https://github.com/alvii147/datacheese

Python library with implementations of popular data science and machine learning algorithms
https://github.com/alvii147/datacheese

data-science machine-learning python

Last synced: about 1 year ago
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Python library with implementations of popular data science and machine learning algorithms

• Host: GitHub
• URL: https://github.com/alvii147/datacheese
• Owner: alvii147
• License: bsd-3-clause
• Created: 2023-03-06T21:43:03.000Z (over 3 years ago)
• Default Branch: master
• Last Pushed: 2024-08-11T23:18:58.000Z (almost 2 years ago)
• Last Synced: 2025-02-16T16:57:55.810Z (over 1 year ago)
• Topics: data-science, machine-learning, python
• Language: Python
• Homepage:
• Size: 2.95 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
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    DataCheese is a Python library with implementations of popular data science and machine learning algorithms.





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## Installation

### :one: [Install Python](https://www.python.org/)

Python 3.10 or above required.

### :two: Install package using [pip](https://pypi.org/project/pip/)

Install directly from the repository:

```bash

pip3 install git+https://github.com/alvii147/DataCheese.git

```

## Usage

The `MultiLayerPerceptron` model can be used to train a feed-forward neural network using data:

```python

import numpy as np

from datacheese.neural_networks import (

    MultiLayerPerceptron,

    SigmoidLayer,

    ReLULayer,

)

# number of data patterns

n_patterns = 5

# number of feature dimensions

n_dimensions = 3

# number of target classes

n_classes = 2

# generate random data

rng = np.random.default_rng()

X = rng.random(size=(n_patterns, n_dimensions))

Y = rng.random(size=(n_patterns, n_classes))

# initialize multi-layer perceptron model

model = MultiLayerPerceptron(lr=0.5)

# add relu layer

model.add_layer(ReLULayer(n_dimensions, 4))

# add sigmoid layer

model.add_layer(SigmoidLayer(4, n_classes))

# fit model to data

model.fit(X, Y, epochs=20, verbose=1)

```

When `verbose` is non-zero, progress is logged:

```

Epoch: 0, Loss: 0.15181599599950849

Epoch: 4, Loss: 0.13701115369406147

Epoch: 8, Loss: 0.11337662383705667

Epoch: 12, Loss: 0.10121139637335393

Epoch: 16, Loss: 0.09388681525946835

```

The model can then be used to make predictions:

```python

# predict target values

Y_pred = model.predict(X)

# compute mean squared loss

print(np.mean((Y_pred - Y) ** 2))

```

This outputs the following:

```

0.05310463606057757

```

For more details, visit the [documentation pages](https://alvii147.github.io/DataCheese/build/html/index.html).