Bernoulli Naive Bayes: first draft

This is the first draft of the bernoulli naive bayes implementation. It
is missing the Fit function tests and the Predict function.

naive/bernoulli_nb.go

@@ -0,0 +1,85 @@
+package naive
+
+import (
+    "github.com/gonum/matrix/mat64"
+    base "github.com/sjwhitworth/golearn/base"
+)
+
+// A Bernoulli Naive Bayes Classifier. Naive Bayes classifiers assumes
+// that features probabilities are independent. In order to classify an
+// instance, it is calculated the probability that it was generated by
+// each known class, that is, for each class C, the following
+// probability is calculated.
+//
+// p(C|F1, F2, F3... Fn)
+//
+// Being F1, F2... Fn the instance features. Using the bayes theorem
+// this can be written as:
+//
+// \frac{p(C) \times p(F1, F2... Fn|C)}{p(F1, F2... Fn)}
+//
+// In the Bernoulli Naive Bayes features are considered independent
+// booleans, this means that the likelihood of a document given a class
+// C is given by:
+//
+// p(F1, F2... Fn) =
+// \prod_{i=1}^{n}{[F_i \times p(f_i|C)) + (1-F_i)(1 - p(f_i|C)))]}
+//
+// where
+//     - F_i equals to 1 if feature is present in vector and zero
+//       otherwise
+//     - p(f_i|C) the probability of class C generating the feature
+//       f_i
+//
+// For more information:
+//
+// C.D. Manning, P. Raghavan and H. Schuetze (2008). Introduction to
+// Information Retrieval. Cambridge University Press, pp. 234-265.
+// http://nlp.stanford.edu/IR-book/html/htmledition/the-bernoulli-model-1.html
+type BernoulliNBClassifier struct {
+    base.BaseEstimator
+    // Number of instances in each class. Used for calculating the prior
+    // probability and p(f_i|C)
+    classInstances []int
+}
+
+// Create a new Bernoulli Naive Bayes Classifier. The argument 'classes'
+// is the number of possible labels in the classification task.
+func NewBernoulliNBClassifier(classes int) *BernoulliNBClassifier {
+    nb := BernoulliNBClassifier{}
+    nb.classInstances = make([]int, classes)
+    return &nb
+}
+
+// Fill data matrix with Bernoulli Naive Bayes model. All values
+// necessary for calculating prior probability and p(f_i
+func (nb *BernoulliNBClassifier) Fit(X *mat64.Dense, y []int) {
+    instances, features := X.Dims()
+    if instances != len(y) {
+        panic(mat64.ErrShape)
+    }
+
+    nb.Data = mat64.NewDense(len(nb.classInstances), features, nil)
+
+    for r := 0; r < instances; r++ {
+        // Get label of this instance. This should be a value between
+        // zero and nb.classes.
+        label := y[r]
+        nb.classInstances[label]++
+
+        for c := 0; c < features; c++ {
+            v := X.At(r, c)
+            // In Bernoulli Naive Bayes the presence and absence of
+            // features are considered. All non-zero values are
+            // treated as presence.
+            if v > 0 {
+                // Update number of times this feature appeared within
+                // given label.
+                nb.Data.Set(label, c, nb.Data.At(label, c) + 1.0)
+            }
+        }
+    }
+}
+
+
+

naive/bernoulli_nb_test.go

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+package naive
+
+import (
+    "testing"
+    "github.com/gonum/matrix/mat64"
+)
+
+// Test if panic is correctly called when matrices with different
+// dimensions are used as arguments.
+func TestFitPanic(t *testing.T) {
+    defer func() {
+        if recover() == nil {
+            t.Fatalf("invalid matrix dim did not panic")
+        }
+    }()
+
+    nb := NewBernoulliNBClassifier(2)
+
+    X := mat64.NewDense(10, 20, nil)
+    // simulating user mistake, one extra label
+    y := make([]int, 11)
+
+    nb.Fit(X, y)
+}

naive/naive.go

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+// Package naive implements...
+package naive