def setUp(self):
Cluster.clusterIdCounter = 0
self.docx = Document(1, {1: 2, 2: 4})
self.docy = Document(2, {2: 4})
self.cluster1 = Cluster(self.docx)
self.cluster2 = Cluster(self.docy)
self.doc1 = Document(3, Vector({3: 4}))
self.doc2 = Document(4, Vector({2: 4}))
def generate_cluster_objects(scooter_data: pd.DataFrame,
cluster_labels: list) -> [Cluster]:
"""
Based on cluster labels and scooter data create Scooter and Cluster objects.
Cluster class generates cluster center
:param scooter_data: geospatial data for scooters
:param cluster_labels: list of labels for scooter data
:return: list of clusters
"""
# Add cluster labels as a row to the scooter data dataframe
scooter_data_w_labels = scooter_data.copy()
scooter_data_w_labels["cluster_labels"] = cluster_labels
# Generate series of scooters belonging to each cluster
clusters = []
for cluster_label in np.unique(cluster_labels):
# Filter out scooters within cluster
cluster_scooters = scooter_data_w_labels[
scooter_data_w_labels["cluster_labels"] == cluster_label]
# Generate scooter objets, using index as ID
scooters = [
Scooter(row["lat"], row["lon"], row["battery"], index)
for index, row in cluster_scooters.iterrows()
]
# Adding all scooters to cluster to find center location
clusters.append(Cluster(cluster_label, scooters))
return sorted(clusters, key=lambda cluster: cluster.id)
def inspect_cluster_size(clustered_path: str, sense_inventory_path: str,
fig_path: str):
from classes import Cluster
# cluster_dict_original: Dict[str, List[Cluster]] = pickle.load(open(sense_inventory_path, "rb"))
# clusters_count_original: int = sum([len(y.cluster_items) for x in cluster_dict_original.values() for y in x])
cluster_dict_raw: Dict[str, List[Cluster]] = pickle.load(
open(clustered_path, "rb"))
largest_cluster: Cluster = Cluster([])
for lemma in tqdm(cluster_dict_raw):
for cluster in cluster_dict_raw[lemma]:
if len(cluster.cluster_items) > len(largest_cluster.cluster_items):
largest_cluster = cluster
print(
f"Max cluster size for {largest_cluster.get_dominant_lemma()}: {len(largest_cluster.cluster_items)}"
)
merged_clusters: List[Cluster] = [
y for x in cluster_dict_raw.values() for y in x
if len(y.cluster_items) > 1
]
merged_clusters.sort(key=lambda x: len(x.cluster_items), reverse=True)
merged_cluster_items_count: int = sum(
[len(x.cluster_items) for x in merged_clusters])
x_data: List[str] = list(range(len(merged_clusters) - 1))
y_data: List[int] = [len(x.cluster_items) for x in merged_clusters[1:]]
pyplot.scatter(x_data, y_data)
pyplot.xlabel("Cluster ID")
pyplot.ylabel("Number of contained usage contexts")
pyplot.title("Distribution of cluster size")
pyplot.savefig(fig_path, dpi=600)
pyplot.show()
def json_process(json_out):
data = json_out["frame"]
Xs = [i['x'] for i in data]
Ys = [i['y'] for i in data]
cluster_in = np.column_stack((Xs, Ys))
cluster = Cluster(cluster_in, eps=0.35, min_samples=3)
cluster.plot(fig=plt)
plt.xlim(-10, 10)
plt.ylim(-0.9, 18)
plt.pause(0.00000001)
plt.clf()
def process_data(data):
Xs = [i['x'] for i in data]
Ys = [i['y'] for i in data]
cluster_in = np.column_stack((Xs, Ys))
cluster = Cluster(cluster_in, eps=0.35, min_samples=3)
cluster.plot(fig=plt)
plt.xlim(-10, 10)
plt.ylim(-0.9, 18)
plt.pause(0.00000001)
plt.clf()
def getClusterAndUpdateExistingClusters(self, document):
predictedCluster = self.getClusterForDocument(document)
if predictedCluster != None:
self.clusters[predictedCluster].addDocument(document)
else:
newCluster = Cluster(document)
newCluster.setSignatureUsingVectorPermutations(
self.unitVector, self.vectorPermutations,
self.phraseTextAndDimensionMap)
for permutation in self.signaturePermutations:
permutation.addDocument(newCluster)
self.clusters[newCluster.clusterId] = newCluster
def json_process(json_out):
data = json_out["frame"]
Xs = [i['x'] for i in data]
Ys = [i['y'] for i in data]
cluster_in = np.column_stack((Xs, Ys))
cluster = Cluster(cluster_in, eps=0.35, min_samples=3)
cluster.plot(fig=plt)
# plt.scatter(Xs, Ys)
plt.xlim(-10, 10)
plt.ylim(-0.9, 18)
# plt.set_xbound(lower=xmin, upper=xmax)
# plt.set_ybound(lower=ymin, upper=ymax)
plt.pause(0.00000001)
plt.clf()
# $Id$
#
# pylint: disable-msg=E1101,W0612,W0142
#
"""superclass for all content-objects
"""
__version__ = "$Id$"
# phython imports
from classes import Cluster, Node, Ressource
import MenuSystem as menusystem
import hb_mini
glbmanager = hb_mini.miniManager()
cluster1 = Cluster("172.16.10.172", "hacluster", "ddd", "172.16.10.172", glbmanager)
if(glbmanager.login(cluster1.ip, cluster1.user, cluster1.passwd) == True):
print "Connected succesfully"
else:
print "Connection failure"
exit()
def printNodeNames(data):
print "\n"
for node in cluster1.getNodes():
print "Node: %s" % node.name
def printActiveNodes(data):
print "\n"
for node in cluster1.getActiveNodes():
