deeprob package

Subpackages

• deeprob.flows package
  • Subpackages
    • deeprob.flows.layers package
      • Submodules
      • deeprob.flows.layers.autoregressive module
      • deeprob.flows.layers.coupling module
      • deeprob.flows.layers.densenet module
      • deeprob.flows.layers.resnet module
      • Module contents
    • deeprob.flows.models package
      • Submodules
      • deeprob.flows.models.base module
      • deeprob.flows.models.maf module
      • deeprob.flows.models.realnvp module
      • Module contents
  • Submodules
  • deeprob.flows.utils module
  • Module contents
• deeprob.spn package
  • Subpackages
    • deeprob.spn.algorithms package
      • Submodules
      • deeprob.spn.algorithms.evaluation module
      • deeprob.spn.algorithms.gradient module
      • deeprob.spn.algorithms.inference module
      • deeprob.spn.algorithms.moments module
      • deeprob.spn.algorithms.sampling module
      • deeprob.spn.algorithms.structure module
      • Module contents
    • deeprob.spn.layers package
      • Submodules
      • deeprob.spn.layers.dgcspn module
      • deeprob.spn.layers.ratspn module
      • Module contents
    • deeprob.spn.learning package
      • Subpackages
      • Submodules
      • deeprob.spn.learning.em module
      • deeprob.spn.learning.leaf module
      • deeprob.spn.learning.learnspn module
      • deeprob.spn.learning.wrappers module
      • deeprob.spn.learning.xpc module
      • Module contents
    • deeprob.spn.models package
      • Submodules
      • deeprob.spn.models.dgcspn module
      • deeprob.spn.models.ratspn module
      • deeprob.spn.models.sklearn module
      • Module contents
    • deeprob.spn.structure package
      • Submodules
      • deeprob.spn.structure.cltree module
      • deeprob.spn.structure.io module
      • deeprob.spn.structure.leaf module
      • deeprob.spn.structure.node module
      • Module contents
    • deeprob.spn.utils package
      • Submodules
      • deeprob.spn.utils.filter module
      • deeprob.spn.utils.partitioning module
      • deeprob.spn.utils.statistics module
      • deeprob.spn.utils.validity module
      • Module contents
  • Module contents
• deeprob.torch package
  • Submodules
  • deeprob.torch.base module
  • deeprob.torch.callbacks module
  • deeprob.torch.constraints module
  • deeprob.torch.datasets module
  • deeprob.torch.initializers module
  • deeprob.torch.metrics module
  • deeprob.torch.routines module
  • deeprob.torch.transforms module
  • deeprob.torch.utils module
  • Module contents
• deeprob.utils package
  • Submodules
  • deeprob.utils.data module
  • deeprob.utils.graph module
  • deeprob.utils.random module
  • deeprob.utils.region module
  • deeprob.utils.statistics module
  • Module contents

Submodules

deeprob.context module

deeprob.context.is_check_dtype_enabled()

Returns whether the context flag ‘check_dtype’ is enabled.

Return type

bool

deeprob.context.is_check_spn_enabled()

Returns whether the context flag ‘check_spn’ is enabled.

Return type

bool

class deeprob.context.ContextState(**kwargs)

Bases: ContextDecorator

Thread-safe Context State that disables some flags during execution.

Current supported flags are the following: - check_dtype: bool = True, Whether to check (and cast when needed) Numpy arrays data types. - check_spn: bool = True, Whether to check the SPNs structure properties.

Module contents