[SOLVED] 代写 R algorithm —

—
title: “ST340 Programming for Data Science”
author: “Assignment 2”
date: “Released: Monday week 5, 2019-10-28; Deadline: 12:00 on Monday week 8, 2019-11-18.”
output: pdf_document
—

# Instructions
* Work individually.
* Specify your student numbers and names on your assignment.
* Any programming should be in R. Your report should be created using R markdown. Submit a single knitted pdf document which includes any code you have written.

## Q1 Expectation Maximization

For the EM algorithm with the mixture of Bernoullis model, we need to maximize the function
[
f(boldsymbol{mu}_{1:K})=f(boldsymbol{mu}_{1},ldots,boldsymbol{mu}_{K})=sum_{i=1}^{n}sum_{k=1}^{K}gamma_{ik}log p(boldsymbol{x}_i|boldsymbol{mu}_{k}),
]
where for $iin{1,ldots,N}$ each $boldsymbol{x}_iin{0,1}^{p}$, for $kin{1,ldots,K}$ each $boldsymbol{mu}_{k} = (mu_{k1},mu_{k2},ldots,mu_{kp})in[0,1]^{p}$, and
[
p(boldsymbol{x}_i|boldsymbol{mu}_{k})=prod_{j=1}^{p}mu_{kj}^{x_{ij}}(1-mu_{kj})^{1-x_{ij}}.
]

(a) Show that the unique stationary point is obtained by choosing for each $kin{1,ldots,K}$
[
boldsymbol{mu}_{k}=frac{sum_{i=1}^{n}gamma_{ik}boldsymbol{x}_i}{sum_{i=1}^{n}gamma_{ik}}.
]
(b) The newsgroups dataset contains binary occurrence data for 100 words across 16,242 postings. Postings are tagged by their highest level domain; that is, into four broad topics `comp.*`, `rec.*`, `sci.*`, `talk.*`. The dataset includes `documents`, a $16,242 times 100$ matrix whose $(i,j)$th entry is an indicator for the presence of the $j$th word in the $i$ post; `newsgroups`, a vector of length $16,242$ whose $i$th entry denotes the true label for the $i$th post (i.e., to which of the four topics the $i$th post belongs); `groupnames`, naming the four topics; and `wordlist`, listing the 100 words.
(i) Run the EM algorithm for the mixture of Bernoullis model on the newsgroups data with $K=4$. You should use some of the code from the EM Lab to help you. A run on the newsgroups dataset could take over 10 minutes so it is recommended to test your code on a small synthetic dataset first.
(ii) Comment on the clustering provided by your run of the algorithm. Can you measure its accuracy?

## Q2 Two-armed Bernoulli bandits

(a) Implement both Thompson sampling and the $epsilon$-decreasing strategy in this setting with the unknown success probabilities of the arms being $0.6$ and $0.4$.
(b) Describe the behaviour of $epsilon$-decreasing when the sequence $(epsilon_{n})_{ngeq1}$ is defined by $epsilon_{n}=min{1,Cn^{-1}}$, where $C$ is some positive constant, and check whether it is consistent with your implementation.
(c) Describe the behaviour of $epsilon$-decreasing when the sequence $(epsilon_{n})_{ngeq1}$ is defined by $epsilon_{n}=min{1,Cn^{-2}}$, where $C$ is some positive constant, and check whether it is consistent with your implementation.
(d) Compare and contrast the implementations of $epsilon$-decreasing and Thompson sampling for this problem.

## Q3 $k$ nearest neighbours

(a) Create a function to do $k$NN regression using a user-supplied distance function, i.e.
“`{r eval=FALSE}
knn.regression.test <- function(k,train.X,train.Y,test.X,test.Y,distances) {# YOUR CODE HEREprint(sum((test.Y-estimates)^2))}“`Predicted labels should use the inverse-distance weighting to each neighbour.(b) Test your function on the following two toy datasets using `distances.l1` from lab 6. Try different values of $k$ and report your results.**Toy dataset 1**:“`{r eval=FALSE}n <- 100train.X <- matrix(sort(rnorm(n)),n,1)train.Y <- (train.X < -0.5) + train.X*(train.X>0)+rnorm(n,sd=0.03)
plot(train.X,train.Y)
test.X <- matrix(sort(rnorm(n)),n,1)test.Y <- (test.X < -0.5) + test.X*(test.X>0)+rnorm(n,sd=0.03)
k <- 2knn.regression.test(k,train.X,train.Y,test.X,test.Y,distances.l1)“`**Toy dataset 2**:“`{r eval=FALSE}train.X <- matrix(rnorm(200),100,2)train.Y <- train.X[,1]test.X <- matrix(rnorm(100),50,2)test.Y <- test.X[,1]k <- 3knn.regression.test(k,train.X,train.Y,test.X,test.Y,distances.l1)“`(c) Load the Iowa dataset (see `?lasso2::Iowa` for details). Try to predict the yield in the years 1931, 1933, … based on the data from 1930, 1932, …“`{r eval=FALSE}install.packages(“lasso2”)library(“lasso2”)data(Iowa)train.X=as.matrix(Iowa[seq(1,33,2),1:9])train.Y=c(Iowa[seq(1,33,2),10])test.X=as.matrix(Iowa[seq(2,32,2),1:9])test.Y=c(Iowa[seq(2,32,2),10])k <- 5knn.regression.test(k,train.X,train.Y,test.X,test.Y,distances.l2)“`(d) Try different values of $k$, and compare your results with ordinary least squares regression and ridge regression.