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Quentin Yeche authored424496c0
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from collections import OrderedDict
import numpy as np
from extreme_fit.distribution.gev.gev_params import GevParams
from extreme_fit.function.param_function.linear_coef import LinearCoef
from spatio_temporal_dataset.coordinates.abstract_coordinates import AbstractCoordinates
def convertFloatVector_to_float(f):
return np.array(f)[0]
def get_margin_coef_ordered_dict(param_name_to_dims, mle_values, type_for_mle="GEV", dim_to_coordinate_name=None):
assert param_name_to_dims is not None
# Build the Coeff dict from param_name_to_dim
coef_dict = OrderedDict()
i = 0
for param_name in GevParams.PARAM_NAMES:
# Add intercept (i.e. stationary parameter)
intercept_coef_name = LinearCoef.coef_template_str(param_name, LinearCoef.INTERCEPT_NAME).format(1)
if type_for_mle == "Gumbel" and param_name == GevParams.SHAPE:
coef_value = 0
else:
coef_value = mle_values[i]
coef_dict[intercept_coef_name] = coef_value
i += 1
# Add a potential linear temporal trend
if param_name in param_name_to_dims:
dims = param_name_to_dims[param_name]
if isinstance(dims[0], int):
coef_name = LinearCoef.coef_template_str(param_name, AbstractCoordinates.COORDINATE_T).format(1)
coef_dict[coef_name] = mle_values[i]
i += 1
else:
for dim, max_degree in dims[:]:
coefficient_name = LinearCoef.coefficient_name(dim, dim_to_coordinate_name)
coef_template = LinearCoef.coef_template_str(param_name, coefficient_name)
offset = LinearCoef.offset_from_coefficient_name(coefficient_name)
for k in range(1, max_degree + 1):
coef_name = coef_template.format(k + offset)
coef_dict[coef_name] = mle_values[i]
i += 1
return coef_dict