def update(self, grid, v, admissibleSet):
# prepare data
gpsi = list(admissibleSet.values())
# prepare list of grid points
gs = grid.getStorage()
gpsj = [None] * gs.getSize()
for i in range(gs.getSize()):
gpsj[i] = gs.getPoint(i)
# compute stiffness matrix for next run
basis = getBasis(grid)
A = self._strategy.computeBilinearFormByList(basis, gpsi, gpsj)
# compute the expectation value term for the new points
b = self._strategy.computeBilinearFormIdentity(basis, gpsi)
# estimate missing coefficients
w = np.ndarray(admissibleSet.getSize())
for i, gp in enumerate(admissibleSet.values()):
w[i] = estimateSurplus(grid, gp, v)
# w[i] = estimateConvergence(grid, gp, v)
# update the ranking
values = self.__computeRanking(v, w, A, b)
self._ranking = {}
for i, gpi in enumerate(admissibleSet.values()):
self._ranking[gpi.getHash()] = values[i]
def update(self, grid, v, admissibleSet):
# prepare data
gpsi = admissibleSet.values()
# prepare list of grid points
gs = grid.getStorage()
gpsj = [None] * gs.size()
for i in xrange(gs.size()):
gpsj[i] = gs.get(i)
# compute stiffness matrix for next run
basis = getBasis(grid)
A = self._strategy.computeBilinearFormByList(basis, gpsi, gpsj)
# compute the expectation value term for the new points
b = self._strategy.computeBilinearFormIdentity(basis, gpsi)
# estimate missing coefficients
w = DataVector(admissibleSet.getSize())
for i, gp in enumerate(admissibleSet.values()):
w[i] = estimateSurplus(grid, gp, v)
# w[i] = estimateConvergence(grid, gp, v)
# update the ranking
values = self.__computeRanking(v, w, A, b)
self._ranking = {}
for i, gpi in enumerate(admissibleSet.values()):
self._ranking[gpi.hash()] = values[i]
def rank(self, grid, gp, alphas, params, *args, **kws):
# get grid point associated to ix
gs = grid.getStorage()
p = [gs.getCoordinates(gp, j) for j in range(gs.getDimension())]
# get joint distribution
ap = params.activeParams()
U = ap.getIndependentJointDistribution()
T = ap.getJointTransformation()
q = T.unitToProbabilistic(p)
# estimate the surplus
alpha = estimateSurplus(grid, gp, alphas)
# get area of basis function
A = 1.0
for d in range(gs.getDimension()):
A *= getIntegral(grid, gp.getLevel(d), gp.getIndex(d))
return abs(alpha) * U.pdf(q) * A
def rank(self, grid, gp, alphas, params, *args, **kws):
# get grid point associated to ix
gs = grid.getStorage()
p = [gp.getCoord(j) for j in xrange(gs.dim())]
# get joint distribution
ap = params.activeParams()
U = ap.getIndependentJointDistribution()
T = ap.getJointTransformation()
q = T.unitToProbabilistic(p)
# estimate the surplus
alpha = estimateSurplus(grid, gp, alphas)
# get area of basis function
A = 1.0
for d in xrange(gs.dim()):
A *= getIntegral(grid, gp.getLevel(d), gp.getIndex(d))
return abs(alpha) * U.pdf(q) * A
def rank(self, grid, gp, alphas, params, *args, **kws):
gs = grid.getStorage()
# estimate the convergence of ancestors of gp
ratio = estimateSurplus(grid, gp, alphas)
# get grid point associated to ix
p = [gp.getCoord(j) for j in xrange(gs.dim())]
# get joint distribution
ap = params.activeParams()
U = ap.getIndependentJointDistribution()
T = ap.getJointTransformation()
q = T.unitToProbabilistic(p)
# scale surplus by probability density
a = abs(ratio) * U.pdf(q)
# get area of basis function
A = 1.0
for d in xrange(gs.dim()):
A *= getIntegral(grid, gp.getLevel(d), gp.getIndex(d))
return a * A
def rank(self, grid, gp, alphas, params, *args, **kws):
gs = grid.getStorage()
# estimate the convergence of ancestors of gp
ratio = estimateSurplus(grid, gp, alphas)
# get grid point associated to ix
p = [gp.getCoord(j) for j in xrange(gs.dim())]
# get joint distribution
ap = params.activeParams()
U = ap.getIndependentJointDistribution()
T = ap.getJointTransformation()
q = T.unitToProbabilistic(p)
# scale surplus by probability density
a = abs(ratio) * U.pdf(q)
# get area of basis function
A = 1.0
for d in xrange(gs.dim()):
A *= getIntegral(grid, gp.getLevel(d), gp.getIndex(d))
return a * A
def rank(self, grid, gp, alphas, params, *args, **kws):
return np.abs(estimateSurplus(grid, gp, alphas))
def rank(self, grid, gp, alphas, params, *args, **kws):
return abs(estimateSurplus(grid, gp, alphas))
for i in range(gs.getSize()):
gs.getCoordinates(gs.getPoint(i), p)
nodalValues[i] = f(p.array())
v = hierarchize(grid, nodalValues)
ipar = 1
assert ipar < len(v)
gp = gs.getPoint(ipar)
print("Grand father:", gs.getCoordinate(gs.getPoint(0), 0), v[0])
print("Father:", gs.getCoordinate(gs.getPoint(0), 0), v[ipar])
gpl = HashGridPoint(gp)
gpl.getLeftChild(0)
gpr = HashGridPoint(gp)
gpr.getRightChild(0)
assert gsc.isContaining(gpl)
assert gsc.isContaining(gpr)
print(gs.getCoordinate(gpl, 0), \
estimateSurplus(grid, gpl, v), \
estimateConvergence(grid, gpl, v), \
w[gsc.getSequenceNumber(gpl)])
print(gs.getCoordinate(gpr, 0), \
estimateSurplus(grid, gpr, v), \
estimateConvergence(grid, gpr, v), \
w[gsc.getSequenceNumber(gpr)])