Hello,
I have noticed a significant speed difference between the array and the matrix
implementation of the dot product, especially for not-so-big matrices.
For example:
In [1]: import numpy as np
In [2]: b = np.random.rand(104,1)
In [3]: bm = np.mat(b)
In [4]: a = np.random.rand(8, 104)
In [5]: am = np.mat(a)
In [6]: %timeit np.dot(a, b)
1000000 loops, best of 3: 1.74 us per loop
In [7]: %timeit am * bm
100000 loops, best of 3: 6.38 us per loop
The results for two different PCs (PC1 with windows/EPD6.2-2 and PC2 with
ubuntu/numpy-1.3.0) and two different sizes are below:
array matrix
8x104 * 104x1
PC1 1.74us 6.38us
PC2 1.23us 5.85us
8x10 * 10x5
PC1 2.38us 7.55us
PC2 1.56us 6.01us
For bigger matrices the timings seem to asymptotically approach.
Is it something worth trying to fix or should I just accept this as a fact and,
when working with small matrices, stick to array?
Thanks,
Luca
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