from frovedis.mllib.gmm import GaussianMixture
# initializing the Frovedis server
argvs = sys.argv
argc = len(argvs)
if (argc < 2):
print(
'Please give frovedis_server calling command as the first argument \n(e.g. "mpirun -np 2 /opt/nec/frovedis/ve/bin/frovedis_server")'
)
quit()
FrovedisServer.initialize(argvs[1])
train_mat = np.loadtxt("./input/gmm_data.txt")
# creating spectral agglomerative object
n_components = 2
try:
gmm_model = GaussianMixture(n_components=n_components)
# fitting the training matrix on gaussian mixture object
gmm_model.fit(train_mat)
cov = gmm_model.covariances_
except Exception as e:
print("status=Exception: " + str(e))
sys.exit(1)
if ((cov.shape[0] == n_components) and (cov.shape[1] == train_mat.shape[1])
and (cov.shape[2] == train_mat.shape[1])):
print("status=Passed")
else:
print("status=Failed")