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Run newest php-cs-fixer (#108)
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@ -101,7 +101,8 @@ class DecisionTree implements Classifier
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} elseif (count($this->columnNames) > $this->featureCount) {
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$this->columnNames = array_slice($this->columnNames, 0, $this->featureCount);
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} elseif (count($this->columnNames) < $this->featureCount) {
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$this->columnNames = array_merge($this->columnNames,
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$this->columnNames = array_merge(
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$this->columnNames,
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range(count($this->columnNames), $this->featureCount - 1)
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);
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}
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@ -39,9 +39,12 @@ class Adaline extends Perceptron
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*
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* @throws \Exception
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*/
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public function __construct(float $learningRate = 0.001, int $maxIterations = 1000,
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bool $normalizeInputs = true, int $trainingType = self::BATCH_TRAINING)
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{
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public function __construct(
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float $learningRate = 0.001,
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int $maxIterations = 1000,
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bool $normalizeInputs = true,
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int $trainingType = self::BATCH_TRAINING
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) {
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if (!in_array($trainingType, [self::BATCH_TRAINING, self::ONLINE_TRAINING])) {
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throw new \Exception("Adaline can only be trained with batch and online/stochastic gradient descent algorithm");
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}
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@ -67,10 +67,13 @@ class LogisticRegression extends Adaline
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*
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* @throws \Exception
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*/
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public function __construct(int $maxIterations = 500, bool $normalizeInputs = true,
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int $trainingType = self::CONJUGATE_GRAD_TRAINING, string $cost = 'sse',
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string $penalty = 'L2')
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{
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public function __construct(
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int $maxIterations = 500,
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bool $normalizeInputs = true,
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int $trainingType = self::CONJUGATE_GRAD_TRAINING,
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string $cost = 'sse',
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string $penalty = 'L2'
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) {
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$trainingTypes = range(self::BATCH_TRAINING, self::CONJUGATE_GRAD_TRAINING);
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if (!in_array($trainingType, $trainingTypes)) {
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throw new \Exception("Logistic regression can only be trained with " .
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@ -99,11 +99,11 @@ class Perceptron implements Classifier, IncrementalEstimator
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$this->trainByLabel($samples, $targets, $labels);
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}
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/**
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* @param array $samples
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* @param array $targets
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* @param array $labels
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*/
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/**
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* @param array $samples
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* @param array $targets
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* @param array $labels
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*/
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public function trainBinary(array $samples, array $targets, array $labels)
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{
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if ($this->normalizer) {
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@ -22,8 +22,14 @@ class SVC extends SupportVectorMachine implements Classifier
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* @param bool $probabilityEstimates
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*/
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public function __construct(
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int $kernel = Kernel::LINEAR, float $cost = 1.0, int $degree = 3, float $gamma = null, float $coef0 = 0.0,
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float $tolerance = 0.001, int $cacheSize = 100, bool $shrinking = true,
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int $kernel = Kernel::LINEAR,
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float $cost = 1.0,
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int $degree = 3,
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float $gamma = null,
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float $coef0 = 0.0,
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float $tolerance = 0.001,
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int $cacheSize = 100,
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bool $shrinking = true,
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bool $probabilityEstimates = false
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) {
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parent::__construct(Type::C_SVC, $kernel, $cost, 0.5, $degree, $gamma, $coef0, 0.1, $tolerance, $cacheSize, $shrinking, $probabilityEstimates);
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@ -243,7 +243,9 @@ class StochasticGD extends Optimizer
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function ($w1, $w2) {
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return abs($w1 - $w2) > $this->threshold ? 1 : 0;
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},
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$oldTheta, $this->theta);
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$oldTheta,
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$this->theta
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);
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if (array_sum($diff) == 0) {
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return true;
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@ -301,7 +301,7 @@ class EigenvalueDecomposition
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$p = -$s * $s2 * $c3 * $el1 * $this->e[$l] / $dl1;
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$this->e[$l] = $s * $p;
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$this->d[$l] = $c * $p;
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// Check for convergence.
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// Check for convergence.
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} while (abs($this->e[$l]) > $eps * $tst1);
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}
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$this->d[$l] = $this->d[$l] + $f;
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@ -493,7 +493,7 @@ class EigenvalueDecomposition
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$this->e[$n] = 0.0;
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--$n;
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$iter = 0;
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// Two roots found
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// Two roots found
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} elseif ($l == $n - 1) {
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$w = $this->H[$n][$n - 1] * $this->H[$n - 1][$n];
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$p = ($this->H[$n - 1][$n - 1] - $this->H[$n][$n]) / 2.0;
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@ -541,7 +541,7 @@ class EigenvalueDecomposition
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$this->V[$i][$n - 1] = $q * $z + $p * $this->V[$i][$n];
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$this->V[$i][$n] = $q * $this->V[$i][$n] - $p * $z;
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}
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// Complex pair
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// Complex pair
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} else {
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$this->d[$n - 1] = $x + $p;
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$this->d[$n] = $x + $p;
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@ -550,7 +550,7 @@ class EigenvalueDecomposition
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}
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$n = $n - 2;
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$iter = 0;
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// No convergence yet
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// No convergence yet
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} else {
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// Form shift
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$x = $this->H[$n][$n];
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@ -715,7 +715,7 @@ class EigenvalueDecomposition
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} else {
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$this->H[$i][$n] = -$r / ($eps * $norm);
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}
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// Solve real equations
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// Solve real equations
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} else {
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$x = $this->H[$i][$i + 1];
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$y = $this->H[$i + 1][$i];
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@ -737,7 +737,7 @@ class EigenvalueDecomposition
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}
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}
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}
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// Complex vector
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// Complex vector
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} elseif ($q < 0) {
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$l = $n - 1;
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// Last vector component imaginary so matrix is triangular
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@ -150,7 +150,12 @@ class Covariance
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$cov[$i][$k] = $cov[$k][$i];
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} else {
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$cov[$i][$k] = self::fromDataset(
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$data, $i, $k, true, $means[$i], $means[$k]
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$data,
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$i,
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$k,
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true,
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$means[$i],
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$means[$k]
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);
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}
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}
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@ -22,9 +22,15 @@ class SVR extends SupportVectorMachine implements Regression
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* @param bool $shrinking
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*/
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public function __construct(
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int $kernel = Kernel::RBF, int $degree = 3, float $epsilon = 0.1, float $cost = 1.0,
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float $gamma = null, float $coef0 = 0.0, float $tolerance = 0.001,
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int $cacheSize = 100, bool $shrinking = true
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int $kernel = Kernel::RBF,
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int $degree = 3,
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float $epsilon = 0.1,
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float $cost = 1.0,
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float $gamma = null,
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float $coef0 = 0.0,
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float $tolerance = 0.001,
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int $cacheSize = 100,
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bool $shrinking = true
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) {
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parent::__construct(Type::EPSILON_SVR, $kernel, $cost, 0.5, $degree, $gamma, $coef0, $epsilon, $tolerance, $cacheSize, $shrinking, false);
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}
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@ -105,9 +105,18 @@ class SupportVectorMachine
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* @param bool $probabilityEstimates
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*/
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public function __construct(
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int $type, int $kernel, float $cost = 1.0, float $nu = 0.5, int $degree = 3,
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float $gamma = null, float $coef0 = 0.0, float $epsilon = 0.1, float $tolerance = 0.001,
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int $cacheSize = 100, bool $shrinking = true, bool $probabilityEstimates = false
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int $type,
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int $kernel,
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float $cost = 1.0,
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float $nu = 0.5,
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int $degree = 3,
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float $gamma = null,
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float $coef0 = 0.0,
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float $epsilon = 0.1,
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float $tolerance = 0.001,
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int $cacheSize = 100,
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bool $shrinking = true,
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bool $probabilityEstimates = false
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) {
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$this->type = $type;
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$this->kernel = $kernel;
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@ -241,7 +250,8 @@ class SupportVectorMachine
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*/
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private function buildTrainCommand(string $trainingSetFileName, string $modelFileName): string
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{
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return sprintf('%ssvm-train%s -s %s -t %s -c %s -n %s -d %s%s -r %s -p %s -m %s -e %s -h %d -b %d %s %s',
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return sprintf(
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'%ssvm-train%s -s %s -t %s -c %s -n %s -d %s%s -r %s -p %s -m %s -e %s -h %d -b %d %s %s',
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$this->binPath,
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$this->getOSExtension(),
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$this->type,
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@ -124,10 +124,12 @@ class NormalizerTest extends TestCase
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// Values in the vector should be some value between -3 and +3
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$this->assertCount(10, $samples);
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foreach ($samples as $sample) {
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$errors = array_filter($sample,
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$errors = array_filter(
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$sample,
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function ($element) {
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return $element < -3 || $element > 3;
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});
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}
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);
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$this->assertCount(0, $errors);
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$this->assertEquals(0, $sample[3]);
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}
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