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bengal\nOptimized Go implementation of Naive Bayes classifiers for multilabel text classification.\n\n## install\nIn your project:\n\n`$ go get github.com/haydenhigg/bengal`\n\nThen, import it as:\n```go\nimport \"github.com/haydenhigg/bengal\"\n```\n\n## use\n### modeling\n- `TrainMultinomial(xs, ys [][]string, smoothing float64) NaiveBayesModel`: Creates and trains a multinomial model.\n- `(model *NaiveBayesModel) PredictMultinomial(x []string) []string`: Predicts the labels for an input using token presence only.\n- `NewBernoulli(xs, ys [][]string, smoothing float64) NaiveBayesModel`: Creates and trains a Bernoulli model.\n- `(model *NaiveBayesModel) PredictBernoulli(x []string) []string`: Predicts the labels for an input using token presence and absence.\n\n### example\n```go\npackage main\n\nimport (\n  \"fmt\"\n  \"github.com/haydenhigg/bengal\"\n)\n\nfunc main() {\n\tinputs := [][]string{\n\t\t[]string{\"the\", \"cat\", \"was\", \"crying\"},\n\t\t[]string{\"dogs\", \"like\", \"to\", \"smile\"},\n\t\t...,\n\t}\n\n\toutputs := [][]string{\n\t\t[]string{\"cat\", \"sad\"},\n\t\t[]string{\"dog\", \"happy\"},\n\t\t...,\n\t}\n\n\tsmoothing := 1.0 // fix the zero-probability problem, 1.0 is common\n\tmodel := bengal.NewBernoulli(inputs, outputs, smoothing)\n\n\tfmt.Println(model.PredictBernoulli([]string{...}))\n}\n```\n\n## notes\n- It is recommended to stem all input examples using something like [this](https://github.com/dchest/stemmer) before training or predicting.\n- This uses log probabilities and smoothing for robustness.\n- It's viable to use a different training function than prediction function. You can use `NewBernoulli` for training but `PredictMultinomial` for faster -- and similarly accurate on short documents -- predictions.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhaydenhigg%2Fbengal","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fhaydenhigg%2Fbengal","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhaydenhigg%2Fbengal/lists"}