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Update NaiveBayes.php (#30)

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* Update NaiveBayes.php

* Update NaiveBayes.php

* Update NaiveBayes.php

Update to fix "predictSample" function to enable it handle samples given as multi-dimensional arrays.

* Update NaiveBayes.php

* Update NaiveBayes.php
This commit is contained in:
Mustafa Karabulut 2017-01-17 17:21:58 +02:00 committed by Arkadiusz Kondas
parent cb5a9956c6
commit e603d60841
1 changed files with 135 additions and 14 deletions
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149
src/Phpml/Classification/NaiveBayes.php
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@ -6,31 +6,152 @@ namespace Phpml\Classification;
use Phpml\Helper\Predictable;
use Phpml\Helper\Trainable;
use Phpml\Math\Statistic\Mean;
use Phpml\Math\Statistic\StandardDeviation;
class NaiveBayes implements Classifier
{
use Trainable, Predictable;
const CONTINUOS = 1;
const NOMINAL = 2;
const EPSILON = 1e-10;
private $std = array();
private $mean= array();
private $discreteProb = array();
private $dataType = array();
private $p = array();
private $sampleCount = 0;
private $featureCount = 0;
private $labels = array();
public function train(array $samples, array $targets)
{
$this->samples = $samples;
$this->targets = $targets;
$this->sampleCount = count($samples);
$this->featureCount = count($samples[0]);
// Get distinct targets
$this->labels = $targets;
array_unique($this->labels);
foreach ($this->labels as $label) {
$samples = $this->getSamplesByLabel($label);
$this->p[$label] = count($samples) / $this->sampleCount;
$this->calculateStatistics($label, $samples);
}
}
/**
* Calculates vital statistics for each label & feature. Stores these
* values in private array in order to avoid repeated calculation
* @param string $label
* @param array $samples
*/
private function calculateStatistics($label, $samples)
{
$this->std[$label] = array_fill(0, $this->featureCount, 0);
$this->mean[$label]= array_fill(0, $this->featureCount, 0);
$this->dataType[$label] = array_fill(0, $this->featureCount, self::CONTINUOS);
$this->discreteProb[$label] = array_fill(0, $this->featureCount, self::CONTINUOS);
for ($i=0; $i<$this->featureCount; $i++) {
// Get the values of nth column in the samples array
// Mean::arithmetic is called twice, can be optimized
$values = array_column($samples, $i);
$numValues = count($values);
// if the values contain non-numeric data,
// then it should be treated as nominal/categorical/discrete column
if ($values !== array_filter($values, 'is_numeric')) {
$this->dataType[$label][$i] = self::NOMINAL;
$this->discreteProb[$label][$i] = array_count_values($values);
$db = &$this->discreteProb[$label][$i];
$db = array_map(function ($el) use ($numValues) {
return $el / $numValues;
}, $db);
} else {
$this->mean[$label][$i] = Mean::arithmetic($values);
// Add epsilon in order to avoid zero stdev
$this->std[$label][$i] = 1e-10 + StandardDeviation::population($values, false);
}
}
}
/**
* Calculates the probability P(label|sample_n)
*
* @param array $sample
* @param int $feature
* @param string $label
*/
private function sampleProbability($sample, $feature, $label)
{
$value = $sample[$feature];
if ($this->dataType[$label][$feature] == self::NOMINAL) {
if (! isset($this->discreteProb[$label][$feature][$value]) ||
$this->discreteProb[$label][$feature][$value] == 0) {
return self::EPSILON;
}
return $this->discreteProb[$label][$feature][$value];
}
$std = $this->std[$label][$feature] ;
$mean= $this->mean[$label][$feature];
// Calculate the probability density by use of normal/Gaussian distribution
// Ref: https://en.wikipedia.org/wiki/Normal_distribution
//
// In order to avoid numerical errors because of small or zero values,
// some libraries adopt taking log of calculations such as
// scikit-learn did.
// (See : https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/naive_bayes.py)
$pdf = -0.5 * log(2.0 * pi() * $std * $std);
$pdf -= 0.5 * pow($value - $mean, 2) / ($std * $std);
return $pdf;
}
/**
* Return samples belonging to specific label
* @param string $label
* @return array
*/
private function getSamplesByLabel($label)
{
$samples = array();
for ($i=0; $i<$this->sampleCount; $i++) {
if ($this->targets[$i] == $label) {
$samples[] = $this->samples[$i];
}
}
return $samples;
}
/**
* @param array $sample
*
* @return mixed
*/
protected function predictSample(array $sample)
{
$predictions = [];
foreach ($this->targets as $index => $label) {
$predictions[$label] = 0;
foreach ($sample as $token => $count) {
if (array_key_exists($token, $this->samples[$index])) {
$predictions[$label] += $count * $this->samples[$index][$token];
}
}
$isArray = is_array($sample[0]);
$samples = $sample;
if (!$isArray) {
$samples = array($sample);
}
arsort($predictions, SORT_NUMERIC);
reset($predictions);
return key($predictions);
$samplePredictions = array();
foreach ($samples as $sample) {
// Use NaiveBayes assumption for each label using:
// P(label|features) = P(label) * P(feature0|label) * P(feature1|label) .... P(featureN|label)
// Then compare probability for each class to determine which label is most likely
$predictions = array();
foreach ($this->labels as $label) {
$p = $this->p[$label];
for ($i=0; $i<$this->featureCount; $i++) {
$Plf = $this->sampleProbability($sample, $i, $label);
$p += $Plf;
}
$predictions[$label] = $p;
}
arsort($predictions, SORT_NUMERIC);
reset($predictions);
$samplePredictions[] = key($predictions);
}
if (! $isArray) {
return $samplePredictions[0];
}
return $samplePredictions;
}
}