Release 0.2.5

README.md

@@ -137,7 +137,7 @@ Plain text
 
 ```
 Y. Nakahara, N. Ichijo, K. Shimada, Y. Iikubo, 
-S. Saito, K. Kazama, T. Matsushima, BayesML Developers, ``BayesML 0.2.4,'' 
+S. Saito, K. Kazama, T. Matsushima, BayesML Developers, ``BayesML 0.2.5,'' 
 [Online] https://github.com/yuta-nakahara/BayesML
 ```
 
@@ -148,7 +148,7 @@ BibTeX
   author = {Nakahara, Yuta and Ichijo, Naoki and Shimada, Koshi and
             Iikubo, Yuji and Saito, Shota and Kazama, Koki and
             Matsushima, Toshiyasu and {BayesML Developers}},
-  title = {BayesML 0.2.4},
+  title = {BayesML 0.2.5},
   howpublished = {\url{https://github.com/yuta-nakahara/BayesML}},
   year = {2022}
 }

README_jp.md

@@ -134,7 +134,7 @@ BayesMLへのコントリビューションを考えてくださってありが
 
 ```
 Y. Nakahara, N. Ichijo, K. Shimada, Y. Iikubo, 
-S. Saito, K. Kazama, T. Matsushima, BayesML Developers, ``BayesML 0.2.4,'' 
+S. Saito, K. Kazama, T. Matsushima, BayesML Developers, ``BayesML 0.2.5,'' 
 [Online] https://github.com/yuta-nakahara/BayesML
 ```
 
@@ -145,7 +145,7 @@ BibTeX
   author = {Nakahara, Yuta and Ichijo, Naoki and Shimada, Koshi and
             Iikubo, Yuji and Saito, Shota and Kazama, Koki and
             Matsushima, Toshiyasu and {BayesML Developers}},
-  title = {BayesML 0.2.4},
+  title = {BayesML 0.2.5},
   howpublished = {\url{https://github.com/yuta-nakahara/BayesML}},
   year = {2022}
 }

bayesml/gaussianmixture/_gaussianmixture.py

@@ -556,7 +556,7 @@ def set_h0_params(
             self.h0_w_mats[:] = h0_w_mats
             self.h0_w_mats_inv[:] = np.linalg.inv(self.h0_w_mats)
 
-        self._calc_prior_char()
+        self._calc_prior_features()
         self.reset_hn_params()
         return self
 
@@ -634,8 +634,8 @@ def set_hn_params(
             self.hn_w_mats[:] = hn_w_mats
             self.hn_w_mats_inv[:] = np.linalg.inv(self.hn_w_mats)
 
-        self._calc_q_pi_char()
-        self._calc_q_lambda_char()
+        self._calc_q_pi_features()
+        self._calc_q_lambda_features()
 
         self.calc_pred_dist()
         return self
@@ -658,7 +658,7 @@ def get_hn_params(self):
                 "hn_nus":self.hn_nus,
                 "hn_w_mats":self.hn_w_mats}
 
-    def _calc_prior_char(self):
+    def _calc_prior_features(self):
         self._ln_c_h0_alpha = gammaln(self.h0_alpha_vec.sum()) - gammaln(self.h0_alpha_vec).sum()
         self._ln_b_h0_w_nus = (
             - self.h0_nus*np.linalg.slogdet(self.h0_w_mats)[1]
@@ -700,13 +700,13 @@ def _calc_vl(self):
             - np.sum(self.h0_w_mats_inv * self._e_lambda_mats,axis=(1,2))
             ) / 2.0
 
-        # E[ln q(Z|pi)]
+        # -E[ln q(Z|pi)]
         self._vl_q_z = -np.sum(xlogy(self.r_vecs,self.r_vecs))
 
-        # E[ln q(pi)]
+        # -E[ln q(pi)]
         self._vl_q_pi = ss_dirichlet.entropy(self.hn_alpha_vec)
 
-        # E[ln q(mu,Lambda)]
+        # -E[ln q(mu,Lambda)]
         self._vl_q_mu_lambda =  np.sum(
             + self.c_degree * (1.0 + np.log(2.0*np.pi) - np.log(self.hn_kappas))
             - self._ln_b_hn_w_nus * 2.0
@@ -742,14 +742,14 @@ def _init_random_responsibility(self,x):
         self.r_vecs[:] = self.rng.dirichlet(np.ones(self.c_num_classes),self.r_vecs.shape[0])
         self._calc_n_x_bar_s(x)
 
-    def _calc_q_pi_char(self):
+    def _calc_q_pi_features(self):
         self._e_ln_pi_vec[:] = digamma(self.hn_alpha_vec) - digamma(self.hn_alpha_vec.sum())
 
     def _update_q_pi(self):
         self.hn_alpha_vec[:] = self.h0_alpha_vec + self.ns
-        self._calc_q_pi_char()
+        self._calc_q_pi_features()
 
-    def _calc_q_lambda_char(self):
+    def _calc_q_lambda_features(self):
         self._e_lambda_mats[:] = self.hn_nus[:,np.newaxis,np.newaxis] * self.hn_w_mats
         self._e_ln_lambda_dets[:] = (np.sum(digamma((self.hn_nus[:,np.newaxis]-np.arange(self.c_degree)) / 2.0),axis=1)
                             + self.c_degree*np.log(2.0)
@@ -774,7 +774,7 @@ def _update_q_mu_lambda(self):
                                       @ (self.x_bar_vecs - self.h0_m_vecs)[:,np.newaxis,:])
                                  )
         self.hn_w_mats[:] = np.linalg.inv(self.hn_w_mats_inv)
-        self._calc_q_lambda_char()
+        self._calc_q_lambda_features()
 
     def _update_q_z(self,x):
         self._ln_rho[:] = (self._e_ln_pi_vec
@@ -801,7 +801,7 @@ def _init_subsampling(self,x):
                                  / _size * self.hn_nus[k]
                                  + np.eye(self.c_degree) * 1.0E-5) # avoid singular matrix
             self.hn_w_mats[k] = np.linalg.inv(self.hn_w_mats_inv[k])
-        self._calc_q_lambda_char()
+        self._calc_q_lambda_features()
 
     def _init_rho_r(self):
         self._ln_rho[:] = 0.0
@@ -820,6 +820,7 @@ def update_posterior(
         Parameters
         ----------
         x : numpy.ndarray
+            (sample_size,c_degree)-dimensional ndarray.
             All the elements must be real number.
         max_itr : int, optional
             maximum number of iterations, by default 100
@@ -897,8 +898,8 @@ def update_posterior(
         self.hn_nus[:] = tmp_nus
         self.hn_w_mats[:] = tmp_w_mats
         self.hn_w_mats_inv[:] = tmp_w_mats_inv
-        self._calc_q_pi_char()
-        self._calc_q_lambda_char()
+        self._calc_q_pi_features()
+        self._calc_q_lambda_features()
         self._update_q_z(x)
         return self
 
@@ -1168,6 +1169,9 @@ def estimate_latent_vars(self,x,loss="0-1"):
 
         Parameters
         ----------
+        x : numpy.ndarray
+            (sample_size,c_degree)-dimensional ndarray.
+            All the elements must be real number.
         loss : str, optional
             Loss function underlying the Bayes risk function, by default \"0-1\".
             This function supports \"squared\", \"0-1\", and \"KL\".

bayesml/hiddenmarkovnormal/_hiddenmarkovnormal.py

@@ -694,7 +694,7 @@ def set_h0_params(
 
         self.h0_w_mats_inv[:] = np.linalg.inv(self.h0_w_mats)
 
-        self._calc_prior_char()
+        self._calc_prior_features()
         self.reset_hn_params()
         return self
 
@@ -796,9 +796,9 @@ def set_hn_params(
 
         self.hn_w_mats_inv[:] = np.linalg.inv(self.hn_w_mats)
 
-        self._calc_q_pi_char()
-        self._calc_q_a_char()
-        self._calc_q_lambda_char()
+        self._calc_q_pi_features()
+        self._calc_q_a_features()
+        self._calc_q_lambda_features()
 
         self.calc_pred_dist()
         return self
@@ -823,7 +823,7 @@ def get_hn_params(self):
                 'hn_nus':self.hn_nus,
                 'hn_w_mats':self.hn_w_mats}
 
-    def _calc_prior_char(self):
+    def _calc_prior_features(self):
         self._ln_c_h0_eta_vec = gammaln(self.h0_eta_vec.sum()) - gammaln(self.h0_eta_vec).sum()
         self._ln_c_h0_zeta_vecs_sum = np.sum(gammaln(self.h0_zeta_vecs.sum(axis=1)) - gammaln(self.h0_zeta_vecs).sum(axis=1))
         self._ln_b_h0_w_nus = (
@@ -851,16 +851,16 @@ def _calc_n_m_x_bar_s(self,x):
                                        @ (x[:,np.newaxis,:] - self.x_bar_vecs[indices])[:,:,np.newaxis,:]),
                                     axis=0) / self.ns[indices,np.newaxis,np.newaxis]
 
-    def _calc_q_pi_char(self):
+    def _calc_q_pi_features(self):
         self._ln_pi_tilde_vec[:] = digamma(self.hn_eta_vec) - digamma(self.hn_eta_vec.sum())
         self._pi_tilde_vec[:] = np.exp(self._ln_pi_tilde_vec - self._ln_pi_tilde_vec.max())
 
-    def _calc_q_a_char(self):
+    def _calc_q_a_features(self):
         self._ln_a_tilde_mat[:] = digamma(self.hn_zeta_vecs) - digamma(self.hn_zeta_vecs.sum(axis=1,keepdims=True))
         self._a_tilde_mat[:] = np.exp(self._ln_a_tilde_mat - self._ln_a_tilde_mat.max())
         self._ln_c_hn_zeta_vecs_sum = np.sum(gammaln(self.hn_zeta_vecs.sum(axis=1)) - gammaln(self.hn_zeta_vecs).sum(axis=1))
 
-    def _calc_q_lambda_char(self):
+    def _calc_q_lambda_features(self):
         self._e_lambda_mats[:] = self.hn_nus[:,np.newaxis,np.newaxis] * self.hn_w_mats
         self._e_ln_lambda_dets[:] = (np.sum(digamma((self.hn_nus[:,np.newaxis]-np.arange(self.c_degree)) / 2.0),axis=1)
                             + self.c_degree*np.log(2.0)
@@ -908,19 +908,19 @@ def _calc_vl(self):
             - np.sum(self.h0_w_mats_inv * self._e_lambda_mats,axis=(1,2))
             ) / 2.0
 
-        # E[ln q(Z|pi)]
+        # -E[ln q(Z|pi)]
         self._vl_q_z = (-(self.gamma_vecs * self._ln_rho).sum()
                         -(self.ms * (self._ln_a_tilde_mat - self._ln_a_tilde_mat.max())).sum()
                         -(self.gamma_vecs[0] * (self._ln_pi_tilde_vec - self._ln_pi_tilde_vec.max())).sum()
                         +np.log(self._cs).sum())
 
-        # E[ln q(pi)]
+        # -E[ln q(pi)]
         self._vl_q_pi = ss_dirichlet.entropy(self.hn_eta_vec)
 
-        # E[ln p(A)]
+        # -E[ln p(A)]
         self._vl_q_a = -self._ln_c_hn_zeta_vecs_sum - ((self.hn_zeta_vecs - 1) * self._ln_a_tilde_mat).sum()
 
-        # E[ln q(mu,Lambda)]
+        # -E[ln q(mu,Lambda)]
         self._vl_q_mu_lambda =  np.sum(
             + self.c_degree * (1.0 + np.log(2.0*np.pi) - np.log(self.hn_kappas))
             - self._ln_b_hn_w_nus * 2.0
@@ -968,7 +968,7 @@ def _init_subsampling(self,x):
                                  / _size * self.hn_nus[k]
                                  + np.eye(self.c_degree) * 1.0E-5) # avoid singular matrix
             self.hn_w_mats[k] = np.linalg.inv(self.hn_w_mats_inv[k])
-        self._calc_q_lambda_char()
+        self._calc_q_lambda_features()
 
     def _update_q_mu_lambda(self):
         self.hn_kappas[:] = self.h0_kappas + self.ns
@@ -982,15 +982,15 @@ def _update_q_mu_lambda(self):
                                       @ (self.x_bar_vecs - self.h0_m_vecs)[:,np.newaxis,:])
                                  )
         self.hn_w_mats[:] = np.linalg.inv(self.hn_w_mats_inv)
-        self._calc_q_lambda_char()
+        self._calc_q_lambda_features()
 
     def _update_q_pi(self):
         self.hn_eta_vec[:] = self.h0_eta_vec + self.ns
-        self._calc_q_pi_char()
+        self._calc_q_pi_features()
 
     def _update_q_a(self):
         self.hn_zeta_vecs[:] = self.h0_zeta_vecs + self.ms
-        self._calc_q_a_char()
+        self._calc_q_a_features()
 
     def _calc_rho(self,x):
         self._ln_rho[:] = ((self._e_ln_lambda_dets
@@ -1135,9 +1135,9 @@ def update_posterior(
         self.hn_nus[:] = tmp_nus
         self.hn_w_mats[:] = tmp_w_mats
         self.hn_w_mats_inv[:] = tmp_w_mats_inv
-        self._calc_q_pi_char()
-        self._calc_q_a_char()
-        self._calc_q_lambda_char()
+        self._calc_q_pi_features()
+        self._calc_q_a_features()
+        self._calc_q_lambda_features()
         self._update_q_z(x)
         return self
 

bayesml/metatree/_metatree.py

@@ -1091,7 +1091,7 @@ class LearnModel(base.Posterior,base.PredictiveMixin):
     h0_k_weight_vec : numpy.ndarray, optional
         A vector of positive real numbers whose length is 
         ``c_dim_continuous+c_dim_categorical``, 
-        by default [1/c_num_assignment_vec.sum(),...,1/c_num_assignment_vec.sum()].
+        by default [1,...,1].
     h0_g : float, optional
         A real number in :math:`[0, 1]`, by default 0.5
     sub_h0_params : dict, optional
@@ -2218,7 +2218,7 @@ def _visualize_model_recursion(self,tree_graph,node:_Node,node_id,parent_id,pare
                         label_string += f'\\l{key}:{value}'
             label_string += '}\\l'
         else:
-            label_string += '\\lNone}\\l'
+            label_string += 'sub_model=\\lNone\\l'
 
         tree_graph.node(name=f'{tmp_id}',label=label_string,fillcolor=f'{rgb2hex(_CMAP(tmp_p_s))}')
         if tmp_p_s > 0.65:

doc/conf.py

@@ -22,7 +22,7 @@
 author = 'BayesML Developers'
 
 # The full version, including alpha/beta/rc tags
-release = '0.2.1'
+release = '0.2.5'
 
 
 # -- General configuration ---------------------------------------------------
@@ -81,7 +81,7 @@
 
 #numpydoc_show_class_members = False
 
-html_style = 'css/customize.css'
+# html_style = 'css/customize.css'
 
 autosummary_generate = True