def __init__(self, C, kernel=LinearKernel(), eps=1e-4,
max_iter=consts.MAX_ITER, min_iter=1, info=[]):
super(SequentialMinimalOptimization, self).__init__(kernel=kernel,
info=info)
self.C = max(0, float(C))
self.eps = max(consts.FLOAT_EPSILON, float(eps))
self.min_iter = max(1, int(min_iter))
self.max_iter = max(self.min_iter, int(max_iter))
def __init__(self, X, y, l, kernel=None, penalty_start=0, mean=False):
"""
Parameters
----------
X : numpy array (n, p)
The data matrix.
y : numpy array (n, 1)
The output vector. Must only contain values of -1 and 1.
l : float
Must be non-negative. The ridge parameter.
kernel : kernel object, optional
The kernel for non-linear SVM, of type
parsimony.algorithms.utils.Kernel. Default is a linear kernel.
penalty_start : int
Must be non-negative. The number of columns, variables etc., to
except from penalisation. Equivalently, the first index to be
penalised. Default is 0, all columns are included.
mean : bool
Whether to compute the squared loss or the mean squared loss.
Default is False, the loss.
"""
self.X = X
self.y = y
self.l = max(0.0, float(l))
if kernel is None:
from parsimony.algorithms.utils import LinearKernel
self.kernel = LinearKernel(X=self.X, use_cache=True)
self._reset_kernel = True
else:
self.kernel = kernel
self._reset_kernel = False
self.penalty_start = max(0, int(penalty_start))
self.mean = bool(mean)
self.reset()
