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Demo Details 
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The repository contains demo python scripts named **demo/demo_1.py** and **demo/demo_2.py** that demonstrate two different uses of the software package. 


Demo 1
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The demo shows an implementation of EM algorithm-based cluster parameter and order estimation for Gaussian mixture model followed by unsupervised classification of datapoints from different clusters.

**Steps**
	• First, generate 500 observations from a Gaussian mixture model with 3 clusters.
	• Then implement “estimate_gm_params” function on the data for the estimation.
	• Then use “split_classes” followed by “compute_class_likelihood” functions to classify observations from different clusters.

**Results**

.. figure:: demo_1_1.png
   :width: 100%
   :alt: generated samples
   :align: center
   
   Generated samples
   
.. figure:: demo_1_2.png
   :width: 100%
   :alt: unsupervised clustering results
   :align: center
   
   Unsupervised classification results
   
   
Demo 2
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The demo shows an implementation of the EM algorithm to estimate the orders and parameters of 2 different Gaussian Mixture models and perform binary maximum likelihood classification.

**Steps**
	• First, generate data from 2 Gaussian mixture model each with 3 clusters. The generated data includes training dataset from both mixtures and a combined testing dataset.
	• Then implement “estimate_gm_params” function on both the training datasets for the estimation.
	• Finally, use “compute_class_likelihood” function to get the likelihood value to classify testing dataset.
    
**Results**

.. figure:: demo_2_1.png
   :width: 100%
   :alt: training samples
   :align: center
   
   Training samples
   
.. figure:: demo_2_2.png
   :width: 100%
   :alt: classification results
   :align: center
   
   Classification results