def doLearningIteration(self, set):
# initialize values
self.linearSystem = DMSystemMatrix(self.grid, set.getPoints(),
self.specification.getCOperator(),
self.specification.getL())
size = self.grid.getSize()
# Reuse data from old alpha vector increasing its dimension
if self.solver.getReuse() and self.alpha is not None:
alpha = DataVector(self.alpha)
alpha.resize(size)
# Use new alpha vector
else:
alpha = DataVector(size)
alpha.setAll(0.0)
b = DataVector(size)
self.linearSystem.generateb(set.getValues(), b)
# calculates alphas
self.solver.solve(self.linearSystem, alpha, b, self.solver.getReuse(),
False, self.solver.getThreshold())
return alpha
# create regular grid, level 3
level = 3
gridGen = grid.createGridGenerator()
gridGen.regular(level)
print "number of initial grid points: {}".format(gridStorage.size())
# definition of function to interpolate - nonsymmetric(!)
f = lambda x0, x1: 16.0 * (x0 - 1) * x0 * (x1 - 1) * x1 * x1
# create coefficient vector
alpha = DataVector(gridStorage.size())
print "length of alpha vector: {}".format(alpha.getSize())
# now refine adaptively 5 times
for refnum in range(5):
# set function values in alpha
for i in xrange(gridStorage.size()):
gp = gridStorage.get(i)
alpha[i] = f(gp.getCoord(0), gp.getCoord(1))
# hierarchize
createOperationHierarchisation(grid).doHierarchisation(alpha)
# refine a single grid point each time
gridGen.refine(SurplusRefinementFunctor(alpha, 1))
print "refinement step {}, new grid size: {}".format(
refnum + 1, gridStorage.size())
# extend alpha vector (new entries uninitialized)
alpha.resize(gridStorage.size())
# set function values in alpha
for i in range(gs.getSize()):
gs.getPoint(i).getStandardCoordinates(p)
alpha[i] = dist.pdf(p.array())
# hierarchize
createOperationHierarchisation(grid).doHierarchisation(alpha)
for refnum in range(refnums):
# refine a single grid point each time
gridGen.refine(SurplusRefinementFunctor(alpha, 1))
print("refinement step {}, new grid size: {}".format(
refnum + 1, gs.getSize()))
# extend alpha vector (new entries uninitialized)
alpha.resize(gs.getSize())
# set function values in alpha
for i in range(gs.getSize()):
gs.getPoint(i).getStandardCoordinates(p)
alpha[i] = dist.pdf(p.array())
# hierarchize
createOperationHierarchisation(grid).doHierarchisation(alpha)
alpha = alpha.array()
sgdeDist = SGDEdist(grid,
alpha,
trainData=trainSamples,
bounds=dist.getBounds())
print(
# create regular grid, level 3
level = 3
gridGen = grid.createGridGenerator()
gridGen.regular(level)
print "number of initial grid points: {}".format(gridStorage.size())
# definition of function to interpolate - nonsymmetric(!)
f = lambda x0, x1: 16.0 * (x0-1)*x0 * (x1-1)*x1*x1
# create coefficient vector
alpha = DataVector(gridStorage.size())
print "length of alpha vector: {}".format(alpha.getSize())
# now refine adaptively 5 times
for refnum in range(5):
# set function values in alpha
for i in xrange(gridStorage.size()):
gp = gridStorage.get(i)
alpha[i] = f(gp.getCoord(0), gp.getCoord(1))
# hierarchize
createOperationHierarchisation(grid).doHierarchisation(alpha)
# refine a single grid point each time
gridGen.refine(SurplusRefinementFunctor(alpha, 1))
print "refinement step {}, new grid size: {}".format(refnum+1, gridStorage.size())
# extend alpha vector (new entries uninitialized)
alpha.resize(gridStorage.size())
