standard_scaler.rb in svmkit-0.1.2

- old
+ new
@@ -4,81 +4,76 @@
 module SVMKit
   # This module consists of the classes that perform preprocessings.
   module Preprocessing
     # Normalize samples by centering and scaling to unit variance.
     #
+    # @example
     #   normalizer = SVMKit::Preprocessing::StandardScaler.new
     #   new_training_samples = normalizer.fit_transform(training_samples)
     #   new_testing_samples = normalizer.transform(testing_samples)
     class StandardScaler
       include Base::BaseEstimator
       include Base::Transformer
 
-      # The vector consists of the mean value for each feature.
-      attr_reader :mean_vec # :nodoc:
+      # Return the vector consists of the mean value for each feature.
+      # @return [NMatrix] (shape: [1, n_features])
+      attr_reader :mean_vec
 
-      # The vector consists of the standard deviation for each feature.
-      attr_reader :std_vec # :nodoc:
+      # Return the vector consists of the standard deviation for each feature.
+      # @return [NMatrix] (shape: [1, n_features])
+      attr_reader :std_vec
 
       # Create a new normalizer for centering and scaling to unit variance.
       #
-      # :call-seq:
-      #   new() -> StandardScaler
+      # @overload new() -> StandardScaler
       def initialize(_params = {})
         @mean_vec = nil
         @std_vec = nil
       end
 
       # Calculate the mean value and standard deviation of each feature for scaling.
       #
-      # :call-seq:
-      #   fit(x) -> StandardScaler
+      # @overload fit(x) -> StandardScaler
       #
-      # * *Arguments* :
-      #   - +x+ (NMatrix, shape: [n_samples, n_features]) -- The samples to calculate the mean values and standard deviations.
-      # * *Returns* :
-      #   - StandardScaler
+      # @param x [NMatrix] (shape: [n_samples, n_features])
+      #   The samples to calculate the mean values and standard deviations.
+      # @return [StandardScaler]
       def fit(x, _y = nil)
         @mean_vec = x.mean(0)
         @std_vec = x.std(0)
         self
       end
 
       # Calculate the mean values and standard deviations, and then normalize samples using them.
       #
-      # :call-seq:
-      #   fit_transform(x) -> NMatrix
+      # @overload fit_transform(x) -> NMatrix
       #
-      # * *Arguments* :
-      #   - +x+ (NMatrix, shape: [n_samples, n_features]) -- The samples to calculate the mean values and standard deviations.
-      # * *Returns* :
-      #   - The scaled samples (NMatrix)
+      # @param x [NMatrix] (shape: [n_samples, n_features])
+      #   The samples to calculate the mean values and standard deviations.
+      # @return [NMatrix] The scaled samples.
       def fit_transform(x, _y = nil)
         fit(x).transform(x)
       end
 
       # Perform standardization the given samples.
       #
-      # 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
         (x - @mean_vec.repeat(n_samples, 0)) / @std_vec.repeat(n_samples, 0)
       end
 
-      # Serializes object through Marshal#dump.
-      def marshal_dump # :nodoc:
+      # Dump marshal data.
+      # @return [Hash] The marshal data about StandardScaler.
+      def marshal_dump
         { mean_vec: Utils.dump_nmatrix(@mean_vec),
           std_vec: Utils.dump_nmatrix(@std_vec) }
       end
 
-      # Deserialize object through Marshal#load.
-      def marshal_load(obj) # :nodoc:
+      # Load marshal data.
+      # @return [nil]
+      def marshal_load(obj)
         @mean_vec = Utils.restore_nmatrix(obj[:mean_vec])
         @std_vec = Utils.restore_nmatrix(obj[:std_vec])
         nil
       end
     end