demo for GP regressio with uncertain inputs

GPy/examples/uncertain_input_GP_regression_demo.py

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+# Copyright (c) 2012, GPy authors (see AUTHORS.txt).
+# Licensed under the BSD 3-clause license (see LICENSE.txt)
+
+
+import pylab as pb
+import numpy as np
+import GPy
+pb.ion()
+pb.close('all')
+
+
+######################################
+## 1 dimensional example
+
+# sample inputs and outputs
+S = np.ones((20,1))
+X = np.random.uniform(-3.,3.,(20,1))
+Y = np.sin(X)+np.random.randn(20,1)*0.05
+
+k = GPy.kern.bias(1) + GPy.kern.white(1)
+
+# create simple GP model
+m = GPy.models.uncertain_input_GP_regression(X,Y,S,kernel=k)
+
+# contrain all parameters to be positive
+m.constrain_positive('(variance|prec)')
+
+# optimize and plot
+m.optimize('tnc', max_f_eval = 1000, messages=1)
+m.plot()
+print(m)