@@ -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\Classification\Ensemble; |
| 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\Classification\Ensemble; |
| 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\Classification\DecisionTree; |
| 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\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 @@ |
||
| 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 | |
@@ -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 | |
@@ -170,7 +170,7 @@ discard block |
||
| 170 | 170 | protected function runTraining(array $samples, array $targets) |
| 171 | 171 | { |
| 172 | 172 | // The cost function is the sum of squares |
| 173 | - $callback = function ($weights, $sample, $target) { |
|
| 173 | + $callback = function($weights, $sample, $target) { |
|
| 174 | 174 | $this->weights = $weights; |
| 175 | 175 | |
| 176 | 176 | $prediction = $this->outputClass($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\Classification\Linear; |
| 6 | 6 | |
@@ -64,7 +64,7 @@ discard block |
||
| 64 | 64 | protected function runTraining(array $samples, array $targets) |
| 65 | 65 | { |
| 66 | 66 | // The cost function is the sum of squares |
| 67 | - $callback = function ($weights, $sample, $target) { |
|
| 67 | + $callback = function($weights, $sample, $target) { |
|
| 68 | 68 | $this->weights = $weights; |
| 69 | 69 | |
| 70 | 70 | $output = $this->output($sample); |
@@ -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\Regression; |
| 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\Regression; |
| 6 | 6 | |