.. _fwflag_fit_reducer:
=============
--fit_reducer
=============
Switch
======

--fit_reducer

Description
===========

Reduces a feature space to clusters.

Argument and Default Value
==========================

If --n_components is not specified then the default number of clusers is 24 (when applicable).

Details
=======

Using :doc:`fwflag_model` one can specify the following clustering algorithms:

	* NMF - Non:doc:`fwflag_Negative` matrix factorization by Projected Gradient (NMF)
	* PCA - (Principal component analysis) Linear dimensionality reduction using Singular Value Decomposition of the data and keeping only the most significant singular vectors to project the data to a lower dimensional space.
	* SPARSEPCA - (Sparse Principal Components Analysis) Finds the set of sparse components that can optimally reconstruct the data. The amount of sparseness is controllable by the coefficient of the L1 penalty.
	* LDA - (Linear Discriminant Analysis) A classifier with a linear decision boundary, generated by fitting class conditional densities to the data and using Bayes’ rule.
	* KMEANS - K:doc:`fwflag_Means` clustering
	* DBSCAN - (Density:doc:`fwflag_Based` Spatial Clustering of Applications with Noise) Finds core samples of high density and expands clusters from them. Good for data which contains clusters of similar density.
	* SPECTRAL - Apply clustering to a projection to the normalized laplacian. In practice Spectral Clustering is very useful when the structure of the individual clusters is highly non:doc:`fwflag_convex` or more generally when a measure of the center and spread of the cluster is not a suitable description of the complete cluster. For instance when clusters are nested circles on the 2D plan.
	* GMM - (Gaussian Mixture Model)

Other Switches
==============

Required Switches:

* :doc:`fwflag_d`, :doc:`fwflag_c`, :doc:`fwflag_t` 
* :doc:`fwflag_f` FEATURE_TABLE_NAME 
* :doc:`fwflag_model` nmf, pca, sparsepca, lda, kmeans, dbscan, spectral or gmm

Optional Switches:

* :doc:`fwflag_n_components` N
* :doc:`fwflag_group_freq_thresh` GROUP_THRESH
* --save_models
* --load_models
* :doc:`fwflag_picklefile` FILE_NAME
* :doc:`fwflag_sparse`
* :doc:`fwflag_no_standardize` 

Example Commands
================

.. code-block:: bash


	# General syntax
	dlatkInterface.py -d <DATABASE> -t <TABLE> -c <> -f <FEATURE_TABLE> --fit_reducer --model <MODEL_NAME> 

	# Example command
	dlatkInterface.py -d primals -t primals_new -c dp_id -f 'feat$1to3gram$primals_new$dp_id$16to1$0_0001' --fit_reducer --model spectral --group_freq_thresh 100