python - concatenating scipy matrices -
i want concatenate 2 csr_matrix, each shape=(1,n).
i know should use scipy.sparse.vstack:
from scipy.sparse import csr_matrix,vstack c1 = csr_matrix([[1, 2]]) c2 = csr_matrix([[3, 4]]) print c1.shape,c2.shape print vstack([c1, c2], format='csr') #prints: (1, 2) (1, 2) (0, 0) 1 (0, 1) 2 (1, 0) 3 (1, 1) 4 however, code fails:
from scipy.sparse import csr_matrix,vstack import numpy np y_train = np.array([1, 0, 1, 0, 1, 0]) x_train = csr_matrix([[1, 1], [-1, 1], [1, 0], [-1, 0], [1, -1], [-1, -1]]) c0 = x_train[y_train == 0].mean(axis=0) c1 = x_train[y_train == 1].mean(axis=0) print c0.shape, c1.shape #prints (1l, 2l) (1l, 2l) print c0,c1 #prints [[-1. 0.]] [[ 1. 0.]] print vstack([c0,c1], format='csr') the last line raises exception -
file "c:\anaconda\lib\site-packages\scipy\sparse\construct.py", line 484, in vstack
return bmat([[b] b in blocks], format=format, dtype=dtype)file "c:\anaconda\lib\site-packages\scipy\sparse\construct.py", line 533, in bmat
raise valueerror('blocks must 2-d') valueerror: blocks must 2-d
i guess using mean has out. ideas?
taking mean of sparse matrix returns numpy matrix (which not sparse). c0 , c1 matrices:
in [76]: type(c0) out[76]: numpy.matrixlib.defmatrix.matrix in [89]: sparse.issparse(c0) out[94]: false vstack expects first argument sequence of sparse matrices. make (at least) first matrix sparse matrix:
in [31]: vstack([coo_matrix(c0), c1]) out[31]: <2x2 sparse matrix of type '<type 'numpy.float64'>' 2 stored elements in coordinate format> in [32]: vstack([coo_matrix(c0), c1]).todense() out[32]: matrix([[-1., 0.], [ 1., 0.]]) 
Comments
Post a Comment