@@ -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\Classification\Linear; |
| 6 | 6 | |
@@ -193,7 +193,7 @@ discard block |
||
| 193 | 193 | * The gradient of the cost function to be used with gradient descent: |
| 194 | 194 | * ∇J(x) = -(y - h(x)) = (h(x) - y) |
| 195 | 195 | */ |
| 196 | - $callback = function ($weights, $sample, $y) use ($penalty) { |
|
| 196 | + $callback = function($weights, $sample, $y) use ($penalty) { |
|
| 197 | 197 | $this->weights = $weights; |
| 198 | 198 | $hX = $this->output($sample); |
| 199 | 199 | |
@@ -224,7 +224,7 @@ discard block |
||
| 224 | 224 | * The gradient of the cost function: |
| 225 | 225 | * ∇J(x) = -(h(x) - y) . h(x) . (1 - h(x)) |
| 226 | 226 | */ |
| 227 | - $callback = function ($weights, $sample, $y) use ($penalty) { |
|
| 227 | + $callback = function($weights, $sample, $y) use ($penalty) { |
|
| 228 | 228 | $this->weights = $weights; |
| 229 | 229 | $hX = $this->output($sample); |
| 230 | 230 | |
@@ -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, $overallMean); |
@@ -1,6 +1,6 @@ |
||
| 1 | 1 | <?php |
| 2 | 2 | |
| 3 | -declare(strict_types=1); |
|
| 3 | +declare(strict_types = 1); |
|
| 4 | 4 | |
| 5 | 5 | namespace Phpml\DimensionReduction; |
| 6 | 6 | |
@@ -1,6 +1,6 @@ |
||
| 1 | 1 | <?php |
| 2 | 2 | |
| 3 | -declare(strict_types=1); |
|
| 3 | +declare(strict_types = 1); |
|
| 4 | 4 | |
| 5 | 5 | namespace Phpml\Math\Statistic; |
| 6 | 6 | |
@@ -1,6 +1,6 @@ |
||
| 1 | 1 | <?php |
| 2 | 2 | |
| 3 | -declare(strict_types=1); |
|
| 3 | +declare(strict_types = 1); |
|
| 4 | 4 | |
| 5 | 5 | namespace Phpml\NeuralNetwork\ActivationFunction; |
| 6 | 6 | |
@@ -1,6 +1,6 @@ |
||
| 1 | 1 | <?php |
| 2 | 2 | |
| 3 | -declare(strict_types=1); |
|
| 3 | +declare(strict_types = 1); |
|
| 4 | 4 | |
| 5 | 5 | namespace Phpml\Math; |
| 6 | 6 | |
@@ -1,6 +1,6 @@ discard block |
||
| 1 | 1 | <?php |
| 2 | 2 | |
| 3 | -declare(strict_types=1); |
|
| 3 | +declare(strict_types = 1); |
|
| 4 | 4 | |
| 5 | 5 | namespace Phpml\FeatureExtraction; |
| 6 | 6 | |
@@ -73,7 +73,7 @@ discard block |
||
| 73 | 73 | } |
| 74 | 74 | } |
| 75 | 75 | |
| 76 | - private function transformSample(string &$sample): void |
|
| 76 | + private function transformSample(string & $sample): void |
|
| 77 | 77 | { |
| 78 | 78 | $counts = []; |
| 79 | 79 | $tokens = $this->tokenizer->tokenize($sample); |
@@ -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\NeuralNetwork\Network; |
| 6 | 6 | |
@@ -132,7 +132,7 @@ discard block |
||
| 132 | 132 | $this->addLayer(new Layer($nodes, Input::class)); |
| 133 | 133 | } |
| 134 | 134 | |
| 135 | - private function addNeuronLayers(array $layers, ?ActivationFunction $activationFunction = null): void |
|
| 135 | + private function addNeuronLayers(array $layers, ?ActivationFunction $activationFunction = null) : void |
|
| 136 | 136 | { |
| 137 | 137 | foreach ($layers as $neurons) { |
| 138 | 138 | $this->addLayer(new Layer($neurons, Neuron::class, $activationFunction)); |
@@ -132,6 +132,9 @@ |
||
| 132 | 132 | $this->addLayer(new Layer($nodes, Input::class)); |
| 133 | 133 | } |
| 134 | 134 | |
| 135 | + /** |
|
| 136 | + * @param ActivationFunction $activationFunction |
|
| 137 | + */ |
|
| 135 | 138 | private function addNeuronLayers(array $layers, ?ActivationFunction $activationFunction = null): void |
| 136 | 139 | { |
| 137 | 140 | foreach ($layers as $neurons) { |
@@ -1,6 +1,6 @@ discard block |
||
| 1 | 1 | <?php |
| 2 | 2 | |
| 3 | -declare(strict_types=1); |
|
| 3 | +declare(strict_types = 1); |
|
| 4 | 4 | |
| 5 | 5 | namespace Phpml\SupportVectorMachine; |
| 6 | 6 | |
@@ -234,7 +234,7 @@ discard block |
||
| 234 | 234 | ); |
| 235 | 235 | } |
| 236 | 236 | |
| 237 | - private function ensureDirectorySeparator(string &$path): void |
|
| 237 | + private function ensureDirectorySeparator(string & $path): void |
|
| 238 | 238 | { |
| 239 | 239 | if (substr($path, -1) !== DIRECTORY_SEPARATOR) { |
| 240 | 240 | $path .= DIRECTORY_SEPARATOR; |