cross validation: return intermediate models used for XV

These are interesting for statistics, e.g. to determine the average dtree size

1 files changed, 5 insertions, 3 deletions

diff --git a/bin/analyze-timing.py b/bin/analyze-timing.py
index d67c553..c37ea65 100755
--- a/bin/analyze-timing.py
+++ b/bin/analyze-timing.py
@@ -305,7 +305,7 @@ if __name__ == "__main__":
                 )
 
     if xv_method == "montecarlo":
-        static_quality = xv.montecarlo(lambda m: m.get_static(), xv_count)
+        static_quality, _ = xv.montecarlo(lambda m: m.get_static(), xv_count)
     else:
         static_quality = model.assess(static_model)
 
@@ -314,7 +314,9 @@ if __name__ == "__main__":
     lut_model = model.get_param_lut()
 
     if xv_method == "montecarlo":
-        lut_quality = xv.montecarlo(lambda m: m.get_param_lut(fallback=True), xv_count)
+        lut_quality, _ = xv.montecarlo(
+            lambda m: m.get_param_lut(fallback=True), xv_count
+        )
     else:
         lut_quality = model.assess(lut_model)
 
@@ -412,7 +414,7 @@ if __name__ == "__main__":
                     print("{:10s}  {:10s}  {}".format("", "", info.model_args))
 
     if xv_method == "montecarlo":
-        analytic_quality = xv.montecarlo(lambda m: m.get_fitted()[0], xv_count)
+        analytic_quality, _ = xv.montecarlo(lambda m: m.get_fitted()[0], xv_count)
     else:
         analytic_quality = model.assess(param_model)