def predict(self, data):
o = self.target_learner.predict(data)
is_target = data.is_target
o_source = self.source_learner.predict(data.get_subset(is_target))
if not data.is_regression:
assert o.fu.ndim == 2
else:
assert o.fu.ndim == 1
assert o_source.fu.ndim == 1
o.fu = o.fu.reshape((o.fu.size,1))
o_source.fu = o_source.fu.reshape((o_source.fu.size,1))
fu_orig = o.fu
for i in range(o.fu.shape[1]):
fu_t = o.fu[is_target,i]
fu_s = o_source.fu[:,i]
o.fu[is_target,i] = self.a*fu_s + (1-self.a)*fu_t
#o.fu[is_target] = np.multiply(o.fu[is_target],(1-self.g)) + np.multiply(self.g,o_source.fu)
if data.is_regression:
o.y = o.fu
else:
fu = array_functions.replace_invalid(o.fu,0,1)
fu = array_functions.normalize_rows(fu)
o.fu = fu
o.y = fu.argmax(1)
assert not (np.isnan(o.y)).any()
assert not (np.isnan(o.fu)).any()
return o
def predict(self, data):
o = self.target_learner.predict(data)
is_target = data.is_target
o_source = self.source_learner.predict(data.get_subset(is_target))
if not data.is_regression:
assert o.fu.ndim == 2
else:
assert np.squeeze(o.fu).ndim == 1
assert np.squeeze(o_source.fu).ndim == 1
o.fu = o.fu.reshape((o.fu.size,1))
o_source.fu = o_source.fu.reshape((o_source.fu.size,1))
for i in range(o.fu.shape[1]):
fu_t = o.fu[is_target,i]
fu_s = o_source.fu[:,i]
if self.g_learner is not None:
pred = self.g_learner.combine_predictions(data.x[is_target,:],fu_s,fu_t)
if data.x.shape[1] == 1:
x = scipy.linspace(data.x.min(),data.x.max(),100)
x = array_functions.vec_to_2d(x)
g = self.g_learner.predict_g(x)
o.x = x
o.g = g
else:
pred = np.multiply(fu_t,1-self.g) + np.multiply(fu_s,self.g)
o.fu[is_target,i] = pred
#o.fu[is_target] = np.multiply(o.fu[is_target],(1-self.g)) + np.multiply(self.g,o_source.fu)
if data.is_regression:
o.y = o.fu
else:
fu = array_functions.replace_invalid(o.fu,0,1)
fu = array_functions.normalize_rows(fu)
o.fu = fu
o.y = fu.argmax(1)
if data.x.shape[1] == 1:
x = array_functions.vec_to_2d(scipy.linspace(data.x.min(),data.x.max(),100))
o.linspace_x = x
o.linspace_g = self.g_learner.predict_g(x)
assert not (np.isnan(o.y)).any()
assert not (np.isnan(o.fu)).any()
return o
