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0035dd184e
This is the first draft of the bernoulli naive bayes implementation. It is missing the Fit function tests and the Predict function.
86 lines
2.7 KiB
Go
86 lines
2.7 KiB
Go
package naive
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import (
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"github.com/gonum/matrix/mat64"
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base "github.com/sjwhitworth/golearn/base"
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)
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// A Bernoulli Naive Bayes Classifier. Naive Bayes classifiers assumes
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// that features probabilities are independent. In order to classify an
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// instance, it is calculated the probability that it was generated by
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// each known class, that is, for each class C, the following
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// probability is calculated.
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//
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// p(C|F1, F2, F3... Fn)
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//
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// Being F1, F2... Fn the instance features. Using the bayes theorem
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// this can be written as:
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//
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// \frac{p(C) \times p(F1, F2... Fn|C)}{p(F1, F2... Fn)}
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//
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// In the Bernoulli Naive Bayes features are considered independent
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// booleans, this means that the likelihood of a document given a class
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// C is given by:
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//
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// p(F1, F2... Fn) =
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// \prod_{i=1}^{n}{[F_i \times p(f_i|C)) + (1-F_i)(1 - p(f_i|C)))]}
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//
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// where
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// - F_i equals to 1 if feature is present in vector and zero
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// otherwise
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// - p(f_i|C) the probability of class C generating the feature
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// f_i
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//
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// For more information:
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//
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// C.D. Manning, P. Raghavan and H. Schuetze (2008). Introduction to
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// Information Retrieval. Cambridge University Press, pp. 234-265.
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// http://nlp.stanford.edu/IR-book/html/htmledition/the-bernoulli-model-1.html
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type BernoulliNBClassifier struct {
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base.BaseEstimator
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// Number of instances in each class. Used for calculating the prior
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// probability and p(f_i|C)
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classInstances []int
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}
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// Create a new Bernoulli Naive Bayes Classifier. The argument 'classes'
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// is the number of possible labels in the classification task.
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func NewBernoulliNBClassifier(classes int) *BernoulliNBClassifier {
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nb := BernoulliNBClassifier{}
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nb.classInstances = make([]int, classes)
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return &nb
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}
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// Fill data matrix with Bernoulli Naive Bayes model. All values
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// necessary for calculating prior probability and p(f_i
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func (nb *BernoulliNBClassifier) Fit(X *mat64.Dense, y []int) {
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instances, features := X.Dims()
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if instances != len(y) {
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panic(mat64.ErrShape)
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}
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nb.Data = mat64.NewDense(len(nb.classInstances), features, nil)
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for r := 0; r < instances; r++ {
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// Get label of this instance. This should be a value between
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// zero and nb.classes.
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label := y[r]
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nb.classInstances[label]++
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for c := 0; c < features; c++ {
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v := X.At(r, c)
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// In Bernoulli Naive Bayes the presence and absence of
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// features are considered. All non-zero values are
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// treated as presence.
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if v > 0 {
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// Update number of times this feature appeared within
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// given label.
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nb.Data.Set(label, c, nb.Data.At(label, c) + 1.0)
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}
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}
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}
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}
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