[NumPy-Tickets] [NumPy] #1980: Matrix A from zeros, after changing values, yields strange A*A.T behavior

[NumPy Trac]

#1980: Matrix A from zeros, after changing values, yields strange A*A.T behavior
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 Reporter:  ehassler  |       Owner:  somebody   
     Type:  defect    |      Status:  new        
 Priority:  normal    |   Milestone:  Unscheduled
Component:  Other     |     Version:  1.6.0      
 Keywords:            |  
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 Apologies if this is expected behavior, or if dupe.  I searched trac for
 "zeros" and "multiply" and found nothing that described this.

 I'm making a permutation matrix P, starting with zeros and setting the
 1's.  When I do P.T*P or P*P.T I get all 0's back.  But if I multiply P by
 an identical matrix manually constructed, or if I use asmatrix to redefine
 P as itself, I get the expected behavior (that is, I get an identity
 matrix back).  I've included example code at the bottom.

 It seems like either one of the following should happen:
 - P should zero out everything it's multiplied into and raise errors when
 I change a value, forcing it to be all zeros all the time.
 - P*P.T = I



 import numpy;

 P0 = numpy.zeros((6,6),dtype=numpy.float64);
 for coord in zip(range(6),(1,3,5,0,2,4)):
         P0[coord[0],coord[1]] = 1.;

 P1 = numpy.matrix([
         [ 0.,  1.,  0.,  0.,  0.,  0.],
         [ 0.,  0.,  0.,  1.,  0.,  0.],
         [ 0.,  0.,  0.,  0.,  0.,  1.],
         [ 1.,  0.,  0.,  0.,  0.,  0.],
         [ 0.,  0.,  1.,  0.,  0.,  0.],
         [ 0.,  0.,  0.,  0.,  1.,  0.]
 ], dtype=numpy.float64);

 print numpy.all(P0 == P1);  # True

 print numpy.all(P0*P0.T == P1*P1.T) # False

 print numpy.all(P0*P1.T == P1*P1.T) # True

 print numpy.all(P1*P0.T == P1*P1.T) # True

 P0 = numpy.asmatrix(P0,dtype=numpy.float64);

 print numpy.all(P0*P0.T == P1*P1.T) # True

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Ticket URL: <http://projects.scipy.org/numpy/ticket/1980>
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