X = m.matrix(numpy.random.standard_normal((3,1000))) C = m.matrix(m.array([[1,.3,.3],[.3,1.,.3],[.3,.3,1.]])) U = m.cholesky(C) Y = U*X #And to test that this works: m.corrcoef(Y[0,:],Y[1,:]) m.corrcoef(Y[0,:],Y[2,:]) m.corrcoef(Y[1,:],Y[2,:])
X = m.matrix(numpy.random.standard_normal((3,1000))) C = m.matrix(m.array([[1,.3,.3],[.3,1.,.3],[.3,.3,1.]])) U = m.cholesky(C) Y = U*X #And to test that this works: m.corrcoef(Y[0,:],Y[1,:]) m.corrcoef(Y[0,:],Y[2,:]) m.corrcoef(Y[1,:],Y[2,:])