def RunLinearRegressionShogun(q): totalTimer = Timer() # Load input dataset. # If the dataset contains two files then the second file is the responses # file. try: Log.Info("Loading dataset", self.verbose) if len(self.dataset) == 2: X = np.genfromtxt(self.dataset[0], delimiter=',') y = np.genfromtxt(self.dataset[1], delimiter=',') else: X = np.genfromtxt(self.dataset, delimiter=',') y = X[:, (X.shape[1] - 1)] X = X[:,:-1] with totalTimer: # Perform linear regression. model = LeastSquaresRegression(RealFeatures(X.T), RegressionLabels(y)) model.train() b = model.get_w() except Exception as e: q.put(-1) return -1 time = totalTimer.ElapsedTime() q.put(time) return time
def RunLinearRegressionShogun(q): totalTimer = Timer() # Load input dataset. # If the dataset contains two files then the second file is the responses # file. try: Log.Info("Loading dataset", self.verbose) if len(self.dataset) == 2: X = np.genfromtxt(self.dataset[0], delimiter=',') y = np.genfromtxt(self.dataset[1], delimiter=',') else: X = np.genfromtxt(self.dataset, delimiter=',') y = X[:, (X.shape[1] - 1)] X = X[:, :-1] with totalTimer: # Perform linear regression. model = LeastSquaresRegression(RealFeatures(X.T), RegressionLabels(y)) model.train() b = model.get_w() except Exception as e: q.put(-1) return -1 time = totalTimer.ElapsedTime() q.put(time) return time
def regression_least_squares_modular (fm_train=traindat,fm_test=testdat,label_train=label_traindat,tau=1e-6): from shogun.Features import RegressionLabels, RealFeatures from shogun.Kernel import GaussianKernel from shogun.Regression import LeastSquaresRegression ls=LeastSquaresRegression(RealFeatures(traindat), RegressionLabels(label_train)) ls.train() out = ls.apply(RealFeatures(fm_test)).get_labels() return out,ls
def regression_least_squares_modular (fm_train=traindat,fm_test=testdat,label_train=label_traindat,tau=1e-6): from shogun.Features import Labels, RealFeatures from shogun.Kernel import GaussianKernel from shogun.Regression import LeastSquaresRegression ls=LeastSquaresRegression(RealFeatures(traindat), Labels(label_train)) ls.train() out = ls.apply(RealFeatures(fm_test)).get_labels() return out,ls