ad8f38c5c2687793d88c990a54bf9f3c9aeecf42,nonconformist/cp.py,TcpClassifier,predict,#TcpClassifier#,107

Before Change

				train_y = np.hstack([self.train_y, y])
				self.base_icp.fit(train_x, train_y)
				self.base_icp.calibrate(train_x, train_y)
				p[i, j] = self.base_icp.predict(np.array([x[i, :]]))[0, j]
				p[i, j] *= (n_train + 2)
				if self.smoothing:
					p[i, j] -= np.random.uniform(0, 1)
				else:

After Change

				self.base_icp.fit(train_x, train_y)
				scores = self.base_icp.nc_function.score(train_x, train_y)
				n_gt = sum(scores > scores[-1])
				n_eq = sum(scores == scores[-1])
				if self.smoothing:
					r = np.random.uniform(0, 1)
					p[i, j] = (n_gt + r*n_eq) / (n_train + 1)
				else:
					p[i, j] = (n_gt + n_eq) / (n_train + 1)

		if significance is not None:

Instances