[SOLVED] 代写 R math ST340 Programming for Data Science

ST340 Programming for Data Science
Assignment 3
Released: Monday week 8, 2019-11-18; Deadline: 12:00 on Monday week 11, 2019-12-09.
Instructions
• Work individually.
• Specify your student number and name 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 Gradient descent
Here is a function that does gradient descent with a fixed number of iterations to find local minima:
Example:
(a) Write a short function that uses gradient.descent to find a local maximum. (For the purpose of this question, gradient.descent is a “black box”. Don’t worry about the printed output, just the return value matters.)
i.e.
gradient.ascent <- function(f, df, x0, iterations=1000, eta=0.2) {# … use gradient.descent(…) here …}f <-function(x) { (1+xˆ2)ˆ(-1) }gradf<-function(x) { -2*x*(1+xˆ2)ˆ(-2) }gradient.ascent(f,gradf,3,40,0.5)(b) Consider the function f : R2 → R given byf <- function(x) (x[1]-1)^2 + 100*(x[1]^2-x[2])^2i) Give a short mathematical proof that f has a unique minimum.ii) Write a function gradf to calculate ∇f, i.e. gradf <- function(x) { # … use x[1] and x[2] … } gradient.descent <- function(f, gradf, x0, iterations=1000, eta=0.2) { x<-x0for (i in 1:iterations) { cat(i,”/”,iterations,”: “,x,” “,f(x),”
“) x<-x-eta*gradf(x)}x } f <-function(x) { sum(x^2) } gradf<-function(x) { 2*x } gradient.descent(f,gradf,c(10,20),10,0.2) 1iii) Starting from the point x0=c(3,4), try to find the minimum using gradient descent. gradient.descent(f,gradf,c(3,4), … , …)(c) Write a function to do gradient descent with momentum. Starting from the point x0=c(3,4), use your function to find the minimum of the function from part (b).Q2 Support vector machinesRun the following code to load the tiny MNIST dataset:and then show some digits:(a) Use three-fold cross validation on the training set to compare SVMs with linear kernels, polynomial kernels and RBF kernels, i.e.etc. (The flag warning=FALSE is helpful here. What is the suppressed warning message warning you about?)(b) For the RBF kernels, write a grid search function that takes two lists, log.C.range and log.gamma.range, and for each pair (lc,lg) of entries in the pair of lists attempts cross-validation with parameters cost = exp(lc) and gamma=exp(lg). Once you have found the model with the best cross-validation error, train it on the full tiny’ training set and then test it on thetiny’ test set. load(“mnist.tiny.RData”) train.X=train.X/255 test.X=test.X/255 library(grid) grid.raster(array(aperm(array(train.X[1:50,],c(5,10,28,28)),c(4,1,3,2)),c(140,280)),interpolate=FALSE) library(e1071) svm(train.X,train.labels,type=”C-classification”,kernel=”linear”,cross=3)$tot.accuracy svm(train.X,train.labels,type=”C-classification”,kernel=”poly”,degree=2,coef=1,cross=3)$tot.accuracy2