@@ -1,6 +1,6 @@ |
||
| 1 | 1 | <?php |
| 2 | 2 | |
| 3 | -declare(strict_types=1); |
|
| 3 | +declare(strict_types = 1); |
|
| 4 | 4 | |
| 5 | 5 | namespace Phpml\Helper; |
| 6 | 6 | |
@@ -1,6 +1,6 @@ |
||
| 1 | 1 | <?php |
| 2 | 2 | |
| 3 | -declare(strict_types=1); |
|
| 3 | +declare(strict_types = 1); |
|
| 4 | 4 | |
| 5 | 5 | namespace Phpml; |
| 6 | 6 | |
@@ -1,6 +1,6 @@ |
||
| 1 | 1 | <?php |
| 2 | 2 | |
| 3 | -declare(strict_types=1); |
|
| 3 | +declare(strict_types = 1); |
|
| 4 | 4 | /** |
| 5 | 5 | * @package JAMA |
| 6 | 6 | * |
@@ -1,6 +1,6 @@ discard block |
||
| 1 | 1 | <?php |
| 2 | 2 | |
| 3 | -declare(strict_types=1); |
|
| 3 | +declare(strict_types = 1); |
|
| 4 | 4 | /** |
| 5 | 5 | * |
| 6 | 6 | * Class to obtain eigenvalues and eigenvectors of a real matrix. |
@@ -838,7 +838,7 @@ discard block |
||
| 838 | 838 | |
| 839 | 839 | // Always return the eigenvectors of length 1.0 |
| 840 | 840 | $vectors = new Matrix($vectors); |
| 841 | - $vectors = array_map(function ($vect) { |
|
| 841 | + $vectors = array_map(function($vect) { |
|
| 842 | 842 | $sum = 0; |
| 843 | 843 | for ($i = 0; $i < count($vect); ++$i) { |
| 844 | 844 | $sum += $vect[$i] ** 2; |
@@ -1,6 +1,6 @@ |
||
| 1 | 1 | <?php |
| 2 | 2 | |
| 3 | -declare(strict_types=1); |
|
| 3 | +declare(strict_types = 1); |
|
| 4 | 4 | |
| 5 | 5 | namespace Phpml\Math\Statistic; |
| 6 | 6 | |
@@ -1,6 +1,6 @@ discard block |
||
| 1 | 1 | <?php |
| 2 | 2 | |
| 3 | -declare(strict_types=1); |
|
| 3 | +declare(strict_types = 1); |
|
| 4 | 4 | |
| 5 | 5 | namespace Phpml\Math; |
| 6 | 6 | |
@@ -157,7 +157,7 @@ discard block |
||
| 157 | 157 | public function transpose() |
| 158 | 158 | { |
| 159 | 159 | if ($this->rows == 1) { |
| 160 | - $matrix = array_map(function ($el) { |
|
| 160 | + $matrix = array_map(function($el) { |
|
| 161 | 161 | return [$el]; |
| 162 | 162 | }, $this->matrix[0]); |
| 163 | 163 | } else { |
@@ -1,6 +1,6 @@ |
||
| 1 | 1 | <?php |
| 2 | 2 | |
| 3 | -declare(strict_types=1); |
|
| 3 | +declare(strict_types = 1); |
|
| 4 | 4 | |
| 5 | 5 | namespace Phpml\DimensionReduction; |
| 6 | 6 | |
@@ -1,6 +1,6 @@ discard block |
||
| 1 | 1 | <?php |
| 2 | 2 | |
| 3 | -declare(strict_types=1); |
|
| 3 | +declare(strict_types = 1); |
|
| 4 | 4 | |
| 5 | 5 | namespace Phpml\DimensionReduction; |
| 6 | 6 | |
@@ -133,7 +133,7 @@ discard block |
||
| 133 | 133 | */ |
| 134 | 134 | protected function centerMatrix(array $matrix, int $n) |
| 135 | 135 | { |
| 136 | - $N = array_fill(0, $n, array_fill(0, $n, 1.0/$n)); |
|
| 136 | + $N = array_fill(0, $n, array_fill(0, $n, 1.0 / $n)); |
|
| 137 | 137 | $N = new Matrix($N, false); |
| 138 | 138 | $K = new Matrix($matrix, false); |
| 139 | 139 | |
@@ -162,19 +162,19 @@ discard block |
||
| 162 | 162 | switch ($this->kernel) { |
| 163 | 163 | case self::KERNEL_LINEAR: |
| 164 | 164 | // k(x,y) = xT.y |
| 165 | - return function ($x, $y) { |
|
| 165 | + return function($x, $y) { |
|
| 166 | 166 | return Matrix::dot($x, $y)[0]; |
| 167 | 167 | }; |
| 168 | 168 | case self::KERNEL_RBF: |
| 169 | 169 | // k(x,y)=exp(-γ.|x-y|) where |..| is Euclidean distance |
| 170 | 170 | $dist = new Euclidean(); |
| 171 | - return function ($x, $y) use ($dist) { |
|
| 171 | + return function($x, $y) use ($dist) { |
|
| 172 | 172 | return exp(-$this->gamma * $dist->sqDistance($x, $y)); |
| 173 | 173 | }; |
| 174 | 174 | |
| 175 | 175 | case self::KERNEL_SIGMOID: |
| 176 | 176 | // k(x,y)=tanh(γ.xT.y+c0) where c0=1 |
| 177 | - return function ($x, $y) { |
|
| 177 | + return function($x, $y) { |
|
| 178 | 178 | $res = Matrix::dot($x, $y)[0] + 1.0; |
| 179 | 179 | return tanh($this->gamma * $res); |
| 180 | 180 | }; |
@@ -182,7 +182,7 @@ discard block |
||
| 182 | 182 | case self::KERNEL_LAPLACIAN: |
| 183 | 183 | // k(x,y)=exp(-γ.|x-y|) where |..| is Manhattan distance |
| 184 | 184 | $dist = new Manhattan(); |
| 185 | - return function ($x, $y) use ($dist) { |
|
| 185 | + return function($x, $y) use ($dist) { |
|
| 186 | 186 | return exp(-$this->gamma * $dist->distance($x, $y)); |
| 187 | 187 | }; |
| 188 | 188 | |
@@ -216,7 +216,7 @@ discard block |
||
| 216 | 216 | protected function projectSample(array $pairs) |
| 217 | 217 | { |
| 218 | 218 | // Normalize eigenvectors by eig = eigVectors / eigValues |
| 219 | - $func = function ($eigVal, $eigVect) { |
|
| 219 | + $func = function($eigVal, $eigVect) { |
|
| 220 | 220 | $m = new Matrix($eigVect, false); |
| 221 | 221 | $a = $m->divideByScalar($eigVal)->toArray(); |
| 222 | 222 | |
@@ -1,6 +1,6 @@ discard block |
||
| 1 | 1 | <?php |
| 2 | 2 | |
| 3 | -declare(strict_types=1); |
|
| 3 | +declare(strict_types = 1); |
|
| 4 | 4 | |
| 5 | 5 | namespace Phpml\DimensionReduction; |
| 6 | 6 | |
@@ -118,7 +118,7 @@ discard block |
||
| 118 | 118 | protected function calculateMeans(array $data, array $classes) : array |
| 119 | 119 | { |
| 120 | 120 | $means = []; |
| 121 | - $counts= []; |
|
| 121 | + $counts = []; |
|
| 122 | 122 | $overallMean = array_fill(0, count($data[0]), 0.0); |
| 123 | 123 | |
| 124 | 124 | foreach ($data as $index => $row) { |
@@ -147,7 +147,7 @@ discard block |
||
| 147 | 147 | |
| 148 | 148 | // Calculate overall mean of the dataset for each column |
| 149 | 149 | $numElements = array_sum($counts); |
| 150 | - $map = function ($el) use ($numElements) { |
|
| 150 | + $map = function($el) use ($numElements) { |
|
| 151 | 151 | return $el / $numElements; |
| 152 | 152 | }; |
| 153 | 153 | $this->overallMean = array_map($map, $overallMean); |