pairwise_metric.rb in rumale-0.14.0

- old
+ new

@@ -11,24 +11,24 @@
       # @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?
-        Rumale::Validation.check_sample_array(x)
-        Rumale::Validation.check_sample_array(y)
+        x = Rumale::Validation.check_convert_sample_array(x)
+        y = Rumale::Validation.check_convert_sample_array(y)
         Numo::NMath.sqrt(squared_error(x, y).abs)
       end
 
       # Calculate the pairwise manhattan distances between x and y.
       #
       # @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 manhattan_distance(x, y = nil)
         y = x if y.nil?
-        Rumale::Validation.check_sample_array(x)
-        Rumale::Validation.check_sample_array(y)
+        x = Rumale::Validation.check_convert_sample_array(x)
+        y = Rumale::Validation.check_convert_sample_array(y)
         n_samples_x = x.shape[0]
         n_samples_y = y.shape[0]
         distance_mat = Numo::DFloat.zeros(n_samples_x, n_samples_y)
         n_samples_x.times do |n|
           distance_mat[n, true] = (y - x[n, true]).abs.sum(axis: 1)
@@ -41,12 +41,12 @@
       # @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 squared_error(x, y = nil)
         y = x if y.nil?
-        Rumale::Validation.check_sample_array(x)
-        Rumale::Validation.check_sample_array(y)
+        x = Rumale::Validation.check_convert_sample_array(x)
+        y = Rumale::Validation.check_convert_sample_array(y)
         n_features = x.shape[1]
         one_vec = Numo::DFloat.ones(n_features).expand_dims(1)
         sum_x_vec = (x**2).dot(one_vec)
         sum_y_vec = (y**2).dot(one_vec).transpose
         dot_xy_mat = x.dot(y.transpose)
@@ -60,25 +60,25 @@
       # @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]
-        Rumale::Validation.check_sample_array(x)
-        Rumale::Validation.check_sample_array(y)
-        Rumale::Validation.check_params_float(gamma: gamma)
+        x = Rumale::Validation.check_convert_sample_array(x)
+        y = Rumale::Validation.check_convert_sample_array(y)
+        Rumale::Validation.check_params_numeric(gamma: gamma)
         Numo::NMath.exp(-gamma * squared_error(x, y).abs)
       end
 
       # Calculate the linear kernel between x and y.
       #
       # @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?
-        Rumale::Validation.check_sample_array(x)
-        Rumale::Validation.check_sample_array(y)
+        x = Rumale::Validation.check_convert_sample_array(x)
+        y = Rumale::Validation.check_convert_sample_array(y)
         x.dot(y.transpose)
       end
 
       # Calculate the polynomial kernel between x and y.
       #
@@ -89,14 +89,13 @@
       # @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 polynomial_kernel(x, y = nil, degree = 3, gamma = nil, coef = 1)
         y = x if y.nil?
         gamma ||= 1.0 / x.shape[1]
-        Rumale::Validation.check_sample_array(x)
-        Rumale::Validation.check_sample_array(y)
-        Rumale::Validation.check_params_float(gamma: gamma)
-        Rumale::Validation.check_params_integer(degree: degree, coef: coef)
+        x = Rumale::Validation.check_convert_sample_array(x)
+        y = Rumale::Validation.check_convert_sample_array(y)
+        Rumale::Validation.check_params_numeric(gamma: gamma, degree: degree, coef: coef)
         (x.dot(y.transpose) * gamma + coef)**degree
       end
 
       # Calculate the sigmoid kernel between x and y.
       #
@@ -106,13 +105,12 @@
       # @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]
-        Rumale::Validation.check_sample_array(x)
-        Rumale::Validation.check_sample_array(y)
-        Rumale::Validation.check_params_float(gamma: gamma)
-        Rumale::Validation.check_params_integer(coef: coef)
+        x = Rumale::Validation.check_convert_sample_array(x)
+        y = Rumale::Validation.check_convert_sample_array(y)
+        Rumale::Validation.check_params_numeric(gamma: gamma, coef: coef)
         Numo::NMath.tanh(x.dot(y.transpose) * gamma + coef)
       end
     end
   end
 end