factor_analysis.rb in rumale-0.14.0

- old
+ new

@@ -44,12 +44,12 @@
       # @param n_components [Integer] The number of components (dimensionality of latent space).
       # @param max_iter [Integer] The maximum number of iterations.
       # @param tol [Float/Nil] The tolerance of termination criterion for EM algorithm.
       #   If nil is given, iterate EM steps up to the maximum number of iterations.
       def initialize(n_components: 2, max_iter: 100, tol: 1e-8)
-        check_params_integer(n_components: n_components, max_iter: max_iter)
-        check_params_type_or_nil(Float, tol: tol)
+        check_params_numeric(n_components: n_components, max_iter: max_iter)
+        check_params_numeric_or_nil(tol: tol)
         check_params_positive(n_components: n_components, max_iter: max_iter)
         @params = {}
         @params[:n_components] = n_components
         @params[:max_iter] = max_iter
         @params[:tol] = tol
@@ -105,21 +105,21 @@
       #
       # @overload fit_transform(x) -> Numo::DFloat
       #   @param x [Numo::DFloat] (shape: [n_samples, n_features]) The training data to be used for fitting the model.
       # @return [Numo::DFloat] (shape: [n_samples, n_components]) The transformed data
       def fit_transform(x, _y = nil)
-        check_sample_array(x)
+        x = check_convert_sample_array(x)
         raise 'FactorAnalysis#fit_transform requires Numo::Linalg but that is not loaded.' unless enable_linalg?
 
         fit(x).transform(x)
       end
 
       # Transform the given data with the learned model.
       #
       # @param x [Numo::DFloat] (shape: [n_samples, n_features]) The data to be transformed with the learned model.
       # @return [Numo::DFloat] (shape: [n_samples, n_components]) The transformed data.
       def transform(x)
-        check_sample_array(x)
+        x = check_convert_sample_array(x)
         raise 'FactorAnalysis#transform requires Numo::Linalg but that is not loaded.' unless enable_linalg?
 
         factors = @params[:n_components] == 1 ? @components.expand_dims(0) : @components
         centered_x = x - @mean
         beta = Numo::Linalg.inv(Numo::DFloat.eye(factors.shape[0]) + (factors / @noise_variance).dot(factors.transpose))