线性分类jupyter实践

线性分类的Jupyter实践

目录

• 线性分类的Jupyter实践
  • 一、实验步骤

一、实验步骤

准备工作：

安装anaconda后配置好一个虚拟环境并下载所需要的的包

pip install -i https://pypi.tuna.tsinghua.edu.cn/simple numpy
pip install -i https://pypi.tuna.tsinghua.edu.cn/simple pandas
pip install -i https://pypi.tuna.tsinghua.edu.cn/simple sklearn
pip install -i https://pypi.tuna.tsinghua.edu.cn/simple matplotlib

步骤：

1.取萼片的长宽作为特征进行分类，导入相应包库

import numpy as np
from sklearn.linear_model import LogisticRegression
import matplotlib.pyplot as plt
import matplotlib as mpl
from sklearn import datasets
from sklearn import preprocessing
import pandas as pd
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline

2.获取数据集

import numpy as np
from sklearn.linear_model import LogisticRegression
import matplotlib.pyplot as plt
import matplotlib as mpl
from sklearn import datasets
from sklearn import preprocessing
import pandas as pd
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline

3.对数据进行处理

#归一化处理
X = StandardScaler().fit_transform(X)
print(X)

4.训练模型

lr = LogisticRegression()   # Logistic回归模型
lr.fit(X, Y)        # 根据数据[x,y]，计算回归参数

5.绘制分类后的图像

N, M = 500, 500     # 横纵各采样多少个值
x1_min, x1_max = X[:, 0].min(), X[:, 0].max()   # 第0列的范围
x2_min, x2_max = X[:, 1].min(), X[:, 1].max()   # 第1列的范围
t1 = np.linspace(x1_min, x1_max, N)
t2 = np.linspace(x2_min, x2_max, M)
x1, x2 = np.meshgrid(t1, t2)                    # 生成网格采样点
x_test = np.stack((x1.flat, x2.flat), axis=1)   # 测试点

cm_light = mpl.colors.ListedColormap(['#77E0A0', '#FF8080', '#A0A0FF'])
cm_dark = mpl.colors.ListedColormap(['g', 'r', 'b'])
y_hat = lr.predict(x_test)       # 预测值
y_hat = y_hat.reshape(x1.shape)                 # 使之与输入的形状相同
plt.pcolormesh(x1, x2, y_hat, cmap=cm_light)     # 预测值的显示
plt.scatter(X[:, 0], X[:, 1], c=Y.ravel(), edgecolors='k', s=50, cmap=cm_dark)    
plt.xlabel('petal length')
plt.ylabel('petal width')
plt.xlim(x1_min, x1_max)
plt.ylim(x2_min, x2_max)
plt.grid()
plt.show()

6.预测模型

y_hat = lr.predict(X)
Y = Y.reshape(-1)
result = y_hat == Y
print(y_hat)
print(result)
acc = np.mean(result)
print('准确度: %.2f%%' % (100 * acc))