Initialization bug fix

Author: yuta-nakahara

bayesml/gaussianmixture/_gaussianmixture.py

@@ -770,9 +770,12 @@ def _init_subsampling(self,x):
                                  / _size * self.hn_nus[k]
                                  + np.identity(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_pi_char()
         self._calc_q_lambda_char()
 
+    def _init_rho_r(self):
+        self._ln_rho[:] = 0.0
+        self.r_vecs[:] = 1/self.c_num_classes
+
     def update_posterior(
             self,
             x,
@@ -818,6 +821,8 @@ def update_posterior(
 
         convergence_flag = True
         for i in range(num_init):
+            self.reset_hn_params()
+            self._init_rho_r()
             if init_type == 'subsampling':
                 self._init_subsampling(x)
                 self._update_q_z(x)

bayesml/gaussianmixture/test.py

@@ -11,8 +11,8 @@
     lambda_mats=np.array([[[6.25]],[[6.25]],[[100]]])
     )
 
-x,z = gen_model.gen_sample(1000)
+x,z = gen_model.gen_sample(300)
 
 learn_model = gaussianmixture.LearnModel(3,1)
-learn_model.update_posterior(x)
+learn_model.update_posterior(x)#,init_type='random_responsibility')
 learn_model.visualize_posterior()