Feature Weighting/Selection Sun08

A feature weighting/selection algorithm described in [Sun08].

class mlpy.FSSun(T=1000, sigma=1.0, theta=0.001, lmbd=1.0, eps=0.001, alpha0=1.0, c=0.01, rho=0.5)

Sun Algorithm for feature weighting/selection

Initialize the FSSun class

Parameters:
T : int (> 0)

max loops

sigma : float (> 0.0)

kernel width

theta : float (> 0.0)

convergence parameter

lmbd : float

regularization parameter

eps : float (0 < eps << 1)

termination tolerance for steepest descent method

alpha0 : float (> 0.0)

initial step length (usually 1.0) for line search

c : float (0 < c < 1/2)

costant for line search

rho : flaot (0 < rho < 1)

alpha coefficient for line search

New in version 2.0.9.

weights(x, y)

Compute the feature weights

Parameters:
x : 2d ndarray float (samples x feats)

training data

y : 1d ndarray integer (-1 or 1)

classes

Returns:
fw : 1d ndarray float

feature weights

Attributes:
FSSun.loops : int

number of loops

Raises:
ValueError

if classes are not -1 or 1

SigmaError

if sigma parameter is too small

[Sun08]Yijun Sun, S. Todorovic, and S. Goodison. A Feature Selection Algorithm Capable of Handling Extremely Large Data Dimensionality. In Proc. 8th SIAM International Conference on Data Mining (SDM08), pp. 530-540, April 2008.

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