Simple way to create matrix of random numbers

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.

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]])

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 ]])

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

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

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

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)

For creating an array of random numbers NumPy provides array creation using:

1. Real numbers

2. Integers

For creating array using random Real numbers: there are 2 options

1. random.rand (for uniform distribution of the generated random numbers )
2. random.randn (for normal distribution of the generated random numbers )

random.rand

import numpy as np 
arr = np.random.rand(row_size, column_size) 

random.randn

import numpy as np 
arr = np.random.randn(row_size, column_size) 

For creating array using random Integers:

import numpy as np
numpy.random.randint(low, high=None, size=None, dtype='l')

where

• low = Lowest (signed) integer to be drawn from the distribution
• high(optional)= If provided, one above the largest (signed) integer to be drawn from the distribution
• size(optional) = Output shape i.e. if the given shape is, e.g., (m, n, k), then m * n * k samples are drawn
• dtype(optional) = Desired dtype of the result.

eg:

The given example will produce an array of random integers between 0 and 4, its size will be 5*5 and have 25 integers

arr2 = np.random.randint(0,5,size = (5,5))

#in order to create 5 by 5 matrix, it should be modified to arr2 = np.random.randint(0,5,size = (5,5)), change the multiplication symbol* to a comma ,#

> [[2 1 1 0 1][3 2 1 4 3][2 3 0 3 3][1 3 1 0 0][4 1 2 0 1]]

eg2:

The given example will produce an array of random integers between 0 and 1, its size will be 1*10 and will have 10 integers

arr3= np.random.randint(2, size = 10)

> [0 0 0 0 1 1 0 0 1 1]

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

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

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

A simple way of creating an array of random integers is:

matrix = np.random.randint(maxVal, size=(rows, columns))

The following outputs a 2 by 3 matrix of random integers from 0 to 10:

a = np.random.randint(10, size=(2,3))

An answer using map-reduce:-

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

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

#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

numpy.random.rand(row, column) generates random numbers between 0 and 1, according to the specified (m,n) parameters given. So use it to create a (m,n) matrix and multiply the matrix for the range limit and sum it with the high limit.

Analyzing: If zero is generated just the low limit will be held, but if one is generated just the high limit will be held. In order words, generating the limits using rand numpy you can generate the extreme desired numbers.

import numpy as np

high = 10
low = 5
m,n = 2,2

a = (high - low)*np.random.rand(m,n) + low

Output:

a = array([[5.91580065, 8.1117106 ],
          [6.30986984, 5.720437  ]])