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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 Exception; |
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8 | use Phpml\Classification\Classifier; |
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9 | use Phpml\Classification\Linear\DecisionStump; |
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10 | use Phpml\Classification\WeightedClassifier; |
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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, Trainable; |
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20 | |||
21 | /** |
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22 | * Actual labels given in the targets array |
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23 | * |
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24 | * @var array |
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25 | */ |
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26 | protected $labels = []; |
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27 | |||
28 | /** |
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29 | * @var int |
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30 | */ |
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31 | protected $sampleCount; |
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32 | |||
33 | /** |
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34 | * @var int |
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35 | */ |
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36 | protected $featureCount; |
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37 | |||
38 | /** |
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39 | * Number of maximum iterations to be done |
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40 | * |
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41 | * @var int |
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42 | */ |
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43 | protected $maxIterations; |
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44 | |||
45 | /** |
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46 | * Sample weights |
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47 | * |
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48 | * @var array |
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49 | */ |
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50 | protected $weights = []; |
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51 | |||
52 | /** |
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53 | * List of selected 'weak' classifiers |
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54 | * |
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55 | * @var array |
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56 | */ |
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57 | protected $classifiers = []; |
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58 | |||
59 | /** |
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60 | * Base classifier weights |
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61 | * |
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62 | * @var array |
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63 | */ |
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64 | protected $alpha = []; |
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65 | |||
66 | /** |
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67 | * @var string |
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68 | */ |
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69 | protected $baseClassifier = DecisionStump::class; |
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70 | |||
71 | /** |
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72 | * @var array |
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73 | */ |
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74 | protected $classifierOptions = []; |
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75 | |||
76 | /** |
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77 | * ADAptive BOOSTing (AdaBoost) is an ensemble algorithm to |
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78 | * improve classification performance of 'weak' classifiers such as |
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79 | * DecisionStump (default base classifier of AdaBoost). |
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80 | */ |
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81 | public function __construct(int $maxIterations = 50) |
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82 | { |
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83 | $this->maxIterations = $maxIterations; |
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84 | } |
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85 | |||
86 | /** |
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87 | * Sets the base classifier that will be used for boosting (default = DecisionStump) |
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88 | */ |
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89 | public function setBaseClassifier(string $baseClassifier = DecisionStump::class, array $classifierOptions = []): void |
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90 | { |
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91 | $this->baseClassifier = $baseClassifier; |
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92 | $this->classifierOptions = $classifierOptions; |
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93 | } |
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94 | |||
95 | /** |
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96 | * @throws \Exception |
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97 | */ |
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98 | public function train(array $samples, array $targets): void |
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99 | { |
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100 | // Initialize usual variables |
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101 | $this->labels = array_keys(array_count_values($targets)); |
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102 | if (count($this->labels) != 2) { |
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103 | throw new Exception('AdaBoost is a binary classifier and can classify between two classes only'); |
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104 | } |
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105 | |||
106 | // Set all target values to either -1 or 1 |
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107 | $this->labels = [ |
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108 | 1 => $this->labels[0], |
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109 | -1 => $this->labels[1], |
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110 | ]; |
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111 | foreach ($targets as $target) { |
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112 | $this->targets[] = $target == $this->labels[1] ? 1 : -1; |
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113 | } |
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114 | |||
115 | $this->samples = array_merge($this->samples, $samples); |
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116 | $this->featureCount = count($samples[0]); |
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117 | $this->sampleCount = count($this->samples); |
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118 | |||
119 | // Initialize AdaBoost parameters |
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120 | $this->weights = array_fill(0, $this->sampleCount, 1.0 / $this->sampleCount); |
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121 | $this->classifiers = []; |
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122 | $this->alpha = []; |
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123 | |||
124 | // Execute the algorithm for a maximum number of iterations |
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125 | $currIter = 0; |
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126 | while ($this->maxIterations > $currIter++) { |
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127 | // Determine the best 'weak' classifier based on current weights |
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128 | $classifier = $this->getBestClassifier(); |
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129 | $errorRate = $this->evaluateClassifier($classifier); |
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130 | |||
131 | // Update alpha & weight values at each iteration |
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132 | $alpha = $this->calculateAlpha($errorRate); |
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133 | $this->updateWeights($classifier, $alpha); |
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134 | |||
135 | $this->classifiers[] = $classifier; |
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136 | $this->alpha[] = $alpha; |
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137 | } |
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138 | } |
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139 | |||
140 | /** |
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141 | * @return mixed |
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142 | */ |
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143 | public function predictSample(array $sample) |
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144 | { |
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145 | $sum = 0; |
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146 | foreach ($this->alpha as $index => $alpha) { |
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147 | $h = $this->classifiers[$index]->predict($sample); |
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148 | $sum += $h * $alpha; |
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149 | } |
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150 | |||
151 | return $this->labels[$sum > 0 ? 1 : -1]; |
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152 | } |
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153 | |||
154 | /** |
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155 | * Returns the classifier with the lowest error rate with the |
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156 | * consideration of current sample weights |
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157 | */ |
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158 | protected function getBestClassifier(): Classifier |
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159 | { |
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160 | $ref = new ReflectionClass($this->baseClassifier); |
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161 | if ($this->classifierOptions) { |
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162 | $classifier = $ref->newInstanceArgs($this->classifierOptions); |
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163 | } else { |
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164 | $classifier = $ref->newInstance(); |
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165 | } |
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166 | |||
167 | if (is_subclass_of($classifier, WeightedClassifier::class)) { |
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168 | $classifier->setSampleWeights($this->weights); |
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169 | $classifier->train($this->samples, $this->targets); |
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170 | } else { |
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171 | [$samples, $targets] = $this->resample(); |
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172 | $classifier->train($samples, $targets); |
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173 | } |
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174 | |||
175 | return $classifier; |
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176 | } |
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177 | |||
178 | /** |
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179 | * Resamples the dataset in accordance with the weights and |
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180 | * returns the new dataset |
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181 | */ |
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182 | protected function resample(): array |
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183 | { |
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184 | $weights = $this->weights; |
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185 | $std = StandardDeviation::population($weights); |
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186 | $mean = Mean::arithmetic($weights); |
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187 | $min = min($weights); |
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188 | $minZ = (int) round(($min - $mean) / $std); |
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189 | |||
190 | $samples = []; |
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191 | $targets = []; |
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192 | foreach ($weights as $index => $weight) { |
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193 | $z = (int) round(($weight - $mean) / $std) - $minZ + 1; |
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194 | for ($i = 0; $i < $z; ++$i) { |
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195 | if (random_int(0, 1) == 0) { |
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196 | continue; |
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197 | } |
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198 | |||
199 | $samples[] = $this->samples[$index]; |
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200 | $targets[] = $this->targets[$index]; |
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201 | } |
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202 | } |
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203 | |||
204 | return [$samples, $targets]; |
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205 | } |
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206 | |||
207 | /** |
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208 | * Evaluates the classifier and returns the classification error rate |
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209 | */ |
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210 | protected function evaluateClassifier(Classifier $classifier): float |
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211 | { |
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212 | $total = (float) array_sum($this->weights); |
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213 | $wrong = 0; |
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214 | foreach ($this->samples as $index => $sample) { |
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215 | $predicted = $classifier->predict($sample); |
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216 | if ($predicted != $this->targets[$index]) { |
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217 | $wrong += $this->weights[$index]; |
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218 | } |
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219 | } |
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220 | |||
221 | return $wrong / $total; |
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222 | } |
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223 | |||
224 | /** |
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225 | * Calculates alpha of a classifier |
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226 | */ |
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227 | protected function calculateAlpha(float $errorRate): float |
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228 | { |
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229 | if ($errorRate == 0) { |
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230 | $errorRate = 1e-10; |
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231 | } |
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232 | |||
233 | return 0.5 * log((1 - $errorRate) / $errorRate); |
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234 | } |
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235 | |||
236 | /** |
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237 | * Updates the sample weights |
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238 | */ |
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239 | protected function updateWeights(Classifier $classifier, float $alpha): void |
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240 | { |
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241 | $sumOfWeights = array_sum($this->weights); |
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242 | $weightsT1 = []; |
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243 | foreach ($this->weights as $index => $weight) { |
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244 | $desired = $this->targets[$index]; |
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245 | $output = $classifier->predict($this->samples[$index]); |
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246 | |||
247 | $weight *= exp(-$alpha * $desired * $output) / $sumOfWeights; |
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248 | |||
249 | $weightsT1[] = $weight; |
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250 | } |
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251 | |||
252 | $this->weights = $weightsT1; |
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253 | } |
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254 | } |
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255 |
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.