diff options
Diffstat (limited to 'admin/survey/excel/PHPExcel/Shared/JAMA/examples')
9 files changed, 0 insertions, 2620 deletions
diff --git a/admin/survey/excel/PHPExcel/Shared/JAMA/examples/LMQuadTest.php b/admin/survey/excel/PHPExcel/Shared/JAMA/examples/LMQuadTest.php deleted file mode 100644 index 706d9e9..0000000 --- a/admin/survey/excel/PHPExcel/Shared/JAMA/examples/LMQuadTest.php +++ /dev/null @@ -1,116 +0,0 @@ -<?php
-/**
- * quadratic (p-o)'S'S(p-o)
- * solve for o, S
- * S is a single scale factor
- */
-class LMQuadTest {
-
- /**
- * @param array[] $x
- * @param array[] $a
- */
- function val($x, $a) {
- if (count($a) != 3) die ("Wrong number of elements in array a");
- if (count($x) != 2) die ("Wrong number of elements in array x");
-
- $ox = $a[0];
- $oy = $a[1];
- $s = $a[2];
-
- $sdx = $s * ($x[0] - $ox);
- $sdy = $s * ($x[1] - $oy);
-
- return ($sdx * $sdx) + ($sdy * $sdy);
- } // function val()
-
-
- /**
- * z = (p-o)'S'S(p-o)
- * dz/dp = 2S'S(p-o)
- *
- * z = (s*(px-ox))^2 + (s*(py-oy))^2
- * dz/dox = -2(s*(px-ox))*s
- * dz/ds = 2*s*[(px-ox)^2 + (py-oy)^2]
- *
- * z = (s*dx)^2 + (s*dy)^2
- * dz/ds = 2(s*dx)*dx + 2(s*dy)*dy
- *
- * @param array[] $x
- * @param array[] $a
- * @param int $a_k
- * @param array[] $a
- */
- function grad($x, $a, $a_k) {
- if (count($a) != 3) die ("Wrong number of elements in array a");
- if (count($x) != 2) die ("Wrong number of elements in array x");
- if ($a_k < 3) die ("a_k=".$a_k);
-
- $ox = $a[0];
- $oy = $a[1];
- $s = $a[2];
-
- $dx = ($x[0] - $ox);
- $dy = ($x[1] - $oy);
-
- if ($a_k == 0)
- return -2.*$s*$s*$dx;
- elseif ($a_k == 1)
- return -2.*$s*$s*$dy;
- else
- return 2.*$s*($dx*$dx + $dy*$dy);
- } // function grad()
-
-
- /**
- * @return array[] $a
- */
- function initial() {
- $a[0] = 0.05;
- $a[1] = 0.1;
- $a[2] = 1.0;
-
- return $a;
- } // function initial()
-
-
- /**
- * @return Object[] $a
- */
- function testdata() {
- $npts = 25;
-
- $a[0] = 0.;
- $a[1] = 0.;
- $a[2] = 0.9;
-
- $i = 0;
-
- for ($r = -2; $r <= 2; ++$r) {
- for ($c = -2; $c <= 2; ++$c) {
- $x[$i][0] = $c;
- $x[$i][1] = $r;
- $y[$i] = $this->val($x[$i], $a);
- print("Quad ".$c.",".$r." -> ".$y[$i]."<br />");
- $s[$i] = 1.;
- ++$i;
- }
- }
- print("quad x= ");
-
- $qx = new Matrix($x);
- $qx->print(10, 2);
-
- print("quad y= ");
- $qy = new Matrix($y, $npts);
- $qy->print(10, 2);
-
- $o[0] = $x;
- $o[1] = $a;
- $o[2] = $y;
- $o[3] = $s;
-
- return $o;
- } // function testdata()
-
-} // class LMQuadTest
diff --git a/admin/survey/excel/PHPExcel/Shared/JAMA/examples/LagrangeInterpolation.php b/admin/survey/excel/PHPExcel/Shared/JAMA/examples/LagrangeInterpolation.php deleted file mode 100644 index 27c8937..0000000 --- a/admin/survey/excel/PHPExcel/Shared/JAMA/examples/LagrangeInterpolation.php +++ /dev/null @@ -1,59 +0,0 @@ -<?php
-
-require_once "../Matrix.php";
-
-/**
- * Given n points (x0,y0)...(xn-1,yn-1), the following methid computes
- * the polynomial factors of the n-1't degree polynomial passing through
- * the n points.
- *
- * Example: Passing in three points (2,3) (1,4) and (3,7) will produce
- * the results [2.5, -8.5, 10] which means that the points are on the
- * curve y = 2.5x² - 8.5x + 10.
- *
- * @see http://geosoft.no/software/lagrange/LagrangeInterpolation.java.html
- * @author Jacob Dreyer
- * @author Paul Meagher (port to PHP and minor changes)
- *
- * @param x[] float
- * @param y[] float
- */
-class LagrangeInterpolation {
-
- public function findPolynomialFactors($x, $y) {
- $n = count($x);
-
- $data = array(); // double[n][n];
- $rhs = array(); // double[n];
-
- for ($i = 0; $i < $n; ++$i) {
- $v = 1;
- for ($j = 0; $j < $n; ++$j) {
- $data[$i][$n-$j-1] = $v;
- $v *= $x[$i];
- }
- $rhs[$i] = $y[$i];
- }
-
- // Solve m * s = b
- $m = new Matrix($data);
- $b = new Matrix($rhs, $n);
-
- $s = $m->solve($b);
-
- return $s->getRowPackedCopy();
- } // function findPolynomialFactors()
-
-} // class LagrangeInterpolation
-
-
-$x = array(2.0, 1.0, 3.0);
-$y = array(3.0, 4.0, 7.0);
-
-$li = new LagrangeInterpolation;
-$f = $li->findPolynomialFactors($x, $y);
-
-
-for ($i = 0; $i < 3; ++$i) {
- echo $f[$i]."<br />";
-}
diff --git a/admin/survey/excel/PHPExcel/Shared/JAMA/examples/LagrangeInterpolation2.php b/admin/survey/excel/PHPExcel/Shared/JAMA/examples/LagrangeInterpolation2.php deleted file mode 100644 index cd9cb9a..0000000 --- a/admin/survey/excel/PHPExcel/Shared/JAMA/examples/LagrangeInterpolation2.php +++ /dev/null @@ -1,59 +0,0 @@ -<?php
-
-require_once "../Matrix.php";
-
-/**
- * Given n points (x0,y0)...(xn-1,yn-1), the following method computes
- * the polynomial factors of the n-1't degree polynomial passing through
- * the n points.
- *
- * Example: Passing in three points (2,3) (1,4) and (3,7) will produce
- * the results [2.5, -8.5, 10] which means that the points are on the
- * curve y = 2.5x² - 8.5x + 10.
- *
- * @see http://geosoft.no/software/lagrange/LagrangeInterpolation.java.html
- * @see http://source.freehep.org/jcvsweb/ilc/LCSIM/wdview/lcsim/src/org/lcsim/fit/polynomial/PolynomialFitter.java
- * @author Jacob Dreyer
- * @author Paul Meagher (port to PHP and minor changes)
- *
- * @param x[] float
- * @param y[] float
- */
-class LagrangeInterpolation {
-
- public function findPolynomialFactors($x, $y) {
- $n = count($x);
-
- $data = array(); // double[n][n];
- $rhs = array(); // double[n];
-
- for ($i = 0; $i < $n; ++$i) {
- $v = 1;
- for ($j = 0; $j < $n; ++$j) {
- $data[$i][$n-$j-1] = $v;
- $v *= $x[$i];
- }
- $rhs[$i] = $y[$i];
- }
-
- // Solve m * s = b
- $m = new Matrix($data);
- $b = new Matrix($rhs, $n);
-
- $s = $m->solve($b);
-
- return $s->getRowPackedCopy();
- } // function findPolynomialFactors()
-
-} // class LagrangeInterpolation
-
-
-$x = array(2.0, 1.0, 3.0);
-$y = array(3.0, 4.0, 7.0);
-
-$li = new LagrangeInterpolation;
-$f = $li->findPolynomialFactors($x, $y);
-
-for ($i = 0; $i < 3; ++$i) {
- echo $f[$i]."<br />";
-}
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 $lambda;
- } // function solve()
-
-} // class LevenbergMarquardt
diff --git a/admin/survey/excel/PHPExcel/Shared/JAMA/examples/MagicSquareExample.php b/admin/survey/excel/PHPExcel/Shared/JAMA/examples/MagicSquareExample.php deleted file mode 100644 index 8a66903..0000000 --- a/admin/survey/excel/PHPExcel/Shared/JAMA/examples/MagicSquareExample.php +++ /dev/null @@ -1,182 +0,0 @@ -<?php
-/**
-* @package JAMA
-*/
-
-require_once "../Matrix.php";
-
-/**
-* Example of use of Matrix Class, featuring magic squares.
-*/
-class MagicSquareExample {
-
- /**
- * Generate magic square test matrix.
- * @param int n dimension of matrix
- */
- function magic($n) {
-
- // Odd order
-
- if (($n % 2) == 1) {
- $a = ($n+1)/2;
- $b = ($n+1);
- for ($j = 0; $j < $n; ++$j)
- for ($i = 0; $i < $n; ++$i)
- $M[$i][$j] = $n*(($i+$j+$a) % $n) + (($i+2*$j+$b) % $n) + 1;
-
- // Doubly Even Order
-
- } else if (($n % 4) == 0) {
- for ($j = 0; $j < $n; ++$j) {
- for ($i = 0; $i < $n; ++$i) {
- if ((($i+1)/2)%2 == (($j+1)/2)%2)
- $M[$i][$j] = $n*$n-$n*$i-$j;
- else
- $M[$i][$j] = $n*$i+$j+1;
- }
- }
-
- // Singly Even Order
-
- } else {
-
- $p = $n/2;
- $k = ($n-2)/4;
- $A = $this->magic($p);
- $M = array();
- for ($j = 0; $j < $p; ++$j) {
- for ($i = 0; $i < $p; ++$i) {
- $aij = $A->get($i,$j);
- $M[$i][$j] = $aij;
- $M[$i][$j+$p] = $aij + 2*$p*$p;
- $M[$i+$p][$j] = $aij + 3*$p*$p;
- $M[$i+$p][$j+$p] = $aij + $p*$p;
- }
- }
-
- for ($i = 0; $i < $p; ++$i) {
- for ($j = 0; $j < $k; ++$j) {
- $t = $M[$i][$j];
- $M[$i][$j] = $M[$i+$p][$j];
- $M[$i+$p][$j] = $t;
- }
- for ($j = $n-$k+1; $j < $n; ++$j) {
- $t = $M[$i][$j];
- $M[$i][$j] = $M[$i+$p][$j];
- $M[$i+$p][$j] = $t;
- }
- }
-
- $t = $M[$k][0]; $M[$k][0] = $M[$k+$p][0]; $M[$k+$p][0] = $t;
- $t = $M[$k][$k]; $M[$k][$k] = $M[$k+$p][$k]; $M[$k+$p][$k] = $t;
-
- }
-
- return new Matrix($M);
-
- }
-
- /**
- * Simple function to replicate PHP 5 behaviour
- */
- function microtime_float() {
- list($usec, $sec) = explode(" ", microtime());
- return ((float)$usec + (float)$sec);
- }
-
- /**
- * Tests LU, QR, SVD and symmetric Eig decompositions.
- *
- * n = order of magic square.
- * trace = diagonal sum, should be the magic sum, (n^3 + n)/2.
- * max_eig = maximum eigenvalue of (A + A')/2, should equal trace.
- * rank = linear algebraic rank, should equal n if n is odd,
- * be less than n if n is even.
- * cond = L_2 condition number, ratio of singular values.
- * lu_res = test of LU factorization, norm1(L*U-A(p,:))/(n*eps).
- * qr_res = test of QR factorization, norm1(Q*R-A)/(n*eps).
- */
- function main() {
- ?>
- <p>Test of Matrix Class, using magic squares.</p>
- <p>See MagicSquareExample.main() for an explanation.</p>
- <table border='1' cellspacing='0' cellpadding='4'>
- <tr>
- <th>n</th>
- <th>trace</th>
- <th>max_eig</th>
- <th>rank</th>
- <th>cond</th>
- <th>lu_res</th>
- <th>qr_res</th>
- </tr>
- <?php
- $start_time = $this->microtime_float();
- $eps = pow(2.0,-52.0);
- for ($n = 3; $n <= 6; ++$n) {
- echo "<tr>";
-
- echo "<td align='right'>$n</td>";
-
- $M = $this->magic($n);
- $t = (int) $M->trace();
-
- echo "<td align='right'>$t</td>";
-
- $O = $M->plus($M->transpose());
- $E = new EigenvalueDecomposition($O->times(0.5));
- $d = $E->getRealEigenvalues();
-
- echo "<td align='right'>".$d[$n-1]."</td>";
-
- $r = $M->rank();
-
- echo "<td align='right'>".$r."</td>";
-
- $c = $M->cond();
-
- if ($c < 1/$eps)
- echo "<td align='right'>".sprintf("%.3f",$c)."</td>";
- else
- echo "<td align='right'>Inf</td>";
-
- $LU = new LUDecomposition($M);
- $L = $LU->getL();
- $U = $LU->getU();
- $p = $LU->getPivot();
- // Java version: R = L.times(U).minus(M.getMatrix(p,0,n-1));
- $S = $L->times($U);
- $R = $S->minus($M->getMatrix($p,0,$n-1));
- $res = $R->norm1()/($n*$eps);
-
- echo "<td align='right'>".sprintf("%.3f",$res)."</td>";
-
- $QR = new QRDecomposition($M);
- $Q = $QR->getQ();
- $R = $QR->getR();
- $S = $Q->times($R);
- $R = $S->minus($M);
- $res = $R->norm1()/($n*$eps);
-
- echo "<td align='right'>".sprintf("%.3f",$res)."</td>";
-
- echo "</tr>";
-
- }
- echo "<table>";
- echo "<br />";
-
- $stop_time = $this->microtime_float();
- $etime = $stop_time - $start_time;
-
- echo "<p>Elapsed time is ". sprintf("%.4f",$etime) ." seconds.</p>";
-
- }
-
-}
-
-$magic = new MagicSquareExample();
-$magic->main();
-
-?>
diff --git a/admin/survey/excel/PHPExcel/Shared/JAMA/examples/Stats.php b/admin/survey/excel/PHPExcel/Shared/JAMA/examples/Stats.php deleted file mode 100644 index 7d1359b..0000000 --- a/admin/survey/excel/PHPExcel/Shared/JAMA/examples/Stats.php +++ /dev/null @@ -1,1605 +0,0 @@ -<?php
-//
-// +----------------------------------------------------------------------+
-// | PHP Version 4 |
-// +----------------------------------------------------------------------+
-// | Copyright (c) 1997-2003 The PHP Group |
-// +----------------------------------------------------------------------+
-// | This source file is subject to version 2.0 of the PHP license, |
-// | that is bundled with this package in the file LICENSE, and is |
-// | available at through the world-wide-web at |
-// | http://www.php.net/license/2_02.txt. |
-// | If you did not receive a copy of the PHP license and are unable to |
-// | obtain it through the world-wide-web, please send a note to |
-// | license@php.net so we can mail you a copy immediately. |
-// +----------------------------------------------------------------------+
-// | Authors: Jesus M. Castagnetto <jmcastagnetto@php.net> |
-// +----------------------------------------------------------------------+
-//
-// $Id: Stats.php,v 1.15 2003/06/01 11:40:30 jmcastagnetto Exp $
-//
-
-include_once 'PEAR.php';
-
-/**
-* @package Math_Stats
-*/
-
-// Constants for defining the statistics to calculate /*{{{*/
-/**
-* STATS_BASIC to generate the basic descriptive statistics
-*/
-define('STATS_BASIC', 1);
-/**
-* STATS_FULL to generate also higher moments, mode, median, etc.
-*/
-define('STATS_FULL', 2);
-/*}}}*/
-
-// Constants describing the data set format /*{{{*/
-/**
-* STATS_DATA_SIMPLE for an array of numeric values. This is the default.
-* e.g. $data = array(2,3,4,5,1,1,6);
-*/
-define('STATS_DATA_SIMPLE', 0);
-/**
-* STATS_DATA_CUMMULATIVE for an associative array of frequency values,
-* where in each array entry, the index is the data point and the
-* value the count (frequency):
-* e.g. $data = array(3=>4, 2.3=>5, 1.25=>6, 0.5=>3)
-*/
-define('STATS_DATA_CUMMULATIVE', 1);
-/*}}}*/
-
-// Constants defining how to handle nulls /*{{{*/
-/**
-* STATS_REJECT_NULL, reject data sets with null values. This is the default.
-* Any non-numeric value is considered a null in this context.
-*/
-define('STATS_REJECT_NULL', -1);
-/**
-* STATS_IGNORE_NULL, ignore null values and prune them from the data.
-* Any non-numeric value is considered a null in this context.
-*/
-define('STATS_IGNORE_NULL', -2);
-/**
-* STATS_USE_NULL_AS_ZERO, assign the value of 0 (zero) to null values.
-* Any non-numeric value is considered a null in this context.
-*/
-define('STATS_USE_NULL_AS_ZERO', -3);
-/*}}}*/
-
-/**
-* A class to calculate descriptive statistics from a data set.
-* Data sets can be simple arrays of data, or a cummulative hash.
-* The second form is useful when passing large data set,
-* for example the data set:
-*
-* <pre>
-* $data1 = array (1,2,1,1,1,1,3,3,4.1,3,2,2,4.1,1,1,2,3,3,2,2,1,1,2,2);
-* </pre>
-*
-* can be epxressed more compactly as:
-*
-* <pre>
-* $data2 = array('1'=>9, '2'=>8, '3'=>5, '4.1'=>2);
-* </pre>
-*
-* Example of use:
-*
-* <pre>
-* include_once 'Math/Stats.php';
-* $s = new Math_Stats();
-* $s->setData($data1);
-* // or
-* // $s->setData($data2, STATS_DATA_CUMMULATIVE);
-* $stats = $s->calcBasic();
-* echo 'Mean: '.$stats['mean'].' StDev: '.$stats['stdev'].' <br />\n';
-*
-* // using data with nulls
-* // first ignoring them:
-* $data3 = array(1.2, 'foo', 2.4, 3.1, 4.2, 3.2, null, 5.1, 6.2);
-* $s->setNullOption(STATS_IGNORE_NULL);
-* $s->setData($data3);
-* $stats3 = $s->calcFull();
-*
-* // and then assuming nulls == 0
-* $s->setNullOption(STATS_USE_NULL_AS_ZERO);
-* $s->setData($data3);
-* $stats3 = $s->calcFull();
-* </pre>
-*
-* Originally this class was part of NumPHP (Numeric PHP package)
-*
-* @author Jesus M. Castagnetto <jmcastagnetto@php.net>
-* @version 0.8
-* @access public
-* @package Math_Stats
-*/
-class Base {/*{{{*/
- // properties /*{{{*/
-
- /**
- * The simple or cummulative data set.
- * Null by default.
- *
- * @access private
- * @var array
- */
- public $_data = null;
-
- /**
- * Expanded data set. Only set when cummulative data
- * is being used. Null by default.
- *
- * @access private
- * @var array
- */
- public $_dataExpanded = null;
-
- /**
- * Flag for data type, one of STATS_DATA_SIMPLE or
- * STATS_DATA_CUMMULATIVE. Null by default.
- *
- * @access private
- * @var int
- */
- public $_dataOption = null;
-
- /**
- * Flag for null handling options. One of STATS_REJECT_NULL,
- * STATS_IGNORE_NULL or STATS_USE_NULL_AS_ZERO
- *
- * @access private
- * @var int
- */
- public $_nullOption;
-
- /**
- * Array for caching result values, should be reset
- * when using setData()
- *
- * @access private
- * @var array
- */
- public $_calculatedValues = array();
-
- /*}}}*/
-
- /**
- * Constructor for the class
- *
- * @access public
- * @param optional int $nullOption how to handle null values
- * @return object Math_Stats
- */
- function Math_Stats($nullOption=STATS_REJECT_NULL) {/*{{{*/
- $this->_nullOption = $nullOption;
- }/*}}}*/
-
- /**
- * Sets and verifies the data, checking for nulls and using
- * the current null handling option
- *
- * @access public
- * @param array $arr the data set
- * @param optional int $opt data format: STATS_DATA_CUMMULATIVE or STATS_DATA_SIMPLE (default)
- * @return mixed true on success, a PEAR_Error object otherwise
- */
- function setData($arr, $opt=STATS_DATA_SIMPLE) {/*{{{*/
- if (!is_array($arr)) {
- return PEAR::raiseError('invalid data, an array of numeric data was expected');
- }
- $this->_data = null;
- $this->_dataExpanded = null;
- $this->_dataOption = null;
- $this->_calculatedValues = array();
- if ($opt == STATS_DATA_SIMPLE) {
- $this->_dataOption = $opt;
- $this->_data = array_values($arr);
- } else if ($opt == STATS_DATA_CUMMULATIVE) {
- $this->_dataOption = $opt;
- $this->_data = $arr;
- $this->_dataExpanded = array();
- }
- return $this->_validate();
- }/*}}}*/
-
- /**
- * Returns the data which might have been modified
- * according to the current null handling options.
- *
- * @access public
- * @param boolean $expanded whether to return a expanded list, default is false
- * @return mixed array of data on success, a PEAR_Error object otherwise
- * @see _validate()
- */
- function getData($expanded=false) {/*{{{*/
- if ($this->_data == null) {
- return PEAR::raiseError('data has not been set');
- }
- if ($this->_dataOption == STATS_DATA_CUMMULATIVE && $expanded) {
- return $this->_dataExpanded;
- } else {
- return $this->_data;
- }
- }/*}}}*/
-
- /**
- * Sets the null handling option.
- * Must be called before assigning a new data set containing null values
- *
- * @access public
- * @return mixed true on success, a PEAR_Error object otherwise
- * @see _validate()
- */
- function setNullOption($nullOption) {/*{{{*/
- if ($nullOption == STATS_REJECT_NULL
- || $nullOption == STATS_IGNORE_NULL
- || $nullOption == STATS_USE_NULL_AS_ZERO) {
- $this->_nullOption = $nullOption;
- return true;
- } else {
- return PEAR::raiseError('invalid null handling option expecting: '.
- 'STATS_REJECT_NULL, STATS_IGNORE_NULL or STATS_USE_NULL_AS_ZERO');
- }
- }/*}}}*/
-
- /**
- * Transforms the data by substracting each entry from the mean and
- * dividing by its standard deviation. This will reset all pre-calculated
- * values to their original (unset) defaults.
- *
- * @access public
- * @return mixed true on success, a PEAR_Error object otherwise
- * @see mean()
- * @see stDev()
- * @see setData()
- */
- function studentize() {/*{{{*/
- $mean = $this->mean();
- if (PEAR::isError($mean)) {
- return $mean;
- }
- $std = $this->stDev();
- if (PEAR::isError($std)) {
- return $std;
- }
- if ($std == 0) {
- return PEAR::raiseError('cannot studentize data, standard deviation is zero.');
- }
- $arr = array();
- if ($this->_dataOption == STATS_DATA_CUMMULATIVE) {
- foreach ($this->_data as $val=>$freq) {
- $newval = ($val - $mean) / $std;
- $arr["$newval"] = $freq;
- }
- } else {
- foreach ($this->_data as $val) {
- $newval = ($val - $mean) / $std;
- $arr[] = $newval;
- }
- }
- return $this->setData($arr, $this->_dataOption);
- }/*}}}*/
-
- /**
- * Transforms the data by substracting each entry from the mean.
- * This will reset all pre-calculated values to their original (unset) defaults.
- *
- * @access public
- * @return mixed true on success, a PEAR_Error object otherwise
- * @see mean()
- * @see setData()
- */
- function center() {/*{{{*/
- $mean = $this->mean();
- if (PEAR::isError($mean)) {
- return $mean;
- }
- $arr = array();
- if ($this->_dataOption == STATS_DATA_CUMMULATIVE) {
- foreach ($this->_data as $val=>$freq) {
- $newval = $val - $mean;
- $arr["$newval"] = $freq;
- }
- } else {
- foreach ($this->_data as $val) {
- $newval = $val - $mean;
- $arr[] = $newval;
- }
- }
- return $this->setData($arr, $this->_dataOption);
- }/*}}}*/
-
- /**
- * Calculates the basic or full statistics for the data set
- *
- * @access public
- * @param int $mode one of STATS_BASIC or STATS_FULL
- * @param boolean $returnErrorObject whether the raw PEAR_Error (when true, default),
- * or only the error message will be returned (when false), if an error happens.
- * @return mixed an associative array of statistics on success, a PEAR_Error object otherwise
- * @see calcBasic()
- * @see calcFull()
- */
- function calc($mode, $returnErrorObject=true) {/*{{{*/
- if ($this->_data == null) {
- return PEAR::raiseError('data has not been set');
- }
- if ($mode == STATS_BASIC) {
- return $this->calcBasic($returnErrorObject);
- } elseif ($mode == STATS_FULL) {
- return $this->calcFull($returnErrorObject);
- } else {
- return PEAR::raiseError('incorrect mode, expected STATS_BASIC or STATS_FULL');
- }
- }/*}}}*/
-
- /**
- * Calculates a basic set of statistics
- *
- * @access public
- * @param boolean $returnErrorObject whether the raw PEAR_Error (when true, default),
- * or only the error message will be returned (when false), if an error happens.
- * @return mixed an associative array of statistics on success, a PEAR_Error object otherwise
- * @see calc()
- * @see calcFull()
- */
- function calcBasic($returnErrorObject=true) {/*{{{*/
- return array (
- 'min' => $this->__format($this->min(), $returnErrorObject),
- 'max' => $this->__format($this->max(), $returnErrorObject),
- 'sum' => $this->__format($this->sum(), $returnErrorObject),
- 'sum2' => $this->__format($this->sum2(), $returnErrorObject),
- 'count' => $this->__format($this->count(), $returnErrorObject),
- 'mean' => $this->__format($this->mean(), $returnErrorObject),
- 'stdev' => $this->__format($this->stDev(), $returnErrorObject),
- 'variance' => $this->__format($this->variance(), $returnErrorObject),
- 'range' => $this->__format($this->range(), $returnErrorObject)
- );
- }/*}}}*/
-
- /**
- * Calculates a full set of statistics
- *
- * @access public
- * @param boolean $returnErrorObject whether the raw PEAR_Error (when true, default),
- * or only the error message will be returned (when false), if an error happens.
- * @return mixed an associative array of statistics on success, a PEAR_Error object otherwise
- * @see calc()
- * @see calcBasic()
- */
- function calcFull($returnErrorObject=true) {/*{{{*/
- return array (
- 'min' => $this->__format($this->min(), $returnErrorObject),
- 'max' => $this->__format($this->max(), $returnErrorObject),
- 'sum' => $this->__format($this->sum(), $returnErrorObject),
- 'sum2' => $this->__format($this->sum2(), $returnErrorObject),
- 'count' => $this->__format($this->count(), $returnErrorObject),
- 'mean' => $this->__format($this->mean(), $returnErrorObject),
- 'median' => $this->__format($this->median(), $returnErrorObject),
- 'mode' => $this->__format($this->mode(), $returnErrorObject),
- 'midrange' => $this->__format($this->midrange(), $returnErrorObject),
- 'geometric_mean' => $this->__format($this->geometricMean(), $returnErrorObject),
- 'harmonic_mean' => $this->__format($this->harmonicMean(), $returnErrorObject),
- 'stdev' => $this->__format($this->stDev(), $returnErrorObject),
- 'absdev' => $this->__format($this->absDev(), $returnErrorObject),
- 'variance' => $this->__format($this->variance(), $returnErrorObject),
- 'range' => $this->__format($this->range(), $returnErrorObject),
- 'std_error_of_mean' => $this->__format($this->stdErrorOfMean(), $returnErrorObject),
- 'skewness' => $this->__format($this->skewness(), $returnErrorObject),
- 'kurtosis' => $this->__format($this->kurtosis(), $returnErrorObject),
- 'coeff_of_variation' => $this->__format($this->coeffOfVariation(), $returnErrorObject),
- 'sample_central_moments' => array (
- 1 => $this->__format($this->sampleCentralMoment(1), $returnErrorObject),
- 2 => $this->__format($this->sampleCentralMoment(2), $returnErrorObject),
- 3 => $this->__format($this->sampleCentralMoment(3), $returnErrorObject),
- 4 => $this->__format($this->sampleCentralMoment(4), $returnErrorObject),
- 5 => $this->__format($this->sampleCentralMoment(5), $returnErrorObject)
- ),
- 'sample_raw_moments' => array (
- 1 => $this->__format($this->sampleRawMoment(1), $returnErrorObject),
- 2 => $this->__format($this->sampleRawMoment(2), $returnErrorObject),
- 3 => $this->__format($this->sampleRawMoment(3), $returnErrorObject),
- 4 => $this->__format($this->sampleRawMoment(4), $returnErrorObject),
- 5 => $this->__format($this->sampleRawMoment(5), $returnErrorObject)
- ),
- 'frequency' => $this->__format($this->frequency(), $returnErrorObject),
- 'quartiles' => $this->__format($this->quartiles(), $returnErrorObject),
- 'interquartile_range' => $this->__format($this->interquartileRange(), $returnErrorObject),
- 'interquartile_mean' => $this->__format($this->interquartileMean(), $returnErrorObject),
- 'quartile_deviation' => $this->__format($this->quartileDeviation(), $returnErrorObject),
- 'quartile_variation_coefficient' => $this->__format($this->quartileVariationCoefficient(), $returnErrorObject),
- 'quartile_skewness_coefficient' => $this->__format($this->quartileSkewnessCoefficient(), $returnErrorObject)
- );
- }/*}}}*/
-
- /**
- * Calculates the minimum of a data set.
- * Handles cummulative data sets correctly
- *
- * @access public
- * @return mixed the minimum value on success, a PEAR_Error object otherwise
- * @see calc()
- * @see max()
- */
- function min() {/*{{{*/
- if ($this->_data == null) {
- return PEAR::raiseError('data has not been set');
- }
- if (!array_key_exists('min', $this->_calculatedValues)) {
- if ($this->_dataOption == STATS_DATA_CUMMULATIVE) {
- $min = min(array_keys($this->_data));
- } else {
- $min = min($this->_data);
- }
- $this->_calculatedValues['min'] = $min;
- }
- return $this->_calculatedValues['min'];
- }/*}}}*/
-
- /**
- * Calculates the maximum of a data set.
- * Handles cummulative data sets correctly
- *
- * @access public
- * @return mixed the maximum value on success, a PEAR_Error object otherwise
- * @see calc()
- * @see min()
- */
- function max() {/*{{{*/
- if ($this->_data == null) {
- return PEAR::raiseError('data has not been set');
- }
- if (!array_key_exists('max', $this->_calculatedValues)) {
- if ($this->_dataOption == STATS_DATA_CUMMULATIVE) {
- $max = max(array_keys($this->_data));
- } else {
- $max = max($this->_data);
- }
- $this->_calculatedValues['max'] = $max;
- }
- return $this->_calculatedValues['max'];
- }/*}}}*/
-
- /**
- * Calculates SUM { xi }
- * Handles cummulative data sets correctly
- *
- * @access public
- * @return mixed the sum on success, a PEAR_Error object otherwise
- * @see calc()
- * @see sum2()
- * @see sumN()
- */
- function sum() {/*{{{*/
- if (!array_key_exists('sum', $this->_calculatedValues)) {
- $sum = $this->sumN(1);
- if (PEAR::isError($sum)) {
- return $sum;
- } else {
- $this->_calculatedValues['sum'] = $sum;
- }
- }
- return $this->_calculatedValues['sum'];
- }/*}}}*/
-
- /**
- * Calculates SUM { (xi)^2 }
- * Handles cummulative data sets correctly
- *
- * @access public
- * @return mixed the sum on success, a PEAR_Error object otherwise
- * @see calc()
- * @see sum()
- * @see sumN()
- */
- function sum2() {/*{{{*/
- if (!array_key_exists('sum2', $this->_calculatedValues)) {
- $sum2 = $this->sumN(2);
- if (PEAR::isError($sum2)) {
- return $sum2;
- } else {
- $this->_calculatedValues['sum2'] = $sum2;
- }
- }
- return $this->_calculatedValues['sum2'];
- }/*}}}*/
-
- /**
- * Calculates SUM { (xi)^n }
- * Handles cummulative data sets correctly
- *
- * @access public
- * @param numeric $n the exponent
- * @return mixed the sum on success, a PEAR_Error object otherwise
- * @see calc()
- * @see sum()
- * @see sum2()
- */
- function sumN($n) {/*{{{*/
- if ($this->_data == null) {
- return PEAR::raiseError('data has not been set');
- }
- $sumN = 0;
- if ($this->_dataOption == STATS_DATA_CUMMULATIVE) {
- foreach($this->_data as $val=>$freq) {
- $sumN += $freq * pow((double)$val, (double)$n);
- }
- } else {
- foreach($this->_data as $val) {
- $sumN += pow((double)$val, (double)$n);
- }
- }
- return $sumN;
- }/*}}}*/
-
- /**
- * Calculates PROD { (xi) }, (the product of all observations)
- * Handles cummulative data sets correctly
- *
- * @access public
- * @return mixed the product on success, a PEAR_Error object otherwise
- * @see productN()
- */
- function product() {/*{{{*/
- if (!array_key_exists('product', $this->_calculatedValues)) {
- $product = $this->productN(1);
- if (PEAR::isError($product)) {
- return $product;
- } else {
- $this->_calculatedValues['product'] = $product;
- }
- }
- return $this->_calculatedValues['product'];
- }/*}}}*/
-
- /**
- * Calculates PROD { (xi)^n }, which is the product of all observations
- * Handles cummulative data sets correctly
- *
- * @access public
- * @param numeric $n the exponent
- * @return mixed the product on success, a PEAR_Error object otherwise
- * @see product()
- */
- function productN($n) {/*{{{*/
- if ($this->_data == null) {
- return PEAR::raiseError('data has not been set');
- }
- $prodN = 1.0;
- if ($this->_dataOption == STATS_DATA_CUMMULATIVE) {
- foreach($this->_data as $val=>$freq) {
- if ($val == 0) {
- return 0.0;
- }
- $prodN *= $freq * pow((double)$val, (double)$n);
- }
- } else {
- foreach($this->_data as $val) {
- if ($val == 0) {
- return 0.0;
- }
- $prodN *= pow((double)$val, (double)$n);
- }
- }
- return $prodN;
-
- }/*}}}*/
-
- /**
- * Calculates the number of data points in the set
- * Handles cummulative data sets correctly
- *
- * @access public
- * @return mixed the count on success, a PEAR_Error object otherwise
- * @see calc()
- */
- function count() {/*{{{*/
- if ($this->_data == null) {
- return PEAR::raiseError('data has not been set');
- }
- if (!array_key_exists('count', $this->_calculatedValues)) {
- if ($this->_dataOption == STATS_DATA_CUMMULATIVE) {
- $count = count($this->_dataExpanded);
- } else {
- $count = count($this->_data);
- }
- $this->_calculatedValues['count'] = $count;
- }
- return $this->_calculatedValues['count'];
- }/*}}}*/
-
- /**
- * Calculates the mean (average) of the data points in the set
- * Handles cummulative data sets correctly
- *
- * @access public
- * @return mixed the mean value on success, a PEAR_Error object otherwise
- * @see calc()
- * @see sum()
- * @see count()
- */
- function mean() {/*{{{*/
- if (!array_key_exists('mean', $this->_calculatedValues)) {
- $sum = $this->sum();
- if (PEAR::isError($sum)) {
- return $sum;
- }
- $count = $this->count();
- if (PEAR::isError($count)) {
- return $count;
- }
- $this->_calculatedValues['mean'] = $sum / $count;
- }
- return $this->_calculatedValues['mean'];
- }/*}}}*/
-
- /**
- * Calculates the range of the data set = max - min
- *
- * @access public
- * @return mixed the value of the range on success, a PEAR_Error object otherwise.
- */
- function range() {/*{{{*/
- if (!array_key_exists('range', $this->_calculatedValues)) {
- $min = $this->min();
- if (PEAR::isError($min)) {
- return $min;
- }
- $max = $this->max();
- if (PEAR::isError($max)) {
- return $max;
- }
- $this->_calculatedValues['range'] = $max - $min;
- }
- return $this->_calculatedValues['range'];
-
- }/*}}}*/
-
- /**
- * Calculates the variance (unbiased) of the data points in the set
- * Handles cummulative data sets correctly
- *
- * @access public
- * @return mixed the variance value on success, a PEAR_Error object otherwise
- * @see calc()
- * @see __sumdiff()
- * @see count()
- */
- function variance() {/*{{{*/
- if (!array_key_exists('variance', $this->_calculatedValues)) {
- $variance = $this->__calcVariance();
- if (PEAR::isError($variance)) {
- return $variance;
- }
- $this->_calculatedValues['variance'] = $variance;
- }
- return $this->_calculatedValues['variance'];
- }/*}}}*/
-
- /**
- * Calculates the standard deviation (unbiased) of the data points in the set
- * Handles cummulative data sets correctly
- *
- * @access public
- * @return mixed the standard deviation on success, a PEAR_Error object otherwise
- * @see calc()
- * @see variance()
- */
- function stDev() {/*{{{*/
- if (!array_key_exists('stDev', $this->_calculatedValues)) {
- $variance = $this->variance();
- if (PEAR::isError($variance)) {
- return $variance;
- }
- $this->_calculatedValues['stDev'] = sqrt($variance);
- }
- return $this->_calculatedValues['stDev'];
- }/*}}}*/
-
- /**
- * Calculates the variance (unbiased) of the data points in the set
- * given a fixed mean (average) value. Not used in calcBasic(), calcFull()
- * or calc().
- * Handles cummulative data sets correctly
- *
- * @access public
- * @param numeric $mean the fixed mean value
- * @return mixed the variance on success, a PEAR_Error object otherwise
- * @see __sumdiff()
- * @see count()
- * @see variance()
- */
- function varianceWithMean($mean) {/*{{{*/
- return $this->__calcVariance($mean);
- }/*}}}*/
-
- /**
- * Calculates the standard deviation (unbiased) of the data points in the set
- * given a fixed mean (average) value. Not used in calcBasic(), calcFull()
- * or calc().
- * Handles cummulative data sets correctly
- *
- * @access public
- * @param numeric $mean the fixed mean value
- * @return mixed the standard deviation on success, a PEAR_Error object otherwise
- * @see varianceWithMean()
- * @see stDev()
- */
- function stDevWithMean($mean) {/*{{{*/
- $varianceWM = $this->varianceWithMean($mean);
- if (PEAR::isError($varianceWM)) {
- return $varianceWM;
- }
- return sqrt($varianceWM);
- }/*}}}*/
-
- /**
- * Calculates the absolute deviation of the data points in the set
- * Handles cummulative data sets correctly
- *
- * @access public
- * @return mixed the absolute deviation on success, a PEAR_Error object otherwise
- * @see calc()
- * @see __sumabsdev()
- * @see count()
- * @see absDevWithMean()
- */
- function absDev() {/*{{{*/
- if (!array_key_exists('absDev', $this->_calculatedValues)) {
- $absDev = $this->__calcAbsoluteDeviation();
- if (PEAR::isError($absdev)) {
- return $absdev;
- }
- $this->_calculatedValues['absDev'] = $absDev;
- }
- return $this->_calculatedValues['absDev'];
- }/*}}}*/
-
- /**
- * Calculates the absolute deviation of the data points in the set
- * given a fixed mean (average) value. Not used in calcBasic(), calcFull()
- * or calc().
- * Handles cummulative data sets correctly
- *
- * @access public
- * @param numeric $mean the fixed mean value
- * @return mixed the absolute deviation on success, a PEAR_Error object otherwise
- * @see __sumabsdev()
- * @see absDev()
- */
- function absDevWithMean($mean) {/*{{{*/
- return $this->__calcAbsoluteDeviation($mean);
- }/*}}}*/
-
- /**
- * Calculates the skewness of the data distribution in the set
- * The skewness measures the degree of asymmetry of a distribution,
- * and is related to the third central moment of a distribution.
- * A normal distribution has a skewness = 0
- * A distribution with a tail off towards the high end of the scale
- * (positive skew) has a skewness > 0
- * A distribution with a tail off towards the low end of the scale
- * (negative skew) has a skewness < 0
- * Handles cummulative data sets correctly
- *
- * @access public
- * @return mixed the skewness value on success, a PEAR_Error object otherwise
- * @see __sumdiff()
- * @see count()
- * @see stDev()
- * @see calc()
- */
- function skewness() {/*{{{*/
- if (!array_key_exists('skewness', $this->_calculatedValues)) {
- $count = $this->count();
- if (PEAR::isError($count)) {
- return $count;
- }
- $stDev = $this->stDev();
- if (PEAR::isError($stDev)) {
- return $stDev;
- }
- $sumdiff3 = $this->__sumdiff(3);
- if (PEAR::isError($sumdiff3)) {
- return $sumdiff3;
- }
- $this->_calculatedValues['skewness'] = ($sumdiff3 / ($count * pow($stDev, 3)));
- }
- return $this->_calculatedValues['skewness'];
- }/*}}}*/
-
- /**
- * Calculates the kurtosis of the data distribution in the set
- * The kurtosis measures the degrees of peakedness of a distribution.
- * It is also called the "excess" or "excess coefficient", and is
- * a normalized form of the fourth central moment of a distribution.
- * A normal distributions has kurtosis = 0
- * A narrow and peaked (leptokurtic) distribution has a
- * kurtosis > 0
- * A flat and wide (platykurtic) distribution has a kurtosis < 0
- * Handles cummulative data sets correctly
- *
- * @access public
- * @return mixed the kurtosis value on success, a PEAR_Error object otherwise
- * @see __sumdiff()
- * @see count()
- * @see stDev()
- * @see calc()
- */
- function kurtosis() {/*{{{*/
- if (!array_key_exists('kurtosis', $this->_calculatedValues)) {
- $count = $this->count();
- if (PEAR::isError($count)) {
- return $count;
- }
- $stDev = $this->stDev();
- if (PEAR::isError($stDev)) {
- return $stDev;
- }
- $sumdiff4 = $this->__sumdiff(4);
- if (PEAR::isError($sumdiff4)) {
- return $sumdiff4;
- }
- $this->_calculatedValues['kurtosis'] = ($sumdiff4 / ($count * pow($stDev, 4))) - 3;
- }
- return $this->_calculatedValues['kurtosis'];
- }/*}}}*/
-
- /**
- * Calculates the median of a data set.
- * The median is the value such that half of the points are below it
- * in a sorted data set.
- * If the number of values is odd, it is the middle item.
- * If the number of values is even, is the average of the two middle items.
- * Handles cummulative data sets correctly
- *
- * @access public
- * @return mixed the median value on success, a PEAR_Error object otherwise
- * @see count()
- * @see calc()
- */
- function median() {/*{{{*/
- if ($this->_data == null) {
- return PEAR::raiseError('data has not been set');
- }
- if (!array_key_exists('median', $this->_calculatedValues)) {
- if ($this->_dataOption == STATS_DATA_CUMMULATIVE) {
- $arr =& $this->_dataExpanded;
- } else {
- $arr =& $this->_data;
- }
- $n = $this->count();
- if (PEAR::isError($n)) {
- return $n;
- }
- $h = intval($n / 2);
- if ($n % 2 == 0) {
- $median = ($arr[$h] + $arr[$h - 1]) / 2;
- } else {
- $median = $arr[$h + 1];
- }
- $this->_calculatedValues['median'] = $median;
- }
- return $this->_calculatedValues['median'];
- }/*}}}*/
-
- /**
- * Calculates the mode of a data set.
- * The mode is the value with the highest frequency in the data set.
- * There can be more than one mode.
- * Handles cummulative data sets correctly
- *
- * @access public
- * @return mixed an array of mode value on success, a PEAR_Error object otherwise
- * @see frequency()
- * @see calc()
- */
- function mode() {/*{{{*/
- if ($this->_data == null) {
- return PEAR::raiseError('data has not been set');
- }
- if (!array_key_exists('mode', $this->_calculatedValues)) {
- if ($this->_dataOption == STATS_DATA_CUMMULATIVE) {
- $arr = $this->_data;
- } else {
- $arr = $this->frequency();
- }
- arsort($arr);
- $mcount = 1;
- foreach ($arr as $val=>$freq) {
- if ($mcount == 1) {
- $mode = array($val);
- $mfreq = $freq;
- ++$mcount;
- continue;
- }
- if ($mfreq == $freq)
- $mode[] = $val;
- if ($mfreq > $freq)
- break;
- }
- $this->_calculatedValues['mode'] = $mode;
- }
- return $this->_calculatedValues['mode'];
- }/*}}}*/
-
- /**
- * Calculates the midrange of a data set.
- * The midrange is the average of the minimum and maximum of the data set.
- * Handles cummulative data sets correctly
- *
- * @access public
- * @return mixed the midrange value on success, a PEAR_Error object otherwise
- * @see min()
- * @see max()
- * @see calc()
- */
- function midrange() {/*{{{*/
- if (!array_key_exists('midrange', $this->_calculatedValues)) {
- $min = $this->min();
- if (PEAR::isError($min)) {
- return $min;
- }
- $max = $this->max();
- if (PEAR::isError($max)) {
- return $max;
- }
- $this->_calculatedValues['midrange'] = (($max + $min) / 2);
- }
- return $this->_calculatedValues['midrange'];
- }/*}}}*/
-
- /**
- * Calculates the geometrical mean of the data points in the set
- * Handles cummulative data sets correctly
- *
- * @access public
- * @return mixed the geometrical mean value on success, a PEAR_Error object otherwise
- * @see calc()
- * @see product()
- * @see count()
- */
- function geometricMean() {/*{{{*/
- if (!array_key_exists('geometricMean', $this->_calculatedValues)) {
- $count = $this->count();
- if (PEAR::isError($count)) {
- return $count;
- }
- $prod = $this->product();
- if (PEAR::isError($prod)) {
- return $prod;
- }
- if ($prod == 0.0) {
- return 0.0;
- }
- if ($prod < 0) {
- return PEAR::raiseError('The product of the data set is negative, geometric mean undefined.');
- }
- $this->_calculatedValues['geometricMean'] = pow($prod , 1 / $count);
- }
- return $this->_calculatedValues['geometricMean'];
- }/*}}}*/
-
- /**
- * Calculates the harmonic mean of the data points in the set
- * Handles cummulative data sets correctly
- *
- * @access public
- * @return mixed the harmonic mean value on success, a PEAR_Error object otherwise
- * @see calc()
- * @see count()
- */
- function harmonicMean() {/*{{{*/
- if ($this->_data == null) {
- return PEAR::raiseError('data has not been set');
- }
- if (!array_key_exists('harmonicMean', $this->_calculatedValues)) {
- $count = $this->count();
- if (PEAR::isError($count)) {
- return $count;
- }
- $invsum = 0.0;
- if ($this->_dataOption == STATS_DATA_CUMMULATIVE) {
- foreach($this->_data as $val=>$freq) {
- if ($val == 0) {
- return PEAR::raiseError('cannot calculate a '.
- 'harmonic mean with data values of zero.');
- }
- $invsum += $freq / $val;
- }
- } else {
- foreach($this->_data as $val) {
- if ($val == 0) {
- return PEAR::raiseError('cannot calculate a '.
- 'harmonic mean with data values of zero.');
- }
- $invsum += 1 / $val;
- }
- }
- $this->_calculatedValues['harmonicMean'] = $count / $invsum;
- }
- return $this->_calculatedValues['harmonicMean'];
- }/*}}}*/
-
- /**
- * Calculates the nth central moment (m{n}) of a data set.
- *
- * The definition of a sample central moment is:
- *
- * m{n} = 1/N * SUM { (xi - avg)^n }
- *
- * where: N = sample size, avg = sample mean.
- *
- * @access public
- * @param integer $n moment to calculate
- * @return mixed the numeric value of the moment on success, PEAR_Error otherwise
- */
- function sampleCentralMoment($n) {/*{{{*/
- if (!is_int($n) || $n < 1) {
- return PEAR::isError('moment must be a positive integer >= 1.');
- }
-
- if ($n == 1) {
- return 0;
- }
- $count = $this->count();
- if (PEAR::isError($count)) {
- return $count;
- }
- if ($count == 0) {
- return PEAR::raiseError("Cannot calculate {$n}th sample moment, ".
- 'there are zero data entries');
- }
- $sum = $this->__sumdiff($n);
- if (PEAR::isError($sum)) {
- return $sum;
- }
- return ($sum / $count);
- }/*}}}*/
-
- /**
- * Calculates the nth raw moment (m{n}) of a data set.
- *
- * The definition of a sample central moment is:
- *
- * m{n} = 1/N * SUM { xi^n }
- *
- * where: N = sample size, avg = sample mean.
- *
- * @access public
- * @param integer $n moment to calculate
- * @return mixed the numeric value of the moment on success, PEAR_Error otherwise
- */
- function sampleRawMoment($n) {/*{{{*/
- if (!is_int($n) || $n < 1) {
- return PEAR::isError('moment must be a positive integer >= 1.');
- }
-
- $count = $this->count();
- if (PEAR::isError($count)) {
- return $count;
- }
- if ($count == 0) {
- return PEAR::raiseError("Cannot calculate {$n}th raw moment, ".
- 'there are zero data entries.');
- }
- $sum = $this->sumN($n);
- if (PEAR::isError($sum)) {
- return $sum;
- }
- return ($sum / $count);
- }/*}}}*/
-
-
- /**
- * Calculates the coefficient of variation of a data set.
- * The coefficient of variation measures the spread of a set of data
- * as a proportion of its mean. It is often expressed as a percentage.
- * Handles cummulative data sets correctly
- *
- * @access public
- * @return mixed the coefficient of variation on success, a PEAR_Error object otherwise
- * @see stDev()
- * @see mean()
- * @see calc()
- */
- function coeffOfVariation() {/*{{{*/
- if (!array_key_exists('coeffOfVariation', $this->_calculatedValues)) {
- $mean = $this->mean();
- if (PEAR::isError($mean)) {
- return $mean;
- }
- if ($mean == 0.0) {
- return PEAR::raiseError('cannot calculate the coefficient '.
- 'of variation, mean of sample is zero');
- }
- $stDev = $this->stDev();
- if (PEAR::isError($stDev)) {
- return $stDev;
- }
-
- $this->_calculatedValues['coeffOfVariation'] = $stDev / $mean;
- }
- return $this->_calculatedValues['coeffOfVariation'];
- }/*}}}*/
-
- /**
- * Calculates the standard error of the mean.
- * It is the standard deviation of the sampling distribution of
- * the mean. The formula is:
- *
- * S.E. Mean = SD / (N)^(1/2)
- *
- * This formula does not assume a normal distribution, and shows
- * that the size of the standard error of the mean is inversely
- * proportional to the square root of the sample size.
- *
- * @access public
- * @return mixed the standard error of the mean on success, a PEAR_Error object otherwise
- * @see stDev()
- * @see count()
- * @see calc()
- */
- function stdErrorOfMean() {/*{{{*/
- if (!array_key_exists('stdErrorOfMean', $this->_calculatedValues)) {
- $count = $this->count();
- if (PEAR::isError($count)) {
- return $count;
- }
- $stDev = $this->stDev();
- if (PEAR::isError($stDev)) {
- return $stDev;
- }
- $this->_calculatedValues['stdErrorOfMean'] = $stDev / sqrt($count);
- }
- return $this->_calculatedValues['stdErrorOfMean'];
- }/*}}}*/
-
- /**
- * Calculates the value frequency table of a data set.
- * Handles cummulative data sets correctly
- *
- * @access public
- * @return mixed an associative array of value=>frequency items on success, a PEAR_Error object otherwise
- * @see min()
- * @see max()
- * @see calc()
- */
- function frequency() {/*{{{*/
- if ($this->_data == null) {
- return PEAR::raiseError('data has not been set');
- }
- if (!array_key_exists('frequency', $this->_calculatedValues)) {
- if ($this->_dataOption == STATS_DATA_CUMMULATIVE) {
- $freq = $this->_data;
- } else {
- $freq = array();
- foreach ($this->_data as $val) {
- $freq["$val"]++;
- }
- ksort($freq);
- }
- $this->_calculatedValues['frequency'] = $freq;
- }
- return $this->_calculatedValues['frequency'];
- }/*}}}*/
-
- /**
- * The quartiles are defined as the values that divide a sorted
- * data set into four equal-sized subsets, and correspond to the
- * 25th, 50th, and 75th percentiles.
- *
- * @access public
- * @return mixed an associative array of quartiles on success, a PEAR_Error otherwise
- * @see percentile()
- */
- function quartiles() {/*{{{*/
- if (!array_key_exists('quartiles', $this->_calculatedValues)) {
- $q1 = $this->percentile(25);
- if (PEAR::isError($q1)) {
- return $q1;
- }
- $q2 = $this->percentile(50);
- if (PEAR::isError($q2)) {
- return $q2;
- }
- $q3 = $this->percentile(75);
- if (PEAR::isError($q3)) {
- return $q3;
- }
- $this->_calculatedValues['quartiles'] = array (
- '25' => $q1,
- '50' => $q2,
- '75' => $q3
- );
- }
- return $this->_calculatedValues['quartiles'];
- }/*}}}*/
-
- /**
- * The interquartile mean is defined as the mean of the values left
- * after discarding the lower 25% and top 25% ranked values, i.e.:
- *
- * interquart mean = mean(<P(25),P(75)>)
- *
- * where: P = percentile
- *
- * @todo need to double check the equation
- * @access public
- * @return mixed a numeric value on success, a PEAR_Error otherwise
- * @see quartiles()
- */
- function interquartileMean() {/*{{{*/
- if (!array_key_exists('interquartileMean', $this->_calculatedValues)) {
- $quart = $this->quartiles();
- if (PEAR::isError($quart)) {
- return $quart;
- }
- $q3 = $quart['75'];
- $q1 = $quart['25'];
- $sum = 0;
- $n = 0;
- foreach ($this->getData(true) as $val) {
- if ($val >= $q1 && $val <= $q3) {
- $sum += $val;
- ++$n;
- }
- }
- if ($n == 0) {
- return PEAR::raiseError('error calculating interquartile mean, '.
- 'empty interquartile range of values.');
- }
- $this->_calculatedValues['interquartileMean'] = $sum / $n;
- }
- return $this->_calculatedValues['interquartileMean'];
- }/*}}}*/
-
- /**
- * The interquartile range is the distance between the 75th and 25th
- * percentiles. Basically the range of the middle 50% of the data set,
- * and thus is not affected by outliers or extreme values.
- *
- * interquart range = P(75) - P(25)
- *
- * where: P = percentile
- *
- * @access public
- * @return mixed a numeric value on success, a PEAR_Error otherwise
- * @see quartiles()
- */
- function interquartileRange() {/*{{{*/
- if (!array_key_exists('interquartileRange', $this->_calculatedValues)) {
- $quart = $this->quartiles();
- if (PEAR::isError($quart)) {
- return $quart;
- }
- $q3 = $quart['75'];
- $q1 = $quart['25'];
- $this->_calculatedValues['interquartileRange'] = $q3 - $q1;
- }
- return $this->_calculatedValues['interquartileRange'];
- }/*}}}*/
-
- /**
- * The quartile deviation is half of the interquartile range value
- *
- * quart dev = (P(75) - P(25)) / 2
- *
- * where: P = percentile
- *
- * @access public
- * @return mixed a numeric value on success, a PEAR_Error otherwise
- * @see quartiles()
- * @see interquartileRange()
- */
- function quartileDeviation() {/*{{{*/
- if (!array_key_exists('quartileDeviation', $this->_calculatedValues)) {
- $iqr = $this->interquartileRange();
- if (PEAR::isError($iqr)) {
- return $iqr;
- }
- $this->_calculatedValues['quartileDeviation'] = $iqr / 2;
- }
- return $this->_calculatedValues['quartileDeviation'];
- }/*}}}*/
-
- /**
- * The quartile variation coefficient is defines as follows:
- *
- * quart var coeff = 100 * (P(75) - P(25)) / (P(75) + P(25))
- *
- * where: P = percentile
- *
- * @todo need to double check the equation
- * @access public
- * @return mixed a numeric value on success, a PEAR_Error otherwise
- * @see quartiles()
- */
- function quartileVariationCoefficient() {/*{{{*/
- if (!array_key_exists('quartileVariationCoefficient', $this->_calculatedValues)) {
- $quart = $this->quartiles();
- if (PEAR::isError($quart)) {
- return $quart;
- }
- $q3 = $quart['75'];
- $q1 = $quart['25'];
- $d = $q3 - $q1;
- $s = $q3 + $q1;
- $this->_calculatedValues['quartileVariationCoefficient'] = 100 * $d / $s;
- }
- return $this->_calculatedValues['quartileVariationCoefficient'];
- }/*}}}*/
-
- /**
- * The quartile skewness coefficient (also known as Bowley Skewness),
- * is defined as follows:
- *
- * quart skewness coeff = (P(25) - 2*P(50) + P(75)) / (P(75) - P(25))
- *
- * where: P = percentile
- *
- * @todo need to double check the equation
- * @access public
- * @return mixed a numeric value on success, a PEAR_Error otherwise
- * @see quartiles()
- */
- function quartileSkewnessCoefficient() {/*{{{*/
- if (!array_key_exists('quartileSkewnessCoefficient', $this->_calculatedValues)) {
- $quart = $this->quartiles();
- if (PEAR::isError($quart)) {
- return $quart;
- }
- $q3 = $quart['75'];
- $q2 = $quart['50'];
- $q1 = $quart['25'];
- $d = $q3 - 2*$q2 + $q1;
- $s = $q3 - $q1;
- $this->_calculatedValues['quartileSkewnessCoefficient'] = $d / $s;
- }
- return $this->_calculatedValues['quartileSkewnessCoefficient'];
- }/*}}}*/
-
- /**
- * The pth percentile is the value such that p% of the a sorted data set
- * is smaller than it, and (100 - p)% of the data is larger.
- *
- * A quick algorithm to pick the appropriate value from a sorted data
- * set is as follows:
- *
- * - Count the number of values: n
- * - Calculate the position of the value in the data list: i = p * (n + 1)
- * - if i is an integer, return the data at that position
- * - if i < 1, return the minimum of the data set
- * - if i > n, return the maximum of the data set
- * - otherwise, average the entries at adjacent positions to i
- *
- * The median is the 50th percentile value.
- *
- * @todo need to double check generality of the algorithm
- *
- * @access public
- * @param numeric $p the percentile to estimate, e.g. 25 for 25th percentile
- * @return mixed a numeric value on success, a PEAR_Error otherwise
- * @see quartiles()
- * @see median()
- */
- function percentile($p) {/*{{{*/
- $count = $this->count();
- if (PEAR::isError($count)) {
- return $count;
- }
- if ($this->_dataOption == STATS_DATA_CUMMULATIVE) {
- $data =& $this->_dataExpanded;
- } else {
- $data =& $this->_data;
- }
- $obsidx = $p * ($count + 1) / 100;
- if (intval($obsidx) == $obsidx) {
- return $data[($obsidx - 1)];
- } elseif ($obsidx < 1) {
- return $data[0];
- } elseif ($obsidx > $count) {
- return $data[($count - 1)];
- } else {
- $left = floor($obsidx - 1);
- $right = ceil($obsidx - 1);
- return ($data[$left] + $data[$right]) / 2;
- }
- }/*}}}*/
-
- // private methods
-
- /**
- * Utility function to calculate: SUM { (xi - mean)^n }
- *
- * @access private
- * @param numeric $power the exponent
- * @param optional double $mean the data set mean value
- * @return mixed the sum on success, a PEAR_Error object otherwise
- *
- * @see stDev()
- * @see variaceWithMean();
- * @see skewness();
- * @see kurtosis();
- */
- function __sumdiff($power, $mean=null) {/*{{{*/
- if ($this->_data == null) {
- return PEAR::raiseError('data has not been set');
- }
- if (is_null($mean)) {
- $mean = $this->mean();
- if (PEAR::isError($mean)) {
- return $mean;
- }
- }
- $sdiff = 0;
- if ($this->_dataOption == STATS_DATA_CUMMULATIVE) {
- foreach ($this->_data as $val=>$freq) {
- $sdiff += $freq * pow((double)($val - $mean), (double)$power);
- }
- } else {
- foreach ($this->_data as $val)
- $sdiff += pow((double)($val - $mean), (double)$power);
- }
- return $sdiff;
- }/*}}}*/
-
- /**
- * Utility function to calculate the variance with or without
- * a fixed mean
- *
- * @access private
- * @param $mean the fixed mean to use, null as default
- * @return mixed a numeric value on success, a PEAR_Error otherwise
- * @see variance()
- * @see varianceWithMean()
- */
- function __calcVariance($mean = null) {/*{{{*/
- if ($this->_data == null) {
- return PEAR::raiseError('data has not been set');
- }
- $sumdiff2 = $this->__sumdiff(2, $mean);
- if (PEAR::isError($sumdiff2)) {
- return $sumdiff2;
- }
- $count = $this->count();
- if (PEAR::isError($count)) {
- return $count;
- }
- if ($count == 1) {
- return PEAR::raiseError('cannot calculate variance of a singe data point');
- }
- return ($sumdiff2 / ($count - 1));
- }/*}}}*/
-
- /**
- * Utility function to calculate the absolute deviation with or without
- * a fixed mean
- *
- * @access private
- * @param $mean the fixed mean to use, null as default
- * @return mixed a numeric value on success, a PEAR_Error otherwise
- * @see absDev()
- * @see absDevWithMean()
- */
- function __calcAbsoluteDeviation($mean = null) {/*{{{*/
- if ($this->_data == null) {
- return PEAR::raiseError('data has not been set');
- }
- $count = $this->count();
- if (PEAR::isError($count)) {
- return $count;
- }
- $sumabsdev = $this->__sumabsdev($mean);
- if (PEAR::isError($sumabsdev)) {
- return $sumabsdev;
- }
- return $sumabsdev / $count;
- }/*}}}*/
-
- /**
- * Utility function to calculate: SUM { | xi - mean | }
- *
- * @access private
- * @param optional double $mean the mean value for the set or population
- * @return mixed the sum on success, a PEAR_Error object otherwise
- *
- * @see absDev()
- * @see absDevWithMean()
- */
- function __sumabsdev($mean=null) {/*{{{*/
- if ($this->_data == null) {
- return PEAR::raiseError('data has not been set');
- }
- if (is_null($mean)) {
- $mean = $this->mean();
- }
- $sdev = 0;
- if ($this->_dataOption == STATS_DATA_CUMMULATIVE) {
- foreach ($this->_data as $val=>$freq) {
- $sdev += $freq * abs($val - $mean);
- }
- } else {
- foreach ($this->_data as $val) {
- $sdev += abs($val - $mean);
- }
- }
- return $sdev;
- }/*}}}*/
-
- /**
- * Utility function to format a PEAR_Error to be used by calc(),
- * calcBasic() and calcFull()
- *
- * @access private
- * @param mixed $v value to be formatted
- * @param boolean $returnErrorObject whether the raw PEAR_Error (when true, default),
- * or only the error message will be returned (when false)
- * @return mixed if the value is a PEAR_Error object, and $useErrorObject
- * is false, then a string with the error message will be returned,
- * otherwise the value will not be modified and returned as passed.
- */
- function __format($v, $useErrorObject=true) {/*{{{*/
- if (PEAR::isError($v) && $useErrorObject == false) {
- return $v->getMessage();
- } else {
- return $v;
- }
- }/*}}}*/
-
- /**
- * Utility function to validate the data and modify it
- * according to the current null handling option
- *
- * @access private
- * @return mixed true on success, a PEAR_Error object otherwise
- *
- * @see setData()
- */
- function _validate() {/*{{{*/
- $flag = ($this->_dataOption == STATS_DATA_CUMMULATIVE);
- foreach ($this->_data as $key=>$value) {
- $d = ($flag) ? $key : $value;
- $v = ($flag) ? $value : $key;
- if (!is_numeric($d)) {
- switch ($this->_nullOption) {
- case STATS_IGNORE_NULL :
- unset($this->_data["$key"]);
- break;
- case STATS_USE_NULL_AS_ZERO:
- if ($flag) {
- unset($this->_data["$key"]);
- $this->_data[0] += $v;
- } else {
- $this->_data[$key] = 0;
- }
- break;
- case STATS_REJECT_NULL :
- default:
- return PEAR::raiseError('data rejected, contains NULL values');
- break;
- }
- }
- }
- if ($flag) {
- ksort($this->_data);
- $this->_dataExpanded = array();
- foreach ($this->_data as $val=>$freq) {
- $this->_dataExpanded = array_pad($this->_dataExpanded, count($this->_dataExpanded) + $freq, $val);
- }
- sort($this->_dataExpanded);
- } else {
- sort($this->_data);
- }
- return true;
- }/*}}}*/
-
-}/*}}}*/
-
-// vim: ts=4:sw=4:et:
-// vim6: fdl=1: fdm=marker:
-
-?>
diff --git a/admin/survey/excel/PHPExcel/Shared/JAMA/examples/benchmark.php b/admin/survey/excel/PHPExcel/Shared/JAMA/examples/benchmark.php deleted file mode 100644 index 42a4884..0000000 --- a/admin/survey/excel/PHPExcel/Shared/JAMA/examples/benchmark.php +++ /dev/null @@ -1,263 +0,0 @@ -<?php
-
-error_reporting(E_ALL);
-
-/**
- * @package JAMA
- */
-
-require_once '../Matrix.php';
-require_once 'Stats.php';
-
-
-/**
- * Example of use of Matrix Class, featuring magic squares.
- */
-class Benchmark {
- public $stat;
-
-
- /**
- * Simple function to replicate PHP 5 behaviour
- */
- function microtime_float() {
- list($usec, $sec) = explode(" ", microtime());
-
- return ((float)$usec + (float)$sec);
- } // function microtime_float()
-
-
- function displayStats($times = null) {
- $this->stat->setData($times);
- $stats = $this->stat->calcFull();
-
- echo '<table style="margin-left:32px;">';
- echo '<tr><td style="text-align:right;"><b>n:</b><td style="text-align:right;">' . $stats['count'] . ' </td></tr>';
- echo '<tr><td style="text-align:right;"><b>Mean:</b><td style="text-align:right;">' . $stats['mean'] . ' </td></tr>';
- echo '<tr><td style="text-align:right;"><b>Min.:</b><td style="text-align:right;">' . $stats['min'] . ' </td></tr>';
- echo '<tr><td style="text-align:right;"><b>Max.:</b><td style="text-align:right;">' . $stats['max'] . ' </td></tr>';
- echo '<tr><td style="text-align:right;"><b>σ:</b><td style="text-align:right;">' . $stats['stdev'] . ' </td></tr>';
- echo '<tr><td style="text-align:right;"><b>Variance:</b><td style="text-align:right;">' . $stats['variance'] . ' </td></tr>';
- echo '<tr><td style="text-align:right;"><b>Range:</b><td style="text-align:right;">' . $stats['range'] . ' </td></tr>';
- echo '</table>';
-
- return $stats;
- } // function displayStats()
-
-
- function runEig($n = 4, $t = 100) {
- $times = array();
-
- for ($i = 0; $i < $t; ++$i) {
- $M = Matrix::random($n, $n);
- $start_time = $this->microtime_float();
- $E = new EigenvalueDecomposition($M);
- $stop_time = $this->microtime_float();
- $times[] = $stop_time - $start_time;
- }
-
- return $times;
- } // function runEig()
-
-
- function runLU($n = 4, $t = 100) {
- $times = array();
-
- for ($i = 0; $i < $t; ++$i) {
- $M = Matrix::random($n, $n);
- $start_time = $this->microtime_float();
- $E = new LUDecomposition($M);
- $stop_time = $this->microtime_float();
- $times[] = $stop_time - $start_time;
- }
-
- return $times;
- } // function runLU()
-
-
- function runQR($n = 4, $t = 100) {
- $times = array();
-
- for ($i = 0; $i < $t; ++$i) {
- $M = Matrix::random($n, $n);
- $start_time = $this->microtime_float();
- $E = new QRDecomposition($M);
- $stop_time = $this->microtime_float();
- $times[] = $stop_time - $start_time;
- }
-
- return $times;
- } // function runQR()
-
-
- function runCholesky($n = 4, $t = 100) {
- $times = array();
-
- for ($i = 0; $i < $t; ++$i) {
- $M = Matrix::random($n, $n);
- $start_time = $this->microtime_float();
- $E = new CholeskyDecomposition($M);
- $stop_time = $this->microtime_float();
- $times[] = $stop_time - $start_time;
- }
-
- return $times;
- } // function runCholesky()
-
-
- function runSVD($n = 4, $t = 100) {
- $times = array();
-
- for ($i = 0; $i < $t; ++$i) {
- $M = Matrix::random($n, $n);
- $start_time = $this->microtime_float();
- $E = new SingularValueDecomposition($M);
- $stop_time = $this->microtime_float();
- $times[] = $stop_time - $start_time;
- }
-
- return $times;
- } // function runSVD()
-
-
- function run() {
- $n = 8;
- $t = 16;
- $sum = 0;
- echo "<b>Cholesky decomposition: $t random {$n}x{$n} matrices</b><br />";
- $r = $this->displayStats($this->runCholesky($n, $t));
- $sum += $r['mean'] * $n;
-
- echo '<hr />';
-
- echo "<b>Eigenvalue decomposition: $t random {$n}x{$n} matrices</b><br />";
- $r = $this->displayStats($this->runEig($n, $t));
- $sum += $r['mean'] * $n;
-
- echo '<hr />';
-
- echo "<b>LU decomposition: $t random {$n}x{$n} matrices</b><br />";
- $r = $this->displayStats($this->runLU($n, $t));
- $sum += $r['mean'] * $n;
-
- echo '<hr />';
-
- echo "<b>QR decomposition: $t random {$n}x{$n} matrices</b><br />";
- $r = $this->displayStats($this->runQR($n, $t));
- $sum += $r['mean'] * $n;
-
- echo '<hr />';
-
- echo "<b>Singular Value decomposition: $t random {$n}x{$n} matrices</b><br />";
- $r = $this->displayStats($this->runSVD($n, $t));
- $sum += $r['mean'] * $n;
-
- return $sum;
- } // function run()
-
-
- public function __construct() {
- $this->stat = new Base();
- } // function Benchmark()
-
-} // class Benchmark (end MagicSquareExample)
-
-
-$benchmark = new Benchmark();
-
-switch($_REQUEST['decomposition']) {
- case 'cholesky':
- $m = array();
- for ($i = 2; $i <= 8; $i *= 2) {
- $t = 32 / $i;
- echo "<b>Cholesky decomposition: $t random {$i}x{$i} matrices</b><br />";
- $s = $benchmark->displayStats($benchmark->runCholesky($i, $t));
- $m[$i] = $s['mean'];
- echo "<br />";
- }
- echo '<pre>';
- foreach($m as $x => $y) {
- echo "$x\t" . 1000*$y . "\n";
- }
- echo '</pre>';
- break;
- case 'eigenvalue':
- $m = array();
- for ($i = 2; $i <= 8; $i *= 2) {
- $t = 32 / $i;
- echo "<b>Eigenvalue decomposition: $t random {$i}x{$i} matrices</b><br />";
- $s = $benchmark->displayStats($benchmark->runEig($i, $t));
- $m[$i] = $s['mean'];
- echo "<br />";
- }
- echo '<pre>';
- foreach($m as $x => $y) {
- echo "$x\t" . 1000*$y . "\n";
- }
- echo '</pre>';
- break;
- case 'lu':
- $m = array();
- for ($i = 2; $i <= 8; $i *= 2) {
- $t = 32 / $i;
- echo "<b>LU decomposition: $t random {$i}x{$i} matrices</b><br />";
- $s = $benchmark->displayStats($benchmark->runLU($i, $t));
- $m[$i] = $s['mean'];
- echo "<br />";
- }
- echo '<pre>';
- foreach($m as $x => $y) {
- echo "$x\t" . 1000*$y . "\n";
- }
- echo '</pre>';
- break;
- case 'qr':
- $m = array();
- for ($i = 2; $i <= 8; $i *= 2) {
- $t = 32 / $i;
- echo "<b>QR decomposition: $t random {$i}x{$i} matrices</b><br />";
- $s = $benchmark->displayStats($benchmark->runQR($i, $t));
- $m[$i] = $s['mean'];
- echo "<br />";
- }
- echo '<pre>';
- foreach($m as $x => $y) {
- echo "$x\t" . 1000*$y . "\n";
- }
- echo '</pre>';
- break;
- case 'svd':
- $m = array();
- for($i = 2; $i <= 8; $i *= 2) {
- $t = 32 / $i;
- echo "<b>Singular value decomposition: $t random {$i}x{$i} matrices</b><br />";
- $s = $benchmark->displayStats($benchmark->runSVD($i, $t));
- $m[$i] = $s['mean'];
- echo "<br />";
- }
- echo '<pre>';
- foreach($m as $x => $y) {
- echo "$x\t" . 1000*$y . "\n";
- }
- echo '</pre>';
- break;
- case 'all':
- $s = $benchmark->run();
- print("<br /><b>Total<b>: {$s}s<br />");
- break;
- default:
- ?>
- <ul>
- <li><a href="benchmark.php?decomposition=all">Complete Benchmark</a>
- <ul>
- <li><a href="benchmark.php?decomposition=cholesky">Cholesky</a></li>
- <li><a href="benchmark.php?decomposition=eigenvalue">Eigenvalue</a></li>
- <li><a href="benchmark.php?decomposition=lu">LU</a></li>
- <li><a href="benchmark.php?decomposition=qr">QR</a></li>
- <li><a href="benchmark.php?decomposition=svd">Singular Value</a></li>
- </ul>
- </li>
- </ul>
- <?php
- break;
-}
diff --git a/admin/survey/excel/PHPExcel/Shared/JAMA/examples/polyfit.php b/admin/survey/excel/PHPExcel/Shared/JAMA/examples/polyfit.php deleted file mode 100644 index fffc864..0000000 --- a/admin/survey/excel/PHPExcel/Shared/JAMA/examples/polyfit.php +++ /dev/null @@ -1,73 +0,0 @@ -<?php
-require_once "../Matrix.php";
-/*
-* @package JAMA
-* @author Michael Bommarito
-* @author Paul Meagher
-* @version 0.1
-*
-* Function to fit an order n polynomial function through
-* a series of x-y data points using least squares.
-*
-* @param $X array x values
-* @param $Y array y values
-* @param $n int order of polynomial to be used for fitting
-* @returns array $coeffs of polynomial coefficients
-* Pre-Conditions: the system is not underdetermined: sizeof($X) > $n+1
-*/
-function polyfit($X, $Y, $n) {
- for ($i = 0; $i < sizeof($X); ++$i)
- for ($j = 0; $j <= $n; ++$j)
- $A[$i][$j] = pow($X[$i], $j);
- for ($i=0; $i < sizeof($Y); ++$i)
- $B[$i] = array($Y[$i]);
- $matrixA = new Matrix($A);
- $matrixB = new Matrix($B);
- $C = $matrixA->solve($matrixB);
- return $C->getMatrix(0, $n, 0, 1);
-}
-
-function printpoly( $C = null ) {
- for($i = $C->m - 1; $i >= 0; --$i) {
- $r = $C->get($i, 0);
- if ( abs($r) <= pow(10, -9) )
- $r = 0;
- if ($i == $C->m - 1)
- echo $r . "x<sup>$i</sup>";
- else if ($i < $C->m - 1)
- echo " + " . $r . "x<sup>$i</sup>";
- else if ($i == 0)
- echo " + " . $r;
- }
-}
-
-$X = array(0,1,2,3,4,5);
-$Y = array(4,3,12,67,228, 579);
-$points = new Matrix(array($X, $Y));
-$points->toHTML();
-printpoly(polyfit($X, $Y, 4));
-
-echo '<hr />';
-
-$X = array(0,1,2,3,4,5);
-$Y = array(1,2,5,10,17, 26);
-$points = new Matrix(array($X, $Y));
-$points->toHTML();
-printpoly(polyfit($X, $Y, 2));
-
-echo '<hr />';
-
-$X = array(0,1,2,3,4,5,6);
-$Y = array(-90,-104,-178,-252,-26, 1160, 4446);
-$points = new Matrix(array($X, $Y));
-$points->toHTML();
-printpoly(polyfit($X, $Y, 5));
-
-echo '<hr />';
-
-$X = array(0,1,2,3,4);
-$Y = array(mt_rand(0, 10), mt_rand(40, 80), mt_rand(240, 400), mt_rand(1800, 2215), mt_rand(8000, 9000));
-$points = new Matrix(array($X, $Y));
-$points->toHTML();
-printpoly(polyfit($X, $Y, 3));
-?>
diff --git a/admin/survey/excel/PHPExcel/Shared/JAMA/examples/tile.php b/admin/survey/excel/PHPExcel/Shared/JAMA/examples/tile.php deleted file mode 100644 index b5c48e1..0000000 --- a/admin/survey/excel/PHPExcel/Shared/JAMA/examples/tile.php +++ /dev/null @@ -1,78 +0,0 @@ -<?php
-
-include "../Matrix.php";
-
-/**
-* Tiling of matrix X in [rowWise by colWise] dimension. Tiling
-* creates a larger matrix than the original data X. Example, if
-* X is to be tiled in a [3 x 4] manner, then:
-*
-* / \
-* | X X X X |
-* C = | X X X X |
-* | X X X X |
-* \ /
-*
-* @param X Matrix
-* @param rowWise int
-* @param colWise int
-* @return Matrix
-*/
-
-function tile(&$X, $rowWise, $colWise){
-
- $xArray = $X->getArray();
- print_r($xArray);
-
- $countRow = 0;
- $countColumn = 0;
-
- $m = $X->getRowDimension();
- $n = $X->getColumnDimension();
-
- if( $rowWise<1 || $colWise<1 ){
- die("tile : Array index is out-of-bound.");
- }
-
- $newRowDim = $m*$rowWise;
- $newColDim = $n*$colWise;
-
- $result = array();
-
- for($i=0 ; $i<$newRowDim; ++$i) {
-
- $holder = array();
-
- for($j=0 ; $j<$newColDim ; ++$j) {
-
- $holder[$j] = $xArray[$countRow][$countColumn++];
-
- // reset the column-index to zero to avoid reference to out-of-bound index in xArray[][]
-
- if($countColumn == $n) { $countColumn = 0; }
-
- } // end for
-
- ++$countRow;
-
- // reset the row-index to zero to avoid reference to out-of-bound index in xArray[][]
-
- if($countRow == $m) { $countRow = 0; }
-
- $result[$i] = $holder;
-
- } // end for
-
- return new Matrix($result);
-
-}
-
-
-$X =array(1,2,3,4,5,6,7,8,9);
-$nRow = 3;
-$nCol = 3;
-$tiled_matrix = tile(new Matrix($X), $nRow, $nCol);
-echo "<pre>";
-print_r($tiled_matrix);
-echo "</pre>";
-?>
|