# 实验三 朴素贝叶斯算法及应用

1、
#导入包 import numpy as np import pandas as pd import matplotlib.pyplot as plt %matplotlib inline from sklearn.datasets import load_iris from sklearn.model_selection import train_test_split from collections import Counter import math
2、
# data def 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]
3、
X, y = create_data() X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)
4、
#测试 X_test[0], y_test[0]
5、
(array([5.6, 3. , 4.5, 1.5]), 1.0)
"""

"""

# GaussianNB 高斯朴素贝叶斯,特征的可能性被假设为高斯

class NaiveBayes:
def init(self):
self.model = None

# 数学期望
@staticmethod
def mean(X):
return sum(X) / float(len(X))

# 标准差（方差）
def stdev(self, X):
avg = self.mean(X)
return math.sqrt(sum([pow(x - avg, 2) for x in X]) / float(len(X)))

# 概率密度函数
def gaussian_probability(self, x, mean, stdev):
exponent = math.exp(-(math.pow(x - mean, 2) /(2 * math.pow(stdev, 2))))
return (1 / (math.sqrt(2 * math.pi) * stdev)) * exponent

# 处理X_train
def summarize(self, train_data):
summaries = [(self.mean(i), self.stdev(i)) for i in zip(*train_data)]
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):
# 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 = {}
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)
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

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))


6、
model = NaiveBayes()#生成一个算法对象 model.fit(X_train, y_train)#将训练数据代入算法中
7、
print(model.predict([4.4, 3.2, 1.3, 0.2]))
8、
model.score(X_test, y_test)
9、
#生成scikit-learn结果与上面手写函数的结果对比 from sklearn.naive_bayes import GaussianNB #导入模型
10、
clf = GaussianNB(；) clf.fit(X_train, y_train)#训练数据
11、
clf.score(X_test, y_test)

posted @ 2021-06-28 16:45  陶凌子  阅读(114)  评论(0编辑  收藏  举报