**STAT 339: HOMEWORK 7 (CLUSTERING) **

美国统计代写推荐 You may also use any typesetting software to prepare your writeup, but the fifinal document should be a PDF. LATEXis highly encouraged.

**Instructions. 美国统计代写推荐**

The final grade will be calculated based on the highest of the following three calculations:

(1) 50% of the average of the top fifive out of seven homework grades + 20% of the project grade + 20% of the midterm grade + 10% of the participation grade (closest to the syllabus: two lowest homeworks dropped, and relative weight of homework preserved)

(2) 60% of the average of the top fifive out of seven homework grades + 15% of the project grade + 15% of the midterm grade + 10% of the participation grade (increased weight to homeworks if they are higher than the average of the midterm and project grades)

(3) 50% of the average of the top four out of six homework grades + 20% of the project grade + 20% of the midterm grade + 10% of the participation grade(equivalent to if this homework didn’t exist) 美国统计代写推荐

Create a directory called hw7 in your stat339 GitHub repo. Your main writeup should be called hw7.pdf.

You may also use any typesetting software to prepare your writeup, but the fifinal document should be a PDF. LATEXis highly encouraged.

I will access your work by cloning your repository; make sure that any fifile path information is written relative to your repo – don’t use absolute paths on your machine,

or the code won’t run for me!

All data fifiles referred to in the problems below can be found at

http://colindawson.net/data/<filename>.csv.

*Date*: Due alongside the fifinal project.

### 1.Clustering with a Mixture of Normals Model

Consider a mixture of *D*-dimensional multivariate Normals model with *K *mixture components, where *K *is specifified in advance.

The data consists of *N *vectors, **x**1*, . . . , ***x***N *, each with *D *real-valued entries, representing observable features.

**1.Implementing K-Means **Write a function called kmeans(), which takes the following arguments:

X the *N **× **D *data matrix **X **to be clustered

K an integer (2 or larger) giving the number of clusters

initialization the name of an initialization function (which in turn can be expected to take a data matrix X and a number of clusters K, and perhaps other optional inputs)

The function should do the following: 美国统计代写推荐

(i) Standardize the columns of **X **as *z*-scores, so that Euclidean distances between points and centers are meaningful (store the means and standard deviations so that the cluster centers can be transformed back at the end)

(ii) Call the initialization function passed to intialization= to get an initial assignment of points to clusters

(iii) Run one pass of *K*-means, terminating when **z **stops changing

Your function should return a dictionary with the following entries:

“z” an array of cluster indicators

“mu” a *K **× **D *array whose rows are cluster centers

**2.Plotting Clusters 美国统计代写推荐**

Write a plotting function, plotClusters(), with the following arguments:

X an *N **× **D *dataset **X **

z the vector **z **indicating the cluster assignment of each data points in X

mu a *K **× **D *matrix whose *k*th row is the mean vector for cluster *k *

dims a tuple of two column indices, *d*1 and *d*2, each between 1 and D, which defifine the plot axes

The function should produce a scatterplot of columns *d*1 and *d*2 of the data, color coded by the clusters assigned in **z**, overlaying the *d*1 and *d*2 coordinates of the cluster centers in a distinct symbol.

**3.Testing K-Means with the Iris Data **Test your kmeans() and plotting function using the iris data (complete data with labels in iris.csv, or if you prefer,features only in iris features.csv and labels only in iris labels.csv), including the plot corresponding to the best of 10 runs of the function

**4.Mixture of Normals with EM for Maximum Likelihood 美国统计代写推荐**

** **Write a function,clusterNormalMix() that does clustering with a mixture of Normals model, with parameters estimated using local Maximum Likelihood as per the EM algorithm.The unknown parameters are ** θ**, which consists of

*D*-dimensional mean vectors for each cluster:1*µ**, . . . ,**µ**K**D**×**D*covariance matrices for each cluster,**Σ**1*, . . . ,***Σ***K*

and ** π**, the vector of mixture weights:

**= (**

*π**π*1

*, . . . ,*

*π**K*) where each entry

*π*

*k*is scalar, and the entries collectively must sum to 1

The inputs to your function should be the same as the inputs for the K-means function, plus an additional input to specify the covariance structure of the Normals. This covariance argument should allow the following possibilities:

“fixed” No learning of covariance matrices (e.g., just set the covariances to the identity matrix in the “standardized”feature space)

“diagonal” Each **Σ***k *is assumed diagonal, but the diagonal entriescan vary by cluster (*k*) and by feature (*d*). This is equivalent to making the “naive Bayes” approximation, in which each feature is conditionally independent of the others given the cluster assignment — that is, each coordinate of **x***n *is modeled as an independent univariate Normal distribution whose parameters are estimated separately within each M step

#### “unconstrained” The covariance matrices can have arbitrary structures,with the entries estimated as per the equations shown in class

The return value should also be a dictionary with the following entries:

“params” a dictionary containing the fifinal parameter estimates,with entries, “pi”, “mu”, and “Sigma”, which are a one dimensional array of length *K*, a *K **×**D *array whose rows are the cluster means, and a *K **× **D **× **D *array of cluster covariances, respectively

“Q” The *N **× **K *array of “soft” clustering assignments based on the fifinal parameter estimates (essentially, do one extra “E”-step after convergence and return the **Q **matrix)

“logLik” An array of “per point” log likelihoods (i.e., overall log likelihood divided by sample size); one entry per iteration(for diagnostic purposes — the values should be strictly decreasing if there is not a bug in the code)

“lwr” An array whose entries consist of the Jensen’s inequality lower bound on the log likelihood computed at each iteration (also for diagnostic purposes)

**5.Testing the using the Iris dataset 美国统计代写推荐**

** **Test the Mixture of Normals model with EM estimated Maximum Likelihood on the iris data for a few small values of *K *(say between 2 and 5), plotting the log likelihood and lower bound over iterations,as well as plotting the data (two features at a time), color-coded by the most likely cluster (if you’re feeling fancy you could try to use a blended color scheme that takes soft clustering into account, but you don’t have to do this). Comment on what effffect the choice of *K *has on the fifinal log likelihood.

**6.Cross-Validation Using Log Likelihood **Implement *J*-fold cross-validation to select the best value of *K *for the EM case, using the per-point log likelihood of the validation set as the performance metric. You should be able to call your code from HW1, passing clusterNormalMix as the training function, and a function that calculates the per point log likelihood as the “error” metric.

Your code should plot the training set and validation set per-point log likelihoods.

Does your cross-validation procedure select a value of *K *which is close to the true number of iris species in the data (i.e., *K *= 3)?

**7.Applying clustering to cancer microarray data **

This problem should not require any new implementation; just applying what you did in the previous problem to a more interesting dataset.

The fifile nci60 reduced.csv comes from a dataset in which cell lines from 64 cancerous tumors were analyzed using a “microarray”, which measures the degree to which particular genes are expressed in the sample. In the original data, gene expression measurements were taken from every cell line. However, we do not want to cluster data with this many dimensions using the simple techniques we have learned; so I have preprocessed the data using Principle Components

Analysis (PCA) to reduce the number of dimensions to 4. 美国统计代写推荐

(a) Using a mixture of Normals, estimating parameters using EM, and using 10-fold cross-validation, fifind a suitable number of clusters for this data, and plot the fifirst two dimensions of the data, labeling by highest probabilty cluster for your fifinal choice of *K*.

(b) The diagnosed cancer types are listed in the fifile nci60 labels.csv. Inves tigate the extent to which your clusters line up with the human-provided catgories. (There is no one correct way to measure this; be creative)

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