- If you are interested in these labs or you just want to suggest your OWN solution, then:
- 1️⃣. Fork the repository! How:interrobang:
- 2️⃣. Create your feature branch
- 3️⃣. Commit your changes
- 4️⃣. Push to the branch
- 5️⃣. Submit a pull request to this repository
-
First task:
Description:
Here we need to create a
space.For example : two-dimensional array, List(Vector2) or struct with two double or float points.
How to use my work as an example:
- Choose first task in main menu.
- Input from keyboard number of points in your
space.
-
Second task:
Description:
Here you need to create a feature space, calculate the points inside it, merge neighboring points into one class/set
The next task is to calculate the intraset distance.
Explanation: we multiply each point of set A to all other points of set A (the same set).
Next, we are calculating distance between the sets, this is the same, but we multiply each point of the set A by each point of the set B.
Use all formulas from here: Интеллектуальный анализ данных - 1.pptx
How to use my work as an example:
- Choose second task in main menu.
- Inject number of classes.
-
Third task:
Description:
You need to create space, fill it with x and y axis, calculate the Eulidean distance between all points and choose those that are closest
How to use my work as an example:
- Choose third task in main menu.
- Input number of neighbors you want to find.
- Input from keyboard number of points in your
space. - Choose any point from the proposed.
- Main task:
Description:
Select a multispectral image from open sources. The number of image channels must be at least 3.
Set manually the classes of areas (objects) in the image (at least 3).
Classify the image points using the Euclidean-Mahalanobis metric.