SG++
python.leja Namespace Reference

Functions

def calc_min (f, lower_bound, upper_bound)
 
def invert_maximum_leja (next_point, z, lower_bound, upper_bound, weight=lambda x:1)
 
def leja_points (start, count, lower_bound, upper_bound, weight=lambda x:1, debug=False, view=False)
 
def leja_poly (next_point, z, lower_bound, upper_bound, weight=lambda x:1)
 
def maximum_leja (next_point, z, lower_bound, upper_bound, weight=lambda x:1)
 

Variables

int count = 8
 
int lower_bound = 0
 
 points = leja_points(start, count, lower_bound, upper_bound, weight)
 
float start = 0.5
 
int upper_bound = 1
 
 weight = lambdax:np.sin(x * np.pi)
 

Function Documentation

def python.leja.calc_min (   f,
  lower_bound,
  upper_bound 
)

Referenced by python.leja.leja_points().

def python.leja.invert_maximum_leja (   next_point,
  z,
  lower_bound,
  upper_bound,
  weight = lambda x : 1 
)
The Nelder-Mead Simplex algorithm used to find the next leja point searches
for the minimum of the function, so we just invert our maximum function

References python.leja.maximum_leja().

Referenced by python.leja.leja_points().

def python.leja.leja_points (   start,
  count,
  lower_bound,
  upper_bound,
  weight = lambda x : 1,
  debug = False,
  view = False 
)
calculates the next COUNT leja points with START = z_0
returns the leja points in a list

References python.leja.calc_min(), python.test.f, and python.leja.invert_maximum_leja().

def python.leja.leja_poly (   next_point,
  z,
  lower_bound,
  upper_bound,
  weight = lambda x : 1 
)

References python.leja.weight.

def python.leja.maximum_leja (   next_point,
  z,
  lower_bound,
  upper_bound,
  weight = lambda x : 1 
)
the maximums function for the leja points
the next leja point is the input so that this function returns
its maximum

References python.leja.weight.

Referenced by python.leja.invert_maximum_leja().

Variable Documentation

float python.leja.start = 0.5

Referenced by sgpp::datadriven::ClusteringOCL::OperationClusteringOCL< T >.calculate_clusters(), sgpp::combigrid::LTwoScalarProductHashMapNakBsplineBoundaryCombigrid.calculateScalarProduct(), sgpp::datadriven::DensityOCLMultiPlatform::OperationCreateGraphOCLSingleDevice< T >.create_graph(), sgpp::datadriven::DensityOCLMultiPlatform::OperationDensityOCLMultiPlatform< T >.generateb(), sgpp::datadriven::PartitioningTool.getPartitionSegment(), sgpp::base::LinearLoadBalancer.getPartitionSegments(), sgpp::base::LinearLoadBalancerMultiPlatform.getPartitionSegments(), main(), sgpp::datadriven::OperationMultiEvalMPI.mult(), sgpp::datadriven::OperationMultiEvalStreaming.mult(), sgpp::datadriven::OperationMultiEvalModMaskStreaming.mult(), sgpp::datadriven::AbstractOperationMultipleEvalSubspace.mult(), sgpp::datadriven::OperationMultiEvalStreamingBSplineOCL< T >.mult(), sgpp::datadriven::StreamingModOCLMaskMultiPlatform::KernelMult< T >.mult(), sgpp::datadriven::OperationMultiEvalStreamingModOCLUnified< T >.mult(), sgpp::datadriven::OperationMultiEvalStreamingModOCLFastMultiPlatform< T >.mult(), sgpp::datadriven::OperationMultiEvalStreamingModOCLMaskMultiPlatform< T >.mult(), sgpp::datadriven::OperationMultiEvalStreamingModOCLOpt< T >.mult(), sgpp::datadriven::StreamingOCLMultiPlatform::KernelMult< T >.mult(), sgpp::datadriven::DensityOCLMultiPlatform::OperationDensityOCLMultiPlatform< T >.mult(), sgpp::datadriven::OperationMultiEvalMPI.multSlave(), sgpp::datadriven::AbstractOperationMultipleEvalSubspace.multTranspose(), sgpp::datadriven::OperationMultiEvalMPI.multTranspose(), sgpp::datadriven::OperationMultiEvalStreaming.multTranspose(), sgpp::datadriven::OperationMultiEvalModMaskStreaming.multTranspose(), sgpp::datadriven::StreamingModOCLMaskMultiPlatform::KernelMultTranspose< T >.multTranspose(), sgpp::datadriven::OperationMultiEvalStreamingBSplineOCL< T >.multTranspose(), sgpp::datadriven::StreamingOCLMultiPlatform::KernelMultTranspose< T >.multTranspose(), sgpp::datadriven::OperationMultiEvalStreamingModOCLUnified< T >.multTranspose(), sgpp::datadriven::OperationMultiEvalStreamingModOCLFastMultiPlatform< T >.multTranspose(), sgpp::datadriven::OperationMultiEvalStreamingModOCLOpt< T >.multTranspose(), sgpp::datadriven::OperationMultiEvalStreamingModOCLMaskMultiPlatform< T >.multTranspose(), sgpp::datadriven::DensityOCLMultiPlatform::OperationPruneGraphOCLMultiPlatform< T >.prune_graph(), sgpp::base::PrewaveletGridGenerator.refine(), sgpp::datadriven::BatchLearner.stringToDataMatrix(), sgpp::datadriven::StringTokenizer.tokenize(), sgpp::datadriven::OperationMultiEvalModMaskStreaming.~OperationMultiEvalModMaskStreaming(), and sgpp::datadriven::OperationMultiEvalStreaming.~OperationMultiEvalStreaming().

int python.leja.upper_bound = 1