def templateClusterAllocationOneDimensionData(ccore_flag):
input_data = [ [random()] for i in range(10) ] + [ [random() + 3] for i in range(10) ] + [ [random() + 5] for i in range(10) ] + [ [random() + 8] for i in range(10) ];
somsc_instance = somsc(input_data, 4, 100, ccore_flag);
somsc_instance.process();
clusters = somsc_instance.get_clusters();
assert len(clusters) == 4;
for cluster in clusters:
assert len(cluster) == 10;
def templateClusterAllocationOneDimensionData(self, ccore_flag):
input_data = [[random()] for i in range(10)] + [[random() + 3] for i in range(10)] + \
[[random() + 5] for i in range(10)] + [[random() + 8] for i in range(10)]
somsc_instance = somsc(input_data, 4, 100, ccore_flag)
somsc_instance.process()
clusters = somsc_instance.get_clusters()
self.assertEqual(len(clusters), 4)
for cluster in clusters:
self.assertEqual(len(cluster), 10)
def predict(self, path_to_file, amount_clusters, points,
expected_closest_clusters, ccore):
sample = read_sample(path_to_file)
somsc_instance = somsc(sample, amount_clusters, 100, ccore)
somsc_instance.process()
closest_clusters = somsc_instance.predict(points)
self.assertEqual(len(expected_closest_clusters), len(closest_clusters))
self.assertTrue(
numpy.array_equal(numpy.array(expected_closest_clusters),
closest_clusters))
def template_clustering(path, amount_clusters, epouch=100, ccore=True):
sample = read_sample(path)
somsc_instance = somsc(sample, amount_clusters, epouch, ccore)
(ticks, _) = timedcall(somsc_instance.process)
clusters = somsc_instance.get_clusters()
print("Sample: ", path, "\t\tExecution time: ", ticks, "\n")
visualizer = cluster_visualizer()
visualizer.append_clusters(clusters, sample)
visualizer.show()
def templateLengthProcessData(self, path_to_file, amount_clusters,
expected_cluster_length, ccore):
sample = read_sample(path_to_file)
somsc_instance = somsc(sample, amount_clusters, 100, ccore)
somsc_instance.process()
clusters = somsc_instance.get_clusters()
obtained_cluster_sizes = [len(cluster) for cluster in clusters]
self.assertEqual(len(sample), sum(obtained_cluster_sizes))
if expected_cluster_length is not None:
obtained_cluster_sizes.sort()
expected_cluster_length.sort()
self.assertEqual(obtained_cluster_sizes, expected_cluster_length)
def templateLengthProcessData(path_to_file, amount_clusters, expected_cluster_length, ccore):
sample = read_sample(path_to_file);
somsc_instance = somsc(sample, amount_clusters, 100, ccore);
somsc_instance.process();
clusters = somsc_instance.get_clusters();
obtained_cluster_sizes = [len(cluster) for cluster in clusters];
assert len(sample) == sum(obtained_cluster_sizes);
if (expected_cluster_length != None):
obtained_cluster_sizes.sort();
expected_cluster_length.sort();
if (obtained_cluster_sizes != expected_cluster_length):
print
assert obtained_cluster_sizes == expected_cluster_length;
def som(arrayNormalizedCharacters,scaler,k): #from pyclustering.cluster import cluster_visualizer # Load list of points for cluster analysis data = arrayNormalizedCharacters # Create instance of SOM-SC algorithm to allocated two clusters somsc_instance = somsc(data, k) # Run cluster analysis and obtain results somsc_instance.process() clusters = somsc_instance.get_clusters() # Visualize clustering results. #visualizer = cluster_visualizer() #visualizer.append_clusters(clusters, data) #visualizer.show() clustersResult = [] k = len(clusters) labels = [] for i in range(0,k): clustersResult.append([]) i=0 for player in arrayNormalizedCharacters: j=0 for cluster in clusters: if(i in cluster): clustersResult[j].append(arrayNormalizedCharacters[i]) labels.append(j) j= j+1 i=i+1 for i in range(0,k): clustersResult[i] = scaler.inverse_transform(clustersResult[i]) return (clustersResult,labels)
