def __init__(self,
X,
Y,
kernel='LinearKernel',
basis_vectors=None,
regparams=None,
**kwargs):
if regparams is None:
grid = [2**x for x in range(-15, 16)]
else:
grid = regparams
learner = RLS(X, Y, grid[0], kernel, basis_vectors, **kwargs)
crossvalidator = LPOCV(learner)
self.cv_performances, self.cv_predictions, self.regparam = grid_search(
crossvalidator, grid)
self.predictor = learner.predictor
def __init__(self,
X,
Y,
qids,
kernel='LinearKernel',
basis_vectors=None,
regparams=None,
measure=None,
**kwargs):
if regparams is None:
grid = [2**x for x in range(-15, 15)]
else:
grid = regparams
if measure is None:
self.measure = cindex
else:
self.measure = measure
learner = QueryRankRLS(X, Y, qids, grid[0], kernel, basis_vectors,
**kwargs)
crossvalidator = LQOCV(learner, measure)
self.cv_performances, self.cv_predictions, self.regparam = grid_search(
crossvalidator, grid)
self.predictor = learner.predictor
def __init__(self,
X,
Y,
folds,
kernel='LinearKernel',
basis_vectors=None,
regparams=None,
measure=None,
save_predictions=False,
**kwargs):
if regparams is None:
grid = [2**x for x in range(-15, 16)]
else:
grid = regparams
if measure is None:
self.measure = sqerror
else:
self.measure = measure
learner = RLS(X, Y, grid[0], kernel, basis_vectors, **kwargs)
crossvalidator = NfoldCV(learner, measure, folds)
self.cv_performances, self.cv_predictions, self.regparam = grid_search(
crossvalidator, grid)
self.predictor = learner.predictor