Make flow parent class, change splines class name.

astro-informatics · Oct 23, 2023 · a6edbfe · a6edbfe
1 parent 97c22f8
commit a6edbfe
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Showing 5 changed files with 84 additions and 206 deletions.
diff --git a/examples/normal_gamma_splines.py b/examples/normal_gamma_splines.py
@@ -268,7 +268,7 @@ def run_example(
         # Fit model
         # =======================================================================
         hm.logs.info_log("Fit model for {} epochs...".format(epochs_num))
-        model = model_nf.RQSplineFlow(
+        model = model_nf.RQSplineModel(
             ndim, standardize=standardize, temperature=var_scale
         )
         model.fit(chains_train.samples, epochs=epochs_num)

diff --git a/examples/radiata_pine_splines.py b/examples/radiata_pine_splines.py
@@ -433,7 +433,7 @@ def run_example(
     Fit model by selecing the configuration of hyper-parameters which 
     minimises the validation variances.
     """
-    model = model_nf.RQSplineFlow(ndim, standardize=standardize, temperature=var_scale)
+    model = model_nf.RQSplineModel(ndim, standardize=standardize, temperature=var_scale)
     model.fit(chains_train.samples, epochs=epochs_num)
 
     # ===========================================================================

diff --git a/examples/rastrigin_splines.py b/examples/rastrigin_splines.py
@@ -200,7 +200,7 @@ def run_example(
         """
         Fit model.
         """
-        model = model_nf.RQSplineFlow(
+        model = model_nf.RQSplineModel(
             ndim,
             n_layers=n_layers,
             n_bins=n_bins,

diff --git a/examples/rosenbrock_splines.py b/examples/rosenbrock_splines.py
@@ -220,7 +220,7 @@ def run_example(
         # Fit model
         # =======================================================================
         hm.logs.info_log("Fit model for {} epochs...".format(epochs_num))
-        model = model_nf.RQSplineFlow(
+        model = model_nf.RQSplineModel(
             ndim, standardize=standardize, temperature=var_scale
         )
         model.fit(chains_train.samples, epochs=epochs_num)