Class: Rumale::LinearModel::LinearRegression
- Inherits:
-
BaseLinearModel
- Object
- BaseLinearModel
- Rumale::LinearModel::LinearRegression
- Includes:
- Base::Regressor
- Defined in:
- lib/rumale/linear_model/linear_regression.rb
Overview
LinearRegression is a class that implements ordinary least square linear regression with mini-batch stochastic gradient descent optimization.
Instance Attribute Summary collapse
-
#bias_term ⇒ Numo::DFloat
readonly
Return the bias term (a.k.a. intercept).
-
#rng ⇒ Random
readonly
Return the random generator for random sampling.
-
#weight_vec ⇒ Numo::DFloat
readonly
Return the weight vector.
Attributes included from Base::BaseEstimator
Instance Method Summary collapse
-
#fit(x, y) ⇒ LinearRegression
Fit the model with given training data.
-
#initialize(fit_bias: false, bias_scale: 1.0, max_iter: 1000, batch_size: 10, optimizer: nil, random_seed: nil) ⇒ LinearRegression
constructor
Create a new ordinary least square linear regressor.
-
#marshal_dump ⇒ Hash
Dump marshal data.
-
#marshal_load(obj) ⇒ nil
Load marshal data.
-
#predict(x) ⇒ Numo::DFloat
Predict values for samples.
Methods included from Base::Regressor
Constructor Details
#initialize(fit_bias: false, bias_scale: 1.0, 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/rumale/linear_model/linear_regression.rb', line 41 def initialize(fit_bias: false, bias_scale: 1.0, max_iter: 1000, batch_size: 10, optimizer: nil, random_seed: nil) check_params_float(bias_scale: bias_scale) 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) keywd_args = method(:initialize).parameters.map { |_t, arg| [arg, binding.local_variable_get(arg)] }.to_h.merge(reg_param: 0.0) super(keywd_args) end |
Instance Attribute Details
#bias_term ⇒ Numo::DFloat (readonly)
Return the bias term (a.k.a. intercept).
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# File 'lib/rumale/linear_model/linear_regression.rb', line 26 def bias_term @bias_term end |
#rng ⇒ Random (readonly)
Return the random generator for random sampling.
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# File 'lib/rumale/linear_model/linear_regression.rb', line 30 def rng @rng end |
#weight_vec ⇒ Numo::DFloat (readonly)
Return the weight vector.
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# File 'lib/rumale/linear_model/linear_regression.rb', line 22 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/rumale/linear_model/linear_regression.rb', line 56 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] = partial_fit(x, y[true, n]) } else @weight_vec, @bias_term = partial_fit(x, y) end self end |
#marshal_dump ⇒ Hash
Dump marshal data.
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# File 'lib/rumale/linear_model/linear_regression.rb', line 86 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/rumale/linear_model/linear_regression.rb', line 95 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/rumale/linear_model/linear_regression.rb', line 79 def predict(x) check_sample_array(x) x.dot(@weight_vec.transpose) + @bias_term end |