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https://issues.apache.org/jira/browse/FLINK-1695?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14363531#comment-14363531
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ASF GitHub Bot commented on FLINK-1695:
---------------------------------------
Github user tillrohrmann commented on a diff in the pull request:
https://github.com/apache/flink/pull/479#discussion_r26507107
--- Diff: docs/ml/polynomial_base_feature_mapper.md ---
@@ -0,0 +1,91 @@
+---
+mathjax: include
+title: Polynomial Base Feature Mapper
+---
+<!--
+Licensed to the Apache Software Foundation (ASF) under one
+or more contributor license agreements. See the NOTICE file
+distributed with this work for additional information
+regarding copyright ownership. The ASF licenses this file
+to you under the Apache License, Version 2.0 (the
+"License"); you may not use this file except in compliance
+with the License. You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing,
+software distributed under the License is distributed on an
+"AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+KIND, either express or implied. See the License for the
+specific language governing permissions and limitations
+under the License.
+-->
+
+* This will be replaced by the TOC
+{:toc}
+
+## Description
+
+The polynomial base feature mapper maps a vector into the polynomial
feature space of degree $d$.
+The dimension of the input vector determines the number of polynomial
factors whose values are the respective vector entries.
+Given a vector $(x, y, z, \ldots)^T$ the resulting feature vector looks
the following:
+
+$$\left(x, y, z, x^2, xy, y^2, yz, z^2, x^3, x^2y, x^2z, xy^2, xyz, xz^2,
y^3, \ldots\right)^T$$
+
+Flink's implementation orders the polynomials in decreasing order of their
degree.
+
+Given the vector $\left(3,2\right)^T$, the polynomial base feature vector
of degree 3 would look like
+
+ $$\left(3^3, 3^2\cdot2, 3\cdot2^2, 2^3, 3^2, 3\cdot2, 2^2, 3, 2\right)^T$$
+
+This transformer can be prepended to all `Transformer` and `Learner`
implmeentations which expec an input of type `LabeledVector`.
--- End diff --
Good catch again :-)
> Create machine learning library
> -------------------------------
>
> Key: FLINK-1695
> URL: https://issues.apache.org/jira/browse/FLINK-1695
> Project: Flink
> Issue Type: Improvement
> Reporter: Till Rohrmann
> Assignee: Till Rohrmann
>
> Create the infrastructure for Flink's machine learning library. This includes
> the creation of the module structure and the implementation of basic types
> such as vectors and matrices.
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