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Convert print to function for Python 3 compatibility.

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This commit is contained in:
Mike Croucher 2015-02-26 08:31:44 +00:00
parent 29da6ff065
commit 906f69e20e
5 changed files with 61 additions and 61 deletions
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26
GPy/examples/regression.py
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@ -15,7 +15,7 @@ def olympic_marathon_men(optimize=True, plot=True):
"""Run a standard Gaussian process regression on the Olympic marathon data."""
try:import pods
except ImportError:
print 'pods unavailable, see https://github.com/sods/ods for example datasets'
print('pods unavailable, see https://github.com/sods/ods for example datasets')
return
data = pods.datasets.olympic_marathon_men()
@ -88,7 +88,7 @@ def epomeo_gpx(max_iters=200, optimize=True, plot=True):
"""
try:import pods
except ImportError:
print 'pods unavailable, see https://github.com/sods/ods for example datasets'
print('pods unavailable, see https://github.com/sods/ods for example datasets')
return
data = pods.datasets.epomeo_gpx()
num_data_list = []
@ -135,7 +135,7 @@ def multiple_optima(gene_number=937, resolution=80, model_restarts=10, seed=1000
try:import pods
except ImportError:
print 'pods unavailable, see https://github.com/sods/ods for example datasets'
print('pods unavailable, see https://github.com/sods/ods for example datasets')
return
data = pods.datasets.della_gatta_TRP63_gene_expression(data_set='della_gatta',gene_number=gene_number)
# data['Y'] = data['Y'][0::2, :]
@ -219,7 +219,7 @@ def olympic_100m_men(optimize=True, plot=True):
"""Run a standard Gaussian process regression on the Rogers and Girolami olympics data."""
try:import pods
except ImportError:
print 'pods unavailable, see https://github.com/sods/ods for example datasets'
print('pods unavailable, see https://github.com/sods/ods for example datasets')
return
data = pods.datasets.olympic_100m_men()
@ -240,7 +240,7 @@ def toy_rbf_1d(optimize=True, plot=True):
"""Run a simple demonstration of a standard Gaussian process fitting it to data sampled from an RBF covariance."""
try:import pods
except ImportError:
print 'pods unavailable, see https://github.com/sods/ods for example datasets'
print('pods unavailable, see https://github.com/sods/ods for example datasets')
return
data = pods.datasets.toy_rbf_1d()
@ -258,7 +258,7 @@ def toy_rbf_1d_50(optimize=True, plot=True):
"""Run a simple demonstration of a standard Gaussian process fitting it to data sampled from an RBF covariance."""
try:import pods
except ImportError:
print 'pods unavailable, see https://github.com/sods/ods for example datasets'
print('pods unavailable, see https://github.com/sods/ods for example datasets')
return
data = pods.datasets.toy_rbf_1d_50()
@ -377,7 +377,7 @@ def robot_wireless(max_iters=100, kernel=None, optimize=True, plot=True):
"""Predict the location of a robot given wirelss signal strength readings."""
try:import pods
except ImportError:
print 'pods unavailable, see https://github.com/sods/ods for example datasets'
print('pods unavailable, see https://github.com/sods/ods for example datasets')
return
data = pods.datasets.robot_wireless()
@ -398,14 +398,14 @@ def robot_wireless(max_iters=100, kernel=None, optimize=True, plot=True):
sse = ((data['Xtest'] - Xpredict)**2).sum()
print('Sum of squares error on test data: ' + str(sse))
print(('Sum of squares error on test data: ' + str(sse)))
return m
def silhouette(max_iters=100, optimize=True, plot=True):
"""Predict the pose of a figure given a silhouette. This is a task from Agarwal and Triggs 2004 ICML paper."""
try:import pods
except ImportError:
print 'pods unavailable, see https://github.com/sods/ods for example datasets'
print('pods unavailable, see https://github.com/sods/ods for example datasets')
return
data = pods.datasets.silhouette()
@ -416,7 +416,7 @@ def silhouette(max_iters=100, optimize=True, plot=True):
if optimize:
m.optimize(messages=True, max_iters=max_iters)
print m
print(m)
return m
def sparse_GP_regression_1D(num_samples=400, num_inducing=5, max_iters=100, optimize=True, plot=True, checkgrad=False):
@ -468,7 +468,7 @@ def sparse_GP_regression_2D(num_samples=400, num_inducing=50, max_iters=100, opt
if plot:
m.plot()
print m
print(m)
return m
def uncertain_inputs_sparse_regression(max_iters=200, optimize=True, plot=True):
@ -492,7 +492,7 @@ def uncertain_inputs_sparse_regression(max_iters=200, optimize=True, plot=True):
if plot:
m.plot(ax=axes[0])
axes[0].set_title('no input uncertainty')
print m
print(m)
# the same Model with uncertainty
m = GPy.models.SparseGPRegression(X, Y, kernel=GPy.kern.RBF(1), Z=Z, X_variance=S)
@ -503,5 +503,5 @@ def uncertain_inputs_sparse_regression(max_iters=200, optimize=True, plot=True):
axes[1].set_title('with input uncertainty')
fig.canvas.draw()
print m
print(m)
return m