class Cadmium::BayesClassifier

• Cadmium::BayesClassifier
• 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.bayes_classifier.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.categorize("You shit head!")
# => "angry"

puts classifier.categorize("You're the best :)")
# => "happy"

classifier.categorize("idk, my bff jill?")
# => "indifferent"

Included Modules

• JSON::Serializable
• YAML::Serializable

Defined in:

cadmium/classifier/bayes.cr

Constant Summary

DEFAULT_TOKENIZER = Cadmium::WordTokenizer.new

Constructors

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

Instance Method Summary

• #categories : Array(String)

  Category names

• #categorize(text)

  Determines what category the text belongs to.

• #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
• #tokenizer=(tokenizer : Cadmium::Tokenizer)
• #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 : Array(String)

  The words to learn from.

• #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