update README

LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) [2020] [Maksym Shylo and Ruslan Khomenko]
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

@@ -1,25 +1,67 @@
 # Pattern Recognition
 
 **Table of contents**
-- [Decoding of a noised number](#lab-1--decoding-of-a-noised-number)
-- [Risk minimization](#lab-2--risk-minimization)
-- [Risk minimization](#lab-3--risk-minimization)
-- [Analysis of Nonbayesian Strategy](#lab-4--analysis-of-nonbayesian-strategy)
-- [Efficient computation of a sum of a subarray in 1D](#lab-5--efficient-computation-of-a-sum-of-a-subarray-in-1d-2d-and-3d)
-- [Divisibility sum of digits](#lab-6--divisibility-sum-of-digits)
-- [Binocular stereo vision](#lab-7--binocular-stereo-vision)
-- [Binary Clustering](#lab-8--binary-clustering)
-- [Finding the separating circle](#lab-9--finding-the-separating-circle)
+
+- [Lab 1. Decoding of a noisy number](#lab-1--decoding-of-a-noisy-number)
+- [Lab 2. Risk minimization (interval loss function)](#lab-2--risk-minimization-interval-loss-function)
+- [Lab 3. Risk minimization (l1 loss function)](#lab-3--risk-minimization-l1-loss-function)
+- [Lab 4. Analysis of Nonbayesian Strategy](#lab-4--analysis-of-nonbayesian-strategy)
+- [Lab 5. Efficient computation of a sum of a subarray in 1D, 2D, 3D](#lab-5--efficient-computation-of-a-sum-of-a-subarray-in-1d-2d-and-3d)
+- [Lab 6. Divisibility sum of digits](#lab-6--divisibility-sum-of-digits)
+- [Lab 7. Binocular stereo vision](#lab-7--binocular-stereo-vision)
+- [Lab 8. Binary Clustering](#lab-8--binary-clustering)
+- [Lab 9. Finding the separating circle](#lab-9--finding-the-separating-circle)
 
 **Tasks and mathematical solutions**
 
-- [solutions.pdf](`all-labs.pdf`)
-- [tasks.pdf](`tasks.pdf`)
+- [solutions.pdf](solutions.pdf).
+- [tasks.pdf](tasks.pdf);
+
+The tasks document is the 
+intellectual property of Valerii Krygin ([definability](https://github.com/definability)) and 
+is included in this repository with explicit permission.
+
+## Acknowledgements
+
+We would like to express our sincere gratitude to the course instructor, 
+Valerii Krygin ([definability](https://github.com/definability)), <!-- (a.k.a. `Boss of this Gym` ♂️💪) --> 
+for the tremendous effort, dedication, and passion invested in teaching 📚 this course. 
+His commitment to delivering high-quality material and supporting students throughout 
+the learning process has been truly invaluable.
+
+This course laid the foundation for our understanding of Python programming 🐍 and the 
+fundamentals of computer vision 👁️🖥️, opening the door to further growth in these fields.
+
+We will always remember the sleepless nights 😵‍💫 it required-<!-- to spend in the dungeon --> 
+and even more so, the lasting value, skills, and mindset it has given us for the future 🚀.
+
+<!--Thank you, master. -->
+
+## Setup
 
-## Lab 1 – Decoding of a noised number
+To run these applications, you need to have **Python3.12**.
+
+1. Clone repo.
+
+2. Create virtual environment.
+    ```bash
+    python3.12 -m venv .venv
+    ```
+
+3. Activate it
+    ```bash
+    source .venv/bin/activate
+    ```
+
+4. Install requirements:
+    ```bash
+    pip install -r requirements.txt
+    ```
+
+## Lab 1 – Decoding of a noisy number
 
 ### Description
-The program converts a number to a noised (bernoulli noise) image and then decodes it.
+The program converts a number to a noisy (Bernoulli noise) image and then decodes it.
 Bayesian strategy for a binary loss function is used.
 
 ### Usage
@@ -51,12 +93,12 @@ Time: 0.022138417001769994 sec
 Decoded number: 9
 ```
 
-| Original image                   |                Noised image                | Decoded number |
+| Original image                   |                Noisy image                 | Decoded number |
 |----------------------------------|:------------------------------------------:|----------------|
 | ![](.imgs/lab1/number_img.png)   | ![](.imgs/lab1/number_img_noised_0.56.png) | 9              |
 
 
-## Lab 2 – Risk minimization
+## Lab 2 – Risk minimization (interval loss function)
 
 ### Description
 Bayesian strategy for **interval** loss function is used.
@@ -83,7 +125,7 @@ Heatmap normalized:  [0.10251256 0.1798995  0.09246231 0.01407035 0.10653266 0.0
 Result: 4
 ```
 
-## Lab 3 – Risk minimization
+## Lab 3 – Risk minimization (L1 loss function)
 
 ### Description
 Bayesian strategy for **L1** loss function is used.
@@ -157,7 +199,7 @@ $ python subsum.py
 | ![](.imgs/lab5/benchmark_numpy.png) | ![](.imgs/lab5/benchmark_prefix.png) |
 
 
-## Lab 6 – Divisibility sum of digits 
+## Lab 6 – Divisibility sum of digits
 
 ### Description
 The program generates a number image and then apply noise and checks whether it is divisible by a given number.
@@ -199,7 +241,7 @@ Success divisions:  2/5
 time :  0.018097166999723413 sec
 ```
 
-| Image                                  |                      Noised Image                       |
+| Image                                  |                       Noisy Image                       |
 |----------------------------------------|:-------------------------------------------------------:|
 | ![](.imgs/lab6/digits_img__iter_0.png) | ![](.imgs/lab6/digits_img_noised__iter_0_div_True.png)  |
 | ![](.imgs/lab6/digits_img__iter_1.png) | ![](.imgs/lab6/digits_img_noised__iter_1_div_False.png) |
@@ -232,11 +274,14 @@ options:
 python3 stereo.py --left examples/im2.ppm --right examples/im5.ppm --alpha 10 --maxd 50
 ```
 
-| Left Image                        | Right Image             |                      Disparity Map                      |
-|-----------------------------------|-------------------------|:-------------------------------------------------------:|
-| ![](.imgs/lab7/im2.png)   | ![](.imgs/lab7/im5.png) | ![](.imgs/lab7/disparity_map.png)  |
+| Left Image                  | Right Image             |            Disparity Map             |
+|-----------------------------|-------------------------|:------------------------------------:|
+| ![](.imgs/lab7/im2.png)     | ![](.imgs/lab7/im5.png) | ![](.imgs/lab7/disparity_map.png)    |
 
-Images - http://vision.middlebury.edu/stereo/data/
+[Images](http://vision.middlebury.edu/stereo/data/): 
+D. Scharstein and R. Szeliski. A taxonomy and evaluation of dense two-frame 
+stereo correspondence algorithms.
+International Journal of Computer Vision, 47(1/2/3):7-42, April-June 2002.
 
 
 ## Lab 8 – Binary Clustering
@@ -271,7 +316,7 @@ Testing on 3893 samples...
 Test set accuracy: 0.9604
 ```
 
-## Kab 9 - Finding the separating circle
+## Lab 9 – Finding the separating circle
 
 ### Description
 Modification of Perceptron Algorithm where hyperplane is a circle (modified scalar product).
@@ -296,4 +341,9 @@ $ python3 perceptron_circle.py --n 300 --seed 31
 Original:  a=0, b=8, r=9
 Predicted: a=0.261, b=8.462, r=8.711
 ```
-![](.imgs/lab9/Figure_1.png)
+![](.imgs/lab9/Figure_1.png)
+
+## Authors
+
+- Maksym Shylo ([maksymshylo](https://github.com/maksymshylo))
+- Ruslan Khomenko ([Ruslan3584](https://github.com/Ruslan3584))