Simple way to create matrix of random numbers

Question

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.