This package provides several primal linear SVM solvers, which are appropriate for dense design matrices (feature matrices). The main entry point for new users is the asgd.LinearSVM object which is a sklearn-compatible class that provides a somewhat uniform interface to various internal and external solvers.

import numpy as np
import asgd

# -- True model (w, b)
w = np.random.randn(5)       # weights
b = np.random.randn()        # bias

# -- fake dataset (X, y)
X = np.random.randn(100, 5)  # 100 fake 5-d examples
y = np.sign(np.dot(X, w) + b)

svm = asgd.LinearSVM(l2_regularization=0.01)
svm.fit(X, y)
print w, svm.svm.weights
print b, svm.svm.bias
print np.mean(svm.predict(X) == y)

• "Towards Optimal One Pass Large Scale Learning with Averaged Stochastic Gradient Descent" Wei Xu (2011)
• "Large-scale Image Classification: Fast Feature Extraction and SVM Training" Yuanqing Lin, Fengjun Lv, Shenghuo Zhu, Ming Yang, Timothee Cour, Kai Yu, Liangliang Cao, and Thomas Huang (CVPR 2011)
• "Large-Scale Machine Learning with Stochastic Gradient Descent" Leon Bottou (2010)
• "Non-Asymptotic Analysis of Stochastic Approximation Algorithms for Machine Learning" Francis Bach, Eric Moulines (NIPS/HAL 2011)