def test_perf_ckks_enc_matmul(benchmark, matrix_shape):
ctx = ckks_context()
vector_shape = matrix_shape[-1]
matrix, vec = generate_input(matrix_shape, vector_shape)
ckks_vector = ts.enc_matmul_encoding(ctx, matrix.tolist())
benchmark.pedantic(
helper_perf,
args=("enc_matmul", ckks_vector, vec, matrix_shape[0]),
rounds=rounds,
iterations=iterations,
)
def test_enc_matmul_plain(context, matrix_shape, vector_size, precision):
def generate_input(matrix_shape, vector_size):
# generated random values
matrix = np.random.randn(*matrix_shape)
vector = np.random.randn(vector_size)
return matrix, vector
matrix, vector = generate_input(matrix_shape, vector_size)
expected = matrix @ vector
context.generate_galois_keys()
ckks_vector = ts.enc_matmul_encoding(context, matrix.tolist())
result = ckks_vector.enc_matmul_plain(vector.tolist(), matrix_shape[0])
assert _almost_equal(result.decrypt(), expected,
precision), "Matrix multiplication is incorrect."
