machine_learning_algorithms/unsupervised_learning_clustering_affinity_propagation.py at main · SyedMuhammadMuhsinKarim/machine_learning_algorithms · GitHub

Name: machine_learning_algorithms/unsupervised_learning_clustering_affinity_propagation.py at main · SyedMuhammadMuhsinKarim/machine_learning_algorithms · GitHub
Rating: 4.8 (2880 reviews)
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importnumpyasnp

classAffinityPropagation:
def__init__(self, org_matrix) ->None:
self.org_matrix=org_matrix
self.org_matrix_len=len(org_matrix)

deffind_clusters(self):
returnself.__find_similarity_matrix()

def__find_similarity_matrix(self):
sim_matrix=np.zeros((self.org_matrix_len, self.org_matrix_len), dtype=int)

foridx, _innp.ndenumerate(self.org_matrix):
ith_row=self.org_matrix[idx[0]]
jth_row=self.org_matrix[idx[1]]
power_of_diff= (ith_row-jth_row) **2
sim_matrix[idx[0], idx[1]] =-sum(power_of_diff)

sim_matrix=np.fill_diagonal(sim_matrix, sim_matrix.min())
returnself.__find_responsibility_matrix(sim_matrix)

def__find_responsibility_matrix(self, sim_matrix):
responsibility_matrix=np.zeros((self.org_matrix_len, self.org_matrix_len), dtype=int)

foridx, _innp.ndenumerate(sim_matrix):
i, j=idx
row_excludng= (sim_matrix[i, :j], sim_matrix[i, j+1:])
row_excludng_max=max(np.concatenate(row_excludng))
responsibility_matrix[i, j] =sim_matrix[i, j] -row_excludng_max

returnself.__find_availibilty_matrix(responsibility_matrix)

def__find_availibilty_matrix(self, responsibility_matrix):
avail_matrix=np.zeros((self.org_matrix_len, self.org_matrix_len), dtype=int)

foridx, _innp.ndenumerate(self.org_matrix):
i, j=idx
ifi==j:
col_exclud= (responsibility_matrix[:i, j], responsibility_matrix[i:, j])
col_exclud=np.concatenate(col_exclud)
avail_matrix[i, j] =np.int64((col_exclud+np.abs(col_exclud)).sum()/2)
else:
exclude= (responsibility_matrix[:i, j], responsibility_matrix[i:, j])
exclude=np.concatenate(exclude)
iflen(exclude) >1:
subs=np.zeros((len(exclude)), dtype=int)
subs[i], subs[j] =responsibility_matrix[i,j], responsibility_matrix[j,j]
col_exclud=exclude-subs
inclusion=responsibility_matrix[j,j] +np.int64((col_exclud+np.abs(col_exclud)).sum()/2)
avail_matrix[i,j] =min([0, inclusion])

returnself.__find_criterion_matrix(responsibility_matrix, avail_matrix)

def__find_criterion_matrix(rself, responsibility_matrix, avail_matrix):
returnresponsibility_matrix+avail_matrix

if__name__=="__main__":
org_matrix=np.array([
 [3, 4, 3, 2, 1],
 [4, 3, 5, 1, 1],
 [3, 5, 3, 3, 3],
 [2, 1, 3, 3, 2],
 [1, 1, 3, 2, 3]
 ])
print(AffinityPropagation(org_matrix).find_clusters())