diff options
Diffstat (limited to 'tests/run/cpp_stl_random.pyx')
| -rw-r--r-- | tests/run/cpp_stl_random.pyx | 319 |
1 files changed, 306 insertions, 13 deletions
diff --git a/tests/run/cpp_stl_random.pyx b/tests/run/cpp_stl_random.pyx index 58f7db040..3b074c278 100644 --- a/tests/run/cpp_stl_random.pyx +++ b/tests/run/cpp_stl_random.pyx @@ -1,7 +1,16 @@ # mode: run # tag: cpp, cpp11 -from libcpp.random cimport mt19937 +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(): @@ -9,8 +18,8 @@ def mt19937_seed_test(): >>> print(mt19937_seed_test()) 1608637542 """ - cdef mt19937 rd = mt19937(42) - return rd() + cdef mt19937 gen = mt19937(42) + return gen() def mt19937_reseed_test(): @@ -18,9 +27,9 @@ def mt19937_reseed_test(): >>> print(mt19937_reseed_test()) 1608637542 """ - cdef mt19937 rd - rd.seed(42) - return rd() + cdef mt19937 gen + gen.seed(42) + return gen() def mt19937_min_max(): @@ -31,8 +40,8 @@ def mt19937_min_max(): >>> print(y) # 2 ** 32 - 1 because mt19937 is 32 bit. 4294967295 """ - cdef mt19937 rd - return rd.min(), rd.max() + cdef mt19937 gen + return gen.min(), gen.max() def mt19937_discard(z): @@ -43,13 +52,297 @@ def mt19937_discard(z): >>> print(y) 1972458954 """ - cdef mt19937 rd = mt19937(42) + cdef mt19937 gen = mt19937(42) # Throw away z random numbers. - rd.discard(z) - a = rd() + gen.discard(z) + a = gen() # Iterate over z random numbers. - rd.seed(42) + gen.seed(42) for _ in range(z + 1): - b = rd() + 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) |