Simple way to create matrix of random numbers

Question

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 ra         


        

Answer 1

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)

Answer 2

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

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

Answer 3

#this is a function for a square matrix so on the while loop rows does not have to be less than cols.
#you can make your own condition. But if you want your a square matrix, use this code.

import random

import numpy as np

def random_matrix(R, cols):

        matrix = []

        rows =  0

        while  rows < cols:

            N = random.sample(R, cols)

            matrix.append(N)

            rows = rows + 1

    return np.array(matrix)

print(random_matrix(range(10), 5))
#make sure you understand the function random.sample

Answer 4

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]])

Answer 5

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

Answer 6

An answer using map-reduce:-

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