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import numpy as np
import scipy.sparse
from ..base import BaseLibSVM
class SparseBaseLibSVM(BaseLibSVM):
def __init__(self, impl, kernel, degree, gamma, coef0,
tol, C, nu, epsilon, shrinking, probability, cache_size,
class_weight, verbose):
assert kernel in self._sparse_kernels, \
"kernel should be one of %s, "\
"%s was given." % (self._kernel_types, kernel)
super(SparseBaseLibSVM, self).__init__(impl, kernel, degree, gamma,
coef0, tol, C, nu, epsilon, shrinking, probability, cache_size,
True, class_weight, verbose)
def fit(self, X, y, sample_weight=None):
X = scipy.sparse.csr_matrix(X, dtype=np.float64)
return super(SparseBaseLibSVM, self).fit(X, y, sample_weight)
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