@@ -138,8 +138,8 @@ |
||
| 138 | 138 | } |
| 139 | 139 | |
| 140 | 140 | /** |
| 141 | - * @param array $newCoordinates |
|
| 142 | - */ |
|
| 141 | + * @param array $newCoordinates |
|
| 142 | + */ |
|
| 143 | 143 | public function setCoordinates(array $newCoordinates) |
| 144 | 144 | { |
| 145 | 145 | $this->coordinates = $newCoordinates; |
@@ -9,11 +9,11 @@ |
||
| 9 | 9 | |
| 10 | 10 | class SupportVectorMachine |
| 11 | 11 | { |
| 12 | - use Trainable; |
|
| 12 | + use Trainable; |
|
| 13 | 13 | |
| 14 | - /** |
|
| 15 | - * @var int |
|
| 16 | - */ |
|
| 14 | + /** |
|
| 15 | + * @var int |
|
| 16 | + */ |
|
| 17 | 17 | private $type; |
| 18 | 18 | |
| 19 | 19 | /** |
@@ -60,7 +60,6 @@ |
||
| 60 | 60 | * Cost function can be 'log' for log-likelihood and 'sse' for sum of squared errors <br> |
| 61 | 61 | * |
| 62 | 62 | * Penalty (Regularization term) can be 'L2' or empty string to cancel penalty term |
| 63 | - |
|
| 64 | 63 | * @param int $maxIterations |
| 65 | 64 | * @param \Phpml\Classification\Linear\type $normalizeInputs |
| 66 | 65 | * @param \Phpml\Classification\Linear\type $trainingType |
@@ -15,7 +15,7 @@ discard block |
||
| 15 | 15 | { |
| 16 | 16 | use Predictable, OneVsRest; |
| 17 | 17 | |
| 18 | - /** |
|
| 18 | + /** |
|
| 19 | 19 | * @var array |
| 20 | 20 | */ |
| 21 | 21 | protected $samples = []; |
@@ -83,7 +83,7 @@ discard block |
||
| 83 | 83 | $this->maxIterations = $maxIterations; |
| 84 | 84 | } |
| 85 | 85 | |
| 86 | - /** |
|
| 86 | + /** |
|
| 87 | 87 | * @param array $samples |
| 88 | 88 | * @param array $targets |
| 89 | 89 | */ |