Simple way to create matrix of random numbers

问题:

I am trying to create a matrix of random numbers, but my solution is too long and looks ugly

random_matrix = [[random.random() for e in range(2)] for e in range(3)]

this looks ok, but in my implementation it is

weights_h = [[random.random() for e in range(len(inputs[0]))] for e in range(hiden_neurons)]

which is extremely unreadable and does not fit on one line.

回答1:

Take a look at numpy.random.rand:

Docstring: rand(d0, d1, ..., dn)

Random values in a given shape.

Create an array of the given shape and propagate it with random samples from a uniform distribution over [0, 1).

---

>>> import numpy as np
>>> np.random.rand(2,3)
array([[ 0.22568268,  0.0053246 ,  0.41282024],
       [ 0.68824936,  0.68086462,  0.6854153 ]])

回答2:

You can drop the range(len()):

weights_h = [[random.random() for e in inputs[0]] for e in range(hiden_neurons)]

But really, you should probably use numpy.

In [9]: numpy.random.random((3, 3))
Out[9]:
array([[ 0.37052381,  0.03463207,  0.10669077],
       [ 0.05862909,  0.8515325 ,  0.79809676],
       [ 0.43203632,  0.54633635,  0.09076408]])

回答3:

use np.random.randint() as numpy.random.random_integers() is deprecated

random_matrix = numpy.random.randint(min_val,max_val,(<num_rows>,<num_cols>))

回答4:

Looks like you are doing a Python implementation of the Coursera Machine Learning Neural Network exercise. Here's what I did for randInitializeWeights(L_in, L_out)

#get a random array of floats between 0 and 1 as Pavel mentioned 
W = numpy.random.random((L_out, L_in +1))

#normalize so that it spans a range of twice epsilon
W = W * 2 * epsilon

#shift so that mean is at zero
W = W - epsilon

回答5:

First, create numpy array then convert it into matrix. See the code below:

import numpy

B = numpy.random.random((3, 4)) #its ndArray
C = numpy.matrix(B)# it is matrix
print(type(B))
print(type(C)) 
print(C)

回答6:

random_matrix = [[random.random for j in range(collumns)] for i in range(rows)
for i in range(rows):
    print random_matrix[i]

回答7:

x = np.int_(np.random.rand(10) * 10)

For random numbers out of 10. For out of 20 we have to multiply by 20.

回答8:

When you say "a matrix of random numbers", you can use numpy as Pavel https://stackoverflow.com/a/15451997/6169225 mentioned above, in this case I'm assuming to you it is irrelevant what distribution these (pseudo) random numbers adhere to.

However, if you require a particular distribution (I imagine you are interested in the uniform distribution), numpy.random has very useful methods for you. For example, let's say you want a 3x2 matrix with a pseudo random uniform distribution bounded by [low,high]. You can do this like so:

numpy.random.uniform(low,high,(3,2))

Note, you can replace uniform by any number of distributions supported by this library.

Further reading: https://docs.scipy.org/doc/numpy/reference/routines.random.html

回答9:

An answer using map-reduce:-

map(lambda x: map(lambda y: ran(),range(len(inputs[0]))),range(hiden_neurons))