pairwise_metric.rb in svmkit-0.2.8

- old
+ new

@@ -9,10 +9,12 @@
       # @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?
+        SVMKit::Validation.check_sample_array(x)
+        SVMKit::Validation.check_sample_array(y)
         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.tile(y.shape[0], 1).transpose +
@@ -27,10 +29,13 @@
       # @param gamma [Float] The parameter of rbf kernel, if nil it is 1 / n_features.
       # @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]
+        SVMKit::Validation.check_sample_array(x)
+        SVMKit::Validation.check_sample_array(y)
+        SVMKit::Validation.check_params_float(gamma: gamma)
         distance_matrix = euclidean_distance(x, y)
         Numo::NMath.exp((distance_matrix**2) * -gamma)
       end
 
       # Calculate the linear kernel between x and y.
@@ -38,10 +43,12 @@
       # @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?
+        SVMKit::Validation.check_sample_array(x)
+        SVMKit::Validation.check_sample_array(y)
         x.dot(y.transpose)
       end
 
       # Calculate the polynomial kernel between x and y.
       #
@@ -52,10 +59,14 @@
       # @param coef [Integer] The parameter of polynomial kernel.
       # @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]
+        SVMKit::Validation.check_sample_array(x)
+        SVMKit::Validation.check_sample_array(y)
+        SVMKit::Validation.check_params_float(gamma: gamma)
+        SVMKit::Validation.check_params_integer(degree: degree, coef: coef)
         (x.dot(y.transpose) * gamma + coef)**degree
       end
 
       # Calculate the sigmoid kernel between x and y.
       #
@@ -65,9 +76,13 @@
       # @param coef [Integer] The parameter of polynomial kernel.
       # @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]
+        SVMKit::Validation.check_sample_array(x)
+        SVMKit::Validation.check_sample_array(y)
+        SVMKit::Validation.check_params_float(gamma: gamma)
+        SVMKit::Validation.check_params_integer(coef: coef)
         Numo::NMath.tanh(x.dot(y.transpose) * gamma + coef)
       end
     end
   end
 end