mirror of
https://github.com/SheffieldML/GPy.git
synced 2026-05-24 14:15:14 +02:00
45 lines
1.1 KiB
Python
45 lines
1.1 KiB
Python
# Copyright (c) 2012, GPy authors (see AUTHORS.txt).
|
|
# Licensed under the BSD 3-clause license (see LICENSE.txt)
|
|
|
|
|
|
import numpy as np
|
|
import scipy as sp
|
|
import pdb, sys, pickle
|
|
import matplotlib.pylab as plt
|
|
import GPy
|
|
np.random.seed(1)
|
|
|
|
N = 100
|
|
# sample inputs and outputs
|
|
X = np.random.uniform(-np.pi,np.pi,(N,1))
|
|
Y = np.sin(X)+np.random.randn(N,1)*0.05
|
|
# Y += np.abs(Y.min()) + 0.5
|
|
Z = np.exp(Y)# Y**(1/3.0)
|
|
|
|
# rescaling targets?
|
|
Zmax = Z.max()
|
|
Zmin = Z.min()
|
|
Z = (Z-Zmin)/(Zmax-Zmin) - 0.5
|
|
|
|
m = GPy.models.warpedGP(X, Z, warping_terms = 2)
|
|
m.constrain_positive('(tanh_a|tanh_b|tanh_d|rbf|white|bias)')
|
|
m.randomize()
|
|
plt.figure()
|
|
plt.xlabel('predicted f(Z)')
|
|
plt.ylabel('actual f(Z)')
|
|
plt.plot(m.Y, Y, 'o', alpha = 0.5, label = 'before training')
|
|
m.optimize(messages = True)
|
|
plt.plot(m.Y, Y, 'o', alpha = 0.5, label = 'after training')
|
|
plt.legend(loc = 0)
|
|
m.plot_warping()
|
|
plt.figure()
|
|
plt.title('warped GP fit')
|
|
m.plot()
|
|
|
|
m1 = GPy.models.GP_regression(X, Z)
|
|
m1.constrain_positive('(rbf|white|bias)')
|
|
m1.randomize()
|
|
m1.optimize(messages = True)
|
|
plt.figure()
|
|
plt.title('GP fit')
|
|
m1.plot()
|