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))