Return variance explained for all components

This PR aims to give the necessary data to build a variance explained graph for the case when all possible components are kept.
ME-ICA · Feb 25, 2022 · 019e0d5 · 019e0d5
1 parent cf42a93
commit 019e0d5
Showing 1 changed file with 10 additions and 2 deletions.
diff --git a/mapca/mapca.py b/mapca/mapca.py
@@ -100,16 +100,20 @@ class MovingAveragePCA:
             - 'n_components': The number of components chosen by the MDL criterion.
             - 'value': The MDL curve values.
             - 'explained_variance_total': The total explained variance of the components.
-    varexp_90 : dict
+    varexp_90_ : dict
         Dictionary containing the 90% variance explained results:
             - 'n_components': The number of components chosen by the 90% variance explained
                               criterion.
             - 'explained_variance_total': The total explained variance of the components.
-    varexp_95 : dict
+    varexp_95_ : dict
         Dictionary containing the 95% variance explained results:
             - 'n_components': The number of components chosen by the 95% variance explained
                               criterion.
             - 'explained_variance_total': The total explained variance of the components.
+    all_ : dict
+        Dictionary containing the results for all possible components:
+            - 'n_components': Total number of possible components.
+            - 'explained_variance_total': The total explained variance of the components.
 
     References
     ----------
@@ -336,6 +340,10 @@ def _fit(self, img, mask):
             "n_components": n_comp_varexp_95,
             "explained_variance_total": cumsum_varexp[n_comp_varexp_95 - 1],
         }
+        self.all_ = {
+            "n_components": ppca.n_components_,
+            "explained_variance_total": cumsum_varexp,
+        }
 
         # Assign attributes from model
         self.components_ = ppca.components_[:n_components, :]