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#!/usr/bin/env python
""" Test functions for the 'dictsampler' derived class.
Author: Ed Schofield, 2003-2006
Copyright: Ed Schofield, 2003-2006
"""
from numpy import arange, add, array, dot, zeros, identity
import sys
from numpy.testing import *
set_package_path()
from numpy import *
#from scipy.montecarlo import *
from scipy.sandbox.montecarlo import *
from scipy import stats
restore_path()
import unittest
class test_dictsampler(NumpyTestCase):
def check_simple(self):
"""
# Sample from this discrete distribution:
# x 'a' 'b' 'c'
# p(x) 10/180 150/180 20/180
"""
table = {}
table['a'] = 10
table['b'] = 150
table['c'] = 20
sampler = dictsampler(table)
n = 1000
#import pdb
#pdb.set_trace()
s = sampler.sample(n)
assert sum((s[i]=='b' for i in range(n)),axis=0)*1./n > 0.75
#lam = 10.0
#n = 35
#numsamples = 10000
#a = array([stats.poisson.pmf(i, lam) for i in range(n)])
#sampler = intsampler(a)
#x = sampler.sample(numsamples)
#m = x.mean()
#s = x.std()
## Use a normal approximation for confidence intervals for the mean
#z = 2.5758 # = norminv(0.995), for a 1% confidence interval
#assert abs(m - lam) < z * lam/sqrt(numsamples)
def check_sanity(self):
# Sample from this pmf:
# x 0 1 2 3 4
# p(x) 0.5 0.1 0.15 0 0.25
# The true mean and variance are:
truemean = 1.4
truevar = 2.74
numsamples = 10000
a = array([0.5, 0.1, 0.15, 0., 0.25])
sampler = intsampler(a)
x = sampler.sample(numsamples)
m = x.mean()
v = x.var()
assert x.max() == 4
assert x.min() == 0
assert sum(x==3,axis=0) == 0
assert 0.08 < average(x==1,axis=0) < 0.12
# Use a normal approx for confidence intervals for the mean
z = 2.5758 # = norminv(0.995), for a 1% confidence interval
assert abs(m - truemean) < z * sqrt(truevar/numsamples)
if __name__ == "__main__":
NumpyTest().run()
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