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Improve matrix implementations
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@ -21,8 +21,12 @@ class CholeskyDecomposition
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private $m = 0;
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/**
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* Is symmetric positive definite
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*/
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private $isSpd = true;
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// see http://www.aip.de/groups/soe/local/numres/bookfpdf/f2-9.pdf
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public function __construct(Matrix $M)
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{
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$this->L = $M->toArray();
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@ -33,14 +37,15 @@ class CholeskyDecomposition
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for ($sum = $this->L[$i][$j], $k = $i - 1; $k >= 0; --$k) {
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$sum -= $this->L[$i][$k] * $this->L[$j][$k];
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}
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if ($i == $j) {
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if ($i === $j) {
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if ($sum >= 0) {
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$this->L[$i][$i] = sqrt($sum);
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} else {
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$this->isSpd = false;
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}
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} else {
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if ($this->L[$i][$i] != 0) {
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if ($this->L[$i][$i] !== 0) {
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$this->L[$j][$i] = $sum / $this->L[$i][$i];
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}
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}
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@ -52,12 +57,12 @@ class CholeskyDecomposition
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}
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}
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public function isSpd()
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public function isSpd() : bool
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{
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return $this->isSpd;
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}
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public function getL()
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public function getL() : Matrix
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{
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$matrix = new Matrix();
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$matrix->setMatrix($this->L);
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@ -65,41 +70,44 @@ class CholeskyDecomposition
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return $matrix;
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}
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public function solve(Matrix $B)
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public function solve(Matrix $B) : Matrix
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{
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if ($B->getM() !== $this->m) {
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// invalid dimension
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throw new \Exception();
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}
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if (!$this->isSpd) {
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// is not positive definite
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throw new \Exception();
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}
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$X = $B->toArray();
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$nx = $B->getN();
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$X = $B->toArray();
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$n = $B->getN();
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for ($k = 0; $k < $this->m; ++$k) {
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for ($i = $k + 1; $i < $this->m; ++$i) {
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for ($j = 0; $j < $nx; ++$j) {
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$X[$i][$j] -= $X[$k][$j] * $this->L[$i][$k];
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// Solve L*Y = B;
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for ($k = 0; $k < $this->m; $k++) {
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for ($j = 0; $j < $n; $j++) {
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for ($i = 0; $i < $k ; $i++) {
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$X[$k][$j] -= $X[$i][$j] * $this->L[$k][$i];
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}
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}
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for ($j = 0; $j < $nx; ++$j) {
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$X[$k][$j] /= $this->L[$k][$k];
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}
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}
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for ($k = $this->m - 1; $k >= 0; --$k) {
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for ($j = 0; $j < $nx; ++$j) {
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$X[$k][$j] /= $this->L[$k][$k];
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}
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for ($i = 0; $i < $k; ++$i) {
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for ($j = 0; $j < $nx; ++$j) {
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$X[$i][$j] -= $X[$k][$j] * $this->L[$k][$i];
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$X[$k][$j] /= $this->L[$k][$k];
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}
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}
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// Solve L'*X = Y;
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for ($k = $this->m - 1; $k >= 0; $k--) {
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for ($j = 0; $j < $n; $j++) {
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for ($i = $k + 1; $i < $this->m ; $i++) {
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$X[$k][$j] -= $X[$i][$j] * $this->L[$i][$k];
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}
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}
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}
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$X[$k][$j] /= $this->L[$k][$k];
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}
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}
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return new Matrix($X, $this->m, $nx);
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$solution = new Matrix();
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$solution->setMatrix($X);
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return $solution;
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}
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}
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@ -40,40 +40,41 @@ class LUDecomposition
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$LUrowi = $LUcolj = [];
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for ($j = 0; $j < $this->n; ++$j) {
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// Make a copy of the j-th column to localize references.
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for ($i = 0; $i < $this->m; ++$i) {
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$LUcolj[$i] = &$this->LU[$i][$j];
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}
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// Apply previous transformations.
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for ($i = 0; $i < $this->m; ++$i) {
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$LUrowi = $this->LU[$i];
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// Most of the time is spent in the following dot product.
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$kmax = min($i, $j);
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$s = 0.0;
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for ($k = 0; $k < $kmax; ++$k) {
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$s += $LUrowi[$k] * $LUcolj[$k];
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}
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$LUrowi[$j] = $LUcolj[$i] -= $s;
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}
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// Find pivot and exchange if necessary.
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$p = $j;
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for ($i = $j + 1; $i < $this->m; ++$i) {
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if (abs($LUcolj[$i]) > abs($LUcolj[$p])) {
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$p = $i;
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}
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}
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if ($p != $j) {
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for ($k = 0; $k < $this->n; ++$k) {
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$t = $this->LU[$p][$k];
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$this->LU[$p][$k] = $this->LU[$j][$k];
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$this->LU[$j][$k] = $t;
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}
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$k = $this->piv[$p];
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$this->piv[$p] = $this->piv[$j];
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$this->piv[$j] = $k;
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$this->pivSign = $this->pivSign * -1;
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}
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// Compute multipliers.
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if (($j < $this->m) && ($this->LU[$j][$j] != 0.0)) {
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for ($i = $j + 1; $i < $this->m; ++$i) {
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$this->LU[$i][$j] /= $this->LU[$j][$j];
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@ -82,7 +83,7 @@ class LUDecomposition
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}
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}
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public function getL()
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public function getL() : Matrix
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{
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$L = [[]];
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@ -104,7 +105,7 @@ class LUDecomposition
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return $matrix;
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}
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public function getU()
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public function getU() : Matrix
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{
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$U = [[]];
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@ -150,21 +151,25 @@ class LUDecomposition
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return $d;
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}
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public function solve(Matrix $B)
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public function solve(Matrix $B) : Matrix
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{
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if ($B->getM() !== $this->m) {
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throw new \Exception();
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}
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if (!$this->isNonsingular()) {
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throw new \Exception();
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}
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$nx = $B->getM();
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$X = $B->getMatrix($this->piv, 0, $nx - 1);
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$n = $B->getN();
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$X = $B->getMatrix($this->piv, 0, $n - 1);
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// todo: fix get extract
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// Solve L*Y = B(piv,:)
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for ($k = 0; $k < $this->n; ++$k) {
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for ($i = $k + 1; $i < $this->n; ++$i) {
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for ($j = 0; $j < $nx; ++$j) {
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for ($j = 0; $j < $n; ++$j) {
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$X[$i][$j] -= $X[$k][$j] * $this->LU[$i][$k];
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}
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}
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@ -172,16 +177,19 @@ class LUDecomposition
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// Solve U*X = Y;
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for ($k = $this->n - 1; $k >= 0; --$k) {
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for ($j = 0; $j < $nx; ++$j) {
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for ($j = 0; $j < $n; ++$j) {
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$X[$k][$j] /= $this->LU[$k][$k];
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}
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for ($i = 0; $i < $k; ++$i) {
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for ($j = 0; $j < $nx; ++$j) {
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for ($j = 0; $j < $n; ++$j) {
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$X[$i][$j] -= $X[$k][$j] * $this->LU[$i][$k];
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}
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}
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}
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return $X;
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$solution = new Matrix();
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$solution->setMatrix($X);
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return $solution;
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}
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}
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@ -33,8 +33,10 @@ class Vector extends Matrix
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*
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* @since 1.0.0
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*/
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public function __construct(int $m)
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public function __construct(int $m = 1)
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{
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parent::__construct($m);
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}
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// todo: maybe overwrite setMatrix since only one column (is only a visual improvement)
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}
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