@@ -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 @@ |
||
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 |
@@ -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\Classification\Linear; |
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; |
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\Helper; |
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; |
6 | 6 |
@@ -136,9 +136,9 @@ |
||
136 | 136 | $N_K_N = $N->multiply($K_N); |
137 | 137 | |
138 | 138 | return $K->subtract($N_K) |
139 | - ->subtract($K_N) |
|
140 | - ->add($N_K_N) |
|
141 | - ->toArray(); |
|
139 | + ->subtract($K_N) |
|
140 | + ->add($N_K_N) |
|
141 | + ->toArray(); |
|
142 | 142 | } |
143 | 143 | |
144 | 144 | /** |
@@ -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 | |
@@ -145,20 +145,20 @@ discard block |
||
145 | 145 | switch ($this->kernel) { |
146 | 146 | case self::KERNEL_LINEAR: |
147 | 147 | // k(x,y) = xT.y |
148 | - return function ($x, $y) { |
|
148 | + return function($x, $y) { |
|
149 | 149 | return Matrix::dot($x, $y)[0]; |
150 | 150 | }; |
151 | 151 | case self::KERNEL_RBF: |
152 | 152 | // k(x,y)=exp(-γ.|x-y|) where |..| is Euclidean distance |
153 | 153 | $dist = new Euclidean(); |
154 | 154 | |
155 | - return function ($x, $y) use ($dist) { |
|
155 | + return function($x, $y) use ($dist) { |
|
156 | 156 | return exp(-$this->gamma * $dist->sqDistance($x, $y)); |
157 | 157 | }; |
158 | 158 | |
159 | 159 | case self::KERNEL_SIGMOID: |
160 | 160 | // k(x,y)=tanh(γ.xT.y+c0) where c0=1 |
161 | - return function ($x, $y) { |
|
161 | + return function($x, $y) { |
|
162 | 162 | $res = Matrix::dot($x, $y)[0] + 1.0; |
163 | 163 | |
164 | 164 | return tanh($this->gamma * $res); |
@@ -168,7 +168,7 @@ discard block |
||
168 | 168 | // k(x,y)=exp(-γ.|x-y|) where |..| is Manhattan distance |
169 | 169 | $dist = new Manhattan(); |
170 | 170 | |
171 | - return function ($x, $y) use ($dist) { |
|
171 | + return function($x, $y) use ($dist) { |
|
172 | 172 | return exp(-$this->gamma * $dist->distance($x, $y)); |
173 | 173 | }; |
174 | 174 | |
@@ -192,7 +192,7 @@ discard block |
||
192 | 192 | protected function projectSample(array $pairs) : array |
193 | 193 | { |
194 | 194 | // Normalize eigenvectors by eig = eigVectors / eigValues |
195 | - $func = function ($eigVal, $eigVect) { |
|
195 | + $func = function($eigVal, $eigVect) { |
|
196 | 196 | $m = new Matrix($eigVect, false); |
197 | 197 | $a = $m->divideByScalar($eigVal)->toArray(); |
198 | 198 |
@@ -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 |