Zuyao Li lizuyao2010
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| test |
lizuyao2010
/ naivebayes.py
Created
May 24, 2013 07:53
— forked from kvorion/naivebayes.py
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| #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) |
lizuyao2010
/ Exponentiation
Created
May 24, 2013 07:34
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| #include <stdio.h> | |
| void do_cal(char str[],int n,int p); | |
| int main() | |
| { | |
| char str[6]; | |
| int n,i,t,r[125]; | |
| while(scanf("%s%d",str,&n)==2) | |
| { | |
| for(i=0;str[i]!='.';i++); | |
| t=(5-i)*n; |
lizuyao2010
/ naivebayes-classify.py
Created
May 24, 2013 07:32
— forked from kvorion/naivebayes-classify.py
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| def Classify(self, featureVector): #featureVector is a simple list like the ones that we use to train | |
| probabilityPerLabel = {} #store the final probability for each class label | |
| 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]) |