pairwise_metric.rb in svmkit-0.2.0

- old
+ new
@@ -2,71 +2,71 @@
   # Module for calculating pairwise distances, similarities, and kernels.
   module PairwiseMetric
     class << self
       # Calculate the pairwise euclidean distances between x and y.
       #
-      # @param x [NMatrix] (shape: [n_samples_x, n_features])
-      # @param y [NMatrix] (shape: [n_samples_y, n_features])
-      # @return [NMatrix] (shape: [n_samples_x, n_samples_x] or [n_samples_x, n_samples_y] if y is given)
+      # @param x [Numo::DFloat] (shape: [n_samples_x, n_features])
+      # @param y [Numo::DFloat] (shape: [n_samples_y, n_features])
+      # @return [Numo::DFloat] (shape: [n_samples_x, n_samples_x] or [n_samples_x, n_samples_y] if y is given)
       def euclidean_distance(x, y = nil)
         y = x if y.nil?
         sum_x_vec = (x**2).sum(1)
         sum_y_vec = (y**2).sum(1)
         dot_xy_mat = x.dot(y.transpose)
         distance_matrix = dot_xy_mat * -2.0 +
-                          sum_x_vec.repeat(y.shape[0], 1) +
-                          sum_y_vec.transpose.repeat(x.shape[0], 0)
-        distance_matrix.abs.sqrt
+                          sum_x_vec.tile(y.shape[0], 1).transpose +
+                          sum_y_vec.tile(x.shape[0], 1)
+        Numo::NMath.sqrt(distance_matrix.abs)
       end
 
       # Calculate the rbf kernel between x and y.
       #
-      # @param x [NMatrix] (shape: [n_samples_x, n_features])
-      # @param y [NMatrix] (shape: [n_samples_y, n_features])
+      # @param x [Numo::DFloat] (shape: [n_samples_x, n_features])
+      # @param y [Numo::DFloat] (shape: [n_samples_y, n_features])
       # @param gamma [Float] The parameter of rbf kernel, if nil it is 1 / n_features.
-      # @return [NMatrix] (shape: [n_samples_x, n_samples_x] or [n_samples_x, n_samples_y] if y is given)
+      # @return [Numo::DFloat] (shape: [n_samples_x, n_samples_x] or [n_samples_x, n_samples_y] if y is given)
       def rbf_kernel(x, y = nil, gamma = nil)
         y = x if y.nil?
         gamma ||= 1.0 / x.shape[1]
         distance_matrix = euclidean_distance(x, y)
-        ((distance_matrix**2) * -gamma).exp
+        Numo::NMath.exp((distance_matrix**2) * -gamma)
       end
 
       # Calculate the linear kernel between x and y.
       #
-      # @param x [NMatrix] (shape: [n_samples_x, n_features])
-      # @param y [NMatrix] (shape: [n_samples_y, n_features])
-      # @return [NMatrix] (shape: [n_samples_x, n_samples_x] or [n_samples_x, n_samples_y] if y is given)
+      # @param x [Numo::DFloat] (shape: [n_samples_x, n_features])
+      # @param y [Numo::DFloat] (shape: [n_samples_y, n_features])
+      # @return [Numo::DFloat] (shape: [n_samples_x, n_samples_x] or [n_samples_x, n_samples_y] if y is given)
       def linear_kernel(x, y = nil)
         y = x if y.nil?
         x.dot(y.transpose)
       end
 
       # Calculate the polynomial kernel between x and y.
       #
-      # @param x [NMatrix] (shape: [n_samples_x, n_features])
-      # @param y [NMatrix] (shape: [n_samples_y, n_features])
+      # @param x [Numo::DFloat] (shape: [n_samples_x, n_features])
+      # @param y [Numo::DFloat] (shape: [n_samples_y, n_features])
       # @param degree [Integer] The parameter of polynomial kernel.
       # @param gamma [Float] The parameter of polynomial kernel, if nil it is 1 / n_features.
       # @param coef [Integer] The parameter of polynomial kernel.
-      # @return [NMatrix] (shape: [n_samples_x, n_samples_x] or [n_samples_x, n_samples_y] if y is given)
+      # @return [Numo::DFloat] (shape: [n_samples_x, n_samples_x] or [n_samples_x, n_samples_y] if y is given)
       def polynomial_kernel(x, y = nil, degree = 3, gamma = nil, coef = 1)
         y = x if y.nil?
         gamma ||= 1.0 / x.shape[1]
         (x.dot(y.transpose) * gamma + coef)**degree
       end
 
       # Calculate the sigmoid kernel between x and y.
       #
-      # @param x [NMatrix] (shape: [n_samples_x, n_features])
-      # @param y [NMatrix] (shape: [n_samples_y, n_features])
+      # @param x [Numo::DFloat] (shape: [n_samples_x, n_features])
+      # @param y [Numo::DFloat] (shape: [n_samples_y, n_features])
       # @param gamma [Float] The parameter of polynomial kernel, if nil it is 1 / n_features.
       # @param coef [Integer] The parameter of polynomial kernel.
-      # @return [NMatrix] (shape: [n_samples_x, n_samples_x] or [n_samples_x, n_samples_y] if y is given)
+      # @return [Numo::DFloat] (shape: [n_samples_x, n_samples_x] or [n_samples_x, n_samples_y] if y is given)
       def sigmoid_kernel(x, y = nil, gamma = nil, coef = 1)
         y = x if y.nil?
         gamma ||= 1.0 / x.shape[1]
-        (x.dot(y.transpose) * gamma + coef).tanh
+        Numo::NMath.tanh(x.dot(y.transpose) * gamma + coef)
       end
     end
   end
 end