Wrapping arbitrary function into a neural network :class:`Module`. This class wraps an arbitrary function or lambda expression into a ne

Class for obtaining mini-batch iterators of sliding windows. Each mini-batch will have `batch_size` windows. If the final batch contain

OmniAnomaly predictor. Args: model (OmniAnomaly): The :class:`OmniAnomaly` model instance. n_z (int or None): Number of `z`

This class allows to run SPOT algorithm on univariate dataset (upper-bound) Attributes ---------- proba : float Detectio

A wrapper class for `tfp.distributions.Distribution`

OmniAnomaly trainer. Args: model (OmniAnomaly): The :class:`OmniAnomaly` model instance. model_vs (str or tf.VariableScope):

A general implementation of variational auto-encoder as module. The variational auto-encoder ("Auto-Encoding Variational Bayes", Kingma,

A multi-variable distribution integrated with recurrent structure.

This class allows to run biSPOT algorithm on univariate dataset (upper and lower bounds) Attributes ---------- proba : float

This class allows to run DSPOT algorithm on univariate dataset (upper and lower bounds) Attributes ---------- proba : float

This class allows to run DSPOT algorithm on univariate dataset (upper-bound) Attributes ---------- proba : float Detecti