1 files changed, 23 insertions, 8 deletions
diff --git a/lib/dfatool.py b/lib/dfatool.py
index 82ed35f..c4529ce 100755
--- a/lib/dfatool.py
+++ b/lib/dfatool.py
@@ -295,7 +295,7 @@ class CrossValidator:
         self.parameters = sorted(parameters)
         self.arg_count = arg_count
 
-    def montecarlo(self, model_getter, count = 2):
+    def montecarlo(self, model_getter, count = 200):
         """
         Perform Monte Carlo cross-validation and return average model quality.
 
@@ -1013,18 +1013,27 @@ class AnalyticModel:
 
         return static_mean_getter
 
-    def get_param_lut(self):
+    def get_param_lut(self, fallback = False):
         """
         Get parameter-look-up-table model function: name, attribute, parameter values -> model value.
 
         The function can only give model values for parameter combinations
-        present in by_param. It raises KeyError for other values.
+        present in by_param. By default, it raises KeyError for other values.
+
+        arguments:
+        fallback -- Fall back to the (non-parameter-aware) static model when encountering unknown parameter values
         """
+        static_model = self._get_model_from_dict(self.by_name, np.median)
         lut_model = self._get_model_from_dict(self.by_param, np.median)
 
         def lut_median_getter(name, key, param, arg = [], **kwargs):
             param.extend(map(soft_cast_int, arg))
-            return lut_model[(name, tuple(param))][key]
+            try:
+                return lut_model[(name, tuple(param))][key]
+            except KeyError:
+                if fallback:
+                    return static_model[name][key]
+                raise
 
         return lut_median_getter
 
@@ -1380,21 +1389,27 @@ class PTAModel:
 
         return static_mean_getter
 
-    def get_param_lut(self):
+    def get_param_lut(self, fallback = False):
         """
         Get parameter-look-up-table model function: name, attribute, parameter values -> model value.
 
         The function can only give model values for parameter combinations
-        present in by_param. It raises KeyError for other values.
+        present in by_param. By default, it raises KeyError for other values.
+
+        arguments:
+        fallback -- Fall back to the (non-parameter-aware) static model when encountering unknown parameter values
         """
         static_model = self._get_model_from_dict(self.by_name, np.median)
         lut_model = self._get_model_from_dict(self.by_param, np.median)
 
         def lut_median_getter(name, key, param, arg = [], **kwargs):
             param.extend(map(soft_cast_int, arg))
-            if (name, tuple(param)) in lut_model:
+            try:
                 return lut_model[(name, tuple(param))][key]
-            return static_model[name][key]
+            except KeyError:
+                if fallback:
+                    return static_model[name][key]
+                raise
 
         return lut_median_getter