def test_omp(self):
data = np.vstack((np.random.randn(100, 2)+1, \
np.random.randn(100, 2)-1))
k = 20
centers = omp_mpi.omp1(data, k, max_iter=10)
self.assertEqual(centers.shape[0], k)
self.assertEqual(centers.shape[1], data.shape[1])
def train(self, incoming_patches):
centroid = omp_mpi.omp1(incoming_patches,
self.specs['k'],
max_iter = self.specs.get('max_iter', 100),
tol = self.specs.get('tol', 0.0001)
)
return centroid, ()
def test_omp(self):
data = np.vstack((np.random.randn(100, 2)+1, \
np.random.randn(100, 2)-1))
k = 20
centers = omp_mpi.omp1(data, k, max_iter=10)
self.assertEqual(centers.shape[0], k)
self.assertEqual(centers.shape[1], data.shape[1])
def test_omp_singlenode(self):
""" Test a simple OMP where the result is apparent
"""
data = np.vstack(( np.ones((100, 2)), \
np.tile([-1.,1.], (100,1))))
k = 2
centers = omp_mpi.omp1(data, k, max_iter=100)
self.assertEqual(centers.shape[0], k)
self.assertEqual(centers.shape[1], data.shape[1])
np.testing.assert_array_almost_equal(np.abs(centers), 1./math.sqrt(2))
self.assertTrue((centers[0,0] * centers[0,1] > 0) ^ \
(centers[1,0] * centers[1,1] > 0))
def test_omp_singlenode(self):
""" Test a simple OMP where the result is apparent
"""
data = np.vstack(( np.ones((100, 2)), \
np.tile([-1.,1.], (100,1))))
k = 2
centers = omp_mpi.omp1(data, k, max_iter=100)
self.assertEqual(centers.shape[0], k)
self.assertEqual(centers.shape[1], data.shape[1])
np.testing.assert_array_almost_equal(np.abs(centers),
1. / math.sqrt(2))
self.assertTrue((centers[0,0] * centers[0,1] > 0) ^ \
(centers[1,0] * centers[1,1] > 0))
def test_omp_multinode(self):
if mpi.SIZE == 1:
return
if mpi.RANK % 2:
data = np.ones((100, 2))
else:
data = np.tile([-1.,1.], (100,1))
k = 2
centers = omp_mpi.omp1(data, k, max_iter=100)
self.assertEqual(centers.shape[0], k)
self.assertEqual(centers.shape[1], data.shape[1])
np.testing.assert_array_almost_equal(np.abs(centers), 1./math.sqrt(2))
self.assertTrue((centers[0,0] * centers[0,1] > 0) ^ \
(centers[1,0] * centers[1,1] > 0))
def test_omp_multinode(self):
if mpi.SIZE == 1:
return
if mpi.RANK % 2:
data = np.ones((100, 2))
else:
data = np.tile([-1., 1.], (100, 1))
k = 2
centers = omp_mpi.omp1(data, k, max_iter=100)
self.assertEqual(centers.shape[0], k)
self.assertEqual(centers.shape[1], data.shape[1])
np.testing.assert_array_almost_equal(np.abs(centers),
1. / math.sqrt(2))
self.assertTrue((centers[0,0] * centers[0,1] > 0) ^ \
(centers[1,0] * centers[1,1] > 0))
