@@ -567,9 +567,11 @@ def create_z_mat(self, data):
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def create_constraint_mat (self ) -> tuple [NDArray , NDArray ]:
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"""Create constraint matrix.
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- Returns:
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- tuple{numpy.ndarray, numpy.ndarray}:
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- Return linear constraints matrix and its uniform prior.
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+ Returns
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+ -------
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+ tuple[NDArray, NDArray]
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+ Return linear constraints matrix and its uniform prior.
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+
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"""
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# initialize the matrix and the value
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c_mat = np .array ([]).reshape (0 , self .num_x_vars )
@@ -682,9 +684,11 @@ def create_constraint_mat(self) -> tuple[NDArray, NDArray]:
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def create_regularization_mat (self ) -> tuple [NDArray , NDArray ]:
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"""Create constraint matrix.
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- Returns:
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- tuple{numpy.ndarray, numpy.ndarray}:
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- Return linear regularization matrix and its Gaussian prior.
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+ Returns
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+ -------
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+ tuple[NDArray, NDArray]
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+ Return linear regularization matrix and its Gaussian prior.
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+
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"""
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r_mat = np .array ([]).reshape (0 , self .num_x_vars )
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r_val = np .array ([]).reshape (2 , 0 )
@@ -813,9 +817,6 @@ def num_regularizations(self):
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class LinearCovModel (CovModel ):
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"""Linear Covariates Model."""
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- def __init__ (self , * args , ** kwargs ):
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- super ().__init__ (* args , ** kwargs )
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-
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def create_x_fun (self , data : MRData ):
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"""Create design function for the fixed effects."""
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alt_mat , ref_mat = self .create_design_mat (data )
@@ -855,12 +856,6 @@ def create_z_mat(self, data):
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class LogCovModel (CovModel ):
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"""Log Covariates Model."""
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- def __init__ (self , * args , ** kwargs ):
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- super ().__init__ (* args , ** kwargs )
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- # if self.use_spline_intercept:
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- # raise ValueError("LogCovModel does not support use_spline_intercept."
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- # "Please set it to False, or leave it as default.")
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-
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def create_x_fun (self , data ):
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"""Create design functions for the fixed effects.
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