def ClusterPopulation(max_gen, population, data):
"""
:param max_gen:
:param population:
:param data:
:return:
"""
pop = np.array(population)
plab1 = []
pcenter = []
cluster1 = int(input("Enter the cluster for new population\n"))
for i in range(0, max_gen):
if (i % cluster1 == 1): # Checking the condition of Kmeans Clustering
K1.insert(i, cluster1)
u, label, t, l = Kmeans_clu(
cluster1,
population) # Storing the values Center(u) anb label(u)
plab1.insert(i, label)
pcenter.insert(i, u)
plab = np.array(plab1)
plab = plab[0]
pcenter = np.array(pcenter)
pcenter = pcenter[0]
print('Value of label after Clustering of population\n', plab)
One_LengthCluster(
cluster1, plab, pcenter, pop,
max_gen) #To check if any label has one point associated
else:
print("Not need of clustering for this generation of\n", i)
def ClusterPopulation(max_gen, population, data):
# print("Value of population",population)
pop = np.array(population)
# print("Data type of population",pop.dtype)
# print("Population is in array form\n",pop)
plab = []
pcenter = []
cluster1 = int(input("Enter the cluster for new population\n"))
for i in range(max_gen):
if (i % cluster1 == 1):
print("CLustering will occur", i)
K1.insert(i, cluster1)
print('value of K1', K1)
u, label, t, l = Kmeans_clu(cluster1, population)
A1.insert(i, t)
plab.insert(i, label)
pcenter.insert(i, u)
# print('Value of newcenter\n',pcenter)
print('Value of new label after Clustering of population\n', plab)
LC = Counter(l.labels_)
print("VAlue of LAbel and Number Cluster Associated with them\n",
LC)
LC1 = [t for (t, v) in LC.items() if v == 1]
t1 = np.array(LC1)
if (t1.size):
One_LengthCluster(t1, cluster1, plab, pcenter, l, LC, pop)
else:
print("no lenght 1 cluster\n")
return (plab, pcenter)
else:
print("Not need of clustering\n", i)
def Repair(pop, s, i, x): # Checking if it is going beyond the boundary
Newlab = [] ##Store the labels in lab
Newcenter = []
new_center = []
new_pop = []
pbm_index_newpop = []
sil_score_newpop = []
C = []
pop = np.array(pop)
A = pop.max(axis=0) #Getting the maximum from the column
B = pop.min(axis=0) #Getting the minimum from the column
for j in range(0, len(s)):
if (s[j] < A[i]):
s[j] = A[i]
elif (s[j] > B[i]):
s[j] = B[i]
else:
s[j] = s[j]
print("Value of offspring for the {} is {} for axis{}\n".format(x, s, i))
center1 = s[0:4]
#print("Value of center 1 is\n", center1)
center2 = s[4:8]
#print("Value of center 2 is\n", center2)
center3 = s[8:12]
#print("Value of center 3 is\n", center3)
C.append(center1)
C.append(center2)
C.append(center3)
C = np.array(C)
print("Value of Initial Center\n", C)
data = genfromtxt('iris.csv',
delimiter=',',
skip_header=0,
usecols=range(0, 4)) ##Read the input data
actual_label = genfromtxt('iris.csv',
delimiter=',',
dtype=str,
skip_header=0,
usecols=(4))
u, lab = Kmeans_clu(3, data, C)
Newlab.insert(i, lab) #Store the labels in lab
Newcenter.insert(i, u)
features = len(data[0])
max_cluster = 3
cluster = 3
new_pbm = cal_pbm_index(cluster, data, u, lab)
pbm_index_newpop.insert(
i, new_pbm) #Store the second objective function(PBM Ind1ex)
s = silhouette_score(data, lab)
sil_score_newpop.insert(
i, s) #Store the first objective funvtion(Silhouette Score)
new_center = pop_create(max_cluster, features, cluster, u)
new_pop.insert(i, new_center)
pbm_index = []
tau_init = 0.8
CN = {}
RN = {}
for mm in range(
0, max_gen
): ## Initialize dictionary of size upto length maximum generation
CN[mm] = {}
RN[mm] = {}
for i in range(0, chromosome):
cluster = random.randint(
2, max_cluster
) ##Randomly selects cluster number from 2 to root(poplation)
K.insert(i, cluster) ##Store the number of clusters in clu
u, label = Kmeans_clu(cluster, data)
lab.insert(i, label) ##Store the labels in lab
center.insert(i, u)
new_pbm = cal_pbm_index(cluster, data, u, label)
pbm_index.insert(i,
new_pbm) ##Store the second objective function(PBM Index)
dn = calculate_dunn_index(data, label, cluster)
ini_dunn.insert(i, dn)
s = silhouette_score(data, label)
sil_score.insert(i,
s) ##Store the first objective funvtion(Silhouette Score)
new_center = pop_create(max_cluster, features, cluster, u)
population.insert(i, new_center) ##Store the populations in population
file.write("Initial Clusters : " + str(K) + '\n')
file.write("Initial PBM : " + str(pbm_index) + '\n')
population = []
sil_score = []
pbm_index = []
K = []
pcenter = []
plab = []
K1 = []
z2 = {}
A1 = []
{}
for i in range(0, chromosome):
cluster = 3 #random.randint(2, max_cluster) ##Randomly selects cluster number from 2 to root(poplation)
K.insert(i, cluster) ##Store the number of clusters in clu
#print('value of K is ',K)
u, label, z1, A1 = Kmeans_clu(cluster, data)
#print("centers and labels : ", u, label)
lab.insert(i, label) ##Store the labels in lab
center.insert(i, u)
#print('Value of center in main \n',center)
new_pbm = cal_pbm_index(cluster, data, u, label)
pbm_index.insert(
i, new_pbm) ##Store the second objective function(PBM Ind1ex)
s = silhouette_score(data, label)
sil_score.insert(i,
s) ##Store the first objective funvtion(Silhouette Score)
new_center = pop_create(max_cluster, features, cluster, u)
population.insert(i, new_center)
#print("VAlue of population in main\n" ,population)
ClusterPopulation(max_gen, population,