https://github.com/corentinth/ml-myknearestneighbors
[Machine Learning] My own version of the K-Nearest-Neighbors classifier.
https://github.com/corentinth/ml-myknearestneighbors
classifier knearest-neighbors-classifier machine-learning
Last synced: about 2 months ago
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[Machine Learning] My own version of the K-Nearest-Neighbors classifier.
- Host: GitHub
- URL: https://github.com/corentinth/ml-myknearestneighbors
- Owner: CorentinTh
- License: mit
- Created: 2017-05-04T19:18:22.000Z (about 8 years ago)
- Default Branch: master
- Last Pushed: 2017-10-04T08:41:57.000Z (over 7 years ago)
- Last Synced: 2025-02-13T11:35:23.118Z (3 months ago)
- Topics: classifier, knearest-neighbors-classifier, machine-learning
- Language: Python
- Homepage:
- Size: 7.81 KB
- Stars: 1
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# ML-MyKNearestNeighbors
## Overview
The `MyKNN.py` file contain my home-made version of the k-Nearest-Neighbors classifier.
By default `k = 5`, it means that the classifier will checks for the closest 5 neightbors of the data you want to test.
## Dependencies
The classifier only uses basic library (`math`, `sys` and `operator`), so you don't need dependencies to use it.
The main file (`main.py`) uses sklearn to get datasets, but you don't need it if you want to use just the classifier. Run this command to get **sklearn** :
```
pip install scikit-learn
```
## Run the script
You can run this script in terminal with this command line :
```
python main.py
```
## Use the classifier
First create the classifier.
```python
from MyKNN import *
classifier = MyKNN()
# You can change the value of k by this
classifier.k = 5
```
Then, train it.
```python
# x_train is an array of features (like [[1, 2], [5, 9], [6, 8], [2, 3]])
# y_train is an array of labels (like [ 'a' , 'b' , 'b' , 'a' ])
# Labels index must match the corresponding feature index
classifier.train(x_train, y_train)
```
And now, you can predict some output.
```python
# x_test is an array of features you want to get the label (like [[6, 8], [0, 2]])
predictions = classifier.predict(x_test)
print(predictions)
# Display : ['b', 'a']
```