使用sklearn的LogisticRegression多分類模型

程序代碼：

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 Pipelinedf = pd.read_csv('http://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data', header=0)
x = df.values[:, :-1]
y = df.values[:, -1]
print('x = \n', x)
print('y = \n', y)
le = preprocessing.LabelEncoder()
le.fit(['Iris-setosa', 'Iris-versicolor', 'Iris-virginica'])
print(le.classes_)
y = le.transform(y)
print('Last Version, y = \n', y)x = x[:, 0:2]
print(x)
print(y)
#x = StandardScaler().fit_transform(x)
lr = LogisticRegression()   # Logistic回歸模型
lr.fit(x, y.ravel())        # 根據數據[x,y]，計算回歸參數X = x
Y = y
N, M = 500, 500     # 橫縱各采樣多少個值
x1_min, x1_max = X[:, 0].min(), X[:, 0].max()   # 第0列的范圍
x2_min, x2_max = X[:, 1].min(), X[:, 1].max()   # 第1列的范圍
t1 = np.linspace(x1_min, x1_max, N)
t2 = np.linspace(x2_min, x2_max, M)
x1, x2 = np.meshgrid(t1, t2)                    # 生成網格采樣點
x_test = np.stack((x1.flat, x2.flat), axis=1)   # 測試點
print(x_test)cm_light = mpl.colors.ListedColormap(['#009933', '#ff6666', '#33ccff'])
cm_dark = mpl.colors.ListedColormap(['g', 'r', 'b'])
y_hat = lr.predict(x_test)       # 預測值
y_hat = y_hat.reshape(x1.shape)                 # 使之與輸入的形狀相同
plt.pcolormesh(x1, x2, y_hat)     # 預測值的顯示
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()

運行結果：

x =?
[[4.9 3.0 1.4 0.2]
[4.7 3.2 1.3 0.2]
[4.6 3.1 1.5 0.2]
[5.0 3.6 1.4 0.2]
[5.4 3.9 1.7 0.4]
[4.6 3.4 1.4 0.3]
[5.0 3.4 1.5 0.2]
[4.4 2.9 1.4 0.2]
[4.9 3.1 1.5 0.1]
[5.4 3.7 1.5 0.2]
[4.8 3.4 1.6 0.2]
[4.8 3.0 1.4 0.1]
[4.3 3.0 1.1 0.1]
[5.8 4.0 1.2 0.2]
[5.7 4.4 1.5 0.4]
[5.4 3.9 1.3 0.4]
[5.1 3.5 1.4 0.3]
[5.7 3.8 1.7 0.3]
[5.1 3.8 1.5 0.3]
[5.4 3.4 1.7 0.2]
[5.1 3.7 1.5 0.4]
[4.6 3.6 1.0 0.2]
[5.1 3.3 1.7 0.5]
[4.8 3.4 1.9 0.2]
[5.0 3.0 1.6 0.2]
[5.0 3.4 1.6 0.4]
[5.2 3.5 1.5 0.2]
[5.2 3.4 1.4 0.2]
[4.7 3.2 1.6 0.2]
[4.8 3.1 1.6 0.2]
[5.4 3.4 1.5 0.4]
[5.2 4.1 1.5 0.1]
[5.5 4.2 1.4 0.2]
[4.9 3.1 1.5 0.1]
[5.0 3.2 1.2 0.2]
[5.5 3.5 1.3 0.2]
[4.9 3.1 1.5 0.1]
[4.4 3.0 1.3 0.2]
[5.1 3.4 1.5 0.2]
[5.0 3.5 1.3 0.3]
[4.5 2.3 1.3 0.3]
[4.4 3.2 1.3 0.2]
[5.0 3.5 1.6 0.6]
[5.1 3.8 1.9 0.4]
[4.8 3.0 1.4 0.3]
[5.1 3.8 1.6 0.2]
[4.6 3.2 1.4 0.2]
[5.3 3.7 1.5 0.2]
[5.0 3.3 1.4 0.2]
[7.0 3.2 4.7 1.4]
[6.4 3.2 4.5 1.5]
[6.9 3.1 4.9 1.5]
[5.5 2.3 4.0 1.3]
[6.5 2.8 4.6 1.5]
[5.7 2.8 4.5 1.3]
[6.3 3.3 4.7 1.6]
[4.9 2.4 3.3 1.0]
[6.6 2.9 4.6 1.3]
[5.2 2.7 3.9 1.4]
[5.0 2.0 3.5 1.0]
[5.9 3.0 4.2 1.5]
[6.0 2.2 4.0 1.0]
[6.1 2.9 4.7 1.4]
[5.6 2.9 3.6 1.3]
[6.7 3.1 4.4 1.4]
[5.6 3.0 4.5 1.5]
[5.8 2.7 4.1 1.0]
[6.2 2.2 4.5 1.5]
[5.6 2.5 3.9 1.1]
[5.9 3.2 4.8 1.8]
[6.1 2.8 4.0 1.3]
[6.3 2.5 4.9 1.5]
[6.1 2.8 4.7 1.2]
[6.4 2.9 4.3 1.3]
[6.6 3.0 4.4 1.4]
[6.8 2.8 4.8 1.4]
[6.7 3.0 5.0 1.7]
[6.0 2.9 4.5 1.5]
[5.7 2.6 3.5 1.0]
[5.5 2.4 3.8 1.1]
[5.5 2.4 3.7 1.0]
[5.8 2.7 3.9 1.2]
[6.0 2.7 5.1 1.6]
[5.4 3.0 4.5 1.5]
[6.0 3.4 4.5 1.6]
[6.7 3.1 4.7 1.5]
[6.3 2.3 4.4 1.3]
[5.6 3.0 4.1 1.3]
[5.5 2.5 4.0 1.3]
[5.5 2.6 4.4 1.2]
[6.1 3.0 4.6 1.4]
[5.8 2.6 4.0 1.2]
[5.0 2.3 3.3 1.0]
[5.6 2.7 4.2 1.3]
[5.7 3.0 4.2 1.2]
[5.7 2.9 4.2 1.3]
[6.2 2.9 4.3 1.3]
[5.1 2.5 3.0 1.1]
[5.7 2.8 4.1 1.3]
[6.3 3.3 6.0 2.5]
[5.8 2.7 5.1 1.9]
[7.1 3.0 5.9 2.1]
[6.3 2.9 5.6 1.8]
[6.5 3.0 5.8 2.2]
[7.6 3.0 6.6 2.1]
[4.9 2.5 4.5 1.7]
[7.3 2.9 6.3 1.8]
[6.7 2.5 5.8 1.8]
[7.2 3.6 6.1 2.5]
[6.5 3.2 5.1 2.0]
[6.4 2.7 5.3 1.9]
[6.8 3.0 5.5 2.1]
[5.7 2.5 5.0 2.0]
[5.8 2.8 5.1 2.4]
[6.4 3.2 5.3 2.3]
[6.5 3.0 5.5 1.8]
[7.7 3.8 6.7 2.2]
[7.7 2.6 6.9 2.3]
[6.0 2.2 5.0 1.5]
[6.9 3.2 5.7 2.3]
[5.6 2.8 4.9 2.0]
[7.7 2.8 6.7 2.0]
[6.3 2.7 4.9 1.8]
[6.7 3.3 5.7 2.1]
[7.2 3.2 6.0 1.8]
[6.2 2.8 4.8 1.8]
[6.1 3.0 4.9 1.8]
[6.4 2.8 5.6 2.1]
[7.2 3.0 5.8 1.6]
[7.4 2.8 6.1 1.9]
[7.9 3.8 6.4 2.0]
[6.4 2.8 5.6 2.2]
[6.3 2.8 5.1 1.5]
[6.1 2.6 5.6 1.4]
[7.7 3.0 6.1 2.3]
[6.3 3.4 5.6 2.4]
[6.4 3.1 5.5 1.8]
[6.0 3.0 4.8 1.8]
[6.9 3.1 5.4 2.1]
[6.7 3.1 5.6 2.4]
[6.9 3.1 5.1 2.3]
[5.8 2.7 5.1 1.9]
[6.8 3.2 5.9 2.3]
[6.7 3.3 5.7 2.5]
[6.7 3.0 5.2 2.3]
[6.3 2.5 5.0 1.9]
[6.5 3.0 5.2 2.0]
[6.2 3.4 5.4 2.3]
[5.9 3.0 5.1 1.8]]
y =?
['Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa'
'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa'
'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa'
'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa'
'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa'
'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa'
'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa'
'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa'
'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa'
'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-versicolor'
'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor'
'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor'
'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor'
'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor'
'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor'
'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor'
'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor'
'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor'
'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor'
'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor'
'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor'
'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor'
'Iris-versicolor' 'Iris-virginica' 'Iris-virginica' 'Iris-virginica'
'Iris-virginica' 'Iris-virginica' 'Iris-virginica' 'Iris-virginica'
'Iris-virginica' 'Iris-virginica' 'Iris-virginica' 'Iris-virginica'
'Iris-virginica' 'Iris-virginica' 'Iris-virginica' 'Iris-virginica'
'Iris-virginica' 'Iris-virginica' 'Iris-virginica' 'Iris-virginica'
'Iris-virginica' 'Iris-virginica' 'Iris-virginica' 'Iris-virginica'
'Iris-virginica' 'Iris-virginica' 'Iris-virginica' 'Iris-virginica'
'Iris-virginica' 'Iris-virginica' 'Iris-virginica' 'Iris-virginica'
'Iris-virginica' 'Iris-virginica' 'Iris-virginica' 'Iris-virginica'
'Iris-virginica' 'Iris-virginica' 'Iris-virginica' 'Iris-virginica'
'Iris-virginica' 'Iris-virginica' 'Iris-virginica' 'Iris-virginica'
'Iris-virginica' 'Iris-virginica' 'Iris-virginica' 'Iris-virginica'
'Iris-virginica' 'Iris-virginica' 'Iris-virginica']
['Iris-setosa' 'Iris-versicolor' 'Iris-virginica']
Last Version, y =?
[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2
2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
2]
[[4.3 ? ? ? ?2. ? ? ? ?]
[4.30721443 2. ? ? ? ?]
[4.31442886 2. ? ? ? ?]
...
[7.88557114 4.4 ? ? ? ]
[7.89278557 4.4 ? ? ? ]
[7.9 ? ? ? ?4.4 ? ? ? ]]

進程已結束,退出代碼0