svmlite 0.1.0

A lightweight SVM implementation from scratch

SVMLite

Work in Progress

This project is part of CS6375 Machine learning course at University of Texas at Dallas.

A lightweight Python library implementing Support Vector Machines from scratch for educational and experimental use.

Features

• Implemenation of primal form (hard margin and soft margin) of SVM Classification using Stochastic Gradient Descent (SGD).

Modules Implemented from Scratch

• SVM Classifier
• Standard Scaler
• Metric functions: Accuracy

Installation

pip install svmlite

Quick Start

from svmlite.svm import SVCLite
from svmlite.utils import StandardScalerLite
from svmlite.metrics import accuracy_score
import numpy as np

# prepare data
X = np.array([[1, 2], [2, 3], [3, 3], [6, 5], [7, 8], [8, 7]])
y = np.array([-1, -1, -1, 1, 1, 1])

# scale features
scaler = StandardScalerLite()
X_scaled = scaler.fit_transform(X)

# train SVM
model = SVCLite(C=1.0)
model.fit(X_scaled, y, learning_rate=0.01, n_iters=1000)

# predict
predictions = model.predict(X_scaled)
print("Predictions:", predictions)

# evaluate
acc = accuracy_score(y, predictions)
print("Accuracy:", acc)