k_means_test.rb in ai4r-1.13

- old
+ new

@@ -15,33 +15,61 @@
   include Ai4r::Clusterers
   include Ai4r::Data
 
   @@data = [  [10, 3], [3, 10], [2, 8], [2, 5], [3, 8], [10, 3],
               [1, 3], [8, 1], [2, 9], [2, 5], [3, 3], [9, 4]]
-  
+              
+  # k-means will generate an empty cluster with this data and initial centroid assignment            
+  @@empty_cluster_data = [[-0.1, 0], [0, 0], [0.1, 0], [-0.1, 10], [0.1, 10], [0.2, 10]]
+  @@empty_centroid_indices = [0,1,2]
+              
   def test_build
     data_set = DataSet.new(:data_items => @@data, :data_labels => ["X", "Y"])
     clusterer = KMeans.new.build(data_set, 4)
     #draw_map(clusterer)
     # Verify that all 4 clusters are created
     assert_equal 4, clusterer.clusters.length
     assert_equal 4, clusterer.centroids.length
-    # The addition of all instances of every cluster must be equal than
+    # The addition of all instances of every cluster must be equal to
     # the number of data points
     total_length = 0
     clusterer.clusters.each do |cluster| 
       total_length += cluster.data_items.length
     end
     assert_equal @@data.length, total_length
-    # Data inside clusters must be the same as orifinal data
+    # Data inside clusters must be the same as original data
     clusterer.clusters.each do |cluster| 
      cluster.data_items.each do |data_item|
        assert @@data.include?(data_item)
      end
     end
   end
-  
+
+  def test_build_and_eliminate_empty_clusters
+    data_set = DataSet.new(:data_items => @@empty_cluster_data, :data_labels => ["X", "Y"])
+    # :eliminate is the :on_empty default, so we don't need to pass it as a parameter for it
+    clusterer = KMeans.new.set_parameters({:centroid_indices=>@@empty_centroid_indices}).build(data_set, @@empty_centroid_indices.size)
+    
+    # Verify that one cluster was eliminated
+    assert_equal @@empty_centroid_indices.size - 1, clusterer.clusters.length
+    assert_equal @@empty_centroid_indices.size - 1, clusterer.centroids.length
+    
+    # The addition of all instances of every cluster must be equal to
+    # the number of data points
+    total_length = 0
+    clusterer.clusters.each do |cluster| 
+      total_length += cluster.data_items.length
+    end
+    assert_equal @@empty_cluster_data.length, total_length
+    # Data inside clusters must be the same as original data
+    clusterer.clusters.each do |cluster| 
+     cluster.data_items.each do |data_item|
+       assert @@empty_cluster_data.include?(data_item)
+     end
+    end
+  end
+
   def test_eval
     data_set = DataSet.new(:data_items => @@data, :data_labels => ["X", "Y"])
     clusterer = KMeans.new.build(data_set, 4)
     item = [10,0]
     cluster_index = clusterer.eval(item)
@@ -52,17 +80,22 @@
     min_distance = clusterer.distance(clusterer.centroids[cluster_index], item)
     clusterer.centroids.each do |centroid|
       assert clusterer.distance(centroid, item) >= min_distance
     end
   end
-  
+
   def test_distance
     clusterer = KMeans.new
-    # By default, distance returns the eucledian distance to the power of 2
+    # By default, distance returns the euclidean distance to the power of 2
     assert_equal 2385, clusterer.distance(
       [1, 10, "Chicago", 2], 
       [10, 10, "London", 50])
+    
+    # Ensure default distance raises error for nil argument
+    exception = assert_raise(TypeError) {clusterer.distance([1, 10], [nil, nil])}
+    assert_equal("nil can't be coerced into Fixnum", exception.message)
+    
     # Test new distance definition
     manhattan_distance = lambda do |a, b| 
         dist = 0.0
         a.each_index do |index|
           if a[index].is_a?(Numeric) && b[index].is_a?(Numeric)
@@ -82,19 +115,53 @@
     clusterer = KMeans.new.
       set_parameters({:max_iterations=>1}).
       build(data_set, 4)
     assert_equal 1, clusterer.iterations
   end
-  
+
+  def test_centroid_indices
+    data_set = DataSet.new(:data_items => @@data, :data_labels => ["X", "Y"])
+    # centroid_indices need not be specified:
+    KMeans.new.build(data_set, 4)
+    # centroid_indices can be specified:
+    KMeans.new.set_parameters({:centroid_indices=>[0,1,2,3]}).build(data_set, 4)
+    # raises exception if number of clusters differs from length of centroid_indices:
+    exception = assert_raise(ArgumentError) {KMeans.new.set_parameters({:centroid_indices=>[0,1,2,3]}).build(data_set, 2)}
+    assert_equal('Length of centroid indices array differs from the specified number of clusters', exception.message)
+    # raises exception for bad centroid index:
+    exception = assert_raise(ArgumentError) {KMeans.new.set_parameters({:centroid_indices=>[0,1,2,@@data.size+10]}).build(data_set, 4)}
+    assert_equal("Invalid centroid index #{@@data.size+10}", exception.message)
+  end
+
+  def test_on_empty
+    data_set = DataSet.new(:data_items => @@empty_cluster_data, :data_labels => ["X", "Y"])
+    clusterer = KMeans.new.set_parameters({:centroid_indices=>@@empty_centroid_indices}).build(data_set, @@empty_centroid_indices.size)
+    # Verify that one cluster was eliminated
+    assert_equal @@empty_centroid_indices.size - 1, clusterer.clusters.length
+    # Verify that eliminate is the on_empty default
+    assert_equal 'eliminate', clusterer.on_empty
+    # Verify that invalid on_empty option throws an argument error 
+    exception = assert_raise(ArgumentError) {KMeans.new.set_parameters({:centroid_indices=>@@empty_centroid_indices, :on_empty=>'ldkfje'}).build(data_set, @@empty_centroid_indices.size)}
+    assert_equal("Invalid value for on_empty", exception.message)
+    # Verify that on_empty option 'terminate' raises an error when an empty cluster arises
+    exception = assert_raise(TypeError) {KMeans.new.set_parameters({:centroid_indices=>@@empty_centroid_indices, :on_empty=>'terminate'}).build(data_set, @@empty_centroid_indices.size)}
+    assert_equal("nil can't be coerced into Float", exception.message)
+    clusterer = KMeans.new.set_parameters({:centroid_indices=>@@empty_centroid_indices, :on_empty=>'random'}).build(data_set, @@empty_centroid_indices.size)
+    # Verify that cluster was not eliminated
+    assert_equal @@empty_centroid_indices.size, clusterer.clusters.length
+    clusterer = KMeans.new.set_parameters({:centroid_indices=>@@empty_centroid_indices, :on_empty=>'outlier'}).build(data_set, @@empty_centroid_indices.size)
+    # Verify that cluster was not eliminated
+    assert_equal @@empty_centroid_indices.size, clusterer.clusters.length
+  end
+
   private
   def draw_map(clusterer)
     map = Array.new(11) {Array.new(11, 0)}
     clusterer.clusters.each_index do |i|
       clusterer.clusters[i].data_items.each do |point|
         map[point.first][point.last]=(i+1)
       end
     end
     map.each { |row| puts row.inspect}
   end
-  
 end
 