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)