Python 拉丁超立方采样

#coding=utf-8
from __future__ import division
__author__ = 'wanghai'
import numpy as np
from matplotlib.ticker import MultipleLocator, FuncFormatter
import matplotlib.pyplot as pl

def LHSample( D,bounds,N):
'''
:param D:参数个数
:param bounds:参数对应范围（list）
:param N:拉丁超立方层数
:return:样本数据
'''

result = np.empty([N, D])
temp = np.empty(
)
d = 1.0 / N

for i in range(D):

for j in range(N):
temp[j] = np.random.uniform(
low=j * d, high=(j + 1) * d, size = 1)[0]

np.random.shuffle(temp)

for j in range(N):
result[j, i] = temp[j]

#对样本数据进行拉伸
b = np.array(bounds)
lower_bounds = b[:,0]
upper_bounds = b[:,1]
if np.any(lower_bounds > upper_bounds):
print '范围出错'
return None

#   sample * (upper_bound - lower_bound) + lower_bound
np.add(np.multiply(result,
(upper_bounds - lower_bounds),
out=result),
lower_bounds,
out=result)
return result

if __name__ =='__main__':
D = 2
N = 30
bounds = [[0,90],[0,90]]
xs = (bounds[0][1] - bounds[0][0])/N
ys = (bounds[1][1] - bounds[1][0])/N
ax = pl.gca()
pl.ylim(bounds[1][0] - ys,bounds[1][1]+ys)
pl.xlim(bounds[0][0] - xs, bounds[0][1] + xs)
pl.grid()
ax.xaxis.set_major_locator( MultipleLocator(xs) )
ax.yaxis.set_major_locator(MultipleLocator(ys))
samples = LHSample(D,bounds,N)
XY = np.array(samples)
X = XY[:,0]
Y = XY[:,1]
pl.scatter(X,Y)
pl.show()