Question

在Windows上使用Python 2.7。想要使用功能T1和T2来设置逻辑回归模型以解决分类问题，目标是T3。

我会显示T1和T2的值，以及我的代码。问题是，由于T1的维度为5，T2的维度为1，我们应该如何对其进行预处理，以便正确利用scikit-learn逻辑回归培训来充分利用它？

BTW，我的意思是训练样本1，T1的特征是[ 0 -1 -2 -3]，T2的特征是[0]，对于训练样本2，它的特征是T1为[ 1 0 -1 -2]，T2的功能为[1]，...

import numpy as np
from sklearn import linear_model, datasets

arc = lambda r,c: r-c
T1 = np.array([[arc(r,c) for c in xrange(4)] for r in xrange(5)])
print T1
print type(T1)
T2 = np.array([[arc(r,c) for c in xrange(1)] for r in xrange(5)])
print T2
print type(T2)
T3 = np.array([0,0,1,1,1])

logreg = linear_model.LogisticRegression(C=1e5)

# we create an instance of Neighbours Classifier and fit the data.
# using T1 and T2 as features, and T3 as target
logreg.fit(T1+T2, T3)

T1，

[[ 0 -1 -2 -3]
 [ 1  0 -1 -2]
 [ 2  1  0 -1]
 [ 3  2  1  0]
 [ 4  3  2  1]]

T2，

[[0]
 [1]
 [2]
 [3]
 [4]]

Answer 1

它需要使用numpy.concatenate连接特征数据矩阵。

import numpy as np
from sklearn import linear_model, datasets

arc = lambda r,c: r-c
T1 = np.array([[arc(r,c) for c in xrange(4)] for r in xrange(5)])
T2 = np.array([[arc(r,c) for c in xrange(1)] for r in xrange(5)])
T3 = np.array([0,0,1,1,1])

X = np.concatenate((T1,T2), axis=1)
Y = T3
logreg = linear_model.LogisticRegression(C=1e5)

# we create an instance of Neighbours Classifier and fit the data.
# using T1 and T2 as features, and T3 as target
logreg.fit(X, Y)

X_test = np.array([[1, 0, -1, -1, 1],
                   [0, 1, 2, 3, 4,]])

print logreg.predict(X_test)

在scikit学习中训练具有不同特征维度的逻辑回归模型

1 个答案: