大佬教程收集整理的这篇文章主要介绍了实验三 朴素贝叶斯算法及应用,大佬教程大佬觉得挺不错的,现在分享给大家,也给大家做个参考。
博客班级 | AHPU-机器学习-计算机18级 |
---|---|
实验要求 | https://edu.cnblogs.com/campus/ahgc/machinelearning/homework/12085 |
学号 | 3180701232 |
#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
@H_262_24@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]
@H_262_24@3.
X, y = create_data()
X_Train, X_test, y_Train, y_test = Train_test_split(X, y, test_size=0.3)
@H_262_24@4.
部分截图
![](https://img2020.cnblogs.com/blog/2205429/202106/2205429-20210627205942735-496119680.png)
@H_262_24@5.
#测试
X_test[0], y_test[0]
@H_262_24@6.
结果:
(array([5.6, 3. , 4.5, 1.5]), 1.0)
高斯贝叶斯
@H_262_24@7.
#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))
@H_262_24@8.
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
@H_262_24@9.
结果:'gaussiAnnB Train done!'
@H_262_24@10.
print(model.preDict([4.4, 3.2, 1.3, 0.2]))
结果:0.0
scikit-learn实例
@H_262_24@11.
#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
clf = GaussiAnnB()
clf.fit(X_Train, y_Train)#训练数据
@H_262_24@12.
结果:GaussiAnnB(priors=None, var_smoothing=1e-09)
@H_262_24@13.
clf.score(X_test, y_test)
@H_262_24@14.
结果:1.0
@H_262_24@15.
clf.preDict([[4.4, 3.2, 1.3, 0.2]])
@H_262_24@16.
结果:array([0.])
以上是大佬教程为你收集整理的实验三 朴素贝叶斯算法及应用全部内容,希望文章能够帮你解决实验三 朴素贝叶斯算法及应用所遇到的程序开发问题。
如果觉得大佬教程网站内容还不错,欢迎将大佬教程推荐给程序员好友。
本图文内容来源于网友网络收集整理提供,作为学习参考使用,版权属于原作者。
如您有任何意见或建议可联系处理。小编QQ:384754419,请注明来意。