Numpy array of random matrices

Question

I'm new to python/numpy and I need to create an array containing matrices of random numbers.

What I've got so far is this:

for i in xrange(samples):
    SPN[] = np.random.random((6,5)) * np.random.randint(0,100)

Which make sense for me as PHP developer but is not working for python. So how do I create a 3 dimensional array to contain this matrices/arrays?

How would this loop, even in PHP, create a three-dimensional array? What is SPN? An empty list? If so, try SPN.append(np.random....) — tobias_k
– tobias_k, Commented Nov 14, 2013 at 17:44
SPN[] without the index on php. I have not defined SPN but it should be an array — Jorge Zapata
– Jorge Zapata, Commented Nov 14, 2013 at 17:45

DSM · Accepted Answer · 2013-11-20 06:24:12Z

Both np.random.randint and np.random.uniform, like most of the np.random functions, accept a size parameter, so in numpy we'd do it in one step:

>>> SPN = np.random.randint(0, 100, (3, 6, 5))
>>> SPN
array([[[45, 95, 56, 78, 90],
        [87, 68, 24, 62, 12],
        [11, 26, 75, 57, 12],
        [95, 87, 47, 69, 90],
        [58, 24, 49, 62, 85],
        [38,  5, 57, 63, 16]],

       [[61, 67, 73, 23, 34],
        [41,  3, 69, 79, 48],
        [22, 40, 22, 18, 41],
        [86, 23, 58, 38, 69],
        [98, 60, 70, 71,  3],
        [44,  8, 33, 86, 66]],

       [[62, 45, 56, 80, 22],
        [27, 95, 55, 87, 22],
        [42, 17, 48, 96, 65],
        [36, 64,  1, 85, 31],
        [10, 13, 15,  7, 92],
        [27, 74, 31, 91, 60]]])
>>> SPN.shape
(3, 6, 5)
>>> SPN[0].shape
(6, 5)

.. actually, it looks like you may want np.random.uniform(0, 100, (samples, 6, 5)), because you want the elements to be floating point, not integers. Well, it works the same way. :^)

Note that what you did isn't equivalent to np.random.uniform, because you're choosing an array of values between 0 and 1 and then multiplying all of them by a fixed integer. I'm assuming that wasn't actually what you were trying to do, because it's a little unusual; please comment if that is what you actually wanted.

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