作业1：优化代码（lgz同学提供的代码）实现：（1）面向过程的朴素贝叶斯（ver1）要求不能使用sklearn。

作业源码(主要借鉴lgz大佬)

import numpy as np


class GaussianNB:

    def __init__(self, feature, label):
        self.feature = feature  # 特征
        self.label = label  # 标签

    @staticmethod
    def GaussianProbability(x, mean, var):  # 高斯概率密度函数
        return np.array(
            [1 / (np.sqrt(2 * np.pi) * var[i]) *
             np.exp(-np.power(x[i] - mean[i], 2) / (2 * np.power(var[i], 2))) for i in range(len(x))])

    @staticmethod
    def fit():  # 求均值&方差
        mean_var = [[np.mean(X[y == label], axis=0), np.var(X[y == label], axis=0)] for label in np.unique(y)]
        return mean_var

    def score(self, feature, label):  # 模型评估
        return np.sum(np.array([[np.argmax(np.array([np.array(
            self.GaussianProbability(sample, np.array([i[0] for i in self.fit()])[j],
                                     np.array([i[1] for i in self.fit()])[j])).prod() for j in
                                                     range(len(np.unique(label)))]))] for sample in
                                feature]).ravel() == label) / label.size
if __name__ == __main__:
    # 读取数据
    data = np.loadtxt("iris.csv",  # 数据源
                      dtype=str,  # 读取类型
                      delimiter=,,  # 分割符号
                      skiprows=1)

    # 数据预处理
    X = data[::, 0:-1].astype(float32)
    y = data[:, -1]
    y = [0 if i == Setosa else i for i in list(y)]
    y = [1 if i == Versicolor else i for i in list(y)]
    y = [2 if i == Virginica else i for i in list(y)]

    # 选择模型
    bayes = GaussianNB(X, np.array(y))

	# 训练选择模型及结果
    print(f模型准确率为：{
            
     bayes.score(X, np.array(y)) * 100}%)

手撕代码