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Homework Unusupervised Learning: 
  The K-means algorithm and Mixture of Gaussian 
  estimation with Expectation-Maximization algorithm.

The purpose of this assignment is to implement the K-means
algorithm and the Mixture of Gaussians estimation using
the Expectation-Maximization algorithm.

Your assignment should be sent by email to the TA
- Send your email in plain text (no msword, no html, no postscript, no pdf).
- you must implement the code by yourself, but you are 
  encouraged to discuss your results with other students.
- Include your source code as attachment(s).
- modify file main.lsh

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* PART 1: K-means

* QUESTION 1.1: Implementing K-means
 The K-means algorithm is as follows.
 Given a set of P training vectors X1...Xp
 - initialize K prototype vectors P1...Pk
   by setting each of them to a different
   randomly chosen training sample
 - repeat until convergence:
   - for all i in [1..p], for all j in [1..k],
     compute the responsabilities 
     Rij = 1 if Xi is closest to Pj than to any other prototype
     Rij = 0 otherwise.
   - recompute the prototypes so that each prototype is
     equal to the mean of all the samples assigned to it
     Pj = 1/[sum_i Rij] sum_i Rij.Xi

* QUESTION 1.2: : Applying K-means to Image compression 
 A simple technique for image compression, called vector quantization,
 consists in (1) cutting up an image into small non-overlapping tiles
 (e.g. 8 by 8 pixels), (2) viewing this set of tiles as a dataset of
 64-dimensional vectors (each vector component being a pixel) (3)
 running the K-means algorithm on this dataset (4) coding each tile by
 a code that represents the index of the nearest prototype to the
 tile.

 the image testimage.pgm (800x600 pixels, grayscale)
 was cut up into 7500 non-overlapping, 8x8-pixel tiles. Those tiles 
 were turned into 64-dimensional vectors (by lining up the pixels in 
 raster order: left to right and top to bottom), and put together into 
 the Lush .mat file "tiles.mat". The tiles are in raster order (left 
 to right, top to bottom), so reconstructing the image consists 
 in painting those tiles next to each other on a 100 columns by
 75 line grid. 

 Run the K-means algorithm with K=2, 4 and 8 on that set 
 (be careful how you initialize the prototypes).
 For each size, report the mean-squared error incurred by
 replacing each tile by its closest prototype, i.e. the
 average of ||Xi - Tk(i)||^2 over i, where Xi is the i-th tile,
 and Tk(i) is the prototype (among the K prototypes) that 
 is closest to Xi.

* QUESTION 1.3: reconstruct the "compressed" test image obtained by replacing
 each tile by its closest prototype. Save the image in a pgm file.
 Saving a PGM image file can be done in Lush with 
 (libload "libimage/pnm")
 (pgm-write-ubim "image.pgm" image) 
 where image is a 600x800 ubyte-matrix.  Attach the
 reconstructed image to your homework email.

 NOTE: grayscale and RGB images can be displayed in a 
 Lush graphic window with
 (new-window 0 0 800 600 "stuff")
 (rgb-draw-matrix 0 0 image)

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* PART 2: Mixture of Gaussian model and EM

* QUESTION 2.1 Implement EM to estimate the parameters of a Mixture of Gaussians

 NOTE: the Lush code for a single multivariate Gaussian 
   is provided in the file gaussian.lsh 

 Given a set of P training vectors X1...Xp
 the likelihood of the data modeled by a MoG with k 
 component gaussians is equal to:
   P(X1...Xp | W1...Wk,M1...Mk,A1...Ak) = product_i sum_j Wj.gaussian(Xi,Mj,Aj)
   where i ranges over [1,p], and j over [1,k] (k is the
   number of components in the mixture). gaussian(Xi,Mj,Aj) is
   a multivariate Gaussian density with mean Mj and covariance matrix 
   Aj evaluated at point Xi. To ensure normalization, the Wj must be
   between 0 and 1, and must verify:  sum_j Wj = 1

 The EM algorithm for a MoG is as follows:
 - initialize K prototype vectors P1...Pk
   by running the K-means algorithm.
 - repeat until convergence:
   - for all i in [1..p], for all j in [1..k],
     compute the responsabilities 
     Rij = Wj*gaussian(Xi,Mj,Aj) / sum_h Wh*gaussian(Xi,Mh,Ah)
   - recompute the parameters of the gaussians:
     - set Mj to the mean of all the samples *weighted* by their 
       responsabilities Rij
     - set Aj to the covariance of all the samples *weighted* by
       their responsabilities Rij.
     - set Wj to [sum_i Rij]/[sum_j sum_i Rij]

 MoG trained with EM can be seen as a "soft" version of K-means
 where the responsabilities are continuous numbers between 0 and
 1 (that sum to one over j) instead of being binary.


* QUESTION 2.2: Testing EM for a Mixture of Gaussians.  Starting from the
 result obtained for question 4 (result of K-means with K=8 on the
 tile dataset) train a mixture of Gaussians with 8 components using EM
 on the tile dataset.  EM finds the parameters that minimize the
 negative log-likelihood of the dataset given the model: 
 L = - sum_i log( ModelProb(Xi) ) 
 Report the initial value of the negative log-likelihood (after running
 K-means and one M step to compute the covariance matrices, using the
 binary responsabilities produced by K-means).  and the value at the
 end of the convergence of EM.