def test_laplace(grid, lmax):
resolution = 100000
grid.getGenerator().regular(lmax)
gridStorage = grid.getStorage()
size = gridStorage.getSize()
b = getBasis(grid)
op = pysgpp.createOperationLaplace(grid)
alpha = pysgpp.DataVector(size)
result = pysgpp.DataVector(size)
for point_i in range(size):
for point_j in range(size):
gp_i = gridStorage.getPoint(point_i)
gp_j = gridStorage.getPoint(point_j)
print("--------")
for i in range(0, size):
alpha[i] = 0
alpha[point_i] = 1
op.mult(alpha, result)
xs = np.linspace(0, 1, resolution)
approx = sum([
b.evalDx(gp_i.getLevel(0), gp_i.getIndex(0), x) *
b.evalDx(gp_j.getLevel(0), gp_j.getIndex(0), x) for x in xs
]) / resolution
print("i,j: {},{} result: {} approx:{}".format(
point_i, point_j, result[point_j], approx))
if (abs(result.get(point_j) - approx) > 1e-1):
print("--------")
print("points: {},{} ".format(point_i, point_j))
print("approx:{}".format(approx))
print("result:{}".format(result.get(point_j)))
# print result
print("--------")
def loadAll(self, iteration):
self.grid = self.loadGrid(iteration)
self.knowledge = self.loadLearnedKnowledge(iteration)
self.learner = self.__loadLearner(iteration)
self.learner.setGrid(self.grid)
self.learner.setLearnedKnowledge(self.knowledge)
self.learner.attachEventController(self)
# setting C operator here, since the knowledge about grid is needed
# recreation of B operator is not necessary since it will be set in learnData()
cOperatorType = self.__getCOperatorType(iteration)
if cOperatorType == 'laplace':
self.learner.specification.setCOperator(
createOperationLaplace(self.learner.grid))
self.learner.specification.setCOperatorType('laplace')
elif cOperatorType == 'identity':
self.learner.specification.setCOperator(
createOperationIdentity(self.learner.grid))
self.learner.specification.setCOperatorType('identity')
else:
raise Exception('C Operator type is unknown')
for controller in self.__getLearnerEventControllers(iteration):
if controller['module'] == 'bin.controller.InfoToScreenRegressor':
self.learner.attachEventController(InfoToScreenRegressor())
elif controller['module'] == 'bin.controller.InfoToScreen':
self.learner.attachEventController(InfoToScreen())
elif controller['module'] == 'bin.controller.InfoToFile':
self.learner.attachEventController(
InfoToFile(controller['filename']))
elif controller['module'] == 'bin.controller.InfoToGraph':
self.learner.attachEventController(
InfoToGraph(controller['filename']))
for controller in self.__getSolverEventControllers(iteration):
if controller['module'] == 'bin.controller.InfoToScreenRegressor':
self.learner.solver.attachEventController(
InfoToScreenRegressor())
elif controller['module'] == 'bin.controller.InfoToScreen':
self.learner.solver.attachEventController(InfoToScreen())
elif controller['module'] == 'bin.controller.InfoToFile':
self.learner.solver.attachEventController(
InfoToFile(controller['filename']))
elif controller['module'] == 'bin.controller.InfoToGraph':
self.learner.solver.attachEventController(
InfoToGraph(controller['filename']))
return self.learner
def loadAll(self, iteration):
self.grid = self.loadGrid(iteration)
self.knowledge = self.loadLearnedKnowledge(iteration)
self.learner = self.__loadLearner(iteration)
self.learner.setGrid(self.grid)
self.learner.setLearnedKnowledge(self.knowledge)
self.learner.attachEventController(self)
# setting C operator here, since the knowledge about grid is needed
# recreation of B operator is not necessary since it will be set in learnData()
cOperatorType = self.__getCOperatorType(iteration)
if cOperatorType == 'laplace':
self.learner.specification.setCOperator(createOperationLaplace(self.learner.grid))
self.learner.specification.setCOperatorType('laplace')
elif cOperatorType == 'identity':
self.learner.specification.setCOperator(createOperationIdentity(self.learner.grid))
self.learner.specification.setCOperatorType('identity')
else:
raise Exception('C Operator type is unknown')
for controller in self.__getLearnerEventControllers(iteration):
if controller['module'] == 'bin.controller.InfoToScreenRegressor':
self.learner.attachEventController(InfoToScreenRegressor())
elif controller['module'] == 'bin.controller.InfoToScreen':
self.learner.attachEventController(InfoToScreen())
elif controller['module'] == 'bin.controller.InfoToFile':
self.learner.attachEventController(InfoToFile(controller['filename']))
elif controller['module'] == 'bin.controller.InfoToGraph':
self.learner.attachEventController(InfoToGraph(controller['filename']))
for controller in self.__getSolverEventControllers(iteration):
if controller['module'] == 'bin.controller.InfoToScreenRegressor':
self.learner.solver.attachEventController(InfoToScreenRegressor())
elif controller['module'] == 'bin.controller.InfoToScreen':
self.learner.solver.attachEventController(InfoToScreen())
elif controller['module'] == 'bin.controller.InfoToFile':
self.learner.solver.attachEventController(InfoToFile(controller['filename']))
elif controller['module'] == 'bin.controller.InfoToGraph':
self.learner.solver.attachEventController(InfoToGraph(controller['filename']))
return self.learner
def create(self):
grid = self._builder.getSimulationLearner().getGrid()
if grid is None:
raise AttributeError('No grid defined. Needed for creating\
C-Operator')
if self.__specification.getCOperatorType() == 'identity':
self.__specification.setCOperator(createOperationIdentity(grid))
else:
# use laplace as default
self.__specification.setCOperator(createOperationLaplace(grid))
self.__specification.setCOperatorType('laplace')
# make sure that there is a stop policy
if self.__stopPolicyDescriptor is None:
self.withStopPolicy()
# create folding polcy if specified
if self.__foldingPolicyDescriptor is not None:
level = self.__foldingPolicyDescriptor.level
seed = self.__foldingPolicyDescriptor.seed
dtype = self.__foldingPolicyDescriptor.dtype
if level is None:
raise Exception("Folding level has to be defined")
if dtype == FoldingStrategy.SEQUENTIAL:
policy = SequentialFoldingPolicy(level)
elif self.dtype == FoldingStrategy.RANDOM:
policy = RandomFoldingPolicy(level, seed)
elif self.dtype == FoldingStrategy.STRATIFIED:
policy = StratifiedFoldingPolicy(level, seed)
elif self.dtype == FoldingStrategy.FILES:
policy = FilesFoldingPolicy(level)
else:
raise Exception("Folding dtype is not defined or is unproper")
# set the folding policy in the learner
self._builder.getLearner().setFoldingPolicy(policy)
def create(self):
grid = self._builder.getSimulationLearner().getGrid()
if grid is None:
raise AttributeError('No grid defined. Needed for creating\
C-Operator')
if self.__specification.getCOperatorType() == 'identity':
self.__specification.setCOperator(createOperationIdentity(grid))
else:
# use laplace as default
self.__specification.setCOperator(createOperationLaplace(grid))
self.__specification.setCOperatorType('laplace')
# make sure that there is a stop policy
if self.__stopPolicyDescriptor is None:
self.withStopPolicy()
# create folding polcy if specified
if self.__foldingPolicyDescriptor is not None:
level = self.__foldingPolicyDescriptor.level
seed = self.__foldingPolicyDescriptor.seed
dtype = self.__foldingPolicyDescriptor.dtype
if level is None:
raise Exception("Folding level has to be defined")
if dtype == FoldingStrategy.SEQUENTIAL:
policy = SequentialFoldingPolicy(level)
elif self.dtype == FoldingStrategy.RANDOM:
policy = RandomFoldingPolicy(level, seed)
elif self.dtype == FoldingStrategy.STRATIFIED:
policy = StratifiedFoldingPolicy(level, seed)
elif self.dtype == FoldingStrategy.FILES:
policy = FilesFoldingPolicy(level)
else:
raise Exception("Folding dtype is not defined or is unproper")
# set the folding policy in the learner
self._builder.getLearner().setFoldingPolicy(policy)
