Sparse EP

GPy/examples/ep_fix.py

@@ -10,6 +10,7 @@ import numpy as np
 import GPy
 pb.ion()
 
+pb.close('all')
 default_seed=10000
 
 model_type='Full'
@@ -26,11 +27,13 @@ data = GPy.util.datasets.toy_linear_1d_classification(seed=seed)
 likelihood = GPy.inference.likelihoods.probit(data['Y'][:, 0:1])
 
 m = GPy.models.GP(data['X'],likelihood=likelihood)
+#m = GPy.models.GP(data['X'],Y=likelihood.Y)
 
 m.constrain_positive('var')
 m.constrain_positive('len')
 m.tie_param('lengthscale')
-m.approximate_likelihood()
+if not isinstance(m.likelihood,GPy.inference.likelihoods.gaussian):
+    m.approximate_likelihood()
 print m.checkgrad()
 # Optimize and plot
 m.optimize()

GPy/examples/poisson.py

@@ -0,0 +1,50 @@
+# Copyright (c) 2012, GPy authors (see AUTHORS.txt).
+# Licensed under the BSD 3-clause license (see LICENSE.txt)
+
+
+"""
+Simple Gaussian Processes classification
+"""
+import pylab as pb
+import numpy as np
+import GPy
+pb.ion()
+
+pb.close('all')
+default_seed=10000
+
+model_type='Full'
+inducing=4
+seed=default_seed
+"""Simple 1D classification example.
+:param model_type: type of model to fit ['Full', 'FITC', 'DTC'].
+:param seed : seed value for data generation (default is 4).
+:type seed: int
+:param inducing : number of inducing variables (only used for 'FITC' or 'DTC').
+:type inducing: int
+"""
+
+X = np.arange(0,100,5)[:,None]
+F = np.round(np.sin(X/18.) + .1*X)
+E = np.random.randint(-3,3,20)[:,None]
+Y = F + E
+pb.plot(X,F,'k-')
+pb.plot(X,Y,'ro')
+pb.figure()
+likelihood = GPy.inference.likelihoods.poisson(Y,scale=4.)
+
+m = GPy.models.GP(X,likelihood=likelihood)
+#m = GPy.models.GP(data['X'],Y=likelihood.Y)
+
+m.constrain_positive('var')
+m.constrain_positive('len')
+m.tie_param('lengthscale')
+if not isinstance(m.likelihood,GPy.inference.likelihoods.gaussian):
+    m.approximate_likelihood()
+print m.checkgrad()
+# Optimize and plot
+m.optimize()
+#m.em(plot_all=False) # EM algorithm
+m.plot()
+
+print(m)

GPy/examples/sparse_ep_fix.py

@@ -0,0 +1,76 @@
+# Copyright (c) 2012, GPy authors (see AUTHORS.txt).
+# Licensed under the BSD 3-clause license (see LICENSE.txt)
+
+
+import numpy as np
+"""
+Sparse Gaussian Processes regression with an RBF kernel
+"""
+import pylab as pb
+import numpy as np
+import GPy
+np.random.seed(2)
+pb.ion()
+N = 500
+M = 5
+
+######################################
+## 1 dimensional example
+
+# sample inputs and outputs
+X = np.random.uniform(-3.,3.,(N,1))
+#Y = np.sin(X)+np.random.randn(N,1)*0.05
+F = np.sin(X)+np.random.randn(N,1)*0.05
+Y = np.ones([F.shape[0],1])
+Y[F<0] = -1
+likelihood = GPy.inference.likelihoods.probit(Y)
+
+# construct kernel
+rbf =  GPy.kern.rbf(1)
+noise = GPy.kern.white(1)
+kernel = rbf + noise
+
+# create simple GP model
+#m1 = GPy.models.sparse_GP_regression(X, Y, kernel, M=M)
+m1 = GPy.models.sparse_GP(X, kernel, M=M,likelihood= likelihood)
+
+# contrain all parameters to be positive
+m1.constrain_positive('(variance|lengthscale|precision)')
+#m1.constrain_positive('(variance|lengthscale)')
+#m1.constrain_fixed('prec',10.)
+
+
+#check gradient FIXME unit test please
+m1.checkgrad()
+# optimize and plot
+m1.optimize('tnc', messages = 1)
+m1.plot()
+# print(m1)
+
+######################################
+## 2 dimensional example
+
+# # sample inputs and outputs
+# X = np.random.uniform(-3.,3.,(N,2))
+# Y = np.sin(X[:,0:1]) * np.sin(X[:,1:2])+np.random.randn(N,1)*0.05
+
+# # construct kernel
+# rbf =  GPy.kern.rbf(2)
+# noise = GPy.kern.white(2)
+# kernel = rbf + noise
+
+# # create simple GP model
+# m2 = GPy.models.sparse_GP_regression(X,Y,kernel, M = 50)
+# create simple GP model
+
+# # contrain all parameters to be positive (but not inducing inputs)
+# m2.constrain_positive('(variance|lengthscale|precision)')
+
+# #check gradient FIXME unit test please
+# m2.checkgrad()
+
+# # optimize and plot
+# pb.figure()
+# m2.optimize('tnc', messages = 1)
+# m2.plot()
+# print(m2)