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linK2_functions2 merged

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Ricardo 2013-09-13 18:09:59 +01:00
parent f8c9e6b982
commit 1bc9374717
10 changed files with 113 additions and 75 deletions
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66
GPy/examples/regression.py
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@ -46,32 +46,23 @@ def coregionalisation_toy(max_iters=100):
"""
X1 = np.random.rand(50, 1) * 8
X2 = np.random.rand(30, 1) * 5
index = np.vstack((np.zeros_like(X1), np.ones_like(X2)))
X = np.hstack((np.vstack((X1, X2)), index))
X = np.vstack((X1, X2))
Y1 = np.sin(X1) + np.random.randn(*X1.shape) * 0.05
Y2 = -np.sin(X2) + np.random.randn(*X2.shape) * 0.05
Y = np.vstack((Y1, Y2))
k1 = GPy.kern.rbf(1)
k2 = GPy.kern.coregionalise(2, 2)
k = k1**k2 #k1.prod(k2, tensor=True)
m = GPy.models.GPRegression(X, Y, kernel=k)
m = GPy.models.GPMultioutputRegression(X_list=[X1,X2],Y_list=[Y1,Y2],kernel_list=[k1])
m.constrain_fixed('.*rbf_var', 1.)
# m.constrain_positive('kappa')
m.optimize(max_iters=max_iters)
pb.figure()
Xtest1 = np.hstack((np.linspace(0, 9, 100)[:, None], np.zeros((100, 1))))
Xtest2 = np.hstack((np.linspace(0, 9, 100)[:, None], np.ones((100, 1))))
mean, var, low, up = m.predict(Xtest1)
GPy.util.plot.gpplot(Xtest1[:, 0], mean, low, up)
mean, var, low, up = m.predict(Xtest2)
GPy.util.plot.gpplot(Xtest2[:, 0], mean, low, up)
pb.plot(X1[:, 0], Y1[:, 0], 'rx', mew=2)
pb.plot(X2[:, 0], Y2[:, 0], 'gx', mew=2)
fig, axes = pb.subplots(2,1)
m.plot(output=0,ax=axes[0])
m.plot(output=1,ax=axes[1])
axes[0].set_title('Output 0')
axes[1].set_title('Output 1')
return m
def coregionalisation_sparse(max_iters=100):
"""
A simple demonstration of coregionalisation on two sinusoidal functions using sparse approximations.
@ -86,30 +77,39 @@ def coregionalisation_sparse(max_iters=100):
num_inducing = 40
Z = np.hstack((np.random.rand(num_inducing, 1) * 8, np.random.randint(0, 2, num_inducing)[:, None]))
Z = np.hstack((np.random.rand(num_inducing, 1) * 8, np.random.randint(0, 2, num_inducing)[:, None]))
k1 = GPy.kern.rbf(1)
k2 = GPy.kern.coregionalise(2, 2)
k = k1**k2 #.prod(k2, tensor=True) # + GPy.kern.white(2,0.001)
m = GPy.models.SparseGPRegression(X, Y, kernel=k, Z=Z)
m = GPy.models.SparseGPMultioutputRegression(X_list=[X1,X2],Y_list=[Y1,Y2],kernel_list=[k1],num_inducing=20)
#k2 = GPy.kern.coregionalise(2, 2)
#k = k1**k2 #.prod(k2, tensor=True) # + GPy.kern.white(2,0.001)
#m = GPy.models.SparseGPRegression(X, Y, kernel=k, Z=Z)
m.constrain_fixed('.*rbf_var', 1.)
m.constrain_fixed('iip')
m.constrain_bounded('noise_variance', 1e-3, 1e-1)
#m.constrain_fixed('iip')
#m.constrain_bounded('noise_variance', 1e-3, 1e-1)
# m.optimize_restarts(5, robust=True, messages=1, max_iters=max_iters, optimizer='bfgs')
m.optimize(max_iters=max_iters)
fig, axes = pb.subplots(2,1)
m.plot(output=0,ax=axes[0])
m.plot(output=1,ax=axes[1])
axes[0].set_title('Output 0')
axes[1].set_title('Output 1')
# plotting:
pb.figure()
Xtest1 = np.hstack((np.linspace(0, 9, 100)[:, None], np.zeros((100, 1))))
Xtest2 = np.hstack((np.linspace(0, 9, 100)[:, None], np.ones((100, 1))))
mean, var, low, up = m.predict(Xtest1)
GPy.util.plot.gpplot(Xtest1[:, 0], mean, low, up)
mean, var, low, up = m.predict(Xtest2)
GPy.util.plot.gpplot(Xtest2[:, 0], mean, low, up)
pb.plot(X1[:, 0], Y1[:, 0], 'rx', mew=2)
pb.plot(X2[:, 0], Y2[:, 0], 'gx', mew=2)
y = pb.ylim()[0]
pb.plot(Z[:, 0][Z[:, 1] == 0], np.zeros(np.sum(Z[:, 1] == 0)) + y, 'r|', mew=2)
pb.plot(Z[:, 0][Z[:, 1] == 1], np.zeros(np.sum(Z[:, 1] == 1)) + y, 'g|', mew=2)
#pb.figure()
#Xtest1 = np.hstack((np.linspace(0, 9, 100)[:, None], np.zeros((100, 1))))
#Xtest2 = np.hstack((np.linspace(0, 9, 100)[:, None], np.ones((100, 1))))
#mean, var, low, up = m.predict(Xtest1)
#GPy.util.plot.gpplot(Xtest1[:, 0], mean, low, up)
#mean, var, low, up = m.predict(Xtest2)
#GPy.util.plot.gpplot(Xtest2[:, 0], mean, low, up)
#pb.plot(X1[:, 0], Y1[:, 0], 'rx', mew=2)
#pb.plot(X2[:, 0], Y2[:, 0], 'gx', mew=2)
#y = pb.ylim()[0]
#pb.plot(Z[:, 0][Z[:, 1] == 0], np.zeros(np.sum(Z[:, 1] == 0)) + y, 'r|', mew=2)
#pb.plot(Z[:, 0][Z[:, 1] == 1], np.zeros(np.sum(Z[:, 1] == 1)) + y, 'g|', mew=2)
return m
def epomeo_gpx(max_iters=100):