@@ -91,7 +91,7 @@ |
||
91 | 91 | } |
92 | 92 | |
93 | 93 | /** |
94 | - * @param $column |
|
94 | + * @param integer $column |
|
95 | 95 | * |
96 | 96 | * @return array |
97 | 97 | * |
@@ -1,6 +1,6 @@ discard block |
||
1 | 1 | <?php |
2 | 2 | |
3 | -declare(strict_types=1); |
|
3 | +declare(strict_types = 1); |
|
4 | 4 | |
5 | 5 | namespace Phpml\Math; |
6 | 6 | |
@@ -157,7 +157,7 @@ discard block |
||
157 | 157 | public function transpose() |
158 | 158 | { |
159 | 159 | if ($this->rows == 1) { |
160 | - $matrix = array_map(function ($el) { |
|
160 | + $matrix = array_map(function($el) { |
|
161 | 161 | return [$el]; |
162 | 162 | }, $this->matrix[0]); |
163 | 163 | } else { |
@@ -1,6 +1,6 @@ |
||
1 | 1 | <?php |
2 | 2 | |
3 | -declare(strict_types=1); |
|
3 | +declare(strict_types = 1); |
|
4 | 4 | /** |
5 | 5 | * @package JAMA |
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 | * |
6 | 6 | * Class to obtain eigenvalues and eigenvectors of a real matrix. |
@@ -838,7 +838,7 @@ discard block |
||
838 | 838 | |
839 | 839 | // Always return the eigenvectors of length 1.0 |
840 | 840 | $vectors = new Matrix($vectors); |
841 | - $vectors = array_map(function ($vect) { |
|
841 | + $vectors = array_map(function($vect) { |
|
842 | 842 | $sum = 0; |
843 | 843 | for ($i = 0; $i < count($vect); ++$i) { |
844 | 844 | $sum += $vect[$i] ** 2; |
@@ -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\DimensionReduction; |
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 | |
@@ -133,7 +133,7 @@ discard block |
||
133 | 133 | */ |
134 | 134 | protected function centerMatrix(array $matrix, int $n) |
135 | 135 | { |
136 | - $N = array_fill(0, $n, array_fill(0, $n, 1.0/$n)); |
|
136 | + $N = array_fill(0, $n, array_fill(0, $n, 1.0 / $n)); |
|
137 | 137 | $N = new Matrix($N, false); |
138 | 138 | $K = new Matrix($matrix, false); |
139 | 139 | |
@@ -162,19 +162,19 @@ discard block |
||
162 | 162 | switch ($this->kernel) { |
163 | 163 | case self::KERNEL_LINEAR: |
164 | 164 | // k(x,y) = xT.y |
165 | - return function ($x, $y) { |
|
165 | + return function($x, $y) { |
|
166 | 166 | return Matrix::dot($x, $y)[0]; |
167 | 167 | }; |
168 | 168 | case self::KERNEL_RBF: |
169 | 169 | // k(x,y)=exp(-γ.|x-y|) where |..| is Euclidean distance |
170 | 170 | $dist = new Euclidean(); |
171 | - return function ($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, $y)[0] + 1.0; |
179 | 179 | return tanh($this->gamma * $res); |
180 | 180 | }; |
@@ -182,7 +182,7 @@ discard block |
||
182 | 182 | case self::KERNEL_LAPLACIAN: |
183 | 183 | // k(x,y)=exp(-γ.|x-y|) where |..| is Manhattan distance |
184 | 184 | $dist = new Manhattan(); |
185 | - return function ($x, $y) use ($dist) { |
|
185 | + return function($x, $y) use ($dist) { |
|
186 | 186 | return exp(-$this->gamma * $dist->distance($x, $y)); |
187 | 187 | }; |
188 | 188 | |
@@ -216,7 +216,7 @@ discard block |
||
216 | 216 | protected function projectSample(array $pairs) |
217 | 217 | { |
218 | 218 | // Normalize eigenvectors by eig = eigVectors / eigValues |
219 | - $func = function ($eigVal, $eigVect) { |
|
219 | + $func = function($eigVal, $eigVect) { |
|
220 | 220 | $m = new Matrix($eigVect, false); |
221 | 221 | $a = $m->divideByScalar($eigVal)->toArray(); |
222 | 222 |
@@ -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 | |
@@ -118,7 +118,7 @@ discard block |
||
118 | 118 | protected function calculateMeans(array $data, array $classes) : array |
119 | 119 | { |
120 | 120 | $means = []; |
121 | - $counts= []; |
|
121 | + $counts = []; |
|
122 | 122 | $overallMean = array_fill(0, count($data[0]), 0.0); |
123 | 123 | |
124 | 124 | foreach ($data as $index => $row) { |
@@ -147,7 +147,7 @@ discard block |
||
147 | 147 | |
148 | 148 | // Calculate overall mean of the dataset for each column |
149 | 149 | $numElements = array_sum($counts); |
150 | - $map = function ($el) use ($numElements) { |
|
150 | + $map = function($el) use ($numElements) { |
|
151 | 151 | return $el / $numElements; |
152 | 152 | }; |
153 | 153 | $this->overallMean = array_map($map, $overallMean); |
@@ -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 | |
@@ -54,7 +54,7 @@ discard block |
||
54 | 54 | { |
55 | 55 | $eig = new EigenvalueDecomposition($matrix); |
56 | 56 | $eigVals = $eig->getRealEigenvalues(); |
57 | - $eigVects= $eig->getEigenvectors(); |
|
57 | + $eigVects = $eig->getEigenvectors(); |
|
58 | 58 | |
59 | 59 | $totalEigVal = array_sum($eigVals); |
60 | 60 | // Sort eigenvalues in descending order |
@@ -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; |
6 | 6 | |
@@ -13,8 +13,8 @@ discard block |
||
13 | 13 | { |
14 | 14 | use Trainable, Predictable; |
15 | 15 | |
16 | - const CONTINUOS = 1; |
|
17 | - const NOMINAL = 2; |
|
16 | + const CONTINUOS = 1; |
|
17 | + const NOMINAL = 2; |
|
18 | 18 | const EPSILON = 1e-10; |
19 | 19 | |
20 | 20 | /** |
@@ -25,7 +25,7 @@ discard block |
||
25 | 25 | /** |
26 | 26 | * @var array |
27 | 27 | */ |
28 | - private $mean= []; |
|
28 | + private $mean = []; |
|
29 | 29 | |
30 | 30 | /** |
31 | 31 | * @var array |
@@ -86,7 +86,7 @@ discard block |
||
86 | 86 | private function calculateStatistics($label, $samples) |
87 | 87 | { |
88 | 88 | $this->std[$label] = array_fill(0, $this->featureCount, 0); |
89 | - $this->mean[$label]= array_fill(0, $this->featureCount, 0); |
|
89 | + $this->mean[$label] = array_fill(0, $this->featureCount, 0); |
|
90 | 90 | $this->dataType[$label] = array_fill(0, $this->featureCount, self::CONTINUOS); |
91 | 91 | $this->discreteProb[$label] = array_fill(0, $this->featureCount, self::CONTINUOS); |
92 | 92 | for ($i = 0; $i < $this->featureCount; ++$i) { |
@@ -100,7 +100,7 @@ discard block |
||
100 | 100 | $this->dataType[$label][$i] = self::NOMINAL; |
101 | 101 | $this->discreteProb[$label][$i] = array_count_values($values); |
102 | 102 | $db = &$this->discreteProb[$label][$i]; |
103 | - $db = array_map(function ($el) use ($numValues) { |
|
103 | + $db = array_map(function($el) use ($numValues) { |
|
104 | 104 | return $el / $numValues; |
105 | 105 | }, $db); |
106 | 106 | } else { |
@@ -130,8 +130,8 @@ discard block |
||
130 | 130 | } |
131 | 131 | return $this->discreteProb[$label][$feature][$value]; |
132 | 132 | } |
133 | - $std = $this->std[$label][$feature] ; |
|
134 | - $mean= $this->mean[$label][$feature]; |
|
133 | + $std = $this->std[$label][$feature]; |
|
134 | + $mean = $this->mean[$label][$feature]; |
|
135 | 135 | // Calculate the probability density by use of normal/Gaussian distribution |
136 | 136 | // Ref: https://en.wikipedia.org/wiki/Normal_distribution |
137 | 137 | // |
@@ -139,7 +139,7 @@ discard block |
||
139 | 139 | // some libraries adopt taking log of calculations such as |
140 | 140 | // scikit-learn did. |
141 | 141 | // (See : https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/naive_bayes.py) |
142 | - $pdf = -0.5 * log(2.0 * pi() * $std * $std); |
|
142 | + $pdf = -0.5 * log(2.0 * pi() * $std * $std); |
|
143 | 143 | $pdf -= 0.5 * pow($value - $mean, 2) / ($std * $std); |
144 | 144 | return $pdf; |
145 | 145 | } |
@@ -174,7 +174,7 @@ discard block |
||
174 | 174 | $predictions = []; |
175 | 175 | foreach ($this->labels as $label) { |
176 | 176 | $p = $this->p[$label]; |
177 | - for ($i = 0; $i<$this->featureCount; ++$i) { |
|
177 | + for ($i = 0; $i < $this->featureCount; ++$i) { |
|
178 | 178 | $Plf = $this->sampleProbability($sample, $i, $label); |
179 | 179 | $p += $Plf; |
180 | 180 | } |