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https://github.com/wassname/simpeg.git
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ehaber99
63 lines
1.1 KiB
Python
63 lines
1.1 KiB
Python
import numpy as np
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from utils import mkvc
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import scipy.sparse.linalg as spla
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import scipy.sparse as sp
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def matmul(A,B):
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# first check shape
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if np.shape(A)[1] != np.shape(B)[0]:
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print 'error in sizes'
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return
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# Check types
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sA = sp.issparse(A)
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sB = sp.issparse(B)
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if ((sA == False) & (sB == True)): # doesno't work unless we trick it
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return (B.T.dot(A.T)).T
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else:
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return A.dot(B)
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def dot(A,B):
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A = mkvc(A,1)
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B = mkvc(B,1)
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return np.dot(A,B)
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def inner(A,B):
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A = mkvc(A,1)
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B = mkvc(B,1)
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return np.dot(A,B)
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if __name__ == '__main__':
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import numpy as np
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from utils import mkvc
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import scipy.sparse as sp
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# generate sparse and dense matrices
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A = sp.rand(100, 200, density=0.05, format='csr', dtype=None)
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B = sp.rand(200, 150, density=0.05, format='csr', dtype=None)
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C = np.random.rand(200,150)
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D = np.random.rand(150,100)
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b = mkvc(np.arange(200),1)
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c = np.reshape(b,(1,200))
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matmul(A,B)
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matmul(A,C)
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matmul(C,D)
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matmul(D,A)
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matmul(A,b)
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dot(c,b)
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dot(C,C)
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print np.shape(c), np.shape(b)[0]
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print matmul(c,b),dot(c,b)
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