@@ -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\NeuralNetwork\Node; |
| 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\NeuralNetwork\Node; |
| 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\NeuralNetwork\Node\Neuron; |
| 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\NeuralNetwork\Training; |
| 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\NeuralNetwork\Training\Backpropagation; |
| 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\DimensionReduction; |
| 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\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 | |
@@ -161,20 +161,20 @@ discard block |
||
| 161 | 161 | switch ($this->kernel) { |
| 162 | 162 | case self::KERNEL_LINEAR: |
| 163 | 163 | // k(x,y) = xT.y |
| 164 | - return function ($x, $y) { |
|
| 164 | + return function($x, $y) { |
|
| 165 | 165 | return Matrix::dot($x, $y)[0]; |
| 166 | 166 | }; |
| 167 | 167 | case self::KERNEL_RBF: |
| 168 | 168 | // k(x,y)=exp(-γ.|x-y|) where |..| is Euclidean distance |
| 169 | 169 | $dist = new Euclidean(); |
| 170 | 170 | |
| 171 | - return function ($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, $y)[0] + 1.0; |
| 179 | 179 | |
| 180 | 180 | return tanh($this->gamma * $res); |
@@ -184,7 +184,7 @@ discard block |
||
| 184 | 184 | // k(x,y)=exp(-γ.|x-y|) where |..| is Manhattan distance |
| 185 | 185 | $dist = new Manhattan(); |
| 186 | 186 | |
| 187 | - return function ($x, $y) use ($dist) { |
|
| 187 | + return function($x, $y) use ($dist) { |
|
| 188 | 188 | return exp(-$this->gamma * $dist->distance($x, $y)); |
| 189 | 189 | }; |
| 190 | 190 | |
@@ -218,7 +218,7 @@ discard block |
||
| 218 | 218 | protected function projectSample(array $pairs): array |
| 219 | 219 | { |
| 220 | 220 | // Normalize eigenvectors by eig = eigVectors / eigValues |
| 221 | - $func = function ($eigVal, $eigVect) { |
|
| 221 | + $func = function($eigVal, $eigVect) { |
|
| 222 | 222 | $m = new Matrix($eigVect, false); |
| 223 | 223 | $a = $m->divideByScalar($eigVal)->toArray(); |
| 224 | 224 | |
@@ -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 | |
@@ -146,7 +146,7 @@ discard block |
||
| 146 | 146 | |
| 147 | 147 | // Calculate overall mean of the dataset for each column |
| 148 | 148 | $numElements = array_sum($counts); |
| 149 | - $map = function ($el) use ($numElements) { |
|
| 149 | + $map = function($el) use ($numElements) { |
|
| 150 | 150 | return $el / $numElements; |
| 151 | 151 | }; |
| 152 | 152 | $this->overallMean = array_map($map, $overallMean); |