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实验三 朴素贝叶斯算法及应用

博客班级 https://edu.cnblogs.com/campus/ahgc/machinelearning
作业要求 https://edu.cnblogs.com/campus/ahgc/machinelearning/homework/12085
作业目标 <实验三 朴素贝叶斯算法及应用>
学号 <3180701229>

一、实验目的

理解朴素贝叶斯算法原理,掌握朴素贝叶斯算法框架;
掌握常见的高斯模型,多项式模型和伯努利模型;
能根据不同的数据类型,选择不同的概率模型实现朴素贝叶斯算法;
针对特定应用场景及数据,能应用朴素贝叶斯解决实际问题。

二、实验内容

实现高斯朴素贝叶斯算法。
熟悉sklearn库中的朴素贝叶斯算法;
针对iris数据集,应用sklearn的朴素贝叶斯算法进行类别预测。
针对iris数据集,利用自编朴素贝叶斯算法进行类别预测。

三、实验报告要求

对照实验内容,撰写实验过程、算法及测试结果;
代码规范化:命名规则、注释;
分析核心算法的复杂度;
查阅文献,讨论各种朴素贝叶斯算法的应用场景;
讨论朴素贝叶斯算法的优缺点。

四、实验内容及结果

实验代码

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
# 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]
X, y = create_data()
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)
X_test[0], y_test[0]
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))
model = NaiveBayes()
model.fit(X_train, y_train)
print(model.predict([4.4, 3.2, 1.3, 0.2]))
model.score(X_test, y_test)
from sklearn.naive_bayes import GaussianNB
clf = GaussianNB()
clf.fit(X_train, y_train)
clf.score(X_test, y_test)
clf.predict([[4.4, 3.2, 1.3, 0.2]])
from sklearn.naive_bayes import BernoulliNB, MultinomialNB # 伯努利模型和多项式模型

运行截图


posted on 2021-06-24 15:32  桃夭不妖  阅读(50)  评论(0编辑  收藏  举报