty basisFunctions");
int n = x.length;
ArgChecker.isTrue(n > 0, "no data");
ArgChecker.isTrue(y.length == n, "y wrong length");
ArgChecker.isTrue(sigma.length == n, "sigma wrong length");
ArgChecker.isTrue(lambda >= 0.0, "negative lambda");
ArgChecker.isTrue(differenceOrder >= 0, "difference order");
List lx = Lists.newArrayList(x);
List ly = Lists.newArrayList(Doubles.asList(y));
List lsigma = Lists.newArrayList(Doubles.asList(sigma));
return solveImp(lx, ly, lsigma, basisFunctions, lambda, differenceOrder);
}
GeneralizedLeastSquareResults solve(
double[] x, double[] y, double[] sigma, List> basisFunctions,
double lambda, int differenceOrder) {
return solve(DoubleArrayMath.toObject(x), y, sigma, basisFunctions, lambda, differenceOrder);
}
/**
*
* @param The type of the independent variables (e.g. Double, double[], DoubleArray etc)
* @param x independent variables
* @param y dependent (scalar) variables
* @param sigma (Gaussian) measurement error on dependent variables
* @param basisFunctions set of basis functions - the fitting function is formed by these basis functions times a set of weights
* @return the results of the least square
*/
public GeneralizedLeastSquareResults solve(
List x, List y, List sigma, List> basisFunctions) {
return solve(x, y, sigma, basisFunctions, 0.0, 0);
}
/**
* Generalised least square with penalty on (higher-order) finite differences of weights.
* @param The type of the independent variables (e.g. Double, double[], DoubleArray etc)
* @param x independent variables
* @param y dependent (scalar) variables
* @param sigma (Gaussian) measurement error on dependent variables
* @param basisFunctions set of basis functions - the fitting function is formed by these basis functions times a set of weights
* @param lambda strength of penalty function
* @param differenceOrder difference order between weights used in penalty function
* @return the results of the least square
*/
public GeneralizedLeastSquareResults solve(
List x, List y, List sigma, List> basisFunctions,
double lambda, int differenceOrder) {
ArgChecker.notEmpty(x, "empty measurement points");
ArgChecker.notEmpty(y, "empty measurement values");
ArgChecker.notEmpty(sigma, "empty measurement errors");
ArgChecker.notEmpty(basisFunctions, "empty basisFunctions");
int n = x.size();
ArgChecker.isTrue(n > 0, "no data");
ArgChecker.isTrue(y.size() == n, "y wrong length");
ArgChecker.isTrue(sigma.size() == n, "sigma wrong length");
ArgChecker.isTrue(lambda >= 0.0, "negative lambda");
ArgChecker.isTrue(differenceOrder >= 0, "difference order");
return solveImp(x, y, sigma, basisFunctions, lambda, differenceOrder);
}
/**
* Specialist method used mainly for solving multidimensional P-spline problems where the basis functions
* (B-splines) span a N-dimension space, and the weights sit on an N-dimension
* grid and are treated as a N-order tensor rather than a vector, so k-order differencing is
* done for each tensor index while varying the other indices.
*
* @param The type of the independent variables (e.g. Double, double[], DoubleArray etc)
* @param x independent variables
* @param y dependent (scalar) variables
* @param sigma (Gaussian) measurement error on dependent variables
* @param basisFunctions set of basis functions - the fitting function is formed by these basis functions times a set of weights
* @param sizes The size the weights tensor in each dimension (the product of this must equal the number of basis functions)
* @param lambda strength of penalty function in each dimension
* @param differenceOrder difference order between weights used in penalty function for each dimension
* @return the results of the least square
*/
public GeneralizedLeastSquareResults solve(
List x, List y, List sigma, List> basisFunctions,
int[] sizes, double[] lambda, int[] differenceOrder) {
ArgChecker.notEmpty(x, "empty measurement points");
ArgChecker.notEmpty(y, "empty measurement values");
ArgChecker.notEmpty(sigma, "empty measurement errors");
ArgChecker.notEmpty(basisFunctions, "empty basisFunctions");
int n = x.size();
ArgChecker.isTrue(n > 0, "no data");
ArgChecker.isTrue(y.size() == n, "y wrong length");
ArgChecker.isTrue(sigma.size() == n, "sigma wrong length");
int dim = sizes.length;
ArgChecker.isTrue(dim == lambda.length, "number of penalty functions {} must be equal to number of directions {}",
lambda.length, dim);
ArgChecker.isTrue(dim == differenceOrder.length, "number of difference order {} must be equal to number of directions {}",
differenceOrder.length, dim);
for (int i = 0; i < dim; i++) {
ArgChecker.isTrue(sizes[i] > 0, "sizes must be >= 1");
ArgChecker.isTrue(lambda[i] >= 0.0, "negative lambda");
ArgChecker.isTrue(differenceOrder[i] >= 0, "difference order");
}
return solveImp(x, y, sigma, basisFunctions, sizes, lambda, differenceOrder);
}
private GeneralizedLeastSquareResults solveImp(
List x, List y, List sigma, List> basisFunctions,
double lambda, int differenceOrder) {
int n = x.size();
int m = basisFunctions.size();
double[] b = new double[m];
double[] invSigmaSqr = new double[n];
double[][] f = new double[m][n];
int i, j, k;
for (i = 0; i < n; i++) {
double temp = sigma.get(i);
ArgChecker.isTrue(temp > 0, "sigma must be greater than zero");
invSigmaSqr[i] = 1.0 / temp / temp;
}
for (i = 0; i < m; i++) {
for (j = 0; j < n; j++) {
f[i][j] = basisFunctions.get(i).apply(x.get(j));
}
}
double sum;
for (i = 0; i < m; i++) {
sum = 0;
for (k = 0; k < n; k++) {
sum += y.get(k) * f[i][k] * invSigmaSqr[k];
}
b[i] = sum;
}
DoubleArray mb = DoubleArray.copyOf(b);
DoubleMatrix ma = getAMatrix(f, invSigmaSqr);
if (lambda > 0.0) {
DoubleMatrix d = getDiffMatrix(m, differenceOrder);
ma = (DoubleMatrix) _algebra.add(ma, _algebra.scale(d, lambda));
}
DecompositionResult decmp = _decomposition.apply(ma);
DoubleArray w = decmp.solve(mb);
DoubleMatrix covar = decmp.solve(DoubleMatrix.identity(m));
double chiSq = 0;
for (i = 0; i < n; i++) {
double temp = 0;
for (k = 0; k < m; k++) {
temp += w.get(k) * f[k][i];
}
chiSq += pow2(y.get(i) - temp) * invSigmaSqr[i];
}
return new GeneralizedLeastSquareResults<>(basisFunctions, chiSq, w, covar);
}
private GeneralizedLeastSquareResults solveImp(
List x, List y, List sigma, List> basisFunctions,
int[] sizes, double[] lambda, int[] differenceOrder) {
int dim = sizes.length;
int n = x.size();
int m = basisFunctions.size();
double[] b = new double[m];
double[] invSigmaSqr = new double[n];
double[][] f = new double[m][n];
int i, j, k;
for (i = 0; i < n; i++) {
double temp = sigma.get(i);
ArgChecker.isTrue(temp > 0, "sigma must be great than zero");
invSigmaSqr[i] = 1.0 / temp / temp;
}
for (i = 0; i < m; i++) {
for (j = 0; j < n; j++) {
f[i][j] = basisFunctions.get(i).apply(x.get(j));
}
}
double sum;
for (i = 0; i < m; i++) {
sum = 0;
for (k = 0; k < n; k++) {
sum += y.get(k) * f[i][k] * invSigmaSqr[k];
}
b[i] = sum;
}
DoubleArray mb = DoubleArray.copyOf(b);
DoubleMatrix ma = getAMatrix(f, invSigmaSqr);
for (i = 0; i < dim; i++) {
if (lambda[i] > 0.0) {
DoubleMatrix d = getDiffMatrix(sizes, differenceOrder[i], i);
ma = (DoubleMatrix) _algebra.add(ma, _algebra.scale(d, lambda[i]));
}
}
DecompositionResult decmp = _decomposition.apply(ma);
DoubleArray w = decmp.solve(mb);
DoubleMatrix covar = decmp.solve(DoubleMatrix.identity(m));
double chiSq = 0;
for (i = 0; i < n; i++) {
double temp = 0;
for (k = 0; k < m; k++) {
temp += w.get(k) * f[k][i];
}
chiSq += pow2(y.get(i) - temp) * invSigmaSqr[i];
}
return new GeneralizedLeastSquareResults<>(basisFunctions, chiSq, w, covar);
}
private DoubleMatrix getAMatrix(double[][] funcMatrix, double[] invSigmaSqr) {
int m = funcMatrix.length;
int n = funcMatrix[0].length;
double[][] a = new double[m][m];
for (int i = 0; i < m; i++) {
double sum = 0;
for (int k = 0; k < n; k++) {
sum += pow2(funcMatrix[i][k]) * invSigmaSqr[k];
}
a[i][i] = sum;
for (int j = i + 1; j < m; j++) {
sum = 0;
for (int k = 0; k < n; k++) {
sum += funcMatrix[i][k] * funcMatrix[j][k] * invSigmaSqr[k];
}
a[i][j] = sum;
a[j][i] = sum;
}
}
return DoubleMatrix.copyOf(a);
}
private DoubleMatrix getDiffMatrix(int m, int k) {
ArgChecker.isTrue(k < m, "difference order too high");
double[][] data = new double[m][m];
if (m == 0) {
return DoubleMatrix.copyOf(data);
}
int[] coeff = new int[k + 1];
int sign = 1;
for (int i = k; i >= 0; i--) {
coeff[i] = (int) (sign * binomialCoefficient(k, i));
sign *= -1;
}
for (int i = k; i < m; i++) {
for (int j = 0; j < k + 1; j++) {
data[i][j + i - k] = coeff[j];
}
}
DoubleMatrix d = DoubleMatrix.copyOf(data);
DoubleMatrix dt = _algebra.getTranspose(d);
return (DoubleMatrix) _algebra.multiply(dt, d);
}
private DoubleMatrix getDiffMatrix(int[] size, int k, int indices) {
int dim = size.length;
DoubleMatrix d = getDiffMatrix(size[indices], k);
int preProduct = 1;
int postProduct = 1;
for (int j = indices + 1; j < dim; j++) {
preProduct *= size[j];
}
for (int j = 0; j < indices; j++) {
postProduct *= size[j];
}
DoubleMatrix temp = d;
if (preProduct != 1) {
temp = (DoubleMatrix) _algebra.kroneckerProduct(DoubleMatrix.identity(preProduct), temp);
}
if (postProduct != 1) {
temp = (DoubleMatrix) _algebra.kroneckerProduct(temp, DoubleMatrix.identity(postProduct));
}
return temp;
}
}
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