Class: Libmf::Model

Inherits:
Object
  • Object
show all
Defined in:
lib/libmf/model.rb

Instance Method Summary collapse

Constructor Details

#initialize(**options) ⇒ Model

Returns a new instance of Model.



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# File 'lib/libmf/model.rb', line 3

def initialize(**options)
  @options = options
end

Instance Method Details

#bias ⇒ Object 



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# File 'lib/libmf/model.rb', line 50

def bias
  model[:b]
end

#columns ⇒ Object 



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# File 'lib/libmf/model.rb', line 42

def columns
  model[:n]
end

#cv(data, folds: 5) ⇒ Object 



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# File 'lib/libmf/model.rb', line 25

def cv(data, folds: 5)
  problem = create_problem(data)
  FFI.mf_cross_validation(problem, folds, param)
end

#factors ⇒ Object 



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# File 'lib/libmf/model.rb', line 46

def factors
  model[:k]
end

#fit(data, eval_set: nil) ⇒ Object 



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# File 'lib/libmf/model.rb', line 7

def fit(data, eval_set: nil)
  train_set = create_problem(data)

  @model =
    if eval_set
      eval_set = create_problem(eval_set)
      FFI.mf_train_with_validation(train_set, eval_set, param)
    else
      FFI.mf_train(train_set, param)
    end

  nil
end

#load_model(path) ⇒ Object 



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# File 'lib/libmf/model.rb', line 34

def load_model(path)
  @model = FFI.mf_load_model(path)
end

#p_factors(format: nil) ⇒ Object 



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# File 'lib/libmf/model.rb', line 54

def p_factors(format: nil)
  _factors(model[:p], rows, format)
end

#predict(row, column) ⇒ Object 



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# File 'lib/libmf/model.rb', line 21

def predict(row, column)
  FFI.mf_predict(model, row, column)
end

#q_factors(format: nil) ⇒ Object 



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# File 'lib/libmf/model.rb', line 58

def q_factors(format: nil)
  _factors(model[:q], columns, format)
end

#rows ⇒ Object 



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# File 'lib/libmf/model.rb', line 38

def rows
  model[:m]
end

#save_model(path) ⇒ Object 



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# File 'lib/libmf/model.rb', line 30

def save_model(path)
  FFI.mf_save_model(model, path)
end