朴素贝叶斯python实现

""" #-*- coding: utf-8 -*- 年龄：0代表<=30，1代表31~40，2代表>40 收入：0代表高，1代表中，2代表低 学生：0代表否，1代表是 信誉：0代表中，1代表优 类别：1代表是,0代表否 """ if __name__ == '__main__': train_samples=[ [0, 0, 0, 0, 0], [0, 0, 0, 1, 0], [1, 0, 0, 0, 1], [2, 1, 0, 0, 1], [2, 2, 1, 0, 1], [2, 2, 1, 1, 0], [1, 2, 1, 1, 1], [0, 1, 0, 0, 0], [0, 2, 1, 0, 1], [2, 1, 1, 0, 1]] # 待分类样本 X=[1, 1, 0, 1] # 训练样本数量 10 n_sample = len(train_samples) # 单个样本的维度： 描述属性和类别属性个数 5 dim_sample = len(train_samples[0]) # 计算每个属性有哪些取值 attr = [] for i in range(0, dim_sample): attr.append([]) for sample in train_samples: for i in range(0, dim_sample): if sample[i] not in attr[i]: attr[i].append(sample[i]) # 每个属性取值的个数 n_attr = [len(attr) for attr in attr] # 记录不同类别的样本个数 n_c = [] for i in range(0, n_attr[dim_sample - 1]): n_c.append(0) # 计算不同类别的样本个数 for sample in train_samples: n_c[sample[dim_sample - 1]] += 1 # 计算不同类别样本所占概率 p_c = [n_cx / sum(n_c) for n_cx in n_c] # print(p_c) # 将用户按照类别分类 samples_at_c = {} for c in attr[dim_sample - 1]: samples_at_c[c] = [] for sample in train_samples: samples_at_c[sample[dim_sample - 1]].append(sample) # 记录 每个类别的训练样本中，取待分类样本的某个属性值的样本个数 n_attr_X = {} for c in attr[dim_sample - 1]: n_attr_X[c] = [] for j in range(0, dim_sample - 1): n_attr_X[c].append(0) # 计算 每个类别的训练样本中，取待分类样本的某个属性值的样本个数 for c, samples_at_cx in zip(samples_at_c.keys(), samples_at_c.values()): for sample in samples_at_cx: for i in range(0, dim_sample - 1): if X[i] == sample[i]: n_attr_X[c][i] += 1 # 字典转化为list n_attr_X = list(n_attr_X.values()) n_attr_X[0], n_attr_X[1] = n_attr_X[1], n_attr_X[0] # print(n_attr_X) # 存储最终的概率 result_p = [] for i in range(0, n_attr[dim_sample - 1]): result_p.append(p_c[i]) result_p=list(reversed(result_p)) n_c=list(reversed(n_c)) # 计算概率 for i in range(0,n_attr[dim_sample - 1]): n_attr_X[i] = [x / n_c[i] for x in n_attr_X[i]] for x in n_attr_X[i]: result_p[i] *= x print('概率分别为', result_p) # 找到概率最大对应的那个类别，就是预测样本的分类情况 predict_class = result_p.index(max(result_p)) print(predict_class)

原文链接;https://blog.csdn.net/ten_sory/article/details/81237169