决策树分类器算法实现

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# -*- coding: cp936 -*-#决策树分类器my_data=[['slashdot','USA','yes',18,'None'],['google','France','yes',23,'Premium'],         ['digg','USA','yes',24,'Basic']]class decisionnode:    def __init__(self,col=-1,value=None,results=None,tb=None,fb=None):        self.col=col        self.value=value        self.results=results        self.tb=tb        self.fb=fbdef divideset(rows,column,value):    split_function=None    if isinstance(value,int) or isinstance(value,float):        split_function=lambda row:row[column]>=value    else:        split_function=lambda row:row[column]==value    set1=[row for row in rows if split_function(row)]    set2=[row for row in rows if not split_function(row)]    return (set1,set2)def uniquecounts(rows):    results={}    for row in rows:        r=row[len(row)-1]        if r not in results:            results[r]=0        results[r]+=1    return resultsdef entropy(rows):    from math import log    log2=lambda x:log(x)/log(2)    results=uniquecounts(rows)    ent=0.0    for r in results.keys():        p=float(results[r])/len(rows)        ent=ent-p*log2(p)    return entdef buildtree(rows,scoref=entropy):    if len(rows)==0:        return decisionnode()    current_score=scoref(rows)    best_gain=0.0    best_criteria=None    best_sets=None    column_count=len(rows[0])-1    for col in range(0,column_count):        column_values={}        for row in rows:            column_values[row[col]]=1        for value in column_values.keys():            (set1,set2)=divideset(rows,col,value)                        p=float(len(set1))/len(rows)            gain=current_score-p*scoref(set1)-(1-p)*scoref(set2)            if gain>best_gain and len(set1)>0 and len(set2)>0:                best_gain=gain                best_criteria=(col,value)                best_sets=(set1,set2)    if best_gain>0:        trueBranch=buildtree(best_sets[0])        falseBranch=buildtree(best_sets[1])        return decisionnode(col=best_criteria[0],value=best_criteria[1],tb=trueBranch,fb=falseBranch)    else:        return decisionnode(results=uniquecounts(rows))    def printtree(tree,indent=''):    if tree.results!=None:        print str(tree.results)    else:        print str(tree.col)+':'+str(tree.value)+'?'        print indent+'T->'        printtree(tree.tb,indent+'  ')        print indent+'F->'        printtree(tree.fb,indent+'  ')def classify(observation,tree):    if tree.results!=None:        return tree.results    else:        v=observation[tree.col]        branch=None        if isinstance(v,int) or isinstance(v,float):            if v>=tree.value:branc=tree.tb            else:branch=tree.fb        else:            if v==tree.value:branch=tree.tb            else:branch=tree.fb        return classify(observation,branch)
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