PCA inverseTransformPoint

doc/PCA.rst

@@ -8,8 +8,6 @@
 
    https://scikit-learn.org/stable/modules/decomposition.html#principal-component-analysis-pca
 
-
-
 :control numDimensions:
 
    The number of dimensions to reduce to
@@ -32,6 +30,17 @@
    :arg action: Run when done
 
    Given a trained model, apply the reduction to a source :fluid-obj:`DataSet` and write to a destination. Can be the same for both input and output (in-place). Returns the fraction of accounted variance, aka the fidelity of the new representation: a value near 1.0 means a higher fidelity to the original.
+
+
+:message inverseTransform:
+
+   :arg sourceDataSet: Source data, or the DataSet name
+
+   :arg destDataSet: Destination data, or the DataSet name
+
+   :arg action: Run when done
+
+   Given a trained model, invert a source :fluid-obj:`DataSet` containing dimensions that are principal components to a destination :fluid-obj:`DataSet` with the dimensionality of the data that was used to ``fit``. :fluid-obj:`DataSet` can be the same for both input and output (the operation will be performed in-place). 
 
 :message fitTransform:
 
@@ -51,4 +60,14 @@
 
    :arg action: Run when done
 
-   Given a trained model, transform the data point in a |buffer| and write to an output buffer.
+   Given a trained model, transform the data point in ``sourceBuffer`` from the original dimensional space to ``numDimensions`` principal components and write into ``destBuffer``.
+
+:message inverseTransformPoint:
+
+  :arg sourceBuffer: Input data
+
+  :arg destBuffer: Output data
+
+  :arg action: Run when done
+
+  Given a trained model, transform the data point in ``sourceBuffer`` from being ``numDimensions`` principal components into the original dimensional space and write into ```destBuffer``.