hdbscan.rb in rumale-0.14.0

- old
+ new

@@ -32,12 +32,12 @@
       # @param min_cluster_size [Integer/Nil] The minimum size of cluster. If nil is given, it is set equal to min_samples.
       # @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(min_samples: 10, min_cluster_size: nil, metric: 'euclidean')
-        check_params_integer(min_samples: min_samples)
-        check_params_type_or_nil(Integer, min_cluster_size: min_cluster_size)
+        check_params_numeric(min_samples: min_samples)
+        check_params_numeric_or_nil(min_cluster_size: min_cluster_size)
         check_params_string(metric: metric)
         check_params_positive(min_samples: min_samples)
         @params = {}
         @params[:min_samples] = min_samples
         @params[:min_cluster_size] = min_cluster_size || min_samples
@@ -51,11 +51,11 @@
       #
       # @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 [HDBSCAN] 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
 
@@ -63,10 +63,10 @@
       #
       # @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
 