def main():
test = False
heuristic = 'Centroid'
reader = DataReader()
data = reader.loadData()
dataArray = reader.getDataArray()
if test == True:
clusters = [
Cluster(dataPoint, data[dataPoint])
for dataPoint in list(data.keys())[:5]
]
else:
clusters = [
Cluster(dataPoint, data[dataPoint])
for dataPoint in list(data.keys())[:]
]
Cluster.generateInitialDistanceMatrix(test)
Uni = UnionTracker(len(clusters))
# print('')
iteration = 0
while (Cluster.currentClusterCount() > 1):
clsA, clsB, dist = Cluster.findMinDistance()
mergedRC = min(clsA, clsB)
toDelete = max(clsA, clsB)
newIDm, newIDd, pts, factor = Cluster.mergeSimilarClusters(
mergedRC, toDelete, iteration, dist, heuristic=heuristic)
Uni.union(newIDd, newIDm, dist, pts, iteration)
iteration += 1
labels = list(data.keys())
drawDendrogram(Uni, labels, heuristic)
def main():
test = False
# heuristic = 'Centroid'
# heuristic = 'Min'
# heuristic = 'Max'
# heuristic = parser2()
parser = argparse.ArgumentParser()
parser.add_argument("echo",
help="Please give arguments as 'Centroid','Min','Max'")
args = parser.parse_args()
heuristic = args.echo
reader = DataReader()
data = reader.loadData()
dataArray = reader.getDataArray()
if test == True:
clusters = [
Cluster(dataPoint, data[dataPoint])
for dataPoint in list(data.keys())[:5]
]
else:
clusters = [
Cluster(dataPoint, data[dataPoint])
for dataPoint in list(data.keys())[:]
]
Cluster.generateInitialDistanceMatrix(test)
Uni = UnionTracker(len(clusters))
print('')
iteration = 0
while (Cluster.currentClusterCount() > 1):
clsA, clsB, dist = Cluster.findMinDistance()
mergedRC = min(clsA, clsB)
toDelete = max(clsA, clsB)
newIDm, newIDd, pts, factor = Cluster.mergeSimilarClusters(
mergedRC, toDelete, iteration, dist, heuristic=heuristic)
Uni.union(newIDd, newIDm, dist, pts, iteration)
iteration += 1
labels = list(data.keys())
# print(len(linkage_matrix))
print("Clustering Done!!")
drawDendrogram(Uni, labels, heuristic)
print("Output file stored in data/results")
