if sum(np.isnan(perf) == False) == 0:
best_e = 10 * max(e)
best_x = 0.08
best_m = 5
else:
best_e = e[np.nanargmax(perf)]
best_x = x[np.nanargmax(perf)]
best_m = m[np.nanargmax(perf)]
algorithm = select_algorithm(a_name, best_m, best_e, int(dim_m * 0.05),
best_x)
y = algorithm.fit_predict(X)
cc = cr.cluster_context(X, y)
gv = cr.gval(cc)
if p_outliers == False:
y = cr.reassign_outliers(X, y, 0, cc.centroids,
gv.extR).astype(int)
S, CH, DB = cr.other_validations(X, y)
rc = cr.refinement_context(X, y, cc, gv)
AMI = adjusted_mutual_info_score(ygt, y)
df_val.loc[(d_name, a_name, 'Best'), 'Grex'] = gv.Grex
df_val.loc[(d_name, a_name, 'Best'), 'Gstr'] = gv.Gstr
df_val.loc[(d_name, a_name, 'Best'), 'Gmin'] = gv.Gmin
df_val.loc[(d_name, a_name, 'Best'), 'Sil'] = S
df_val.loc[(d_name, a_name, 'Best'), 'CH'] = CH
df_val.loc[(d_name, a_name, 'Best'), 'DB'] = DB
clustering_algorithms = ['mkm']
for a_name in clustering_algorithms:
print("Clustering with wrong parameters:", a_name)
start = time.time()
k = 10 + p_n_clusters
algorithm = select_algorithm(a_name, k)
y = algorithm.fit_predict(X)
end = time.time()
T = (end - start)
cc = cr2.cluster_context(X, y)
gv = cr2.gval(cc)
if p_outliers == False:
y = cr2.reassign_outliers(X, y, 0, cc.centroids,
gv.extR).astype(int)
S, CH, DB = cr2.other_validations(X, y)
rc = cr2.refinement_context(X, y, cc, gv)
AMI = adjusted_mutual_info_score(ygt, y)
df_val.loc[(d_name, a_name, 'Normal'), 'Grex'] = gv.Grex
df_val.loc[(d_name, a_name, 'Normal'), 'Gstr'] = gv.Gstr
df_val.loc[(d_name, a_name, 'Normal'), 'Gmin'] = gv.Gmin
df_val.loc[(d_name, a_name, 'Normal'), 'Sil'] = S
df_val.loc[(d_name, a_name, 'Normal'), 'CH'] = CH
df_val.loc[(d_name, a_name, 'Normal'), 'DB'] = DB