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# mode: run
# tag: cpp, cpp11
from libcpp.random cimport mt19937, mt19937_64, random_device, uniform_int_distribution, \
uniform_real_distribution, bernoulli_distribution, binomial_distribution, \
geometric_distribution, negative_binomial_distribution, poisson_distribution, \
exponential_distribution, gamma_distribution, weibull_distribution, \
extreme_value_distribution, normal_distribution, lognormal_distribution, \
chi_squared_distribution, cauchy_distribution, fisher_f_distribution, student_t_distribution
from libc.float cimport DBL_MAX as DBL_MAX_
DBL_MAX = DBL_MAX_
def mt19937_seed_test():
"""
>>> print(mt19937_seed_test())
1608637542
"""
cdef mt19937 gen = mt19937(42)
return gen()
def mt19937_reseed_test():
"""
>>> print(mt19937_reseed_test())
1608637542
"""
cdef mt19937 gen
gen.seed(42)
return gen()
def mt19937_min_max():
"""
>>> x, y = mt19937_min_max()
>>> print(x)
0
>>> print(y) # 2 ** 32 - 1 because mt19937 is 32 bit.
4294967295
"""
cdef mt19937 gen
return gen.min(), gen.max()
def mt19937_discard(z):
"""
>>> x, y = mt19937_discard(13)
>>> print(x)
1972458954
>>> print(y)
1972458954
"""
cdef mt19937 gen = mt19937(42)
# Throw away z random numbers.
gen.discard(z)
a = gen()
# Iterate over z random numbers.
gen.seed(42)
for _ in range(z + 1):
b = gen()
return a, b
def mt19937_64_seed_test():
"""
>>> print(mt19937_64_seed_test())
13930160852258120406
"""
cdef mt19937_64 gen = mt19937_64(42)
return gen()
def mt19937_64_reseed_test():
"""
>>> print(mt19937_64_reseed_test())
13930160852258120406
"""
cdef mt19937_64 gen
gen.seed(42)
return gen()
def mt19937_64_min_max():
"""
>>> x, y = mt19937_64_min_max()
>>> print(x)
0
>>> print(y) # 2 ** 64 - 1 because mt19937_64 is 64 bit.
18446744073709551615
"""
cdef mt19937_64 gen
return gen.min(), gen.max()
def mt19937_64_discard(z):
"""
>>> x, y = mt19937_64_discard(13)
>>> print(x)
11756813601242511406
>>> print(y)
11756813601242511406
"""
cdef mt19937_64 gen = mt19937_64(42)
# Throw away z random numbers.
gen.discard(z)
a = gen()
# Iterate over z random numbers.
gen.seed(42)
for _ in range(z + 1):
b = gen()
return a, b
ctypedef fused any_dist:
uniform_int_distribution[int]
uniform_real_distribution[double]
bernoulli_distribution
binomial_distribution[int]
geometric_distribution[int]
negative_binomial_distribution[int]
poisson_distribution[int]
exponential_distribution[double]
gamma_distribution[double]
weibull_distribution[double]
extreme_value_distribution[double]
normal_distribution[double]
lognormal_distribution[double]
chi_squared_distribution[double]
cauchy_distribution[double]
fisher_f_distribution[double]
student_t_distribution[double]
cdef sample_or_range(any_dist dist, bint sample):
"""
This helper function returns a sample if `sample` is truthy and the range of the distribution
if `sample` is falsy. We use a fused type to avoid duplicating the conditional statement in each
distribution test.
"""
cdef random_device rd
if sample:
dist(mt19937(rd()))
else:
return dist.min(), dist.max()
def uniform_int_distribution_test(a, b, sample=True):
"""
>>> uniform_int_distribution_test(2, 3)
>>> uniform_int_distribution_test(5, 9, False)
(5, 9)
"""
cdef uniform_int_distribution[int] dist = uniform_int_distribution[int](a, b)
return sample_or_range[uniform_int_distribution[int]](dist, sample)
def uniform_real_distribution_test(a, b, sample=True):
"""
>>> x = uniform_real_distribution_test(4, 5)
>>> uniform_real_distribution_test(3, 8, False)
(3.0, 8.0)
"""
cdef uniform_real_distribution[double] dist = uniform_real_distribution[double](a, b)
return sample_or_range[uniform_real_distribution[double]](dist, sample)
def bernoulli_distribution_test(proba, sample=True):
"""
>>> bernoulli_distribution_test(0.2)
>>> bernoulli_distribution_test(0.7, False)
(False, True)
"""
cdef bernoulli_distribution dist = bernoulli_distribution(proba)
return sample_or_range[bernoulli_distribution](dist, sample)
def binomial_distribution_test(n, proba, sample=True):
"""
>>> binomial_distribution_test(10, 0.7)
>>> binomial_distribution_test(75, 0.3, False)
(0, 75)
"""
cdef binomial_distribution[int] dist = binomial_distribution[int](n, proba)
return sample_or_range[binomial_distribution[int]](dist, sample)
def geometric_distribution_test(proba, sample=True):
"""
>>> geometric_distribution_test(.4)
>>> geometric_distribution_test(0.2, False) # 2147483647 = 2 ** 32 - 1
(0, 2147483647)
"""
cdef geometric_distribution[int] dist = geometric_distribution[int](proba)
return sample_or_range[geometric_distribution[int]](dist, sample)
def negative_binomial_distribution_test(n, p, sample=True):
"""
>>> negative_binomial_distribution_test(5, .1)
>>> negative_binomial_distribution_test(10, 0.2, False) # 2147483647 = 2 ** 32 - 1
(0, 2147483647)
"""
cdef negative_binomial_distribution[int] dist = negative_binomial_distribution[int](n, p)
return sample_or_range[negative_binomial_distribution[int]](dist, sample)
def poisson_distribution_test(rate, sample=True):
"""
>>> poisson_distribution_test(7)
>>> poisson_distribution_test(7, False) # 2147483647 = 2 ** 32 - 1
(0, 2147483647)
"""
cdef poisson_distribution[int] dist = poisson_distribution[int](rate)
return sample_or_range[poisson_distribution[int]](dist, sample)
def exponential_distribution_test(rate, sample=True):
"""
>>> x = exponential_distribution_test(6)
>>> l, u = exponential_distribution_test(1, False)
>>> l
0.0
>>> u == DBL_MAX or u == float("inf")
True
"""
cdef exponential_distribution[double] dist = exponential_distribution[double](rate)
return sample_or_range[exponential_distribution[double]](dist, sample)
def gamma_distribution_test(shape, scale, sample=True):
"""
>>> gamma_distribution_test(3, 4)
>>> l, u = gamma_distribution_test(1, 1, False)
>>> l
0.0
>>> u == DBL_MAX or u == float("inf")
True
"""
cdef gamma_distribution[double] dist = gamma_distribution[double](shape, scale)
return sample_or_range[gamma_distribution[double]](dist, sample)
def weibull_distribution_test(shape, scale, sample=True):
"""
>>> weibull_distribution_test(3, 2)
>>> l, u = weibull_distribution_test(1, 1, False)
>>> l
0.0
>>> u == DBL_MAX or u == float("inf")
True
"""
cdef weibull_distribution[double] dist = weibull_distribution[double](shape, scale)
return sample_or_range[weibull_distribution[double]](dist, sample)
def extreme_value_distribution_test(shape, scale, sample=True):
"""
>>> extreme_value_distribution_test(3, 0.1)
>>> l, u = extreme_value_distribution_test(1, 1, False)
>>> l == -DBL_MAX or l == -float("inf")
True
>>> u == DBL_MAX or u == float("inf")
True
"""
cdef extreme_value_distribution[double] dist = extreme_value_distribution[double](shape, scale)
return sample_or_range[extreme_value_distribution[double]](dist, sample)
def normal_distribution_test(loc, scale, sample=True):
"""
>>> normal_distribution_test(3, 2)
>>> l, u = normal_distribution_test(1, 1, False)
>>> l == -DBL_MAX or l == -float("inf")
True
>>> u == DBL_MAX or u == float("inf")
True
"""
cdef normal_distribution[double] dist = normal_distribution[double](loc, scale)
return sample_or_range[normal_distribution[double]](dist, sample)
def lognormal_distribution_test(loc, scale, sample=True):
"""
>>> lognormal_distribution_test(3, 2)
>>> l, u = lognormal_distribution_test(1, 1, False)
>>> l
0.0
>>> u == DBL_MAX or u == float("inf")
True
"""
cdef lognormal_distribution[double] dist = lognormal_distribution[double](loc, scale)
return sample_or_range[lognormal_distribution[double]](dist, sample)
def chi_squared_distribution_test(dof, sample=True):
"""
>>> x = chi_squared_distribution_test(9)
>>> l, u = chi_squared_distribution_test(5, False)
>>> l
0.0
>>> u == DBL_MAX or u == float("inf")
True
"""
cdef chi_squared_distribution[double] dist = chi_squared_distribution[double](dof)
return sample_or_range[chi_squared_distribution[double]](dist, sample)
def cauchy_distribution_test(loc, scale, sample=True):
"""
>>> cauchy_distribution_test(3, 9)
>>> l, u = cauchy_distribution_test(1, 1, False)
>>> l == -DBL_MAX or l == -float("inf")
True
>>> u == DBL_MAX or u == float("inf")
True
"""
cdef cauchy_distribution[double] dist = cauchy_distribution[double](loc, scale)
return sample_or_range[cauchy_distribution[double]](dist, sample)
def fisher_f_distribution_test(m, n, sample=True):
"""
>>> x = fisher_f_distribution_test(9, 11)
>>> l, u = fisher_f_distribution_test(1, 1, False)
>>> l
0.0
>>> u == DBL_MAX or u == float("inf")
True
"""
cdef fisher_f_distribution[double] dist = fisher_f_distribution[double](m, n)
return sample_or_range[fisher_f_distribution[double]](dist, sample)
def student_t_distribution_test(dof, sample=True):
"""
>>> x = student_t_distribution_test(13)
>>> l, u = student_t_distribution_test(1, False)
>>> l == -DBL_MAX or l == -float("inf")
True
>>> u == DBL_MAX or u == float("inf")
True
"""
cdef student_t_distribution[double] dist = student_t_distribution[double](dof)
return sample_or_range[student_t_distribution[double]](dist, sample)
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