nmf.rb in rumale-0.14.0

- old
+ new

@@ -32,13 +32,12 @@
       # @param max_iter [Integer] The maximum number of iterations.
       # @param tol [Float] The tolerance of termination criterion.
       # @param eps [Float] A small value close to zero to avoid zero division error.
       # @param random_seed [Integer] The seed value using to initialize the random generator.
       def initialize(n_components: 2, max_iter: 500, tol: 1.0e-4, eps: 1.0e-16, random_seed: nil)
-        check_params_integer(n_components: n_components, max_iter: max_iter)
-        check_params_float(tol: tol, eps: eps)
-        check_params_type_or_nil(Integer, random_seed: random_seed)
+        check_params_numeric(n_components: n_components, max_iter: max_iter, tol: tol, eps: eps)
+        check_params_numeric_or_nil(random_seed: random_seed)
         check_params_positive(n_components: n_components, max_iter: max_iter, tol: tol, eps: eps)
         @params = {}
         @params[:n_components] = n_components
         @params[:max_iter] = max_iter
         @params[:tol] = tol
@@ -54,11 +53,11 @@
       # @overload fit(x) -> NMF
       #
       # @param x [Numo::DFloat] (shape: [n_samples, n_features]) The training data to be used for fitting the model.
       # @return [NMF] The learned transformer itself.
       def fit(x, _y = nil)
-        check_sample_array(x)
+        x = check_convert_sample_array(x)
         partial_fit(x)
         self
       end
 
       # Fit the model with training data, and then transform them with the learned model.
@@ -66,28 +65,28 @@
       # @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)
         partial_fit(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)
         partial_fit(x, false)
       end
 
       # Inverse transform the given transformed data with the learned model.
       #
       # @param z [Numo::DFloat] (shape: [n_samples, n_components]) The data to be restored into original space with the learned model.
       # @return [Numo::DFloat] (shape: [n_samples, n_featuress]) The restored data.
       def inverse_transform(z)
-        check_sample_array(z)
+        z = check_convert_sample_array(z)
         z.dot(@components)
       end
 
       # Dump marshal data.
       # @return [Hash] The marshal data.