972 lines
34 KiB
Python
972 lines
34 KiB
Python
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"""
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Implementation of method overloads for Generator objects.
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"""
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import numpy as np
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from numba.core import types
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from numba.core.extending import overload_method, register_jitable
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from numba.np.numpy_support import as_dtype, from_dtype
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from numba.np.random.generator_core import next_float, next_double
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from numba.np.numpy_support import is_nonelike
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from numba.core.errors import TypingError
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from numba.core.types.containers import Tuple, UniTuple
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from numba.np.random.distributions import \
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(random_standard_exponential_inv_f, random_standard_exponential_inv,
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random_standard_exponential, random_standard_normal_f,
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random_standard_gamma, random_standard_normal, random_uniform,
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random_standard_exponential_f, random_standard_gamma_f, random_normal,
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random_exponential, random_gamma, random_beta, random_power,
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random_f,random_chisquare,random_standard_cauchy,random_pareto,
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random_weibull, random_laplace, random_logistic,
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random_lognormal, random_rayleigh, random_standard_t, random_wald,
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random_geometric, random_zipf, random_triangular,
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random_poisson, random_negative_binomial, random_logseries,
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random_noncentral_chisquare, random_noncentral_f, random_binomial)
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from numba.np.random import random_methods
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def _get_proper_func(func_32, func_64, dtype, dist_name="the given"):
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"""
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Most of the standard NumPy distributions that accept dtype argument
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only support either np.float32 or np.float64 as dtypes.
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This is a helper function that helps Numba select the proper underlying
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implementation according to provided dtype.
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"""
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if isinstance(dtype, types.Omitted):
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dtype = dtype.value
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np_dt = dtype
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if isinstance(dtype, type):
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nb_dt = from_dtype(np.dtype(dtype))
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elif isinstance(dtype, types.NumberClass):
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nb_dt = dtype
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np_dt = as_dtype(nb_dt)
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if np_dt not in [np.float32, np.float64]:
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raise TypingError("Argument dtype is not one of the" +
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" expected type(s): " +
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" np.float32 or np.float64")
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if np_dt == np.float32:
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next_func = func_32
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else:
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next_func = func_64
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return next_func, nb_dt
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def check_size(size):
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if not any([isinstance(size, UniTuple) and
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isinstance(size.dtype, types.Integer),
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isinstance(size, Tuple) and size.count == 0,
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isinstance(size, types.Integer)]):
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raise TypingError("Argument size is not one of the" +
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" expected type(s): " +
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" an integer, an empty tuple or a tuple of integers")
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def check_types(obj, type_list, arg_name):
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"""
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Check if given object is one of the provided types.
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If not raises an TypeError
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"""
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if isinstance(obj, types.Omitted):
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obj = obj.value
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if not isinstance(type_list, (list, tuple)):
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type_list = [type_list]
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if not any([isinstance(obj, _type) for _type in type_list]):
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raise TypingError(f"Argument {arg_name} is not one of the" +
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f" expected type(s): {type_list}")
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# Overload the Generator().integers()
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@overload_method(types.NumPyRandomGeneratorType, 'integers')
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def NumPyRandomGeneratorType_integers(inst, low, high, size=None,
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dtype=np.int64, endpoint=False):
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check_types(low, [types.Integer,
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types.Boolean, bool, int], 'low')
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check_types(high, [types.Integer, types.Boolean,
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bool, int], 'high')
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check_types(endpoint, [types.Boolean, bool], 'endpoint')
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if isinstance(size, types.Omitted):
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size = size.value
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if isinstance(dtype, types.Omitted):
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dtype = dtype.value
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if isinstance(dtype, type):
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nb_dt = from_dtype(np.dtype(dtype))
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_dtype = dtype
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elif isinstance(dtype, types.NumberClass):
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nb_dt = dtype
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_dtype = as_dtype(nb_dt)
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else:
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raise TypingError("Argument dtype is not one of the" +
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" expected type(s): " +
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"np.int32, np.int64, np.int16, np.int8, "
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"np.uint32, np.uint64, np.uint16, np.uint8, "
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"np.bool_")
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if _dtype == np.bool_:
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int_func = random_methods.random_bounded_bool_fill
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lower_bound = -1
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upper_bound = 2
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else:
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try:
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i_info = np.iinfo(_dtype)
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except ValueError:
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raise TypingError("Argument dtype is not one of the" +
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" expected type(s): " +
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"np.int32, np.int64, np.int16, np.int8, "
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"np.uint32, np.uint64, np.uint16, np.uint8, "
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"np.bool_")
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int_func = getattr(random_methods,
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f'random_bounded_uint{i_info.bits}_fill')
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lower_bound = i_info.min
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upper_bound = i_info.max
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if is_nonelike(size):
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def impl(inst, low, high, size=None,
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dtype=np.int64, endpoint=False):
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random_methods._randint_arg_check(low, high, endpoint,
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lower_bound, upper_bound)
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if not endpoint:
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high -= dtype(1)
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low = dtype(low)
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high = dtype(high)
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rng = high - low
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return int_func(inst.bit_generator, low, rng, 1, dtype)[0]
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else:
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low = dtype(low)
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high = dtype(high)
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rng = high - low
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return int_func(inst.bit_generator, low, rng, 1, dtype)[0]
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return impl
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else:
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check_size(size)
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def impl(inst, low, high, size=None,
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dtype=np.int64, endpoint=False):
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random_methods._randint_arg_check(low, high, endpoint,
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lower_bound, upper_bound)
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if not endpoint:
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high -= dtype(1)
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low = dtype(low)
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high = dtype(high)
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rng = high - low
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return int_func(inst.bit_generator, low, rng, size, dtype)
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else:
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low = dtype(low)
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high = dtype(high)
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rng = high - low
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return int_func(inst.bit_generator, low, rng, size, dtype)
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return impl
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# The following `shuffle` implementation is a direct translation from:
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# https://github.com/numpy/numpy/blob/95e3e7f445407e4f355b23d6a9991d8774f0eb0c/numpy/random/_generator.pyx#L4578
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# Overload the Generator().shuffle()
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@overload_method(types.NumPyRandomGeneratorType, 'shuffle')
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def NumPyRandomGeneratorType_shuffle(inst, x, axis=0):
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check_types(x, [types.Array], 'x')
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check_types(axis, [int, types.Integer], 'axis')
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def impl(inst, x, axis=0):
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if axis < 0:
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axis = axis + x.ndim
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if axis > x.ndim - 1 or axis < 0:
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raise IndexError("Axis is out of bounds for the given array")
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z = np.swapaxes(x, 0, axis)
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buf = np.empty_like(z[0, ...])
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for i in range(len(z) - 1, 0, -1):
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j = types.intp(random_methods.random_interval(inst.bit_generator,
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i))
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if i == j:
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continue
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buf[...] = z[j, ...]
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z[j, ...] = z[i, ...]
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z[i, ...] = buf
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return impl
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# The following `permutation` implementation is a direct translation from:
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# https://github.com/numpy/numpy/blob/95e3e7f445407e4f355b23d6a9991d8774f0eb0c/numpy/random/_generator.pyx#L4710
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# Overload the Generator().permutation()
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@overload_method(types.NumPyRandomGeneratorType, 'permutation')
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def NumPyRandomGeneratorType_permutation(inst, x, axis=0):
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check_types(x, [types.Array, types.Integer], 'x')
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check_types(axis, [int, types.Integer], 'axis')
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IS_INT = isinstance(x, types.Integer)
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def impl(inst, x, axis=0):
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if IS_INT:
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new_arr = np.arange(x)
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# NumPy ignores the axis argument when x is an integer
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inst.shuffle(new_arr)
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else:
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new_arr = x.copy()
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inst.shuffle(new_arr, axis=axis)
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return new_arr
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return impl
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# Overload the Generator().random()
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@overload_method(types.NumPyRandomGeneratorType, 'random')
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def NumPyRandomGeneratorType_random(inst, size=None, dtype=np.float64):
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dist_func, nb_dt = _get_proper_func(next_float, next_double,
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dtype, "random")
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if isinstance(size, types.Omitted):
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size = size.value
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if is_nonelike(size):
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def impl(inst, size=None, dtype=np.float64):
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return nb_dt(dist_func(inst.bit_generator))
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return impl
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else:
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check_size(size)
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def impl(inst, size=None, dtype=np.float64):
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out = np.empty(size, dtype=dtype)
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out_f = out.flat
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for i in range(out.size):
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out_f[i] = dist_func(inst.bit_generator)
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return out
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return impl
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# Overload the Generator().standard_exponential() method
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@overload_method(types.NumPyRandomGeneratorType, 'standard_exponential')
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def NumPyRandomGeneratorType_standard_exponential(inst, size=None,
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dtype=np.float64,
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method='zig'):
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check_types(method, [types.UnicodeType, str], 'method')
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dist_func_inv, nb_dt = _get_proper_func(
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random_standard_exponential_inv_f,
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random_standard_exponential_inv,
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dtype
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)
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dist_func, nb_dt = _get_proper_func(random_standard_exponential_f,
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random_standard_exponential,
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dtype)
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if isinstance(size, types.Omitted):
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size = size.value
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if is_nonelike(size):
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def impl(inst, size=None, dtype=np.float64, method='zig'):
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if method == 'zig':
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return nb_dt(dist_func(inst.bit_generator))
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elif method == 'inv':
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return nb_dt(dist_func_inv(inst.bit_generator))
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else:
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raise ValueError("Method must be either 'zig' or 'inv'")
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return impl
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else:
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check_size(size)
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def impl(inst, size=None, dtype=np.float64, method='zig'):
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out = np.empty(size, dtype=dtype)
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out_f = out.flat
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if method == 'zig':
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for i in range(out.size):
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out_f[i] = dist_func(inst.bit_generator)
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elif method == 'inv':
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for i in range(out.size):
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out_f[i] = dist_func_inv(inst.bit_generator)
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else:
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raise ValueError("Method must be either 'zig' or 'inv'")
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return out
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return impl
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# Overload the Generator().standard_normal() method
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@overload_method(types.NumPyRandomGeneratorType, 'standard_normal')
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def NumPyRandomGeneratorType_standard_normal(inst, size=None, dtype=np.float64):
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dist_func, nb_dt = _get_proper_func(random_standard_normal_f,
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random_standard_normal,
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dtype)
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if isinstance(size, types.Omitted):
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size = size.value
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if is_nonelike(size):
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def impl(inst, size=None, dtype=np.float64):
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return nb_dt(dist_func(inst.bit_generator))
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return impl
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else:
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check_size(size)
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def impl(inst, size=None, dtype=np.float64):
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out = np.empty(size, dtype=dtype)
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out_f = out.flat
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for i in range(out.size):
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out_f[i] = dist_func(inst.bit_generator)
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return out
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return impl
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# Overload the Generator().standard_gamma() method
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@overload_method(types.NumPyRandomGeneratorType, 'standard_gamma')
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def NumPyRandomGeneratorType_standard_gamma(inst, shape, size=None,
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dtype=np.float64):
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check_types(shape, [types.Float, types.Integer, int, float], 'shape')
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dist_func, nb_dt = _get_proper_func(random_standard_gamma_f,
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random_standard_gamma,
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dtype)
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if isinstance(size, types.Omitted):
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size = size.value
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if is_nonelike(size):
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def impl(inst, shape, size=None, dtype=np.float64):
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return nb_dt(dist_func(inst.bit_generator, shape))
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return impl
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else:
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check_size(size)
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def impl(inst, shape, size=None, dtype=np.float64):
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out = np.empty(size, dtype=dtype)
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out_f = out.flat
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for i in range(out.size):
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out_f[i] = dist_func(inst.bit_generator, shape)
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return out
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return impl
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# Overload the Generator().normal() method
|
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@overload_method(types.NumPyRandomGeneratorType, 'normal')
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def NumPyRandomGeneratorType_normal(inst, loc=0.0, scale=1.0,
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size=None):
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check_types(loc, [types.Float, types.Integer, int, float], 'loc')
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check_types(scale, [types.Float, types.Integer, int, float], 'scale')
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if isinstance(size, types.Omitted):
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size = size.value
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if is_nonelike(size):
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|
|
def impl(inst, loc=0.0, scale=1.0, size=None):
|
||
|
|
return random_normal(inst.bit_generator, loc, scale)
|
||
|
|
return impl
|
||
|
|
else:
|
||
|
|
check_size(size)
|
||
|
|
|
||
|
|
def impl(inst, loc=0.0, scale=1.0, size=None):
|
||
|
|
out = np.empty(size, dtype=np.float64)
|
||
|
|
out_f = out.flat
|
||
|
|
for i in range(out.size):
|
||
|
|
out_f[i] = random_normal(inst.bit_generator, loc, scale)
|
||
|
|
return out
|
||
|
|
return impl
|
||
|
|
|
||
|
|
|
||
|
|
# Overload the Generator().uniform() method
|
||
|
|
@overload_method(types.NumPyRandomGeneratorType, 'uniform')
|
||
|
|
def NumPyRandomGeneratorType_uniform(inst, low=0.0, high=1.0,
|
||
|
|
size=None):
|
||
|
|
check_types(low, [types.Float, types.Integer, int, float], 'low')
|
||
|
|
check_types(high, [types.Float, types.Integer, int, float], 'high')
|
||
|
|
if isinstance(size, types.Omitted):
|
||
|
|
size = size.value
|
||
|
|
|
||
|
|
if is_nonelike(size):
|
||
|
|
def impl(inst, low=0.0, high=1.0, size=None):
|
||
|
|
return random_uniform(inst.bit_generator, low, high - low)
|
||
|
|
return impl
|
||
|
|
else:
|
||
|
|
check_size(size)
|
||
|
|
|
||
|
|
def impl(inst, low=0.0, high=1.0, size=None):
|
||
|
|
out = np.empty(size, dtype=np.float64)
|
||
|
|
out_f = out.flat
|
||
|
|
for i in range(out.size):
|
||
|
|
out_f[i] = random_uniform(inst.bit_generator, low, high - low)
|
||
|
|
return out
|
||
|
|
return impl
|
||
|
|
|
||
|
|
|
||
|
|
# Overload the Generator().exponential() method
|
||
|
|
@overload_method(types.NumPyRandomGeneratorType, 'exponential')
|
||
|
|
def NumPyRandomGeneratorType_exponential(inst, scale=1.0, size=None):
|
||
|
|
check_types(scale, [types.Float, types.Integer, int, float], 'scale')
|
||
|
|
if isinstance(size, types.Omitted):
|
||
|
|
size = size.value
|
||
|
|
|
||
|
|
if is_nonelike(size):
|
||
|
|
def impl(inst, scale=1.0, size=None):
|
||
|
|
return random_exponential(inst.bit_generator, scale)
|
||
|
|
return impl
|
||
|
|
else:
|
||
|
|
check_size(size)
|
||
|
|
|
||
|
|
def impl(inst, scale=1.0, size=None):
|
||
|
|
out = np.empty(size, dtype=np.float64)
|
||
|
|
out_f = out.flat
|
||
|
|
for i in range(out.size):
|
||
|
|
out_f[i] = random_exponential(inst.bit_generator, scale)
|
||
|
|
return out
|
||
|
|
return impl
|
||
|
|
|
||
|
|
|
||
|
|
# Overload the Generator().gamma() method
|
||
|
|
@overload_method(types.NumPyRandomGeneratorType, 'gamma')
|
||
|
|
def NumPyRandomGeneratorType_gamma(inst, shape, scale=1.0, size=None):
|
||
|
|
check_types(shape, [types.Float, types.Integer, int, float], 'shape')
|
||
|
|
check_types(scale, [types.Float, types.Integer, int, float], 'scale')
|
||
|
|
if isinstance(size, types.Omitted):
|
||
|
|
size = size.value
|
||
|
|
|
||
|
|
if is_nonelike(size):
|
||
|
|
def impl(inst, shape, scale=1.0, size=None):
|
||
|
|
return random_gamma(inst.bit_generator, shape, scale)
|
||
|
|
return impl
|
||
|
|
else:
|
||
|
|
check_size(size)
|
||
|
|
|
||
|
|
def impl(inst, shape, scale=1.0, size=None):
|
||
|
|
out = np.empty(size, dtype=np.float64)
|
||
|
|
out_f = out.flat
|
||
|
|
for i in range(out.size):
|
||
|
|
out_f[i] = random_gamma(inst.bit_generator, shape, scale)
|
||
|
|
return out
|
||
|
|
return impl
|
||
|
|
|
||
|
|
|
||
|
|
# Overload the Generator().beta() method
|
||
|
|
@overload_method(types.NumPyRandomGeneratorType, 'beta')
|
||
|
|
def NumPyRandomGeneratorType_beta(inst, a, b, size=None):
|
||
|
|
check_types(a, [types.Float, types.Integer, int, float], 'a')
|
||
|
|
check_types(b, [types.Float, types.Integer, int, float], 'b')
|
||
|
|
if isinstance(size, types.Omitted):
|
||
|
|
size = size.value
|
||
|
|
|
||
|
|
if is_nonelike(size):
|
||
|
|
def impl(inst, a, b, size=None):
|
||
|
|
return random_beta(inst.bit_generator, a, b)
|
||
|
|
return impl
|
||
|
|
else:
|
||
|
|
check_size(size)
|
||
|
|
|
||
|
|
def impl(inst, a, b, size=None):
|
||
|
|
out = np.empty(size)
|
||
|
|
out_f = out.flat
|
||
|
|
for i in range(out.size):
|
||
|
|
out_f[i] = random_beta(inst.bit_generator, a, b)
|
||
|
|
return out
|
||
|
|
return impl
|
||
|
|
|
||
|
|
|
||
|
|
# Overload the Generator().f() method
|
||
|
|
@overload_method(types.NumPyRandomGeneratorType, 'f')
|
||
|
|
def NumPyRandomGeneratorType_f(inst, dfnum, dfden, size=None):
|
||
|
|
check_types(dfnum, [types.Float, types.Integer, int, float], 'dfnum')
|
||
|
|
check_types(dfden, [types.Float, types.Integer, int, float], 'dfden')
|
||
|
|
if isinstance(size, types.Omitted):
|
||
|
|
size = size.value
|
||
|
|
|
||
|
|
if is_nonelike(size):
|
||
|
|
def impl(inst, dfnum, dfden, size=None):
|
||
|
|
return random_f(inst.bit_generator, dfnum, dfden)
|
||
|
|
return impl
|
||
|
|
else:
|
||
|
|
check_size(size)
|
||
|
|
|
||
|
|
def impl(inst, dfnum, dfden, size=None):
|
||
|
|
out = np.empty(size)
|
||
|
|
out_f = out.flat
|
||
|
|
for i in range(out.size):
|
||
|
|
out_f[i] = random_f(inst.bit_generator, dfnum, dfden)
|
||
|
|
return out
|
||
|
|
return impl
|
||
|
|
|
||
|
|
|
||
|
|
# Overload the Generator().chisquare() method
|
||
|
|
@overload_method(types.NumPyRandomGeneratorType, 'chisquare')
|
||
|
|
def NumPyRandomGeneratorType_chisquare(inst, df, size=None):
|
||
|
|
check_types(df, [types.Float, types.Integer, int, float], 'df')
|
||
|
|
if isinstance(size, types.Omitted):
|
||
|
|
size = size.value
|
||
|
|
|
||
|
|
if is_nonelike(size):
|
||
|
|
def impl(inst, df, size=None):
|
||
|
|
return random_chisquare(inst.bit_generator, df)
|
||
|
|
return impl
|
||
|
|
else:
|
||
|
|
check_size(size)
|
||
|
|
|
||
|
|
def impl(inst, df, size=None):
|
||
|
|
out = np.empty(size)
|
||
|
|
out_f = out.flat
|
||
|
|
for i in range(out.size):
|
||
|
|
out_f[i] = random_chisquare(inst.bit_generator, df)
|
||
|
|
return out
|
||
|
|
return impl
|
||
|
|
|
||
|
|
|
||
|
|
@overload_method(types.NumPyRandomGeneratorType, 'standard_cauchy')
|
||
|
|
def NumPyRandomGeneratorType_standard_cauchy(inst, size=None):
|
||
|
|
|
||
|
|
if isinstance(size, types.Omitted):
|
||
|
|
size = size.value
|
||
|
|
|
||
|
|
if is_nonelike(size):
|
||
|
|
def impl(inst, size=None):
|
||
|
|
return random_standard_cauchy(inst.bit_generator)
|
||
|
|
return impl
|
||
|
|
else:
|
||
|
|
check_size(size)
|
||
|
|
|
||
|
|
def impl(inst, size=None):
|
||
|
|
out = np.empty(size)
|
||
|
|
out_f = out.flat
|
||
|
|
for i in range(out.size):
|
||
|
|
out_f[i] = random_standard_cauchy(inst.bit_generator)
|
||
|
|
return out
|
||
|
|
return impl
|
||
|
|
|
||
|
|
|
||
|
|
@overload_method(types.NumPyRandomGeneratorType, 'pareto')
|
||
|
|
def NumPyRandomGeneratorType_pareto(inst, a, size=None):
|
||
|
|
check_types(a, [types.Float, types.Integer, int, float], 'a')
|
||
|
|
if isinstance(size, types.Omitted):
|
||
|
|
size = size.value
|
||
|
|
|
||
|
|
if is_nonelike(size):
|
||
|
|
def impl(inst, a, size=None):
|
||
|
|
return random_pareto(inst.bit_generator, a)
|
||
|
|
return impl
|
||
|
|
else:
|
||
|
|
check_size(size)
|
||
|
|
|
||
|
|
def impl(inst, a, size=None):
|
||
|
|
out = np.empty(size)
|
||
|
|
out_f = out.flat
|
||
|
|
for i in range(out.size):
|
||
|
|
out_f[i] = random_pareto(inst.bit_generator, a)
|
||
|
|
return out
|
||
|
|
return impl
|
||
|
|
|
||
|
|
|
||
|
|
@overload_method(types.NumPyRandomGeneratorType, 'weibull')
|
||
|
|
def NumPyRandomGeneratorType_weibull(inst, a, size=None):
|
||
|
|
check_types(a, [types.Float, types.Integer, int, float], 'a')
|
||
|
|
if isinstance(size, types.Omitted):
|
||
|
|
size = size.value
|
||
|
|
|
||
|
|
if is_nonelike(size):
|
||
|
|
def impl(inst, a, size=None):
|
||
|
|
return random_weibull(inst.bit_generator, a)
|
||
|
|
return impl
|
||
|
|
else:
|
||
|
|
check_size(size)
|
||
|
|
|
||
|
|
def impl(inst, a, size=None):
|
||
|
|
out = np.empty(size)
|
||
|
|
out_f = out.flat
|
||
|
|
for i in range(out.size):
|
||
|
|
out_f[i] = random_weibull(inst.bit_generator, a)
|
||
|
|
return out
|
||
|
|
return impl
|
||
|
|
|
||
|
|
|
||
|
|
@overload_method(types.NumPyRandomGeneratorType, 'power')
|
||
|
|
def NumPyRandomGeneratorType_power(inst, a, size=None):
|
||
|
|
check_types(a, [types.Float, types.Integer, int, float], 'a')
|
||
|
|
if isinstance(size, types.Omitted):
|
||
|
|
size = size.value
|
||
|
|
|
||
|
|
if is_nonelike(size):
|
||
|
|
def impl(inst, a, size=None):
|
||
|
|
return random_power(inst.bit_generator, a)
|
||
|
|
return impl
|
||
|
|
else:
|
||
|
|
check_size(size)
|
||
|
|
|
||
|
|
def impl(inst, a, size=None):
|
||
|
|
out = np.empty(size)
|
||
|
|
out_f = out.flat
|
||
|
|
for i in range(out.size):
|
||
|
|
out_f[i] = random_power(inst.bit_generator, a)
|
||
|
|
return out
|
||
|
|
return impl
|
||
|
|
|
||
|
|
|
||
|
|
@overload_method(types.NumPyRandomGeneratorType, 'laplace')
|
||
|
|
def NumPyRandomGeneratorType_laplace(inst, loc=0.0, scale=1.0, size=None):
|
||
|
|
check_types(loc, [types.Float, types.Integer, int, float], 'loc')
|
||
|
|
check_types(scale, [types.Float, types.Integer, int, float], 'scale')
|
||
|
|
if isinstance(size, types.Omitted):
|
||
|
|
size = size.value
|
||
|
|
|
||
|
|
if is_nonelike(size):
|
||
|
|
def impl(inst, loc=0.0, scale=1.0, size=None):
|
||
|
|
return random_laplace(inst.bit_generator, loc, scale)
|
||
|
|
return impl
|
||
|
|
else:
|
||
|
|
check_size(size)
|
||
|
|
|
||
|
|
def impl(inst, loc=0.0, scale=1.0, size=None):
|
||
|
|
out = np.empty(size)
|
||
|
|
out_f = out.flat
|
||
|
|
for i in range(out.size):
|
||
|
|
out_f[i] = random_laplace(inst.bit_generator, loc, scale)
|
||
|
|
return out
|
||
|
|
return impl
|
||
|
|
|
||
|
|
|
||
|
|
@overload_method(types.NumPyRandomGeneratorType, 'logistic')
|
||
|
|
def NumPyRandomGeneratorType_logistic(inst, loc=0.0, scale=1.0, size=None):
|
||
|
|
check_types(loc, [types.Float, types.Integer, int, float], 'loc')
|
||
|
|
check_types(scale, [types.Float, types.Integer, int, float], 'scale')
|
||
|
|
if isinstance(size, types.Omitted):
|
||
|
|
size = size.value
|
||
|
|
|
||
|
|
if is_nonelike(size):
|
||
|
|
def impl(inst, loc=0.0, scale=1.0, size=None):
|
||
|
|
return random_logistic(inst.bit_generator, loc, scale)
|
||
|
|
return impl
|
||
|
|
else:
|
||
|
|
check_size(size)
|
||
|
|
|
||
|
|
def impl(inst, loc=0.0, scale=1.0, size=None):
|
||
|
|
out = np.empty(size)
|
||
|
|
out_f = out.flat
|
||
|
|
for i in range(out.size):
|
||
|
|
out_f[i] = random_logistic(inst.bit_generator, loc, scale)
|
||
|
|
return out
|
||
|
|
return impl
|
||
|
|
|
||
|
|
|
||
|
|
@overload_method(types.NumPyRandomGeneratorType, 'lognormal')
|
||
|
|
def NumPyRandomGeneratorType_lognormal(inst, mean=0.0, sigma=1.0, size=None):
|
||
|
|
check_types(mean, [types.Float, types.Integer, int, float], 'mean')
|
||
|
|
check_types(sigma, [types.Float, types.Integer, int, float], 'sigma')
|
||
|
|
if isinstance(size, types.Omitted):
|
||
|
|
size = size.value
|
||
|
|
|
||
|
|
if is_nonelike(size):
|
||
|
|
def impl(inst, mean=0.0, sigma=1.0, size=None):
|
||
|
|
return random_lognormal(inst.bit_generator, mean, sigma)
|
||
|
|
return impl
|
||
|
|
else:
|
||
|
|
check_size(size)
|
||
|
|
|
||
|
|
def impl(inst, mean=0.0, sigma=1.0, size=None):
|
||
|
|
out = np.empty(size)
|
||
|
|
out_f = out.flat
|
||
|
|
for i in range(out.size):
|
||
|
|
out_f[i] = random_lognormal(inst.bit_generator, mean, sigma)
|
||
|
|
return out
|
||
|
|
return impl
|
||
|
|
|
||
|
|
|
||
|
|
@overload_method(types.NumPyRandomGeneratorType, 'rayleigh')
|
||
|
|
def NumPyRandomGeneratorType_rayleigh(inst, scale=1.0, size=None):
|
||
|
|
check_types(scale, [types.Float, types.Integer, int, float], 'scale')
|
||
|
|
if isinstance(size, types.Omitted):
|
||
|
|
size = size.value
|
||
|
|
|
||
|
|
if is_nonelike(size):
|
||
|
|
def impl(inst, scale=1.0, size=None):
|
||
|
|
return random_rayleigh(inst.bit_generator, scale)
|
||
|
|
return impl
|
||
|
|
else:
|
||
|
|
check_size(size)
|
||
|
|
|
||
|
|
def impl(inst, scale=1.0, size=None):
|
||
|
|
out = np.empty(size)
|
||
|
|
out_f = out.flat
|
||
|
|
for i in range(out.size):
|
||
|
|
out_f[i] = random_rayleigh(inst.bit_generator, scale)
|
||
|
|
return out
|
||
|
|
return impl
|
||
|
|
|
||
|
|
|
||
|
|
@overload_method(types.NumPyRandomGeneratorType, 'standard_t')
|
||
|
|
def NumPyRandomGeneratorType_standard_t(inst, df, size=None):
|
||
|
|
check_types(df, [types.Float, types.Integer, int, float], 'df')
|
||
|
|
if isinstance(size, types.Omitted):
|
||
|
|
size = size.value
|
||
|
|
|
||
|
|
if is_nonelike(size):
|
||
|
|
def impl(inst, df, size=None):
|
||
|
|
return random_standard_t(inst.bit_generator, df)
|
||
|
|
return impl
|
||
|
|
else:
|
||
|
|
check_size(size)
|
||
|
|
|
||
|
|
def impl(inst, df, size=None):
|
||
|
|
out = np.empty(size)
|
||
|
|
out_f = out.flat
|
||
|
|
for i in range(out.size):
|
||
|
|
out_f[i] = random_standard_t(inst.bit_generator, df)
|
||
|
|
return out
|
||
|
|
return impl
|
||
|
|
|
||
|
|
|
||
|
|
@overload_method(types.NumPyRandomGeneratorType, 'wald')
|
||
|
|
def NumPyRandomGeneratorType_wald(inst, mean, scale, size=None):
|
||
|
|
check_types(mean, [types.Float, types.Integer, int, float], 'mean')
|
||
|
|
check_types(scale, [types.Float, types.Integer, int, float], 'scale')
|
||
|
|
if isinstance(size, types.Omitted):
|
||
|
|
size = size.value
|
||
|
|
|
||
|
|
if is_nonelike(size):
|
||
|
|
def impl(inst, mean, scale, size=None):
|
||
|
|
return random_wald(inst.bit_generator, mean, scale)
|
||
|
|
return impl
|
||
|
|
else:
|
||
|
|
check_size(size)
|
||
|
|
|
||
|
|
def impl(inst, mean, scale, size=None):
|
||
|
|
out = np.empty(size)
|
||
|
|
out_f = out.flat
|
||
|
|
for i in range(out.size):
|
||
|
|
out_f[i] = random_wald(inst.bit_generator, mean, scale)
|
||
|
|
return out
|
||
|
|
return impl
|
||
|
|
|
||
|
|
|
||
|
|
@overload_method(types.NumPyRandomGeneratorType, 'geometric')
|
||
|
|
def NumPyRandomGeneratorType_geometric(inst, p, size=None):
|
||
|
|
check_types(p, [types.Float, types.Integer, int, float], 'p')
|
||
|
|
if isinstance(size, types.Omitted):
|
||
|
|
size = size.value
|
||
|
|
|
||
|
|
if is_nonelike(size):
|
||
|
|
def impl(inst, p, size=None):
|
||
|
|
return np.int64(random_geometric(inst.bit_generator, p))
|
||
|
|
return impl
|
||
|
|
else:
|
||
|
|
check_size(size)
|
||
|
|
|
||
|
|
def impl(inst, p, size=None):
|
||
|
|
out = np.empty(size, dtype=np.int64)
|
||
|
|
out_f = out.flat
|
||
|
|
for i in range(out.size):
|
||
|
|
out_f[i] = random_geometric(inst.bit_generator, p)
|
||
|
|
return out
|
||
|
|
return impl
|
||
|
|
|
||
|
|
|
||
|
|
@overload_method(types.NumPyRandomGeneratorType, 'zipf')
|
||
|
|
def NumPyRandomGeneratorType_zipf(inst, a, size=None):
|
||
|
|
check_types(a, [types.Float, types.Integer, int, float], 'a')
|
||
|
|
if isinstance(size, types.Omitted):
|
||
|
|
size = size.value
|
||
|
|
|
||
|
|
if is_nonelike(size):
|
||
|
|
def impl(inst, a, size=None):
|
||
|
|
return np.int64(random_zipf(inst.bit_generator, a))
|
||
|
|
return impl
|
||
|
|
else:
|
||
|
|
check_size(size)
|
||
|
|
|
||
|
|
def impl(inst, a, size=None):
|
||
|
|
out = np.empty(size, dtype=np.int64)
|
||
|
|
out_f = out.flat
|
||
|
|
for i in range(out.size):
|
||
|
|
out_f[i] = random_zipf(inst.bit_generator, a)
|
||
|
|
return out
|
||
|
|
return impl
|
||
|
|
|
||
|
|
|
||
|
|
@overload_method(types.NumPyRandomGeneratorType, 'triangular')
|
||
|
|
def NumPyRandomGeneratorType_triangular(inst, left, mode, right, size=None):
|
||
|
|
check_types(left, [types.Float, types.Integer, int, float], 'left')
|
||
|
|
check_types(mode, [types.Float, types.Integer, int, float], 'mode')
|
||
|
|
check_types(right, [types.Float, types.Integer, int, float], 'right')
|
||
|
|
if isinstance(size, types.Omitted):
|
||
|
|
size = size.value
|
||
|
|
|
||
|
|
if is_nonelike(size):
|
||
|
|
def impl(inst, left, mode, right, size=None):
|
||
|
|
return random_triangular(inst.bit_generator, left, mode, right)
|
||
|
|
return impl
|
||
|
|
else:
|
||
|
|
check_size(size)
|
||
|
|
|
||
|
|
def impl(inst, left, mode, right, size=None):
|
||
|
|
out = np.empty(size)
|
||
|
|
out_f = out.flat
|
||
|
|
for i in range(out.size):
|
||
|
|
out_f[i] = random_triangular(inst.bit_generator,
|
||
|
|
left, mode, right)
|
||
|
|
return out
|
||
|
|
return impl
|
||
|
|
|
||
|
|
|
||
|
|
@overload_method(types.NumPyRandomGeneratorType, 'poisson')
|
||
|
|
def NumPyRandomGeneratorType_poisson(inst, lam , size=None):
|
||
|
|
check_types(lam, [types.Float, types.Integer, int, float], 'lam')
|
||
|
|
if isinstance(size, types.Omitted):
|
||
|
|
size = size.value
|
||
|
|
|
||
|
|
if is_nonelike(size):
|
||
|
|
def impl(inst, lam , size=None):
|
||
|
|
return np.int64(random_poisson(inst.bit_generator, lam))
|
||
|
|
return impl
|
||
|
|
else:
|
||
|
|
check_size(size)
|
||
|
|
|
||
|
|
def impl(inst, lam , size=None):
|
||
|
|
out = np.empty(size, dtype=np.int64)
|
||
|
|
out_f = out.flat
|
||
|
|
for i in range(out.size):
|
||
|
|
out_f[i] = random_poisson(inst.bit_generator, lam)
|
||
|
|
return out
|
||
|
|
return impl
|
||
|
|
|
||
|
|
|
||
|
|
@overload_method(types.NumPyRandomGeneratorType, 'negative_binomial')
|
||
|
|
def NumPyRandomGeneratorType_negative_binomial(inst, n, p, size=None):
|
||
|
|
check_types(n, [types.Float, types.Integer, int, float], 'n')
|
||
|
|
check_types(p, [types.Float, types.Integer, int, float], 'p')
|
||
|
|
if isinstance(size, types.Omitted):
|
||
|
|
size = size.value
|
||
|
|
|
||
|
|
if is_nonelike(size):
|
||
|
|
def impl(inst, n, p , size=None):
|
||
|
|
return np.int64(random_negative_binomial(inst.bit_generator, n, p))
|
||
|
|
return impl
|
||
|
|
else:
|
||
|
|
check_size(size)
|
||
|
|
|
||
|
|
def impl(inst, n, p , size=None):
|
||
|
|
out = np.empty(size, dtype=np.int64)
|
||
|
|
out_f = out.flat
|
||
|
|
for i in range(out.size):
|
||
|
|
out_f[i] = random_negative_binomial(inst.bit_generator, n, p)
|
||
|
|
return out
|
||
|
|
return impl
|
||
|
|
|
||
|
|
|
||
|
|
@overload_method(types.NumPyRandomGeneratorType, 'noncentral_chisquare')
|
||
|
|
def NumPyRandomGeneratorType_noncentral_chisquare(inst, df, nonc, size=None):
|
||
|
|
check_types(df, [types.Float, types.Integer, int, float], 'df')
|
||
|
|
check_types(nonc, [types.Float, types.Integer, int, float], 'nonc')
|
||
|
|
if isinstance(size, types.Omitted):
|
||
|
|
size = size.value
|
||
|
|
|
||
|
|
@register_jitable
|
||
|
|
def check_arg_bounds(df, nonc):
|
||
|
|
if df <= 0:
|
||
|
|
raise ValueError("df <= 0")
|
||
|
|
if nonc < 0:
|
||
|
|
raise ValueError("nonc < 0")
|
||
|
|
|
||
|
|
if is_nonelike(size):
|
||
|
|
def impl(inst, df, nonc, size=None):
|
||
|
|
check_arg_bounds(df, nonc)
|
||
|
|
return np.float64(random_noncentral_chisquare(inst.bit_generator,
|
||
|
|
df, nonc))
|
||
|
|
return impl
|
||
|
|
else:
|
||
|
|
check_size(size)
|
||
|
|
|
||
|
|
def impl(inst, df, nonc, size=None):
|
||
|
|
check_arg_bounds(df, nonc)
|
||
|
|
out = np.empty(size, dtype=np.float64)
|
||
|
|
out_f = out.flat
|
||
|
|
for i in range(out.size):
|
||
|
|
out_f[i] = random_noncentral_chisquare(inst.bit_generator,
|
||
|
|
df, nonc)
|
||
|
|
return out
|
||
|
|
return impl
|
||
|
|
|
||
|
|
|
||
|
|
@overload_method(types.NumPyRandomGeneratorType, 'noncentral_f')
|
||
|
|
def NumPyRandomGeneratorType_noncentral_f(inst, dfnum, dfden, nonc, size=None):
|
||
|
|
check_types(dfnum, [types.Float, types.Integer, int, float], 'dfnum')
|
||
|
|
check_types(dfden, [types.Float, types.Integer, int, float], 'dfden')
|
||
|
|
check_types(nonc, [types.Float, types.Integer, int, float], 'nonc')
|
||
|
|
if isinstance(size, types.Omitted):
|
||
|
|
size = size.value
|
||
|
|
|
||
|
|
@register_jitable
|
||
|
|
def check_arg_bounds(dfnum, dfden, nonc):
|
||
|
|
if dfnum <= 0:
|
||
|
|
raise ValueError("dfnum <= 0")
|
||
|
|
if dfden <= 0:
|
||
|
|
raise ValueError("dfden <= 0")
|
||
|
|
if nonc < 0:
|
||
|
|
raise ValueError("nonc < 0")
|
||
|
|
|
||
|
|
if is_nonelike(size):
|
||
|
|
def impl(inst, dfnum, dfden, nonc, size=None):
|
||
|
|
check_arg_bounds(dfnum, dfden, nonc)
|
||
|
|
return np.float64(random_noncentral_f(inst.bit_generator,
|
||
|
|
dfnum, dfden, nonc))
|
||
|
|
return impl
|
||
|
|
else:
|
||
|
|
check_size(size)
|
||
|
|
|
||
|
|
def impl(inst, dfnum, dfden, nonc, size=None):
|
||
|
|
check_arg_bounds(dfnum, dfden, nonc)
|
||
|
|
out = np.empty(size, dtype=np.float64)
|
||
|
|
out_f = out.flat
|
||
|
|
for i in range(out.size):
|
||
|
|
out_f[i] = random_noncentral_f(inst.bit_generator,
|
||
|
|
dfnum, dfden, nonc)
|
||
|
|
return out
|
||
|
|
return impl
|
||
|
|
|
||
|
|
|
||
|
|
@overload_method(types.NumPyRandomGeneratorType, 'logseries')
|
||
|
|
def NumPyRandomGeneratorType_logseries(inst, p, size=None):
|
||
|
|
check_types(p, [types.Float, types.Integer, int, float], 'p')
|
||
|
|
if isinstance(size, types.Omitted):
|
||
|
|
size = size.value
|
||
|
|
|
||
|
|
@register_jitable
|
||
|
|
def check_arg_bounds(p):
|
||
|
|
if p < 0 or p >= 1 or np.isnan(p):
|
||
|
|
raise ValueError("p < 0, p >= 1 or p is NaN")
|
||
|
|
|
||
|
|
if is_nonelike(size):
|
||
|
|
def impl(inst, p, size=None):
|
||
|
|
check_arg_bounds(p)
|
||
|
|
return np.int64(random_logseries(inst.bit_generator, p))
|
||
|
|
return impl
|
||
|
|
else:
|
||
|
|
check_size(size)
|
||
|
|
|
||
|
|
def impl(inst, p, size=None):
|
||
|
|
check_arg_bounds(p)
|
||
|
|
out = np.empty(size, dtype=np.int64)
|
||
|
|
out_f = out.flat
|
||
|
|
for i in range(out.size):
|
||
|
|
out_f[i] = random_logseries(inst.bit_generator, p)
|
||
|
|
return out
|
||
|
|
return impl
|
||
|
|
|
||
|
|
|
||
|
|
@overload_method(types.NumPyRandomGeneratorType, 'binomial')
|
||
|
|
def NumPyRandomGeneratorType_binomial(inst, n, p, size=None):
|
||
|
|
check_types(n, [types.Float, types.Integer, int, float], 'n')
|
||
|
|
check_types(p, [types.Float, types.Integer, int, float], 'p')
|
||
|
|
|
||
|
|
if isinstance(size, types.Omitted):
|
||
|
|
size = size.value
|
||
|
|
|
||
|
|
if is_nonelike(size):
|
||
|
|
def impl(inst, n, p, size=None):
|
||
|
|
return np.int64(random_binomial(inst.bit_generator, n, p))
|
||
|
|
return impl
|
||
|
|
else:
|
||
|
|
check_size(size)
|
||
|
|
|
||
|
|
def impl(inst, n, p, size=None):
|
||
|
|
out = np.empty(size, dtype=np.int64)
|
||
|
|
for i in np.ndindex(size):
|
||
|
|
out[i] = random_binomial(inst.bit_generator, n, p)
|
||
|
|
return out
|
||
|
|
return impl
|