python - train logistic regression model with different feature dimension in scikit learn -

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using python 2.7 on windows. want fit logistic regression model using feature t1 , t2 classification problem, , target t3.

i show values of t1 , t2, code. question is, since t1 has dimension 5, , t2 has dimension 1, how should pre-process them leveraged scikit-learn logistic regression training correctly?

btw, mean training sample 1, feature of t1 [ 0 -1 -2 -3], , feature of t2 [0], training sample 2, feature of t1 [ 1 0 -1 -2] , feature of t2 [1], ...

import numpy np sklearn import linear_model, datasets  arc = lambda r,c: r-c t1 = np.array([[arc(r,c) c in xrange(4)] r in xrange(5)]) print t1 print type(t1) t2 = np.array([[arc(r,c) c in xrange(1)] r in xrange(5)]) print t2 print type(t2) t3 = np.array([0,0,1,1,1])  logreg = linear_model.logisticregression(c=1e5)  # create instance of neighbours classifier , fit data. # using t1 , t2 features, , t3 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]]

it needs concatenate feature data matrices using numpy.concatenate.

import numpy np sklearn import linear_model, datasets  arc = lambda r,c: r-c t1 = np.array([[arc(r,c) c in xrange(4)] r in xrange(5)]) t2 = np.array([[arc(r,c) c in xrange(1)] 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)  # create instance of neighbours classifier , fit data. # using t1 , t2 features, , t3 target logreg.fit(x, y)  x_test = np.array([[1, 0, -1, -1, 1],                    [0, 1, 2, 3, 4,]])  print logreg.predict(x_test)

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