Symmetrical matrix with numpy

from random import *
N = 100
gamma = 0.7
connect = zeros((N,N))

for i in range(N):
    for j in range(i+1):
        if random() < gamma:
            connect[i,j] = 1
            connect[j,i] = 1
        else:
            connect[i,j] = 0
            connect[j,i] = 0

What I try to do is to create a symmetrical matrix, filled with zeros and ones (ones with 开发者_C百科a probability of 0.7). Here is the double for loop, very inefficient...I shall make something with numpy, which I believe could speed up thing a great deal? Does anyone know how to proceed? Thank you very much!

You could use the numpy random module to generate random vectors, and use those vectors to seed the matrix. For example:

import numpy as np

N = 100
gamma = 0.7
connect = np.zeros((N,N),dtype=np.int32)

for i in range(0,N):
        dval = np.diag((np.random.random_sample(size=(N-i))<gamma).astype(np.int32),i)
        connect += dval
        if (i>0):
                connect += dval.T

does this diagonally using numpy.diag, but you could do it row-wise to assemble the upper or lower triangular portion, then use addition to form the symmetrical matrix. I don't have a feeling for which might be faster.

EDIT: In fact this row wise version is about 5 times faster than the diagonal version, which I guess shouldn't be all that surprising given the memory access patterns it uses compared to diagonal assembly.

N = 100
gamma = 0.7
connect = np.zeros((N,N),dtype=np.int32)

for i in range(0,N):
    rval = (np.random.random_sample(size=(N-i))<gamma).astype(np.int32)
    connect[i,i:] = rval

connect += np.triu(connect,1).T

EDIT 2

This is even simpler and about 4 times faster than the row-wise version above. Here a triangular matrix is formed directly from a full matrix of weights, then added to its transpose to produce the symmetric matrix:

N = 100
gamma = 0.7
a=np.triu((np.random.random_sample(size=(N,N))<gamma).astype(np.int32))
connect = a + np.triu(a,1).T

On the Linux system I tested it on, version 1 takes about 6.5 milliseconds, version 2 takes about 1.5 milliseconds, version 3 takes about 450 microseconds.