目录

一、使用Python开发工具，运行对iris数据进行分类的例子程序NaiveBayes.py，熟悉sklearn机器实习开源库。

1. NaiveBayes.py

2. 运行结果

二、登录https://archive-beta.ics.uci.edu/

三、使用sklearn机器学习开源库，使用贝叶斯分类器对breast-cancer-wisconsin.data进行分类。

1. Python代码

2. 运行截图

四、用java实现贝叶斯分类器算法，并对上述数据进行分类。

1. 流程图

2. 数据结构

3. 算法

4. 测试结果

五、心得体会

一、使用Python开发工具，运行对iris数据进行分类的例子程序NaiveBayes.py，熟悉sklearn机器实习开源库。

1. NaiveBayes.py

<code>import numpy as np

import matplotlib.pyplot as plt

from sklearn import datasets

from sklearn.naive_bayes import GaussianNB

import matplotlib

# %matplotlib inline

# 生成所有测试样本点

def make_meshgrid(x, y, h=.02):

x_min, x_max = x.min() - 1, x.max() + 1

y_min, y_max = y.min() - 1, y.max() + 1

xx, yy = np.meshgrid(np.arange(x_min, x_max, h),

np.arange(y_min, y_max, h))

return xx, yy

# 对测试样本进行预测，并显示

def plot_test_results(ax, clf, xx, yy, **params):

Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])

Z = Z.reshape(xx.shape)

ax.contourf(xx, yy, Z, **params)

# 载入iris数据集

iris = datasets.load_iris()

# 只使用前面连个特征

X = iris.data[:, :2]

# 样本标签值

y = iris.target

# 创建并训练正态朴素贝叶斯分类器

clf = GaussianNB()

clf.fit(X, y)

title = ('GaussianBayesClassifier')

fig, ax = plt.subplots(figsize=(5, 5))

plt.subplots_adjust(wspace=0.4, hspace=0.4)

X0, X1 = X[:, 0], X[:, 1]

# 生成所有测试样本点

xx, yy = make_meshgrid(X0, X1)

# 显示测试样本的分类结果

plot_test_results(ax, clf, xx, yy, cmap=plt.cm.coolwarm, alpha=0.8)

# 显示训练样本

ax.scatter(X0, X1, c=y, cmap=plt.cm.coolwarm, s=20, edgecolors='k')code>

ax.set_xlim(xx.min(), xx.max())

ax.set_ylim(yy.min(), yy.max())

ax.set_xlabel('x1')

ax.set_ylabel('x2')

ax.set_xticks(())

ax.set_yticks(())

ax.set_title(title)

plt.show()

2. 运行结果

<code>from sklearn import datasets

from matplotlib import pyplot as plt

from sklearn.linear_model import LinearRegression, SGDRegressor, Ridge, LogisticRegression

from sklearn.model_selection import train_test_split

from sklearn.naive_bayes import GaussianNB

from sklearn.preprocessing import StandardScaler

from sklearn.metrics import mean_squared_error, classification_report

import pandas as pd

import numpy as np

# 构造列标签名字

column = ['Sample code number', 'Clump Thickness', 'Uniformity of Cell Size', 'Uniformity of Cell Shape',

'Marginal Adhesion', 'Single Epithelial Cell Size', 'Bare Nuclei', 'Bland Chromatin', 'Normal Nucleoli',

'Mitoses', 'Class']

# 读取数据

data = pd.read_csv(

"https://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/breast-cancer-wisconsin.data",

names=column)

print(data)

# 缺失值进行处理

data = data.replace(to_replace='?', value=np.nan)code>

# 删除

data = data.dropna()

# 1-10列是特征值，最后一列10 代表11列目标值

x_train, x_test, y_train, y_test = train_test_split(data[column[1:10]], data[column[10]], test_size=0.25)

#

clf = GaussianNB()

clf.fit(x_train, y_train)

title = ('GaussianBayesClassifier')

y_predict = clf.predict(x_test)

# 首先用分类器自带的.score方法来对准确性进行打印：

print("准确率：", clf.score(x_test, y_test))

print("召回率：", classification_report(y_test, y_predict, labels=[2, 4], target_names=["良性", "恶性"]))

2. 运行截图