def template_clustering(data_path, levels, **kwargs):
print("Sample: '%s'." % os.path.basename(data_path))
density_threshold = kwargs.get("density_threshold", 0.0)
amount_threshold = kwargs.get("amount_threshold", 0)
data = read_sample(data_path)
bang_instance = bang(data,
levels,
density_threshold=density_threshold,
amount_threshold=amount_threshold)
bang_instance.process()
clusters = bang_instance.get_clusters()
noise = bang_instance.get_noise()
directory = bang_instance.get_directory()
dendrogram = bang_instance.get_dendrogram()
bang_visualizer.show_blocks(directory)
bang_visualizer.show_dendrogram(dendrogram)
bang_visualizer.show_clusters(data, clusters, noise)
if len(data[0]) == 2:
animator = bang_animator(directory, clusters)
animator.animate()
def template_segmentation(source, levels, threshold):
data = read_image(source)
bang_instance = bang(data, levels, threshold)
bang_instance.process()
clusters = bang_instance.get_clusters()
draw_image_mask_segments(source, clusters)
def template_segmentation(source, levels, threshold):
data = read_image(source)
bang_instance = bang(data, levels, threshold)
bang_instance.process()
clusters = bang_instance.get_clusters()
draw_image_mask_segments(source, clusters)
def visualize(path, levels, threshold, ccore, **kwargs):
sample = read_sample(path)
bang_instance = bang(sample, levels, threshold, ccore)
bang_instance.process()
directory = bang_instance.get_directory()
dendrogram = bang_instance.get_dendrogram()
bang_visualizer.show_blocks(directory)
bang_visualizer.show_dendrogram(dendrogram)
def animate(path, levels, threshold, ccore, **kwargs):
sample = read_sample(path)
bang_instance = bang(sample, levels, threshold, ccore)
bang_instance.process()
directory = bang_instance.get_directory()
clusters = bang_instance.get_clusters()
noise = bang_instance.get_noise()
animator = bang_animator(directory, clusters, noise)
animator.animate()
def getCluster(arrayNormalizedCharacters,scaler,clusterName): if(clusterName == 'K-means'): return kMeans(arrayNormalizedCharacters,scaler,3) if(clusterName == 'WARD'): return hierarchicalWard(arrayNormalizedCharacters,scaler, 3) if(clusterName == 'Spectral'): return spectral(arrayNormalizedCharacters,scaler, 3) if(clusterName == 'DBSCAN'): return dbscan(arrayNormalizedCharacters,scaler) if(clusterName == 'BANG'): return bang(arrayNormalizedCharacters,scaler) if(clusterName == 'SOM'): return som(arrayNormalizedCharacters,scaler,3) if(clusterName == 'Fuzzy C-Means'): return cMeans(arrayNormalizedCharacters,scaler,3)
def exception(type, sample_storage, levels, threshold, ccore):
try:
sample = sample_storage
if isinstance(sample_storage, str):
sample = read_sample(sample_storage)
bang_instance = bang(sample, levels, threshold, ccore)
bang_instance.process()
except type:
return
except Exception as ex:
raise AssertionError("Expected: '%s', Actual: '%s'" % (type, type(ex).__name__))
raise AssertionError("Expected: '%s', Actual: 'None'" % type)
def clustering(path, levels, density_threshold, expected_clusters,
expected_noise, ccore, **kwargs):
sample = read_sample(path)
amount_threshold = kwargs.get('amount_threshold', 0)
bang_instance = bang(sample,
levels,
ccore,
density_threshold=density_threshold,
amount_threshold=amount_threshold)
bang_instance.process()
clusters = bang_instance.get_clusters()
noise = bang_instance.get_noise()
directory = bang_instance.get_directory()
dendrogram = bang_instance.get_dendrogram()
assertion.eq(len(clusters), len(dendrogram))
obtained_length = len(noise)
obtained_cluster_length = []
for cluster in clusters:
obtained_length += len(cluster)
obtained_cluster_length.append(len(cluster))
obtained_cluster_length.sort()
assertion.eq(len(sample), obtained_length)
assertion.eq(expected_noise, len(noise))
if expected_clusters is not None:
assertion.eq(len(expected_clusters), len(clusters))
assertion.eq(expected_clusters, obtained_cluster_length)
leafs = directory.get_leafs()
covered_points = set()
for leaf in leafs:
points = leaf.get_points()
for index_point in points:
covered_points.add(index_point)
assertion.eq(len(sample), len(covered_points))
return bang_instance
def setup(self, keywords={}):
"""
Setup the algorithms
"""
for p in keywords.keys():
setattr(self, p, keywords[p])
if self.method == "bang":
self.obj = bang(self.data_list,
self.levels,
ccore=self.ccore,
density_threshold=self.density_threshold,
amount_threshold=self.amount_threshold)
if self.method == "clique":
self.obj = clique(self.data_list,
self.amount_threshold,
self.density_threshold,
ccore=self.ccore)
return
def template_clustering(data_path, levels, **kwargs):
print("Sample: '%s'." % os.path.basename(data_path))
density_threshold = kwargs.get("density_threshold", 0.0)
amount_threshold = kwargs.get("amount_threshold", 0)
data = read_sample(data_path)
bang_instance = bang(data, levels, density_threshold=density_threshold, amount_threshold=amount_threshold)
bang_instance.process()
clusters = bang_instance.get_clusters()
noise = bang_instance.get_noise()
directory = bang_instance.get_directory()
dendrogram = bang_instance.get_dendrogram()
bang_visualizer.show_blocks(directory)
bang_visualizer.show_dendrogram(dendrogram)
bang_visualizer.show_clusters(data, clusters, noise)
if len(data[0]) == 2:
animator = bang_animator(directory, clusters)
animator.animate()
def bang(arrayNormalizedCharacters,scaler): from pyclustering.cluster.bang import bang, bang_visualizer # Read data three dimensional data. data = arrayNormalizedCharacters # Prepare algorithm's parameters. levels = 11 # Create instance of BANG algorithm. bang_instance = bang(data, levels) bang_instance.process() # Obtain clustering results. clusters = bang_instance.get_clusters() #dendrogram = bang_instance.get_dendrogram() # Visualize BANG clustering results. #bang_visualizer.show_dendrogram(dendrogram) 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)
