本文整理汇总了PHP中hypo函数的典型用法代码示例。如果您正苦于以下问题：PHP hypo函数的具体用法？PHP hypo怎么用？PHP hypo使用的例子？那么, 这里精选的函数代码示例或许可以为您提供帮助。
在下文中一共展示了hypo函数的6个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的PHP代码示例。
示例1: __construct
/**
* 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);
}
}示例2: QRDecomposition
/**
* QR Decomposition computed by Householder reflections.
* @param matrix $A Rectangular matrix
* @return Structure to access R and the Householder vectors and compute Q.
*/
function QRDecomposition($A)
{
if (is_a($A, '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 {
trigger_error(ArgumentTypeException, ERROR);
}
}示例3: normF
/**
* normF
* Frobenius norm
* @return float Square root of the sum of all elements squared
*/
function normF()
{
$f = 0;
for ($i = 0; $i < $this->m; $i++) {
for ($j = 0; $j < $this->n; $j++) {
$f = hypo($f, $this->A[$i][$j]);
}
}
return $f;
}示例4: normF
/**
* normF
*
* Frobenius norm
* @return float Square root of the sum of all elements squared
*/
public function normF()
{
$f = 0;
for ($i = 0; $i < $this->m; ++$i) {
for ($j = 0; $j < $this->n; ++$j) {
$f = hypo($f, $this->A[$i][$j]);
}
}
return $f;
}示例5: tql2
/**
* Symmetric tridiagonal QL algorithm.
*
* This is derived from the Algol procedures tql2, by
* Bowdler, Martin, Reinsch, and Wilkinson, Handbook for
* Auto. Comp., Vol.ii-Linear Algebra, and the corresponding
* Fortran subroutine in EISPACK.
*
* @access private
*/
private function tql2()
{
for ($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];
//.........这里部分代码省略.........
示例6: __construct
/**
* 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];
}
}
//.........这里部分代码省略.........