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

vendor/github.com/jbrukh/bayesian/doc.go

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+/*Package bayesian is a Naive Bayesian Classifier
+  Jake Brukhman <jbrukh@gmail.com>
+
+  BAYESIAN CLASSIFICATION REFRESHER: suppose you have a set
+  of classes (e.g. categories) C := {C_1, ..., C_n}, and a
+  document D consisting of words D := {W_1, ..., W_k}.
+  We wish to ascertain the probability that the document
+  belongs to some class C_j given some set of training data
+  associating documents and classes.
+
+  By Bayes' Theorem, we have that
+
+    P(C_j|D) = P(D|C_j)*P(C_j)/P(D).
+
+  The LHS is the probability that the document belongs to class
+  C_j given the document itself (by which is meant, in practice,
+  the word frequencies occurring in this document), and our program
+  will calculate this probability for each j and spit out the
+  most likely class for this document.
+
+  P(C_j) is referred to as the "prior" probability, or the
+  probability that a document belongs to C_j in general, without
+  seeing the document first. P(D|C_j) is the probability of seeing
+  such a document, given that it belongs to C_j. Here, by assuming
+  that words appear independently in documents (this being the
+  "naive" assumption), we can estimate
+
+    P(D|C_j) ~= P(W_1|C_j)*...*P(W_k|C_j)
+
+  where P(W_i|C_j) is the probability of seeing the given word
+  in a document of the given class. Finally, P(D) can be seen as
+  merely a scaling factor and is not strictly relevant to
+  classificiation, unless you want to normalize the resulting
+  scores and actually see probabilities. In this case, note that
+
+    P(D) = SUM_j(P(D|C_j)*P(C_j))
+
+  One practical issue with performing these calculations is the
+  possibility of float64 underflow when calculating P(D|C_j), as
+  individual word probabilities can be arbitrarily small, and
+  a document can have an arbitrarily large number of them. A
+  typical method for dealing with this case is to transform the
+  probability to the log domain and perform additions instead
+  of multiplications:
+
+    log P(C_j|D) ~ log(P(C_j)) + SUM_i(log P(W_i|C_j))
+
+  where i = 1, ..., k. Note that by doing this, we are discarding
+  the scaling factor P(D) and our scores are no longer
+  probabilities; however, the monotonic relationship of the
+  scores is preserved by the log function.
+*/
+package bayesian