Create a random vector with 35 elements using the function runif()

INSTRUCTIONS TO CANDIDATES

ANSWER ALL QUESTIONS

Question 1: 

A). R-markdown

Create a Rmarkdown File (name it as “R Assignment”)

Keep knitting by answring following questions..

Question 2: Manipulating Arrays 

A). Set a variable student no equal to your student number (as an integer). Run the following code to

seed the random number generator.

set.seed(500455)

B). Create a random vector with 35 elements using the function runif(). Print your vector as below (its

entries will be different to the one below example).

My vector:

[1] 0.13657 0.84154 0.14272 0.49104 0.51371 0.12455 0.26841 0.42449

[9] 0.55263 0.23255 0.58731 0.27877 0.06764 0.45776 0.84827 0.89885

[17] 0.19844 0.29119 0.19489 0.08537 0.59395 0.05255 0.90160 0.33161

[25] 0.05589 0.14042 0.58746 0.50247 0.17255 0.67475 0.12326 0.12625

[33] 0.42804 0.00201 0.46450

C). Print the following elements of your vector. Be sure to format the result in the same way as shown

below, including the labels Minimum value, etc.

Minimum value: 0.002009551. Maximum value: 0.9015985

Average value: 0.3655426. Median value: 0.2911931

D). Choose a random number (called n) between 8 and 12 using sample(). Print the nth smallest and

nth largest value in the vector.

The 11th smallest value: 0.1427226

The 11th largest value: 0.5024719

E). Reshape your vector into a 5 by 7 matrix. Calculate the following characteristics of your matrix.

Column 3 has the largest sum of all the columns.

Row 4 has the smallest sum of all the rows.

F). Create a new matrix, which is the same as the previous matrix, except that any element m[i,j] that

is greater than 0.4 is replaced with m[i,j]-1. Print the sum of the matrix.

The sum of the new matrix is -3.20601

Question 3: Plotting 

In a normal RGB colour image, there are 3 channels, one each for red, green and blue colour. It is

organised in a 3D array, e.g. an array of size 100 * 120 * 3, for an image that is 100 (vertical) by 120

(horizontal) pixels. Then, X[ , , 1] is an array that represents the red colour component of the image.

Similarly X[ , , 2] is for green and X[ , , 3] for blue.

A hyperspectral image (HSI) has more than three colour components. Each pixel in a HSI is a spectrum: it

represents the brightness of the image in particular wavelength ranges. For example, X[2,3,] is the

spectrum at the second row and the third column of the image. Here we will look at a HSI remotely sensed

by the ROSIS sensor http://www.opairs.aero/rosis_en.html. The whole data set is packed in a matlab file

called paviauni.mat which is attached separately

A). Use the function readMat() in the R package called R.matlab to read imn the matlab file. Save the

contents as a list named A, as follows.

library(R.matlab)

A <- readMat('paviauni.mat')

B). The list A has two enties. A$X is the HSI. A$ groundtruth classifies each pixel into one of 10 classes.

Print the dimensions of A$X and A$groundtruth, as follows.

A$X is 610 by 340 y 103

A$groundtruth is 610 by 340

C) For each of the classifications 0, 1, 2, ... , 9 in A$groundtruth, count how many pixels out of

the total of 610 × 340 = 207400 in the image have that classification.

Class 0: 164624 pixels

Class 1: 6631 pixels

Class 2: 18649 pixels

Class 3: 2099 pixels

Class 4: 3064 pixels

Class 5: 1345 pixels

Class 6: 5029 pixels

Class 7: 1330 pixels

Class 8: 3682 pixels

Class 9: 947 pixels

D) Produce a plot of 25 spectra randomly chosen from classes 1 to 9, plotting each class in separate

subfigure as below. Hint: consider the which() function, including the argument arr.ind.

Attachments:

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