examples

GPy/examples/regression.py

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+"""
+Gaussian Processes regression examples
+"""
+import pylab as pb
+import numpy as np
+import GPy
+pb.ion()
+pb.close('all')
+
+
+def toy_rbf_1d():
+    """Run a simple demonstration of a standard Gaussian process fitting it to data sampled from an RBF covariance."""
+    data = GPy.util.datasets.toy_rbf_1d()
+
+    # create simple GP model
+    m = GPy.models.GP_regression(data['X'],data['Y'])
+
+    # contrain all parameters to be positive
+    m.constrain_positive('')
+
+    # optimize
+    m.optimize()
+
+    # plot
+    m.plot()
+    print(m)
+    return m
+
+def rogers_girolami_olympics():
+    """Run a standard Gaussian process regression on the Rogers and Girolami olympics data."""
+    data = GPy.util.datasets.rogers_girolami_olympics()
+
+    # create simple GP model
+    m = GPy.models.GP_regression(data['X'],data['Y'])
+
+    # contrain all parameters to be positive
+    m.constrain_positive('')
+
+    # optimize
+    m.optimize()
+
+    # plot
+    m.plot(plot_limits = (1850, 2050))
+    print(m)
+    return m
+
+def toy_rbf_1d_50():
+    """Run a simple demonstration of a standard Gaussian process fitting it to data sampled from an RBF covariance."""
+    data = GPy.util.datasets.toy_rbf_1d_50()
+
+    # create simple GP model
+    m = GPy.models.GP_regression(data['X'],data['Y'])
+
+    # contrain all parameters to be positive
+    m.constrain_positive('')
+
+    # optimize
+    m.optimize()
+
+    # plot
+    m.plot()
+    print(m)
+    return m
+
+def silhouette():
+    """Predict the pose of a figure given a silhouette. This is a task from Agarwal and Triggs 2004 ICML paper."""
+    data = GPy.util.datasets.silhouette()
+
+    # create simple GP model
+    m = GPy.models.GP_regression(data['X'],data['Y'])
+
+    # contrain all parameters to be positive
+    m.constrain_positive('')
+
+    # optimize
+    m.optimize()
+
+    print(m)
+    return m