数据集

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Pos 为分类属性

数据和代码可以在下面链接github上下载。

github数据集和代码

import pandas as pdimport math# 数据读取与预处理path = 'NBA_Season_Stats(1984-)-new.csv'data = pd.read_csv(path, encoding='gbk')data = data.dropna()data = data.reset_index(drop=True)# 去掉没用属性X = data.drop(['Pos'], 1)X1 = datay = data['Pos']# 将Pos属性的值变为一个for i in range(len(y)):x = str(y[i])index = x.find('-')if(index != -1):y[i] = x[0:index]# 划分数据集from sklearn.model_selection import train_test_splitXtrain, Xtest, ytrain, ytest = train_test_split(X, y, test_size=0.3, random_state=2)X = Xtrainy = ytrainX = pd.DataFrame(X).reset_index(drop=True)y = y.reset_index(drop=True)############## 训练 #################### 计算先验概率与似然函数# 先验概率：pricount_each = {'C': 0, 'PF': 0, 'PG': 0, 'SF': 0, 'SG': 0}pri = {'C': 0, 'PF': 0, 'PG': 0, 'SF': 0, 'SG': 0}len_y = len(y)for i in range(len(y)):print(y[i])count_each[y[i]] += 1for i in pri:pri[i] = count_each[i]/len_y# 似然 likeliholdlikelihold = {'C': {}, 'PF': {}, 'PG': {}, 'SF': {}, 'SG': {}}for pos in likelihold:X['Pos'] = yX2 = X[(X['Pos']) == pos]X2 = X2.drop(['Pos'], 1)for attr, row in X2.iteritems():attr_prob = {}attr_prob['mean'] = X2[attr].mean()attr_prob['std'] = X2[attr].std()likelihold[pos][attr] = attr_probprint(likelihold)# 测试#def test_all(Xtest, ytest):Xtest = pd.DataFrame(Xtest).reset_index(drop=True)ytest = ytest.reset_index(drop=True)print(Xtest, ytest)attrs = {}for attr, row in Xtest.iteritems():attrs[attr] = 0print(attrs)result = {'C': 0, 'PF': 0, 'PG': 0, 'SF': 0, 'SG': 0}res = 0for i in range(len(Xtest)):result = {'C': 0, 'PF': 0, 'PG': 0, 'SF': 0, 'SG': 0}row = Xtest.loc[i]# print(Xtest.loc[i])# for attr in Xtest.loc[i]:# print(attr)# print(row[]) # 输出每行的索引值for cla in result:prob = pri[cla]for attr in attrs:mean = likelihold[cla][attr]['mean']std = likelihold[cla][attr]['std']prob *= 1/math.sqrt(2*math.pi)/std*math.exp(-1*pow((row[attr]-mean),2)/2/pow(std,2))result[cla] = prob#print(i, " ", result)print("第", i, "个测试的分类为", max(result, key=result.get))if ytest.loc[i] == max(result, key=result.get):res += 1print("正确率为", res/len(ytest))# for attr, row in Xtest.iteritems():# prob *= likelihold[cla][attr]#testdef test():# 0.58 0.7 7 3 1 22# inputtest_class = input("请输入类别")arr = list(map(float, input("请输入各属性值").split()))testcase = {'2P%': arr[0], 'FT%': arr[1], 'TRB': arr[2], 'AST': arr[3], 'STL': arr[4], 'PTS': arr[5]}#直接填写# #testcase = {'2P%': 0.58, 'FT%': 0.7, 'TRB': 7, 'AST': 3, 'STL': 1, 'PTS': 22}## testcase = {'2P%': 0.53, 'FT%': 0.7, 'TRB': 6, 'AST': 4, 'STL': 1, 'PTS': 30}# test_class = 'SF'result = {'C': 0, 'PF': 0, 'PG': 0, 'SF': 0, 'SG': 0}prob = 0for cla in result:prob = pri[cla]for attr in testcase:mean = likelihold[cla][attr]['mean']std = likelihold[cla][attr]['std']prob *= 1 / math.sqrt(2 * math.pi) / std * math.exp(-1 * pow((testcase[attr] - mean), 2) / 2 / pow(std, 2))result[cla] = prob# print(i, " ", result)print(result)print("测试的分类为", max(result, key=result.get), "实际分类为",test_class)test_all(Xtest, ytest)# test()# from sklearn.naive_bayes import GaussianNB# model = GaussianNB()# model.fit(Xtrain, ytrain)# y_model = model.predict(Xtest)## from sklearn.metrics import accuracy_score# print(accuracy_score(ytest, y_model))