def train_all(self, g):
X = np.concatenate([self.train_X, self.val_X], axis=0)
if self.use_scale:
self.scale.fit(X)
X = self.scale.transform(X)
for i in range(3):
y = np.concatenate([self.train_y, self.val_y], axis=0)
y[y!=i+1]=0
y[y!=0]=1
clf = SVC()
clf.set_params(**g)
self.model_a.append(clf.fit(X, y))
def train(self, g):
self.model = []
X = self.train_X.copy()
if self.use_scale:
self.scale.fit(X)
X = self.scale.transform(X)
for i in range(3):
y = self.train_y.copy()
y[y!=i+1]=0
y[y!=0]=1
clf = SVC()
clf.set_params(**g)
self.model.append(clf.fit(X, y))
