def predict(self, data):
o = Output(data)
x = self.transform.transform(data.x)
y = x.dot(self.w) + self.b
o.fu = y
o.y = y
return o
def predict(self, data):
o = Output(data)
x = self.transform.transform(data.x)
y = x.dot(self.w) + self.b
o.fu = y
o.y = y
return o
def predict(self, data):
o = Output(data)
#W = pairwise.rbf_kernel(data.x,self.x,self.sigma)
x = data.x
if self.use_stacking:
x = self.get_stacking_x(data)
x = self.transform.transform(x)
if self.method == MixedFeatureGuidanceMethod.METHOD_LASSO:
o.y = self.learner_lasso.predict(x)
o.fu = o.y
else:
o.y = x.dot(self.w) + self.b
o.fu = o.y
o.w = self.w
o.true_w = self.true_w
return o
def predict(self, data):
o = Output(data)
#W = pairwise.rbf_kernel(data.x,self.x,self.sigma)
x = data.x
if self.use_stacking:
x = self.get_stacking_x(data)
x = self.transform.transform(x)
o.y = x.dot(self.w) + self.b
o.fu = o.y
o.w = self.w
o.true_w = self.true_w
return o
def predict(self, data):
o = Output(data)
x = data.x
if self.transform is not None:
x = self.transform.transform(x)
y = x.dot(self.w) + self.b
# y = np.round(y)
# y[y >= .5] = 1
# y[y < .5] = 0
y = np.sign(y)
o.y = y
o.fu = y
if self.label_transform is not None:
o.true_y = self.label_transform.transform(o.true_y)
if not self.running_cv:
is_correct = o.y == o.true_y
mean_train = is_correct[o.is_train].mean()
mean_test = is_correct[o.is_test].mean()
mean_train_labeled = is_correct[data.is_train & data.is_labeled].mean()
pass
return o
def predict(self, data):
o = Output(data)
x = data.x
if self.transform is not None:
x = self.transform.transform(x)
y = x.dot(self.w) + self.b
#y = np.round(y)
#y[y >= .5] = 1
#y[y < .5] = 0
y = np.sign(y)
o.y = y
o.fu = y
if self.label_transform is not None:
o.true_y = self.label_transform.transform(o.true_y)
if not self.running_cv:
is_correct = (o.y == o.true_y)
mean_train = is_correct[o.is_train].mean()
mean_test = is_correct[o.is_test].mean()
mean_train_labeled = is_correct[data.is_train
& data.is_labeled].mean()
pass
return o