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| author | Anton Luka Šijanec <anton@sijanec.eu> | 2024-05-27 13:12:17 +0200 |
|---|---|---|
| committer | Anton Luka Šijanec <anton@sijanec.eu> | 2024-05-27 13:12:17 +0200 |
| commit | f1ab2f022fdc780aca0944d90e9a0e844a0820d7 (patch) | |
| tree | 79942a40514f5ab40c5901349c9fcd30c6c8dc0e /admin/survey/excel/PHPExcel/Shared/JAMA | |
| parent | 2024-02-19 upstream (diff) | |
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Diffstat (limited to 'admin/survey/excel/PHPExcel/Shared/JAMA')
19 files changed, 0 insertions, 6264 deletions
diff --git a/admin/survey/excel/PHPExcel/Shared/JAMA/CHANGELOG.TXT b/admin/survey/excel/PHPExcel/Shared/JAMA/CHANGELOG.TXT deleted file mode 100644 index 2fc9cd4..0000000 --- a/admin/survey/excel/PHPExcel/Shared/JAMA/CHANGELOG.TXT +++ /dev/null @@ -1,16 +0,0 @@ -Mar 1, 2005 11:15 AST by PM
-
-+ For consistency, renamed Math.php to Maths.java, utils to util,
- tests to test, docs to doc -
-
-+ Removed conditional logic from top of Matrix class.
-
-+ Switched to using hypo function in Maths.php for all php-hypot calls.
- NOTE TO SELF: Need to make sure that all decompositions have been
- switched over to using the bundled hypo.
-
-Feb 25, 2005 at 10:00 AST by PM
-
-+ Recommend using simpler Error.php instead of JAMA_Error.php but
- can be persuaded otherwise.
-
diff --git a/admin/survey/excel/PHPExcel/Shared/JAMA/CholeskyDecomposition.php b/admin/survey/excel/PHPExcel/Shared/JAMA/CholeskyDecomposition.php deleted file mode 100644 index 2d1fdfb..0000000 --- a/admin/survey/excel/PHPExcel/Shared/JAMA/CholeskyDecomposition.php +++ /dev/null @@ -1,149 +0,0 @@ -<?php
-/**
- * @package JAMA
- *
- * Cholesky decomposition class
- *
- * For a symmetric, positive definite matrix A, the Cholesky decomposition
- * is an lower triangular matrix L so that A = L*L'.
- *
- * If the matrix is not symmetric or positive definite, the constructor
- * returns a partial decomposition and sets an internal flag that may
- * be queried by the isSPD() method.
- *
- * @author Paul Meagher
- * @author Michael Bommarito
- * @version 1.2
- */
-class CholeskyDecomposition {
-
- /**
- * Decomposition storage
- * @var array
- * @access private
- */
- private $L = array();
-
- /**
- * Matrix row and column dimension
- * @var int
- * @access private
- */
- private $m;
-
- /**
- * Symmetric positive definite flag
- * @var boolean
- * @access private
- */
- private $isspd = true;
-
-
- /**
- * CholeskyDecomposition
- *
- * Class constructor - decomposes symmetric positive definite matrix
- * @param mixed Matrix square symmetric positive definite matrix
- */
- public function __construct($A = null) {
- if ($A instanceof Matrix) {
- $this->L = $A->getArray();
- $this->m = $A->getRowDimension();
-
- for($i = 0; $i < $this->m; ++$i) {
- for($j = $i; $j < $this->m; ++$j) {
- for($sum = $this->L[$i][$j], $k = $i - 1; $k >= 0; --$k) {
- $sum -= $this->L[$i][$k] * $this->L[$j][$k];
- }
- if ($i == $j) {
- if ($sum >= 0) {
- $this->L[$i][$i] = sqrt($sum);
- } else {
- $this->isspd = false;
- }
- } else {
- if ($this->L[$i][$i] != 0) {
- $this->L[$j][$i] = $sum / $this->L[$i][$i];
- }
- }
- }
-
- for ($k = $i+1; $k < $this->m; ++$k) {
- $this->L[$i][$k] = 0.0;
- }
- }
- } else {
- throw new Exception(JAMAError(ArgumentTypeException));
- }
- } // function __construct()
-
-
- /**
- * Is the matrix symmetric and positive definite?
- *
- * @return boolean
- */
- public function isSPD() {
- return $this->isspd;
- } // function isSPD()
-
-
- /**
- * getL
- *
- * Return triangular factor.
- * @return Matrix Lower triangular matrix
- */
- public function getL() {
- return new Matrix($this->L);
- } // function getL()
-
-
- /**
- * Solve A*X = B
- *
- * @param $B Row-equal matrix
- * @return Matrix L * L' * X = B
- */
- public function solve($B = null) {
- if ($B instanceof Matrix) {
- if ($B->getRowDimension() == $this->m) {
- if ($this->isspd) {
- $X = $B->getArrayCopy();
- $nx = $B->getColumnDimension();
-
- for ($k = 0; $k < $this->m; ++$k) {
- for ($i = $k + 1; $i < $this->m; ++$i) {
- for ($j = 0; $j < $nx; ++$j) {
- $X[$i][$j] -= $X[$k][$j] * $this->L[$i][$k];
- }
- }
- for ($j = 0; $j < $nx; ++$j) {
- $X[$k][$j] /= $this->L[$k][$k];
- }
- }
-
- for ($k = $this->m - 1; $k >= 0; --$k) {
- for ($j = 0; $j < $nx; ++$j) {
- $X[$k][$j] /= $this->L[$k][$k];
- }
- for ($i = 0; $i < $k; ++$i) {
- for ($j = 0; $j < $nx; ++$j) {
- $X[$i][$j] -= $X[$k][$j] * $this->L[$k][$i];
- }
- }
- }
-
- return new Matrix($X, $this->m, $nx);
- } else {
- throw new Exception(JAMAError(MatrixSPDException));
- }
- } else {
- throw new Exception(JAMAError(MatrixDimensionException));
- }
- } else {
- throw new Exception(JAMAError(ArgumentTypeException));
- }
- } // function solve()
-
-} // class CholeskyDecomposition
diff --git a/admin/survey/excel/PHPExcel/Shared/JAMA/EigenvalueDecomposition.php b/admin/survey/excel/PHPExcel/Shared/JAMA/EigenvalueDecomposition.php deleted file mode 100644 index 716af82..0000000 --- a/admin/survey/excel/PHPExcel/Shared/JAMA/EigenvalueDecomposition.php +++ /dev/null @@ -1,862 +0,0 @@ -<?php
-/**
- * @package JAMA
- *
- * Class to obtain eigenvalues and eigenvectors of a real matrix.
- *
- * If A is symmetric, then A = V*D*V' where the eigenvalue matrix D
- * is diagonal and the eigenvector matrix V is orthogonal (i.e.
- * A = V.times(D.times(V.transpose())) and V.times(V.transpose())
- * equals the identity matrix).
- *
- * If A is not symmetric, then the eigenvalue matrix D is block diagonal
- * with the real eigenvalues in 1-by-1 blocks and any complex eigenvalues,
- * lambda + i*mu, in 2-by-2 blocks, [lambda, mu; -mu, lambda]. The
- * columns of V represent the eigenvectors in the sense that A*V = V*D,
- * i.e. A.times(V) equals V.times(D). The matrix V may be badly
- * conditioned, or even singular, so the validity of the equation
- * A = V*D*inverse(V) depends upon V.cond().
- *
- * @author Paul Meagher
- * @license PHP v3.0
- * @version 1.1
- */
-class EigenvalueDecomposition {
-
- /**
- * Row and column dimension (square matrix).
- * @var int
- */
- private $n;
-
- /**
- * Internal symmetry flag.
- * @var int
- */
- private $issymmetric;
-
- /**
- * Arrays for internal storage of eigenvalues.
- * @var array
- */
- private $d = array();
- private $e = array();
-
- /**
- * Array for internal storage of eigenvectors.
- * @var array
- */
- private $V = array();
-
- /**
- * Array for internal storage of nonsymmetric Hessenberg form.
- * @var array
- */
- private $H = array();
-
- /**
- * Working storage for nonsymmetric algorithm.
- * @var array
- */
- private $ort;
-
- /**
- * Used for complex scalar division.
- * @var float
- */
- private $cdivr;
- private $cdivi;
-
-
- /**
- * Symmetric Householder reduction to tridiagonal form.
- *
- * @access private
- */
- private function tred2 () {
- // This is derived from the Algol procedures tred2 by
- // Bowdler, Martin, Reinsch, and Wilkinson, Handbook for
- // Auto. Comp., Vol.ii-Linear Algebra, and the corresponding
- // Fortran subroutine in EISPACK.
- $this->d = $this->V[$this->n-1];
- // Householder reduction to tridiagonal form.
- for ($i = $this->n-1; $i > 0; --$i) {
- $i_ = $i -1;
- // Scale to avoid under/overflow.
- $h = $scale = 0.0;
- $scale += array_sum(array_map(abs, $this->d));
- if ($scale == 0.0) {
- $this->e[$i] = $this->d[$i_];
- $this->d = array_slice($this->V[$i_], 0, $i_);
- for ($j = 0; $j < $i; ++$j) {
- $this->V[$j][$i] = $this->V[$i][$j] = 0.0;
- }
- } else {
- // Generate Householder vector.
- for ($k = 0; $k < $i; ++$k) {
- $this->d[$k] /= $scale;
- $h += pow($this->d[$k], 2);
- }
- $f = $this->d[$i_];
- $g = sqrt($h);
- if ($f > 0) {
- $g = -$g;
- }
- $this->e[$i] = $scale * $g;
- $h = $h - $f * $g;
- $this->d[$i_] = $f - $g;
- for ($j = 0; $j < $i; ++$j) {
- $this->e[$j] = 0.0;
- }
- // Apply similarity transformation to remaining columns.
- for ($j = 0; $j < $i; ++$j) {
- $f = $this->d[$j];
- $this->V[$j][$i] = $f;
- $g = $this->e[$j] + $this->V[$j][$j] * $f;
- for ($k = $j+1; $k <= $i_; ++$k) {
- $g += $this->V[$k][$j] * $this->d[$k];
- $this->e[$k] += $this->V[$k][$j] * $f;
- }
- $this->e[$j] = $g;
- }
- $f = 0.0;
- for ($j = 0; $j < $i; ++$j) {
- $this->e[$j] /= $h;
- $f += $this->e[$j] * $this->d[$j];
- }
- $hh = $f / (2 * $h);
- for ($j=0; $j < $i; ++$j) {
- $this->e[$j] -= $hh * $this->d[$j];
- }
- for ($j = 0; $j < $i; ++$j) {
- $f = $this->d[$j];
- $g = $this->e[$j];
- for ($k = $j; $k <= $i_; ++$k) {
- $this->V[$k][$j] -= ($f * $this->e[$k] + $g * $this->d[$k]);
- }
- $this->d[$j] = $this->V[$i-1][$j];
- $this->V[$i][$j] = 0.0;
- }
- }
- $this->d[$i] = $h;
- }
-
- // Accumulate transformations.
- for ($i = 0; $i < $this->n-1; ++$i) {
- $this->V[$this->n-1][$i] = $this->V[$i][$i];
- $this->V[$i][$i] = 1.0;
- $h = $this->d[$i+1];
- if ($h != 0.0) {
- for ($k = 0; $k <= $i; ++$k) {
- $this->d[$k] = $this->V[$k][$i+1] / $h;
- }
- for ($j = 0; $j <= $i; ++$j) {
- $g = 0.0;
- for ($k = 0; $k <= $i; ++$k) {
- $g += $this->V[$k][$i+1] * $this->V[$k][$j];
- }
- for ($k = 0; $k <= $i; ++$k) {
- $this->V[$k][$j] -= $g * $this->d[$k];
- }
- }
- }
- for ($k = 0; $k <= $i; ++$k) {
- $this->V[$k][$i+1] = 0.0;
- }
- }
-
- $this->d = $this->V[$this->n-1];
- $this->V[$this->n-1] = array_fill(0, $j, 0.0);
- $this->V[$this->n-1][$this->n-1] = 1.0;
- $this->e[0] = 0.0;
- }
-
-
- /**
- * Symmetric tridiagonal QL algorithm.
- *
- * This is derived from the Algol procedures tql2, by
- * Bowdler, Martin, Reinsch, and Wilkinson, Handbook for
- * Auto. Comp., Vol.ii-Linear Algebra, and the corresponding
- * Fortran subroutine in EISPACK.
- *
- * @access private
- */
- private function tql2() {
- for ($i = 1; $i < $this->n; ++$i) {
- $this->e[$i-1] = $this->e[$i];
- }
- $this->e[$this->n-1] = 0.0;
- $f = 0.0;
- $tst1 = 0.0;
- $eps = pow(2.0,-52.0);
-
- for ($l = 0; $l < $this->n; ++$l) {
- // Find small subdiagonal element
- $tst1 = max($tst1, abs($this->d[$l]) + abs($this->e[$l]));
- $m = $l;
- while ($m < $this->n) {
- if (abs($this->e[$m]) <= $eps * $tst1)
- break;
- ++$m;
- }
- // If m == l, $this->d[l] is an eigenvalue,
- // otherwise, iterate.
- if ($m > $l) {
- $iter = 0;
- do {
- // Could check iteration count here.
- $iter += 1;
- // Compute implicit shift
- $g = $this->d[$l];
- $p = ($this->d[$l+1] - $g) / (2.0 * $this->e[$l]);
- $r = hypo($p, 1.0);
- if ($p < 0)
- $r *= -1;
- $this->d[$l] = $this->e[$l] / ($p + $r);
- $this->d[$l+1] = $this->e[$l] * ($p + $r);
- $dl1 = $this->d[$l+1];
- $h = $g - $this->d[$l];
- for ($i = $l + 2; $i < $this->n; ++$i)
- $this->d[$i] -= $h;
- $f += $h;
- // Implicit QL transformation.
- $p = $this->d[$m];
- $c = 1.0;
- $c2 = $c3 = $c;
- $el1 = $this->e[$l + 1];
- $s = $s2 = 0.0;
- for ($i = $m-1; $i >= $l; --$i) {
- $c3 = $c2;
- $c2 = $c;
- $s2 = $s;
- $g = $c * $this->e[$i];
- $h = $c * $p;
- $r = hypo($p, $this->e[$i]);
- $this->e[$i+1] = $s * $r;
- $s = $this->e[$i] / $r;
- $c = $p / $r;
- $p = $c * $this->d[$i] - $s * $g;
- $this->d[$i+1] = $h + $s * ($c * $g + $s * $this->d[$i]);
- // Accumulate transformation.
- for ($k = 0; $k < $this->n; ++$k) {
- $h = $this->V[$k][$i+1];
- $this->V[$k][$i+1] = $s * $this->V[$k][$i] + $c * $h;
- $this->V[$k][$i] = $c * $this->V[$k][$i] - $s * $h;
- }
- }
- $p = -$s * $s2 * $c3 * $el1 * $this->e[$l] / $dl1;
- $this->e[$l] = $s * $p;
- $this->d[$l] = $c * $p;
- // Check for convergence.
- } while (abs($this->e[$l]) > $eps * $tst1);
- }
- $this->d[$l] = $this->d[$l] + $f;
- $this->e[$l] = 0.0;
- }
-
- // Sort eigenvalues and corresponding vectors.
- for ($i = 0; $i < $this->n - 1; ++$i) {
- $k = $i;
- $p = $this->d[$i];
- for ($j = $i+1; $j < $this->n; ++$j) {
- if ($this->d[$j] < $p) {
- $k = $j;
- $p = $this->d[$j];
- }
- }
- if ($k != $i) {
- $this->d[$k] = $this->d[$i];
- $this->d[$i] = $p;
- for ($j = 0; $j < $this->n; ++$j) {
- $p = $this->V[$j][$i];
- $this->V[$j][$i] = $this->V[$j][$k];
- $this->V[$j][$k] = $p;
- }
- }
- }
- }
-
-
- /**
- * Nonsymmetric reduction to Hessenberg form.
- *
- * This is derived from the Algol procedures orthes and ortran,
- * by Martin and Wilkinson, Handbook for Auto. Comp.,
- * Vol.ii-Linear Algebra, and the corresponding
- * Fortran subroutines in EISPACK.
- *
- * @access private
- */
- private function orthes () {
- $low = 0;
- $high = $this->n-1;
-
- for ($m = $low+1; $m <= $high-1; ++$m) {
- // Scale column.
- $scale = 0.0;
- for ($i = $m; $i <= $high; ++$i) {
- $scale = $scale + abs($this->H[$i][$m-1]);
- }
- if ($scale != 0.0) {
- // Compute Householder transformation.
- $h = 0.0;
- for ($i = $high; $i >= $m; --$i) {
- $this->ort[$i] = $this->H[$i][$m-1] / $scale;
- $h += $this->ort[$i] * $this->ort[$i];
- }
- $g = sqrt($h);
- if ($this->ort[$m] > 0) {
- $g *= -1;
- }
- $h -= $this->ort[$m] * $g;
- $this->ort[$m] -= $g;
- // Apply Householder similarity transformation
- // H = (I -u * u' / h) * H * (I -u * u') / h)
- for ($j = $m; $j < $this->n; ++$j) {
- $f = 0.0;
- for ($i = $high; $i >= $m; --$i) {
- $f += $this->ort[$i] * $this->H[$i][$j];
- }
- $f /= $h;
- for ($i = $m; $i <= $high; ++$i) {
- $this->H[$i][$j] -= $f * $this->ort[$i];
- }
- }
- for ($i = 0; $i <= $high; ++$i) {
- $f = 0.0;
- for ($j = $high; $j >= $m; --$j) {
- $f += $this->ort[$j] * $this->H[$i][$j];
- }
- $f = $f / $h;
- for ($j = $m; $j <= $high; ++$j) {
- $this->H[$i][$j] -= $f * $this->ort[$j];
- }
- }
- $this->ort[$m] = $scale * $this->ort[$m];
- $this->H[$m][$m-1] = $scale * $g;
- }
- }
-
- // Accumulate transformations (Algol's ortran).
- for ($i = 0; $i < $this->n; ++$i) {
- for ($j = 0; $j < $this->n; ++$j) {
- $this->V[$i][$j] = ($i == $j ? 1.0 : 0.0);
- }
- }
- for ($m = $high-1; $m >= $low+1; --$m) {
- if ($this->H[$m][$m-1] != 0.0) {
- for ($i = $m+1; $i <= $high; ++$i) {
- $this->ort[$i] = $this->H[$i][$m-1];
- }
- for ($j = $m; $j <= $high; ++$j) {
- $g = 0.0;
- for ($i = $m; $i <= $high; ++$i) {
- $g += $this->ort[$i] * $this->V[$i][$j];
- }
- // Double division avoids possible underflow
- $g = ($g / $this->ort[$m]) / $this->H[$m][$m-1];
- for ($i = $m; $i <= $high; ++$i) {
- $this->V[$i][$j] += $g * $this->ort[$i];
- }
- }
- }
- }
- }
-
-
- /**
- * Performs complex division.
- *
- * @access private
- */
- private function cdiv($xr, $xi, $yr, $yi) {
- if (abs($yr) > abs($yi)) {
- $r = $yi / $yr;
- $d = $yr + $r * $yi;
- $this->cdivr = ($xr + $r * $xi) / $d;
- $this->cdivi = ($xi - $r * $xr) / $d;
- } else {
- $r = $yr / $yi;
- $d = $yi + $r * $yr;
- $this->cdivr = ($r * $xr + $xi) / $d;
- $this->cdivi = ($r * $xi - $xr) / $d;
- }
- }
-
-
- /**
- * Nonsymmetric reduction from Hessenberg to real Schur form.
- *
- * Code is derived from the Algol procedure hqr2,
- * by Martin and Wilkinson, Handbook for Auto. Comp.,
- * Vol.ii-Linear Algebra, and the corresponding
- * Fortran subroutine in EISPACK.
- *
- * @access private
- */
- private function hqr2 () {
- // Initialize
- $nn = $this->n;
- $n = $nn - 1;
- $low = 0;
- $high = $nn - 1;
- $eps = pow(2.0, -52.0);
- $exshift = 0.0;
- $p = $q = $r = $s = $z = 0;
- // Store roots isolated by balanc and compute matrix norm
- $norm = 0.0;
-
- for ($i = 0; $i < $nn; ++$i) {
- if (($i < $low) OR ($i > $high)) {
- $this->d[$i] = $this->H[$i][$i];
- $this->e[$i] = 0.0;
- }
- for ($j = max($i-1, 0); $j < $nn; ++$j) {
- $norm = $norm + abs($this->H[$i][$j]);
- }
- }
-
- // Outer loop over eigenvalue index
- $iter = 0;
- while ($n >= $low) {
- // Look for single small sub-diagonal element
- $l = $n;
- while ($l > $low) {
- $s = abs($this->H[$l-1][$l-1]) + abs($this->H[$l][$l]);
- if ($s == 0.0) {
- $s = $norm;
- }
- if (abs($this->H[$l][$l-1]) < $eps * $s) {
- break;
- }
- --$l;
- }
- // Check for convergence
- // One root found
- if ($l == $n) {
- $this->H[$n][$n] = $this->H[$n][$n] + $exshift;
- $this->d[$n] = $this->H[$n][$n];
- $this->e[$n] = 0.0;
- --$n;
- $iter = 0;
- // Two roots found
- } else if ($l == $n-1) {
- $w = $this->H[$n][$n-1] * $this->H[$n-1][$n];
- $p = ($this->H[$n-1][$n-1] - $this->H[$n][$n]) / 2.0;
- $q = $p * $p + $w;
- $z = sqrt(abs($q));
- $this->H[$n][$n] = $this->H[$n][$n] + $exshift;
- $this->H[$n-1][$n-1] = $this->H[$n-1][$n-1] + $exshift;
- $x = $this->H[$n][$n];
- // Real pair
- if ($q >= 0) {
- if ($p >= 0) {
- $z = $p + $z;
- } else {
- $z = $p - $z;
- }
- $this->d[$n-1] = $x + $z;
- $this->d[$n] = $this->d[$n-1];
- if ($z != 0.0) {
- $this->d[$n] = $x - $w / $z;
- }
- $this->e[$n-1] = 0.0;
- $this->e[$n] = 0.0;
- $x = $this->H[$n][$n-1];
- $s = abs($x) + abs($z);
- $p = $x / $s;
- $q = $z / $s;
- $r = sqrt($p * $p + $q * $q);
- $p = $p / $r;
- $q = $q / $r;
- // Row modification
- for ($j = $n-1; $j < $nn; ++$j) {
- $z = $this->H[$n-1][$j];
- $this->H[$n-1][$j] = $q * $z + $p * $this->H[$n][$j];
- $this->H[$n][$j] = $q * $this->H[$n][$j] - $p * $z;
- }
- // Column modification
- for ($i = 0; $i <= n; ++$i) {
- $z = $this->H[$i][$n-1];
- $this->H[$i][$n-1] = $q * $z + $p * $this->H[$i][$n];
- $this->H[$i][$n] = $q * $this->H[$i][$n] - $p * $z;
- }
- // Accumulate transformations
- for ($i = $low; $i <= $high; ++$i) {
- $z = $this->V[$i][$n-1];
- $this->V[$i][$n-1] = $q * $z + $p * $this->V[$i][$n];
- $this->V[$i][$n] = $q * $this->V[$i][$n] - $p * $z;
- }
- // Complex pair
- } else {
- $this->d[$n-1] = $x + $p;
- $this->d[$n] = $x + $p;
- $this->e[$n-1] = $z;
- $this->e[$n] = -$z;
- }
- $n = $n - 2;
- $iter = 0;
- // No convergence yet
- } else {
- // Form shift
- $x = $this->H[$n][$n];
- $y = 0.0;
- $w = 0.0;
- if ($l < $n) {
- $y = $this->H[$n-1][$n-1];
- $w = $this->H[$n][$n-1] * $this->H[$n-1][$n];
- }
- // Wilkinson's original ad hoc shift
- if ($iter == 10) {
- $exshift += $x;
- for ($i = $low; $i <= $n; ++$i) {
- $this->H[$i][$i] -= $x;
- }
- $s = abs($this->H[$n][$n-1]) + abs($this->H[$n-1][$n-2]);
- $x = $y = 0.75 * $s;
- $w = -0.4375 * $s * $s;
- }
- // MATLAB's new ad hoc shift
- if ($iter == 30) {
- $s = ($y - $x) / 2.0;
- $s = $s * $s + $w;
- if ($s > 0) {
- $s = sqrt($s);
- if ($y < $x) {
- $s = -$s;
- }
- $s = $x - $w / (($y - $x) / 2.0 + $s);
- for ($i = $low; $i <= $n; ++$i) {
- $this->H[$i][$i] -= $s;
- }
- $exshift += $s;
- $x = $y = $w = 0.964;
- }
- }
- // Could check iteration count here.
- $iter = $iter + 1;
- // Look for two consecutive small sub-diagonal elements
- $m = $n - 2;
- while ($m >= $l) {
- $z = $this->H[$m][$m];
- $r = $x - $z;
- $s = $y - $z;
- $p = ($r * $s - $w) / $this->H[$m+1][$m] + $this->H[$m][$m+1];
- $q = $this->H[$m+1][$m+1] - $z - $r - $s;
- $r = $this->H[$m+2][$m+1];
- $s = abs($p) + abs($q) + abs($r);
- $p = $p / $s;
- $q = $q / $s;
- $r = $r / $s;
- if ($m == $l) {
- break;
- }
- if (abs($this->H[$m][$m-1]) * (abs($q) + abs($r)) <
- $eps * (abs($p) * (abs($this->H[$m-1][$m-1]) + abs($z) + abs($this->H[$m+1][$m+1])))) {
- break;
- }
- --$m;
- }
- for ($i = $m + 2; $i <= $n; ++$i) {
- $this->H[$i][$i-2] = 0.0;
- if ($i > $m+2) {
- $this->H[$i][$i-3] = 0.0;
- }
- }
- // Double QR step involving rows l:n and columns m:n
- for ($k = $m; $k <= $n-1; ++$k) {
- $notlast = ($k != $n-1);
- if ($k != $m) {
- $p = $this->H[$k][$k-1];
- $q = $this->H[$k+1][$k-1];
- $r = ($notlast ? $this->H[$k+2][$k-1] : 0.0);
- $x = abs($p) + abs($q) + abs($r);
- if ($x != 0.0) {
- $p = $p / $x;
- $q = $q / $x;
- $r = $r / $x;
- }
- }
- if ($x == 0.0) {
- break;
- }
- $s = sqrt($p * $p + $q * $q + $r * $r);
- if ($p < 0) {
- $s = -$s;
- }
- if ($s != 0) {
- if ($k != $m) {
- $this->H[$k][$k-1] = -$s * $x;
- } elseif ($l != $m) {
- $this->H[$k][$k-1] = -$this->H[$k][$k-1];
- }
- $p = $p + $s;
- $x = $p / $s;
- $y = $q / $s;
- $z = $r / $s;
- $q = $q / $p;
- $r = $r / $p;
- // Row modification
- for ($j = $k; $j < $nn; ++$j) {
- $p = $this->H[$k][$j] + $q * $this->H[$k+1][$j];
- if ($notlast) {
- $p = $p + $r * $this->H[$k+2][$j];
- $this->H[$k+2][$j] = $this->H[$k+2][$j] - $p * $z;
- }
- $this->H[$k][$j] = $this->H[$k][$j] - $p * $x;
- $this->H[$k+1][$j] = $this->H[$k+1][$j] - $p * $y;
- }
- // Column modification
- for ($i = 0; $i <= min($n, $k+3); ++$i) {
- $p = $x * $this->H[$i][$k] + $y * $this->H[$i][$k+1];
- if ($notlast) {
- $p = $p + $z * $this->H[$i][$k+2];
- $this->H[$i][$k+2] = $this->H[$i][$k+2] - $p * $r;
- }
- $this->H[$i][$k] = $this->H[$i][$k] - $p;
- $this->H[$i][$k+1] = $this->H[$i][$k+1] - $p * $q;
- }
- // Accumulate transformations
- for ($i = $low; $i <= $high; ++$i) {
- $p = $x * $this->V[$i][$k] + $y * $this->V[$i][$k+1];
- if ($notlast) {
- $p = $p + $z * $this->V[$i][$k+2];
- $this->V[$i][$k+2] = $this->V[$i][$k+2] - $p * $r;
- }
- $this->V[$i][$k] = $this->V[$i][$k] - $p;
- $this->V[$i][$k+1] = $this->V[$i][$k+1] - $p * $q;
- }
- } // ($s != 0)
- } // k loop
- } // check convergence
- } // while ($n >= $low)
-
- // Backsubstitute to find vectors of upper triangular form
- if ($norm == 0.0) {
- return;
- }
-
- for ($n = $nn-1; $n >= 0; --$n) {
- $p = $this->d[$n];
- $q = $this->e[$n];
- // Real vector
- if ($q == 0) {
- $l = $n;
- $this->H[$n][$n] = 1.0;
- for ($i = $n-1; $i >= 0; --$i) {
- $w = $this->H[$i][$i] - $p;
- $r = 0.0;
- for ($j = $l; $j <= $n; ++$j) {
- $r = $r + $this->H[$i][$j] * $this->H[$j][$n];
- }
- if ($this->e[$i] < 0.0) {
- $z = $w;
- $s = $r;
- } else {
- $l = $i;
- if ($this->e[$i] == 0.0) {
- if ($w != 0.0) {
- $this->H[$i][$n] = -$r / $w;
- } else {
- $this->H[$i][$n] = -$r / ($eps * $norm);
- }
- // Solve real equations
- } else {
- $x = $this->H[$i][$i+1];
- $y = $this->H[$i+1][$i];
- $q = ($this->d[$i] - $p) * ($this->d[$i] - $p) + $this->e[$i] * $this->e[$i];
- $t = ($x * $s - $z * $r) / $q;
- $this->H[$i][$n] = $t;
- if (abs($x) > abs($z)) {
- $this->H[$i+1][$n] = (-$r - $w * $t) / $x;
- } else {
- $this->H[$i+1][$n] = (-$s - $y * $t) / $z;
- }
- }
- // Overflow control
- $t = abs($this->H[$i][$n]);
- if (($eps * $t) * $t > 1) {
- for ($j = $i; $j <= $n; ++$j) {
- $this->H[$j][$n] = $this->H[$j][$n] / $t;
- }
- }
- }
- }
- // Complex vector
- } else if ($q < 0) {
- $l = $n-1;
- // Last vector component imaginary so matrix is triangular
- if (abs($this->H[$n][$n-1]) > abs($this->H[$n-1][$n])) {
- $this->H[$n-1][$n-1] = $q / $this->H[$n][$n-1];
- $this->H[$n-1][$n] = -($this->H[$n][$n] - $p) / $this->H[$n][$n-1];
- } else {
- $this->cdiv(0.0, -$this->H[$n-1][$n], $this->H[$n-1][$n-1] - $p, $q);
- $this->H[$n-1][$n-1] = $this->cdivr;
- $this->H[$n-1][$n] = $this->cdivi;
- }
- $this->H[$n][$n-1] = 0.0;
- $this->H[$n][$n] = 1.0;
- for ($i = $n-2; $i >= 0; --$i) {
- // double ra,sa,vr,vi;
- $ra = 0.0;
- $sa = 0.0;
- for ($j = $l; $j <= $n; ++$j) {
- $ra = $ra + $this->H[$i][$j] * $this->H[$j][$n-1];
- $sa = $sa + $this->H[$i][$j] * $this->H[$j][$n];
- }
- $w = $this->H[$i][$i] - $p;
- if ($this->e[$i] < 0.0) {
- $z = $w;
- $r = $ra;
- $s = $sa;
- } else {
- $l = $i;
- if ($this->e[$i] == 0) {
- $this->cdiv(-$ra, -$sa, $w, $q);
- $this->H[$i][$n-1] = $this->cdivr;
- $this->H[$i][$n] = $this->cdivi;
- } else {
- // Solve complex equations
- $x = $this->H[$i][$i+1];
- $y = $this->H[$i+1][$i];
- $vr = ($this->d[$i] - $p) * ($this->d[$i] - $p) + $this->e[$i] * $this->e[$i] - $q * $q;
- $vi = ($this->d[$i] - $p) * 2.0 * $q;
- if ($vr == 0.0 & $vi == 0.0) {
- $vr = $eps * $norm * (abs($w) + abs($q) + abs($x) + abs($y) + abs($z));
- }
- $this->cdiv($x * $r - $z * $ra + $q * $sa, $x * $s - $z * $sa - $q * $ra, $vr, $vi);
- $this->H[$i][$n-1] = $this->cdivr;
- $this->H[$i][$n] = $this->cdivi;
- if (abs($x) > (abs($z) + abs($q))) {
- $this->H[$i+1][$n-1] = (-$ra - $w * $this->H[$i][$n-1] + $q * $this->H[$i][$n]) / $x;
- $this->H[$i+1][$n] = (-$sa - $w * $this->H[$i][$n] - $q * $this->H[$i][$n-1]) / $x;
- } else {
- $this->cdiv(-$r - $y * $this->H[$i][$n-1], -$s - $y * $this->H[$i][$n], $z, $q);
- $this->H[$i+1][$n-1] = $this->cdivr;
- $this->H[$i+1][$n] = $this->cdivi;
- }
- }
- // Overflow control
- $t = max(abs($this->H[$i][$n-1]),abs($this->H[$i][$n]));
- if (($eps * $t) * $t > 1) {
- for ($j = $i; $j <= $n; ++$j) {
- $this->H[$j][$n-1] = $this->H[$j][$n-1] / $t;
- $this->H[$j][$n] = $this->H[$j][$n] / $t;
- }
- }
- } // end else
- } // end for
- } // end else for complex case
- } // end for
-
- // Vectors of isolated roots
- for ($i = 0; $i < $nn; ++$i) {
- if ($i < $low | $i > $high) {
- for ($j = $i; $j < $nn; ++$j) {
- $this->V[$i][$j] = $this->H[$i][$j];
- }
- }
- }
-
- // Back transformation to get eigenvectors of original matrix
- for ($j = $nn-1; $j >= $low; --$j) {
- for ($i = $low; $i <= $high; ++$i) {
- $z = 0.0;
- for ($k = $low; $k <= min($j,$high); ++$k) {
- $z = $z + $this->V[$i][$k] * $this->H[$k][$j];
- }
- $this->V[$i][$j] = $z;
- }
- }
- } // end hqr2
-
-
- /**
- * Constructor: Check for symmetry, then construct the eigenvalue decomposition
- *
- * @access public
- * @param A Square matrix
- * @return Structure to access D and V.
- */
- public function __construct($Arg) {
- $this->A = $Arg->getArray();
- $this->n = $Arg->getColumnDimension();
-
- $issymmetric = true;
- for ($j = 0; ($j < $this->n) & $issymmetric; ++$j) {
- for ($i = 0; ($i < $this->n) & $issymmetric; ++$i) {
- $issymmetric = ($this->A[$i][$j] == $this->A[$j][$i]);
- }
- }
-
- if ($issymmetric) {
- $this->V = $this->A;
- // Tridiagonalize.
- $this->tred2();
- // Diagonalize.
- $this->tql2();
- } else {
- $this->H = $this->A;
- $this->ort = array();
- // Reduce to Hessenberg form.
- $this->orthes();
- // Reduce Hessenberg to real Schur form.
- $this->hqr2();
- }
- }
-
-
- /**
- * Return the eigenvector matrix
- *
- * @access public
- * @return V
- */
- public function getV() {
- return new Matrix($this->V, $this->n, $this->n);
- }
-
-
- /**
- * Return the real parts of the eigenvalues
- *
- * @access public
- * @return real(diag(D))
- */
- public function getRealEigenvalues() {
- return $this->d;
- }
-
-
- /**
- * Return the imaginary parts of the eigenvalues
- *
- * @access public
- * @return imag(diag(D))
- */
- public function getImagEigenvalues() {
- return $this->e;
- }
-
-
- /**
- * Return the block diagonal eigenvalue matrix
- *
- * @access public
- * @return D
- */
- public function getD() {
- for ($i = 0; $i < $this->n; ++$i) {
- $D[$i] = array_fill(0, $this->n, 0.0);
- $D[$i][$i] = $this->d[$i];
- if ($this->e[$i] == 0) {
- continue;
- }
- $o = ($this->e[$i] > 0) ? $i + 1 : $i - 1;
- $D[$i][$o] = $this->e[$i];
- }
- return new Matrix($D);
- }
-
-} // class EigenvalueDecomposition
diff --git a/admin/survey/excel/PHPExcel/Shared/JAMA/LUDecomposition.php b/admin/survey/excel/PHPExcel/Shared/JAMA/LUDecomposition.php deleted file mode 100644 index 98e918c..0000000 --- a/admin/survey/excel/PHPExcel/Shared/JAMA/LUDecomposition.php +++ /dev/null @@ -1,258 +0,0 @@ -<?php
-/**
- * @package JAMA
- *
- * For an m-by-n matrix A with m >= n, the LU decomposition is an m-by-n
- * unit lower triangular matrix L, an n-by-n upper triangular matrix U,
- * and a permutation vector piv of length m so that A(piv,:) = L*U.
- * If m < n, then L is m-by-m and U is m-by-n.
- *
- * The LU decompostion with pivoting always exists, even if the matrix is
- * singular, so the constructor will never fail. The primary use of the
- * LU decomposition is in the solution of square systems of simultaneous
- * linear equations. This will fail if isNonsingular() returns false.
- *
- * @author Paul Meagher
- * @author Bartosz Matosiuk
- * @author Michael Bommarito
- * @version 1.1
- * @license PHP v3.0
- */
-class PHPExcel_Shared_JAMA_LUDecomposition {
-
- const MatrixSingularException = "Can only perform operation on singular matrix.";
- const MatrixSquareException = "Mismatched Row dimension";
-
- /**
- * Decomposition storage
- * @var array
- */
- private $LU = array();
-
- /**
- * Row dimension.
- * @var int
- */
- private $m;
-
- /**
- * Column dimension.
- * @var int
- */
- private $n;
-
- /**
- * Pivot sign.
- * @var int
- */
- private $pivsign;
-
- /**
- * Internal storage of pivot vector.
- * @var array
- */
- private $piv = array();
-
-
- /**
- * LU Decomposition constructor.
- *
- * @param $A Rectangular matrix
- * @return Structure to access L, U and piv.
- */
- public function __construct($A) {
- if ($A instanceof PHPExcel_Shared_JAMA_Matrix) {
- // Use a "left-looking", dot-product, Crout/Doolittle algorithm.
- $this->LU = $A->getArray();
- $this->m = $A->getRowDimension();
- $this->n = $A->getColumnDimension();
- for ($i = 0; $i < $this->m; ++$i) {
- $this->piv[$i] = $i;
- }
- $this->pivsign = 1;
- $LUrowi = $LUcolj = array();
-
- // Outer loop.
- for ($j = 0; $j < $this->n; ++$j) {
- // Make a copy of the j-th column to localize references.
- for ($i = 0; $i < $this->m; ++$i) {
- $LUcolj[$i] = &$this->LU[$i][$j];
- }
- // Apply previous transformations.
- for ($i = 0; $i < $this->m; ++$i) {
- $LUrowi = $this->LU[$i];
- // Most of the time is spent in the following dot product.
- $kmax = min($i,$j);
- $s = 0.0;
- for ($k = 0; $k < $kmax; ++$k) {
- $s += $LUrowi[$k] * $LUcolj[$k];
- }
- $LUrowi[$j] = $LUcolj[$i] -= $s;
- }
- // Find pivot and exchange if necessary.
- $p = $j;
- for ($i = $j+1; $i < $this->m; ++$i) {
- if (abs($LUcolj[$i]) > abs($LUcolj[$p])) {
- $p = $i;
- }
- }
- if ($p != $j) {
- for ($k = 0; $k < $this->n; ++$k) {
- $t = $this->LU[$p][$k];
- $this->LU[$p][$k] = $this->LU[$j][$k];
- $this->LU[$j][$k] = $t;
- }
- $k = $this->piv[$p];
- $this->piv[$p] = $this->piv[$j];
- $this->piv[$j] = $k;
- $this->pivsign = $this->pivsign * -1;
- }
- // Compute multipliers.
- if (($j < $this->m) && ($this->LU[$j][$j] != 0.0)) {
- for ($i = $j+1; $i < $this->m; ++$i) {
- $this->LU[$i][$j] /= $this->LU[$j][$j];
- }
- }
- }
- } else {
- throw new Exception(PHPExcel_Shared_JAMA_Matrix::ArgumentTypeException);
- }
- } // function __construct()
-
-
- /**
- * Get lower triangular factor.
- *
- * @return array Lower triangular factor
- */
- public function getL() {
- for ($i = 0; $i < $this->m; ++$i) {
- for ($j = 0; $j < $this->n; ++$j) {
- if ($i > $j) {
- $L[$i][$j] = $this->LU[$i][$j];
- } elseif ($i == $j) {
- $L[$i][$j] = 1.0;
- } else {
- $L[$i][$j] = 0.0;
- }
- }
- }
- return new PHPExcel_Shared_JAMA_Matrix($L);
- } // function getL()
-
-
- /**
- * Get upper triangular factor.
- *
- * @return array Upper triangular factor
- */
- public function getU() {
- for ($i = 0; $i < $this->n; ++$i) {
- for ($j = 0; $j < $this->n; ++$j) {
- if ($i <= $j) {
- $U[$i][$j] = $this->LU[$i][$j];
- } else {
- $U[$i][$j] = 0.0;
- }
- }
- }
- return new PHPExcel_Shared_JAMA_Matrix($U);
- } // function getU()
-
-
- /**
- * Return pivot permutation vector.
- *
- * @return array Pivot vector
- */
- public function getPivot() {
- return $this->piv;
- } // function getPivot()
-
-
- /**
- * Alias for getPivot
- *
- * @see getPivot
- */
- public function getDoublePivot() {
- return $this->getPivot();
- } // function getDoublePivot()
-
-
- /**
- * Is the matrix nonsingular?
- *
- * @return true if U, and hence A, is nonsingular.
- */
- public function isNonsingular() {
- for ($j = 0; $j < $this->n; ++$j) {
- if ($this->LU[$j][$j] == 0) {
- return false;
- }
- }
- return true;
- } // function isNonsingular()
-
-
- /**
- * Count determinants
- *
- * @return array d matrix deterninat
- */
- public function det() {
- if ($this->m == $this->n) {
- $d = $this->pivsign;
- for ($j = 0; $j < $this->n; ++$j) {
- $d *= $this->LU[$j][$j];
- }
- return $d;
- } else {
- throw new Exception(PHPExcel_Shared_JAMA_Matrix::MatrixDimensionException);
- }
- } // function det()
-
-
- /**
- * Solve A*X = B
- *
- * @param $B A Matrix with as many rows as A and any number of columns.
- * @return X so that L*U*X = B(piv,:)
- * @exception IllegalArgumentException Matrix row dimensions must agree.
- * @exception RuntimeException Matrix is singular.
- */
- public function solve($B) {
- if ($B->getRowDimension() == $this->m) {
- if ($this->isNonsingular()) {
- // Copy right hand side with pivoting
- $nx = $B->getColumnDimension();
- $X = $B->getMatrix($this->piv, 0, $nx-1);
- // Solve L*Y = B(piv,:)
- for ($k = 0; $k < $this->n; ++$k) {
- for ($i = $k+1; $i < $this->n; ++$i) {
- for ($j = 0; $j < $nx; ++$j) {
- $X->A[$i][$j] -= $X->A[$k][$j] * $this->LU[$i][$k];
- }
- }
- }
- // Solve U*X = Y;
- for ($k = $this->n-1; $k >= 0; --$k) {
- for ($j = 0; $j < $nx; ++$j) {
- $X->A[$k][$j] /= $this->LU[$k][$k];
- }
- for ($i = 0; $i < $k; ++$i) {
- for ($j = 0; $j < $nx; ++$j) {
- $X->A[$i][$j] -= $X->A[$k][$j] * $this->LU[$i][$k];
- }
- }
- }
- return $X;
- } else {
- throw new Exception(self::MatrixSingularException);
- }
- } else {
- throw new Exception(self::MatrixSquareException);
- }
- } // function solve()
-
-} // class PHPExcel_Shared_JAMA_LUDecomposition
diff --git a/admin/survey/excel/PHPExcel/Shared/JAMA/Matrix.php b/admin/survey/excel/PHPExcel/Shared/JAMA/Matrix.php deleted file mode 100644 index e66e8e0..0000000 --- a/admin/survey/excel/PHPExcel/Shared/JAMA/Matrix.php +++ /dev/null @@ -1,1059 +0,0 @@ -<?php
-/**
- * @package JAMA
- */
-
-/** PHPExcel root directory */
-if (!defined('PHPEXCEL_ROOT')) {
- /**
- * @ignore
- */
- define('PHPEXCEL_ROOT', dirname(__FILE__) . '/../../../');
- require(PHPEXCEL_ROOT . 'PHPExcel/Autoloader.php');
-}
-
-
-/*
- * Matrix class
- *
- * @author Paul Meagher
- * @author Michael Bommarito
- * @author Lukasz Karapuda
- * @author Bartek Matosiuk
- * @version 1.8
- * @license PHP v3.0
- * @see http://math.nist.gov/javanumerics/jama/
- */
-class PHPExcel_Shared_JAMA_Matrix {
-
-
- const PolymorphicArgumentException = "Invalid argument pattern for polymorphic function.";
- const ArgumentTypeException = "Invalid argument type.";
- const ArgumentBoundsException = "Invalid argument range.";
- const MatrixDimensionException = "Matrix dimensions are not equal.";
- const ArrayLengthException = "Array length must be a multiple of m.";
-
- /**
- * Matrix storage
- *
- * @var array
- * @access public
- */
- public $A = array();
-
- /**
- * Matrix row dimension
- *
- * @var int
- * @access private
- */
- private $m;
-
- /**
- * Matrix column dimension
- *
- * @var int
- * @access private
- */
- private $n;
-
-
- /**
- * Polymorphic constructor
- *
- * As PHP has no support for polymorphic constructors, we hack our own sort of polymorphism using func_num_args, func_get_arg, and gettype. In essence, we're just implementing a simple RTTI filter and calling the appropriate constructor.
- */
- public function __construct() {
- if (func_num_args() > 0) {
- $args = func_get_args();
- $match = implode(",", array_map('gettype', $args));
-
- switch($match) {
- //Rectangular matrix - m x n initialized from 2D array
- case 'array':
- $this->m = count($args[0]);
- $this->n = count($args[0][0]);
- $this->A = $args[0];
- break;
- //Square matrix - n x n
- case 'integer':
- $this->m = $args[0];
- $this->n = $args[0];
- $this->A = array_fill(0, $this->m, array_fill(0, $this->n, 0));
- break;
- //Rectangular matrix - m x n
- case 'integer,integer':
- $this->m = $args[0];
- $this->n = $args[1];
- $this->A = array_fill(0, $this->m, array_fill(0, $this->n, 0));
- break;
- //Rectangular matrix - m x n initialized from packed array
- case 'array,integer':
- $this->m = $args[1];
- if ($this->m != 0) {
- $this->n = count($args[0]) / $this->m;
- } else {
- $this->n = 0;
- }
- if (($this->m * $this->n) == count($args[0])) {
- for($i = 0; $i < $this->m; ++$i) {
- for($j = 0; $j < $this->n; ++$j) {
- $this->A[$i][$j] = $args[0][$i + $j * $this->m];
- }
- }
- } else {
- throw new Exception(self::ArrayLengthException);
- }
- break;
- default:
- throw new Exception(self::PolymorphicArgumentException);
- break;
- }
- } else {
- throw new Exception(self::PolymorphicArgumentException);
- }
- } // function __construct()
-
-
- /**
- * getArray
- *
- * @return array Matrix array
- */
- public function getArray() {
- return $this->A;
- } // function getArray()
-
-
- /**
- * getRowDimension
- *
- * @return int Row dimension
- */
- public function getRowDimension() {
- return $this->m;
- } // function getRowDimension()
-
-
- /**
- * getColumnDimension
- *
- * @return int Column dimension
- */
- public function getColumnDimension() {
- return $this->n;
- } // function getColumnDimension()
-
-
- /**
- * get
- *
- * Get the i,j-th element of the matrix.
- * @param int $i Row position
- * @param int $j Column position
- * @return mixed Element (int/float/double)
- */
- public function get($i = null, $j = null) {
- return $this->A[$i][$j];
- } // function get()
-
-
- /**
- * getMatrix
- *
- * Get a submatrix
- * @param int $i0 Initial row index
- * @param int $iF Final row index
- * @param int $j0 Initial column index
- * @param int $jF Final column index
- * @return Matrix Submatrix
- */
- public function getMatrix() {
- if (func_num_args() > 0) {
- $args = func_get_args();
- $match = implode(",", array_map('gettype', $args));
-
- switch($match) {
- //A($i0...; $j0...)
- case 'integer,integer':
- list($i0, $j0) = $args;
- if ($i0 >= 0) { $m = $this->m - $i0; } else { throw new Exception(self::ArgumentBoundsException); }
- if ($j0 >= 0) { $n = $this->n - $j0; } else { throw new Exception(self::ArgumentBoundsException); }
- $R = new PHPExcel_Shared_JAMA_Matrix($m, $n);
- for($i = $i0; $i < $this->m; ++$i) {
- for($j = $j0; $j < $this->n; ++$j) {
- $R->set($i, $j, $this->A[$i][$j]);
- }
- }
- return $R;
- break;
- //A($i0...$iF; $j0...$jF)
- case 'integer,integer,integer,integer':
- list($i0, $iF, $j0, $jF) = $args;
- if (($iF > $i0) && ($this->m >= $iF) && ($i0 >= 0)) { $m = $iF - $i0; } else { throw new Exception(self::ArgumentBoundsException); }
- if (($jF > $j0) && ($this->n >= $jF) && ($j0 >= 0)) { $n = $jF - $j0; } else { throw new Exception(self::ArgumentBoundsException); }
- $R = new PHPExcel_Shared_JAMA_Matrix($m+1, $n+1);
- for($i = $i0; $i <= $iF; ++$i) {
- for($j = $j0; $j <= $jF; ++$j) {
- $R->set($i - $i0, $j - $j0, $this->A[$i][$j]);
- }
- }
- return $R;
- break;
- //$R = array of row indices; $C = array of column indices
- case 'array,array':
- list($RL, $CL) = $args;
- if (count($RL) > 0) { $m = count($RL); } else { throw new Exception(self::ArgumentBoundsException); }
- if (count($CL) > 0) { $n = count($CL); } else { throw new Exception(self::ArgumentBoundsException); }
- $R = new PHPExcel_Shared_JAMA_Matrix($m, $n);
- for($i = 0; $i < $m; ++$i) {
- for($j = 0; $j < $n; ++$j) {
- $R->set($i - $i0, $j - $j0, $this->A[$RL[$i]][$CL[$j]]);
- }
- }
- return $R;
- break;
- //$RL = array of row indices; $CL = array of column indices
- case 'array,array':
- list($RL, $CL) = $args;
- if (count($RL) > 0) { $m = count($RL); } else { throw new Exception(self::ArgumentBoundsException); }
- if (count($CL) > 0) { $n = count($CL); } else { throw new Exception(self::ArgumentBoundsException); }
- $R = new PHPExcel_Shared_JAMA_Matrix($m, $n);
- for($i = 0; $i < $m; ++$i) {
- for($j = 0; $j < $n; ++$j) {
- $R->set($i, $j, $this->A[$RL[$i]][$CL[$j]]);
- }
- }
- return $R;
- break;
- //A($i0...$iF); $CL = array of column indices
- case 'integer,integer,array':
- list($i0, $iF, $CL) = $args;
- if (($iF > $i0) && ($this->m >= $iF) && ($i0 >= 0)) { $m = $iF - $i0; } else { throw new Exception(self::ArgumentBoundsException); }
- if (count($CL) > 0) { $n = count($CL); } else { throw new Exception(self::ArgumentBoundsException); }
- $R = new PHPExcel_Shared_JAMA_Matrix($m, $n);
- for($i = $i0; $i < $iF; ++$i) {
- for($j = 0; $j < $n; ++$j) {
- $R->set($i - $i0, $j, $this->A[$RL[$i]][$j]);
- }
- }
- return $R;
- break;
- //$RL = array of row indices
- case 'array,integer,integer':
- list($RL, $j0, $jF) = $args;
- if (count($RL) > 0) { $m = count($RL); } else { throw new Exception(self::ArgumentBoundsException); }
- if (($jF >= $j0) && ($this->n >= $jF) && ($j0 >= 0)) { $n = $jF - $j0; } else { throw new Exception(self::ArgumentBoundsException); }
- $R = new PHPExcel_Shared_JAMA_Matrix($m, $n+1);
- for($i = 0; $i < $m; ++$i) {
- for($j = $j0; $j <= $jF; ++$j) {
- $R->set($i, $j - $j0, $this->A[$RL[$i]][$j]);
- }
- }
- return $R;
- break;
- default:
- throw new Exception(self::PolymorphicArgumentException);
- break;
- }
- } else {
- throw new Exception(self::PolymorphicArgumentException);
- }
- } // function getMatrix()
-
-
- /**
- * checkMatrixDimensions
- *
- * Is matrix B the same size?
- * @param Matrix $B Matrix B
- * @return boolean
- */
- public function checkMatrixDimensions($B = null) {
- if ($B instanceof PHPExcel_Shared_JAMA_Matrix) {
- if (($this->m == $B->getRowDimension()) && ($this->n == $B->getColumnDimension())) {
- return true;
- } else {
- throw new Exception(self::MatrixDimensionException);
- }
- } else {
- throw new Exception(self::ArgumentTypeException);
- }
- } // function checkMatrixDimensions()
-
-
-
- /**
- * set
- *
- * Set the i,j-th element of the matrix.
- * @param int $i Row position
- * @param int $j Column position
- * @param mixed $c Int/float/double value
- * @return mixed Element (int/float/double)
- */
- public function set($i = null, $j = null, $c = null) {
- // Optimized set version just has this
- $this->A[$i][$j] = $c;
- } // function set()
-
-
- /**
- * identity
- *
- * Generate an identity matrix.
- * @param int $m Row dimension
- * @param int $n Column dimension
- * @return Matrix Identity matrix
- */
- public function identity($m = null, $n = null) {
- return $this->diagonal($m, $n, 1);
- } // function identity()
-
-
- /**
- * diagonal
- *
- * Generate a diagonal matrix
- * @param int $m Row dimension
- * @param int $n Column dimension
- * @param mixed $c Diagonal value
- * @return Matrix Diagonal matrix
- */
- public function diagonal($m = null, $n = null, $c = 1) {
- $R = new PHPExcel_Shared_JAMA_Matrix($m, $n);
- for($i = 0; $i < $m; ++$i) {
- $R->set($i, $i, $c);
- }
- return $R;
- } // function diagonal()
-
-
- /**
- * getMatrixByRow
- *
- * Get a submatrix by row index/range
- * @param int $i0 Initial row index
- * @param int $iF Final row index
- * @return Matrix Submatrix
- */
- public function getMatrixByRow($i0 = null, $iF = null) {
- if (is_int($i0)) {
- if (is_int($iF)) {
- return $this->getMatrix($i0, 0, $iF + 1, $this->n);
- } else {
- return $this->getMatrix($i0, 0, $i0 + 1, $this->n);
- }
- } else {
- throw new Exception(self::ArgumentTypeException);
- }
- } // function getMatrixByRow()
-
-
- /**
- * getMatrixByCol
- *
- * Get a submatrix by column index/range
- * @param int $i0 Initial column index
- * @param int $iF Final column index
- * @return Matrix Submatrix
- */
- public function getMatrixByCol($j0 = null, $jF = null) {
- if (is_int($j0)) {
- if (is_int($jF)) {
- return $this->getMatrix(0, $j0, $this->m, $jF + 1);
- } else {
- return $this->getMatrix(0, $j0, $this->m, $j0 + 1);
- }
- } else {
- throw new Exception(self::ArgumentTypeException);
- }
- } // function getMatrixByCol()
-
-
- /**
- * transpose
- *
- * Tranpose matrix
- * @return Matrix Transposed matrix
- */
- public function transpose() {
- $R = new PHPExcel_Shared_JAMA_Matrix($this->n, $this->m);
- for($i = 0; $i < $this->m; ++$i) {
- for($j = 0; $j < $this->n; ++$j) {
- $R->set($j, $i, $this->A[$i][$j]);
- }
- }
- return $R;
- } // function transpose()
-
-
- /**
- * trace
- *
- * Sum of diagonal elements
- * @return float Sum of diagonal elements
- */
- public function trace() {
- $s = 0;
- $n = min($this->m, $this->n);
- for($i = 0; $i < $n; ++$i) {
- $s += $this->A[$i][$i];
- }
- return $s;
- } // function trace()
-
-
- /**
- * uminus
- *
- * Unary minus matrix -A
- * @return Matrix Unary minus matrix
- */
- public function uminus() {
- } // function uminus()
-
-
- /**
- * plus
- *
- * A + B
- * @param mixed $B Matrix/Array
- * @return Matrix Sum
- */
- public function plus() {
- if (func_num_args() > 0) {
- $args = func_get_args();
- $match = implode(",", array_map('gettype', $args));
-
- switch($match) {
- case 'object':
- if ($args[0] instanceof PHPExcel_Shared_JAMA_Matrix) { $M = $args[0]; } else { throw new Exception(self::ArgumentTypeException); }
- break;
- case 'array':
- $M = new PHPExcel_Shared_JAMA_Matrix($args[0]);
- break;
- default:
- throw new Exception(self::PolymorphicArgumentException);
- break;
- }
- $this->checkMatrixDimensions($M);
- for($i = 0; $i < $this->m; ++$i) {
- for($j = 0; $j < $this->n; ++$j) {
- $M->set($i, $j, $M->get($i, $j) + $this->A[$i][$j]);
- }
- }
- return $M;
- } else {
- throw new Exception(self::PolymorphicArgumentException);
- }
- } // function plus()
-
-
- /**
- * plusEquals
- *
- * A = A + B
- * @param mixed $B Matrix/Array
- * @return Matrix Sum
- */
- public function plusEquals() {
- if (func_num_args() > 0) {
- $args = func_get_args();
- $match = implode(",", array_map('gettype', $args));
-
- switch($match) {
- case 'object':
- if ($args[0] instanceof PHPExcel_Shared_JAMA_Matrix) { $M = $args[0]; } else { throw new Exception(self::ArgumentTypeException); }
- break;
- case 'array':
- $M = new PHPExcel_Shared_JAMA_Matrix($args[0]);
- break;
- default:
- throw new Exception(self::PolymorphicArgumentException);
- break;
- }
- $this->checkMatrixDimensions($M);
- for($i = 0; $i < $this->m; ++$i) {
- for($j = 0; $j < $this->n; ++$j) {
- $validValues = True;
- $value = $M->get($i, $j);
- if ((is_string($this->A[$i][$j])) && (strlen($this->A[$i][$j]) > 0) && (!is_numeric($this->A[$i][$j]))) {
- $this->A[$i][$j] = trim($this->A[$i][$j],'"');
- $validValues &= PHPExcel_Shared_String::convertToNumberIfFraction($this->A[$i][$j]);
- }
- if ((is_string($value)) && (strlen($value) > 0) && (!is_numeric($value))) {
- $value = trim($value,'"');
- $validValues &= PHPExcel_Shared_String::convertToNumberIfFraction($value);
- }
- if ($validValues) {
- $this->A[$i][$j] += $value;
- } else {
- $this->A[$i][$j] = PHPExcel_Calculation_Functions::NaN();
- }
- }
- }
- return $this;
- } else {
- throw new Exception(self::PolymorphicArgumentException);
- }
- } // function plusEquals()
-
-
- /**
- * minus
- *
- * A - B
- * @param mixed $B Matrix/Array
- * @return Matrix Sum
- */
- public function minus() {
- if (func_num_args() > 0) {
- $args = func_get_args();
- $match = implode(",", array_map('gettype', $args));
-
- switch($match) {
- case 'object':
- if ($args[0] instanceof PHPExcel_Shared_JAMA_Matrix) { $M = $args[0]; } else { throw new Exception(self::ArgumentTypeException); }
- break;
- case 'array':
- $M = new PHPExcel_Shared_JAMA_Matrix($args[0]);
- break;
- default:
- throw new Exception(self::PolymorphicArgumentException);
- break;
- }
- $this->checkMatrixDimensions($M);
- for($i = 0; $i < $this->m; ++$i) {
- for($j = 0; $j < $this->n; ++$j) {
- $M->set($i, $j, $M->get($i, $j) - $this->A[$i][$j]);
- }
- }
- return $M;
- } else {
- throw new Exception(self::PolymorphicArgumentException);
- }
- } // function minus()
-
-
- /**
- * minusEquals
- *
- * A = A - B
- * @param mixed $B Matrix/Array
- * @return Matrix Sum
- */
- public function minusEquals() {
- if (func_num_args() > 0) {
- $args = func_get_args();
- $match = implode(",", array_map('gettype', $args));
-
- switch($match) {
- case 'object':
- if ($args[0] instanceof PHPExcel_Shared_JAMA_Matrix) { $M = $args[0]; } else { throw new Exception(self::ArgumentTypeException); }
- break;
- case 'array':
- $M = new PHPExcel_Shared_JAMA_Matrix($args[0]);
- break;
- default:
- throw new Exception(self::PolymorphicArgumentException);
- break;
- }
- $this->checkMatrixDimensions($M);
- for($i = 0; $i < $this->m; ++$i) {
- for($j = 0; $j < $this->n; ++$j) {
- $validValues = True;
- $value = $M->get($i, $j);
- if ((is_string($this->A[$i][$j])) && (strlen($this->A[$i][$j]) > 0) && (!is_numeric($this->A[$i][$j]))) {
- $this->A[$i][$j] = trim($this->A[$i][$j],'"');
- $validValues &= PHPExcel_Shared_String::convertToNumberIfFraction($this->A[$i][$j]);
- }
- if ((is_string($value)) && (strlen($value) > 0) && (!is_numeric($value))) {
- $value = trim($value,'"');
- $validValues &= PHPExcel_Shared_String::convertToNumberIfFraction($value);
- }
- if ($validValues) {
- $this->A[$i][$j] -= $value;
- } else {
- $this->A[$i][$j] = PHPExcel_Calculation_Functions::NaN();
- }
- }
- }
- return $this;
- } else {
- throw new Exception(self::PolymorphicArgumentException);
- }
- } // function minusEquals()
-
-
- /**
- * arrayTimes
- *
- * Element-by-element multiplication
- * Cij = Aij * Bij
- * @param mixed $B Matrix/Array
- * @return Matrix Matrix Cij
- */
- public function arrayTimes() {
- if (func_num_args() > 0) {
- $args = func_get_args();
- $match = implode(",", array_map('gettype', $args));
-
- switch($match) {
- case 'object':
- if ($args[0] instanceof PHPExcel_Shared_JAMA_Matrix) { $M = $args[0]; } else { throw new Exception(self::ArgumentTypeException); }
- break;
- case 'array':
- $M = new PHPExcel_Shared_JAMA_Matrix($args[0]);
- break;
- default:
- throw new Exception(self::PolymorphicArgumentException);
- break;
- }
- $this->checkMatrixDimensions($M);
- for($i = 0; $i < $this->m; ++$i) {
- for($j = 0; $j < $this->n; ++$j) {
- $M->set($i, $j, $M->get($i, $j) * $this->A[$i][$j]);
- }
- }
- return $M;
- } else {
- throw new Exception(self::PolymorphicArgumentException);
- }
- } // function arrayTimes()
-
-
- /**
- * arrayTimesEquals
- *
- * Element-by-element multiplication
- * Aij = Aij * Bij
- * @param mixed $B Matrix/Array
- * @return Matrix Matrix Aij
- */
- public function arrayTimesEquals() {
- if (func_num_args() > 0) {
- $args = func_get_args();
- $match = implode(",", array_map('gettype', $args));
-
- switch($match) {
- case 'object':
- if ($args[0] instanceof PHPExcel_Shared_JAMA_Matrix) { $M = $args[0]; } else { throw new Exception(self::ArgumentTypeException); }
- break;
- case 'array':
- $M = new PHPExcel_Shared_JAMA_Matrix($args[0]);
- break;
- default:
- throw new Exception(self::PolymorphicArgumentException);
- break;
- }
- $this->checkMatrixDimensions($M);
- for($i = 0; $i < $this->m; ++$i) {
- for($j = 0; $j < $this->n; ++$j) {
- $validValues = True;
- $value = $M->get($i, $j);
- if ((is_string($this->A[$i][$j])) && (strlen($this->A[$i][$j]) > 0) && (!is_numeric($this->A[$i][$j]))) {
- $this->A[$i][$j] = trim($this->A[$i][$j],'"');
- $validValues &= PHPExcel_Shared_String::convertToNumberIfFraction($this->A[$i][$j]);
- }
- if ((is_string($value)) && (strlen($value) > 0) && (!is_numeric($value))) {
- $value = trim($value,'"');
- $validValues &= PHPExcel_Shared_String::convertToNumberIfFraction($value);
- }
- if ($validValues) {
- $this->A[$i][$j] *= $value;
- } else {
- $this->A[$i][$j] = PHPExcel_Calculation_Functions::NaN();
- }
- }
- }
- return $this;
- } else {
- throw new Exception(self::PolymorphicArgumentException);
- }
- } // function arrayTimesEquals()
-
-
- /**
- * arrayRightDivide
- *
- * Element-by-element right division
- * A / B
- * @param Matrix $B Matrix B
- * @return Matrix Division result
- */
- public function arrayRightDivide() {
- if (func_num_args() > 0) {
- $args = func_get_args();
- $match = implode(",", array_map('gettype', $args));
-
- switch($match) {
- case 'object':
- if ($args[0] instanceof PHPExcel_Shared_JAMA_Matrix) { $M = $args[0]; } else { throw new Exception(self::ArgumentTypeException); }
- break;
- case 'array':
- $M = new PHPExcel_Shared_JAMA_Matrix($args[0]);
- break;
- default:
- throw new Exception(self::PolymorphicArgumentException);
- break;
- }
- $this->checkMatrixDimensions($M);
- for($i = 0; $i < $this->m; ++$i) {
- for($j = 0; $j < $this->n; ++$j) {
- $validValues = True;
- $value = $M->get($i, $j);
- if ((is_string($this->A[$i][$j])) && (strlen($this->A[$i][$j]) > 0) && (!is_numeric($this->A[$i][$j]))) {
- $this->A[$i][$j] = trim($this->A[$i][$j],'"');
- $validValues &= PHPExcel_Shared_String::convertToNumberIfFraction($this->A[$i][$j]);
- }
- if ((is_string($value)) && (strlen($value) > 0) && (!is_numeric($value))) {
- $value = trim($value,'"');
- $validValues &= PHPExcel_Shared_String::convertToNumberIfFraction($value);
- }
- if ($validValues) {
- if ($value == 0) {
- // Trap for Divide by Zero error
- $M->set($i, $j, '#DIV/0!');
- } else {
- $M->set($i, $j, $this->A[$i][$j] / $value);
- }
- } else {
- $M->set($i, $j, PHPExcel_Calculation_Functions::NaN());
- }
- }
- }
- return $M;
- } else {
- throw new Exception(self::PolymorphicArgumentException);
- }
- } // function arrayRightDivide()
-
-
- /**
- * arrayRightDivideEquals
- *
- * Element-by-element right division
- * Aij = Aij / Bij
- * @param mixed $B Matrix/Array
- * @return Matrix Matrix Aij
- */
- public function arrayRightDivideEquals() {
- if (func_num_args() > 0) {
- $args = func_get_args();
- $match = implode(",", array_map('gettype', $args));
-
- switch($match) {
- case 'object':
- if ($args[0] instanceof PHPExcel_Shared_JAMA_Matrix) { $M = $args[0]; } else { throw new Exception(self::ArgumentTypeException); }
- break;
- case 'array':
- $M = new PHPExcel_Shared_JAMA_Matrix($args[0]);
- break;
- default:
- throw new Exception(self::PolymorphicArgumentException);
- break;
- }
- $this->checkMatrixDimensions($M);
- for($i = 0; $i < $this->m; ++$i) {
- for($j = 0; $j < $this->n; ++$j) {
- $this->A[$i][$j] = $this->A[$i][$j] / $M->get($i, $j);
- }
- }
- return $M;
- } else {
- throw new Exception(self::PolymorphicArgumentException);
- }
- } // function arrayRightDivideEquals()
-
-
- /**
- * arrayLeftDivide
- *
- * Element-by-element Left division
- * A / B
- * @param Matrix $B Matrix B
- * @return Matrix Division result
- */
- public function arrayLeftDivide() {
- if (func_num_args() > 0) {
- $args = func_get_args();
- $match = implode(",", array_map('gettype', $args));
-
- switch($match) {
- case 'object':
- if ($args[0] instanceof PHPExcel_Shared_JAMA_Matrix) { $M = $args[0]; } else { throw new Exception(self::ArgumentTypeException); }
- break;
- case 'array':
- $M = new PHPExcel_Shared_JAMA_Matrix($args[0]);
- break;
- default:
- throw new Exception(self::PolymorphicArgumentException);
- break;
- }
- $this->checkMatrixDimensions($M);
- for($i = 0; $i < $this->m; ++$i) {
- for($j = 0; $j < $this->n; ++$j) {
- $M->set($i, $j, $M->get($i, $j) / $this->A[$i][$j]);
- }
- }
- return $M;
- } else {
- throw new Exception(self::PolymorphicArgumentException);
- }
- } // function arrayLeftDivide()
-
-
- /**
- * arrayLeftDivideEquals
- *
- * Element-by-element Left division
- * Aij = Aij / Bij
- * @param mixed $B Matrix/Array
- * @return Matrix Matrix Aij
- */
- public function arrayLeftDivideEquals() {
- if (func_num_args() > 0) {
- $args = func_get_args();
- $match = implode(",", array_map('gettype', $args));
-
- switch($match) {
- case 'object':
- if ($args[0] instanceof PHPExcel_Shared_JAMA_Matrix) { $M = $args[0]; } else { throw new Exception(self::ArgumentTypeException); }
- break;
- case 'array':
- $M = new PHPExcel_Shared_JAMA_Matrix($args[0]);
- break;
- default:
- throw new Exception(self::PolymorphicArgumentException);
- break;
- }
- $this->checkMatrixDimensions($M);
- for($i = 0; $i < $this->m; ++$i) {
- for($j = 0; $j < $this->n; ++$j) {
- $this->A[$i][$j] = $M->get($i, $j) / $this->A[$i][$j];
- }
- }
- return $M;
- } else {
- throw new Exception(self::PolymorphicArgumentException);
- }
- } // function arrayLeftDivideEquals()
-
-
- /**
- * times
- *
- * Matrix multiplication
- * @param mixed $n Matrix/Array/Scalar
- * @return Matrix Product
- */
- public function times() {
- if (func_num_args() > 0) {
- $args = func_get_args();
- $match = implode(",", array_map('gettype', $args));
-
- switch($match) {
- case 'object':
- if ($args[0] instanceof PHPExcel_Shared_JAMA_Matrix) { $B = $args[0]; } else { throw new Exception(self::ArgumentTypeException); }
- if ($this->n == $B->m) {
- $C = new PHPExcel_Shared_JAMA_Matrix($this->m, $B->n);
- for($j = 0; $j < $B->n; ++$j) {
- for ($k = 0; $k < $this->n; ++$k) {
- $Bcolj[$k] = $B->A[$k][$j];
- }
- for($i = 0; $i < $this->m; ++$i) {
- $Arowi = $this->A[$i];
- $s = 0;
- for($k = 0; $k < $this->n; ++$k) {
- $s += $Arowi[$k] * $Bcolj[$k];
- }
- $C->A[$i][$j] = $s;
- }
- }
- return $C;
- } else {
- throw new Exception(JAMAError(MatrixDimensionMismatch));
- }
- break;
- case 'array':
- $B = new PHPExcel_Shared_JAMA_Matrix($args[0]);
- if ($this->n == $B->m) {
- $C = new PHPExcel_Shared_JAMA_Matrix($this->m, $B->n);
- for($i = 0; $i < $C->m; ++$i) {
- for($j = 0; $j < $C->n; ++$j) {
- $s = "0";
- for($k = 0; $k < $C->n; ++$k) {
- $s += $this->A[$i][$k] * $B->A[$k][$j];
- }
- $C->A[$i][$j] = $s;
- }
- }
- return $C;
- } else {
- throw new Exception(JAMAError(MatrixDimensionMismatch));
- }
- return $M;
- break;
- case 'integer':
- $C = new PHPExcel_Shared_JAMA_Matrix($this->A);
- for($i = 0; $i < $C->m; ++$i) {
- for($j = 0; $j < $C->n; ++$j) {
- $C->A[$i][$j] *= $args[0];
- }
- }
- return $C;
- break;
- case 'double':
- $C = new PHPExcel_Shared_JAMA_Matrix($this->m, $this->n);
- for($i = 0; $i < $C->m; ++$i) {
- for($j = 0; $j < $C->n; ++$j) {
- $C->A[$i][$j] = $args[0] * $this->A[$i][$j];
- }
- }
- return $C;
- break;
- case 'float':
- $C = new PHPExcel_Shared_JAMA_Matrix($this->A);
- for($i = 0; $i < $C->m; ++$i) {
- for($j = 0; $j < $C->n; ++$j) {
- $C->A[$i][$j] *= $args[0];
- }
- }
- return $C;
- break;
- default:
- throw new Exception(self::PolymorphicArgumentException);
- break;
- }
- } else {
- throw new Exception(self::PolymorphicArgumentException);
- }
- } // function times()
-
-
- /**
- * power
- *
- * A = A ^ B
- * @param mixed $B Matrix/Array
- * @return Matrix Sum
- */
- public function power() {
- if (func_num_args() > 0) {
- $args = func_get_args();
- $match = implode(",", array_map('gettype', $args));
-
- switch($match) {
- case 'object':
- if ($args[0] instanceof PHPExcel_Shared_JAMA_Matrix) { $M = $args[0]; } else { throw new Exception(self::ArgumentTypeException); }
- break;
- case 'array':
- $M = new PHPExcel_Shared_JAMA_Matrix($args[0]);
- break;
- default:
- throw new Exception(self::PolymorphicArgumentException);
- break;
- }
- $this->checkMatrixDimensions($M);
- for($i = 0; $i < $this->m; ++$i) {
- for($j = 0; $j < $this->n; ++$j) {
- $validValues = True;
- $value = $M->get($i, $j);
- if ((is_string($this->A[$i][$j])) && (strlen($this->A[$i][$j]) > 0) && (!is_numeric($this->A[$i][$j]))) {
- $this->A[$i][$j] = trim($this->A[$i][$j],'"');
- $validValues &= PHPExcel_Shared_String::convertToNumberIfFraction($this->A[$i][$j]);
- }
- if ((is_string($value)) && (strlen($value) > 0) && (!is_numeric($value))) {
- $value = trim($value,'"');
- $validValues &= PHPExcel_Shared_String::convertToNumberIfFraction($value);
- }
- if ($validValues) {
- $this->A[$i][$j] = pow($this->A[$i][$j],$value);
- } else {
- $this->A[$i][$j] = PHPExcel_Calculation_Functions::NaN();
- }
- }
- }
- return $this;
- } else {
- throw new Exception(self::PolymorphicArgumentException);
- }
- } // function power()
-
-
- /**
- * concat
- *
- * A = A & B
- * @param mixed $B Matrix/Array
- * @return Matrix Sum
- */
- public function concat() {
- if (func_num_args() > 0) {
- $args = func_get_args();
- $match = implode(",", array_map('gettype', $args));
-
- switch($match) {
- case 'object':
- if ($args[0] instanceof PHPExcel_Shared_JAMA_Matrix) { $M = $args[0]; } else { throw new Exception(self::ArgumentTypeException); }
- case 'array':
- $M = new PHPExcel_Shared_JAMA_Matrix($args[0]);
- break;
- default:
- throw new Exception(self::PolymorphicArgumentException);
- break;
- }
- $this->checkMatrixDimensions($M);
- for($i = 0; $i < $this->m; ++$i) {
- for($j = 0; $j < $this->n; ++$j) {
- $this->A[$i][$j] = trim($this->A[$i][$j],'"').trim($M->get($i, $j),'"');
- }
- }
- return $this;
- } else {
- throw new Exception(self::PolymorphicArgumentException);
- }
- } // function concat()
-
-
- /**
- * Solve A*X = B.
- *
- * @param Matrix $B Right hand side
- * @return Matrix ... Solution if A is square, least squares solution otherwise
- */
- public function solve($B) {
- if ($this->m == $this->n) {
- $LU = new PHPExcel_Shared_JAMA_LUDecomposition($this);
- return $LU->solve($B);
- } else {
- $QR = new QRDecomposition($this);
- return $QR->solve($B);
- }
- } // function solve()
-
-
- /**
- * Matrix inverse or pseudoinverse.
- *
- * @return Matrix ... Inverse(A) if A is square, pseudoinverse otherwise.
- */
- public function inverse() {
- return $this->solve($this->identity($this->m, $this->m));
- } // function inverse()
-
-
- /**
- * det
- *
- * Calculate determinant
- * @return float Determinant
- */
- public function det() {
- $L = new PHPExcel_Shared_JAMA_LUDecomposition($this);
- return $L->det();
- } // function det()
-
-
-} // class PHPExcel_Shared_JAMA_Matrix
diff --git a/admin/survey/excel/PHPExcel/Shared/JAMA/QRDecomposition.php b/admin/survey/excel/PHPExcel/Shared/JAMA/QRDecomposition.php deleted file mode 100644 index 4492fa5..0000000 --- a/admin/survey/excel/PHPExcel/Shared/JAMA/QRDecomposition.php +++ /dev/null @@ -1,234 +0,0 @@ -<?php
-/**
- * @package JAMA
- *
- * For an m-by-n matrix A with m >= n, the QR decomposition is an m-by-n
- * orthogonal matrix Q and an n-by-n upper triangular matrix R so that
- * A = Q*R.
- *
- * The QR decompostion always exists, even if the matrix does not have
- * full rank, so the constructor will never fail. The primary use of the
- * QR decomposition is in the least squares solution of nonsquare systems
- * of simultaneous linear equations. This will fail if isFullRank()
- * returns false.
- *
- * @author Paul Meagher
- * @license PHP v3.0
- * @version 1.1
- */
-class PHPExcel_Shared_JAMA_QRDecomposition {
-
- const MatrixRankException = "Can only perform operation on full-rank matrix.";
-
- /**
- * Array for internal storage of decomposition.
- * @var array
- */
- private $QR = array();
-
- /**
- * Row dimension.
- * @var integer
- */
- private $m;
-
- /**
- * Column dimension.
- * @var integer
- */
- private $n;
-
- /**
- * Array for internal storage of diagonal of R.
- * @var array
- */
- private $Rdiag = array();
-
-
- /**
- * QR Decomposition computed by Householder reflections.
- *
- * @param matrix $A Rectangular matrix
- * @return Structure to access R and the Householder vectors and compute Q.
- */
- public function __construct($A) {
- if($A instanceof PHPExcel_Shared_JAMA_Matrix) {
- // Initialize.
- $this->QR = $A->getArrayCopy();
- $this->m = $A->getRowDimension();
- $this->n = $A->getColumnDimension();
- // Main loop.
- for ($k = 0; $k < $this->n; ++$k) {
- // Compute 2-norm of k-th column without under/overflow.
- $nrm = 0.0;
- for ($i = $k; $i < $this->m; ++$i) {
- $nrm = hypo($nrm, $this->QR[$i][$k]);
- }
- if ($nrm != 0.0) {
- // Form k-th Householder vector.
- if ($this->QR[$k][$k] < 0) {
- $nrm = -$nrm;
- }
- for ($i = $k; $i < $this->m; ++$i) {
- $this->QR[$i][$k] /= $nrm;
- }
- $this->QR[$k][$k] += 1.0;
- // Apply transformation to remaining columns.
- for ($j = $k+1; $j < $this->n; ++$j) {
- $s = 0.0;
- for ($i = $k; $i < $this->m; ++$i) {
- $s += $this->QR[$i][$k] * $this->QR[$i][$j];
- }
- $s = -$s/$this->QR[$k][$k];
- for ($i = $k; $i < $this->m; ++$i) {
- $this->QR[$i][$j] += $s * $this->QR[$i][$k];
- }
- }
- }
- $this->Rdiag[$k] = -$nrm;
- }
- } else {
- throw new Exception(PHPExcel_Shared_JAMA_Matrix::ArgumentTypeException);
- }
- } // function __construct()
-
-
- /**
- * Is the matrix full rank?
- *
- * @return boolean true if R, and hence A, has full rank, else false.
- */
- public function isFullRank() {
- for ($j = 0; $j < $this->n; ++$j) {
- if ($this->Rdiag[$j] == 0) {
- return false;
- }
- }
- return true;
- } // function isFullRank()
-
-
- /**
- * Return the Householder vectors
- *
- * @return Matrix Lower trapezoidal matrix whose columns define the reflections
- */
- public function getH() {
- for ($i = 0; $i < $this->m; ++$i) {
- for ($j = 0; $j < $this->n; ++$j) {
- if ($i >= $j) {
- $H[$i][$j] = $this->QR[$i][$j];
- } else {
- $H[$i][$j] = 0.0;
- }
- }
- }
- return new PHPExcel_Shared_JAMA_Matrix($H);
- } // function getH()
-
-
- /**
- * Return the upper triangular factor
- *
- * @return Matrix upper triangular factor
- */
- public function getR() {
- for ($i = 0; $i < $this->n; ++$i) {
- for ($j = 0; $j < $this->n; ++$j) {
- if ($i < $j) {
- $R[$i][$j] = $this->QR[$i][$j];
- } elseif ($i == $j) {
- $R[$i][$j] = $this->Rdiag[$i];
- } else {
- $R[$i][$j] = 0.0;
- }
- }
- }
- return new PHPExcel_Shared_JAMA_Matrix($R);
- } // function getR()
-
-
- /**
- * Generate and return the (economy-sized) orthogonal factor
- *
- * @return Matrix orthogonal factor
- */
- public function getQ() {
- for ($k = $this->n-1; $k >= 0; --$k) {
- for ($i = 0; $i < $this->m; ++$i) {
- $Q[$i][$k] = 0.0;
- }
- $Q[$k][$k] = 1.0;
- for ($j = $k; $j < $this->n; ++$j) {
- if ($this->QR[$k][$k] != 0) {
- $s = 0.0;
- for ($i = $k; $i < $this->m; ++$i) {
- $s += $this->QR[$i][$k] * $Q[$i][$j];
- }
- $s = -$s/$this->QR[$k][$k];
- for ($i = $k; $i < $this->m; ++$i) {
- $Q[$i][$j] += $s * $this->QR[$i][$k];
- }
- }
- }
- }
- /*
- for($i = 0; $i < count($Q); ++$i) {
- for($j = 0; $j < count($Q); ++$j) {
- if(! isset($Q[$i][$j]) ) {
- $Q[$i][$j] = 0;
- }
- }
- }
- */
- return new PHPExcel_Shared_JAMA_Matrix($Q);
- } // function getQ()
-
-
- /**
- * Least squares solution of A*X = B
- *
- * @param Matrix $B A Matrix with as many rows as A and any number of columns.
- * @return Matrix Matrix that minimizes the two norm of Q*R*X-B.
- */
- public function solve($B) {
- if ($B->getRowDimension() == $this->m) {
- if ($this->isFullRank()) {
- // Copy right hand side
- $nx = $B->getColumnDimension();
- $X = $B->getArrayCopy();
- // Compute Y = transpose(Q)*B
- for ($k = 0; $k < $this->n; ++$k) {
- for ($j = 0; $j < $nx; ++$j) {
- $s = 0.0;
- for ($i = $k; $i < $this->m; ++$i) {
- $s += $this->QR[$i][$k] * $X[$i][$j];
- }
- $s = -$s/$this->QR[$k][$k];
- for ($i = $k; $i < $this->m; ++$i) {
- $X[$i][$j] += $s * $this->QR[$i][$k];
- }
- }
- }
- // Solve R*X = Y;
- for ($k = $this->n-1; $k >= 0; --$k) {
- for ($j = 0; $j < $nx; ++$j) {
- $X[$k][$j] /= $this->Rdiag[$k];
- }
- for ($i = 0; $i < $k; ++$i) {
- for ($j = 0; $j < $nx; ++$j) {
- $X[$i][$j] -= $X[$k][$j]* $this->QR[$i][$k];
- }
- }
- }
- $X = new PHPExcel_Shared_JAMA_Matrix($X);
- return ($X->getMatrix(0, $this->n-1, 0, $nx));
- } else {
- throw new Exception(self::MatrixRankException);
- }
- } else {
- throw new Exception(PHPExcel_Shared_JAMA_Matrix::MatrixDimensionException);
- }
- } // function solve()
-
-} // PHPExcel_Shared_JAMA_class QRDecomposition
diff --git a/admin/survey/excel/PHPExcel/Shared/JAMA/SingularValueDecomposition.php b/admin/survey/excel/PHPExcel/Shared/JAMA/SingularValueDecomposition.php deleted file mode 100644 index ecfb0cd..0000000 --- a/admin/survey/excel/PHPExcel/Shared/JAMA/SingularValueDecomposition.php +++ /dev/null @@ -1,526 +0,0 @@ -<?php
-/**
- * @package JAMA
- *
- * For an m-by-n matrix A with m >= n, the singular value decomposition is
- * an m-by-n orthogonal matrix U, an n-by-n diagonal matrix S, and
- * an n-by-n orthogonal matrix V so that A = U*S*V'.
- *
- * The singular values, sigma[$k] = S[$k][$k], are ordered so that
- * sigma[0] >= sigma[1] >= ... >= sigma[n-1].
- *
- * The singular value decompostion always exists, so the constructor will
- * never fail. The matrix condition number and the effective numerical
- * rank can be computed from this decomposition.
- *
- * @author Paul Meagher
- * @license PHP v3.0
- * @version 1.1
- */
-class SingularValueDecomposition {
-
- /**
- * Internal storage of U.
- * @var array
- */
- private $U = array();
-
- /**
- * Internal storage of V.
- * @var array
- */
- private $V = array();
-
- /**
- * Internal storage of singular values.
- * @var array
- */
- private $s = array();
-
- /**
- * Row dimension.
- * @var int
- */
- private $m;
-
- /**
- * Column dimension.
- * @var int
- */
- private $n;
-
-
- /**
- * Construct the singular value decomposition
- *
- * Derived from LINPACK code.
- *
- * @param $A Rectangular matrix
- * @return Structure to access U, S and V.
- */
- public function __construct($Arg) {
-
- // Initialize.
- $A = $Arg->getArrayCopy();
- $this->m = $Arg->getRowDimension();
- $this->n = $Arg->getColumnDimension();
- $nu = min($this->m, $this->n);
- $e = array();
- $work = array();
- $wantu = true;
- $wantv = true;
- $nct = min($this->m - 1, $this->n);
- $nrt = max(0, min($this->n - 2, $this->m));
-
- // Reduce A to bidiagonal form, storing the diagonal elements
- // in s and the super-diagonal elements in e.
- for ($k = 0; $k < max($nct,$nrt); ++$k) {
-
- if ($k < $nct) {
- // Compute the transformation for the k-th column and
- // place the k-th diagonal in s[$k].
- // Compute 2-norm of k-th column without under/overflow.
- $this->s[$k] = 0;
- for ($i = $k; $i < $this->m; ++$i) {
- $this->s[$k] = hypo($this->s[$k], $A[$i][$k]);
- }
- if ($this->s[$k] != 0.0) {
- if ($A[$k][$k] < 0.0) {
- $this->s[$k] = -$this->s[$k];
- }
- for ($i = $k; $i < $this->m; ++$i) {
- $A[$i][$k] /= $this->s[$k];
- }
- $A[$k][$k] += 1.0;
- }
- $this->s[$k] = -$this->s[$k];
- }
-
- for ($j = $k + 1; $j < $this->n; ++$j) {
- if (($k < $nct) & ($this->s[$k] != 0.0)) {
- // Apply the transformation.
- $t = 0;
- for ($i = $k; $i < $this->m; ++$i) {
- $t += $A[$i][$k] * $A[$i][$j];
- }
- $t = -$t / $A[$k][$k];
- for ($i = $k; $i < $this->m; ++$i) {
- $A[$i][$j] += $t * $A[$i][$k];
- }
- // Place the k-th row of A into e for the
- // subsequent calculation of the row transformation.
- $e[$j] = $A[$k][$j];
- }
- }
-
- if ($wantu AND ($k < $nct)) {
- // Place the transformation in U for subsequent back
- // multiplication.
- for ($i = $k; $i < $this->m; ++$i) {
- $this->U[$i][$k] = $A[$i][$k];
- }
- }
-
- if ($k < $nrt) {
- // Compute the k-th row transformation and place the
- // k-th super-diagonal in e[$k].
- // Compute 2-norm without under/overflow.
- $e[$k] = 0;
- for ($i = $k + 1; $i < $this->n; ++$i) {
- $e[$k] = hypo($e[$k], $e[$i]);
- }
- if ($e[$k] != 0.0) {
- if ($e[$k+1] < 0.0) {
- $e[$k] = -$e[$k];
- }
- for ($i = $k + 1; $i < $this->n; ++$i) {
- $e[$i] /= $e[$k];
- }
- $e[$k+1] += 1.0;
- }
- $e[$k] = -$e[$k];
- if (($k+1 < $this->m) AND ($e[$k] != 0.0)) {
- // Apply the transformation.
- for ($i = $k+1; $i < $this->m; ++$i) {
- $work[$i] = 0.0;
- }
- for ($j = $k+1; $j < $this->n; ++$j) {
- for ($i = $k+1; $i < $this->m; ++$i) {
- $work[$i] += $e[$j] * $A[$i][$j];
- }
- }
- for ($j = $k + 1; $j < $this->n; ++$j) {
- $t = -$e[$j] / $e[$k+1];
- for ($i = $k + 1; $i < $this->m; ++$i) {
- $A[$i][$j] += $t * $work[$i];
- }
- }
- }
- if ($wantv) {
- // Place the transformation in V for subsequent
- // back multiplication.
- for ($i = $k + 1; $i < $this->n; ++$i) {
- $this->V[$i][$k] = $e[$i];
- }
- }
- }
- }
-
- // Set up the final bidiagonal matrix or order p.
- $p = min($this->n, $this->m + 1);
- if ($nct < $this->n) {
- $this->s[$nct] = $A[$nct][$nct];
- }
- if ($this->m < $p) {
- $this->s[$p-1] = 0.0;
- }
- if ($nrt + 1 < $p) {
- $e[$nrt] = $A[$nrt][$p-1];
- }
- $e[$p-1] = 0.0;
- // If required, generate U.
- if ($wantu) {
- for ($j = $nct; $j < $nu; ++$j) {
- for ($i = 0; $i < $this->m; ++$i) {
- $this->U[$i][$j] = 0.0;
- }
- $this->U[$j][$j] = 1.0;
- }
- for ($k = $nct - 1; $k >= 0; --$k) {
- if ($this->s[$k] != 0.0) {
- for ($j = $k + 1; $j < $nu; ++$j) {
- $t = 0;
- for ($i = $k; $i < $this->m; ++$i) {
- $t += $this->U[$i][$k] * $this->U[$i][$j];
- }
- $t = -$t / $this->U[$k][$k];
- for ($i = $k; $i < $this->m; ++$i) {
- $this->U[$i][$j] += $t * $this->U[$i][$k];
- }
- }
- for ($i = $k; $i < $this->m; ++$i ) {
- $this->U[$i][$k] = -$this->U[$i][$k];
- }
- $this->U[$k][$k] = 1.0 + $this->U[$k][$k];
- for ($i = 0; $i < $k - 1; ++$i) {
- $this->U[$i][$k] = 0.0;
- }
- } else {
- for ($i = 0; $i < $this->m; ++$i) {
- $this->U[$i][$k] = 0.0;
- }
- $this->U[$k][$k] = 1.0;
- }
- }
- }
-
- // If required, generate V.
- if ($wantv) {
- for ($k = $this->n - 1; $k >= 0; --$k) {
- if (($k < $nrt) AND ($e[$k] != 0.0)) {
- for ($j = $k + 1; $j < $nu; ++$j) {
- $t = 0;
- for ($i = $k + 1; $i < $this->n; ++$i) {
- $t += $this->V[$i][$k]* $this->V[$i][$j];
- }
- $t = -$t / $this->V[$k+1][$k];
- for ($i = $k + 1; $i < $this->n; ++$i) {
- $this->V[$i][$j] += $t * $this->V[$i][$k];
- }
- }
- }
- for ($i = 0; $i < $this->n; ++$i) {
- $this->V[$i][$k] = 0.0;
- }
- $this->V[$k][$k] = 1.0;
- }
- }
-
- // Main iteration loop for the singular values.
- $pp = $p - 1;
- $iter = 0;
- $eps = pow(2.0, -52.0);
-
- while ($p > 0) {
- // Here is where a test for too many iterations would go.
- // This section of the program inspects for negligible
- // elements in the s and e arrays. On completion the
- // variables kase and k are set as follows:
- // kase = 1 if s(p) and e[k-1] are negligible and k<p
- // kase = 2 if s(k) is negligible and k<p
- // kase = 3 if e[k-1] is negligible, k<p, and
- // s(k), ..., s(p) are not negligible (qr step).
- // kase = 4 if e(p-1) is negligible (convergence).
- for ($k = $p - 2; $k >= -1; --$k) {
- if ($k == -1) {
- break;
- }
- if (abs($e[$k]) <= $eps * (abs($this->s[$k]) + abs($this->s[$k+1]))) {
- $e[$k] = 0.0;
- break;
- }
- }
- if ($k == $p - 2) {
- $kase = 4;
- } else {
- for ($ks = $p - 1; $ks >= $k; --$ks) {
- if ($ks == $k) {
- break;
- }
- $t = ($ks != $p ? abs($e[$ks]) : 0.) + ($ks != $k + 1 ? abs($e[$ks-1]) : 0.);
- if (abs($this->s[$ks]) <= $eps * $t) {
- $this->s[$ks] = 0.0;
- break;
- }
- }
- if ($ks == $k) {
- $kase = 3;
- } else if ($ks == $p-1) {
- $kase = 1;
- } else {
- $kase = 2;
- $k = $ks;
- }
- }
- ++$k;
-
- // Perform the task indicated by kase.
- switch ($kase) {
- // Deflate negligible s(p).
- case 1:
- $f = $e[$p-2];
- $e[$p-2] = 0.0;
- for ($j = $p - 2; $j >= $k; --$j) {
- $t = hypo($this->s[$j],$f);
- $cs = $this->s[$j] / $t;
- $sn = $f / $t;
- $this->s[$j] = $t;
- if ($j != $k) {
- $f = -$sn * $e[$j-1];
- $e[$j-1] = $cs * $e[$j-1];
- }
- if ($wantv) {
- for ($i = 0; $i < $this->n; ++$i) {
- $t = $cs * $this->V[$i][$j] + $sn * $this->V[$i][$p-1];
- $this->V[$i][$p-1] = -$sn * $this->V[$i][$j] + $cs * $this->V[$i][$p-1];
- $this->V[$i][$j] = $t;
- }
- }
- }
- break;
- // Split at negligible s(k).
- case 2:
- $f = $e[$k-1];
- $e[$k-1] = 0.0;
- for ($j = $k; $j < $p; ++$j) {
- $t = hypo($this->s[$j], $f);
- $cs = $this->s[$j] / $t;
- $sn = $f / $t;
- $this->s[$j] = $t;
- $f = -$sn * $e[$j];
- $e[$j] = $cs * $e[$j];
- if ($wantu) {
- for ($i = 0; $i < $this->m; ++$i) {
- $t = $cs * $this->U[$i][$j] + $sn * $this->U[$i][$k-1];
- $this->U[$i][$k-1] = -$sn * $this->U[$i][$j] + $cs * $this->U[$i][$k-1];
- $this->U[$i][$j] = $t;
- }
- }
- }
- break;
- // Perform one qr step.
- case 3:
- // Calculate the shift.
- $scale = max(max(max(max(
- abs($this->s[$p-1]),abs($this->s[$p-2])),abs($e[$p-2])),
- abs($this->s[$k])), abs($e[$k]));
- $sp = $this->s[$p-1] / $scale;
- $spm1 = $this->s[$p-2] / $scale;
- $epm1 = $e[$p-2] / $scale;
- $sk = $this->s[$k] / $scale;
- $ek = $e[$k] / $scale;
- $b = (($spm1 + $sp) * ($spm1 - $sp) + $epm1 * $epm1) / 2.0;
- $c = ($sp * $epm1) * ($sp * $epm1);
- $shift = 0.0;
- if (($b != 0.0) || ($c != 0.0)) {
- $shift = sqrt($b * $b + $c);
- if ($b < 0.0) {
- $shift = -$shift;
- }
- $shift = $c / ($b + $shift);
- }
- $f = ($sk + $sp) * ($sk - $sp) + $shift;
- $g = $sk * $ek;
- // Chase zeros.
- for ($j = $k; $j < $p-1; ++$j) {
- $t = hypo($f,$g);
- $cs = $f/$t;
- $sn = $g/$t;
- if ($j != $k) {
- $e[$j-1] = $t;
- }
- $f = $cs * $this->s[$j] + $sn * $e[$j];
- $e[$j] = $cs * $e[$j] - $sn * $this->s[$j];
- $g = $sn * $this->s[$j+1];
- $this->s[$j+1] = $cs * $this->s[$j+1];
- if ($wantv) {
- for ($i = 0; $i < $this->n; ++$i) {
- $t = $cs * $this->V[$i][$j] + $sn * $this->V[$i][$j+1];
- $this->V[$i][$j+1] = -$sn * $this->V[$i][$j] + $cs * $this->V[$i][$j+1];
- $this->V[$i][$j] = $t;
- }
- }
- $t = hypo($f,$g);
- $cs = $f/$t;
- $sn = $g/$t;
- $this->s[$j] = $t;
- $f = $cs * $e[$j] + $sn * $this->s[$j+1];
- $this->s[$j+1] = -$sn * $e[$j] + $cs * $this->s[$j+1];
- $g = $sn * $e[$j+1];
- $e[$j+1] = $cs * $e[$j+1];
- if ($wantu && ($j < $this->m - 1)) {
- for ($i = 0; $i < $this->m; ++$i) {
- $t = $cs * $this->U[$i][$j] + $sn * $this->U[$i][$j+1];
- $this->U[$i][$j+1] = -$sn * $this->U[$i][$j] + $cs * $this->U[$i][$j+1];
- $this->U[$i][$j] = $t;
- }
- }
- }
- $e[$p-2] = $f;
- $iter = $iter + 1;
- break;
- // Convergence.
- case 4:
- // Make the singular values positive.
- if ($this->s[$k] <= 0.0) {
- $this->s[$k] = ($this->s[$k] < 0.0 ? -$this->s[$k] : 0.0);
- if ($wantv) {
- for ($i = 0; $i <= $pp; ++$i) {
- $this->V[$i][$k] = -$this->V[$i][$k];
- }
- }
- }
- // Order the singular values.
- while ($k < $pp) {
- if ($this->s[$k] >= $this->s[$k+1]) {
- break;
- }
- $t = $this->s[$k];
- $this->s[$k] = $this->s[$k+1];
- $this->s[$k+1] = $t;
- if ($wantv AND ($k < $this->n - 1)) {
- for ($i = 0; $i < $this->n; ++$i) {
- $t = $this->V[$i][$k+1];
- $this->V[$i][$k+1] = $this->V[$i][$k];
- $this->V[$i][$k] = $t;
- }
- }
- if ($wantu AND ($k < $this->m-1)) {
- for ($i = 0; $i < $this->m; ++$i) {
- $t = $this->U[$i][$k+1];
- $this->U[$i][$k+1] = $this->U[$i][$k];
- $this->U[$i][$k] = $t;
- }
- }
- ++$k;
- }
- $iter = 0;
- --$p;
- break;
- } // end switch
- } // end while
-
- } // end constructor
-
-
- /**
- * Return the left singular vectors
- *
- * @access public
- * @return U
- */
- public function getU() {
- return new Matrix($this->U, $this->m, min($this->m + 1, $this->n));
- }
-
-
- /**
- * Return the right singular vectors
- *
- * @access public
- * @return V
- */
- public function getV() {
- return new Matrix($this->V);
- }
-
-
- /**
- * Return the one-dimensional array of singular values
- *
- * @access public
- * @return diagonal of S.
- */
- public function getSingularValues() {
- return $this->s;
- }
-
-
- /**
- * Return the diagonal matrix of singular values
- *
- * @access public
- * @return S
- */
- public function getS() {
- for ($i = 0; $i < $this->n; ++$i) {
- for ($j = 0; $j < $this->n; ++$j) {
- $S[$i][$j] = 0.0;
- }
- $S[$i][$i] = $this->s[$i];
- }
- return new Matrix($S);
- }
-
-
- /**
- * Two norm
- *
- * @access public
- * @return max(S)
- */
- public function norm2() {
- return $this->s[0];
- }
-
-
- /**
- * Two norm condition number
- *
- * @access public
- * @return max(S)/min(S)
- */
- public function cond() {
- return $this->s[0] / $this->s[min($this->m, $this->n) - 1];
- }
-
-
- /**
- * Effective numerical matrix rank
- *
- * @access public
- * @return Number of nonnegligible singular values.
- */
- public function rank() {
- $eps = pow(2.0, -52.0);
- $tol = max($this->m, $this->n) * $this->s[0] * $eps;
- $r = 0;
- for ($i = 0; $i < count($this->s); ++$i) {
- if ($this->s[$i] > $tol) {
- ++$r;
- }
- }
- return $r;
- }
-
-} // class SingularValueDecomposition
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>";
-?>
diff --git a/admin/survey/excel/PHPExcel/Shared/JAMA/tests/TestMatrix.php b/admin/survey/excel/PHPExcel/Shared/JAMA/tests/TestMatrix.php deleted file mode 100644 index cf8128b..0000000 --- a/admin/survey/excel/PHPExcel/Shared/JAMA/tests/TestMatrix.php +++ /dev/null @@ -1,415 +0,0 @@ -<?php
-
-require_once "../Matrix.php";
-
-class TestMatrix {
-
- function TestMatrix() {
-
- // define test variables
-
- $errorCount = 0;
- $warningCount = 0;
- $columnwise = array(1.,2.,3.,4.,5.,6.,7.,8.,9.,10.,11.,12.);
- $rowwise = array(1.,4.,7.,10.,2.,5.,8.,11.,3.,6.,9.,12.);
- $avals = array(array(1.,4.,7.,10.),array(2.,5.,8.,11.),array(3.,6.,9.,12.));
- $rankdef = $avals;
- $tvals = array(array(1.,2.,3.),array(4.,5.,6.),array(7.,8.,9.),array(10.,11.,12.));
- $subavals = array(array(5.,8.,11.),array(6.,9.,12.));
- $rvals = array(array(1.,4.,7.),array(2.,5.,8.,11.),array(3.,6.,9.,12.));
- $pvals = array(array(1.,1.,1.),array(1.,2.,3.),array(1.,3.,6.));
- $ivals = array(array(1.,0.,0.,0.),array(0.,1.,0.,0.),array(0.,0.,1.,0.));
- $evals = array(array(0.,1.,0.,0.),array(1.,0.,2.e-7,0.),array(0.,-2.e-7,0.,1.),array(0.,0.,1.,0.));
- $square = array(array(166.,188.,210.),array(188.,214.,240.),array(210.,240.,270.));
- $sqSolution = array(array(13.),array(15.));
- $condmat = array(array(1.,3.),array(7.,9.));
- $rows = 3;
- $cols = 4;
- $invalidID = 5; /* should trigger bad shape for construction with val */
- $raggedr = 0; /* (raggedr,raggedc) should be out of bounds in ragged array */
- $raggedc = 4;
- $validID = 3; /* leading dimension of intended test Matrices */
- $nonconformld = 4; /* leading dimension which is valid, but nonconforming */
- $ib = 1; /* index ranges for sub Matrix */
- $ie = 2;
- $jb = 1;
- $je = 3;
- $rowindexset = array(1,2);
- $badrowindexset = array(1,3);
- $columnindexset = array(1,2,3);
- $badcolumnindexset = array(1,2,4);
- $columnsummax = 33.;
- $rowsummax = 30.;
- $sumofdiagonals = 15;
- $sumofsquares = 650;
-
- /**
- * Test matrix methods
- */
-
- /**
- * Constructors and constructor-like methods:
- *
- * Matrix(double[], int)
- * Matrix(double[][])
- * Matrix(int, int)
- * Matrix(int, int, double)
- * Matrix(int, int, double[][])
- * constructWithCopy(double[][])
- * random(int,int)
- * identity(int)
- */
- echo "<p>Testing constructors and constructor-like methods...</p>";
-
- $A = new Matrix($columnwise, 3);
- if($A instanceof Matrix) {
- $this->try_success("Column-packed constructor...");
- } else
- $errorCount = $this->try_failure($errorCount, "Column-packed constructor...", "Unable to construct Matrix");
-
- $T = new Matrix($tvals);
- if($T instanceof Matrix)
- $this->try_success("2D array constructor...");
- else
- $errorCount = $this->try_failure($errorCount, "2D array constructor...", "Unable to construct Matrix");
-
- $A = new Matrix($columnwise, $validID);
- $B = new Matrix($avals);
- $tmp = $B->get(0,0);
- $avals[0][0] = 0.0;
- $C = $B->minus($A);
- $avals[0][0] = $tmp;
- $B = Matrix::constructWithCopy($avals);
- $tmp = $B->get(0,0);
- $avals[0][0] = 0.0;
- /** check that constructWithCopy behaves properly **/
- if ( ( $tmp - $B->get(0,0) ) != 0.0 )
- $errorCount = $this->try_failure($errorCount,"constructWithCopy... ","copy not effected... data visible outside");
- else
- $this->try_success("constructWithCopy... ","");
-
- $I = new Matrix($ivals);
- if ( $this->checkMatrices($I,Matrix::identity(3,4)) )
- $this->try_success("identity... ","");
- else
- $errorCount = $this->try_failure($errorCount,"identity... ","identity Matrix not successfully created");
-
- /**
- * Access Methods:
- *
- * getColumnDimension()
- * getRowDimension()
- * getArray()
- * getArrayCopy()
- * getColumnPackedCopy()
- * getRowPackedCopy()
- * get(int,int)
- * getMatrix(int,int,int,int)
- * getMatrix(int,int,int[])
- * getMatrix(int[],int,int)
- * getMatrix(int[],int[])
- * set(int,int,double)
- * setMatrix(int,int,int,int,Matrix)
- * setMatrix(int,int,int[],Matrix)
- * setMatrix(int[],int,int,Matrix)
- * setMatrix(int[],int[],Matrix)
- */
- print "<p>Testing access methods...</p>";
-
- $B = new Matrix($avals);
- if($B->getRowDimension() == $rows)
- $this->try_success("getRowDimension...");
- else
- $errorCount = $this->try_failure($errorCount, "getRowDimension...");
-
- if($B->getColumnDimension() == $cols)
- $this->try_success("getColumnDimension...");
- else
- $errorCount = $this->try_failure($errorCount, "getColumnDimension...");
-
- $barray = $B->getArray();
- if($this->checkArrays($barray, $avals))
- $this->try_success("getArray...");
- else
- $errorCount = $this->try_failure($errorCount, "getArray...");
-
- $bpacked = $B->getColumnPackedCopy();
- if($this->checkArrays($bpacked, $columnwise))
- $this->try_success("getColumnPackedCopy...");
- else
- $errorCount = $this->try_failure($errorCount, "getColumnPackedCopy...");
-
- $bpacked = $B->getRowPackedCopy();
- if($this->checkArrays($bpacked, $rowwise))
- $this->try_success("getRowPackedCopy...");
- else
- $errorCount = $this->try_failure($errorCount, "getRowPackedCopy...");
-
- /**
- * Array-like methods:
- * minus
- * minusEquals
- * plus
- * plusEquals
- * arrayLeftDivide
- * arrayLeftDivideEquals
- * arrayRightDivide
- * arrayRightDivideEquals
- * arrayTimes
- * arrayTimesEquals
- * uminus
- */
- print "<p>Testing array-like methods...</p>";
-
- /**
- * I/O methods:
- * read
- * print
- * serializable:
- * writeObject
- * readObject
- */
- print "<p>Testing I/O methods...</p>";
-
- /**
- * Test linear algebra methods
- */
- echo "<p>Testing linear algebra methods...<p>";
-
- $A = new Matrix($columnwise, 3);
- if( $this->checkMatrices($A->transpose(), $T) )
- $this->try_success("Transpose check...");
- else
- $errorCount = $this->try_failure($errorCount, "Transpose check...", "Matrices are not equal");
-
- if($this->checkScalars($A->norm1(), $columnsummax))
- $this->try_success("Maximum column sum...");
- else
- $errorCount = $this->try_failure($errorCount, "Maximum column sum...", "Incorrect: " . $A->norm1() . " != " . $columnsummax);
-
- if($this->checkScalars($A->normInf(), $rowsummax))
- $this->try_success("Maximum row sum...");
- else
- $errorCount = $this->try_failure($errorCount, "Maximum row sum...", "Incorrect: " . $A->normInf() . " != " . $rowsummax );
-
- if($this->checkScalars($A->normF(), sqrt($sumofsquares)))
- $this->try_success("Frobenius norm...");
- else
- $errorCount = $this->try_failure($errorCount, "Frobenius norm...", "Incorrect:" . $A->normF() . " != " . sqrt($sumofsquares));
-
- if($this->checkScalars($A->trace(), $sumofdiagonals))
- $this->try_success("Matrix trace...");
- else
- $errorCount = $this->try_failure($errorCount, "Matrix trace...", "Incorrect: " . $A->trace() . " != " . $sumofdiagonals);
-
- $B = $A->getMatrix(0, $A->getRowDimension(), 0, $A->getRowDimension());
- if( $B->det() == 0 )
- $this->try_success("Matrix determinant...");
- else
- $errorCount = $this->try_failure($errorCount, "Matrix determinant...", "Incorrect: " . $B->det() . " != " . 0);
-
- $A = new Matrix($columnwise,3);
- $SQ = new Matrix($square);
- if ($this->checkMatrices($SQ, $A->times($A->transpose())))
- $this->try_success("times(Matrix)...");
- else {
- $errorCount = $this->try_failure($errorCount, "times(Matrix)...", "Unable to multiply matrices");
- $SQ->toHTML();
- $AT->toHTML();
- }
-
- $A = new Matrix($columnwise, 4);
-
- $QR = $A->qr();
- $R = $QR->getR();
- $Q = $QR->getQ();
- if($this->checkMatrices($A, $Q->times($R)))
- $this->try_success("QRDecomposition...","");
- else
- $errorCount = $this->try_failure($errorCount,"QRDecomposition...","incorrect qr decomposition calculation");
-
- $A = new Matrix($columnwise, 4);
- $SVD = $A->svd();
- $U = $SVD->getU();
- $S = $SVD->getS();
- $V = $SVD->getV();
- if ($this->checkMatrices($A, $U->times($S->times($V->transpose()))))
- $this->try_success("SingularValueDecomposition...","");
- else
- $errorCount = $this->try_failure($errorCount,"SingularValueDecomposition...","incorrect singular value decomposition calculation");
-
- $n = $A->getColumnDimension();
- $A = $A->getMatrix(0,$n-1,0,$n-1);
- $A->set(0,0,0.);
-
- $LU = $A->lu();
- $L = $LU->getL();
- if ( $this->checkMatrices($A->getMatrix($LU->getPivot(),0,$n-1), $L->times($LU->getU())) )
- $this->try_success("LUDecomposition...","");
- else
- $errorCount = $this->try_failure($errorCount,"LUDecomposition...","incorrect LU decomposition calculation");
-
- $X = $A->inverse();
- if ( $this->checkMatrices($A->times($X),Matrix::identity(3,3)) )
- $this->try_success("inverse()...","");
- else
- $errorCount = $this->try_failure($errorCount, "inverse()...","incorrect inverse calculation");
-
- $DEF = new Matrix($rankdef);
- if($this->checkScalars($DEF->rank(), min($DEF->getRowDimension(), $DEF->getColumnDimension())-1))
- $this->try_success("Rank...");
- else
- $this->try_failure("Rank...", "incorrect rank calculation");
-
- $B = new Matrix($condmat);
- $SVD = $B->svd();
- $singularvalues = $SVD->getSingularValues();
- if($this->checkScalars($B->cond(), $singularvalues[0]/$singularvalues[min($B->getRowDimension(), $B->getColumnDimension())-1]))
- $this->try_success("Condition number...");
- else
- $this->try_failure("Condition number...", "incorrect condition number calculation");
-
- $SUB = new Matrix($subavals);
- $O = new Matrix($SUB->getRowDimension(),1,1.0);
- $SOL = new Matrix($sqSolution);
- $SQ = $SUB->getMatrix(0,$SUB->getRowDimension()-1,0,$SUB->getRowDimension()-1);
- if ( $this->checkMatrices($SQ->solve($SOL),$O) )
- $this->try_success("solve()...","");
- else
- $errorCount = $this->try_failure($errorCount,"solve()...","incorrect lu solve calculation");
-
- $A = new Matrix($pvals);
- $Chol = $A->chol();
- $L = $Chol->getL();
- if ( $this->checkMatrices($A, $L->times($L->transpose())) )
- $this->try_success("CholeskyDecomposition...","");
- else
- $errorCount = $this->try_failure($errorCount,"CholeskyDecomposition...","incorrect Cholesky decomposition calculation");
-
- $X = $Chol->solve(Matrix::identity(3,3));
- if ( $this->checkMatrices($A->times($X), Matrix::identity(3,3)) )
- $this->try_success("CholeskyDecomposition solve()...","");
- else
- $errorCount = $this->try_failure($errorCount,"CholeskyDecomposition solve()...","incorrect Choleskydecomposition solve calculation");
-
- $Eig = $A->eig();
- $D = $Eig->getD();
- $V = $Eig->getV();
- if( $this->checkMatrices($A->times($V),$V->times($D)) )
- $this->try_success("EigenvalueDecomposition (symmetric)...","");
- else
- $errorCount = $this->try_failure($errorCount,"EigenvalueDecomposition (symmetric)...","incorrect symmetric Eigenvalue decomposition calculation");
-
- $A = new Matrix($evals);
- $Eig = $A->eig();
- $D = $Eig->getD();
- $V = $Eig->getV();
- if ( $this->checkMatrices($A->times($V),$V->times($D)) )
- $this->try_success("EigenvalueDecomposition (nonsymmetric)...","");
- else
- $errorCount = $this->try_failure($errorCount,"EigenvalueDecomposition (nonsymmetric)...","incorrect nonsymmetric Eigenvalue decomposition calculation");
-
- print("<b>{$errorCount} total errors</b>.");
- }
-
- /**
- * Print appropriate messages for successful outcome try
- * @param string $s
- * @param string $e
- */
- function try_success($s, $e = "") {
- print "> ". $s ."success<br />";
- if ($e != "")
- print "> Message: ". $e ."<br />";
- }
-
- /**
- * Print appropriate messages for unsuccessful outcome try
- * @param int $count
- * @param string $s
- * @param string $e
- * @return int incremented counter
- */
- function try_failure($count, $s, $e="") {
- print "> ". $s ."*** failure ***<br />> Message: ". $e ."<br />";
- return ++$count;
- }
-
- /**
- * Print appropriate messages for unsuccessful outcome try
- * @param int $count
- * @param string $s
- * @param string $e
- * @return int incremented counter
- */
- function try_warning($count, $s, $e="") {
- print "> ". $s ."*** warning ***<br />> Message: ". $e ."<br />";
- return ++$count;
- }
-
- /**
- * Check magnitude of difference of "scalars".
- * @param float $x
- * @param float $y
- */
- function checkScalars($x, $y) {
- $eps = pow(2.0,-52.0);
- if ($x == 0 & abs($y) < 10*$eps) return;
- if ($y == 0 & abs($x) < 10*$eps) return;
- if (abs($x-$y) > 10 * $eps * max(abs($x),abs($y)))
- return false;
- else
- return true;
- }
-
- /**
- * Check norm of difference of "vectors".
- * @param float $x[]
- * @param float $y[]
- */
- function checkVectors($x, $y) {
- $nx = count($x);
- $ny = count($y);
- if ($nx == $ny)
- for($i=0; $i < $nx; ++$i)
- $this->checkScalars($x[$i],$y[$i]);
- else
- die("Attempt to compare vectors of different lengths");
- }
-
- /**
- * Check norm of difference of "arrays".
- * @param float $x[][]
- * @param float $y[][]
- */
- function checkArrays($x, $y) {
- $A = new Matrix($x);
- $B = new Matrix($y);
- return $this->checkMatrices($A,$B);
- }
-
- /**
- * Check norm of difference of "matrices".
- * @param matrix $X
- * @param matrix $Y
- */
- function checkMatrices($X = null, $Y = null) {
- if( $X == null || $Y == null )
- return false;
-
- $eps = pow(2.0,-52.0);
- if ($X->norm1() == 0. & $Y->norm1() < 10*$eps) return true;
- if ($Y->norm1() == 0. & $X->norm1() < 10*$eps) return true;
-
- $A = $X->minus($Y);
-
- if ($A->norm1() > 1000 * $eps * max($X->norm1(),$Y->norm1()))
- die("The norm of (X-Y) is too large: ".$A->norm1());
- else
- return true;
- }
-
-}
-
-$test = new TestMatrix;
-?>
diff --git a/admin/survey/excel/PHPExcel/Shared/JAMA/utils/Error.php b/admin/survey/excel/PHPExcel/Shared/JAMA/utils/Error.php deleted file mode 100644 index c895aa9..0000000 --- a/admin/survey/excel/PHPExcel/Shared/JAMA/utils/Error.php +++ /dev/null @@ -1,82 +0,0 @@ -<?php
-/**
- * @package JAMA
- *
- * Error handling
- * @author Michael Bommarito
- * @version 01292005
- */
-
-//Language constant
-define('JAMALANG', 'EN');
-
-
-//All errors may be defined by the following format:
-//define('ExceptionName', N);
-//$error['lang'][ExceptionName] = 'Error message';
-$error = array();
-
-/*
-I've used Babelfish and a little poor knowledge of Romance/Germanic languages for the translations here.
-Feel free to correct anything that looks amiss to you.
-*/
-
-define('PolymorphicArgumentException', -1);
-$error['EN'][PolymorphicArgumentException] = "Invalid argument pattern for polymorphic function.";
-$error['FR'][PolymorphicArgumentException] = "Modèle inadmissible d'argument pour la fonction polymorphe.".
-$error['DE'][PolymorphicArgumentException] = "Unzulässiges Argumentmuster für polymorphe Funktion.";
-
-define('ArgumentTypeException', -2);
-$error['EN'][ArgumentTypeException] = "Invalid argument type.";
-$error['FR'][ArgumentTypeException] = "Type inadmissible d'argument.";
-$error['DE'][ArgumentTypeException] = "Unzulässige Argumentart.";
-
-define('ArgumentBoundsException', -3);
-$error['EN'][ArgumentBoundsException] = "Invalid argument range.";
-$error['FR'][ArgumentBoundsException] = "Gamme inadmissible d'argument.";
-$error['DE'][ArgumentBoundsException] = "Unzulässige Argumentstrecke.";
-
-define('MatrixDimensionException', -4);
-$error['EN'][MatrixDimensionException] = "Matrix dimensions are not equal.";
-$error['FR'][MatrixDimensionException] = "Les dimensions de Matrix ne sont pas égales.";
-$error['DE'][MatrixDimensionException] = "Matrixmaße sind nicht gleich.";
-
-define('PrecisionLossException', -5);
-$error['EN'][PrecisionLossException] = "Significant precision loss detected.";
-$error['FR'][PrecisionLossException] = "Perte significative de précision détectée.";
-$error['DE'][PrecisionLossException] = "Bedeutender Präzision Verlust ermittelte.";
-
-define('MatrixSPDException', -6);
-$error['EN'][MatrixSPDException] = "Can only perform operation on symmetric positive definite matrix.";
-$error['FR'][MatrixSPDException] = "Perte significative de précision détectée.";
-$error['DE'][MatrixSPDException] = "Bedeutender Präzision Verlust ermittelte.";
-
-define('MatrixSingularException', -7);
-$error['EN'][MatrixSingularException] = "Can only perform operation on singular matrix.";
-
-define('MatrixRankException', -8);
-$error['EN'][MatrixRankException] = "Can only perform operation on full-rank matrix.";
-
-define('ArrayLengthException', -9);
-$error['EN'][ArrayLengthException] = "Array length must be a multiple of m.";
-
-define('RowLengthException', -10);
-$error['EN'][RowLengthException] = "All rows must have the same length.";
-
-/**
- * Custom error handler
- * @param int $num Error number
- */
-function JAMAError($errorNumber = null) {
- global $error;
-
- if (isset($errorNumber)) {
- if (isset($error[JAMALANG][$errorNumber])) {
- return $error[JAMALANG][$errorNumber];
- } else {
- return $error['EN'][$errorNumber];
- }
- } else {
- return ("Invalid argument to JAMAError()");
- }
-}
diff --git a/admin/survey/excel/PHPExcel/Shared/JAMA/utils/Maths.php b/admin/survey/excel/PHPExcel/Shared/JAMA/utils/Maths.php deleted file mode 100644 index 5488e00..0000000 --- a/admin/survey/excel/PHPExcel/Shared/JAMA/utils/Maths.php +++ /dev/null @@ -1,43 +0,0 @@ -<?php
-/**
- * @package JAMA
- *
- * Pythagorean Theorem:
- *
- * a = 3
- * b = 4
- * r = sqrt(square(a) + square(b))
- * r = 5
- *
- * r = sqrt(a^2 + b^2) without under/overflow.
- */
-function hypo($a, $b) {
- if (abs($a) > abs($b)) {
- $r = $b / $a;
- $r = abs($a) * sqrt(1 + $r * $r);
- } elseif ($b != 0) {
- $r = $a / $b;
- $r = abs($b) * sqrt(1 + $r * $r);
- } else {
- $r = 0.0;
- }
- return $r;
-} // function hypo()
-
-
-/**
- * Mike Bommarito's version.
- * Compute n-dimensional hyotheneuse.
- *
-function hypot() {
- $s = 0;
- foreach (func_get_args() as $d) {
- if (is_numeric($d)) {
- $s += pow($d, 2);
- } else {
- throw new Exception(JAMAError(ArgumentTypeException));
- }
- }
- return sqrt($s);
-}
-*/
|