Hello, all
I try to solve issue 2649 which is related to 473 on multiplication of a
matrix and an array. As 2649 shows
import numpy as np
x = np.arange(5)
I = np.asmatrix(np.identity(5))
print np.dot(I, x).shape
# -> (1, 5)
First of all I assume we expect that I.dot(x) and I * x behave the same, so
I suggest add function dot to matrix, like
def dot(self, other):
return self * other
Then the major issue is the constructor of array and matrix interpret a
list differently. array([0,1]).shape = (2,) and matrix([0,1]).shape = (1,
2). It will throw error when run np.dot(I, x), because in __mul__, x will
be converted to a 1*5 matrix first. It's not consistent with
np.dot(np.identity(5),
x), which returns x. To fix that, I suggest to check the dimension of array
when convert it to matrix. If it's 1D array, then convert it to a vertical
vector explicitly like this
if isinstance(data, N.ndarray):
+ if len(data.shape) == 1:
+ data = data.reshape(data.shape[0], 1)
if dtype is None:
intype = data.dtype
else:
Any comments?
--
Kan Huang
Department of Applied math & Statistics
Stony Brook University
917-767-8018
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