AMD

examples/deconvolution/partitions.py

@@ -39,15 +39,13 @@
 ###############################################################################
 # For data with multiple trials (data with a :class:`Case` dimension), the function attempts to use trials evenly across time: 
 
-p = plot.preview_partitions(20, partitions=5)
-p = plot.preview_partitions(20, partitions=2)
+p5 = plot.preview_partitions(20, partitions=5)
+p2 = plot.preview_partitions(20, partitions=2)
 
 ###############################################################################
 # Testing
 # -------
 # Testing the result of a model fit with cross-validation requires data that was never used during training. Testing with cross-validation is enabled in the :func:`boosting` function by setting ``test=True``. When testing is enabled, each data segment is used in turn as testing segment. For each testing segment, the remaining segment are used in different runs as training and validation data. The results of those runs are then averaged to predict responses in the testing data. This nested loop means that the number of boosting runs can get large quickly when using many partitions, so the default is to use just four partitions:
 
 p = plot.preview_partitions(test=True)
-
-""
-p = plot.preview_partitions(20, partitions=5, test=1)
+p5 = plot.preview_partitions(20, partitions=5, test=1)