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https://github.com/SheffieldML/GPy.git
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416 lines
16 KiB
Python
416 lines
16 KiB
Python
import numpy as np
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import unittest
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import GPy
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from GPy.models import GradientChecker
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import functools
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import inspect
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from GPy.likelihoods.noise_models import gp_transformations
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def dparam_partial(inst_func, *args):
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"""
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If we have a instance method that needs to be called but that doesn't
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take the parameter we wish to change to checkgrad, then this function
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will change the variable using set params.
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inst_func: should be a instance function of an object that we would like
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to change
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param: the param that will be given to set_params
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args: anything else that needs to be given to the function (for example
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the f or Y that are being used in the function whilst we tweak the
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param
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"""
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def param_func(param, inst_func, args):
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inst_func.im_self._set_params(param)
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return inst_func(*args)
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return functools.partial(param_func, inst_func=inst_func, args=args)
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def dparam_checkgrad(func, dfunc, params, args, constrain_positive=True, randomize=False, verbose=False):
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"""
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checkgrad expects a f: R^N -> R^1 and df: R^N -> R^N
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However if we are holding other parameters fixed and moving something else
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We need to check the gradient of each of the fixed parameters
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(f and y for example) seperately.
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Whilst moving another parameter. otherwise f: gives back R^N and
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df: gives back R^NxM where M is
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The number of parameters and N is the number of data
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Need to take a slice out from f and a slice out of df
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"""
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#print "\n{} likelihood: {} vs {}".format(func.im_self.__class__.__name__,
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#func.__name__, dfunc.__name__)
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partial_f = dparam_partial(func, *args)
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partial_df = dparam_partial(dfunc, *args)
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gradchecking = True
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for param in params:
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fnum = np.atleast_1d(partial_f(param)).shape[0]
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dfnum = np.atleast_1d(partial_df(param)).shape[0]
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for fixed_val in range(dfnum):
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#dlik and dlik_dvar gives back 1 value for each
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f_ind = min(fnum, fixed_val+1) - 1
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print "fnum: {} dfnum: {} f_ind: {} fixed_val: {}".format(fnum, dfnum, f_ind, fixed_val)
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grad = GradientChecker(lambda x: np.atleast_1d(partial_f(x))[f_ind],
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lambda x : np.atleast_1d(partial_df(x))[fixed_val],
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param, 'p')
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if constrain_positive:
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grad.constrain_positive('p')
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if randomize:
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grad.randomize()
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print grad
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if verbose:
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grad.checkgrad(verbose=1)
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if not grad.checkgrad():
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gradchecking = False
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return gradchecking
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from nose.tools import with_setup
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class TestNoiseModels(object):
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"""
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Generic model checker
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"""
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def setUp(self):
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self.N = 5
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self.D = 3
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self.X = np.random.rand(self.N, self.D)*10
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self.real_std = 0.1
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noise = np.random.randn(*self.X[:, 0].shape)*self.real_std
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self.Y = (np.sin(self.X[:, 0]*2*np.pi) + noise)[:, None]
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self.f = np.random.rand(self.N, 1)
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self.var = 0.2
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self.var = np.random.rand(1)
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#Make a bigger step as lower bound can be quite curved
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self.step = 1e-3
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def tearDown(self):
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self.Y = None
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self.f = None
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self.X = None
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def test_noise_models(self):
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self.setUp()
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"""
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Dictionary where we nest models we would like to check
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Name: {
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"model": model_instance,
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"grad_params": {
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"names": [names_of_params_we_want, to_grad_check],
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"vals": [values_of_params, to_start_at],
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"constrain_positive": [boolean_values, of_whether_to_constrain]
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},
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"laplace": boolean_of_whether_model_should_work_for_laplace
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}
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"""
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noise_models = {"Student_t_default": {
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"model": GPy.likelihoods.student_t(deg_free=5, sigma2=self.var),
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"grad_params": {
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"names": ["t_noise"],
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"vals": [self.var],
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"constrain_positive": [True]
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},
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"laplace": True
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},
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"Student_t_small_var": {
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"model": GPy.likelihoods.student_t(deg_free=5, sigma2=self.var),
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"grad_params": {
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"names": ["t_noise"],
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"vals": [0.01],
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"constrain_positive": [True]
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},
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"laplace": True
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},
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"Student_t_approx_gauss": {
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"model": GPy.likelihoods.student_t(deg_free=1000, sigma2=self.var),
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"grad_params": {
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"names": ["t_noise"],
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"vals": [self.var],
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"constrain_positive": [True]
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},
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"laplace": True
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},
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"Student_t_log": {
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"model": GPy.likelihoods.student_t(gp_link=gp_transformations.Log(), deg_free=5, sigma2=self.var),
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"grad_params": {
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"names": ["t_noise"],
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"vals": [self.var],
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"constrain_positive": [True]
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},
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"laplace": True
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},
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"Gaussian_default": {
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"model": GPy.likelihoods.gaussian(variance=self.var, D=self.D, N=self.N),
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"grad_params": {
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"names": ["noise_model_variance"],
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"vals": [self.var],
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"constrain_positive": [True]
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},
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"laplace": True
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},
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"Gaussian_log": {
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"model": GPy.likelihoods.gaussian(gp_link=gp_transformations.Log(), variance=self.var, D=self.D, N=self.N),
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"grad_params": {
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"names": ["noise_model_variance"],
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"vals": [self.var],
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"constrain_positive": [True]
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},
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"laplace": True
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}
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}
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for name, attributes in noise_models.iteritems():
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model = attributes["model"]
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params = attributes["grad_params"]
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param_vals = params["vals"]
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param_names= params["names"]
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constrain_positive = params["constrain_positive"]
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laplace = attributes["laplace"]
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if len(param_vals) > 1:
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raise NotImplementedError("Cannot support multiple params in likelihood yet!")
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#Required by all
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#Normal derivatives
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yield self.t_logpdf, model
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yield self.t_dlogpdf_df, model
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yield self.t_d2logpdf_df2, model
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#Link derivatives
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yield self.t_dlogpdf_dlink, model
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yield self.t_d2logpdf_dlink2, model
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yield self.t_d3logpdf_dlink3, model
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if laplace:
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#Laplace only derivatives
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yield self.t_d3logpdf_df3, model
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#Params
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yield self.t_dlogpdf_dparams, model, param_vals
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yield self.t_dlogpdf_df_dparams, model, param_vals
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yield self.t_d2logpdf2_df2_dparams, model, param_vals
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#Link params
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yield self.t_dlogpdf_link_dparams, model, param_vals
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yield self.t_dlogpdf_dlink_dparams, model, param_vals
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yield self.t_d2logpdf2_dlink2_dparams, model, param_vals
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#laplace likelihood gradcheck
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yield self.t_laplace_fit_rbf_white, model, param_vals, param_names, constrain_positive
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self.tearDown()
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#############
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# dpdf_df's #
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#############
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@with_setup(setUp, tearDown)
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def t_logpdf(self, model):
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print "\n{}".format(inspect.stack()[0][3])
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np.testing.assert_almost_equal(
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np.log(model.pdf(self.f.copy(), self.Y.copy())),
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model.logpdf(self.f.copy(), self.Y.copy()))
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@with_setup(setUp, tearDown)
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def t_dlogpdf_df(self, model):
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print "\n{}".format(inspect.stack()[0][3])
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self.description = "\n{}".format(inspect.stack()[0][3])
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logpdf = functools.partial(model.logpdf, y=self.Y)
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dlogpdf_df = functools.partial(model.dlogpdf_df, y=self.Y)
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grad = GradientChecker(logpdf, dlogpdf_df, self.f.copy(), 'g')
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grad.randomize()
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grad.checkgrad(verbose=1)
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assert grad.checkgrad()
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@with_setup(setUp, tearDown)
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def t_d2logpdf_df2(self, model):
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print "\n{}".format(inspect.stack()[0][3])
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dlogpdf_df = functools.partial(model.dlogpdf_df, y=self.Y)
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d2logpdf_df2 = functools.partial(model.d2logpdf_df2, y=self.Y)
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grad = GradientChecker(dlogpdf_df, d2logpdf_df2, self.f.copy(), 'g')
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grad.randomize()
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grad.checkgrad(verbose=1)
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assert grad.checkgrad()
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@with_setup(setUp, tearDown)
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def t_d3logpdf_df3(self, model):
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print "\n{}".format(inspect.stack()[0][3])
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d2logpdf_df2 = functools.partial(model.d2logpdf_df2, y=self.Y)
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d3logpdf_df3 = functools.partial(model.d3logpdf_df3, y=self.Y)
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grad = GradientChecker(d2logpdf_df2, d3logpdf_df3, self.f.copy(), 'g')
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grad.randomize()
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grad.checkgrad(verbose=1)
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assert grad.checkgrad()
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##############
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# df_dparams #
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##############
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@with_setup(setUp, tearDown)
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def t_dlogpdf_dparams(self, model, params):
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print "\n{}".format(inspect.stack()[0][3])
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assert (
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dparam_checkgrad(model.logpdf, model.dlogpdf_dtheta,
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params, args=(self.f, self.Y), constrain_positive=True,
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randomize=False, verbose=True)
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)
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@with_setup(setUp, tearDown)
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def t_dlogpdf_df_dparams(self, model, params):
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print "\n{}".format(inspect.stack()[0][3])
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assert (
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dparam_checkgrad(model.dlogpdf_df, model.dlogpdf_df_dtheta,
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params, args=(self.f, self.Y), constrain_positive=True,
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randomize=False, verbose=True)
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)
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@with_setup(setUp, tearDown)
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def t_d2logpdf2_df2_dparams(self, model, params):
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print "\n{}".format(inspect.stack()[0][3])
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assert (
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dparam_checkgrad(model.d2logpdf_df2, model.d2logpdf_df2_dtheta,
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params, args=(self.f, self.Y), constrain_positive=True,
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randomize=False, verbose=True)
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)
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################
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# dpdf_dlink's #
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################
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@with_setup(setUp, tearDown)
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def t_dlogpdf_dlink(self, model):
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print "\n{}".format(inspect.stack()[0][3])
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logpdf = functools.partial(model.logpdf_link, y=self.Y)
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dlogpdf_dlink = functools.partial(model.dlogpdf_dlink, y=self.Y)
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grad = GradientChecker(logpdf, dlogpdf_dlink, self.f.copy(), 'g')
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grad.randomize()
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grad.checkgrad(verbose=1)
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assert grad.checkgrad()
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@with_setup(setUp, tearDown)
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def t_d2logpdf_dlink2(self, model):
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print "\n{}".format(inspect.stack()[0][3])
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dlogpdf_dlink = functools.partial(model.dlogpdf_dlink, y=self.Y)
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d2logpdf_dlink2 = functools.partial(model.d2logpdf_dlink2, y=self.Y)
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grad = GradientChecker(dlogpdf_dlink, d2logpdf_dlink2, self.f.copy(), 'g')
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grad.randomize()
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grad.checkgrad(verbose=1)
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assert grad.checkgrad()
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@with_setup(setUp, tearDown)
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def t_d3logpdf_dlink3(self, model):
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print "\n{}".format(inspect.stack()[0][3])
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d2logpdf_dlink2 = functools.partial(model.d2logpdf_dlink2, y=self.Y)
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d3logpdf_dlink3 = functools.partial(model.d3logpdf_dlink3, y=self.Y)
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grad = GradientChecker(d2logpdf_dlink2, d3logpdf_dlink3, self.f.copy(), 'g')
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grad.randomize()
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grad.checkgrad(verbose=1)
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assert grad.checkgrad()
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#################
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# dlink_dparams #
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#################
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@with_setup(setUp, tearDown)
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def t_dlogpdf_link_dparams(self, model, params):
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print "\n{}".format(inspect.stack()[0][3])
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assert (
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dparam_checkgrad(model.logpdf_link, model.dlogpdf_link_dtheta,
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params, args=(self.f, self.Y), constrain_positive=True,
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randomize=False, verbose=True)
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)
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@with_setup(setUp, tearDown)
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def t_dlogpdf_dlink_dparams(self, model, params):
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print "\n{}".format(inspect.stack()[0][3])
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assert (
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dparam_checkgrad(model.dlogpdf_dlink, model.dlogpdf_dlink_dtheta,
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params, args=(self.f, self.Y), constrain_positive=True,
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randomize=False, verbose=True)
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)
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@with_setup(setUp, tearDown)
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def t_d2logpdf2_dlink2_dparams(self, model, params):
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print "\n{}".format(inspect.stack()[0][3])
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assert (
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dparam_checkgrad(model.d2logpdf_dlink2, model.d2logpdf_dlink2_dtheta,
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params, args=(self.f, self.Y), constrain_positive=True,
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randomize=False, verbose=True)
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)
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################
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# laplace test #
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################
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@with_setup(setUp, tearDown)
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def t_laplace_fit_rbf_white(self, model, param_vals, param_names, constrain_positive):
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print "\n{}".format(inspect.stack()[0][3])
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self.Y = self.Y/self.Y.max()
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white_var = 0.001
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kernel = GPy.kern.rbf(self.X.shape[1]) + GPy.kern.white(self.X.shape[1])
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laplace_likelihood = GPy.likelihoods.Laplace(self.Y.copy(), model)
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m = GPy.models.GPRegression(self.X, self.Y.copy(), kernel, likelihood=laplace_likelihood)
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m.ensure_default_constraints()
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m.constrain_fixed('white', white_var)
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for param_num in range(len(param_names)):
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name = param_names[param_num]
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if constrain_positive[param_num]:
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m.constrain_positive(name)
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m[name] = param_vals[param_num]
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m.randomize()
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m.checkgrad(verbose=1, step=self.step)
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print m
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assert m.checkgrad(step=self.step)
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class LaplaceTests(unittest.TestCase):
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"""
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Specific likelihood tests, not general enough for the above tests
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"""
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def setUp(self):
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self.N = 5
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self.D = 3
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self.X = np.random.rand(self.N, self.D)*10
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self.real_std = 0.1
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noise = np.random.randn(*self.X[:, 0].shape)*self.real_std
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self.Y = (np.sin(self.X[:, 0]*2*np.pi) + noise)[:, None]
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self.f = np.random.rand(self.N, 1)
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self.var = 0.2
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self.var = np.random.rand(1)
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self.stu_t = GPy.likelihoods.student_t(deg_free=5, sigma2=self.var)
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self.gauss = GPy.likelihoods.gaussian(gp_transformations.Log(), variance=self.var, D=self.D, N=self.N)
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#Make a bigger step as lower bound can be quite curved
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self.step = 1e-3
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def tearDown(self):
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self.stu_t = None
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self.gauss = None
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self.Y = None
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self.f = None
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self.X = None
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""" Gradchecker fault """
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@unittest.expectedFailure
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def test_gaussian_d2logpdf_df2_2(self):
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print "\n{}".format(inspect.stack()[0][3])
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self.Y = None
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self.gauss = None
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self.N = 2
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self.D = 1
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self.X = np.linspace(0, self.D, self.N)[:, None]
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self.real_std = 0.2
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noise = np.random.randn(*self.X.shape)*self.real_std
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self.Y = np.sin(self.X*2*np.pi) + noise
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self.f = np.random.rand(self.N, 1)
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self.gauss = GPy.likelihoods.gaussian(variance=self.var, D=self.D, N=self.N)
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dlogpdf_df = functools.partial(self.gauss.dlogpdf_df, y=self.Y)
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d2logpdf_df2 = functools.partial(self.gauss.d2logpdf_df2, y=self.Y)
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grad = GradientChecker(dlogpdf_df, d2logpdf_df2, self.f.copy(), 'g')
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grad.randomize()
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grad.checkgrad(verbose=1)
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self.assertTrue(grad.checkgrad())
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if __name__ == "__main__":
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print "Running unit tests"
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unittest.main()
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