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DEP: removed size check. random #357

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Merged
merged 2 commits into from
Dec 6, 2020
Merged

DEP: removed size check. random #357

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5afa819
DEP: removed size check. random
samir-nasibli Dec 6, 2020
84b74fd
removed size assigne on random funcs level
samir-nasibli Dec 6, 2020
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165 changes: 0 additions & 165 deletions dpnp/random/dpnp_iface_random.py
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Original file line number Diff line number Diff line change
Expand Up @@ -422,15 +422,6 @@ def exponential(scale=1.0, size=None):
"""

if not use_origin_backend(scale):
if size is None:
size = 1
elif isinstance(size, tuple):
for dim in size:
if not isinstance(dim, int):
checker_throw_value_error("exponential", "type(dim)", type(dim), int)
elif not isinstance(size, int):
checker_throw_value_error("exponential", "type(size)", type(size), int)

if scale < 0:
checker_throw_value_error("exponential", "scale", scale, "non-negative")

Expand Down Expand Up @@ -508,15 +499,6 @@ def gamma(shape, scale=1.0, size=None):
# TODO:
# array_like of floats for `scale` and `shape`
if not use_origin_backend(scale) and dpnp_queue_is_cpu():
if size is None:
size = 1
elif isinstance(size, tuple):
for dim in size:
if not isinstance(dim, int):
checker_throw_value_error("gamma", "type(dim)", type(dim), int)
elif not isinstance(size, int):
checker_throw_value_error("gamma", "type(size)", type(size), int)

if scale < 0:
checker_throw_value_error("gamma", "scale", scale, "non-negative")
if shape < 0:
Expand Down Expand Up @@ -567,15 +549,6 @@ def geometric(p, size=None):
"""

if not use_origin_backend(p):
if size is None:
size = 1
elif isinstance(size, tuple):
for dim in size:
if not isinstance(dim, int):
checker_throw_value_error("geometric", "type(dim)", type(dim), int)
elif not isinstance(size, int):
checker_throw_value_error("geometric", "type(size)", type(size), int)

# TODO:
# array_like of floats for `p` param
if p > 1 or p <= 0:
Expand Down Expand Up @@ -620,15 +593,6 @@ def gumbel(loc=0.0, scale=1.0, size=None):
"""

if not use_origin_backend(loc):
if size is None:
size = 1
elif isinstance(size, tuple):
for dim in size:
if not isinstance(dim, int):
checker_throw_value_error("gumbel", "type(dim)", type(dim), int)
elif not isinstance(size, int):
checker_throw_value_error("gumbel", "type(size)", type(size), int)

# TODO:
# array_like of floats for `loc` and `scale` params
if scale < 0:
Expand Down Expand Up @@ -713,15 +677,6 @@ def hypergeometric(ngood, nbad, nsample, size=None):
"""

if not use_origin_backend(ngood) and dpnp_queue_is_cpu():
if size is None:
size = 1
elif isinstance(size, tuple):
for dim in size:
if not isinstance(dim, int):
checker_throw_value_error("hypergeometric", "type(dim)", type(dim), int)
elif not isinstance(size, int):
checker_throw_value_error("hypergeometric", "type(size)", type(size), int)

# TODO:
# array_like of ints for `ngood`, `nbad`, `nsample` param
if ngood < 0:
Expand Down Expand Up @@ -780,15 +735,6 @@ def laplace(loc=0.0, scale=1.0, size=None):
"""

if not use_origin_backend(loc):
if size is None:
size = 1
elif isinstance(size, tuple):
for dim in size:
if not isinstance(dim, int):
checker_throw_value_error("laplace", "type(dim)", type(dim), int)
elif not isinstance(size, int):
checker_throw_value_error("laplace", "type(size)", type(size), int)

# TODO:
# array_like of floats for `loc` and `scale` params
if scale < 0:
Expand Down Expand Up @@ -878,15 +824,6 @@ def lognormal(mean=0.0, sigma=1.0, size=None):
"""

if not use_origin_backend(mean):
if size is None:
size = 1
elif isinstance(size, tuple):
for dim in size:
if not isinstance(dim, int):
checker_throw_value_error("lognormal", "type(dim)", type(dim), int)
elif not isinstance(size, int):
checker_throw_value_error("lognormal", "type(size)", type(size), int)

# TODO:
# array_like of floats for `mean` and `sigma` params
if sigma < 0:
Expand Down Expand Up @@ -1172,15 +1109,6 @@ def negative_binomial(n, p, size=None):
"""

if not use_origin_backend(n) and dpnp_queue_is_cpu():
if size is None:
size = 1
elif isinstance(size, tuple):
for dim in size:
if not isinstance(dim, int):
checker_throw_value_error("negative_binomial", "type(dim)", type(dim), int)
elif not isinstance(size, int):
checker_throw_value_error("negative_binomial", "type(size)", type(size), int)

# TODO:
# array_like of floats for `p` and `n` params
if p > 1 or p < 0:
Expand Down Expand Up @@ -1259,15 +1187,6 @@ def normal(loc=0.0, scale=1.0, size=None):
"""

if not use_origin_backend(loc):
if size is None:
size = 1
elif isinstance(size, tuple):
for dim in size:
if not isinstance(dim, int):
checker_throw_value_error("normal", "type(dim)", type(dim), int)
elif not isinstance(size, int):
checker_throw_value_error("normal", "type(size)", type(size), int)

# TODO:
# array_like of floats for `loc` and `scale` params
if scale < 0:
Expand Down Expand Up @@ -1396,15 +1315,6 @@ def poisson(lam=1.0, size=None):
"""

if not use_origin_backend(lam):
if size is None:
size = 1
elif isinstance(size, tuple):
for dim in size:
if not isinstance(dim, int):
checker_throw_value_error("poisson", "type(dim)", type(dim), int)
elif not isinstance(size, int):
checker_throw_value_error("poisson", "type(size)", type(size), int)

# TODO:
# array_like of floats for `lam` param
if lam < 0:
Expand Down Expand Up @@ -1504,15 +1414,6 @@ def randint(low, high=None, size=None, dtype=int):
"""

if not use_origin_backend(low):
if size is None:
size = 1
elif isinstance(size, tuple):
for dim in size:
if not isinstance(dim, int):
checker_throw_value_error("randint", "type(dim)", type(dim), int)
elif not isinstance(size, int):
checker_throw_value_error("randint", "type(size)", type(size), int)

if high is None:
high = low
low = 0
Expand Down Expand Up @@ -1659,9 +1560,6 @@ def random_sample(size):
"""

if not use_origin_backend(size):
for dim in size:
if not isinstance(dim, int):
checker_throw_value_error("random_sample", "type(dim)", type(dim), int)
return dpnp_random(size)

return call_origin(numpy.random.random_sample, size)
Expand All @@ -1687,9 +1585,6 @@ def ranf(size):
"""

if not use_origin_backend(size):
for dim in size:
if not isinstance(dim, int):
checker_throw_value_error("ranf", "type(dim)", type(dim), int)
return dpnp_random(size)

return call_origin(numpy.random.ranf, size)
Expand Down Expand Up @@ -1720,15 +1615,6 @@ def rayleigh(scale=1.0, size=None):
"""

if not use_origin_backend(scale):
if size is None:
size = 1
elif isinstance(size, tuple):
for dim in size:
if not isinstance(dim, int):
checker_throw_value_error("rayleigh", "type(dim)", type(dim), int)
elif not isinstance(size, int):
checker_throw_value_error("rayleigh", "type(size)", type(size), int)

# TODO:
# array_like of floats for `scale` params
if scale < 0:
Expand Down Expand Up @@ -1759,9 +1645,6 @@ def sample(size):
"""

if not use_origin_backend(size):
for dim in size:
if not isinstance(dim, int):
checker_throw_value_error("sample", "type(dim)", type(dim), int)
return dpnp_random(size)

return call_origin(numpy.random.sample, size)
Expand Down Expand Up @@ -1836,15 +1719,6 @@ def standard_cauchy(size=None):
"""

if not use_origin_backend(size):
if size is None:
size = 1
elif isinstance(size, tuple):
for dim in size:
if not isinstance(dim, int):
checker_throw_value_error("standard_cauchy", "type(dim)", type(dim), int)
elif not isinstance(size, int):
checker_throw_value_error("standard_cauchy", "type(size)", type(size), int)

return dpnp_standard_cauchy(size)

return call_origin(numpy.random.standard_cauchy, size)
Expand Down Expand Up @@ -1878,15 +1752,6 @@ def standard_exponential(size=None):
"""

if not use_origin_backend(size):
if size is None:
size = 1
elif isinstance(size, tuple):
for dim in size:
if not isinstance(dim, int):
checker_throw_value_error("standard_exponential", "type(dim)", type(dim), int)
elif not isinstance(size, int):
checker_throw_value_error("standard_exponential", "type(size)", type(size), int)

return dpnp_standard_exponential(size)

return call_origin(numpy.random.standard_exponential, size)
Expand Down Expand Up @@ -1946,15 +1811,6 @@ def standard_gamma(shape, size=None):
# TODO:
# array_like of floats for and `shape`
if not use_origin_backend(shape) and dpnp_queue_is_cpu():
if size is None:
size = 1
elif isinstance(size, tuple):
for dim in size:
if not isinstance(dim, int):
checker_throw_value_error("standard_gamma", "type(dim)", type(dim), int)
elif not isinstance(size, int):
checker_throw_value_error("standard_gamma", "type(size)", type(size), int)

if shape < 0:
checker_throw_value_error("standard_gamma", "shape", shape, "non-negative")

Expand Down Expand Up @@ -1984,15 +1840,6 @@ def standard_normal(size=None):
"""

if not use_origin_backend(size):
if size is None:
size = 1
elif isinstance(size, tuple):
for dim in size:
if not isinstance(dim, int):
checker_throw_value_error("standard_normal", "type(dim)", type(dim), int)
elif not isinstance(size, int):
checker_throw_value_error("standard_normal", "type(size)", type(size), int)

return dpnp_standard_normal(size)

return call_origin(numpy.random.standard_normal, size)
Expand Down Expand Up @@ -2069,8 +1916,6 @@ def uniform(low=0.0, high=1.0, size=None):
"""

if not use_origin_backend(low):
if size is None:
size = 1
if low == high:
# TODO:
# currently dparray.full is not implemented
Expand Down Expand Up @@ -2155,20 +2000,10 @@ def weibull(a, size=None):
"""

if not use_origin_backend(a):
if size is None:
size = 1
elif isinstance(size, tuple):
for dim in size:
if not isinstance(dim, int):
checker_throw_value_error("weibull", "type(dim)", type(dim), int)
elif not isinstance(size, int):
checker_throw_value_error("weibull", "type(size)", type(size), int)

# TODO:
# array_like of floats for `a` params
if a < 0:
checker_throw_value_error("weibulla", "a", a, "non-negative")

return dpnp_weibull(a, size)

return call_origin(numpy.random.weibull, a, size)
Expand Down