lasso.rb in rumale-0.14.0

- old
+ new

@@ -46,26 +46,25 @@
       #   If zero or less is given, it becomes equal to the number of processors.
       #   This parameter is ignored if the Parallel gem is not loaded.
       # @param random_seed [Integer] The seed value using to initialize the random generator.
       def initialize(reg_param: 1.0, fit_bias: false, bias_scale: 1.0, max_iter: 1000, batch_size: 10, optimizer: nil,
                      n_jobs: nil, random_seed: nil)
-        check_params_float(reg_param: reg_param, bias_scale: bias_scale)
-        check_params_integer(max_iter: max_iter, batch_size: batch_size)
+        check_params_numeric(reg_param: reg_param, bias_scale: bias_scale, max_iter: max_iter, batch_size: batch_size)
         check_params_boolean(fit_bias: fit_bias)
-        check_params_type_or_nil(Integer, n_jobs: n_jobs, random_seed: random_seed)
+        check_params_numeric_or_nil(n_jobs: n_jobs, random_seed: random_seed)
         check_params_positive(reg_param: reg_param, max_iter: max_iter, batch_size: batch_size)
         super
       end
 
       # Fit the model with given training data.
       #
       # @param x [Numo::DFloat] (shape: [n_samples, n_features]) The training data to be used for fitting the model.
       # @param y [Numo::Int32] (shape: [n_samples, n_outputs]) The target values to be used for fitting the model.
       # @return [Lasso] The learned regressor itself.
       def fit(x, y)
-        check_sample_array(x)
-        check_tvalue_array(y)
+        x = check_convert_sample_array(x)
+        y = check_convert_tvalue_array(y)
         check_sample_tvalue_size(x, y)
 
         n_outputs = y.shape[1].nil? ? 1 : y.shape[1]
         n_features = x.shape[1]
 
@@ -87,10 +86,10 @@
       # Predict values for samples.
       #
       # @param x [Numo::DFloat] (shape: [n_samples, n_features]) The samples to predict the values.
       # @return [Numo::DFloat] (shape: [n_samples, n_outputs]) Predicted values per sample.
       def predict(x)
-        check_sample_array(x)
+        x = check_convert_sample_array(x)
         x.dot(@weight_vec.transpose) + @bias_term
       end
 
       # Dump marshal data.
       # @return [Hash] The marshal data about Lasso.