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| -rw-r--r-- | admin/survey/excel/PHPExcel/Shared/JAMA/examples/LevenbergMarquardt.php | 185 |
1 files changed, 0 insertions, 185 deletions
diff --git a/admin/survey/excel/PHPExcel/Shared/JAMA/examples/LevenbergMarquardt.php b/admin/survey/excel/PHPExcel/Shared/JAMA/examples/LevenbergMarquardt.php deleted file mode 100644 index 01c4acc..0000000 --- a/admin/survey/excel/PHPExcel/Shared/JAMA/examples/LevenbergMarquardt.php +++ /dev/null @@ -1,185 +0,0 @@ -<?php
-
-// Levenberg-Marquardt in PHP
-
-// http://www.idiom.com/~zilla/Computer/Javanumeric/LM.java
-
-class LevenbergMarquardt {
-
- /**
- * Calculate the current sum-squared-error
- *
- * Chi-squared is the distribution of squared Gaussian errors,
- * thus the name.
- *
- * @param double[][] $x
- * @param double[] $a
- * @param double[] $y,
- * @param double[] $s,
- * @param object $f
- */
- function chiSquared($x, $a, $y, $s, $f) {
- $npts = count($y);
- $sum = 0.0;
-
- for ($i = 0; $i < $npts; ++$i) {
- $d = $y[$i] - $f->val($x[$i], $a);
- $d = $d / $s[$i];
- $sum = $sum + ($d*$d);
- }
-
- return $sum;
- } // function chiSquared()
-
-
- /**
- * Minimize E = sum {(y[k] - f(x[k],a)) / s[k]}^2
- * The individual errors are optionally scaled by s[k].
- * Note that LMfunc implements the value and gradient of f(x,a),
- * NOT the value and gradient of E with respect to a!
- *
- * @param x array of domain points, each may be multidimensional
- * @param y corresponding array of values
- * @param a the parameters/state of the model
- * @param vary false to indicate the corresponding a[k] is to be held fixed
- * @param s2 sigma^2 for point i
- * @param lambda blend between steepest descent (lambda high) and
- * jump to bottom of quadratic (lambda zero).
- * Start with 0.001.
- * @param termepsilon termination accuracy (0.01)
- * @param maxiter stop and return after this many iterations if not done
- * @param verbose set to zero (no prints), 1, 2
- *
- * @return the new lambda for future iterations.
- * Can use this and maxiter to interleave the LM descent with some other
- * task, setting maxiter to something small.
- */
- function solve($x, $a, $y, $s, $vary, $f, $lambda, $termepsilon, $maxiter, $verbose) {
- $npts = count($y);
- $nparm = count($a);
-
- if ($verbose > 0) {
- print("solve x[".count($x)."][".count($x[0])."]");
- print(" a[".count($a)."]");
- println(" y[".count(length)."]");
- }
-
- $e0 = $this->chiSquared($x, $a, $y, $s, $f);
-
- //double lambda = 0.001;
- $done = false;
-
- // g = gradient, H = hessian, d = step to minimum
- // H d = -g, solve for d
- $H = array();
- $g = array();
-
- //double[] d = new double[nparm];
-
- $oos2 = array();
-
- for($i = 0; $i < $npts; ++$i) {
- $oos2[$i] = 1./($s[$i]*$s[$i]);
- }
- $iter = 0;
- $term = 0; // termination count test
-
- do {
- ++$iter;
-
- // hessian approximation
- for( $r = 0; $r < $nparm; ++$r) {
- for( $c = 0; $c < $nparm; ++$c) {
- for( $i = 0; $i < $npts; ++$i) {
- if ($i == 0) $H[$r][$c] = 0.;
- $xi = $x[$i];
- $H[$r][$c] += ($oos2[$i] * $f->grad($xi, $a, $r) * $f->grad($xi, $a, $c));
- } //npts
- } //c
- } //r
-
- // boost diagonal towards gradient descent
- for( $r = 0; $r < $nparm; ++$r)
- $H[$r][$r] *= (1. + $lambda);
-
- // gradient
- for( $r = 0; $r < $nparm; ++$r) {
- for( $i = 0; $i < $npts; ++$i) {
- if ($i == 0) $g[$r] = 0.;
- $xi = $x[$i];
- $g[$r] += ($oos2[$i] * ($y[$i]-$f->val($xi,$a)) * $f->grad($xi, $a, $r));
- }
- } //npts
-
- // scale (for consistency with NR, not necessary)
- if ($false) {
- for( $r = 0; $r < $nparm; ++$r) {
- $g[$r] = -0.5 * $g[$r];
- for( $c = 0; $c < $nparm; ++$c) {
- $H[$r][$c] *= 0.5;
- }
- }
- }
-
- // solve H d = -g, evaluate error at new location
- //double[] d = DoubleMatrix.solve(H, g);
-// double[] d = (new Matrix(H)).lu().solve(new Matrix(g, nparm)).getRowPackedCopy();
- //double[] na = DoubleVector.add(a, d);
-// double[] na = (new Matrix(a, nparm)).plus(new Matrix(d, nparm)).getRowPackedCopy();
-// double e1 = chiSquared(x, na, y, s, f);
-
-// if (verbose > 0) {
-// System.out.println("\n\niteration "+iter+" lambda = "+lambda);
-// System.out.print("a = ");
-// (new Matrix(a, nparm)).print(10, 2);
-// if (verbose > 1) {
-// System.out.print("H = ");
-// (new Matrix(H)).print(10, 2);
-// System.out.print("g = ");
-// (new Matrix(g, nparm)).print(10, 2);
-// System.out.print("d = ");
-// (new Matrix(d, nparm)).print(10, 2);
-// }
-// System.out.print("e0 = " + e0 + ": ");
-// System.out.print("moved from ");
-// (new Matrix(a, nparm)).print(10, 2);
-// System.out.print("e1 = " + e1 + ": ");
-// if (e1 < e0) {
-// System.out.print("to ");
-// (new Matrix(na, nparm)).print(10, 2);
-// } else {
-// System.out.println("move rejected");
-// }
-// }
-
- // termination test (slightly different than NR)
-// if (Math.abs(e1-e0) > termepsilon) {
-// term = 0;
-// } else {
-// term++;
-// if (term == 4) {
-// System.out.println("terminating after " + iter + " iterations");
-// done = true;
-// }
-// }
-// if (iter >= maxiter) done = true;
-
- // in the C++ version, found that changing this to e1 >= e0
- // was not a good idea. See comment there.
- //
-// if (e1 > e0 || Double.isNaN(e1)) { // new location worse than before
-// lambda *= 10.;
-// } else { // new location better, accept new parameters
-// lambda *= 0.1;
-// e0 = e1;
-// // simply assigning a = na will not get results copied back to caller
-// for( int i = 0; i < nparm; i++ ) {
-// if (vary[i]) a[i] = na[i];
-// }
-// }
- } while(!$done);
-
- return $lambda;
- } // function solve()
-
-} // class LevenbergMarquardt
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