def build_map(self, GP):
self.update_bounds(GP)
y_mean, y_var = self.GenGrid(GP)
self.mean.set_values(y_mean)
if self.var_map == "pca":
G = np.prod(y_var.shape[:-1])
full_rank = np.min((G, y_var.shape[-1]))
if self.svd_rank == "auto":
self.var = PCASplines(
self.bounds[0],
self.bounds[1],
orders=self.grid_num,
svd_rank=full_rank,
)
else:
assert isinstance(self.svd_rank, int), "Please set svd_rank to int or 'auto'"
assert self.svd_rank <= full_rank, f"svd_rank={self.svd_rank} exceeds full_rank={full_rank}"
self.var = PCASplines(
self.bounds[0],
self.bounds[1],
orders=self.grid_num,
svd_rank=self.svd_rank,
)
if self.var_map is not None:
self.var.set_values(y_var)
self.hyps_mask = deepcopy(GP.hyps_mask)
def build_map_container(self):
"""
build 1-d spline function for mean, 2-d for var
"""
if np.any(np.array(self.bounds[1]) <= 0.0):
bounds = [
np.zeros_like(self.bounds[0]),
np.ones_like(self.bounds[1])
]
else:
bounds = self.bounds
self.mean = CubicSpline(bounds[0], bounds[1], orders=self.grid_num)
if self.var_map == "pca":
if self.svd_rank == "auto":
warnings.warn(
"The containers for variance are not built because svd_rank='auto'"
)
elif isinstance(self.svd_rank, int):
self.var = PCASplines(
bounds[0],
bounds[1],
orders=self.grid_num,
svd_rank=self.svd_rank,
)
if self.var_map == "simple":
self.var = CubicSpline(bounds[0], bounds[1], orders=self.grid_num)
def build_map(self, GP):
self.update_bounds(GP)
if not self.load_grid:
y_mean, y_var = self.GenGrid(GP)
else:
if "mgp_grids" not in os.listdir(self.load_grid):
raise FileNotFoundError(
"Please set 'load_grid' as the location of mgp_grids folder"
)
grid_path = f"{self.load_grid}/mgp_grids/{self.bodies}_{self.species_code}"
y_mean = np.load(f"{grid_path}_mean.npy")
y_var = np.load(f"{grid_path}_var.npy", allow_pickle=True)
self.mean.set_values(y_mean)
if self.var_map == "pca" and self.svd_rank == "auto":
self.var = PCASplines(
self.bounds[0],
self.bounds[1],
orders=self.grid_num,
svd_rank=np.min(y_var.shape),
)
if self.var_map is not None:
self.var.set_values(y_var)
self.hyps_mask = deepcopy(GP.hyps_mask)
def build_map_container(self):
"""
build 1-d spline function for mean, 2-d for var
"""
self.mean = CubicSpline(self.bounds[0],
self.bounds[1],
orders=self.grid_num)
if self.var_map == "pca":
if self.svd_rank == "auto":
warnings.warn(
"The containers for variance are not built because svd_rank='auto'"
)
elif isinstance(self.svd_rank, int):
self.var = PCASplines(
self.bounds[0],
self.bounds[1],
orders=self.grid_num,
svd_rank=self.svd_rank,
)
if self.var_map == "simple":
self.var = CubicSpline(self.bounds[0],
self.bounds[1],
orders=self.grid_num)
def build_map_container(self):
self.mean = CubicSpline(self.l_bounds, self.u_bounds,
orders=[self.grid_num])
if not self.mean_only:
self.var = PCASplines(self.l_bounds, self.u_bounds,
orders=[self.grid_num],
svd_rank=self.svd_rank)
def build_map_container(self):
# create spline interpolation class object
self.mean = CubicSpline(self.l_bounds, self.u_bounds,
orders=self.grid_num)
if not self.mean_only:
self.var = PCASplines(self.l_bounds, self.u_bounds,
orders=self.grid_num,
svd_rank=self.svd_rank)
def build_map_container(self):
'''
build 1-d spline function for mean, 2-d for var
'''
self.mean = CubicSpline(self.bounds[0],
self.bounds[1],
orders=[self.grid_num])
if not self.mean_only:
self.var = PCASplines(self.bounds[0],
self.bounds[1],
orders=[self.grid_num],
svd_rank=self.svd_rank)
def build_map_container(self):
'''
build 3-d spline function for mean,
3-d for the low rank approximation of L^{-1}k*
'''
# create spline interpolation class object
self.mean = CubicSpline(self.bounds[0],
self.bounds[1],
orders=self.grid_num)
if not self.mean_only:
self.var = PCASplines(self.bounds[0],
self.bounds[1],
orders=self.grid_num,
svd_rank=self.svd_rank)
def build_map(self, y_mean, y_var):
'''
build 1-d spline function for mean, 2-d for var
'''
self.mean = \
SplinesInterpolation(y_mean, u_bounds=np.array(self.u_bound),
l_bounds=np.array(self.l_bound),
orders=np.array([self.grid_num]))
if not self.mean_only:
self.var = \
PCASplines(y_var, u_bounds=np.array(self.u_bound),
l_bounds=np.array(self.l_bound),
orders=np.array([self.grid_num]),
svd_rank=self.svd_rank)
def build_map(self, y_mean, y_var, svd_rank, load_svd):
'''
build 3-d spline function for mean,
3-d for the low rank approximation of L^{-1}k*
'''
nop = self.grid_num[0]
noa = self.grid_num[2]
self.mean = SplinesInterpolation(y_mean,
u_bounds=self.u_bound,
l_bounds=self.l_bound,
orders=np.array([nop, nop, noa]))
self.var = PCASplines(y_var,
u_bounds=self.u_bound,
l_bounds=self.l_bound,
orders=np.array([nop, nop, noa]),
svd_rank=svd_rank,
load_svd=load_svd)
def build_map_container(self):
'''
build 3-d spline function for mean,
3-d for the low rank approximation of L^{-1}k*
'''
# create spline interpolation class object
nop = self.grid_num[0]
noa = self.grid_num[2]
self.mean = CubicSpline(self.l_bounds,
self.u_bounds,
orders=[nop, nop, noa])
if not self.mean_only:
self.var = PCASplines(self.l_bounds,
self.u_bounds,
orders=[nop, nop, noa],
svd_rank=self.svd_rank)
def build_map(self, GP):
self.update_bounds(GP)
y_mean, y_var = self.GenGrid(GP)
self.mean.set_values(y_mean)
if self.var_map == "pca" and self.svd_rank == "auto":
self.var = PCASplines(
self.bounds[0],
self.bounds[1],
orders=self.grid_num,
svd_rank=np.min(y_var.shape),
)
if self.var_map is not None:
self.var.set_values(y_var)
self.hyps_mask = deepcopy(GP.hyps_mask)
def build_map(self, y_mean, y_var):
'''
build 1-d spline function for mean, 2-d for var
'''
self.mean = SplinesInterpolation(y_mean,
u_bounds=np.array(self.u_bound),
l_bounds=np.array(self.l_bound),
orders=np.array([self.grid_num]))
if self.bodies == '2':
self.var = SplinesInterpolation(
y_var,
u_bounds=np.array([self.u_bound, self.u_bound]),
l_bounds=np.array([self.l_bound, self.l_bound]),
orders=np.array([self.grid_num, self.grid_num]))
elif self.bodies == '2+3':
self.var = PCASplines(y_var,
u_bounds=np.array(self.u_bound),
l_bounds=np.array(self.l_bound),
orders=np.array([self.grid_num]),
svd_rank=self.svd_rank,
load_svd=None)