Add the method predict_values_no_error() to the emulator object to pr…

…ovide predictions without the associated errors, which can be a lot faster.

swiftemulator/emulators/base.py

@@ -80,7 +80,8 @@ def predict_values(
         self, independent: np.array, model_parameters: Dict[str, float]
     ) -> tuple[np.array, np.array]:
         """
-        Predict values from the trained emulator contained within this object.
+        Predict values and the associated variance from the trained emulator contained
+        within this object.
 
         Parameters
         ----------
@@ -123,3 +124,49 @@ def predict_values(
             )
 
         raise NotImplementedError
+
+    def predict_values_no_error(
+        self, independent: np.array, model_parameters: Dict[str, float]
+    ) -> np.array:
+        """
+        Predict values from the trained emulator contained within this object.
+        In cases where the error estimates are not required, this method is
+        significantly faster than predict_values().
+
+        Parameters
+        ----------
+
+        independent, np.array
+            Independent continuous variables to evaluate the emulator
+            at. If the emulator is discrete, these are only allowed to be
+            the discrete independent variables that the emulator was trained at
+            (disregarding the additional 'independent' model parameters, below.)
+
+        model_parameters: Dict[str, float]
+            The point in model parameter space to create predicted
+            values at.
+
+        Returns
+        -------
+
+        dependent_predictions, np.array
+            Array of predictions, if the emulator is a function f, these
+            are the predicted values of f(independent) evaluted at the position
+            of the input ``model_parameters``.
+
+        Raises
+        ------
+
+        AttributeError
+            When the model has not been trained before trying to make a
+            prediction, or when attempting to evaluate the model at
+            disallowed independent variables.
+        """
+
+        if self.emulator is None:
+            raise AttributeError(
+                "Please train the emulator with fit_model before attempting "
+                "to make predictions."
+            )
+
+        raise NotImplementedError

swiftemulator/emulators/gaussian_process.py

@@ -231,7 +231,8 @@ def predict_values(
         self, independent: np.array, model_parameters: Dict[str, float]
     ) -> tuple[np.array, np.array]:
         """
-        Predict values from the trained emulator contained within this object.
+        Predict values and the associated variance from the trained emulator
+        contained within this object.
 
         Parameters
         ----------
@@ -253,7 +254,7 @@ def predict_values(
             of the input model_parameters.
 
         dependent_prediction_errors, np.array
-            Errors on the model predictions.
+            Errors (variances) on the model predictions.
         """
 
         if self.emulator is None:
@@ -279,3 +280,55 @@ def predict_values(
         )
 
         return model, errors
+
+    def predict_values_no_error(
+        self, independent: np.array, model_parameters: Dict[str, float]
+    ) -> np.array:
+        """
+        Predict values from the trained emulator contained within this object.
+        In cases where the error estimates are not required, this method is
+        significantly faster than predict_values().
+
+        Parameters
+        ----------
+
+        independent, np.array
+            Independent continuous variables to evaluate the emulator
+            at.
+
+        model_parameters: Dict[str, float]
+            The point in model parameter space to create predicted
+            values at.
+
+        Returns
+        -------
+
+        dependent_predictions, np.array
+            Array of predictions, if the emulator is a function f, these
+            are the predicted values of f(independent) evaluted at the position
+            of the input model_parameters.
+        """
+
+        if self.emulator is None:
+            raise AttributeError(
+                "Please train the emulator with fit_model before attempting "
+                "to make predictions."
+            )
+
+        model_parameter_array = np.array(
+            [model_parameters[parameter] for parameter in self.parameter_order]
+        )
+
+        t = np.empty(
+            (len(independent), len(model_parameter_array) + 1), dtype=np.float32
+        )
+
+        for line, value in enumerate(independent):
+            t[line][0] = value
+            t[line][1:] = model_parameter_array
+
+        model = self.emulator.predict(
+            y=self.dependent_variables, t=t, return_cov=False, return_var=False
+        )
+
+        return model