def normal(nrows, ncols=1, mean=0.0, std=1.0):
'''
Randomly generates a matrix with normally distributed entries.
normal(nrows, ncols=1, mean=0, std=1)
PURPOSE
Returns a matrix with typecode 'd' and size nrows by ncols, with
its entries randomly generated from a normal distribution with mean
m and standard deviation std.
ARGUMENTS
nrows number of rows
ncols number of columns
mean approximate mean of the distribution
std standard deviation of the distribution
'''
try:
from cvxopt import gsl
except:
from cvxopt.base import matrix
from random import gauss
return matrix([gauss(mean, std) for k in range(nrows * ncols)],
(nrows, ncols), 'd')
return gsl.normal(nrows, ncols, mean, std)
def normal(nrows, ncols=1, mean=0.0, std=1.0):
'''
Randomly generates a matrix with normally distributed entries.
normal(nrows, ncols=1, mean=0, std=1)
PURPOSE
Returns a matrix with typecode 'd' and size nrows by ncols, with
its entries randomly generated from a normal distribution with mean
m and standard deviation std.
ARGUMENTS
nrows number of rows
ncols number of columns
mean approximate mean of the distribution
std standard deviation of the distribution
'''
try:
from cvxopt import gsl
except:
from cvxopt.base import matrix
from random import gauss
return matrix([gauss(mean, std) for k in range(nrows*ncols)],
(nrows,ncols), 'd' )
return gsl.normal(nrows, ncols, mean, std)
def test2(self):
from cvxopt import gsl
x = gsl.normal(3,2)
self.assertTrue(x.size[0] == 3 and x.size[1] == 2)
def test2(self):
from cvxopt import gsl
x = gsl.normal(3, 2)
self.assertTrue(x.size[0] == 3 and x.size[1] == 2)
