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# DAGMM Tensorflow implementation

数据/代码github地址：https://github.com/tnakae/DAGMM

Deep Autoencoding Gaussian Mixture Model.

This implementation is based on the paper

this is UNOFFICIAL implementation.

# Requirements

- python (3.5-3.6)

- Tensorflow <= 1.15

- Numpy

- sklearn

# Usage instructions

To use DAGMM model, you need to create "DAGMM" object.

At initialize, you have to specify next 4 variables at least.

For more details, please check out [dagmm/dagmm.py](dagmm/dagmm.py) docstrings.

# Example

## Small Example

```python

import tensorflow as tf

from dagmm import DAGMM

```

## Jupyter Notebook Example

You can use next jupyter notebook examples using DAGMM model.

- [Simple DAGMM Example notebook](Example_DAGMM.ipynb) :

  This example uses random samples of mixture of gaussian.

  If you want to know simple usage, this notebook is recommended.

  with the same condition of original paper (need pandas)

# Notes

## GMM Implementation

The equation to calculate "energy" for each sample in the original paper

uses direct expression of multivariate gaussian distribution which

has covariance matrix inversion, but it is impossible sometimes

and [another author of DAGMM](https://github.com/danieltan07/dagmm) also points it out)

## Parameter of GMM Covariance (lambda_2)

Default value of lambda_2 is set to 0.0001 (0.005 in original paper).

When lambda_2 is 0.005, covariances of GMM becomes too large to detect

anomaly points. But perhaps it depends on distribution of data and method of preprocessing