feat: change emulate_and_predict_ln_pxl to approx_ln_pxl

src/psimpy/inference/active_learning.py

@@ -243,13 +243,13 @@ class ActiveLearning:
         var_samples : numpy array
             Variable input samples of `ninit` simulations and `niter`
             iterative simulations. 2d array of shape (ninit+niter, ndim).
+        sim_outputs : numpy array
+            Outputs of `ninit` and `niter` simulations, corresponding to `data`.
+            2d array of shape (ninit+niter, len(data)).
         ln_pxl_values : numpy array
             Natural logarithm values of the product of prior and likelihood 
             at `ninit` and `niter` simulations. 
             1d array of shape (ninit+niter,).
-        sim_outputs : numpy array
-            Outputs of `ninit` and `niter` simulations, corresponding to `data`.
-            2d array of shape (ninit+niter, len(data)).
         """
         if init_var_samples.shape != (ninit, self.ndim):
             raise ValueError("init_var_samples must be of shape (ninit, ndim)")
@@ -301,32 +301,28 @@ class ActiveLearning:
                 next_var_sample.reshape(-1), next_sim_output)
             ln_pxl_values.append(next_ln_pxl_value)
         
+        # train final scalar gasp
+        self._emulate_ln_pxl(var_samples, np.array(ln_pxl_values))
+        
         ln_pxl_values = np.array(ln_pxl_values)
         
-        return var_samples, ln_pxl_values, sim_outputs
+        return var_samples, sim_outputs, ln_pxl_values
 
     @beartype
-    def emulate_and_predict_ln_pxl(self, x: np.ndarray, var_samples: np.ndarray,
-        ln_pxl_values: np.ndarray) -> float:
+    def approx_ln_pxl(self, x: np.ndarray) -> float:
         """
-        Build a scalar GP emulator for ln_pxl and make prediction at new x.
+        Approximate ln_pxl value at x based on the trained calar GP emulator.
 
         Parameters
         ----------
         x : numpy array
             One variable sample at which ln_pxl is to be approximated. 1d array
             of shape (ndim,)
-        var_samples : numpy array
-            Samples of variable inputs. 2d array of shape (n, ndim).
-        ln_pxl_values : numpy array
-            Natural logarithm values of the product of prior and likelihood 
-            at `var_samples`. 1d array of shape (n,).
         
         Returns
         -------
         A float value which is the emulator-predicted ln_pxl value at x.
         """
-        self._emulate_ln_pxl(var_samples, ln_pxl_values)
         predict = self._predict_ln_pxl(x)
 
         return float(predict[:,0])