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AugGMM_1.py
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AugGMM_1.py
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from pyclustering.utils import euclidean_distance_square
from clustering import Clustering
from distance import min_distance, max_distance
from normalization import normalized_X
from sklearn.datasets.samples_generator import make_blobs
import numpy as np
import timeit
from Node import Node
numberOfCluster = 20
numberOfLevels = 1
def AugGMM(cluster, X1, indexMap, K, C1):
l = 1
for k in range(K - 2):
LLmin = []
LLmax = []
for node1 in cluster.root.children:
# print("children ", node1.elements)
minmax = 10000000
minmin = 10000000
for e in C1:
id = cluster.documentMap[tuple(e)][l]
distmax, distmin = cluster.dismatrix[l][id][node1.id]
#cluster.dismatrixitem[l][indexMap[tuple(e)]][node1.id]
if minmax > distmax[0]:
minmax = distmax[0]
if minmin > distmin[0]:
minmin = distmin[0]
LLmin.append(minmin)
LLmax.append(minmax)
maxofMin = max(LLmin)
selecteditem = []
i = 0
for it in LLmax:
if it > maxofMin:
selecteditem = selecteditem + cluster.root.children[i].elements
i = i + 1
L = []
for i in selecteditem:
min = 10000000
for j in C1:
dist = euclidean_distance_square(i, j)
if min > dist:
min = dist
L.append(min)
# print(maxOfmins)
index_max = np.argmax(L)
# print(selecteditem[index_max])
C1.append(selecteditem[index_max])
X1.remove(selecteditem[index_max])
id = cluster.documentMap[tuple(selecteditem[index_max])][1]
node = cluster.root.children[id - 1]
node.elements.remove(selecteditem[index_max])
print("Aug-GMM result:", C1)
return C1
def GMM(X, K, C):
for k in range(K - 2):
L = []
for i in X:
min = 10000000
for j in C:
dist = euclidean_distance_square(i, j)
if min > dist:
min = dist
L.append(min)
# print(maxOfmins)
index_max = np.argmax(L)
# print(L[index_max])
# print(X[index_max])
C.append(X[index_max])
X.remove(X[index_max])
# print("C:" , C)
# print("X:", X)
print("final C:", C)
return C
def checkResult(augGmmResult, gmmResult):
if sorted(augGmmResult) == sorted(gmmResult):
print("array equal")
else:
print("array not equal")
for i in gmmResult:
if i not in augGmmResult:
print(i, " not in Aug GMM")
for i in augGmmResult:
if i not in gmmResult:
print(i, " not in GMM")
def main():
# Xin, Y = make_blobs(n_samples=1000, centers=10, cluster_std=0.60, random_state=0)
# np.random.seed(46)
# Xin = np.random.randint(10000, size=(20, 2))
# print(Xin)
Xin = normalized_X
X = Xin.tolist()
# print(X)
K = 15
a = []
b = []
C = []
maxd = 0
for i in X:
for j in X:
if (i[0] == j[0] and i[1] == j[1]) == False:
dis = euclidean_distance_square(i, j)
if maxd < dis:
maxd = dis
a = i
b = j
# print(a,b,max)
C.append(a)
C.append(b)
X.remove(a)
X.remove(b)
start = timeit.default_timer()
gmmResult = GMM(X, K, C)
stop = timeit.default_timer()
print('Time for gmm: ', stop - start)
X1 = Xin.tolist()
a1 = []
b1 = []
C1 = []
maxd = 0
for i in X1:
for j in X1:
if (i[0] == j[0] and i[1] == j[1]) == False:
dis = euclidean_distance_square(i, j)
if maxd < dis:
maxd = dis
a1 = i
b1 = j
# print(a,b,max)
C1.append(a1)
C1.append(b1)
X1.remove(a1)
X1.remove(b1)
indexMap = {}
index = 0
for e in X:
indexMap[tuple(e)] = index
index = index + 1
cluster = Clustering(X1)
cluster.buildTree(cluster.root)
cluster.createLevelMatrix(cluster.root)
cluster.createDistanceMatrix(numberOfCluster, numberOfLevels)
#cluster.createDistanceMatrixforelements(numberOfCluster, numberOfLevels)
start = timeit.default_timer()
augGmmResult = AugGMM(cluster, X1, indexMap, K, C1)
stop = timeit.default_timer()
print('Time for aug-gmm: ', stop - start)
checkResult(augGmmResult, gmmResult)
main()