lib/svmkit/preprocessing/l2_normalizer.rb in svmkit-0.1.3 vs lib/svmkit/preprocessing/l2_normalizer.rb in svmkit-0.2.0

@@ -12,41 +12,38 @@
     class L2Normalizer
       include Base::BaseEstimator
       include Base::Transformer
 
       # Return the vector consists of L2-norm for each sample.
-      # @return [NMatrix] (shape: [1, n_samples])
+      # @return [Numo::DFloat] (shape: [n_samples])
       attr_reader :norm_vec # :nodoc:
 
       # Create a new normalizer for normaliing to unit L2-norm.
-      #
-      # @overload new() -> L2Normalizer
-      def initialize(_params = {})
+      def initialize()
+        self.params = {}
         @norm_vec = nil
       end
 
       # Calculate L2-norms of each sample.
       #
       # @overload fit(x) -> L2Normalizer
       #
-      # @param x [NMatrix] (shape: [n_samples, n_features]) The samples to calculate L2-norms.
+      # @param x [Numo::DFloat] (shape: [n_samples, n_features]) The samples to calculate L2-norms.
       # @return [L2Normalizer]
       def fit(x, _y = nil)
-        n_samples, = x.shape
-        @norm_vec = NMatrix.new([1, n_samples],
-                                Array.new(n_samples) { |n| x.row(n).norm2 })
+        @norm_vec = Numo::NMath.sqrt((x**2).sum(1))
         self
       end
 
       # Calculate L2-norms of each sample, and then normalize samples to unit L2-norm.
       #
-      # @overload fit_transform(x) -> NMatrix
+      # @overload fit_transform(x) -> Numo::DFloat
       #
-      # @param x [NMatrix] (shape: [n_samples, n_features]) The samples to calculate L2-norms.
-      # @return [NMatrix] The normalized samples.
+      # @param x [Numo::DFloat] (shape: [n_samples, n_features]) The samples to calculate L2-norms.
+      # @return [Numo::DFloat] The normalized samples.
       def fit_transform(x, _y = nil)
         fit(x)
-        x / @norm_vec.transpose.repeat(x.shape[1], 1)
+        x / @norm_vec.tile(x.shape[1], 1).transpose
       end
     end
   end
 end