def scalar_function_of_normal(
store_name: str,
N: int,
M: int,
X_std: float,
noise_std: float,
CDF_scale: NP.Array = None,
CDF_loc: NP.Array = None,
pre_function_with_parameters: function.CallableWithParameters = None,
function_with_parameters: function.CallableWithParameters = None
) -> data.Store:
X_marginal = distribution.Univariate('norm', loc=0, scale=X_std)
X_dist = distribution.Multivariate.Independent(M=M, marginals=X_marginal)
noise_dist = (distribution.Multivariate.Normal(mean=zeros(1, dtype=float),
covariance=noise_std**2 *
eye(1, dtype=float))
if noise_std > EFFECTIVELY_ZERO else None)
return function.sample(
store_dir=store_dir(store_name, noise_std, CDF_scale),
N=N,
X_distribution=X_dist,
X_sample_design=distribution.SampleDesign.LATIN_HYPERCUBE,
CDF_scale=CDF_scale,
CDF_loc=CDF_loc,
pre_function_with_parameters=pre_function_with_parameters,
functions_with_parameters=function_with_parameters,
noise_distribution=noise_dist,
noise_sample_design=distribution.SampleDesign.LATIN_HYPERCUBE)
def scalar_function_of_normal(
store_name: str,
N: int,
M: int,
X_std: float,
noise_std: float,
CDF_scale: NP.Array = None,
CDF_loc: NP.Array = None,
pre_function_with_parameters: function.CallableWithParameters = None,
function_with_parameters: function.CallableWithParameters = None
) -> data.Store:
X_marginal = distribution.Univariate('norm', loc=0, scale=X_std)
X_dist = distribution.Multivariate.Independent(M=M, marginals=X_marginal)
noise_dist = (distribution.Multivariate.Normal(mean=zeros(1, dtype=float),
covariance=noise_std**2 *
eye(1, dtype=float))
if noise_std > EFFECTIVELY_ZERO else None)
df, meta = function.sample(
N=N,
X_distribution=X_dist,
X_sample_design=distribution.SampleDesign.LATIN_HYPERCUBE,
CDF_scale=CDF_scale,
CDF_loc=CDF_loc,
pre_function_with_parameters=pre_function_with_parameters,
functions_with_parameters=function_with_parameters,
noise_distribution=noise_dist,
noise_sample_design=distribution.SampleDesign.LATIN_HYPERCUBE)
if noise_dist is None:
dir_ = BASE_PATH / NOISELESS_DIR / store_name
elif CDF_scale is None:
dir_ = BASE_PATH / NORMAL_DIR / store_name
else:
dir_ = BASE_PATH / NORMAL_CDF_DIR / store_name
return data.Store.from_df(dir_=dir_, df=df, meta=meta)
