nbc.rar_naive bayes_朴素贝叶斯_概率预测_贝叶斯概率

import pandas as pd import pprint class Classifier(): data = None class_attr = None priori = {} cp = {} hypothesis = None def __init__(self,filename=None, class_attr=None ): self.data = pd.read_csv(filename, sep=',', header =(0)) self.class_attr = class_attr ''' probability(class) = How many times it appears in cloumn __________________________________________ count of all class attribute ''' def calculate_priori(self): class_values = list(set(self.data[self.class_attr])) class_data = list(self.data[self.class_attr]) # Your Code1 for i in class_values: y=0 for j in class_data: if i==j: y+=1 self.priori[i]=y/float(len(class_data)) print "Priori Values: ", self.priori ''' Here we calculate the individual probabilites P(outcome|evidence) = P(Likelihood of Evidence) x Prior prob of outcome ___________________________________________ P(Evidence) ''' def get_cp(self, attr, attr_type, class_value): data_attr = list(self.data[attr]) class_data = list(self.data[self.class_attr]) total =1 #Your Code2 sum=0 for i in range(14): if class_data[i] == class_value: sum+=1 if data_attr[i] == attr_type: total+=1 t=float(total / (sum*1.0)) return t ''' Here we calculate Likelihood of Evidence and multiple all individual probabilities with priori (Outcome|Multiple Evidence) = P(Evidence1|Outcome) x P(Evidence2|outcome) x ... x P(EvidenceN|outcome) x P(Outcome) scaled by P(Multiple Evidence) ''' def calculate_conditional_probabilities(self, hypothesis): for i in self.priori: self.cp[i] = {} for j in hypothesis: self.cp[i].update({ hypothesis[j]: self.get_cp(j, hypothesis[j], i)}) print "\nCalculated Conditional Probabilities: \n" pprint.pprint(self.cp) def classify(self): print "Result: " for i in self.cp: print i, " ==> ", reduce(lambda x, y: x*y, self.cp[i].values())*self.priori[i] if __name__ == "__main__": c = Classifier(filename="dataset.csv", class_attr="Buys_Computer" ) c.calculate_priori() c.hypothesis = {"Age":'<=30', "Income":'medium', "Student":'yes' , "Credit_Rating":'fair'} c.calculate_conditional_probabilities(c.hypothesis) # Your Code3 c.classify()