# -*- coding: utf-8 -*-
"""
Created on Sun Jul 12 02:42:24 2015
@author: R
"""
import scipy
import scipy.linalg
import numpy.linalg
import numpy
from tabulate import tabulate


precision = 100000000000

def MarkovChain(n,s) :
    """

    """
    matrix = []
    for l in range(n) :
        lineLst = []
        sum = 0
        crtPrec = precision
        for i in range(n-1) :
            val = random.randrange(crtPrec)
            sum += val
            lineLst.append(float(val)/precision)
            crtPrec -= val
        lineLst.append(float(precision - sum)/precision)
        matrix2 = matrix.append(lineLst)
    print("The intial probability matrix.")
    print(tabulate(matrix))
    baseprob = []
    for i in range(1000):
        matrix_n = numpy.linalg.matrix_power(matrix, s)
        baseprob.append(matrix_n.item(0))
    print(baseprob)
    print("The final probability matrix.")
    print(tabulate(matrix_n))
    matrixTranspose = zip(*matrix_n)
    evectors = numpy.linalg.eig(matrixTranspose)[1][:,0]
    print("The steady state vector is:")
    print(evectors)






MarkovChain(6, 10000000000)

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runfile('/home/raleigh/Downloads/Python-3.4.3/MarkovChain.py', wdir='/home/raleigh/Downloads/Python-3.4.3/ipython-3.2.0'