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()