def testSolveSymPos(self):
import scipy.linalg
np.random.seed(1)
data = np.random.randint(1, 10, (20, 20))
data_l = np.tril(data)
data1 = data_l.dot(data_l.T)
data2 = np.random.randint(1, 10, (20, ))
A = tensor(data1, chunk_size=5)
b = tensor(data2, chunk_size=5)
x = solve(A, b, sym_pos=True)
res = self.executor.execute_tensor(x, concat=True)[0]
np.testing.assert_allclose(res, scipy.linalg.solve(data1, data2))
res = self.executor.execute_tensor(A.dot(x), concat=True)[0]
np.testing.assert_allclose(res, data2)
def test_solve_sym_pos(setup):
import scipy.linalg
rs = np.random.RandomState(0)
data = rs.randint(1, 10, (20, 20))
data_l = np.tril(data)
data1 = data_l.dot(data_l.T)
data2 = rs.randint(1, 10, (20, ))
A = tensor(data1, chunk_size=10)
b = tensor(data2, chunk_size=10)
x = solve(A, b, sym_pos=True)
res = x.execute().fetch()
np.testing.assert_allclose(res, scipy.linalg.solve(data1, data2))
res = A.dot(x).execute().fetch()
np.testing.assert_allclose(res, data2)
def testSolve(self):
import scipy.linalg
np.random.seed(1)
data1 = np.random.randint(1, 10, (20, 20))
data2 = np.random.randint(1, 10, (20, ))
A = tensor(data1, chunk_size=5)
b = tensor(data2, chunk_size=5)
x = solve(A, b)
res = self.executor.execute_tensor(x, concat=True)[0]
np.testing.assert_allclose(res, scipy.linalg.solve(data1, data2))
res = self.executor.execute_tensor(A.dot(x), concat=True)[0]
np.testing.assert_allclose(res, data2)
data2 = np.random.randint(1, 10, (20, 5))
A = tensor(data1, chunk_size=5)
b = tensor(data2, chunk_size=5)
x = solve(A, b)
res = self.executor.execute_tensor(x, concat=True)[0]
np.testing.assert_allclose(res, scipy.linalg.solve(data1, data2))
res = self.executor.execute_tensor(A.dot(x), concat=True)[0]
np.testing.assert_allclose(res, data2)
data2 = np.random.randint(1, 10, (20, 20))
A = tensor(data1, chunk_size=5)
b = tensor(data2, chunk_size=5)
x = solve(A, b)
res = self.executor.execute_tensor(x, concat=True)[0]
np.testing.assert_allclose(res, scipy.linalg.solve(data1, data2))
res = self.executor.execute_tensor(A.dot(x), concat=True)[0]
np.testing.assert_allclose(res, data2)
# test for not all chunks are square in matrix A
data2 = np.random.randint(1, 10, (20, ))
A = tensor(data1, chunk_size=6)
b = tensor(data2, chunk_size=6)
x = solve(A, b)
res = self.executor.execute_tensor(x, concat=True)[0]
np.testing.assert_allclose(res, scipy.linalg.solve(data1, data2))
res = self.executor.execute_tensor(A.dot(x), concat=True)[0]
np.testing.assert_allclose(res, data2)
A = tensor(data1, chunk_size=(7, 6))
b = tensor(data2, chunk_size=6)
x = solve(A, b)
res = self.executor.execute_tensor(x, concat=True)[0]
np.testing.assert_allclose(res, scipy.linalg.solve(data1, data2))
res = self.executor.execute_tensor(A.dot(x), concat=True)[0]
np.testing.assert_allclose(res, data2)
# test sparse
data1 = sps.csr_matrix(np.random.randint(1, 10, (20, 20)))
data2 = np.random.randint(1, 10, (20, ))
A = tensor(data1, chunk_size=5)
b = tensor(data2, chunk_size=5)
x = solve(A, b)
res = self.executor.execute_tensor(x, concat=True)[0]
self.assertIsInstance(res, SparseNDArray)
np.testing.assert_allclose(data1.dot(res), data2)
data2 = np.random.randint(1, 10, (20, 5))
A = tensor(data1, chunk_size=5)
b = tensor(data2, chunk_size=5)
x = solve(A, b)
res = self.executor.execute_tensor(A.dot(x), concat=True)[0]
self.assertIsInstance(res, SparseNDArray)
np.testing.assert_allclose(res, data2)
data2 = np.random.randint(1, 10, (20, 20))
A = tensor(data1, chunk_size=5)
b = tensor(data2, chunk_size=5)
x = solve(A, b)
res = self.executor.execute_tensor(A.dot(x), concat=True)[0]
self.assertIsInstance(res, SparseNDArray)
np.testing.assert_allclose(res, data2)
# test for not all chunks are square in matrix A
data2 = np.random.randint(1, 10, (20, ))
A = tensor(data1, chunk_size=6)
b = tensor(data2, chunk_size=6)
x = solve(A, b)
res = self.executor.execute_tensor(A.dot(x), concat=True)[0]
np.testing.assert_allclose(res, data2)
def test_solve(setup):
import scipy.linalg
rs = np.random.RandomState(0)
data1 = rs.randint(1, 10, (20, 20))
data2 = rs.randint(1, 10, (20, ))
A = tensor(data1, chunk_size=10)
b = tensor(data2, chunk_size=10)
x = solve(A, b)
res = x.execute().fetch()
np.testing.assert_allclose(res, scipy.linalg.solve(data1, data2))
res = A.dot(x).execute().fetch()
np.testing.assert_allclose(res, data2)
data2 = rs.randint(1, 10, (20, 5))
A = tensor(data1, chunk_size=10)
b = tensor(data2, chunk_size=10)
x = solve(A, b)
res = x.execute().fetch()
np.testing.assert_allclose(res, scipy.linalg.solve(data1, data2))
res = A.dot(x).execute().fetch()
np.testing.assert_allclose(res, data2)
# test for not all chunks are square in matrix A
data2 = rs.randint(1, 10, (20, ))
A = tensor(data1, chunk_size=10)
b = tensor(data2, chunk_size=10)
x = solve(A, b)
res = x.execute().fetch()
np.testing.assert_allclose(res, scipy.linalg.solve(data1, data2))
res = A.dot(x).execute().fetch()
np.testing.assert_allclose(res, data2)
A = tensor(data1, chunk_size=(10, 15))
b = tensor(data2, chunk_size=10)
x = solve(A, b)
res = x.execute().fetch()
np.testing.assert_allclose(res, scipy.linalg.solve(data1, data2))
res = A.dot(x).execute().fetch()
np.testing.assert_allclose(res, data2)
# test sparse
data1 = sps.csr_matrix(rs.randint(1, 10, (20, 20)))
data2 = rs.randint(1, 10, (20, ))
A = tensor(data1, chunk_size=10)
b = tensor(data2, chunk_size=10)
x = solve(A, b)
res = x.execute().fetch()
assert isinstance(res, SparseNDArray)
np.testing.assert_allclose(data1.dot(res), data2)
data2 = rs.randint(1, 10, (20, 5))
A = tensor(data1, chunk_size=10)
b = tensor(data2, chunk_size=10)
x = solve(A, b)
res = A.dot(x).execute().fetch()
assert isinstance(res, SparseNDArray)
np.testing.assert_allclose(res, data2)
# test for not all chunks are square in matrix A
data2 = rs.randint(1, 10, (20, ))
A = tensor(data1, chunk_size=10)
b = tensor(data2, chunk_size=10)
x = solve(A, b)
res = A.dot(x).execute().fetch()
np.testing.assert_allclose(res, data2)
