torch.distributions.transforms.StickBreakingTransform Class Reference

Inheritance diagram for torch.distributions.transforms.StickBreakingTransform:

## Public Member Functions | |

def | __eq__ (self, other) |

def | log_abs_det_jacobian (self, x, y) |

Public Member Functions inherited from torch.distributions.transforms.Transform | |

def | __init__ (self, cache_size=0) |

def | inv (self) |

def | sign (self) |

def | __eq__ (self, other) |

def | __ne__ (self, other) |

def | __call__ (self, x) |

def | log_abs_det_jacobian (self, x, y) |

def | __repr__ (self) |

## Static Public Attributes | |

domain | |

codomain | |

bijective | |

event_dim | |

Static Public Attributes inherited from torch.distributions.transforms.Transform | |

bijective | |

event_dim | |

Transform from unconstrained space to the simplex of one additional dimension via a stick-breaking process. This transform arises as an iterated sigmoid transform in a stick-breaking construction of the `Dirichlet` distribution: the first logit is transformed via sigmoid to the first probability and the probability of everything else, and then the process recurses. This is bijective and appropriate for use in HMC; however it mixes coordinates together and is less appropriate for optimization.

Definition at line 473 of file transforms.py.

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

- torch/distributions/transforms.py

Generated on Thu Mar 21 2019 13:06:39 for Caffe2 - Python API by 1.8.11