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--- cs401r_w2016:lab13 [2016/01/13 17:14]
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+++ cs401r_w2016:lab13 [2016/02/08 23:14]
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@@ Line 6: / Line 6: @@
 ====Deliverable:====
-For this lab, you will implement the Expectation Maximization algorithm on the Old Faithful dataset.  This involves learning the parameters of a Gaussian mixture model.  Your notebook should produce a visualization of the progress of the algorithm.  The final figure should look something like this:
+For this lab, you will implement the Expectation Maximization algorithm on the Old Faithful dataset.  This involves learning the parameters of a Gaussian mixture model.  Your notebook should produce a visualization of the progress of the algorithm.  The final figure could look something like this (they don't have to be arranged in subplots):
-{{ :cs401r_w2016:lab5_em.png?direct&300|}}
+{{:cs401r_w2016:lab5_em.png?direct&600|}}
 ----
 ====Grading:====
-Your notebook will be
+Your notebook will be graded on the following elements:
+  * 10% Data is correctly mean-centered
+  * 20% Correctly updates responsibilities
+  * 20% Correctly updates means
+  * 20% Correctly updates covariances
+  * 20% Correctly updates mixing weights
+  * 10% Final plot(s) is tidy and legible
 ----
 ====Description:====
+For this lab, we will be using the Expectation Maximization (EM) method to **learn** the parameters of a Gaussian mixture model.  These parameters will reflect cluster structure in the data -- in other words, we will learn probabilistic descriptions of clusters in the data.
+For this lab, you will use the Old Faithful dataset, which you can download here:
+[[http://hatch.cs.byu.edu/courses/stat_ml/old_faithful.mat|Old Faithful dataset]]
+The equations for implementing the EM algorithm are given in MLAPP 11.4.2.2 - 11.4.2.3.
+The algorithm is:
+  - Compute the responsibilities $r_{ik}$ (Eq. 11.27)
+  - Update the mixing weights $\pi_k$ (Eq. 11.28)
+  - Update the means $\mu_k$ (Eq. 11.31)
+  - Update the covariances $\Sigma_k$ (Eq. 11.32)
+Now, repeat until convergence.
+Since the EM algorithm is deterministic, and since precise initial conditions for your algorithm are given below, the progress of your algorithm should closely match the reference image shown above.
+**Note: To help our TA better grade your notebook, you should use the following initial parameters:**
+<code python>
+# the Gaussian means (as column vectors -- ie, the mean for Gaussian 0 is mus[:,0]
+mus = np.asarray( [[-1.17288986, -0.11642103],
+                   [-0.16526981,  0.70142713]])
+# the Gaussian covariance matrices
+covs = list()
+covs.append(
+    np.asarray([[ 0.74072815,  0.09252716],
+                [ 0.09252716,  0.5966275 ]]) )
+covs.append(
+    np.asarray([[ 0.39312776, -0.46488887],
+                [-0.46488887,  1.64990767]]) )
+# The Gaussian mixing weights
+mws = [ 0.68618439, 0.31381561 ]  # called alpha in the slides
+</code>
 ----
@@ Line 33: / Line 79: @@
     pmat = u*2.447*np.sqrt(s) # 95% confidence
     return np.dot(  pmat, sf )
+</code>
+Here are some additional python functions that may be helpful to you:
+<code python>
+# compute the likelihood of a multivariate Gaussian
+scipy.stats.multivariate_normal.pdf
+# scatters a set of points; check out the "c" keyword argument to change color, and the "s" arg to change the size
+plt.scatter
+plt.xlim # sets the range of values for the x axis
+plt.ylim # sets the range of values for the y axis
+# to check the shape of a vector, use the .shape member
+foo = np.random.randn( 100, 200 )
+foo.shape # an array with values (100,200)
+# to transpose a vector, you can use the .T operator
+foo = np.atleast_2d( [42, 43] ) # this is a row vector
+foo.T # this is a column vector
+import numpy as np
+np.atleast_2d
+np.sum
 </code>

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