train logistic regression model with different feature dimension in scikit learn

Question

Using Python 2.7 on Windows. Want to fit a logistic regression model using feature T1 and T2 for a classification problem, and target is T3 .

I show the values of T1 and T2 , as well as my code. The question is, since T1 has dimension 5, and T2 has dimension 1, how should we pre-process them so that it could be leveraged by scikit-learn logistic regression training correctly?

BTW, I mean for training sample 1, its feature of T1 is [ 0 -1 -2 -3] , and feature of T2 is [0] , for training sample 2, its feature of T1 is [ 1 0 -1 -2] and feature of T2 is [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

It needs to concatenate the feature data matrices using numpy.concatenate.

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)