From 6ce2f6fc3634529e5d0476c74d1e2084db51bd23 Mon Sep 17 00:00:00 2001
From: Matthew Taylor <matt@numenta.org>
Date: Mon, 15 May 2017 18:13:02 -0700
Subject: [PATCH] Fixed anomaly likelihood doc problems. (#3629)

---
 docs/README.md                             |  4 +--
 src/nupic/algorithms/anomaly_likelihood.py | 31 ++++++++++++----------
 2 files changed, 19 insertions(+), 16 deletions(-)

diff --git a/docs/README.md b/docs/README.md
index 77f95e52e2..569d0b5f7c 100644
--- a/docs/README.md
+++ b/docs/README.md
@@ -156,8 +156,8 @@ nupic
 │   ├── stats.py [TODO]
 │   └── topology.py [TODO]
 ├── regions
-│   ├── AnomalyLikelihoodRegion.py [TODO]
-│   ├── AnomalyRegion.py [TODO]
+│   ├── AnomalyLikelihoodRegion.py [OK]
+│   ├── AnomalyRegion.py [OK]
 │   ├── CLAClassifierRegion.py [TODO]
 │   ├── KNNAnomalyClassifierRegion.py [TODO]
 │   ├── KNNClassifierRegion.py [TODO]
diff --git a/src/nupic/algorithms/anomaly_likelihood.py b/src/nupic/algorithms/anomaly_likelihood.py
index aa82ffc1cf..ef49c79f0a 100644
--- a/src/nupic/algorithms/anomaly_likelihood.py
+++ b/src/nupic/algorithms/anomaly_likelihood.py
@@ -21,16 +21,18 @@
 
 """
 This module analyzes and estimates the distribution of averaged anomaly scores
-from a given model. Given a new anomaly score `s`, estimates `P(score >= s)`.
+from a given model. Given a new anomaly score ``s``, estimates
+``P(score >= s)``.
 
-The number `P(score >= s)` represents the likelihood of the current state of
+The number ``P(score >= s)`` represents the likelihood of the current state of
 predictability. For example, a likelihood of 0.01 or 1% means we see this much
 predictability about one out of every 100 records. The number is not as unusual
 as it seems. For records that arrive every minute, this means once every hour
 and 40 minutes. A likelihood of 0.0001 or 0.01% means we see it once out of
 10,000 records, or about once every 7 days.
 
-**USAGE**
+USAGE
++++++
 
 There are two ways to use the code: using the
 :class:`.anomaly_likelihood.AnomalyLikelihood` helper class or using the raw
@@ -38,39 +40,39 @@
 :func:`~.anomaly_likelihood.updateAnomalyLikelihoods`.
 
 
-**Low-Level Function Usage**
+Low-Level Function Usage
+++++++++++++++++++++++++
 
-
-There are two primary interface routines:
+There are two primary interface routines.
 
 - :func:`~.anomaly_likelihood.estimateAnomalyLikelihoods`: batch routine, called
-    initially and once in a while
+  initially and once in a while
 - :func:`~.anomaly_likelihood.updateAnomalyLikelihoods`: online routine, called
-    for every new data point
+  for every new data point
 
-1. Initially::
+Initially:
 
 .. code-block:: python
 
    likelihoods, avgRecordList, estimatorParams = \\
      estimateAnomalyLikelihoods(metric_data)
 
-2. Whenever you get new data::
+Whenever you get new data:
 
 .. code-block:: python
 
    likelihoods, avgRecordList, estimatorParams = \\
      updateAnomalyLikelihoods(data2, estimatorParams)
 
-3. And again (make sure you use the new estimatorParams returned in the above
-  call to updateAnomalyLikelihoods!)::
+And again (make sure you use the new estimatorParams returned in the above call
+to updateAnomalyLikelihoods!).
 
 .. code-block:: python
 
    likelihoods, avgRecordList, estimatorParams = \\
      updateAnomalyLikelihoods(data3, estimatorParams)
 
-4. Every once in a while update estimator with a lot of recent data::
+Every once in a while update estimator with a lot of recent data.
 
 .. code-block:: python
 
@@ -78,7 +80,8 @@
      estimateAnomalyLikelihoods(lots_of_metric_data)
 
 
-**PARAMS**
+PARAMS
+++++++
 
 The parameters dict returned by the above functions has the following
 structure. Note: the client does not need to know the details of this.