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Polynomial kernel gradients wrt X working.

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This commit is contained in:
Neil Lawrence 2013-08-28 00:20:12 +02:00
parent a61feb17a5
commit b127c96bf2
8 changed files with 79 additions and 26 deletions
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12
GPy/examples/regression.py
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@ -114,7 +114,7 @@ def coregionalisation_sparse(max_iters=100):
return m
def epomeo_gpx(max_iters=100):
"""Perform Gaussian process regression on the GPX data from the Mount Epomeo runs. Requires gpxpy to be installed on your system."""
"""Perform Gaussian process regression on the latitude and longitude data from the Mount Epomeo runs. Requires gpxpy to be installed on your system to load in the data."""
data = GPy.util.datasets.epomeo_gpx()
num_data_list = []
for Xpart in data['X']:
@ -122,16 +122,16 @@ def epomeo_gpx(max_iters=100):
num_data_array = np.array(num_data_list)
num_data = num_data_array.sum()
Y = np.zeros((num_data, 3))
Y = np.zeros((num_data, 2))
t = np.zeros((num_data, 2))
start = 0
for Xpart, index in zip(data['X'], range(len(data['X']))):
end = start+Xpart.shape[0]
t[start:end, :] = np.hstack((Xpart[:, 0:1],
index*np.ones((Xpart.shape[0], 1))))
Y[start:end, :] = Xpart[:, 1:4]
Y[start:end, :] = Xpart[:, 1:3]
num_inducing = 40
num_inducing = 200
Z = np.hstack((np.linspace(t[:,0].min(), t[:, 0].max(), num_inducing)[:, None],
np.random.randint(0, 4, num_inducing)[:, None]))
@ -139,12 +139,12 @@ def epomeo_gpx(max_iters=100):
k2 = GPy.kern.coregionalise(output_dim=5, rank=5)
k = k1**k2
m = GPy.models.SparseGPRegression(t, Y, kernel=k, Z=Z)
m = GPy.models.SparseGPRegression(t, Y, kernel=k, Z=Z, normalize_Y=True)
m.constrain_fixed('.*rbf_var', 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)
m.optimize(max_iters=max_iters,messages=True)
return m