def test_save(self):
fname = 'ezyrb.tmp'
pod = POD()
rbf = RBF()
db = Database(param, snapshots.T)
rom = ROM(db, pod, rbf)
rom.fit()
rom.save(fname)
def test_with_db_predict(self):
reg = Linear()
pod = POD()
db = Database(np.array([1, 2, 3])[:,None], np.array([1, 5, 3])[:,None])
rom = ReducedOrderModel(db, pod, reg)
rom.fit()
assert rom.predict([1]) == 1
assert rom.predict([2]) == 5
assert rom.predict([3]) == 3
def test_with_db_predict(self):
reg = RadiusNeighborsRegressor(radius=0.5)
pod = POD()
db = Database(
np.array([1, 2, 3])[:, None],
np.array([1, 5, 3])[:, None])
rom = ReducedOrderModel(db, pod, reg)
rom.fit()
assert rom.predict([1]) == 1
assert rom.predict([2]) == 5
assert rom.predict([3]) == 3
def test_load2(self):
fname = 'ezyrb2.tmp'
pod = POD()
rbf = RBF()
db = Database(param, snapshots.T)
rom = ROM(db, pod, rbf)
rom.fit()
rom.save(fname, save_db=False)
new_rom = ROM.load(fname)
new_param = [-0.293344, -0.23120537]
np.testing.assert_array_almost_equal(
rom.predict(new_param),
new_rom.predict(new_param)
)
#POD offline
snapshots = np.load('Snapshots/Snapshot75b.npy')
print(f'Total number of columns in snapshot matrix = {len(snapshots[0])}')
limit = int(0.8 * len(snapshots[0]))
snapshots = snapshots[:, 0:limit]
snapshots = np.transpose(snapshots)
param = np.load('Param/Param75b.npy')
param_snap = param[:, 0:limit]
param_snap = np.transpose(param_snap)
db = Database(param_snap, snapshots)
pod = POD('svd')
rbf = RBF()
rom = ROM(db, pod, rbf)
rom.fit()
#POD Online
param_train = np.transpose(param[:, limit:len(param[0])])
Error_array = np.zeros((len(param[0]) - limit, 1))
train_limit = len(param[0]) - limit
for i in range(0, train_limit):
param_i = param_train[i, :]
[A, b, TleftBC] = buildadvdefop(param_i[0], param_i[1], param_i[2])
wcomp = np.linalg.solve(A, b).T.squeeze()
pred_sol = rom.predict(param_i)
MSE = np.square(np.subtract(wcomp, pred_sol)).mean()
RMSE = math.sqrt(MSE)
Error_array[i, :] = RMSE
