Beispiel #1
0
import matplotlib.pyplot as plt

sym.init_printing(use_unicode=True)

d = 2
p = 0.25
q = 1 - p
E = Matrix([[1, 1], [0, 1]])
Qs = [Matrix([[-5, 0], [0, 5]]), Matrix([[1, 2], [0.5, 0.1]])]

d = E.shape[0]
A = ones((d, d))

import Pymatr.model as Mod

red = Mod.reduced(A, E, Qs)

import random
import math


def Gs(i, j):
    return lambda: random.gauss(Qs[0][i, j], math.sqrt(Qs[1][i, j]))


import Pymatr.synthesis as Syn

from Pymatr.utils import numerical
L = red.dEigen
nsyn = 100
Gen = Syn.MatrixRngOpt(numerical(A), numerical(E / L), Gs, nsyn)
Beispiel #2
0
d=2
p=0.25
q=1-p
E = Matrix( [ [ 1 , 1 ] ,  [0, 1] ] )
Qs =[  Matrix( [ [ -5  ,0 ] ,  [ 0 , 5] ] )
, Matrix( [ [ 1 ,2 ] ,  [0.5 , 0.1] ] ) ]



d=E.shape[0]
A = ones((d,d))

import Pymatr.model as Mod


red= Mod.reduced(A,E, Qs )  

import random
import math
def Gs(i,j):
	return lambda : random.gauss( Qs[0][i,j], math.sqrt(Qs[1][i,j]) )  
import Pymatr.synthesis as Syn

from Pymatr.utils import numerical
L= red.dEigen
nsyn=100
Gen = Syn.MatrixRngOpt(numerical(A),numerical(E/L), Gs, nsyn)

def average():
	s= sum(Gen())
	av= s /nsyn