Update poisson

Author: yuta-nakahara

bayesml/bernoulli/_bernoulli.py

@@ -223,7 +223,7 @@ def __init__(self,h0_alpha=0.5,h0_beta=0.5):
         self.set_h0_params(h0_alpha,h0_beta)
 
     def get_constants(self):
-        """Get constants of GenModel.
+        """Get constants of LearnModel.
 
         This model does not have any constants. 
         Therefore, this function returns an emtpy dict ``{}``.

bayesml/exponential/_exponential.py

@@ -225,7 +225,7 @@ def __init__(self,h0_alpha=1.0, h0_beta=1.0):
         self.set_h0_params(h0_alpha,h0_beta)
 
     def get_constants(self):
-        """Get constants of GenModel.
+        """Get constants of LearnModel.
 
         This model does not have any constants. 
         Therefore, this function returns an emtpy dict ``{}``.

bayesml/normal/_normal.py

@@ -270,7 +270,7 @@ def __init__(self,h0_m=0.0,h0_kappa=1.0,h0_alpha=1.0,h0_beta=1.0):
         )
 
     def get_constants(self):
-        """Get constants of GenModel.
+        """Get constants of LearnModel.
 
         This model does not have any constants. 
         Therefore, this function returns an emtpy dict ``{}``.
@@ -558,6 +558,7 @@ def pred_and_update(self,x,loss="squared"):
             If the loss function is \"KL\", the predictive distribution itself will be returned
             as numpy.ndarray.
         """
+        _check.float_(x,'x',DataFormatError)
         self.calc_pred_dist()
         prediction = self.make_prediction(loss=loss)
         self.update_posterior(x)

bayesml/poisson/_poisson.py

@@ -32,24 +32,45 @@ class GenModel(base.Generative):
         by default None
     """
 
-    def __init__(self,*,lambda_=1.0,h_alpha=1.0,h_beta=1.0,seed=None):
-        self.lambda_ = _check.pos_float(lambda_,'lambda_',ParameterFormatError)
-        self.h_alpha = _check.pos_float(h_alpha,'h_alpha',ParameterFormatError)
-        self.h_beta = _check.pos_float(h_beta,'h_beta',ParameterFormatError)
+    def __init__(self,lambda_=1.0,h_alpha=1.0,h_beta=1.0,seed=None):
         self.rng = np.random.default_rng(seed)
 
-    def set_h_params(self,h_alpha,h_beta):
+        # params
+        self.lambda_ = 1.0
+
+        # h_params
+        self.h_alpha = 1.0
+        self.h_beta = 1.0
+
+        self.set_params(lambda_)
+        self.set_h_params(h_alpha,h_beta)
+
+    def get_constants(self):
+        """Get constants of GenModel.
+
+        This model does not have any constants. 
+        Therefore, this function returns an emtpy dict ``{}``.
+        
+        Returns
+        -------
+        constants : an empty dict
+        """
+        return {}
+
+    def set_h_params(self,h_alpha=None,h_beta=None):
         """Set the hyperparameters of the prior distribution.
         
         Parameters
         ----------
-        h_alpha : float
-            a positive real number
-        h_beta : float
-            a positibe real number
+        h_alpha : float, optional
+            a positive real number, by default None
+        h_beta : float, optional
+            a positibe real number, by default None
         """
-        self.h_alpha = _check.pos_float(h_alpha,'h_alpha',ParameterFormatError)
-        self.h_beta = _check.pos_float(h_beta,'h_beta',ParameterFormatError)
+        if h_alpha is not None:
+            self.h_alpha = _check.pos_float(h_alpha,'h_alpha',ParameterFormatError)
+        if h_beta is not None:
+            self.h_beta = _check.pos_float(h_beta,'h_beta',ParameterFormatError)
         return self
 
     def get_h_params(self):
@@ -71,15 +92,16 @@ def gen_params(self):
         self.lambda_ = self.rng.gamma(shape=self.h_alpha,scale=1.0/self.h_beta)
         return self
 
-    def set_params(self,lambda_):
+    def set_params(self,lambda_=None):
         """Set the parameter of the sthocastic data generative model.
 
         Parameters
         ----------
-        lambda_ : float
-            a positive real number
+        lambda_ : float, optional
+            a positive real number, by default None
         """
-        self.lambda_ = _check.pos_float(lambda_,'lambda_',ParameterFormatError)
+        if lambda_ is not None:
+            self.lambda_ = _check.pos_float(lambda_,'lambda_',ParameterFormatError)
         return self
 
     def get_params(self):
@@ -175,34 +197,55 @@ class LearnModel(base.Posterior,base.PredictiveMixin):
     Attributes
     ----------
     hn_alpha : float
-        a positive real number, by default 1.0
+        a positive real number
     hn_beta : float
-        a positibe real number, by default 1.0
+        a positibe real number
     p_r : float
-        a positive real number, by default 1.0
+        a positive real number
     p_theta : float
-        a real number in :math:`[0, 1]`, by default 0.5
+        a real number in :math:`[0, 1]`
     """
     def __init__(self,h0_alpha=1.0,h0_beta=1.0):
-        self.h0_alpha = _check.pos_float(h0_alpha,'h0_alpha',ParameterFormatError)
-        self.h0_beta = _check.pos_float(h0_beta,'h0_beta',ParameterFormatError)
-        self.hn_alpha = self.h0_alpha
-        self.hn_beta = self.h0_beta
-        self.p_r = self.hn_alpha
-        self.p_theta = 1.0 / (1.0+self.hn_beta)
-    
-    def set_h0_params(self,h0_alpha,h0_beta):
+        # h0_params
+        self.h0_alpha = 1.0
+        self.h0_beta = 1.0
+
+        # hn_params
+        self.hn_alpha = 1.0
+        self.hn_beta = 1.0
+
+        #p_params
+        self.p_r = 1.0
+        self.p_theta = 0.5
+
+        self.set_h0_params(h0_alpha,h0_beta)
+
+    def get_constants(self):
+        """Get constants of LearnModel.
+
+        This model does not have any constants. 
+        Therefore, this function returns an emtpy dict ``{}``.
+        
+        Returns
+        -------
+        constants : an empty dict
+        """
+        return {}
+
+    def set_h0_params(self,h0_alpha=None,h0_beta=None):
         """Set initial values of the hyperparameter of the posterior distribution.
         
         Parameters
         ----------
-        h0_alpha : float
-            a positive real number
-        h0_beta : float
-            a positibe real number
+        h0_alpha : float, optional
+            a positive real number, by default None
+        h0_beta : float, optional
+            a positibe real number, by default None
         """
-        self.h0_alpha = _check.pos_float(h0_alpha,'h0_alpha',ParameterFormatError)
-        self.h0_beta = _check.pos_float(h0_beta,'h0_beta',ParameterFormatError)
+        if h0_alpha is not None:
+            self.h0_alpha = _check.pos_float(h0_alpha,'h0_alpha',ParameterFormatError)
+        if h0_beta is not None:
+            self.h0_beta = _check.pos_float(h0_beta,'h0_beta',ParameterFormatError)
         self.reset_hn_params()
         return self
 
@@ -217,18 +260,20 @@ def get_h0_params(self):
         """
         return {"h0_alpha":self.h0_alpha, "h0_beta":self.h0_beta}
 
-    def set_hn_params(self,hn_alpha,hn_beta):
+    def set_hn_params(self,hn_alpha=None,hn_beta=None):
         """Set updated values of the hyperparameter of the posterior distribution.
         
         Parameters
         ----------
-        hn_alpha : float
-            a positive real number
-        hn_beta : float
-            a positibe real number
+        hn_alpha : float, optional
+            a positive real number, by default None
+        hn_beta : float, optional
+            a positibe real number, by default None
         """
-        self.hn_alpha = _check.pos_float(hn_alpha,'hn_alpha',ParameterFormatError)
-        self.hn_beta = _check.pos_float(hn_beta,'hn_beta',ParameterFormatError)
+        if hn_alpha is not None:
+            self.hn_alpha = _check.pos_float(hn_alpha,'hn_alpha',ParameterFormatError)
+        if hn_beta is not None:
+            self.hn_beta = _check.pos_float(hn_beta,'hn_beta',ParameterFormatError)
         self.calc_pred_dist()
         return self
 
@@ -243,28 +288,6 @@ def get_hn_params(self):
         """
         return {"hn_alpha":self.hn_alpha, "hn_beta":self.hn_beta}
 
-    def overwrite_h0_params(self):
-        """Overwrite the initial values of the hyperparameters of the posterior distribution by the learned values.
-        
-        They are overwritten by `self.hn_alpha` and `self.hn_beta`.
-        Note that the parameters of the predictive distribution are also calculated from `self.hn_alpha` and `self.hn_beta`.
-        """
-        self.h0_alpha = self.hn_alpha
-        self.h0_beta = self.hn_beta
-        self.calc_pred_dist()
-        return self
-
-    def reset_hn_params(self):
-        """Reset the hyperparameters of the posterior distribution to their initial values.
-        
-        They are reset to `self.h0_alpha` and `self.h0_beta`.
-        Note that the parameters of the predictive distribution are also calculated from `self.h0_alpha` and `self.h0_beta`.
-        """
-        self.hn_alpha = self.h0_alpha
-        self.hn_beta = self.h0_beta
-        self.calc_pred_dist()
-        return self
-
     def update_posterior(self,x):
         """Update the hyperparameters of the posterior distribution using traning data.
 
@@ -275,6 +298,15 @@ def update_posterior(self,x):
         """
         _check.nonneg_ints(x,'x',DataFormatError)
         self.hn_alpha += np.sum(x)
+        try:
+            self.hn_beta += x.size
+        except:
+            self.hn_beta += 1
+        return self
+
+    def _update_posterior(self,x):
+        """Update opsterior without input check."""
+        self.hn_alpha += np.sum(x)
         self.hn_beta += x.size
         return self