Add calc_log_marginal_likelihood

bayesml/bernoulli/_bernoulli.py

@@ -5,7 +5,7 @@
 import warnings
 import numpy as np
 from scipy.stats import beta as ss_beta
-# from scipy.stats import betabinom as ss_betabinom
+from scipy.special import gammaln
 import matplotlib.pyplot as plt
 
 from .. import base
@@ -486,3 +486,32 @@ def pred_and_update(self,x,loss="squared"):
         prediction = self.make_prediction(loss=loss)
         self.update_posterior(x)
         return prediction
+
+    def calc_log_marginal_likelihood(self,x,posterior_update=True):
+        """Calculate log marginal likelihood of given data
+
+        Parameters
+        ----------
+        x : numpy.ndarray
+            All the elements must be 0 or 1.
+        posterior_update : bool, optional
+            If True, posterior distribution will be 
+            updated at the same time, by default True.
+
+        Returns
+        -------
+        log_marginal_likelihood : float
+            The log margina likelihood.
+        """
+        self.update_posterior(x)
+        tmp = (gammaln(self.h0_alpha+self.h0_beta)
+                -gammaln(self.h0_alpha)
+                -gammaln(self.h0_beta)
+                -gammaln(self.hn_alpha+self.hn_beta)
+                +gammaln(self.hn_alpha)
+                +gammaln(self.hn_beta))
+
+        if not posterior_update:
+            self.reset_hn_params()
+
+        return tmp