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This commit is contained in:
Pol Dellaiera
Arkadiusz Kondas
parent
4b837fae8e
commit
40f1ca06aa
@ -137,7 +137,7 @@ class DecisionTree implements Classifier
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$sum = array_sum(array_column($countMatrix, $i));
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if ($sum > 0) {
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foreach ($this->labels as $label) {
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$part += pow($countMatrix[$label][$i] / (float) $sum, 2);
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$part += ($countMatrix[$label][$i] / (float) $sum) ** 2;
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}
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}
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@ -131,7 +131,7 @@ class RandomForest extends Bagging
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if (is_float($this->featureSubsetRatio)) {
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$featureCount = (int) ($this->featureSubsetRatio * $this->featureCount);
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} elseif ($this->featureSubsetRatio === 'sqrt') {
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$featureCount = (int) sqrt($this->featureCount) + 1;
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$featureCount = (int) ($this->featureCount ** .5) + 1;
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} else {
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$featureCount = (int) log($this->featureCount, 2) + 1;
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}
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@ -162,7 +162,7 @@ class NaiveBayes implements Classifier
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// scikit-learn did.
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// (See : https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/naive_bayes.py)
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$pdf = -0.5 * log(2.0 * M_PI * $std * $std);
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$pdf -= 0.5 * pow($value - $mean, 2) / ($std * $std);
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$pdf -= 0.5 * (($value - $mean) ** 2) / ($std * $std);
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return $pdf;
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}
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@ -49,7 +49,7 @@ class Point implements ArrayAccess, \Countable
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$distance += $difference * $difference;
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}
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return $precise ? sqrt((float) $distance) : $distance;
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return $precise ? $distance ** .5 : $distance;
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}
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/**
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@ -128,12 +128,13 @@ class EigenvalueDecomposition
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$vectors = new Matrix($vectors);
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$vectors = array_map(function ($vect) {
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$sum = 0;
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for ($i = 0; $i < count($vect); ++$i) {
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$count = count($vect);
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for ($i = 0; $i < $count; ++$i) {
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$sum += $vect[$i] ** 2;
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}
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$sum = sqrt($sum);
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for ($i = 0; $i < count($vect); ++$i) {
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$sum **= .5;
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for ($i = 0; $i < $count; ++$i) {
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$vect[$i] /= $sum;
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}
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@ -208,11 +209,11 @@ class EigenvalueDecomposition
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// Generate Householder vector.
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for ($k = 0; $k < $i; ++$k) {
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$this->d[$k] /= $scale;
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$h += pow($this->d[$k], 2);
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$h += $this->d[$k] ** 2;
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}
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$f = $this->d[$i_];
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$g = sqrt($h);
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$g = $h ** .5;
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if ($f > 0) {
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$g = -$g;
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}
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@ -320,7 +321,7 @@ class EigenvalueDecomposition
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$this->e[$this->n - 1] = 0.0;
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$f = 0.0;
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$tst1 = 0.0;
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$eps = pow(2.0, -52.0);
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$eps = 2.0 ** -52.0;
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for ($l = 0; $l < $this->n; ++$l) {
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// Find small subdiagonal element
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@ -443,7 +444,7 @@ class EigenvalueDecomposition
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$h += $this->ort[$i] * $this->ort[$i];
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}
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$g = sqrt($h);
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$g = $h ** .5;
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if ($this->ort[$m] > 0) {
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$g *= -1;
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}
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@ -548,7 +549,7 @@ class EigenvalueDecomposition
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$n = $nn - 1;
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$low = 0;
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$high = $nn - 1;
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$eps = pow(2.0, -52.0);
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$eps = 2.0 ** -52.0;
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$exshift = 0.0;
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$p = $q = $r = $s = $z = 0;
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// Store roots isolated by balanc and compute matrix norm
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@ -596,7 +597,7 @@ class EigenvalueDecomposition
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$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][$n]) / 2.0;
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$q = $p * $p + $w;
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$z = sqrt(abs($q));
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$z = abs($q) ** .5;
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$this->H[$n][$n] += $exshift;
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$this->H[$n - 1][$n - 1] += $exshift;
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$x = $this->H[$n][$n];
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@ -620,7 +621,7 @@ class EigenvalueDecomposition
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$s = abs($x) + abs($z);
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$p = $x / $s;
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$q = $z / $s;
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$r = sqrt($p * $p + $q * $q);
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$r = ($p * $p + $q * $q) ** .5;
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$p /= $r;
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$q /= $r;
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// Row modification
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@ -682,7 +683,7 @@ class EigenvalueDecomposition
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$s = ($y - $x) / 2.0;
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$s *= $s + $w;
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if ($s > 0) {
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$s = sqrt($s);
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$s **= .5;
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if ($y < $x) {
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$s = -$s;
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}
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@ -750,7 +751,7 @@ class EigenvalueDecomposition
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break;
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}
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$s = sqrt($p * $p + $q * $q + $r * $r);
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$s = ($p * $p + $q * $q + $r * $r) ** .5;
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if ($p < 0) {
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$s = -$s;
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}
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@ -271,7 +271,7 @@ class Matrix
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}
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}
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return sqrt($squareSum);
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return $squareSum ** .5;
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}
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/**
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@ -32,10 +32,10 @@ class Correlation
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$a = $x[$i] - $meanX;
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$b = $y[$i] - $meanY;
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$axb += ($a * $b);
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$a2 += pow($a, 2);
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$b2 += pow($b, 2);
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$a2 += $a ** 2;
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$b2 += $b ** 2;
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}
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return $axb / sqrt((float) ($a2 * $b2));
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return $axb / ($a2 * $b2) ** .5;
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}
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}
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@ -34,7 +34,7 @@ class Gaussian
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$std2 = $this->std ** 2;
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$mean = $this->mean;
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return exp(-(($value - $mean) ** 2) / (2 * $std2)) / sqrt(2 * $std2 * M_PI);
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return exp(-(($value - $mean) ** 2) / (2 * $std2)) / ((2 * $std2 * M_PI) ** .5);
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}
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/**
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@ -32,7 +32,7 @@ class StandardDeviation
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--$n;
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}
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return sqrt($carry / $n);
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return ($carry / $n) ** .5;
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}
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/**
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@ -13,7 +13,7 @@ class Gaussian implements ActivationFunction
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*/
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public function compute($value): float
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{
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return exp(-pow($value, 2));
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return exp(- $value ** 2);
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}
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/**
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@ -32,6 +32,6 @@ class HyperbolicTangent implements ActivationFunction
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*/
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public function differentiate($value, $computedvalue): float
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{
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return 1 - pow($computedvalue, 2);
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return 1 - $computedvalue ** 2;
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}
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}
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@ -106,7 +106,7 @@ class Normalizer implements Preprocessor
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$norm2 += $feature * $feature;
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}
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$norm2 = sqrt((float) $norm2);
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$norm2 **= .5;
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if ($norm2 == 0) {
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$sample = array_fill(0, count($sample), 1);
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