By attending the lab and solving the assignment, a student should…

- understand the rules of the semester.
- solve possible access problems (BRUTE, forum, KOS, …).
- be able to work with the templates.
- have experience with uploading a simple problem solution to the upload system.

To fulfill this assignment, you need to submit these files (all packed in one `.zip`

file) into the upload system:

- a script for data initialisation, calling of the implemented functions and plotting of their results (for your convenience, will not be checked).`basics.ipynb`

- file with implemented methods:`basics.py`

- a method implementing the matrix manipulation tasks specified in the section Matrix manipulation`matrix_manip`

,`initial1_mean.png`

and`initial2_mean.png`

- images specified in the section Simple data task`initials_histograms.png`

** Use template of the assignment.** When preparing a zip file for the upload system, do not include any directories, the files have to be in the zip file root.

Beware of using `for`

loops! :)

We will be using the Python programming language with the NumPy library during the whole semester. Make sure you are comfortable with these so that you don't spend more time dealing with python/numpy issues than solving the assignment tasks.

For the case you are not too sure about your Python/NumPy skills, have a look here: http://cs231n.github.io/python-numpy-tutorial/, search for other materials (duckduckgo, google) or ask your teacher.

**Start by reading** General information for Python development and cloning the assignment template repository.

**We strongly recommend** using the .ipynb notebooks provided in the template. (`$ pip install jupyter`

, then `$ jupyter notebook`

in the directory containing basics.ipynb. Or you can use your favorite IDE jupyter notebook plugin.)

We provide unit tests in the assignment template, see `test_basics.py`

. To execute the tests, run `$ python -m unittest`

.
The tests are provided to simplify local development and debugging of your code. Passing all the unit tests does not automatically mean that your code will pass all BRUTE tests, feel free to write additional tests into `test_basics.py`

if needed. Make sure that all the unittests pass OK, before uploading to BRUTE.

**In the first part of today’s assignment, you will start with some simple matrix manipulation tasks.
TRY TO AVOID USING LOOPS FOR MATRIX MANIPULATION IN YOUR PROGRAM!** (some hints on how to do that here).

Although numpy has a `matrix`

class, we will not be using that. Instead, we will use the `array`

class for representing matrices, vectors, images, lists, etc. We will import numpy using

import numpy as np

Your goal is to complete a function `output = matrix_manip(A, B)`

, where `A`

and `B`

are input matrices (represented by `np.array`

). The `matrix_manip`

function should return a python dict containing the results of the operations described below.

To have some data to work with, lets use the following matrices `A`

and `B`

:

A = np.array([[16, 2, 3, 13], [ 5, 11, 10, 8], [ 9, 7, 6, 12], [ 4, 14, 15, 1]]) B = np.array([[3, 4, 9, 4, 3, 6, 6, 2, 3, 4], [9, 2, 10, 1, 4, 3, 7, 1, 3, 5]])

Your function should work on general input matrices, not only for the `A`

and `B`

shown here or for matrices with the same dimensions.

- Find the transpose of the matrix
`A`

and return it in`output['A_transpose']`

. Example result:>> output['A_transpose'] array([[16, 5, 9, 4], [ 2, 11, 7, 14], [ 3, 10, 6, 15], [13, 8, 12, 1]])

- Select the third column of the matrix
`A`

and return it in`output['A_3rd_col']`

.>> output['A_3rd_col'] array([[ 3], [10], [ 6], [15]])

**Hint:**Don't forget python and numpy use 0-based indexing. Make sure your output dimensions are correct! - Select last two rows from last three columns of the matrix A and return the matrix in
`output['A_slice']`

.>> output['A_slice'] array([[ 7, 6, 12], [14, 15, 1]])

- Find all positions in
`A`

greater then 3 and increment them by 1. Afterwards add a new column of ones to the matrix (from right). Save the result to`output['A_gr_inc']`

.>> output['A_gr_inc'] array([[17, 2, 3, 14, 1], [ 6, 12, 11, 9, 1], [10, 8, 7, 13, 1], [ 5, 15, 16, 1, 1]])

**Hint:**Try`>`

operator on the whole matrix. The output dtype should be the same as the input dtype. Some numpy functions do not make copies of the inputs, but return 'views' of the input arrays instead. Make sure you don't corrupt the other results when computing`output['A_gr_inc']`

- Create matrix
`C`

such that $C_{i,j} = \sum_{k=1}^n A\_gr\_inc_{i,k} \cdot (A\_gr\_inc^T)_{k,j}$ and store it in`output['C']`

.>> output['C'] array([[499, 286, 390, 178], [286, 383, 351, 396], [390, 351, 383, 296], [178, 396, 296, 508]])

**Hint:**No loops are needed, use appropriate numpy matrix function. Try it on a paper with a 2×2 matrix. - Compute $\sum_{c=1}^n c \cdot \sum_{r=1}^m A\_gr\_inc_{r,c}$, store in
`output['A_weighted_col_sum']`

:>> output['A_weighted_col_sum'] 391

**Hint:**Look at`np.arange`

and`np.sum`

. Finally convert the output to Python float (as indicated in the docstring) by calling`float(…)`

. - Subtract a vector $(4,6)^T$ from all columns of matrix
`B`

. Save the result to matrix`output['D']`

.>> output['D'] array([[-1, 0, 5, 0, -1, 2, 2, -2, -1, 0], [ 3, -4, 4, -5, -2, -3, 1, -5, -3, -1]])

**Hint:**numpy broadcasting. - Select all column vectors in the matrix
`D`

, which have greater euclidean length than the average length of column vectors in`D`

. Store the results in`output['D_select']`

>> output['D_select'] array([[ 0, 5, 0, -2], [-4, 4, -5, -5]])

**In this part of the assignment, you are supposed to work with a simple input data which contains images of letters. We will use similar data structures later on during the labs. Do the following:**

- The following variables are stored in the
`data_33rpz_basics.npz`

data file:`images`

(3D array of 2000 10×10 grayscale images)`alphabet`

(letters contained in the`images`

, not full alphabet is included)`labels`

(indexes of the`images`

into`Alphabet`

array).

- Load and access them as follows
loaded_data = np.load("data_33rpz_basics.npz") loaded_data['images']

- Have look at the image with the montage function supplied in the template:
import matplotlib.pyplot as plt plt.imshow(montage(images), cmap='gray') plt.show()

**Hint:**Try to use%matplotlib notebook

after importing matplotlib. - For a given letter, compute its mean image. This means taking all images in the dataset displaying that letter, and making pixel-wise mean.
**Use your name initials**(if present in the dataset) and save them as`initial1_mean.png`

and`initial2_mean.png`

(use any letter if any of your initials is not present in the dataset).**Round the mean image**to integers and return it in the`uint8`

type.**hint**: Image generation is already prepared in`basics.ipynb`

- For the purpose of mean image calculation, complete the function
`compute_letter_mean`

:letter_mean = compute_letter_mean(letter_char, alphabet, images, labels)

where`letter_char`

is a character (e.g. 'A', 'B', 'C') representing the letter whose mean we want to compute,`alphabet`

,`images`

and`labels`

are loaded from the provided data, and`letter_mean`

is the resulting mean image.

- Compute features (from images) for all occurrences of a given letter. For a single image, it is an image feature
*x*- a single number characterizing an image. It is defined asx = sum of pixel values in the left half of image - sum of pixel values in the right half of image

**warning:**The images are stored in unsigned type (uint8), make sure to convert the values to suitable signed type before doing the subtraction. E.g.`np.int32(sum_left) - np.int32(sum_right)`

.

Complete a function for the features computation:lr_features = compute_lr_features(letter_char, alphabet, images, labels)

where`letter_char`

is a character representing the letter whose feature histogram we want to compute,`alphabet`

,`images`

and`labels`

are loaded from the provided data, and`lr_features`

is the resulting vector of features for a given letter.- For reference the following feature vector was computed for a letter A
>> compute_lr_features('A', alphabet, images, labels) array([ 120 1223 -144 -161 197 -2921 -998 -944 -120 -304 -884 -1461 -1233 1444 1705 1332 881 212 92 319 -3104 -2829 255 1 -1763 2230 1916 -335 -257 -3568 -5204 -1144 -641 525 182 -768 -844 1536 1139 522 495 353 -251 1345 439 1114 -2087 -107 -563 1491 -1935 -1640 1979 2215 906 1726 1332 365 825 2776 1282 708 1010 429 1141 1145 1896 7 -642 -657 36 368 1079 79 -483 327 -135 888 2270 2211 3860 1248 1371 -857 100 -134 -946 1954 1979 -1575 -837 1363 803 546 -1916 -1808 370 -435 -363 497])

- Plot feature histograms of your initials into one figure to compare them and save the figure as
`initials_histograms.png`

.- Code for plotting histograms already prepared for you as
`plot_letter_feature_histogram(features_1, features_2, letters)`

courses/be5b33rpz/labs/01_intro/start.txt · Last modified: 2022/10/10 12:49 by serycjon