single_linkage.rb in rumale-0.14.0

- old
+ new

@@ -33,11 +33,11 @@
       # @param n_clusters [Integer] The number of clusters.
       # @param metric [String] The metric to calculate the distances.
       #   If metric is 'euclidean', Euclidean distance is calculated for distance between points.
       #   If metric is 'precomputed', the fit and fit_transform methods expect to be given a distance matrix.
       def initialize(n_clusters: 2, metric: 'euclidean')
-        check_params_integer(n_clusters: n_clusters)
+        check_params_numeric(n_clusters: n_clusters)
         check_params_string(metric: metric)
         @params = {}
         @params[:n_clusters] = n_clusters
         @params[:metric] = metric == 'precomputed' ? 'precomputed' : 'euclidean'
         @labels = nil
@@ -50,11 +50,11 @@
       #
       # @param x [Numo::DFloat] (shape: [n_samples, n_features]) The training data to be used for cluster analysis.
       #   If the metric is 'precomputed', x must be a square distance matrix (shape: [n_samples, n_samples]).
       # @return [SingleLinkage] The learned cluster analyzer itself.
       def fit(x, _y = nil)
-        check_sample_array(x)
+        x = check_convert_sample_array(x)
         raise ArgumentError, 'Expect the input distance matrix to be square.' if @params[:metric] == 'precomputed' && x.shape[0] != x.shape[1]
         fit_predict(x)
         self
       end
 
@@ -62,10 +62,10 @@
       #
       # @param x [Numo::DFloat] (shape: [n_samples, n_features]) The samples to be used for cluster analysis.
       #   If the metric is 'precomputed', x must be a square distance matrix (shape: [n_samples, n_samples]).
       # @return [Numo::Int32] (shape: [n_samples]) Predicted cluster label per sample.
       def fit_predict(x)
-        check_sample_array(x)
+        x = check_convert_sample_array(x)
         raise ArgumentError, 'Expect the input distance matrix to be square.' if @params[:metric] == 'precomputed' && x.shape[0] != x.shape[1]
         distance_mat = @params[:metric] == 'precomputed' ? x : Rumale::PairwiseMetric.euclidean_distance(x)
         @labels = partial_fit(distance_mat)
       end
 