import classification as classif import utils xs, classes = utils.importFruitData() phi = classif.basisNone(xs) t = classif.vectT(classes, 3) w = classif.logRegress(phi, t) utils.plotRegression(xs, classes, w, 3) plt.show()
import utils import regression data = utils.importWarmupData() #data = utils.importTestData() x = data['time'] t = data['force'] #basis = regression.basisPoly basis = regression.basisFourier M = 16 '''Maximum likelihood estimation''' w, var = regression.maximum_likelihood(x, t, basis, M) print w #utils.plt.figure() utils.plotRegression(x, t, w, basis, var, 'blue') utils.plt.savefig("maximum_likelihood_estimation_16") '''Bayesian linear regression''' w, pred = regression.bayesian_linear_regression(x, t, basis, M) print w utils.plotRegression(x, t, w, basis, pred, 'green') utils.plt.savefig("bayesian_linear_regression_16") utils.plt.show()
import classification as classif import utils xs, classes = utils.importFruitData() phi = classif.basisNone(xs) t = classif.vectT(classes, 3) w = classif.logRegress(phi, t) utils.plotRegression(xs,classes, w, 3) plt.show()