Skip to content

Commit 19b4fb3

Browse files
yuta-nakaharayuta-nakahara
committed
revise h_params of multivariate_normal
1 parent 242818c commit 19b4fb3

File tree

2 files changed

+41
-96
lines changed

2 files changed

+41
-96
lines changed

‎bayesml/multivariate_normal/_multivariatenormal.py

Lines changed: 39 additions & 94 deletions
Original file line numberDiff line numberDiff line change
@@ -184,44 +184,25 @@ def __init__(
184184

185185
self.h_kappa = _check.pos_float(h_kappa,'h_kappa',ParameterFormatError)
186186
self.rng = np.random.default_rng(seed)
187-
self._H_PARAM_KEYS = {'h_m_vec','h_kappa','h_nu','h_w_mat'}
188-
self._H0_PARAM_KEYS = {'h0_m_vec','h0_kappa','h0_nu','h0_w_mat'}
189-
self._HN_PARAM_KEYS = {'hn_m_vec','hn_kappa','hn_nu','hn_w_mat'}
190187

191-
def set_h_params(self,**kwargs):
188+
def set_h_params(self,h_m_vec,h_kappa,h_nu,h_w_mat):
192189
"""Set the hyperparameters of the prior distribution.
193190
194191
Parameters
195192
----------
196-
**kwargs
197-
a python dictionary {'h_m_vec':ndarray, 'h_kappa':float, 'h_nu':float, 'h_w_mat':ndarray} or
198-
{'h0_m_vec':ndarray, 'h0_kappa':float, 'h0_nu':float, 'h0_w_mat':ndarray}
199-
or {'hn_m_vec':ndarray, 'hn_kappa':float, 'hn_nu':float, 'hn_w_mat':ndarray}
200-
They are obtained by ``get_h_params()`` of GenModel,
201-
``get_h0_params`` of LearnModel or ``get_hn_params`` of LearnModel.
193+
h_m_vec : numpy.ndarray
194+
a vector of real numbers
195+
h_kappa : float
196+
a positive real number
197+
h_nu : float
198+
a real number > degree-1
199+
h_w_mat : numpy.ndarray
200+
a positive definite symetric matrix
202201
"""
203-
if kwargs.keys() == self._H_PARAM_KEYS:
204-
self.h_m_vec = _check.float_vec(kwargs['h_m_vec'],'h_m_vec',ParameterFormatError)
205-
self.h_kappa = _check.pos_float(kwargs['h_kappa'],'h_kappa',ParameterFormatError)
206-
self.h_nu = _check.pos_float(kwargs['h_nu'],'h_nu',ParameterFormatError)
207-
self.h_w_mat = _check.pos_def_sym_mat(kwargs['h_w_mat'],'h_w_mat',ParameterFormatError)
208-
elif kwargs.keys() == self._H0_PARAM_KEYS:
209-
self.h_m_vec = _check.float_vec(kwargs['h0_m_vec'],'h_m_vec',ParameterFormatError)
210-
self.h_kappa = _check.pos_float(kwargs['h0_kappa'],'h_kappa',ParameterFormatError)
211-
self.h_nu = _check.pos_float(kwargs['h0_nu'],'h_nu',ParameterFormatError)
212-
self.h_w_mat = _check.pos_def_sym_mat(kwargs['h0_w_mat'],'h_w_mat',ParameterFormatError)
213-
elif kwargs.keys() == self._HN_PARAM_KEYS:
214-
self.h_m_vec = _check.float_vec(kwargs['hn_m_vec'],'h_m_vec',ParameterFormatError)
215-
self.h_kappa = _check.pos_float(kwargs['hn_kappa'],'h_kappa',ParameterFormatError)
216-
self.h_nu = _check.pos_float(kwargs['hn_nu'],'h_nu',ParameterFormatError)
217-
self.h_w_mat = _check.pos_def_sym_mat(kwargs['hn_w_mat'],'h_w_mat',ParameterFormatError)
218-
else:
219-
raise(ParameterFormatError(
220-
"The input of this function must be a python dictionary with keys:"
221-
+str(self._H_PARAM_KEYS)+" or "
222-
+str(self._H0_PARAM_KEYS)+" or "
223-
+str(self._HN_PARAM_KEYS)+".")
224-
)
202+
self.h_m_vec = _check.float_vec(h_m_vec,'h_m_vec',ParameterFormatError)
203+
self.h_w_mat = _check.pos_def_sym_mat(h_w_mat,'h_w_mat',ParameterFormatError)
204+
self.h_nu = _check.pos_float(h_nu,'h_nu',ParameterFormatError)
205+
self.h_kappa = _check.pos_float(h_kappa,'h_kappa',ParameterFormatError)
225206

226207
if (self.h_m_vec.shape[0] != self.h_w_mat.shape[0]
227208
or self.h_nu <= self.h_m_vec.shape[0] - 1):
@@ -510,44 +491,24 @@ def __init__(self,degree=None,h0_m_vec=None,h0_kappa=1.0,h0_nu=None,h0_w_mat=Non
510491
self.p_v_mat = self.hn_kappa*self.p_nu/(self.hn_kappa+1) * self.hn_w_mat
511492
self.p_v_mat_inv = (self.hn_kappa+1)/self.hn_kappa/self.p_nu * self.hn_w_mat_inv
512493

513-
self._H_PARAM_KEYS = {'h_m_vec','h_kappa','h_nu','h_w_mat'}
514-
self._H0_PARAM_KEYS = {'h0_m_vec','h0_kappa','h0_nu','h0_w_mat'}
515-
self._HN_PARAM_KEYS = {'hn_m_vec','hn_kappa','hn_nu','hn_w_mat'}
516-
517-
def set_h0_params(self,**kwargs):
494+
def set_h0_params(self,h0_m_vec,h0_kappa,h0_nu,h0_w_mat):
518495
"""Set the hyperparameters of the prior distribution.
519496
520497
Parameters
521498
----------
522-
**kwargs
523-
a python dictionary {'h_m_vec':ndarray, 'h_kappa':float, 'h_nu':float, 'h_w_mat':ndarray} or
524-
{'h0_m_vec':ndarray, 'h0_kappa':float, 'h0_nu':float, 'h0_w_mat':ndarray}
525-
or {'hn_m_vec':ndarray, 'hn_kappa':float, 'hn_nu':float, 'hn_w_mat':ndarray}
526-
They are obtained by ``get_h_params()`` of GenModel,
527-
``get_h0_params`` of LearnModel or ``get_hn_params`` of LearnModel.
499+
h0_m_vec : numpy.ndarray
500+
a vector of real numbers
501+
h0_kappa : float
502+
a positive real number
503+
h0_nu : float
504+
a real number > degree-1
505+
h0_w_mat : numpy.ndarray
506+
a positive definite symetric matrix
528507
"""
529-
if kwargs.keys() == self._H_PARAM_KEYS:
530-
self.h0_m_vec = _check.float_vec(kwargs['h_m_vec'],'h0_m_vec',ParameterFormatError)
531-
self.h0_kappa = _check.pos_float(kwargs['h_kappa'],'h0_kappa',ParameterFormatError)
532-
self.h0_nu = _check.pos_float(kwargs['h_nu'],'h0_nu',ParameterFormatError)
533-
self.h0_w_mat = _check.pos_def_sym_mat(kwargs['h_w_mat'],'h0_w_mat',ParameterFormatError)
534-
elif kwargs.keys() == self._H0_PARAM_KEYS:
535-
self.h0_m_vec = _check.float_vec(kwargs['h0_m_vec'],'h0_m_vec',ParameterFormatError)
536-
self.h0_kappa = _check.pos_float(kwargs['h0_kappa'],'h0_kappa',ParameterFormatError)
537-
self.h0_nu = _check.pos_float(kwargs['h0_nu'],'h0_nu',ParameterFormatError)
538-
self.h0_w_mat = _check.pos_def_sym_mat(kwargs['h0_w_mat'],'h0_w_mat',ParameterFormatError)
539-
elif kwargs.keys() == self._HN_PARAM_KEYS:
540-
self.h0_m_vec = _check.float_vec(kwargs['hn_m_vec'],'h0_m_vec',ParameterFormatError)
541-
self.h0_kappa = _check.pos_float(kwargs['hn_kappa'],'h0_kappa',ParameterFormatError)
542-
self.h0_nu = _check.pos_float(kwargs['hn_nu'],'h0_nu',ParameterFormatError)
543-
self.h0_w_mat = _check.pos_def_sym_mat(kwargs['hn_w_mat'],'h0_w_mat',ParameterFormatError)
544-
else:
545-
raise(ParameterFormatError(
546-
"The input of this function must be a python dictionary with keys:"
547-
+str(self._H_PARAM_KEYS)+" or "
548-
+str(self._H0_PARAM_KEYS)+" or "
549-
+str(self._HN_PARAM_KEYS)+".")
550-
)
508+
self.h0_m_vec = _check.float_vec(h0_m_vec,'h0_m_vec',ParameterFormatError)
509+
self.h0_kappa = _check.pos_float(h0_kappa,'h0_kappa',ParameterFormatError)
510+
self.h0_nu = _check.pos_float(h0_nu,'h0_nu',ParameterFormatError)
511+
self.h0_w_mat = _check.pos_def_sym_mat(h0_w_mat,'h0_w_mat',ParameterFormatError)
551512

552513
if (self.h0_m_vec.shape[0] != self.h0_w_mat.shape[0]
553514
or self.h0_nu <= self.h0_m_vec.shape[0] - 1):
@@ -573,40 +534,24 @@ def get_h0_params(self):
573534
"""
574535
return {"h0_m_vec":self.h0_m_vec, "h0_kappa":self.h0_kappa, "h0_nu":self.h0_nu, "h0_w_mat":self.h0_w_mat}
575536

576-
def set_hn_params(self,**kwargs):
537+
def set_hn_params(self,hn_m_vec,hn_kappa,hn_nu,hn_w_mat):
577538
"""Set updated values of the hyperparameter of the posterior distribution.
578539
579540
Parameters
580541
----------
581-
**kwargs
582-
a python dictionary {'h_m_vec':ndarray, 'h_kappa':float, 'h_nu':float, 'h_w_mat':ndarray} or
583-
{'h0_m_vec':ndarray, 'h0_kappa':float, 'h0_nu':float, 'h0_w_mat':ndarray}
584-
or {'hn_m_vec':ndarray, 'hn_kappa':float, 'hn_nu':float, 'hn_w_mat':ndarray}
585-
They are obtained by ``get_h_params()`` of GenModel,
586-
``get_h0_params`` of LearnModel or ``get_hn_params`` of LearnModel.
542+
hn_m_vec : numpy.ndarray
543+
a vector of real numbers
544+
hn_kappa : float
545+
a positive real number
546+
hn_nu : float
547+
a real number > degree-1
548+
hn_w_mat : numpy.ndarray
549+
a positive definite symetric matrix
587550
"""
588-
if kwargs.keys() == self._H_PARAM_KEYS:
589-
self.hn_m_vec = _check.float_vec(kwargs['h_m_vec'],'hn_m_vec',ParameterFormatError)
590-
self.hn_kappa = _check.pos_float(kwargs['h_kappa'],'hn_kappa',ParameterFormatError)
591-
self.hn_nu = _check.pos_float(kwargs['h_nu'],'hn_nu',ParameterFormatError)
592-
self.hn_w_mat = _check.pos_def_sym_mat(kwargs['h_w_mat'],'hn_w_mat',ParameterFormatError)
593-
elif kwargs.keys() == self._H0_PARAM_KEYS:
594-
self.hn_m_vec = _check.float_vec(kwargs['h0_m_vec'],'hn_m_vec',ParameterFormatError)
595-
self.hn_kappa = _check.pos_float(kwargs['h0_kappa'],'hn_kappa',ParameterFormatError)
596-
self.hn_nu = _check.pos_float(kwargs['h0_nu'],'hn_nu',ParameterFormatError)
597-
self.hn_w_mat = _check.pos_def_sym_mat(kwargs['h0_w_mat'],'hn_w_mat',ParameterFormatError)
598-
elif kwargs.keys() == self._HN_PARAM_KEYS:
599-
self.hn_m_vec = _check.float_vec(kwargs['hn_m_vec'],'hn_m_vec',ParameterFormatError)
600-
self.hn_kappa = _check.pos_float(kwargs['hn_kappa'],'hn_kappa',ParameterFormatError)
601-
self.hn_nu = _check.pos_float(kwargs['hn_nu'],'hn_nu',ParameterFormatError)
602-
self.hn_w_mat = _check.pos_def_sym_mat(kwargs['hn_w_mat'],'hn_w_mat',ParameterFormatError)
603-
else:
604-
raise(ParameterFormatError(
605-
"The input of this function must be a python dictionary with keys:"
606-
+str(self._H_PARAM_KEYS)+" or "
607-
+str(self._H0_PARAM_KEYS)+" or "
608-
+str(self._HN_PARAM_KEYS)+".")
609-
)
551+
self.hn_m_vec = _check.float_vec(hn_m_vec,'hn_m_vec',ParameterFormatError)
552+
self.hn_kappa = _check.pos_float(hn_kappa,'hn_kappa',ParameterFormatError)
553+
self.hn_nu = _check.pos_float(hn_nu,'hn_nu',ParameterFormatError)
554+
self.hn_w_mat = _check.pos_def_sym_mat(hn_w_mat,'hn_w_mat',ParameterFormatError)
610555

611556
if (self.hn_m_vec.shape[0] != self.hn_w_mat.shape[0]
612557
or self.hn_nu <= self.hn_m_vec.shape[0] - 1):

‎doc/devdoc/examples/h_params_test.py

Lines changed: 2 additions & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -1,7 +1,7 @@
1-
from bayesml import normal as bayesml_model
1+
from bayesml import multivariate_normal as bayesml_model
22
import numpy as np
33

4-
h0_params = {'h0_m':2,'h0_kappa':1,'h0_alpha':3,'h0_beta':4}
4+
h0_params = {'h0_m_vec':np.ones(2),'h0_kappa':3,'h0_nu':2,'h0_w_mat':np.eye(2)*2}
55

66
print('Gen to Learn 1')
77
model = bayesml_model.GenModel()

0 commit comments

Comments
 (0)