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merge GPy upstream

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Zhenwen Dai 2016-02-23 13:52:45 +00:00
commit 00e4ac152a
9 changed files with 53 additions and 312 deletions
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2
GPy/__version__.py
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@ -1 +1 @@
__version__ = "0.9.5" __version__ = "0.9.6"

11
GPy/core/gp.py
View file

@ -181,7 +181,7 @@ class GP(Model):
def parameters_changed(self): def parameters_changed(self):
""" """
Method that is called upon any changes to :class:`~GPy.core.parameterization.param.Param` variables within the model. Method that is called upon any changes to :class:`~GPy.core.parameterization.param.Param` variables within the model.
In particular in the GP class this method reperforms inference, recalculating the posterior and log marginal likelihood and gradients of the model In particular in the GP class this method re-performs inference, recalculating the posterior and log marginal likelihood and gradients of the model
.. warning:: .. warning::
This method is not designed to be called manually, the framework is set up to automatically call this method upon changes to parameters, if you call This method is not designed to be called manually, the framework is set up to automatically call this method upon changes to parameters, if you call
@ -371,13 +371,14 @@ class GP(Model):
mean_jac[:,:,i] = kern.gradients_X(self.posterior.woodbury_vector[:,i:i+1].T, Xnew, self._predictive_variable) mean_jac[:,:,i] = kern.gradients_X(self.posterior.woodbury_vector[:,i:i+1].T, Xnew, self._predictive_variable)
dK_dXnew_full = np.empty((self._predictive_variable.shape[0], Xnew.shape[0], Xnew.shape[1])) dK_dXnew_full = np.empty((self._predictive_variable.shape[0], Xnew.shape[0], Xnew.shape[1]))
one = np.ones((1,1))
for i in range(self._predictive_variable.shape[0]): for i in range(self._predictive_variable.shape[0]):
dK_dXnew_full[i] = kern.gradients_X([[1.]], Xnew, self._predictive_variable[[i]]) dK_dXnew_full[i] = kern.gradients_X(one, Xnew, self._predictive_variable[[i]])
if full_cov: if full_cov:
dK2_dXdX = kern.gradients_XX([[1.]], Xnew) dK2_dXdX = kern.gradients_XX(one, Xnew)
else: else:
dK2_dXdX = kern.gradients_XX_diag([[1.]], Xnew) dK2_dXdX = kern.gradients_XX_diag(one, Xnew)
def compute_cov_inner(wi): def compute_cov_inner(wi):
if full_cov: if full_cov:
@ -464,7 +465,7 @@ class GP(Model):
m, v = self._raw_predict(X, full_cov=full_cov, **predict_kwargs) m, v = self._raw_predict(X, full_cov=full_cov, **predict_kwargs)
if self.normalizer is not None: if self.normalizer is not None:
m, v = self.normalizer.inverse_mean(m), self.normalizer.inverse_variance(v) m, v = self.normalizer.inverse_mean(m), self.normalizer.inverse_variance(v)
def sim_one_dim(m, v): def sim_one_dim(m, v):
if not full_cov: if not full_cov:
return np.random.multivariate_normal(m.flatten(), np.diag(v.flatten()), size).T return np.random.multivariate_normal(m.flatten(), np.diag(v.flatten()), size).T

286
GPy/inference/optimization/optimization.py
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@ -1,286 +0,0 @@
# Copyright (c) 2012-2014, GPy authors (see AUTHORS.txt).
# Licensed under the BSD 3-clause license (see LICENSE.txt)
import datetime as dt
from scipy import optimize
from warnings import warn
try:
import rasmussens_minimize as rasm
rasm_available = True
except ImportError:
rasm_available = False
from .scg import SCG
class Optimizer(object):
"""
Superclass for all the optimizers.
:param x_init: initial set of parameters
:param f_fp: function that returns the function AND the gradients at the same time
:param f: function to optimize
:param fp: gradients
:param messages: print messages from the optimizer?
:type messages: (True | False)
:param max_f_eval: maximum number of function evaluations
:rtype: optimizer object.
"""
def __init__(self, x_init, messages=False, model=None, max_f_eval=1e4, max_iters=1e3,
ftol=None, gtol=None, xtol=None, bfgs_factor=None):
self.opt_name = None
self.x_init = x_init
# Turning messages off and using internal structure for print outs:
self.messages = False #messages
self.f_opt = None
self.x_opt = None
self.funct_eval = None
self.status = None
self.max_f_eval = int(max_iters)
self.max_iters = int(max_iters)
self.bfgs_factor = bfgs_factor
self.trace = None
self.time = "Not available"
self.xtol = xtol
self.gtol = gtol
self.ftol = ftol
self.model = model
def run(self, **kwargs):
start = dt.datetime.now()
self.opt(**kwargs)
end = dt.datetime.now()
self.time = str(end - start)
def opt(self, f_fp=None, f=None, fp=None):
raise NotImplementedError("this needs to be implemented to use the optimizer class")
def __str__(self):
diagnostics = "Optimizer: \t\t\t\t %s\n" % self.opt_name
diagnostics += "f(x_opt): \t\t\t\t %.3f\n" % self.f_opt
diagnostics += "Number of function evaluations: \t %d\n" % self.funct_eval
diagnostics += "Optimization status: \t\t\t %s\n" % self.status
diagnostics += "Time elapsed: \t\t\t\t %s\n" % self.time
return diagnostics
class opt_tnc(Optimizer):
def __init__(self, *args, **kwargs):
Optimizer.__init__(self, *args, **kwargs)
self.opt_name = "TNC (Scipy implementation)"
def opt(self, f_fp=None, f=None, fp=None):
"""
Run the TNC optimizer
"""
tnc_rcstrings = ['Local minimum', 'Converged', 'XConverged', 'Maximum number of f evaluations reached',
'Line search failed', 'Function is constant']
assert f_fp != None, "TNC requires f_fp"
opt_dict = {}
if self.xtol is not None:
opt_dict['xtol'] = self.xtol
if self.ftol is not None:
opt_dict['ftol'] = self.ftol
if self.gtol is not None:
opt_dict['pgtol'] = self.gtol
opt_result = optimize.fmin_tnc(f_fp, self.x_init, messages=self.messages,
maxfun=self.max_f_eval, **opt_dict)
self.x_opt = opt_result[0]
self.f_opt = f_fp(self.x_opt)[0]
self.funct_eval = opt_result[1]
self.status = tnc_rcstrings[opt_result[2]]
class opt_lbfgsb(Optimizer):
def __init__(self, *args, **kwargs):
Optimizer.__init__(self, *args, **kwargs)
self.opt_name = "L-BFGS-B (Scipy implementation)"
def opt(self, f_fp=None, f=None, fp=None):
"""
Run the optimizer
"""
rcstrings = ['Converged', 'Maximum number of f evaluations reached', 'Error']
assert f_fp != None, "BFGS requires f_fp"
if self.messages:
iprint = 1
else:
iprint = -1
opt_dict = {}
if self.xtol is not None:
print("WARNING: l-bfgs-b doesn't have an xtol arg, so I'm going to ignore it")
if self.ftol is not None:
print("WARNING: l-bfgs-b doesn't have an ftol arg, so I'm going to ignore it")
if self.gtol is not None:
opt_dict['pgtol'] = self.gtol
if self.bfgs_factor is not None:
opt_dict['factr'] = self.bfgs_factor
opt_result = optimize.fmin_l_bfgs_b(f_fp, self.x_init, iprint=iprint,
maxfun=self.max_iters,maxiter=self.max_iters, **opt_dict)
self.x_opt = opt_result[0]
self.f_opt = f_fp(self.x_opt)[0]
self.funct_eval = opt_result[2]['funcalls']
self.status = rcstrings[opt_result[2]['warnflag']]
#a more helpful error message is available in opt_result in the Error case
if opt_result[2]['warnflag']==2:
self.status = 'Error' + str(opt_result[2]['task'])
class opt_bfgs(Optimizer):
def __init__(self, *args, **kwargs):
Optimizer.__init__(self, *args, **kwargs)
self.opt_name = "BFGS (Scipy implementation)"
def opt(self, f_fp=None, f=None, fp=None):
"""
Run the optimizer
"""
rcstrings = ['','Maximum number of iterations exceeded', 'Gradient and/or function calls not changing']
opt_dict = {}
if self.xtol is not None:
print("WARNING: bfgs doesn't have an xtol arg, so I'm going to ignore it")
if self.ftol is not None:
print("WARNING: bfgs doesn't have an ftol arg, so I'm going to ignore it")
if self.gtol is not None:
opt_dict['pgtol'] = self.gtol
opt_result = optimize.fmin_bfgs(f, self.x_init, fp, disp=self.messages,
maxiter=self.max_iters, full_output=True, **opt_dict)
self.x_opt = opt_result[0]
self.f_opt = f_fp(self.x_opt)[0]
self.funct_eval = opt_result[4]
self.status = rcstrings[opt_result[6]]
class opt_simplex(Optimizer):
def __init__(self, *args, **kwargs):
Optimizer.__init__(self, *args, **kwargs)
self.opt_name = "Nelder-Mead simplex routine (via Scipy)"
def opt(self, f_fp=None, f=None, fp=None):
"""
The simplex optimizer does not require gradients.
"""
statuses = ['Converged', 'Maximum number of function evaluations made', 'Maximum number of iterations reached']
opt_dict = {}
if self.xtol is not None:
opt_dict['xtol'] = self.xtol
if self.ftol is not None:
opt_dict['ftol'] = self.ftol
if self.gtol is not None:
print("WARNING: simplex doesn't have an gtol arg, so I'm going to ignore it")
opt_result = optimize.fmin(f, self.x_init, (), disp=self.messages,
maxfun=self.max_f_eval, full_output=True, **opt_dict)
self.x_opt = opt_result[0]
self.f_opt = opt_result[1]
self.funct_eval = opt_result[3]
self.status = statuses[opt_result[4]]
self.trace = None
class opt_rasm(Optimizer):
def __init__(self, *args, **kwargs):
Optimizer.__init__(self, *args, **kwargs)
self.opt_name = "Rasmussen's Conjugate Gradient"
def opt(self, f_fp=None, f=None, fp=None):
"""
Run Rasmussen's Conjugate Gradient optimizer
"""
assert f_fp != None, "Rasmussen's minimizer requires f_fp"
statuses = ['Converged', 'Line search failed', 'Maximum number of f evaluations reached',
'NaNs in optimization']
opt_dict = {}
if self.xtol is not None:
print("WARNING: minimize doesn't have an xtol arg, so I'm going to ignore it")
if self.ftol is not None:
print("WARNING: minimize doesn't have an ftol arg, so I'm going to ignore it")
if self.gtol is not None:
print("WARNING: minimize doesn't have an gtol arg, so I'm going to ignore it")
opt_result = rasm.minimize(self.x_init, f_fp, (), messages=self.messages,
maxnumfuneval=self.max_f_eval)
self.x_opt = opt_result[0]
self.f_opt = opt_result[1][-1]
self.funct_eval = opt_result[2]
self.status = statuses[opt_result[3]]
self.trace = opt_result[1]
class opt_SCG(Optimizer):
def __init__(self, *args, **kwargs):
if 'max_f_eval' in kwargs:
warn("max_f_eval deprecated for SCG optimizer: use max_iters instead!\nIgnoring max_f_eval!", FutureWarning)
Optimizer.__init__(self, *args, **kwargs)
self.opt_name = "Scaled Conjugate Gradients"
def opt(self, f_fp=None, f=None, fp=None):
assert not f is None
assert not fp is None
opt_result = SCG(f, fp, self.x_init, display=self.messages,
maxiters=self.max_iters,
max_f_eval=self.max_f_eval,
xtol=self.xtol, ftol=self.ftol,
gtol=self.gtol)
self.x_opt = opt_result[0]
self.trace = opt_result[1]
self.f_opt = self.trace[-1]
self.funct_eval = opt_result[2]
self.status = opt_result[3]
class Opt_Adadelta(Optimizer):
def __init__(self, step_rate=0.1, decay=0.9, momentum=0, *args, **kwargs):
Optimizer.__init__(self, *args, **kwargs)
self.opt_name = "Adadelta (climin)"
self.step_rate=step_rate
self.decay = decay
self.momentum = momentum
def opt(self, f_fp=None, f=None, fp=None):
assert not fp is None
import climin
opt = climin.adadelta.Adadelta(self.x_init, fp, step_rate=self.step_rate, decay=self.decay, momentum=self.momentum)
for info in opt:
if info['n_iter']>=self.max_iters:
self.x_opt = opt.wrt
self.status = 'maximum number of function evaluations exceeded '
break
def get_optimizer(f_min):
optimizers = {'fmin_tnc': opt_tnc,
'simplex': opt_simplex,
'lbfgsb': opt_lbfgsb,
'org-bfgs': opt_bfgs,
'scg': opt_SCG,
'adadelta':Opt_Adadelta}
if rasm_available:
optimizers['rasmussen'] = opt_rasm
for opt_name in optimizers.keys():
if opt_name.lower().find(f_min.lower()) != -1:
return optimizers[opt_name]
raise KeyError('No optimizer was found matching the name: %s' % f_min)

4
GPy/kern/src/kern.py
View file

@ -61,12 +61,12 @@ class Kern(Parameterized):
self.psicomp = PSICOMP_GH() self.psicomp = PSICOMP_GH()
def __setstate__(self, state): def __setstate__(self, state):
self._all_dims_active = range(0, max(state['active_dims'])+1) self._all_dims_active = np.arange(0, max(state['active_dims'])+1)
super(Kern, self).__setstate__(state) super(Kern, self).__setstate__(state)
@property @property
def _effective_input_dim(self): def _effective_input_dim(self):
return self._all_dims_active.size return np.size(self._all_dims_active)
@Cache_this(limit=20) @Cache_this(limit=20)
def _slice_X(self, X): def _slice_X(self, X):

4
GPy/kern/src/stationary.py
View file

@ -97,7 +97,7 @@ class Stationary(Kern):
r = self._scaled_dist(X, X2) r = self._scaled_dist(X, X2)
return self.K_of_r(r) return self.K_of_r(r)
@Cache_this(limit=20, ignore_args=()) @Cache_this(limit=3, ignore_args=())
def dK_dr_via_X(self, X, X2): def dK_dr_via_X(self, X, X2):
#a convenience function, so we can cache dK_dr #a convenience function, so we can cache dK_dr
return self.dK_dr(self._scaled_dist(X, X2)) return self.dK_dr(self._scaled_dist(X, X2))
@ -127,7 +127,7 @@ class Stationary(Kern):
r2 = np.clip(r2, 0, np.inf) r2 = np.clip(r2, 0, np.inf)
return np.sqrt(r2) return np.sqrt(r2)
@Cache_this(limit=20, ignore_args=()) @Cache_this(limit=3, ignore_args=())
def _scaled_dist(self, X, X2=None): def _scaled_dist(self, X, X2=None):
""" """
Efficiently compute the scaled distance, r. Efficiently compute the scaled distance, r.

32
GPy/plotting/gpy_plot/gp_plots.py
View file

@ -46,7 +46,7 @@ def plot_mean(self, plot_limits=None, fixed_inputs=None,
""" """
Plot the mean of the GP. Plot the mean of the GP.
You can deactivate the legend for this one plot by supplying None to label. You can deactivate the legend for this one plot by supplying None to label.
Give the Y_metadata in the predict_kw if you need it. Give the Y_metadata in the predict_kw if you need it.
@ -116,7 +116,7 @@ def plot_confidence(self, lower=2.5, upper=97.5, plot_limits=None, fixed_inputs=
E.g. the 95% confidence interval is $2.5, 97.5$. E.g. the 95% confidence interval is $2.5, 97.5$.
Note: Only implemented for one dimension! Note: Only implemented for one dimension!
You can deactivate the legend for this one plot by supplying None to label. You can deactivate the legend for this one plot by supplying None to label.
Give the Y_metadata in the predict_kw if you need it. Give the Y_metadata in the predict_kw if you need it.
@ -170,7 +170,7 @@ def plot_samples(self, plot_limits=None, fixed_inputs=None,
""" """
Plot the mean of the GP. Plot the mean of the GP.
You can deactivate the legend for this one plot by supplying None to label. You can deactivate the legend for this one plot by supplying None to label.
Give the Y_metadata in the predict_kw if you need it. Give the Y_metadata in the predict_kw if you need it.
@ -231,7 +231,7 @@ def plot_density(self, plot_limits=None, fixed_inputs=None,
E.g. the 95% confidence interval is $2.5, 97.5$. E.g. the 95% confidence interval is $2.5, 97.5$.
Note: Only implemented for one dimension! Note: Only implemented for one dimension!
You can deactivate the legend for this one plot by supplying None to label. You can deactivate the legend for this one plot by supplying None to label.
Give the Y_metadata in the predict_kw if you need it. Give the Y_metadata in the predict_kw if you need it.
@ -288,7 +288,7 @@ def plot(self, plot_limits=None, fixed_inputs=None,
""" """
Convenience function for plotting the fit of a GP. Convenience function for plotting the fit of a GP.
You can deactivate the legend for this one plot by supplying None to label. You can deactivate the legend for this one plot by supplying None to label.
Give the Y_metadata in the predict_kw if you need it. Give the Y_metadata in the predict_kw if you need it.
@ -319,9 +319,17 @@ def plot(self, plot_limits=None, fixed_inputs=None,
:param {2d|3d} projection: plot in 2d or 3d? :param {2d|3d} projection: plot in 2d or 3d?
:param bool legend: convenience, whether to put a legend on the plot or not. :param bool legend: convenience, whether to put a legend on the plot or not.
""" """
canvas, _ = pl().new_canvas(projection=projection, **kwargs)
X = get_x_y_var(self)[0] X = get_x_y_var(self)[0]
helper_data = helper_for_plot_data(self, X, plot_limits, visible_dims, fixed_inputs, resolution) helper_data = helper_for_plot_data(self, X, plot_limits, visible_dims, fixed_inputs, resolution)
xmin, xmax = helper_data[5:7]
free_dims = helper_data[1]
if not 'xlim' in kwargs:
kwargs['xlim'] = (xmin[0], xmax[0])
if not 'ylim' in kwargs and len(free_dims) == 2:
kwargs['ylim'] = (xmin[1], xmax[1])
canvas, _ = pl().new_canvas(projection=projection, **kwargs)
helper_prediction = helper_predict_with_model(self, helper_data[2], plot_raw, helper_prediction = helper_predict_with_model(self, helper_data[2], plot_raw,
apply_link, np.linspace(2.5, 97.5, levels*2) if plot_density else (lower,upper), apply_link, np.linspace(2.5, 97.5, levels*2) if plot_density else (lower,upper),
get_which_data_ycols(self, which_data_ycols), get_which_data_ycols(self, which_data_ycols),
@ -330,9 +338,11 @@ def plot(self, plot_limits=None, fixed_inputs=None,
# It does not make sense to plot the data (which lives not in the latent function space) into latent function space. # It does not make sense to plot the data (which lives not in the latent function space) into latent function space.
plot_data = False plot_data = False
plots = {} plots = {}
if hasattr(self, 'Z') and plot_inducing:
plots.update(_plot_inducing(self, canvas, visible_dims, projection, 'Inducing'))
if plot_data: if plot_data:
plots.update(_plot_data(self, canvas, which_data_rows, which_data_ycols, visible_dims, projection, "Data")) plots.update(_plot_data(self, canvas, which_data_rows, which_data_ycols, free_dims, projection, "Data"))
plots.update(_plot_data_error(self, canvas, which_data_rows, which_data_ycols, visible_dims, projection, "Data Error")) plots.update(_plot_data_error(self, canvas, which_data_rows, which_data_ycols, free_dims, projection, "Data Error"))
plots.update(_plot(self, canvas, plots, helper_data, helper_prediction, levels, plot_inducing, plot_density, projection)) plots.update(_plot(self, canvas, plots, helper_data, helper_prediction, levels, plot_inducing, plot_density, projection))
if plot_raw and (samples_likelihood > 0): if plot_raw and (samples_likelihood > 0):
helper_prediction = helper_predict_with_model(self, helper_data[2], False, helper_prediction = helper_predict_with_model(self, helper_data[2], False,
@ -340,8 +350,6 @@ def plot(self, plot_limits=None, fixed_inputs=None,
get_which_data_ycols(self, which_data_ycols), get_which_data_ycols(self, which_data_ycols),
predict_kw, samples_likelihood) predict_kw, samples_likelihood)
plots.update(_plot_samples(canvas, helper_data, helper_prediction, projection, "Lik Samples")) plots.update(_plot_samples(canvas, helper_data, helper_prediction, projection, "Lik Samples"))
if hasattr(self, 'Z') and plot_inducing:
plots.update(_plot_inducing(self, canvas, visible_dims, projection, 'Inducing'))
return pl().add_to_canvas(canvas, plots, legend=legend) return pl().add_to_canvas(canvas, plots, legend=legend)
@ -362,7 +370,7 @@ def plot_f(self, plot_limits=None, fixed_inputs=None,
If you want fine graned control use the specific plotting functions supplied in the model. If you want fine graned control use the specific plotting functions supplied in the model.
You can deactivate the legend for this one plot by supplying None to label. You can deactivate the legend for this one plot by supplying None to label.
Give the Y_metadata in the predict_kw if you need it. Give the Y_metadata in the predict_kw if you need it.
@ -389,7 +397,7 @@ def plot_f(self, plot_limits=None, fixed_inputs=None,
:param dict error_kwargs: kwargs for the error plot for the plotting library you are using :param dict error_kwargs: kwargs for the error plot for the plotting library you are using
:param kwargs plot_kwargs: kwargs for the data plot for the plotting library you are using :param kwargs plot_kwargs: kwargs for the data plot for the plotting library you are using
""" """
plot(self, plot_limits, fixed_inputs, resolution, True, return plot(self, plot_limits, fixed_inputs, resolution, True,
apply_link, which_data_ycols, which_data_rows, apply_link, which_data_ycols, which_data_rows,
visible_dims, levels, samples, 0, visible_dims, levels, samples, 0,
lower, upper, plot_data, plot_inducing, lower, upper, plot_data, plot_inducing,

9
GPy/plotting/gpy_plot/plot_util.py
View file

@ -199,7 +199,7 @@ def subsample_X(X, labels, num_samples=1000):
num_samples and the returned subsampled X. num_samples and the returned subsampled X.
""" """
if X.shape[0] > num_samples: if X.shape[0] > num_samples:
print("Warning: subsampling X, as it has more samples then 1000. X.shape={!s}".format(X.shape)) print("Warning: subsampling X, as it has more samples then {}. X.shape={!s}".format(int(num_samples), X.shape))
if labels is not None: if labels is not None:
subsample = [] subsample = []
for _, _, _, _, index, _ in scatter_label_generator(labels, X, (0, None, None)): for _, _, _, _, index, _ in scatter_label_generator(labels, X, (0, None, None)):
@ -285,7 +285,10 @@ def get_x_y_var(model):
X = model.X.mean.values X = model.X.mean.values
X_variance = model.X.variance.values X_variance = model.X.variance.values
else: else:
X = model.X.values try:
X = model.X.values
except AttributeError:
X = model.X
X_variance = None X_variance = None
try: try:
Y = model.Y.values Y = model.Y.values
@ -346,7 +349,7 @@ def x_frame1D(X,plot_limits=None,resolution=None):
xmin,xmax = X.min(0),X.max(0) xmin,xmax = X.min(0),X.max(0)
xmin, xmax = xmin-0.25*(xmax-xmin), xmax+0.25*(xmax-xmin) xmin, xmax = xmin-0.25*(xmax-xmin), xmax+0.25*(xmax-xmin)
elif len(plot_limits) == 2: elif len(plot_limits) == 2:
xmin, xmax = plot_limits xmin, xmax = map(np.atleast_1d, plot_limits)
else: else:
raise ValueError("Bad limits for plotting") raise ValueError("Bad limits for plotting")

15
GPy/testing/inference_tests.py
View file

@ -50,5 +50,20 @@ class InferenceXTestCase(unittest.TestCase):
x, mi = m.infer_newX(m.Y, optimize=True) x, mi = m.infer_newX(m.Y, optimize=True)
np.testing.assert_array_almost_equal(m.X, mi.X, decimal=2) np.testing.assert_array_almost_equal(m.X, mi.X, decimal=2)
class HMCSamplerTest(unittest.TestCase):
def test_sampling(self):
np.random.seed(1)
x = np.linspace(0.,2*np.pi,100)[:,None]
y = -np.cos(x)+np.random.randn(*x.shape)*0.3+1
m = GPy.models.GPRegression(x,y)
m.kern.lengthscale.set_prior(GPy.priors.Gamma.from_EV(1.,10.))
m.kern.variance.set_prior(GPy.priors.Gamma.from_EV(1.,10.))
m.likelihood.variance.set_prior(GPy.priors.Gamma.from_EV(1.,10.))
hmc = GPy.inference.mcmc.HMC(m,stepsize=1e-2)
s = hmc.sample(num_samples=3)
if __name__ == "__main__": if __name__ == "__main__":
unittest.main() unittest.main()

2
setup.cfg
View file

@ -1,5 +1,5 @@
[bumpversion] [bumpversion]
current_version = 0.9.5 current_version = 0.9.6
tag = True tag = True
commit = True commit = True