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testing priors in the demo

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Nicolo Fusi 2013-02-25 12:20:33 +00:00
parent f661b4b64e
commit 734edfe9d9
1 changed files with 12 additions and 4 deletions
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16
GPy/examples/warped_GP_demo.py
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@ -7,15 +7,14 @@ import scipy as sp
import pdb, sys, pickle
import matplotlib.pylab as plt
import GPy
np.random.seed(3)
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)
Z = np.exp(3.0*Y)#Y**(1/3.0)
# rescaling targets?
Zmax = Z.max()
Zmin = Z.min()
@ -23,12 +22,21 @@ 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|noise|bias)')
# m.unconstrain('tanh_d')
# m.constrain_fixed('tanh_d', 1.0)
# lognormal = GPy.priors.log_Gaussian(1.0, 2.0) # 1,2
# gaussian = GPy.priors.Gaussian(0, 10) # 0, 10
# m.set_prior('tanh_c', gaussian)
# m.set_prior('(tanh_b|tanh_a)', lognormal)
m.randomize()
plt.figure()
plt.xlabel('predicted f(Z)')
plt.ylabel('actual f(Z)')
plt.plot(m.likelihood.Y, Y, 'o', alpha = 0.5, label = 'before training')
m.optimize(messages = True)
# m.optimize(messages = True)
m.optimize_restarts(4, parallel = True)
plt.plot(m.likelihood.Y, Y, 'o', alpha = 0.5, label = 'after training')
plt.legend(loc = 0)
m.plot_warping()