@@ -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\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\Association; |
| 6 | 6 | |
@@ -104,11 +104,11 @@ discard block |
||
| 104 | 104 | */ |
| 105 | 105 | protected function predictSample(array $sample): array |
| 106 | 106 | { |
| 107 | - $predicts = array_values(array_filter($this->getRules(), function ($rule) use ($sample) { |
|
| 107 | + $predicts = array_values(array_filter($this->getRules(), function($rule) use ($sample) { |
|
| 108 | 108 | return $this->equals($rule[self::ARRAY_KEY_ANTECEDENT], $sample); |
| 109 | 109 | })); |
| 110 | 110 | |
| 111 | - return array_map(function ($rule) { |
|
| 111 | + return array_map(function($rule) { |
|
| 112 | 112 | return $rule[self::ARRAY_KEY_CONSEQUENT]; |
| 113 | 113 | }, $predicts); |
| 114 | 114 | } |
@@ -177,7 +177,7 @@ discard block |
||
| 177 | 177 | $cardinality = count($sample); |
| 178 | 178 | $antecedents = $this->powerSet($sample); |
| 179 | 179 | |
| 180 | - return array_filter($antecedents, function ($antecedent) use ($cardinality) { |
|
| 180 | + return array_filter($antecedents, function($antecedent) use ($cardinality) { |
|
| 181 | 181 | return (count($antecedent) != $cardinality) && ($antecedent != []); |
| 182 | 182 | }); |
| 183 | 183 | } |
@@ -199,7 +199,7 @@ discard block |
||
| 199 | 199 | } |
| 200 | 200 | } |
| 201 | 201 | |
| 202 | - return array_map(function ($entry) { |
|
| 202 | + return array_map(function($entry) { |
|
| 203 | 203 | return [$entry]; |
| 204 | 204 | }, $items); |
| 205 | 205 | } |
@@ -213,7 +213,7 @@ discard block |
||
| 213 | 213 | */ |
| 214 | 214 | private function frequent(array $samples): array |
| 215 | 215 | { |
| 216 | - return array_filter($samples, function ($entry) { |
|
| 216 | + return array_filter($samples, function($entry) { |
|
| 217 | 217 | return $this->support($entry) >= $this->support; |
| 218 | 218 | }); |
| 219 | 219 | } |
@@ -287,7 +287,7 @@ discard block |
||
| 287 | 287 | */ |
| 288 | 288 | private function frequency(array $sample): int |
| 289 | 289 | { |
| 290 | - return count(array_filter($this->samples, function ($entry) use ($sample) { |
|
| 290 | + return count(array_filter($this->samples, function($entry) use ($sample) { |
|
| 291 | 291 | return $this->subset($entry, $sample); |
| 292 | 292 | })); |
| 293 | 293 | } |
@@ -302,7 +302,7 @@ discard block |
||
| 302 | 302 | */ |
| 303 | 303 | private function contains(array $system, array $set): bool |
| 304 | 304 | { |
| 305 | - return (bool) array_filter($system, function ($entry) use ($set) { |
|
| 305 | + return (bool) array_filter($system, function($entry) use ($set) { |
|
| 306 | 306 | return $this->equals($entry, $set); |
| 307 | 307 | }); |
| 308 | 308 | } |
@@ -1,6 +1,6 @@ discard block |
||
| 1 | 1 | <?php |
| 2 | 2 | |
| 3 | -declare(strict_types=1); |
|
| 3 | +declare(strict_types = 1); |
|
| 4 | 4 | |
| 5 | 5 | namespace Phpml\Math; |
| 6 | 6 | |
@@ -126,7 +126,7 @@ discard block |
||
| 126 | 126 | public function transpose(): self |
| 127 | 127 | { |
| 128 | 128 | if ($this->rows == 1) { |
| 129 | - $matrix = array_map(function ($el) { |
|
| 129 | + $matrix = array_map(function($el) { |
|
| 130 | 130 | return [$el]; |
| 131 | 131 | }, $this->matrix[0]); |
| 132 | 132 | } else { |
@@ -1,6 +1,6 @@ |
||
| 1 | 1 | <?php |
| 2 | 2 | |
| 3 | -declare(strict_types=1); |
|
| 3 | +declare(strict_types = 1); |
|
| 4 | 4 | |
| 5 | 5 | /** |
| 6 | 6 | * @package JAMA |
@@ -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. |
@@ -128,7 +128,7 @@ discard block |
||
| 128 | 128 | |
| 129 | 129 | // Always return the eigenvectors of length 1.0 |
| 130 | 130 | $vectors = new Matrix($vectors); |
| 131 | - $vectors = array_map(function ($vect) { |
|
| 131 | + $vectors = array_map(function($vect) { |
|
| 132 | 132 | $sum = 0; |
| 133 | 133 | for ($i = 0; $i < count($vect); ++$i) { |
| 134 | 134 | $sum += $vect[$i] ** 2; |
@@ -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\Distance; |
| 6 | 6 | |
@@ -18,7 +18,7 @@ discard block |
||
| 18 | 18 | throw InvalidArgumentException::arraySizeNotMatch(); |
| 19 | 19 | } |
| 20 | 20 | |
| 21 | - return array_sum(array_map(function ($m, $n) { |
|
| 21 | + return array_sum(array_map(function($m, $n) { |
|
| 22 | 22 | return abs($m - $n); |
| 23 | 23 | }, $a, $b)); |
| 24 | 24 | } |
@@ -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\FeatureExtraction; |
| 6 | 6 | |