mymlscratch 0.1.0

A library that returns ML algorithm source code implemented from scratch

MyMLScratch

A Python library that returns the complete source code for various machine learning algorithms implemented from scratch using pure Python and NumPy.

Installation

pip install mymlscratch

Usage

import mymlscratch

# Get Linear Regression code
code = mymlscratch.get_linear_regression_code()
print(code)

# Save to a file
with open('linear_regression.py', 'w') as f:
    f.write(code)

# Execute the code
exec(code)

# List all available algorithms
algorithms = mymlscratch.list_available_algorithms()
print(algorithms)

Available Algorithms

• Linear Regression - get_linear_regression_code()
• Logistic Regression - get_logistic_regression_code()
• K-Nearest Neighbors - get_knn_code()
• Decision Tree - get_decision_tree_code() (coming soon)
• K-Means Clustering - get_kmeans_code() (coming soon)

Features

• ✅ Pure Python/NumPy implementations
• ✅ Complete, runnable source code
• ✅ Educational comments and docstrings
• ✅ Example usage included in each algorithm
• ✅ No dependencies except NumPy

Use Cases

• Learning: Study how ML algorithms work under the hood
• Teaching: Use in educational materials and courses
• Customization: Get base code to modify for specific needs
• Prototyping: Quick algorithm implementations for experiments

Example

import mymlscratch
import numpy as np

# Get the code
code = mymlscratch.get_linear_regression_code()

# Execute it
exec(code)

# Now you can use the LinearRegression class
X = np.array([[1], [2], [3], [4], [5]])
y = np.array([2, 4, 6, 8, 10])

model = LinearRegression(learning_rate=0.01, n_iterations=1000)
model.fit(X, y)
predictions = model.predict(X)

print("Predictions:", predictions)

License

MIT License - see LICENSE file for details

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.