Add bayes classifier stubs, string normalizer, and deps, update sample config

vendor/github.com/jbrukh/bayesian/README.md

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+# Naive Bayesian Classification
+
+Perform naive Bayesian classification into an arbitrary number of classes on sets of strings. `bayesian` also supports term frequency-inverse document frequency calculations ([TF-IDF](https://www.wikiwand.com/en/Tf%E2%80%93idf)).
+
+Copyright (c) 2011-2017. Jake Brukhman. (jbrukh@gmail.com).
+All rights reserved.  See the LICENSE file for BSD-style license.
+
+------------
+
+## Background
+
+This is meant to be an low-entry barrier Go library for basic Bayesian classification. See code comments for a refresher on naive Bayesian classifiers, and please take some time to understand underflow edge cases as this otherwise may result in innacurate classifications.
+
+------------
+
+## Installation
+
+Using the go command:
+
+```shell
+go get github.com/navossoc/bayesian
+go install !$
+```
+
+------------
+
+## Documentation
+
+See the GoPkgDoc documentation [here](https://godoc.org/github.com/navossoc/bayesian).
+
+------------
+
+## Features
+
+- Conditional probability and "log-likelihood"-like scoring.
+- Underflow detection.
+- Simple persistence of classifiers.
+- Statistics.
+- TF-IDF support.
+
+------------
+
+## Example 1 (Simple Classification)
+
+To use the classifier, first you must create some classes
+and train it:
+
+```go
+import "github.com/navossoc/bayesian"
+
+const (
+    Good bayesian.Class = "Good"
+    Bad  bayesian.Class = "Bad"
+)
+
+classifier := bayesian.NewClassifier(Good, Bad)
+goodStuff := []string{"tall", "rich", "handsome"}
+badStuff  := []string{"poor", "smelly", "ugly"}
+classifier.Learn(goodStuff, Good)
+classifier.Learn(badStuff,  Bad)
+```
+
+Then you can ascertain the scores of each class and
+the most likely class your data belongs to:
+
+```go
+scores, likely, _ := classifier.LogScores(
+                        []string{"tall", "girl"},
+                     )
+```
+
+Magnitude of the score indicates likelihood. Alternatively (but
+with some risk of float underflow), you can obtain actual probabilities:
+
+```go
+probs, likely, _ := classifier.ProbScores(
+                        []string{"tall", "girl"},
+                     )
+```
+
+## Example 2 (TF-IDF Support)
+
+To use the TF-IDF classifier, first you must create some classes
+and train it and you need to call ConvertTermsFreqToTfIdf() AFTER training
+and before calling classification methods such as `LogScores`, `SafeProbScores`, and `ProbScores`)
+
+```go
+import "github.com/navossoc/bayesian"
+
+const (
+    Good bayesian.Class = "Good"
+    Bad bayesian.Class = "Bad"
+)
+
+// Create a classifier with TF-IDF support.
+classifier := bayesian.NewClassifierTfIdf(Good, Bad)
+
+goodStuff := []string{"tall", "rich", "handsome"}
+badStuff  := []string{"poor", "smelly", "ugly"}
+
+classifier.Learn(goodStuff, Good)
+classifier.Learn(badStuff,  Bad)
+
+// Required
+classifier.ConvertTermsFreqToTfIdf()
+```
+
+Then you can ascertain the scores of each class and
+the most likely class your data belongs to:
+
+```go
+scores, likely, _ := classifier.LogScores(
+    []string{"tall", "girl"},
+)
+```
+
+Magnitude of the score indicates likelihood. Alternatively (but
+with some risk of float underflow), you can obtain actual probabilities:
+
+```go
+probs, likely, _ := classifier.ProbScores(
+    []string{"tall", "girl"},
+)
+```
+
+Use wisely.