Class: SVMKit::LinearModel::LinearRegression

Inherits:
Object
  • Object
show all
Includes:
Base::BaseEstimator, Base::Regressor
Defined in:
lib/svmkit/linear_model/linear_regression.rb

Overview

LinearRegression is a class that implements ordinary least square linear regression with mini-batch stochastic gradient descent optimization.

Examples:

estimator =
  SVMKit::LinearModel::LinearRegression.new(max_iter: 1000, batch_size: 20, random_seed: 1)
estimator.fit(training_samples, traininig_values)
results = estimator.predict(testing_samples)

Instance Attribute Summary collapse

Attributes included from Base::BaseEstimator

#params

Instance Method Summary collapse

Methods included from Base::Regressor

#score

Constructor Details

#initialize(fit_bias: false, max_iter: 1000, batch_size: 10, optimizer: nil, random_seed: nil) ⇒ LinearRegression

Create a new ordinary least square linear regressor.



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# File 'lib/svmkit/linear_model/linear_regression.rb', line 44

def initialize(fit_bias: false, max_iter: 1000, batch_size: 10, optimizer: nil, random_seed: nil)
  check_params_integer(max_iter: max_iter, batch_size: batch_size)
  check_params_boolean(fit_bias: fit_bias)
  check_params_type_or_nil(Integer, random_seed: random_seed)
  check_params_positive(max_iter: max_iter, batch_size: batch_size)
  @params = {}
  @params[:fit_bias] = fit_bias
  @params[:max_iter] = max_iter
  @params[:batch_size] = batch_size
  @params[:optimizer] = optimizer
  @params[:optimizer] ||= Optimizer::Nadam.new
  @params[:random_seed] = random_seed
  @params[:random_seed] ||= srand
  @weight_vec = nil
  @bias_term = nil
  @rng = Random.new(@params[:random_seed])
end

Instance Attribute Details

#bias_term ⇒ Numo::DFloat (readonly)

Return the bias term (a.k.a. intercept).



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# File 'lib/svmkit/linear_model/linear_regression.rb', line 30

def bias_term
  @bias_term
end

#rng ⇒ Random (readonly)

Return the random generator for random sampling.



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# File 'lib/svmkit/linear_model/linear_regression.rb', line 34

def rng
  @rng
end

#weight_vec ⇒ Numo::DFloat (readonly)

Return the weight vector.



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# File 'lib/svmkit/linear_model/linear_regression.rb', line 26

def weight_vec
  @weight_vec
end

Instance Method Details

#fit(x, y) ⇒ LinearRegression

Fit the model with given training data.



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# File 'lib/svmkit/linear_model/linear_regression.rb', line 67

def fit(x, y)
  check_sample_array(x)
  check_tvalue_array(y)
  check_sample_tvalue_size(x, y)

  n_outputs = y.shape[1].nil? ? 1 : y.shape[1]
  n_features = x.shape[1]

  if n_outputs > 1
    @weight_vec = Numo::DFloat.zeros(n_outputs, n_features)
    @bias_term = Numo::DFloat.zeros(n_outputs)
    n_outputs.times { |n| @weight_vec[n, true], @bias_term[n] = single_fit(x, y[true, n]) }
  else
    @weight_vec, @bias_term = single_fit(x, y)
  end

  self
end

#marshal_dump ⇒ Hash

Dump marshal data.



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# File 'lib/svmkit/linear_model/linear_regression.rb', line 97

def marshal_dump
  { params: @params,
    weight_vec: @weight_vec,
    bias_term: @bias_term,
    rng: @rng }
end

#marshal_load(obj) ⇒ nil

Load marshal data.



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# File 'lib/svmkit/linear_model/linear_regression.rb', line 106

def marshal_load(obj)
  @params = obj[:params]
  @weight_vec = obj[:weight_vec]
  @bias_term = obj[:bias_term]
  @rng = obj[:rng]
  nil
end

#predict(x) ⇒ Numo::DFloat

Predict values for samples.



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# File 'lib/svmkit/linear_model/linear_regression.rb', line 90

def predict(x)
  check_sample_array(x)
  x.dot(@weight_vec.transpose) + @bias_term
end