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@dz1984

dz1984/NaiveBayes.php

Created December 16, 2013 04:50
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I practice to write a Naive Bayes algorithm to classify dataset. The Python script is my reference. The PHP script is written by myself. Just for fun. XD
Raw
data.txt
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B,0.68,0.43,0.34,0.30,0.92,0.91,0.70,0.10,0.31,0.13,0.16,0.09,0.09,0.01,0.49,0.91,0.58,0.56,0.03,0.07,0.07,0.12,0.40,0.15,0.19,0.02,0.50,0.88,0.59,0.56,0.97,0.43,0.39,0.45,0.53,0.29,0.57,0.57,0.68,0.45,0.36,0.41,0.71,0.63,0.43,0.42,0.54,0.73,0.40,0.48,0.46,0.49,0.33,0.71,0.71,0.67,0.35,0.34,0.31,0.69,0.32,0.51,0.42,0.49,0.28,0.30,0.27,0.31,0.22,0.01,0.03,0.03,0.06,0.17,0.08,0.08,0.00,0.35,0.26,0.33,0.22,0.01,0.28,0.54,0.04,0.03,0.07,0.16,0.42,0.13,0.06,0.15,0.46,0.11,0.57,0.46,0.11,0.57,0.15,0.00,0.01,0.88,1.00,0.83
B,0.75,0.34,0.27,0.23,0.32,0.32,0.92,0.28,0.80,0.35,0.63,0.68,0.68,0.20,0.43,0.61,0.46,0.49,0.07,0.17,0.17,0.28,0.58,0.30,0.40,0.14,0.65,0.63,0.42,0.46,0.94,0.26,0.10,0.25,0.16,0.00,0.02,0.02,0.76,0.24,0.25,0.19,0.99,0.79,0.15,0.21,0.15,1.00,0.89,0.83,0.93,0.68,0.50,0.48,0.48,0.74,0.28,0.26,0.24,0.83,0.87,0.83,0.93,0.69,0.51,0.59,0.70,0.64,0.79,0.38,0.65,0.65,0.28,0.84,0.36,0.63,0.18,0.61,0.72,0.64,0.79,0.57,0.48,0.89,0.40,0.57,0.32,0.62,0.58,0.46,0.69,0.41,0.81,0.62,0.87,0.81,0.62,0.87,0.45,0.49,0.41,0.72,0.71,0.71
B,0.59,0.52,0.43,0.32,0.80,0.80,0.66,0.14,0.41,0.18,0.26,0.20,0.20,0.03,0.48,0.51,0.48,0.46,0.09,0.19,0.19,0.14,0.52,0.18,0.31,0.08,0.54,0.54,0.48,0.46,0.91,0.69,0.57,0.71,0.78,0.60,0.75,0.75,0.59,0.56,0.45,0.52,0.52,0.35,0.60,0.69,0.76,0.38,0.44,0.31,0.44,0.32,0.16,0.31,0.31,0.55,0.40,0.44,0.33,0.82,0.34,0.36,0.37,0.29,0.13,0.17,0.14,0.17,0.11,0.01,0.03,0.03,0.13,0.20,0.13,0.11,0.00,0.15,0.17,0.15,0.11,0.01,0.28,0.33,0.20,0.30,0.17,0.30,0.22,0.21,0.35,0.20,0.59,0.28,0.69,0.59,0.28,0.69,0.17,0.19,0.15,0.78,0.70,0.78
Raw
NaiveBayes.php
<?php
// Author: DonaldIsFreak
// Practice to programming Naive Bayes.
define("MAX_CATEGORY",10);
$category = array();
$num_category = 0;
$num_samples = 0;
$num_columns = 0;
function mean($aValues){
return (array_sum($aValues)/count($aValues));
}
function sd_square($x,$mean){
return pow($x-$mean,2);
}
function vars($aValues){
$mean = mean($aValues);
return array_sum(array_map("sd_square",$aValues,array_fill(0,count($aValues),$mean)))/(count($aValues)-1);
}
function sd($aValues){
return sqrt(vars($aValues));
}
function categoryFilter($category=""){
return function($x) use($category) { return $x[0] == $category; };
}
function maleFilter($aValues){
return ($aValues[0]=="A");
}
function femaleFilter($aValues){
return ($aValues[0]=="B");
}
function getCol($aValues,$index){
$temp = array();
foreach($aValues as $v)
array_push($temp,$v[$index]);
return $temp;
}
function readCSVFile($fName){
global $category;
$data = array();
$fp = fopen($fName,'r');
$feature = "NONE";
while (!feof($fp)){
$temp = fgetcsv($fp);
if ($feature!=$temp[0])
array_push($category,$temp[0]);
array_push($data,$temp);
}
$GLOBALS["num_samples"] = count($data);
$GLOBALS["num_columns"] = count($data[0]);
$GLOBALS["num_category"] = count($category);
fclose($fp);
return $data;
}
function trainClassifier($aValue){
global $num_columns,$num_samples;
$male_temp = array_filter($aValue,'maleFilter');
$female_temp = array_filter($aValue,'femaleFilter');
$male = array(count($male_temp)/$num_samples);
$female = array(count($female_temp)/$num_samples);
for ($i=1; $i<$num_columns; $i++){
$male_cols = getCol($male_temp,$i);
$female_cols = getCol($female_temp,$i);
array_push($male,array(mean($male_cols),vars($male_cols)));
array_push($female,array(mean($female_cols,vars($female_cols))));
}
return array($male,$female);
}
function gaussProbability($mean,$vars,$x){
return exp(-pow($x-$mean,2)/(2*$vars))/sqrt(2*M_PI*$vars);
}
function runClassifier($training,$sample){
for ($i=0; $i<2; $i++){
}
}
$data = readCSVFile('data.txt');
$training = trainClassifier($data);
echo gaussProbability($training[0][1][0],$training[0][1][1],6);
?>
Raw
NaiveBayes.py
#Author: Krishnamurthy Koduvayur Viswanathan
from __future__ import division
import collections
import math
class Model:
def __init__(self, arffFile):
self.trainingFile = arffFile
self.features = {} #all feature names and their possible values (including the class label)
self.featureNameList = [] #this is to maintain the order of features as in the arff
self.featureCounts = collections.defaultdict(lambda: 1)#contains tuples of the form (label, feature_name, feature_value)
self.featureVectors = [] #contains all the values and the label as the last entry
self.labelCounts = collections.defaultdict(lambda: 0) #these will be smoothed later
def TrainClassifier(self):
for fv in self.featureVectors:
self.labelCounts[fv[len(fv)-1]] += 1 #udpate count of the label
for counter in range(0, len(fv)-1):
self.featureCounts[(fv[len(fv)-1], self.featureNameList[counter], fv[counter])] += 1
for label in self.labelCounts: #increase label counts (smoothing). remember that the last feature is actually the label
for feature in self.featureNameList[:len(self.featureNameList)-1]:
self.labelCounts[label] += len(self.features[feature])
def Classify(self, featureVector): #featureVector is a simple list like the ones that we use to train
probabilityPerLabel = {}
for label in self.labelCounts:
logProb = 0
for featureValue in featureVector:
logProb += math.log(self.featureCounts[(label, self.featureNameList[featureVector.index(featureValue)], featureValue)]/self.labelCounts[label])
probabilityPerLabel[label] = (self.labelCounts[label]/sum(self.labelCounts.values())) * math.exp(logProb)
print probabilityPerLabel
return max(probabilityPerLabel, key = lambda classLabel: probabilityPerLabel[classLabel])
def GetValues(self):
file = open(self.trainingFile, 'r')
for line in file:
if line[0] != '@': #start of actual data
self.featureVectors.append(line.strip().lower().split(','))
else: #feature definitions
if line.strip().lower().find('@data') == -1 and (not line.lower().startswith('@relation')):
self.featureNameList.append(line.strip().split()[1])
self.features[self.featureNameList[len(self.featureNameList) - 1]] = [featureName.strip() for featureName in line[line.find('{')+1: line.find('}')].strip().split(',')]
file.close()
def TestClassifier(self, arffFile):
file = open(arffFile, 'r')
for line in file:
if line[0] != '@':
vector = line.strip().lower().split(',')
print "classifier: " + self.Classify(vector) + " given " + vector[len(vector) - 1]
if __name__ == "__main__":
model = Model("tennis.arff")
model.GetValues()
model.TrainClassifier()
model.TestClassifier("tennis.arff")
Raw
tennis.arff
@RELATION TENNIS
@ATTRIBUTE outlook {sunny, overcast, rain}
@ATTRIBUTE temperature {hot, mild, cool}
@ATTRIBUTE humidity {high, normal, low}
@ATTRIBUTE wind {weak, strong}
@ATTRIBUTE play {yes, no}
@DATA
Sunny,Hot,High,Weak,No
Sunny,Hot,High,Strong,No
Overcast,Hot,High,Weak,Yes
Rain,Mild,High,Weak,Yes
Rain,Cool,Normal,Weak,Yes
Rain,Cool,Normal,Strong,No
Overcast,Cool,Normal,Strong,Yes
Sunny,Mild,High,Weak,No
Sunny,Cool,Normal,Weak,Yes
Rain,Mild,Normal,Weak,Yes
Sunny,Mild,Normal,Strong,Yes
Overcast,Mild,High,Strong,Yes
Overcast,Hot,Normal,Weak,Yes
Rain,Mild,High,Strong,No
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