| 123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899100101102103104105106107108109110111112113114115116117118119120121122123124125126127128129130131132133134135136137138139140141142143144145146147148149150151152153154155156157158159160161162163164165166167168169170171172173174175176177178179180181182183184185186187188189190191192193194195196197198199200201202203204205206207208209210211212213214215216217218219220221222223224225226227228229230231232233234235236237238239240241242243244245246247248249250251252253254255256257258259260261262263264265266267268269270271272273274275276277278279280281282283284285286287288289290291292293294295296297298299300301302303304305306307308309310311312313314315316317318319320321322323324325326327328329330331332333334335336337338339340341342343344345346347348349350351352353354355356357358359360361362363364365366367368369370371372373374375376377378379380381382383384385386387388389390391392393394395396397398399400401402403404405406407408409410411412413414415416417418419420421422423424425426427428429430431432433434435436437438439440441442443444445446447448449450451452453454455456457458459460461462463464465466467468469470471472473474475476477478479480481482483484485486487488489490491492493494495496497498499500501502503504505506507508509510511512513514515516517518519520521522523524525526527528 | <?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) {                    $kase = 3;                } elseif ($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;    }}
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