def __prepareDataContainer(self, data, name):
"""
Prepare data for learning
@param data: dictionary loaded from UQSetting
@return dictionary {dtype: {t: <DataContainer>}}
"""
ans = {}
U = self.getParameters()\
.activeParams()\
.getIndependentJointDistribution()
for dtype in self.getKnowledgeTypes():
ans[dtype] = {}
dim = self.grid.getStorage().dim()
# prepare data container depending on the given knowledge type
tmp = KnowledgeTypes.transformData(data, U, dtype)
# load data for all time steps
for t, values in tmp.items():
size = len(values)
mydata = DataMatrix(size, dim)
sol = DataVector(size)
for i, (sample, res) in enumerate(values.items()):
p = DataVector(sample.getActiveUnit())
mydata.setRow(i, p)
sol[i] = float(res)
ans[dtype][t] = DataContainer(points=mydata, values=sol, name=name)
return ans
def __prepareDataContainer(self, data, name):
"""
Prepare data for learning
@param data: dictionary loaded from UQSetting
@return dictionary {dtype: {t: <DataContainer>}}
"""
ans = {}
U = self.getParameters()\
.activeParams()\
.getIndependentJointDistribution()
for dtype in self.getKnowledgeTypes():
ans[dtype] = {}
dim = self.grid.getStorage().getDimension()
# prepare data container depending on the given knowledge type
tmp = KnowledgeTypes.transformData(data, U, dtype)
# load data for all time steps
for t, values in list(tmp.items()):
size = len(values)
mydata = DataMatrix(size, dim)
sol = DataVector(size)
for i, (sample, res) in enumerate(values.items()):
p = DataVector(sample.getActiveUnit())
mydata.setRow(i, p)
sol[i] = float(res)
ans[dtype][t] = DataContainer(points=mydata,
values=sol,
name=name)
return ans
def learnData(self, *args, **kws):
# learn data
for dtype, values in self.dataContainer.items():
knowledge = {}
print KnowledgeTypes.toString(dtype)
# do the learning
for t, dataContainer in values.items():
print "t = %g, " % t,
if dataContainer is not None:
learner = self._learners[t]
# learn data, if there is any available
learner.grid = self.getGrid()
learner.dataContainer = dataContainer
alpha = learner.learnData()
# prepare the answer
knowledge[t] = copyGrid(learner.grid), DataVector(alpha)
print
# update results
if len(knowledge) > 0:
self.updateResults(knowledge, dtype)
def learnData(self, *args, **kws):
# learn data
for dtype, values in self.dataContainer.items():
knowledge = {}
print KnowledgeTypes.toString(dtype)
# do the learning
for t, dataContainer in values.items():
print "t = %g, " % t,
if dataContainer is not None:
learner = self._learners[t]
# learn data, if there is any available
learner.grid = self.getGrid()
learner.dataContainer = dataContainer
alpha = learner.learnData()
# prepare the answer
knowledge[t] = copyGrid(learner.grid), DataVector(alpha)
print
# update results
if len(knowledge) > 0:
self.updateResults(knowledge, dtype)
def getAlphasByQoI(self, qoi='_', dtype=KnowledgeTypes.SIMPLE,
iteration=None):
"""
Get all coefficient vectors for the given quantity of interest
@param qoi: string quantity of interest
@param iteration: int, iteration number
"""
if iteration is None:
iteration = self.__iteration
if qoi in self.__alphas[iteration] and \
dtype in self.__alphas[iteration][qoi]:
return self.__alphas[iteration][qoi][dtype]
else:
raise AttributeError('no knowledge for (i=%i, qoi=%s, \
dtype=%i)' % (iteration, qoi,
KnowledgeTypes.toString(dtype)))
def getAlphasByQoI(self,
qoi='_',
dtype=KnowledgeTypes.SIMPLE,
iteration=None):
"""
Get all coefficient vectors for the given quantity of interest
@param qoi: string quantity of interest
@param iteration: int, iteration number
"""
if iteration is None:
iteration = self.__iteration
if qoi in self.__alphas[iteration] and \
dtype in self.__alphas[iteration][qoi]:
return self.__alphas[iteration][qoi][dtype]
else:
raise AttributeError(
'no knowledge for (i=%i, qoi=%s, dtype=%i)' %
(iteration, qoi, KnowledgeTypes.toString(dtype)))
def runASGCAnalysis(self, analysis, label, alabel, blabel):
# ----------------------------------------------
# write stats
# ----------------------------------------------
pathResults = os.path.join(self.pathResults, alabel, blabel)
analysisMC = None
if 'ref' in self.uqSettings:
res = self.uqSettings['ref'].getResults()
analysisMC = MCAnalysis(self.params, res)
# print "sobol indices"
# analysis.writeSensitivityValues(os.path.join(pathResults, label))
print("surpluses")
analysis.writeSurplusesLevelWise(os.path.join(pathResults, label))
print("stats")
analysis.writeStats(os.path.join(pathResults, label))
print("moments")
analysis.writeMoments(os.path.join(pathResults, label))
print("sampling")
analysis.sampleGrids(os.path.join(pathResults, "samples", label))
print("checkpoints")
analysis.writeCheckpoints(
os.path.join(pathResults, "checkpoints", label))
print("density")
kde = analysis.estimateDensity(dtype="kde")
sgde = analysis.estimateDensity(dtype="sgde")
kdeMC = None
if analysisMC is not None:
kdeMC = analysisMC.estimateDensity(dtype="kde")
fig = plt.figure()
plotDensity1d(kde, label="kde")
plotDensity1d(sgde, label="sgde")
if kdeMC is not None:
plotDensity1d(kdeMC, label="kde (ref)")
plt.legend()
plt.savefig(os.path.join(pathResults, "scc.pdf.png"))
plt.close(fig)
fig = plt.figure()
plotCDF1d(kde, label="kde")
plotCDF1d(sgde, label="sgde")
if kdeMC is not None:
plotCDF1d(kdeMC, label="kde (ref)")
plt.legend()
plt.savefig(os.path.join(pathResults, "scc.cdf.png"))
plt.close(fig)
# ----------------------------------------------
# do some plotting
# ----------------------------------------------
uqManager = analysis.getUQManager()
# scatter plot of surpluses level wise
for t in uqManager.getTimeStepsOfInterest():
for dtype in uqManager.getKnowledgeTypes():
surpluses = analysis.computeSurplusesLevelWise(t, dtype)
maxLevel = uqManager.getKnowledge().getGrid(uqManager.getQoI())\
.getStorage().getMaxLevel()
fig = plotSurplusLevelWise(surpluses, maxLevel)
fig.savefig(
os.path.join(
pathResults, "surpluses.%s.t%g.png" %
(KnowledgeTypes.toString(dtype), t)))