lib/svmkit/preprocessing/min_max_scaler.rb in svmkit-0.1.1 vs lib/svmkit/preprocessing/min_max_scaler.rb in svmkit-0.1.2

@@ -4,92 +4,85 @@
 module SVMKit
   # This module consists of the classes that perform preprocessings.
   module Preprocessing
     # Normalize samples by scaling each feature to a given range.
     #
+    # @example
     #   normalizer = SVMKit::Preprocessing::MinMaxScaler.new(feature_range: [0.0, 1.0])
     #   new_training_samples = normalizer.fit_transform(training_samples)
     #   new_testing_samples = normalizer.transform(testing_samples)
     class MinMaxScaler
       include Base::BaseEstimator
       include Base::Transformer
 
-      DEFAULT_PARAMS = { # :nodoc:
+      # @!visibility private
+      DEFAULT_PARAMS = {
         feature_range: [0.0, 1.0]
       }.freeze
 
-      # The vector consists of the minimum value for each feature.
-      attr_reader :min_vec # :nodoc:
+      # Return the vector consists of the minimum value for each feature.
+      # @return [NMatrix] (shape: [1, n_features])
+      attr_reader :min_vec
 
-      # The vector consists of the maximum value for each feature.
-      attr_reader :max_vec # :nodoc:
+      # Return the vector consists of the maximum value for each feature.
+      # @return [NMatrix] (shape: [1, n_features])
+      attr_reader :max_vec
 
       # Creates a new normalizer for scaling each feature to a given range.
       #
-      # call-seq:
-      #   new(feature_range: [0.0, 1.0]) -> MinMaxScaler
+      # @overload new(feature_range: [0.0, 1.0]) -> MinMaxScaler
       #
-      # * *Arguments* :
-      #   - +:feature_range+ (Array) (defaults to: [0.0, 1.0]) -- The desired range of samples.
+      # @param feature_range [Array] (defaults to: [0.0, 1.0]) The desired range of samples.
       def initialize(params = {})
         @params = DEFAULT_PARAMS.merge(Hash[params.map { |k, v| [k.to_sym, v] }])
         @min_vec = nil
         @max_vec = nil
       end
 
       # Calculate the minimum and maximum value of each feature for scaling.
       #
-      # :call-seq:
-      #   fit(x) -> MinMaxScaler
+      # @overload fit(x) -> MinMaxScaler
       #
-      # * *Arguments* :
-      #   - +x+ (NMatrix, shape: [n_samples, n_features]) -- The samples to calculate the minimum and maximum values.
-      # * *Returns* :
-      #   - MinMaxScaler
+      # @param x [NMatrix] (shape: [n_samples, n_features]) The samples to calculate the minimum and maximum values.
+      # @return [MinMaxScaler]
       def fit(x, _y = nil)
         @min_vec = x.min(0)
         @max_vec = x.max(0)
         self
       end
 
       # Calculate the minimum and maximum values, and then normalize samples to feature_range.
       #
-      # :call-seq:
-      #   fit_transform(x) -> NMatrix
+      # @overload fit_transform(x) -> NMatrix
       #
-      # * *Arguments* :
-      #   - +x+ (NMatrix, shape: [n_samples, n_features]) -- The samples to calculate the minimum and maximum values.
-      # * *Returns* :
-      #   - The scaled samples (NMatrix)
+      # @param x [NMatrix] (shape: [n_samples, n_features]) The samples to calculate the minimum and maximum values.
+      # @return [NMatrix] The scaled samples.
       def fit_transform(x, _y = nil)
         fit(x).transform(x)
       end
 
       # Perform scaling the given samples according to feature_range.
       #
-      # call-seq:
-      #   transform(x) -> NMatrix
-      #
-      # * *Arguments* :
-      #   - +x+ (NMatrix, shape: [n_samples, n_features]) -- The samples to be scaled.
-      # * *Returns* :
-      #   - The scaled samples (NMatrix)
+      # @param x [NMatrix] (shape: [n_samples, n_features]) The samples to be scaled.
+      # @return [NMatrix] The scaled samples.
       def transform(x)
         n_samples, = x.shape
         dif_vec = @max_vec - @min_vec
         nx = (x - @min_vec.repeat(n_samples, 0)) / dif_vec.repeat(n_samples, 0)
         nx * (@params[:feature_range][1] - @params[:feature_range][0]) + @params[:feature_range][0]
       end
 
-      # Serializes object through Marshal#dump.
-      def marshal_dump # :nodoc:
+      # Dump marshal data.
+      # @return [Hash] The marshal data about MinMaxScaler.
+      def marshal_dump
         { params: @params,
           min_vec: Utils.dump_nmatrix(@min_vec),
           max_vec: Utils.dump_nmatrix(@max_vec) }
       end
 
-      # Deserialize object through Marshal#load.
-      def marshal_load(obj) # :nodoc:
+      # Load marshal data.
+      # @return [nil]
+      def marshal_load(obj)
         @params = obj[:params]
         @min_vec = Utils.restore_nmatrix(obj[:min_vec])
         @max_vec = Utils.restore_nmatrix(obj[:max_vec])
         nil
       end