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A tiny lib with pocket-sized implementations of machine learning models in NumPy, most of which will fit in a tweet.
| Date | Stars |
|---|---|
| 2026-07-31 | 571 |
| 2026-08-05 | 571 |
| 2026-08-06 | 571 |
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# NapkinML
## About
Pocket-sized implementations of machine learning models, most of which will fit in a tweet.
## Table of Contents
- [NapkinML](#napkinml)
* [About](#about)
* [Table of Contents](#table-of-contents)
* [Installation](#installation)
* [Implementations](#implementations)
+ [K-Means](#k-means)
+ [K-Nearest Neighbors](#k-nearest-neighbors)
+ [Linear Regression](#linear-regression)
+ [Linear Discriminant Analysis](#linear-discriminant-analysis)
+ [Logistic Regression](#logistic-regression)
+ [Multilayer Perceptron](#multilayer-perceptron)
+ [Principal Component Analysis](#principal-component-analysis)
## Installation
$ git clone https://github.com/eriklindernoren/NapkinML
$ cd NapkinML
$ sudo python setup.py install
## Implementations
### K-Means
```python
class KMeans:
def fit(self, X, k, n_iter=200):
centers = random.sample(list(X), k)
for i in range(n_iter):
clusters = np.argmin(cdist(X, centers), axis=1)
centers = np.array([X[clusters == c].mean(0) for c in clusters])
return clusters
```
```
$ python napkin_ml/examples/kmeans.py
```
<p align="center">
<img src="http://eriklindernoren.se/images/napkin_kmeans.png" width="640">
</p>
<p align="center">
Figure: K-Means clustering of the Iris dataset.
</p>
### K-Nearest Neighbors
```python
class KNN:
def predict(self, k, Xt, X, y):
idx = np.argsort(cdist(Xt, X))[:, :k]
y_pred = [np.bincount(y[i]).argmax() for i in idx]
return y_pred
```
```
$ python napkin_ml/examples/knn.py
```
<p align="center">
<img src="http://eriklindernoren.se/images/napkin_knn.png" width="640">
</p>
<p align="center">
Figure: Classification of the Iris dataset with K-Nearest Neighbors.
</p>
### Linear Regression
```python
class LinearRegression:
def fit(self, X, y):
self.w = np.linalg.lstsq(X, y, rcond=None)[0]
def predict(self, X):
return X.dot(self.w)
```
```
$ python napkin_ml/examples/linear_regression.py
```
<p align="center">
<img src="http://eriklindernoren.se/images/napkin_linreg.png" width="640">
</p>
<p align="center">
Figure: Linear Regression.
</p>
### Linear Discriminant Analysis
```python
class LDA:
def fit(self, X, y):
cov_sum = sum([np.cov(X[y == val], rowvar=False) for val in [0, 1]])
mean_diff = X[y == 0].mean(0) - X[y == 1].mean(0)
self.w = np.linalg.inv(cov_sum).dot(mean_diff)
def predict(self, X):
return 1 * (X.dot(self.w) < 0)
```
### Logistic Regression
```python
class LogisticRegression:
def fit(self, X, y, n_iter=4000, lr=0.01):
self.w = np.random.rand(X.shape[1])
for _ in range(n_iter):
self.w -= lr * (self.predict(X) - y).dot(X)
def predict(self, X):
return sigmoid(X.dot(self.w))
```
```
$ python napkin_ml/examples/logistic_regression.py
```
<p align="center">
<img src="http://eriklindernoren.se/images/napkin_logreg.png" width="640">
</p>
<p align="center">
Figure: Classification with Logistic Regression.
</p>
### Multilayer Perceptron
```python
class MLP:
def fit(self, X, y, n_epochs=4000, lr=0.01, n_units=10):
self.w = np.random.rand(X.shape[1], n_units)
self.v = np.random.rand(n_units, y.shape[1])
for _ in range(n_epochs):
h_out = sigmoid(X.dot(self.w))
out = softmax(h_out.dot(self.v))
self.v -= lr * h_out.T.dot(out - y)
self.w -= lr * X.T.dot((out - y).dot(self.v.T) * (h_out * (1 - h_out)))
def predict(self, X):
return softmax(sigmoid(X.dot(self.w)).dot(self.v))
```
```
$ python napkin_ml/examples/mlp.py
```
<p align="center">
<img src="http://eriklindernoren.se/images/napkin_mlp1.png" width="640">
</p>
<p align="center">
Figure: Classification of the Iris dataset with a Multilayer Perceptron <br> with one hidden layer.
</p>
### Principal Component Analysis
```python
class PCA:
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matched fp:962d67796794f0e9, llm:Repository description: "A tiny lib with pocket-sized implementations of machine learning models in NumPy, most of which will fit in a tweet." (Python)
matched fp:962d67796794f0e9, llm:Repository description: "A tiny lib with pocket-sized implementations of machine learning models in NumPy, most of which will fit in a tweet." (Python)