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From tillrohrmann <...@git.apache.org>
Subject [GitHub] flink pull request: [FLINK-2030][FLINK-2274][ml]Online Histograms ...
Date Thu, 20 Aug 2015 13:21:46 GMT
Github user tillrohrmann commented on a diff in the pull request:

    --- Diff: docs/libs/ml/statistics.md ---
    @@ -0,0 +1,69 @@
    +mathjax: include
    +htmlTitle: FlinkML - Statistics
    +title: <a href="../ml">FlinkML</a> - Statistics
    +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
    +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
    +## Description
    + The statistics utility provides features such as building histograms over data.
    +## Methods
    + The Statistics utility provides two major functions: `createHistogram` and
    + `createDiscreteHistogram`.
    +### Creating a histogram
    + There are two types of histograms:
    +   1. **Continuous Histograms**: These histograms are formed on a data set `X: DataSet[Double]`
    +   when the values in `X` are from a continuous range. These histograms support
    +   `quantile` and `sum`  operations. Here `quantile(q)` refers to a value $x_q$ such
that $|x: x
    +   \leq x_q| = q * |X|$. Further, `sum(s)` refers to the number of elements $x \leq s$,
which can
    +    be construed as a cumulative probability value at $s$[Of course, *scaled* probability].
    +   2. A continuous histogram can be formed by calling `X.createHistogram(b)` where `b`
is the
    +    number of bins.
    +    **Discrete Histograms**: These histograms are formed on a data set `X:DataSet[Double]`
    +    when the values in `X` are from a discrete distribution. These histograms
    +    support `count(c)` operation which returns the number of elements associated with
cateogry `c`.
    +    <br>
    --- End diff --
    html tags should be replaced by markdown syntax

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