def addFullGridPoints(self, grid, alpha):
"""
Add all those full grid points with |accLevel|_1 <= n, where n is the
maximun level of the sparse grid
@param grid: Grid sparse grid to be discretized
@param alpha: numpy array hierarchical coefficients
"""
# make grid isotropic by adding all missing hierarchical ancestors
newGridPoints = []
# 1. locate candidates to be refined
candidates = self.findCandidates(grid, alpha)
# 2. do full grid search locally
gridPoinsToBeAdded = self.lookupFullGridPoints(grid, alpha, candidates)
# 3. insert them in a new grid
for gp in gridPoinsToBeAdded:
newGridPoints += insertPoint(grid, gp)
newGridPoints += insertHierarchicalAncestors(grid, gp)
if grid.getType() in [
GridType_LinearBoundary, GridType_PolyBoundary
]:
newGridPoints += insertTruncatedBorder(grid, gp)
# recompute the leaf property and return the result
grid.getStorage().recalcLeafProperty()
return newGridPoints
def refine(self, grid, gp):
ans = insertPoint(grid, gp)
ans += insertHierarchicalAncestors(grid, gp)
if hasBorder(grid):
ans += insertTruncatedBorder(grid, gp)
grid.getStorage().recalcLeafProperty()
return ans
def refine(self, grid, gp):
ans = insertPoint(grid, gp)
ans += insertHierarchicalAncestors(grid, gp)
if hasBorder(grid):
ans += insertTruncatedBorder(grid, gp)
grid.getStorage().recalcLeafProperty()
return ans
def refine(self, grid, gp):
ans = []
gs = grid.getStorage()
for d in range(gs.getDimension()):
gpl = HashGridPoint(gp)
gpl.getLeftChild(d)
if isValid(grid, gpl):
ans += insertPoint(grid, gpl)
if hasBorder(grid.getType()):
ans += insertTruncatedBorder(grid, gpl)
gpr = HashGridPoint(gp)
gpr.getRightChild(d)
if isValid(grid, gpr):
ans += insertPoint(grid, gpr)
if hasBorder(grid.getType()):
ans += insertTruncatedBorder(grid, gpr)
gs.recalcLeafProperty()
return ans
def refine(self, grid, gp):
ans = []
gs = grid.getStorage()
for d in xrange(gs.dim()):
gpl = HashGridIndex(gp)
gs.left_child(gpl, d)
ans += insertPoint(grid, gpl)
ans += insertHierarchicalAncestors(grid, gpl)
if hasBorder(grid):
ans += insertTruncatedBorder(grid, gpl)
gpr = HashGridIndex(gp)
gs.right_child(gpr, d)
ans += insertPoint(grid, gpr)
ans += insertHierarchicalAncestors(grid, gpr)
if hasBorder(grid):
ans += insertTruncatedBorder(grid, gpr)
gs.recalcLeafProperty()
return ans
def refine(self, grid, gp):
ans = []
gs = grid.getStorage()
for d in xrange(gs.dim()):
gpl = HashGridIndex(gp)
gs.left_child(gpl, d)
ans += insertPoint(grid, gpl)
ans += insertHierarchicalAncestors(grid, gpl)
if hasBorder(grid):
ans += insertTruncatedBorder(grid, gpl)
gpr = HashGridIndex(gp)
gs.right_child(gpr, d)
ans += insertPoint(grid, gpr)
ans += insertHierarchicalAncestors(grid, gpr)
if hasBorder(grid):
ans += insertTruncatedBorder(grid, gpr)
gs.recalcLeafProperty()
return ans
def addFullGridPoints(self, grid, alpha):
"""
Add all those full grid points with |accLevel|_1 <= n, where n is the
maximun level of the sparse grid
@param grid: Grid sparse grid to be discretized
@param alpha: DataVector hierarchical coefficients
"""
# make grid isotropic by adding all missing hierarchical ancestors
newGridPoints = []
# 1. locate candidates to be refined
candidates = self.findCandidates(grid, alpha)
# 2. do full grid search locally
gridPoinsToBeAdded = self.lookupFullGridPoints(grid, alpha, candidates)
# 3. insert them in a new grid
for gp in gridPoinsToBeAdded:
newGridPoints += insertPoint(grid, gp)
newGridPoints += insertHierarchicalAncestors(grid, gp)
if grid.getType() in [LinearBoundary, PolyBoundary]:
newGridPoints += insertTruncatedBorder(grid, gp)
# recompute the leaf property and return the result
grid.getStorage().recalcLeafProperty()
return newGridPoints
def addFullGridPoints(self, grid, alpha, candidates, tol=-1e-14):
"""
Add all those full grid points with |accLevel|_1 <= n, where n is the
maximun level of the sparse grid
@param grid: Grid sparse grid to be discretized
@param candidates:
@param tol:
"""
# remove all the already existing candidates
gs = grid.getStorage()
nonExistingCandidates = [
gp for gp in candidates if not gs.isContaining(gp)
]
# sort the non existing grid points by level
finalCandidates = self.sortCandidatesByLevelSum(nonExistingCandidates)
levelSums = sorted(finalCandidates.keys())
if self.lastMinimumCandidateLevelSum is None:
ix = 0
else:
ixs = np.where(
np.array(levelSums) == self.lastMinimumCandidateLevelSum)[0]
if len(ixs) == 0:
ix = 0
elif len(ixs) == 1:
ix = ixs[0] + 1
else:
raise AttributeError("this should never happen")
done = False
addedGridPoints = []
minLevelSum = -1
nextLevelCosts = 0
while not done and ix < len(levelSums):
minLevelSum = levelSums[ix]
self.lastMinimumCandidateLevelSum = minLevelSum
currentCandidates = finalCandidates[minLevelSum]
if self.verbose:
print(("# check candidates : %i/%i (at |l|_1 = %i <= %i)" %
(len(currentCandidates), len(candidates), minLevelSum,
np.max(levelSums)), ))
# evaluate the remaining candidates
samples = np.ndarray((len(currentCandidates), self.numDims))
p = DataVector(self.numDims)
for j, gp in enumerate(currentCandidates):
gp.getStandardCoordinates(p)
samples[j, :] = p.array()
eval = evalSGFunctionMulti(grid, alpha, samples)
negativeNonExistingCandidates = [
gp for j, gp in enumerate(currentCandidates) if eval[j] < tol
]
if self.verbose:
print("-> %i : considered candidates" %
len(negativeNonExistingCandidates))
for gp in negativeNonExistingCandidates:
addedGridPoints += insertPoint(grid, gp)
addedGridPoints += insertHierarchicalAncestors(grid, gp)
if not self.addAllGridPointsOnNextLevel and len(
addedGridPoints) > self.maxNewGridPoints:
done = True
break
if self.addAllGridPointsOnNextLevel and len(addedGridPoints) > 0:
nextLevelCosts = len(finalCandidates[minLevelSum])
done = True
ix += 1
# recompute the leaf property and return the result
grid.getStorage().recalcLeafProperty()
return addedGridPoints, minLevelSum, nextLevelCosts