def train(self, X, y, hypers):
#self.regressor = pyrb.train_ols(X, y.reshape(len(y),1), 0.01, 0.3, verbose=True)
self.regressor = pyrb.train_exact(X, y.reshape(len(y),1), 9.0/hypers)
def train(self, X, y, hypers):
#self.regressor = pyrb.train_ols(X, y.reshape(len(y),1), 0.01, 0.3, verbose=True)
self.regressor = pyrb.train_exact(X, y.reshape(len(y), 1),
9.0 / hypers)
#Defines the mesh x = np.linspace(-1.5, 1.5, 40) x, y = np.meshgrid(x, x) P = np.vstack([x.flatten(), y.flatten()]) #Defines the function heart = lambda x,y: np.abs(x**2.+2*(y-0.5*np.sqrt(np.abs(x)))**2.-1) #Evaluates function over every point of the grid V = heart(P[0:1], P[1:]) #Plot plt.plot(P[0:1][V<0.2], P[1:][V<0.2], '*r', P[0:1][V>=0.2], P[1:][V>=0.2], 'o') plt.show() #defines an exact RBFN enet = pyrb.train_exact(P.T, V.T, 0.3) #simulate S = enet.sim(P.T).T plt.plot(P[0:1][S<0.2], P[1:][S<0.2], '*r', P[0:1][S>=0.2], P[1:][S>=0.2], 'o') plt.show() # small differences are due to ill conditioning #What if we compute points outside training set O=np.random.uniform(size=(2,5000), low=-2., high=2.) S = enet.sim(O.T).T plt.plot(O[0:1][S<0.2], O[1:][S<0.2], '*r', O[0:1][S>=0.2], O[1:][S>=0.2], 'o') plt.show() #To achieve better generalization we can train an inexact RBFN inet = pyrb.train_ols(P.T, V.T, 0.0007, 0.3, verbose=True) #simulate S = inet.sim(P.T).T
