PseudoRandomness vs RH

未分类

PseudoRandomness vs RH

def rand_walk(n):
    step = 0
    for i in range(1, n):
        #temp = randint(0,2)
        temp = ZZ.random_element(0,3)
        if temp == 2:
            step += 1
        elif temp == 1:
            step -= 1
    # Output the result.
    print "Derivation from orgin", step
    print "The expectation from the orgin is:", floor(sqrt(n))
#Define a rand_walk using RH
def rand_walk_rh(n):
    step = 0
    for i in range(1, n + 1):
        fac = factor(i)
        length = len(fac)
        stay = false
        for j in range(length):
            if fac[j][1] > 1:
                print i, "don't move"
                stay = True
                break
        if not stay:  
            if is_odd(length):
                step += 1
                print i, "move right"
            else:  
                step -=1
                print i, "move left"
# Output the result.
    print "Derivation from orgin", step
    print "The expectation from the orgin is:", floor(sqrt(n))

Python code

#https://www.quora.com/How-do-I-solve-the-drunkards-walk-or-random-walk-simulation-using-Python-language
import random
def RandomWalk1(n):
    # n-length random walk on the number line
    return [1 if random.random() > 0.5 else -1 for i in xrange(n)]
def RandomWalk2(n):
    # n-length random walk on the number line
    return [random.choice([-1, 1]) for i in xrange(n)]
def RandomWalkFinalPosition(n):
    return sum(RandomWalk1(n))
import numpy as np
def RandomWalkNP(n):
    return np.random.choice([-1, 1], n)
def RandomWalkGenerator1():
    while True:
        yield 1 if random.random() > 0.5 else -1
def RandomWalkGenerator2(n=float('inf')):
    step = 0
    while step < n:
        step += 1
        yield 1 if random.random() > 0.5 else -1
import random
def Step(n_dim):
    move = [0 for i in xrange(n_dim)]
    dimension_to_move = random.randint(0, n_dim-1)
    direction = 1 if random.random() > 0.5 else -1
    move[dimension_to_move] = direction
    return move
def RandomWalkMulti(n_steps, n_dim):
    return [Step(n_dim) for i in xrange(n_steps)]
#https://plot.ly/python/random-walk/
#http://mooc.ee/MTAT.03.236/2014_fall/uploads/Main/drunkard1.pdf
#https://stackoverflow.com/questions/9421928/another-simple-random-walk-simulation-using-pythontwo-dimensional
#https://stackoverflow.com/questions/26195815/python-drunkards-walk