SG++-Doxygen-Documentation
python.uq.estimators.MonteCarloStrategy.MonteCarloStrategy Class Reference
Inheritance diagram for python.uq.estimators.MonteCarloStrategy.MonteCarloStrategy:

Public Member Functions

def __init__ (self, samples=None, ixs=None, n=5000, npaths=100, isPositive=False, percentile=1)
 
def mean (self, grid, alpha, U, T)
 
def var (self, grid, alpha, U, T, mean)
 

Public Attributes

 samples
 
 verbose
 

Constructor & Destructor Documentation

◆ __init__()

def python.uq.estimators.MonteCarloStrategy.MonteCarloStrategy.__init__ (   self,
  samples = None,
  ixs = None,
  n = 5000,
  npaths = 100,
  isPositive = False,
  percentile = 1 
)
Constructor
@param samples: ndarray containing monte carlo samples
@param ixs: list of indices for which there is data available
@param n: number of samples per path
@param npaths: number of paths
@param epsilon: maximal error with respect to the central limit theorem
@param beta: confidence level for central limit theorem
@param isPositive: forces the function to be positive

Member Function Documentation

◆ mean()

def python.uq.estimators.MonteCarloStrategy.MonteCarloStrategy.mean (   self,
  grid,
  alpha,
  U,
  T 
)
Estimate the expectation value using Monte-Carlo.

\frac{1}{N}\sum\limits_{i = 1}^N f_N(x_i)

where x_i \in \Gamma
@return: (mean, error of bootstrapping)

References python.uq.estimators.MonteCarloStrategy.MonteCarloStrategy.__getSamples(), python.uq.estimators.MCEstimator.MCEstimator.__npaths, python.uq.estimators.MonteCarloStrategy.MonteCarloStrategy.__npaths, python.uq.estimators.MCEstimator.MCEstimator.__percentile, python.uq.estimators.MonteCarloStrategy.MonteCarloStrategy.__percentile, python.uq.estimators.SparseGridEstimationStrategy.SparseGridEstimationStrategy._extractPDFforMomentEstimation(), and python.uq.operations.sparse_grid.evalSGFunctionMulti().

Referenced by python.uq.analysis.mc.MCAnalysis.MCAnalysis.computeMoments(), and python.uq.analysis.asgc.ASGCAnalysis.ASGCAnalysis.computeMoments().

◆ var()

def python.uq.estimators.MonteCarloStrategy.MonteCarloStrategy.var (   self,
  grid,
  alpha,
  U,
  T,
  mean 
)
Estimate the expectation value using Monte-Carlo.

\frac{1}{N}\sum\limits_{i = 1}^N (f_N(x_i) - E(f))^2

where x_i \in \Gamma
@return: (variance, error of bootstrapping)

References python.uq.estimators.MonteCarloStrategy.MonteCarloStrategy.__getSamples(), python.uq.estimators.MCEstimator.MCEstimator.__npaths, python.uq.estimators.MonteCarloStrategy.MonteCarloStrategy.__npaths, python.uq.estimators.MCEstimator.MCEstimator.__percentile, python.uq.estimators.MonteCarloStrategy.MonteCarloStrategy.__percentile, python.uq.estimators.SparseGridEstimationStrategy.SparseGridEstimationStrategy._extractPDFforMomentEstimation(), and python.uq.operations.sparse_grid.evalSGFunctionMulti().

Referenced by python.uq.analysis.mc.MCAnalysis.MCAnalysis.computeMoments(), and python.uq.analysis.asgc.ASGCAnalysis.ASGCAnalysis.computeMoments().

Member Data Documentation

◆ samples

python.uq.estimators.MonteCarloStrategy.MonteCarloStrategy.samples

◆ verbose


The documentation for this class was generated from the following file: