def __init__(self, trainer=svr.LinearSVR(), error_fx=sklm.r2_score):
#super(RegressionMeasure, self).__init__()
Measure.__init__(self)
self.trainer = trainer
self.fx = error_fx
self.mse = sklm.mean_squared_error
def __init__(self, trainer=svr.LinearSVR(), error_fx=sklm.r2_score):
#super(RegressionMeasure, self).__init__()
Measure.__init__(self)
self.trainer = trainer
self.fx = error_fx
self.mse = sklm.mean_squared_error
def __init__(self, p=0.05):
Measure.__init__(self)
#self.space = 'targets'
self.p = p
def __init__(self):
Measure.__init__(self)
self.params = dict()
def train(self, ds):
Measure.train(self, ds)
self.trainer.fit(ds.samples, ds.targets)
def train(self, ds):
Measure.train(self, ds)
self.trainer.transform(ds.samples, ds.targets)
def __init__(self, p=0.05):
Measure.__init__(self)
#self.space = 'targets'
self.p = p
def train(self, ds):
Measure.train(self, ds)
self.trainer.transform(ds.samples, ds.targets)
def __init__(self):
Measure.__init__(self)
self.params = dict()