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1 | <?php |
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2 | |||
3 | declare(strict_types=1); |
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4 | |||
5 | namespace Phpml\Classification\Ensemble; |
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6 | |||
7 | use Phpml\Classification\Classifier; |
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8 | use Phpml\Classification\Linear\DecisionStump; |
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9 | use Phpml\Classification\WeightedClassifier; |
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10 | use Phpml\Exception\InvalidArgumentException; |
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11 | use Phpml\Helper\Predictable; |
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12 | use Phpml\Helper\Trainable; |
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13 | use Phpml\Math\Statistic\Mean; |
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14 | use Phpml\Math\Statistic\StandardDeviation; |
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15 | use ReflectionClass; |
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16 | |||
17 | class AdaBoost implements Classifier |
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18 | { |
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19 | use Predictable; |
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20 | use Trainable; |
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21 | |||
22 | /** |
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23 | * Actual labels given in the targets array |
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24 | * |
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25 | * @var array |
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26 | */ |
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27 | protected $labels = []; |
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28 | |||
29 | /** |
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30 | * @var int |
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31 | */ |
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32 | protected $sampleCount; |
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33 | |||
34 | /** |
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35 | * @var int |
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36 | */ |
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37 | protected $featureCount; |
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38 | |||
39 | /** |
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40 | * Number of maximum iterations to be done |
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41 | * |
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42 | * @var int |
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43 | */ |
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44 | protected $maxIterations; |
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45 | |||
46 | /** |
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47 | * Sample weights |
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48 | * |
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49 | * @var array |
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50 | */ |
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51 | protected $weights = []; |
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52 | |||
53 | /** |
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54 | * List of selected 'weak' classifiers |
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55 | * |
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56 | * @var array |
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57 | */ |
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58 | protected $classifiers = []; |
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59 | |||
60 | /** |
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61 | * Base classifier weights |
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62 | * |
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63 | * @var array |
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64 | */ |
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65 | protected $alpha = []; |
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66 | |||
67 | /** |
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68 | * @var string |
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69 | */ |
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70 | protected $baseClassifier = DecisionStump::class; |
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71 | |||
72 | /** |
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73 | * @var array |
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74 | */ |
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75 | protected $classifierOptions = []; |
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76 | |||
77 | /** |
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78 | * ADAptive BOOSTing (AdaBoost) is an ensemble algorithm to |
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79 | * improve classification performance of 'weak' classifiers such as |
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80 | * DecisionStump (default base classifier of AdaBoost). |
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81 | */ |
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82 | public function __construct(int $maxIterations = 50) |
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83 | { |
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84 | $this->maxIterations = $maxIterations; |
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85 | } |
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86 | |||
87 | /** |
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88 | * Sets the base classifier that will be used for boosting (default = DecisionStump) |
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89 | */ |
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90 | public function setBaseClassifier(string $baseClassifier = DecisionStump::class, array $classifierOptions = []): void |
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91 | { |
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92 | $this->baseClassifier = $baseClassifier; |
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93 | $this->classifierOptions = $classifierOptions; |
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94 | } |
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95 | |||
96 | /** |
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97 | * @throws InvalidArgumentException |
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98 | */ |
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99 | public function train(array $samples, array $targets): void |
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100 | { |
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101 | // Initialize usual variables |
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102 | $this->labels = array_keys(array_count_values($targets)); |
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103 | if (count($this->labels) !== 2) { |
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104 | throw new InvalidArgumentException('AdaBoost is a binary classifier and can classify between two classes only'); |
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105 | } |
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106 | |||
107 | // Set all target values to either -1 or 1 |
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108 | $this->labels = [ |
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109 | 1 => $this->labels[0], |
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110 | -1 => $this->labels[1], |
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111 | ]; |
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112 | foreach ($targets as $target) { |
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113 | $this->targets[] = $target == $this->labels[1] ? 1 : -1; |
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114 | } |
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115 | |||
116 | $this->samples = array_merge($this->samples, $samples); |
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117 | $this->featureCount = count($samples[0]); |
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118 | $this->sampleCount = count($this->samples); |
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119 | |||
120 | // Initialize AdaBoost parameters |
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121 | $this->weights = array_fill(0, $this->sampleCount, 1.0 / $this->sampleCount); |
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122 | $this->classifiers = []; |
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123 | $this->alpha = []; |
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124 | |||
125 | // Execute the algorithm for a maximum number of iterations |
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126 | $currIter = 0; |
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127 | while ($this->maxIterations > $currIter++) { |
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128 | // Determine the best 'weak' classifier based on current weights |
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129 | $classifier = $this->getBestClassifier(); |
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130 | $errorRate = $this->evaluateClassifier($classifier); |
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131 | |||
132 | // Update alpha & weight values at each iteration |
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133 | $alpha = $this->calculateAlpha($errorRate); |
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134 | $this->updateWeights($classifier, $alpha); |
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135 | |||
136 | $this->classifiers[] = $classifier; |
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137 | $this->alpha[] = $alpha; |
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138 | } |
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139 | } |
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140 | |||
141 | /** |
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142 | * @return mixed |
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143 | */ |
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144 | public function predictSample(array $sample) |
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145 | { |
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146 | $sum = 0; |
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147 | foreach ($this->alpha as $index => $alpha) { |
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148 | $h = $this->classifiers[$index]->predict($sample); |
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149 | $sum += $h * $alpha; |
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150 | } |
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151 | |||
152 | return $this->labels[$sum > 0 ? 1 : -1]; |
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153 | } |
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154 | |||
155 | /** |
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156 | * Returns the classifier with the lowest error rate with the |
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157 | * consideration of current sample weights |
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158 | */ |
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159 | protected function getBestClassifier(): Classifier |
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160 | { |
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161 | $ref = new ReflectionClass($this->baseClassifier); |
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162 | /** @var Classifier $classifier */ |
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163 | $classifier = count($this->classifierOptions) === 0 ? $ref->newInstance() : $ref->newInstanceArgs($this->classifierOptions); |
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164 | |||
165 | if ($classifier instanceof WeightedClassifier) { |
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166 | $classifier->setSampleWeights($this->weights); |
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167 | $classifier->train($this->samples, $this->targets); |
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168 | } else { |
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169 | [$samples, $targets] = $this->resample(); |
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170 | $classifier->train($samples, $targets); |
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171 | } |
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172 | |||
173 | return $classifier; |
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174 | } |
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175 | |||
176 | /** |
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177 | * Resamples the dataset in accordance with the weights and |
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178 | * returns the new dataset |
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179 | */ |
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180 | protected function resample(): array |
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181 | { |
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182 | $weights = $this->weights; |
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183 | $std = StandardDeviation::population($weights); |
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184 | $mean = Mean::arithmetic($weights); |
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185 | $min = min($weights); |
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186 | $minZ = (int) round(($min - $mean) / $std); |
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187 | |||
188 | $samples = []; |
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189 | $targets = []; |
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190 | foreach ($weights as $index => $weight) { |
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191 | $z = (int) round(($weight - $mean) / $std) - $minZ + 1; |
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192 | for ($i = 0; $i < $z; ++$i) { |
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193 | if (random_int(0, 1) == 0) { |
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194 | continue; |
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195 | } |
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196 | |||
197 | $samples[] = $this->samples[$index]; |
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198 | $targets[] = $this->targets[$index]; |
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199 | } |
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200 | } |
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201 | |||
202 | return [$samples, $targets]; |
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203 | } |
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204 | |||
205 | /** |
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206 | * Evaluates the classifier and returns the classification error rate |
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207 | */ |
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208 | protected function evaluateClassifier(Classifier $classifier): float |
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209 | { |
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210 | $total = (float) array_sum($this->weights); |
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211 | $wrong = 0; |
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212 | foreach ($this->samples as $index => $sample) { |
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213 | $predicted = $classifier->predict($sample); |
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214 | if ($predicted != $this->targets[$index]) { |
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215 | $wrong += $this->weights[$index]; |
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216 | } |
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217 | } |
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218 | |||
219 | return $wrong / $total; |
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220 | } |
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221 | |||
222 | /** |
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223 | * Calculates alpha of a classifier |
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224 | */ |
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225 | protected function calculateAlpha(float $errorRate): float |
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226 | { |
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227 | if ($errorRate == 0) { |
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228 | $errorRate = 1e-10; |
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229 | } |
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230 | |||
231 | return 0.5 * log((1 - $errorRate) / $errorRate); |
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232 | } |
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233 | |||
234 | /** |
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235 | * Updates the sample weights |
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236 | */ |
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237 | protected function updateWeights(Classifier $classifier, float $alpha): void |
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238 | { |
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239 | $sumOfWeights = array_sum($this->weights); |
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240 | $weightsT1 = []; |
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241 | foreach ($this->weights as $index => $weight) { |
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242 | $desired = $this->targets[$index]; |
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243 | $output = $classifier->predict($this->samples[$index]); |
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244 | |||
245 | $weight *= exp(-$alpha * $desired * $output) / $sumOfWeights; |
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246 | |||
247 | $weightsT1[] = $weight; |
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248 | } |
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249 | |||
250 | $this->weights = $weightsT1; |
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251 | } |
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252 | } |
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253 |
This check marks access to variables or properties that have not been declared yet. While PHP has no explicit notion of declaring a variable, accessing it before a value is assigned to it is most likely a bug.