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Convert print to function for Python 3 compatibility.

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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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24
GPy/examples/classification.py
View file

@ -15,7 +15,7 @@ def oil(num_inducing=50, max_iters=100, kernel=None, optimize=True, plot=True):
""" """
try:import pods try:import pods
except ImportError:print 'pods unavailable, see https://github.com/sods/ods for example datasets' except ImportError:print('pods unavailable, see https://github.com/sods/ods for example datasets')
data = pods.datasets.oil() data = pods.datasets.oil()
X = data['X'] X = data['X']
Xtest = data['Xtest'] Xtest = data['Xtest']
@ -52,7 +52,7 @@ def toy_linear_1d_classification(seed=default_seed, optimize=True, plot=True):
""" """
try:import pods try:import pods
except ImportError:print 'pods unavailable, see https://github.com/sods/ods for example datasets' except ImportError:print('pods unavailable, see https://github.com/sods/ods for example datasets')
data = pods.datasets.toy_linear_1d_classification(seed=seed) data = pods.datasets.toy_linear_1d_classification(seed=seed)
Y = data['Y'][:, 0:1] Y = data['Y'][:, 0:1]
Y[Y.flatten() == -1] = 0 Y[Y.flatten() == -1] = 0
@ -75,7 +75,7 @@ def toy_linear_1d_classification(seed=default_seed, optimize=True, plot=True):
m.plot_f(ax=axes[0]) m.plot_f(ax=axes[0])
m.plot(ax=axes[1]) m.plot(ax=axes[1])
print m print(m)
return m return m
def toy_linear_1d_classification_laplace(seed=default_seed, optimize=True, plot=True): def toy_linear_1d_classification_laplace(seed=default_seed, optimize=True, plot=True):
@ -88,7 +88,7 @@ def toy_linear_1d_classification_laplace(seed=default_seed, optimize=True, plot=
""" """
try:import pods try:import pods
except ImportError:print 'pods unavailable, see https://github.com/sods/ods for example datasets' except ImportError:print('pods unavailable, see https://github.com/sods/ods for example datasets')
data = pods.datasets.toy_linear_1d_classification(seed=seed) data = pods.datasets.toy_linear_1d_classification(seed=seed)
Y = data['Y'][:, 0:1] Y = data['Y'][:, 0:1]
Y[Y.flatten() == -1] = 0 Y[Y.flatten() == -1] = 0
@ -114,7 +114,7 @@ def toy_linear_1d_classification_laplace(seed=default_seed, optimize=True, plot=
m.plot_f(ax=axes[0]) m.plot_f(ax=axes[0])
m.plot(ax=axes[1]) m.plot(ax=axes[1])
print m print(m)
return m return m
def sparse_toy_linear_1d_classification(num_inducing=10, seed=default_seed, optimize=True, plot=True): def sparse_toy_linear_1d_classification(num_inducing=10, seed=default_seed, optimize=True, plot=True):
@ -127,7 +127,7 @@ def sparse_toy_linear_1d_classification(num_inducing=10, seed=default_seed, opti
""" """
try:import pods try:import pods
except ImportError:print 'pods unavailable, see https://github.com/sods/ods for example datasets' except ImportError:print('pods unavailable, see https://github.com/sods/ods for example datasets')
data = pods.datasets.toy_linear_1d_classification(seed=seed) data = pods.datasets.toy_linear_1d_classification(seed=seed)
Y = data['Y'][:, 0:1] Y = data['Y'][:, 0:1]
Y[Y.flatten() == -1] = 0 Y[Y.flatten() == -1] = 0
@ -147,7 +147,7 @@ def sparse_toy_linear_1d_classification(num_inducing=10, seed=default_seed, opti
m.plot_f(ax=axes[0]) m.plot_f(ax=axes[0])
m.plot(ax=axes[1]) m.plot(ax=axes[1])
print m print(m)
return m return m
def toy_heaviside(seed=default_seed, max_iters=100, optimize=True, plot=True): def toy_heaviside(seed=default_seed, max_iters=100, optimize=True, plot=True):
@ -160,7 +160,7 @@ def toy_heaviside(seed=default_seed, max_iters=100, optimize=True, plot=True):
""" """
try:import pods try:import pods
except ImportError:print 'pods unavailable, see https://github.com/sods/ods for example datasets' except ImportError:print('pods unavailable, see https://github.com/sods/ods for example datasets')
data = pods.datasets.toy_linear_1d_classification(seed=seed) data = pods.datasets.toy_linear_1d_classification(seed=seed)
Y = data['Y'][:, 0:1] Y = data['Y'][:, 0:1]
Y[Y.flatten() == -1] = 0 Y[Y.flatten() == -1] = 0
@ -177,7 +177,7 @@ def toy_heaviside(seed=default_seed, max_iters=100, optimize=True, plot=True):
# Parameters optimization: # Parameters optimization:
for _ in range(5): for _ in range(5):
m.optimize(max_iters=int(max_iters/5)) m.optimize(max_iters=int(max_iters/5))
print m print(m)
# Plot # Plot
if plot: if plot:
@ -186,7 +186,7 @@ def toy_heaviside(seed=default_seed, max_iters=100, optimize=True, plot=True):
m.plot_f(ax=axes[0]) m.plot_f(ax=axes[0])
m.plot(ax=axes[1]) m.plot(ax=axes[1])
print m print(m)
return m return m
def crescent_data(model_type='Full', num_inducing=10, seed=default_seed, kernel=None, optimize=True, plot=True): def crescent_data(model_type='Full', num_inducing=10, seed=default_seed, kernel=None, optimize=True, plot=True):
@ -202,7 +202,7 @@ def crescent_data(model_type='Full', num_inducing=10, seed=default_seed, kernel=
:type kernel: a GPy kernel :type kernel: a GPy kernel
""" """
try:import pods try:import pods
except ImportError:print 'pods unavailable, see https://github.com/sods/ods for example datasets' except ImportError:print('pods unavailable, see https://github.com/sods/ods for example datasets')
data = pods.datasets.crescent_data(seed=seed) data = pods.datasets.crescent_data(seed=seed)
Y = data['Y'] Y = data['Y']
Y[Y.flatten()==-1] = 0 Y[Y.flatten()==-1] = 0
@ -224,5 +224,5 @@ def crescent_data(model_type='Full', num_inducing=10, seed=default_seed, kernel=
if plot: if plot:
m.plot() m.plot()
print m print(m)
return m return m

12
GPy/examples/dimensionality_reduction.py
View file

@ -333,7 +333,7 @@ def bgplvm_simulation(optimize=True, verbose=1,
m.likelihood.variance = .1 m.likelihood.variance = .1
if optimize: if optimize:
print "Optimizing model:" print("Optimizing model:")
m.optimize('bfgs', messages=verbose, max_iters=max_iters, m.optimize('bfgs', messages=verbose, max_iters=max_iters,
gtol=.05) gtol=.05)
if plot: if plot:
@ -358,7 +358,7 @@ def ssgplvm_simulation(optimize=True, verbose=1,
m.likelihood.variance = .1 m.likelihood.variance = .1
if optimize: if optimize:
print "Optimizing model:" print("Optimizing model:")
m.optimize('scg', messages=verbose, max_iters=max_iters, m.optimize('scg', messages=verbose, max_iters=max_iters,
gtol=.05) gtol=.05)
if plot: if plot:
@ -388,7 +388,7 @@ def bgplvm_simulation_missing_data(optimize=True, verbose=1,
m.Yreal = Y m.Yreal = Y
if optimize: if optimize:
print "Optimizing model:" print("Optimizing model:")
m.optimize('bfgs', messages=verbose, max_iters=max_iters, m.optimize('bfgs', messages=verbose, max_iters=max_iters,
gtol=.05) gtol=.05)
if plot: if plot:
@ -411,7 +411,7 @@ def mrd_simulation(optimize=True, verbose=True, plot=True, plot_sim=True, **kw):
m['.*noise'] = [Y.var() / 40. for Y in Ylist] m['.*noise'] = [Y.var() / 40. for Y in Ylist]
if optimize: if optimize:
print "Optimizing Model:" print("Optimizing Model:")
m.optimize(messages=verbose, max_iters=8e3) m.optimize(messages=verbose, max_iters=8e3)
if plot: if plot:
m.X.plot("MRD Latent Space 1D") m.X.plot("MRD Latent Space 1D")
@ -439,7 +439,7 @@ def mrd_simulation_missing_data(optimize=True, verbose=True, plot=True, plot_sim
initx="random", initz='permute', **kw) initx="random", initz='permute', **kw)
if optimize: if optimize:
print "Optimizing Model:" print("Optimizing Model:")
m.optimize('bfgs', messages=verbose, max_iters=8e3, gtol=.1) m.optimize('bfgs', messages=verbose, max_iters=8e3, gtol=.1)
if plot: if plot:
m.X.plot("MRD Latent Space 1D") m.X.plot("MRD Latent Space 1D")
@ -603,7 +603,7 @@ def stick_bgplvm(model=None, optimize=True, verbose=True, plot=True):
try: try:
if optimize: m.optimize('bfgs', messages=verbose, max_iters=5e3, bfgs_factor=10) if optimize: m.optimize('bfgs', messages=verbose, max_iters=5e3, bfgs_factor=10)
except KeyboardInterrupt: except KeyboardInterrupt:
print "Keyboard interrupt, continuing to plot and return" print("Keyboard interrupt, continuing to plot and return")
if plot: if plot:
fig, (latent_axes, sense_axes) = plt.subplots(1, 2) fig, (latent_axes, sense_axes) = plt.subplots(1, 2)

44
GPy/examples/non_gaussian.py
View file

@ -37,7 +37,7 @@ def student_t_approx(optimize=True, plot=True):
#Add student t random noise to datapoints #Add student t random noise to datapoints
deg_free = 1 deg_free = 1
print "Real noise: ", real_std print("Real noise: ", real_std)
initial_var_guess = 0.5 initial_var_guess = 0.5
edited_real_sd = initial_var_guess edited_real_sd = initial_var_guess
@ -73,7 +73,7 @@ def student_t_approx(optimize=True, plot=True):
m4['.*t_scale2'].constrain_bounded(1e-6, 10.) m4['.*t_scale2'].constrain_bounded(1e-6, 10.)
m4['.*white'].constrain_fixed(1e-5) m4['.*white'].constrain_fixed(1e-5)
m4.randomize() m4.randomize()
print m4 print(m4)
debug=True debug=True
if debug: if debug:
m4.optimize(messages=1) m4.optimize(messages=1)
@ -81,18 +81,18 @@ def student_t_approx(optimize=True, plot=True):
pb.plot(m4.X, m4.inference_method.f_hat) pb.plot(m4.X, m4.inference_method.f_hat)
pb.plot(m4.X, m4.Y, 'rx') pb.plot(m4.X, m4.Y, 'rx')
m4.plot() m4.plot()
print m4 print(m4)
return m4 return m4
if optimize: if optimize:
optimizer='scg' optimizer='scg'
print "Clean Gaussian" print("Clean Gaussian")
m1.optimize(optimizer, messages=1) m1.optimize(optimizer, messages=1)
print "Corrupt Gaussian" print("Corrupt Gaussian")
m2.optimize(optimizer, messages=1) m2.optimize(optimizer, messages=1)
print "Clean student t" print("Clean student t")
m3.optimize(optimizer, messages=1) m3.optimize(optimizer, messages=1)
print "Corrupt student t" print("Corrupt student t")
m4.optimize(optimizer, messages=1) m4.optimize(optimizer, messages=1)
if plot: if plot:
@ -151,7 +151,7 @@ def boston_example(optimize=True, plot=True):
for n, (train, test) in enumerate(kf): for n, (train, test) in enumerate(kf):
X_train, X_test, Y_train, Y_test = X[train], X[test], Y[train], Y[test] X_train, X_test, Y_train, Y_test = X[train], X[test], Y[train], Y[test]
print "Fold {}".format(n) print("Fold {}".format(n))
noise = 1e-1 #np.exp(-2) noise = 1e-1 #np.exp(-2)
rbf_len = 0.5 rbf_len = 0.5
@ -163,21 +163,21 @@ def boston_example(optimize=True, plot=True):
score_folds[0, n] = rmse(Y_test, np.mean(Y_train)) score_folds[0, n] = rmse(Y_test, np.mean(Y_train))
#Gaussian GP #Gaussian GP
print "Gauss GP" print("Gauss GP")
mgp = GPy.models.GPRegression(X_train.copy(), Y_train.copy(), kernel=kernelgp.copy()) mgp = GPy.models.GPRegression(X_train.copy(), Y_train.copy(), kernel=kernelgp.copy())
mgp.constrain_fixed('.*white', 1e-5) mgp.constrain_fixed('.*white', 1e-5)
mgp['.*len'] = rbf_len mgp['.*len'] = rbf_len
mgp['.*noise'] = noise mgp['.*noise'] = noise
print mgp print(mgp)
if optimize: if optimize:
mgp.optimize(optimizer=optimizer, messages=messages) mgp.optimize(optimizer=optimizer, messages=messages)
Y_test_pred = mgp.predict(X_test) Y_test_pred = mgp.predict(X_test)
score_folds[1, n] = rmse(Y_test, Y_test_pred[0]) score_folds[1, n] = rmse(Y_test, Y_test_pred[0])
pred_density[1, n] = np.mean(mgp.log_predictive_density(X_test, Y_test)) pred_density[1, n] = np.mean(mgp.log_predictive_density(X_test, Y_test))
print mgp print(mgp)
print pred_density print(pred_density)
print "Gaussian Laplace GP" print("Gaussian Laplace GP")
N, D = Y_train.shape N, D = Y_train.shape
g_distribution = GPy.likelihoods.noise_model_constructors.gaussian(variance=noise, N=N, D=D) g_distribution = GPy.likelihoods.noise_model_constructors.gaussian(variance=noise, N=N, D=D)
g_likelihood = GPy.likelihoods.Laplace(Y_train.copy(), g_distribution) g_likelihood = GPy.likelihoods.Laplace(Y_train.copy(), g_distribution)
@ -186,18 +186,18 @@ def boston_example(optimize=True, plot=True):
mg.constrain_fixed('.*white', 1e-5) mg.constrain_fixed('.*white', 1e-5)
mg['rbf_len'] = rbf_len mg['rbf_len'] = rbf_len
mg['noise'] = noise mg['noise'] = noise
print mg print(mg)
if optimize: if optimize:
mg.optimize(optimizer=optimizer, messages=messages) mg.optimize(optimizer=optimizer, messages=messages)
Y_test_pred = mg.predict(X_test) Y_test_pred = mg.predict(X_test)
score_folds[2, n] = rmse(Y_test, Y_test_pred[0]) score_folds[2, n] = rmse(Y_test, Y_test_pred[0])
pred_density[2, n] = np.mean(mg.log_predictive_density(X_test, Y_test)) pred_density[2, n] = np.mean(mg.log_predictive_density(X_test, Y_test))
print pred_density print(pred_density)
print mg print(mg)
for stu_num, df in enumerate(degrees_freedoms): for stu_num, df in enumerate(degrees_freedoms):
#Student T #Student T
print "Student-T GP {}df".format(df) print("Student-T GP {}df".format(df))
t_distribution = GPy.likelihoods.noise_model_constructors.student_t(deg_free=df, sigma2=noise) t_distribution = GPy.likelihoods.noise_model_constructors.student_t(deg_free=df, sigma2=noise)
stu_t_likelihood = GPy.likelihoods.Laplace(Y_train.copy(), t_distribution) stu_t_likelihood = GPy.likelihoods.Laplace(Y_train.copy(), t_distribution)
mstu_t = GPy.models.GPRegression(X_train.copy(), Y_train.copy(), kernel=kernelstu.copy(), likelihood=stu_t_likelihood) mstu_t = GPy.models.GPRegression(X_train.copy(), Y_train.copy(), kernel=kernelstu.copy(), likelihood=stu_t_likelihood)
@ -205,14 +205,14 @@ def boston_example(optimize=True, plot=True):
mstu_t.constrain_bounded('.*t_scale2', 0.0001, 1000) mstu_t.constrain_bounded('.*t_scale2', 0.0001, 1000)
mstu_t['rbf_len'] = rbf_len mstu_t['rbf_len'] = rbf_len
mstu_t['.*t_scale2'] = noise mstu_t['.*t_scale2'] = noise
print mstu_t print(mstu_t)
if optimize: if optimize:
mstu_t.optimize(optimizer=optimizer, messages=messages) mstu_t.optimize(optimizer=optimizer, messages=messages)
Y_test_pred = mstu_t.predict(X_test) Y_test_pred = mstu_t.predict(X_test)
score_folds[3+stu_num, n] = rmse(Y_test, Y_test_pred[0]) score_folds[3+stu_num, n] = rmse(Y_test, Y_test_pred[0])
pred_density[3+stu_num, n] = np.mean(mstu_t.log_predictive_density(X_test, Y_test)) pred_density[3+stu_num, n] = np.mean(mstu_t.log_predictive_density(X_test, Y_test))
print pred_density print(pred_density)
print mstu_t print(mstu_t)
if plot: if plot:
plt.figure() plt.figure()
@ -230,8 +230,8 @@ def boston_example(optimize=True, plot=True):
plt.scatter(X_test[:, data_axis_plot], Y_test, c='r', marker='x') plt.scatter(X_test[:, data_axis_plot], Y_test, c='r', marker='x')
plt.title('Stu t {}df'.format(df)) plt.title('Stu t {}df'.format(df))
print "Average scores: {}".format(np.mean(score_folds, 1)) print("Average scores: {}".format(np.mean(score_folds, 1)))
print "Average pred density: {}".format(np.mean(pred_density, 1)) print("Average pred density: {}".format(np.mean(pred_density, 1)))
if plot: if plot:
#Plotting #Plotting

26
GPy/examples/regression.py
View file

@ -15,7 +15,7 @@ def olympic_marathon_men(optimize=True, plot=True):
"""Run a standard Gaussian process regression on the Olympic marathon data.""" """Run a standard Gaussian process regression on the Olympic marathon data."""
try:import pods try:import pods
except ImportError: 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 return
data = pods.datasets.olympic_marathon_men() data = pods.datasets.olympic_marathon_men()
@ -88,7 +88,7 @@ def epomeo_gpx(max_iters=200, optimize=True, plot=True):
""" """
try:import pods try:import pods
except ImportError: 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 return
data = pods.datasets.epomeo_gpx() data = pods.datasets.epomeo_gpx()
num_data_list = [] num_data_list = []
@ -135,7 +135,7 @@ def multiple_optima(gene_number=937, resolution=80, model_restarts=10, seed=1000
try:import pods try:import pods
except ImportError: 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 return
data = pods.datasets.della_gatta_TRP63_gene_expression(data_set='della_gatta',gene_number=gene_number) data = pods.datasets.della_gatta_TRP63_gene_expression(data_set='della_gatta',gene_number=gene_number)
# data['Y'] = data['Y'][0::2, :] # 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.""" """Run a standard Gaussian process regression on the Rogers and Girolami olympics data."""
try:import pods try:import pods
except ImportError: 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 return
data = pods.datasets.olympic_100m_men() 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.""" """Run a simple demonstration of a standard Gaussian process fitting it to data sampled from an RBF covariance."""
try:import pods try:import pods
except ImportError: 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 return
data = pods.datasets.toy_rbf_1d() 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.""" """Run a simple demonstration of a standard Gaussian process fitting it to data sampled from an RBF covariance."""
try:import pods try:import pods
except ImportError: 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 return
data = pods.datasets.toy_rbf_1d_50() 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.""" """Predict the location of a robot given wirelss signal strength readings."""
try:import pods try:import pods
except ImportError: 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 return
data = pods.datasets.robot_wireless() 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() 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 return m
def silhouette(max_iters=100, optimize=True, plot=True): 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.""" """Predict the pose of a figure given a silhouette. This is a task from Agarwal and Triggs 2004 ICML paper."""
try:import pods try:import pods
except ImportError: 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 return
data = pods.datasets.silhouette() data = pods.datasets.silhouette()
@ -416,7 +416,7 @@ def silhouette(max_iters=100, optimize=True, plot=True):
if optimize: if optimize:
m.optimize(messages=True, max_iters=max_iters) m.optimize(messages=True, max_iters=max_iters)
print m print(m)
return m return m
def sparse_GP_regression_1D(num_samples=400, num_inducing=5, max_iters=100, optimize=True, plot=True, checkgrad=False): 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: if plot:
m.plot() m.plot()
print m print(m)
return m return m
def uncertain_inputs_sparse_regression(max_iters=200, optimize=True, plot=True): 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: if plot:
m.plot(ax=axes[0]) m.plot(ax=axes[0])
axes[0].set_title('no input uncertainty') axes[0].set_title('no input uncertainty')
print m print(m)
# the same Model with uncertainty # the same Model with uncertainty
m = GPy.models.SparseGPRegression(X, Y, kernel=GPy.kern.RBF(1), Z=Z, X_variance=S) 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') axes[1].set_title('with input uncertainty')
fig.canvas.draw() fig.canvas.draw()
print m print(m)
return m return m

16
README.md
View file

@ -1,13 +1,5 @@
# GPy # GPy
# Moving to Python 3
Work is underway to make GPy run on Python 3. We are not there yet! Changes performed so far have retained compatibility with Python 2.6 and abive.
Work done so far:
* Use 2to3 to fix relative imports
* use 2to3 to convert print from statement to function. Some advanced uses of print meant that this could not be done in a way that retained compatibility with old versions of Python. The oldest version of Python that is supported by this version is 2.6 due to the required future imports.
A Gaussian processes framework in Python. A Gaussian processes framework in Python.
* [GPy homepage](http://sheffieldml.github.io/GPy/) * [GPy homepage](http://sheffieldml.github.io/GPy/)
@ -18,6 +10,14 @@ A Gaussian processes framework in Python.
Continuous integration status: ![CI status](https://travis-ci.org/SheffieldML/GPy.png) Continuous integration status: ![CI status](https://travis-ci.org/SheffieldML/GPy.png)
### Moving to Python 3
Work is underway to make GPy run on Python 3. We are not there yet! Changes performed so far have retained compatibility with Python 2.6 and abive.
Work done so far:
* Use 2to3 to fix relative imports
* use 2to3 to convert print from statement to function. Some advanced uses of print meant that this could not be done in a way that retained compatibility with old versions of Python. The oldest version of Python that is supported by this version is 2.6 due to the required future imports.
### Citation ### Citation
@Misc{gpy2014, @Misc{gpy2014,