Naive Bayes classifier. Currently 3 types of NB are supported:

• MultinomialNB - Assumes variables have a multinomial distribution. Good for text classification. See examples/nums.jl for usage.
• GaussianNB - Assumes variables have a multivariate normal distribution. Good for real-valued data. See examples/iris.jl for usage.
• HybridNB - A hybrid empirical naive Bayes model for a mixture of continuous and discrete features. The continuous features are estimated using Kernel Density Estimation. Note: fit/predict methods take Dict{Symbol/AstractString, Vector} rather than a Matrix. Also, discrete features must be integers while continuous features must be floats. If all features are continuous Matrix input is supported.

Since GaussianNB models multivariate distribution, it's not really a "naive" classifier (i.e. no independence assumption is made), so the name may change in the future.

As a subproduct, this package also provides a DataStats type that may be used for incremental calculation of common data statistics such as mean and covariance matrix. See test/datastatstest.jl for a usage example.

1. Continuous and discrete features as Dict{Symbol, Vector}}

   f_c1 = randn(10)
   f_c2 = randn(10)
   f_d1 = rand(1:5, 10)
   f_d2 = randn(3:7, 10)
   training_features_continuous = Dict{Symbol, Vector{Float64}}(:c1=>f_c1, :c2=>f_c2)
   training_features_discrete   = Dict{Symbol, Vector{Int}}(:d1=>f_d1, :d2=>f_d2) #discrete features as Int64
   
   hybrid_model = HybridNB(labels)
   
   # train the model
   fit(hybrid_model, training_features_continuous, training_features_discrete, labels)
   # predict the classification for new events (points): features_c, features_d
   y = predict(hybrid_model, features_c, features_d)

   Alternatively one can skip declaring the model and train it directly:

   model = train(HybridNB, training_features_continuous, training_features_discrete, labels)
   y = predict(hybrid_model, features_c, features_d)

2. Continuous features only as a Matrix

   X_train = randn(3,400);
   X_classify = randn(3,10)
   
   hybrid_model = HybridNB(labels) # the number of discrete features is 0 so it's not needed
   fit(hybrid_model, X_train, labels)
   y = predict(hybrid_model, X_classify)

3. Continuous and discrete features as a Matrix{Float}

   #X is a matrix of features
   # the first 3 rows are continuous
   training_features_continuous = restructure_matrix(X[1:3, :])
   # the last 2 rows are discrete and must be integers
   training_features_discrete = map(Int, restructure_matrix(X[4:5, :]))
   # train the model
   hybrid_model = train(HybridNB, training_features_continuous, training_features_discrete, labels)
   
   # predict the classification for new events (points): features_c, features_d
   y = predict(hybrid_model, features_c, features_d)

It is useful to train a model once and then use it for prediction many times later. For example, train your classifier on a local machine and then use it on a cluster to classify points in parallel.

There is support for writing HybridNB models to HDF5 files via the methods write_model and load_model. This is useful for interacting with other programs/languages. If the model file is going to be read only in Julia it is easier to use JLD.jl for saving and loading the file.