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capturing the training set in a closure to avoid retraining for each … #6

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26 changes: 14 additions & 12 deletions MachineLearningInAction/MachineLearningInAction/NaiveBayes.fs
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Original file line number Diff line number Diff line change
Expand Up @@ -101,6 +101,7 @@ module NaiveBayes =
label,
prop(total, size),
Map.map (fun token count -> prop(count, totTokens)) tokenCount)
|> Seq.toList

// Classifier function:
// the classifier is trained on the dataset,
Expand All @@ -112,16 +113,17 @@ module NaiveBayes =
// and returning the highest scoring label.
// Probabilities are log-transformed to avoid underflow.
// See "Chapter4.fsx" for an illustration.
let classifier frequency dataset words text =
let classifier frequency dataset words =
let estimator = train frequency dataset words
let tokenized = vocabulary text
estimator
|> Seq.map (fun (label, proba, tokens) ->
label,
tokens
|> Map.fold (fun p token value ->
if Set.exists (fun w -> w = token) tokenized
then p + log(value)
else p) (log proba))
|> Seq.maxBy snd
|> fst
fun text ->
let tokenized = vocabulary text
estimator
|> Seq.map (fun (label, proba, tokens) ->
label,
tokens
|> Map.fold (fun p token value ->
if Set.exists (fun w -> w = token) tokenized
then p + log(value)
else p) (log proba))
|> Seq.maxBy snd
|> fst