class Cadmium::Classifier::Bayes

• Cadmium::Classifier::Bayes
• Reference
• Object

Overview

This is a native-bayes classifier which used Laplace Smoothing. It can be trained to categorize sentences based on the words in that sentence.

Example:

classifier = Cadmium::Classifier::Bayes.new

# Train some angry examples
classifier.train("omg I can't believe you would do that to me", "angry")
classifier.train("I hate you so much!", "angry")
classifier.train("Just go. I don't need this.", "angry")
classifier.train("You're so full of shit!", "angry")

# Some happy ones
classifier.train("omg you're the best!", "happy")
classifier.train("I can't believe how happy you make me", "happy")
classifier.train("I love you so damn much!", "happy")
classifier.train("You're the best!", "happy")

# And some indifferent ones
classifier.train("Idk, what do you think?", "indifferent")
classifier.train("yeah that's ok", "indifferent")
classifier.train("cool", "indifferent")
classifier.train("I guess we could do that", "indifferent")

# Now let's test it on a sentence
classifier.classify("You shit head!")
# => {"angry" => 85.5, "happy" => 10.2, "indifferent" => 4.3}

# Or just get the top category
classifier.classify_category("You're the best :)")
# => "happy"

Included Modules

• JSON::Serializable
• MessagePack::Serializable
• YAML::Serializable

Defined in:

Constant Summary

DEFAULT_TOKENIZER = Cadmium::Tokenizer::Word.new

Constructors

• .new(ctx : YAML::ParseContext, node : YAML::Nodes::Node)
• .new(pull : JSON::PullParser)
• .new(pull : MessagePack::Unpacker)
• .new(tokenizer = nil)

Instance Method Summary

• #categories : Array(String)

  Category names

• #classify(text : String)

  Determines what category the text belongs to.

• #classify_category(text : String) : String

  Convenience method that returns just the top category name instead of all probabilities.

• #doc_count : Hash(String, Int32)

  Document frequency table for each of our categories.

• #frequency_table(tokens)

  Build a frequency hash map where - the keys are the entries in tokens - the values are the frequency of each entry in tokens

• #initialize_category(name)

  Intializes each of our data structure entities for this new category and returns self.

• #token_probability(token, category)

  Calculate the probaility that a token belongs to a category.

• #tokenizer : Cadmium::Tokenizer::Base
• #tokenizer=(tokenizer : Cadmium::Tokenizer::Base)
• #total_documents : Int32

  Number of documents we have learned from.

• #train(text, category)

  Train our native-bayes classifier by telling it what category the train text corresponds to.

• #vocabulary : Set(String)

  The words to learn from.

• #vocabulary_size : Int32

  The total number of words in the vocabulary

• #word_count : Hash(String, Int32)

  For each category, how many total words were mapped to it.

• #word_frequency_count : Hash(String, Hash(String, Int32))

  Word frequency table for each category.

Constructor Detail

Instance Method Detail

classifier = Cadmium::Classifier::Bayes.new
classifier.train("I love this!", "positive")
classifier.train("This is terrible", "negative")
classifier.classify_category("This is amazing!") # => "positive"