太赞了！机器学习基础核心算法：贝叶斯分类！(附西瓜书案例及代码实现)

import numpy as npimport pandas as pdimport matplotlib.pyplot as plt%matplotlib inline
from sklearn.datasets import load_irisfrom sklearn.model_selection import train_test_split
from collections import Counterimport mathimport math

# datadef create_data():    iris = load_iris()    df = pd.DataFrame(iris.data, columns=iris.feature_names)    df['label'] = iris.target    df.columns = ['sepal length', 'sepal width', 'petal length', 'petal width', 'label']    data = np.array(df.iloc[:100, :])    # print(data)    return data[:,:-1], data[:,-1]

import mathclass NaiveBayes:    def __init__(self):        self.model = None
    # 数学期望    @staticmethod    def mean(X):        """计算均值        Param: X : list or np.ndarray                Return:            avg : float                """        avg = 0.0        # ========= show me your code ==================        avg = sum(X) / float(len(X))        # ========= show me your code ==================        return avg
    # 标准差（方差）    def stdev(self, X):        """计算标准差        Param: X : list or np.ndarray                Return:            res : float                """        res = 0.0        # ========= show me your code ==================        avg = self.mean(X)        res = math.sqrt(sum([pow(x - avg, 2) for x in X]) / float(len(X)))        # ========= show me your code ==================        return res            # 概率密度函数    def gaussian_probability(self, x, mean, stdev)


    
:        """根据均值和标注差计算x符号该高斯分布的概率        Parameters:        ----------        x : 输入        mean : 均值        stdev : 标准差                Return:                res : float， x符合的概率值                    """        res = 0.0        # ========= show me your code ==================        exponent = math.exp(-(math.pow(x - mean, 2) /                              (2 * math.pow(stdev, 2))))        res = (1 / (math.sqrt(2 * math.pi) * stdev)) * exponent        # ========= show me your code ==================                return res            # 处理X_train    def summarize(self, train_data):        """计算每个类目下对应数据的均值和标准差        Param: train_data : list                Return : [mean, stdev]        """        summaries = [0.0, 0.0]        # ========= show me your code ==================        summaries = [(self.mean(i), self.stdev(i)) for i in zip(*train_data)]                # ========= show me your code ==================        return summaries
    # 分类别求出数学期望和标准差    def fit(self, X, y):        labels = list(set(y))        data = {label: [] for label in labels}        for f, label in zip(X, y):            data[label].append(f)        self.model = {            label: self.summarize(value) for label, value in data.items()        }        return 'gaussianNB train done!'
    # 计算概率    def calculate_probabilities(self, input_data):        """计算数据在各个高斯分布下的概率        Paramter:        input_data : 输入数据                Return:        probabilities : {label : p}        """        # summaries:{0.0: [(5.0, 0.37),(3.42, 0.40)], 1.0: [(5.8, 0.449),(2.7, 0.27)]}        # input_data:[1.1, 2.2]        probabilities = {}        # ========= show me your code ==================        for label, value in self.model.items():            probabilities[label] = 1            for i in range(len(value)):                mean, stdev = value[i]                probabilities[label] *= self.gaussian_probability(                    input_data[i], mean, stdev)        # ========= show me your code ==================        return probabilities
    # 类别    def predict(self, X_test):        # {0.0: 2.9680340789325763e-27, 1.0: 3.5749783019849535e-26}        label = sorted(self.calculate_probabilities(X_test).items(), key=lambda x: x[-1])[-1][0]        return label    # 计算得分    def score(self, X_test, y_test):        right = 0        for X, y in zip(X_test, y_test):            label = self.predict(X)            if label == y:                right += 1
        return


    
 right / float(len(X_test))

model = NaiveBayes()model.fit(X_train, y_train)print(model.predict([3.4,  6.2,  2.0,  0.3]))model.score(X_test, y_test)