# Large Linear Classification from [LIBLINEAR]

Solvers:

> - l2r_lr: L2-regularized logistic regression (primal)

> - l2r_l2loss_svc_dual: L2-regularized L2-loss support vector classification (dual)

> - l2r_l2loss_svc: L2-regularized L2-loss support vector classification (primal)

> - l2r_l1loss_svc_dual: L2-regularized L1-loss support vector classification (dual)

> - mcsvm_cs: multi-class support vector classification by Crammer and Singer

> - l1r_l2loss_svc: L1-regularized L2-loss support vector classification

> - l1r_lr: L1-regularized logistic regression

> - l2r_lr_dual: L2-regularized logistic regression (dual)

class mlpy.LibLinear(solver_type='l2r_lr', C=1, eps=0.01, weight={})

  LibLinear is a simple class for solving large-scale regularized linear classification. It currently supports L2-regularized logistic regression/L2-loss support vector classification/L1-loss support vector classification, and L1-regularized L2-loss support vector classification/ logistic regression.

  Parameters:
    solver_typestring

      solver, can be one of ‘l2r_lr’, ‘l2r_l2loss_svc_dual’, ‘l2r_l2loss_svc’, ‘l2r_l1loss_svc_dual’, ‘mcsvm_cs’, ‘l1r_l2loss_svc’, ‘l1r_lr’, ‘l2r_lr_dual’

    Cfloat

      cost of constraints violation

    epsfloat

      stopping criterion

    weightdict

      changes the penalty for some classes (if the weight for a class is not changed, it is set to 1). For example, to change penalty for classes 1 and 2 to 0.5 and 0.8 respectively set weight={1:0.5, 2:0.8}

Example:

    >>> import numpy as np
    >>> import matplotlib.pyplot as plt
    >>> import mlpy
    >>> np.random.seed(0)
    >>> mean1, cov1, n1 = [1, 5], [[1,1],[1,2]], 200  # 200 samples of class 0
    >>> x1 = np.random.multivariate_normal(mean1, cov1, n1)
    >>> y1 = np.zeros(n1, dtype=int)
    >>> mean2, cov2, n2 = [2.5, 2.5], [[1,0],[0,1]], 300 # 300 samples of class 1
    >>> x2 = np.random.multivariate_normal(mean2, cov2, n2)
    >>> y2 = np.ones(n2, dtype=int)
    >>> mean3, cov3, n3 = [5, 8], [[0.5,0],[0,0.5]], 200 # 200 samples of class 2
    >>> x3 = np.random.multivariate_normal(mean3, cov3, n3)
    >>> y3 = 2 * np.ones(n3, dtype=int)
    >>> x = np.concatenate((x1, x2, x3), axis=0) # concatenate the samples
    >>> y = np.concatenate((y1, y2, y3))
    >>> svm = mlpy.LibLinear(solver_type='l2r_l2loss_svc_dual', C=0.01)
    >>> svm.learn(x, y)
    >>> w = svm.w() # w[i]: coefficients for label svm.labels()[i]
    >>> w
    array([[-0.73225278,  0.33309388],
           [ 0.32295557, -0.44097029],
           [ 0.23192595,  0.11536679]])
    >>> b = svm.bias() # b[i]: bias for label svm.labels()[i]
    >>> b
    array([-0.21631629,  0.96014472, -1.53933202])
    >>> xx = np.arange(np.min(x[:,0]), np.max(x[:,0]), 0.01)
    >>> yy1 = (xx* (w[1][0]-w[0][0]) + b[1] - b[0]) / (w[0][1]-w[1][1])
    >>> yy2 = (xx* (w[2][0]-w[0][0]) + b[2] - b[0]) / (w[0][1]-w[2][1])
    >>> yy3 = (xx* (w[2][0]-w[1][0]) + b[2] - b[1]) / (w[1][1]-w[2][1])
    >>> fig = plt.figure(1) # plot
    >>> plot1 = plt.plot(x1[:, 0], x1[:, 1], 'ob', x2[:, 0], x2[:, 1], 'or', x3[:, 0], x3[:, 1], 'og')
    >>> plot2 = plt.plot(xx, yy1, '--k')
    >>> plot3 = plt.plot(xx, yy2, '--k')
    >>> plot4 = plt.plot(xx, yy3, '--k')
    >>> plt.show()

    >>> test = [[6,7], [4, 2]] # test points
    >>> print svm.pred(test)
    array([2, 1])

[LIBLINEAR]

Machine Learning Group at National Taiwan University. http://www.csie.ntu.edu.tw/~cjlin/liblinear/