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https://github.com/SheffieldML/GPy.git
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Merge branch 'devel'
This commit is contained in:
Alan Saul
commit
8929076ce7
37 changed files with 1181 additions and 758 deletions
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@ -24,3 +24,4 @@ install:
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# command to run tests, e.g. python setup.py test
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script:
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- nosetests GPy/testing
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#- yes | nosetests GPy/testing
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@ -218,8 +218,8 @@ class GPBase(Model):
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Y = self.likelihood.data
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for d in which_data_ycols:
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m_d = m[:,d].reshape(resolution, resolution).T
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ax.contour(x, y, m_d, levels, vmin=m.min(), vmax=m.max(), cmap=pb.cm.jet)
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ax.scatter(self.X[which_data_rows, free_dims[0]], self.X[which_data_rows, free_dims[1]], 40, Y[which_data_rows, d], cmap=pb.cm.jet, vmin=m.min(), vmax=m.max(), linewidth=0.)
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contour = ax.contour(x, y, m_d, levels, vmin=m.min(), vmax=m.max(), cmap=pb.cm.jet)
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scatter = ax.scatter(self.X[which_data_rows, free_dims[0]], self.X[which_data_rows, free_dims[1]], 40, Y[which_data_rows, d], cmap=pb.cm.jet, vmin=m.min(), vmax=m.max(), linewidth=0.)
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#set the limits of the plot to some sensible values
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ax.set_xlim(xmin[0], xmax[0])
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@ -227,7 +227,7 @@ class GPBase(Model):
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if samples:
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warnings.warn("Samples are rather difficult to plot for 2D inputs...")
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return contour, scatter
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else:
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raise NotImplementedError, "Cannot define a frame with more than two input dimensions"
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@ -381,7 +381,7 @@ class SparseGP(GPBase):
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which_data_ycols='all', which_parts='all', fixed_inputs=[],
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plot_raw=False,
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levels=20, samples=0, fignum=None, ax=None, resolution=None):
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"""
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"""
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Plot the posterior of the sparse GP.
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- In one dimension, the function is plotted with a shaded region identifying two standard deviations.
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- In two dimsensions, a contour-plot shows the mean predicted function
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@ -417,6 +417,11 @@ class SparseGP(GPBase):
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:param levels: for 2D plotting, the number of contour levels to use is ax is None, create a new figure
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"""
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#deal work out which ax to plot on
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#Need these because we use which_data_rows in this function not just base
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if which_data_rows == 'all':
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which_data_rows = slice(None)
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if which_data_ycols == 'all':
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which_data_ycols = np.arange(self.output_dim)
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if ax is None:
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fig = pb.figure(num=fignum)
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ax = fig.add_subplot(111)
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@ -37,7 +37,6 @@ class SVIGP(GPBase):
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self.Y = self.likelihood.Y.copy()
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self.Z = Z
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self.num_inducing = Z.shape[0]
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self.batchcounter = 0
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self.epochs = 0
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self.iterations = 0
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@ -305,12 +304,12 @@ class SVIGP(GPBase):
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#Iterate!
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for i in range(iterations):
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#store the current configuration for plotting later
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self._param_trace.append(self._get_params())
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self._ll_trace.append(self.log_likelihood() + self.log_prior())
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#load a batch
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#load a batch and do the appropriate computations (kernel matrices, etc)
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self.load_batch()
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#compute the (stochastic) gradient
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@ -320,7 +319,8 @@ class SVIGP(GPBase):
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#compute the steps in all parameters
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vb_step = self.vb_steplength*natgrads[0]
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if (self.epochs>=1):#only move the parameters after the first epoch
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#only move the parameters after the first epoch and only if the steplength is nonzero
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if (self.epochs>=1) and (self.param_steplength > 0):
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param_step = self.momentum*param_step + self.param_steplength*grads
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else:
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param_step = 0.
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@ -342,6 +342,8 @@ class SVIGP(GPBase):
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if self.epochs > 10:
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self._adapt_steplength()
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self._vb_steplength_trace.append(self.vb_steplength)
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self._param_steplength_trace.append(self.param_steplength)
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self.iterations += 1
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@ -350,17 +352,20 @@ class SVIGP(GPBase):
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if self.adapt_vb_steplength:
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# self._adaptive_vb_steplength()
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self._adaptive_vb_steplength_KL()
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self._vb_steplength_trace.append(self.vb_steplength)
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assert self.vb_steplength > 0
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#self._vb_steplength_trace.append(self.vb_steplength)
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assert self.vb_steplength >= 0
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if self.adapt_param_steplength:
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self._adaptive_param_steplength()
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# self._adaptive_param_steplength_log()
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# self._adaptive_param_steplength_from_vb()
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self._param_steplength_trace.append(self.param_steplength)
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#self._param_steplength_trace.append(self.param_steplength)
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def _adaptive_param_steplength(self):
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decr_factor = 0.02
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if hasattr(self, 'adapt_param_steplength_decr'):
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decr_factor = self.adapt_param_steplength_decr
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else:
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decr_factor = 0.02
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g_tp = self._transform_gradients(self._log_likelihood_gradients())
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self.gbar_tp = (1-1/self.tau_tp)*self.gbar_tp + 1/self.tau_tp * g_tp
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self.hbar_tp = (1-1/self.tau_tp)*self.hbar_tp + 1/self.tau_tp * np.dot(g_tp.T, g_tp)
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@ -1,19 +0,0 @@
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'''
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Created on 6 Nov 2013
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@author: maxz
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'''
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from parameterized import Parameterized
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from parameter import Param
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class Normal(Parameterized):
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'''
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Normal distribution for variational approximations.
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holds the means and variances for a factorizing multivariate normal distribution
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'''
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def __init__(self, name, means, variances):
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Parameterized.__init__(self, name=name)
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self.means = Param("mean", means)
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self.variances = Param('variance', variances)
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self.add_parameters(self.means, self.variances)
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@ -37,20 +37,32 @@ def bgplvm_test_model(seed=default_seed, optimize=False, verbose=1, plot=False):
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# k = GPy.kern.rbf(input_dim, .5, _np.ones(input_dim) * 2., ARD=True) + GPy.kern.linear(input_dim, _np.ones(input_dim) * .2, ARD=True)
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m = GPy.models.BayesianGPLVM(lik, input_dim, kernel=k, num_inducing=num_inducing)
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#===========================================================================
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# randomly obstruct data with percentage p
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p = .8
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Y_obstruct = Y.copy()
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Y_obstruct[_np.random.uniform(size=(Y.shape)) < p] = _np.nan
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#===========================================================================
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m2 = GPy.models.BayesianGPLVMWithMissingData(Y_obstruct, input_dim, kernel=k, num_inducing=num_inducing)
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m.lengthscales = lengthscales
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if plot:
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import matplotlib.pyplot as pb
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m.plot()
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pb.title('PCA initialisation')
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m2.plot()
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pb.title('PCA initialisation')
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if optimize:
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m.optimize('scg', messages=verbose)
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m2.optimize('scg', messages=verbose)
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if plot:
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m.plot()
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pb.title('After optimisation')
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m2.plot()
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pb.title('After optimisation')
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return m
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return m, m2
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def gplvm_oil_100(optimize=True, verbose=1, plot=True):
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import GPy
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@ -217,7 +229,7 @@ def _simulate_sincos(D1, D2, D3, N, num_inducing, Q, plot_sim=False):
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ax.legend()
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for i, Y in enumerate(Ylist):
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ax = fig.add_subplot(2, len(Ylist), len(Ylist) + 1 + i)
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ax.imshow(Y, aspect='auto', cmap=cm.gray)
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ax.imshow(Y, aspect='auto', cmap=cm.gray) # @UndefinedVariable
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ax.set_title("Y{}".format(i + 1))
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pylab.draw()
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pylab.tight_layout()
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@ -451,12 +463,9 @@ def cmu_mocap(subject='35', motion=['01'], in_place=True, optimize=True, verbose
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if in_place:
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# Make figure move in place.
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data['Y'][:, 0:3] = 0.0
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m = GPy.models.GPLVM(data['Y'], 2, normalize_Y=True)
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if optimize:
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m.optimize(messages=verbose, max_f_eval=10000)
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if optimize: m.optimize(messages=verbose, max_f_eval=10000)
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if plot:
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ax = m.plot_latent()
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y = m.likelihood.Y[0, :]
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@ -273,27 +273,6 @@ def toy_rbf_1d_50(optimize=True, plot=True):
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return m
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def toy_poisson_rbf_1d(optimize=True, plot=True):
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"""Run a simple demonstration of a standard Gaussian process fitting it to data sampled from an RBF covariance."""
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x_len = 400
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X = np.linspace(0, 10, x_len)[:, None]
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f_true = np.random.multivariate_normal(np.zeros(x_len), GPy.kern.rbf(1).K(X))
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Y = np.array([np.random.poisson(np.exp(f)) for f in f_true]).reshape(x_len,1)
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noise_model = GPy.likelihoods.poisson()
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likelihood = GPy.likelihoods.EP(Y,noise_model)
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# create simple GP Model
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m = GPy.models.GPRegression(X, Y, likelihood=likelihood)
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if optimize:
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m.optimize('bfgs')
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if plot:
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m.plot()
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return m
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def toy_poisson_rbf_1d_laplace(optimize=True, plot=True):
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"""Run a simple demonstration of a standard Gaussian process fitting it to data sampled from an RBF covariance."""
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optimizer='scg'
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@ -7,7 +7,7 @@ import numpy as np
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def index_to_slices(index):
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"""
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take a numpy array of integers (index) and return a nested list of slices such that the slices describe the start, stop points for each integer in the index.
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take a numpy array of integers (index) and return a nested list of slices such that the slices describe the start, stop points for each integer in the index.
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e.g.
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>>> index = np.asarray([0,0,0,1,1,1,2,2,2])
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@ -38,7 +38,7 @@ class ODE_UY(Kernpart):
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:param input_dim: the number of input dimension, has to be equal to one
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:type input_dim: int
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:param input_lengthU: the number of input U length
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:type input_dim: int
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:type input_dim: int
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:param varianceU: variance of the driving GP
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:type varianceU: float
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:param lengthscaleU: lengthscale of the driving GP (sqrt(3)/lengthscaleU)
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@ -96,7 +96,7 @@ class ODE_UY(Kernpart):
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def K(self, X, X2, target):
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"""Compute the covariance matrix between X and X2."""
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# model : a * dy/dt + b * y = U
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#lu=sqrt(3)/theta1 ly=1/theta2 theta2= a/b :thetay sigma2=1/(2ab) :sigmay
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#lu=sqrt(3)/theta1 ly=1/theta2 theta2= a/b :thetay sigma2=1/(2ab) :sigmay
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X,slices = X[:,:-1],index_to_slices(X[:,-1])
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if X2 is None:
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@ -110,24 +110,31 @@ class ODE_UY(Kernpart):
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ly=1/self.lengthscaleY
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lu=np.sqrt(3)/self.lengthscaleU
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#iu=self.input_lengthU #dimention of U
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Vu=self.varianceU
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Vy=self.varianceY
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# kernel for kuu matern3/2
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kuu = lambda dist:Vu * (1 + lu* np.abs(dist)) * np.exp(-lu * np.abs(dist))
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# kernel for kyy
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k1 = lambda dist:np.exp(-ly*np.abs(dist))*(2*lu+ly)/(lu+ly)**2
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k2 = lambda dist:(np.exp(-lu*dist)*(ly-2*lu+lu*ly*dist-lu**2*dist) + np.exp(-ly*dist)*(2*lu-ly) ) / (ly-lu)**2
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k2 = lambda dist:(np.exp(-lu*dist)*(ly-2*lu+lu*ly*dist-lu**2*dist) + np.exp(-ly*dist)*(2*lu-ly) ) / (ly-lu)**2
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k3 = lambda dist:np.exp(-lu*dist) * ( (1+lu*dist)/(lu+ly) + (lu)/(lu+ly)**2 )
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kyy = lambda dist:Vu*Vy*(k1(dist) + k2(dist) + k3(dist))
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# cross covariance function
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kyu3 = lambda dist:np.exp(-lu*dist)/(lu+ly)*(1+lu*(dist+1/(lu+ly)))
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# cross covariance kyu
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kyup = lambda dist:Vu*Vy*(k1(dist)+k2(dist)) #t>0 kyu
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kyun = lambda dist:Vu*Vy*(kyu3(dist)) #t<0 kyu
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# cross covariance kuy
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kuyp = lambda dist:Vu*Vy*(kyu3(dist)) #t>0 kuy
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kuyn = lambda dist:Vu*Vy*(k1(dist)+k2(dist)) #t<0 kuy
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for i, s1 in enumerate(slices):
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for j, s2 in enumerate(slices2):
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for ss1 in s1:
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@ -135,12 +142,13 @@ class ODE_UY(Kernpart):
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if i==0 and j==0:
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target[ss1,ss2] = kuu(np.abs(rdist[ss1,ss2]))
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elif i==0 and j==1:
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target[ss1,ss2] = np.where( rdist[ss1,ss2]>0 , kuyp(np.abs(rdist[ss1,ss2])), kuyn(np.abs(rdist[s1[0],s2[0]]) ) )
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#target[ss1,ss2] = np.where( rdist[ss1,ss2]>0 , kuyp(np.abs(rdist[ss1,ss2])), kuyn(np.abs(rdist[s1[0],s2[0]]) ) )
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target[ss1,ss2] = np.where( rdist[ss1,ss2]>0 , kuyp(np.abs(rdist[ss1,ss2])), kuyn(np.abs(rdist[ss1,ss2]) ) )
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elif i==1 and j==1:
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target[ss1,ss2] = kyy(np.abs(rdist[ss1,ss2]))
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else:
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target[ss1,ss2] = np.where( rdist[ss1,ss2]>0 , kyup(np.abs(rdist[ss1,ss2])), kyun(np.abs(rdist[s1[0],s2[0]]) ) )
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#target[ss1,ss2] = np.where( rdist[ss1,ss2]>0 , kyup(np.abs(rdist[ss1,ss2])), kyun(np.abs(rdist[s1[0],s2[0]]) ) )
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target[ss1,ss2] = np.where( rdist[ss1,ss2]>0 , kyup(np.abs(rdist[ss1,ss2])), kyun(np.abs(rdist[ss1,ss2]) ) )
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#KUU = kuu(np.abs(rdist[:iu,:iu]))
|
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@ -160,22 +168,22 @@ class ODE_UY(Kernpart):
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lu=np.sqrt(3)/self.lengthscaleU
|
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#ly=self.lengthscaleY
|
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#lu=self.lengthscaleU
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|
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k1 = (2*lu+ly)/(lu+ly)**2
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k2 = (ly-2*lu + 2*lu-ly ) / (ly-lu)**2
|
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k3 = 1/(lu+ly) + (lu)/(lu+ly)**2
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k2 = (ly-2*lu + 2*lu-ly ) / (ly-lu)**2
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k3 = 1/(lu+ly) + (lu)/(lu+ly)**2
|
||||
|
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slices = index_to_slices(X[:,-1])
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for i, ss1 in enumerate(slices):
|
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for s1 in ss1:
|
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if i==0:
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target[s1]+= self.varianceU
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target[s1]+= self.varianceU
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elif i==1:
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target[s1]+= self.varianceU*self.varianceY*(k1+k2+k3)
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else:
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raise ValueError, "invalid input/output index"
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#target[slices[0][0]]+= self.varianceU #matern32 diag
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#target[slices[1][0]]+= self.varianceU*self.varianceY*(k1+k2+k3) # diag
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||||
|
|
@ -186,20 +194,30 @@ class ODE_UY(Kernpart):
|
|||
|
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def dK_dtheta(self, dL_dK, X, X2, target):
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"""derivative of the covariance matrix with respect to the parameters."""
|
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if X2 is None: X2 = X
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dist = np.abs(X - X2.T)
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||||
|
||||
X,slices = X[:,:-1],index_to_slices(X[:,-1])
|
||||
if X2 is None:
|
||||
X2,slices2 = X,slices
|
||||
else:
|
||||
X2,slices2 = X2[:,:-1],index_to_slices(X2[:,-1])
|
||||
|
||||
|
||||
#rdist = X[:,0][:,None] - X2[:,0][:,None].T
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rdist = X - X2.T
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ly=1/self.lengthscaleY
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lu=np.sqrt(3)/self.lengthscaleU
|
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#ly=self.lengthscaleY
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||||
#lu=self.lengthscaleU
|
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|
||||
rd=rdist.shape[0]
|
||||
dktheta1 = np.zeros([rd,rd])
|
||||
dktheta2 = np.zeros([rd,rd])
|
||||
dkUdvar = np.zeros([rd,rd])
|
||||
dkYdvar = np.zeros([rd,rd])
|
||||
|
||||
# dk dtheta for UU
|
||||
UUdtheta1 = lambda dist: np.exp(-lu* dist)*dist + (-dist)*np.exp(-lu* dist)*(1+lu*dist)
|
||||
UUdtheta2 = lambda dist: 0
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#UUdvar = lambda dist: (1 + lu*dist)*np.exp(-lu*dist)
|
||||
UUdvar = lambda dist: (1 + lu* np.abs(dist)) * np.exp(-lu * np.abs(dist))
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|
||||
# dk dtheta for YY
|
||||
|
||||
dk1theta1 = lambda dist: np.exp(-ly*dist)*2*(-lu)/(lu+ly)**3
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||||
#c=np.sqrt(3)
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||||
|
|
@ -207,16 +225,16 @@ class ODE_UY(Kernpart):
|
|||
#t2=1/ly
|
||||
#dk1theta1=np.exp(-dist*ly)*t2*( (2*c*t2+2*t1)/(c*t2+t1)**2 -2*(2*c*t2*t1+t1**2)/(c*t2+t1)**3 )
|
||||
|
||||
dk2theta1 = lambda dist: 1*(
|
||||
np.exp(-lu*dist)*dist*(-ly+2*lu-lu*ly*dist+dist*lu**2)*(ly-lu)**(-2) + np.exp(-lu*dist)*(-2+ly*dist-2*dist*lu)*(ly-lu)**(-2)
|
||||
+np.exp(-dist*lu)*(ly-2*lu+ly*lu*dist-dist*lu**2)*2*(ly-lu)**(-3)
|
||||
dk2theta1 = lambda dist: 1*(
|
||||
np.exp(-lu*dist)*dist*(-ly+2*lu-lu*ly*dist+dist*lu**2)*(ly-lu)**(-2) + np.exp(-lu*dist)*(-2+ly*dist-2*dist*lu)*(ly-lu)**(-2)
|
||||
+np.exp(-dist*lu)*(ly-2*lu+ly*lu*dist-dist*lu**2)*2*(ly-lu)**(-3)
|
||||
+np.exp(-dist*ly)*2*(ly-lu)**(-2)
|
||||
+np.exp(-dist*ly)*2*(2*lu-ly)*(ly-lu)**(-3)
|
||||
)
|
||||
|
||||
|
||||
dk3theta1 = lambda dist: np.exp(-dist*lu)*(lu+ly)**(-2)*((2*lu+ly+dist*lu**2+lu*ly*dist)*(-dist-2/(lu+ly))+2+2*lu*dist+ly*dist)
|
||||
|
||||
dktheta1 = lambda dist: self.varianceU*self.varianceY*(dk1theta1+dk2theta1+dk3theta1)
|
||||
#dktheta1 = lambda dist: self.varianceU*self.varianceY*(dk1theta1+dk2theta1+dk3theta1)
|
||||
|
||||
|
||||
|
||||
|
|
@ -230,14 +248,35 @@ class ODE_UY(Kernpart):
|
|||
|
||||
dk3theta2 = lambda dist: np.exp(-dist*lu) * (-3*lu-ly-dist*lu**2-lu*ly*dist)/(lu+ly)**3
|
||||
|
||||
dktheta2 = lambda dist: self.varianceU*self.varianceY*(dk1theta2 + dk2theta2 +dk3theta2)
|
||||
|
||||
|
||||
#dktheta2 = lambda dist: self.varianceU*self.varianceY*(dk1theta2 + dk2theta2 +dk3theta2)
|
||||
|
||||
# kyy kernel
|
||||
#k1 = lambda dist: np.exp(-ly*dist)*(2*lu+ly)/(lu+ly)**2
|
||||
#k2 = lambda dist: (np.exp(-lu*dist)*(ly-2*lu+lu*ly*dist-lu**2*dist) + np.exp(-ly*dist)*(2*lu-ly) ) / (ly-lu)**2
|
||||
#k3 = lambda dist: np.exp(-lu*dist) * ( (1+lu*dist)/(lu+ly) + (lu)/(lu+ly)**2 )
|
||||
k1 = lambda dist: np.exp(-ly*dist)*(2*lu+ly)/(lu+ly)**2
|
||||
k2 = lambda dist: (np.exp(-lu*dist)*(ly-2*lu+lu*ly*dist-lu**2*dist) + np.exp(-ly*dist)*(2*lu-ly) ) / (ly-lu)**2
|
||||
k2 = lambda dist: (np.exp(-lu*dist)*(ly-2*lu+lu*ly*dist-lu**2*dist) + np.exp(-ly*dist)*(2*lu-ly) ) / (ly-lu)**2
|
||||
k3 = lambda dist: np.exp(-lu*dist) * ( (1+lu*dist)/(lu+ly) + (lu)/(lu+ly)**2 )
|
||||
dkdvar = k1+k2+k3
|
||||
#dkdvar = k1+k2+k3
|
||||
|
||||
#cross covariance kernel
|
||||
kyu3 = lambda dist:np.exp(-lu*dist)/(lu+ly)*(1+lu*(dist+1/(lu+ly)))
|
||||
|
||||
# dk dtheta for UY
|
||||
dkcrtheta2 = lambda dist: np.exp(-lu*dist) * ( (-1)*(lu+ly)**(-2)*(1+lu*dist+lu*(lu+ly)**(-1)) + (lu+ly)**(-1)*(-lu)*(lu+ly)**(-2) )
|
||||
dkcrtheta1 = lambda dist: np.exp(-lu*dist)*(lu+ly)**(-1)* ( (-dist)*(1+dist*lu+lu*(lu+ly)**(-1)) - (lu+ly)**(-1)*(1+dist*lu+lu*(lu+ly)**(-1)) +dist+(lu+ly)**(-1)-lu*(lu+ly)**(-2) )
|
||||
#dkuyp dtheta
|
||||
#dkuyp dtheta1 = self.varianceU*self.varianceY* (dk1theta1() + dk2theta1())
|
||||
#dkuyp dtheta2 = self.varianceU*self.varianceY* (dk1theta2() + dk2theta2())
|
||||
#dkuyp dVar = k1() + k2()
|
||||
|
||||
|
||||
#dkyup dtheta
|
||||
#dkyun dtheta1 = self.varianceU*self.varianceY* (dk1theta1() + dk2theta1())
|
||||
#dkyun dtheta2 = self.varianceU*self.varianceY* (dk1theta2() + dk2theta2())
|
||||
#dkyup dVar = k1() + k2() #
|
||||
|
||||
|
||||
|
||||
|
||||
for i, s1 in enumerate(slices):
|
||||
|
|
@ -246,20 +285,35 @@ class ODE_UY(Kernpart):
|
|||
for ss2 in s2:
|
||||
if i==0 and j==0:
|
||||
#target[ss1,ss2] = kuu(np.abs(rdist[ss1,ss2]))
|
||||
dktheta1[ss1,ss2] = self.varianceU*self.varianceY*UUdtheta1(np.abs(rdist[ss1,ss2]))
|
||||
dktheta2[ss1,ss2] = 0
|
||||
dkUdvar[ss1,ss2] = UUdvar(np.abs(rdist[ss1,ss2]))
|
||||
dkYdvar[ss1,ss2] = 0
|
||||
elif i==0 and j==1:
|
||||
#target[ss1,ss2] = np.where( rdist[ss1,ss2]>0 , kuyp(np.abs(rdist[ss1,ss2])), kuyn(np.abs(rdist[s1[0],s2[0]]) ) )
|
||||
#dktheta1[ss1,ss2] =
|
||||
#dktheta2[ss1,ss2] =
|
||||
#dkdvar[ss1,ss2] = np.where( rdist[ss1,ss2]>0 , kuyp(np.abs(rdist[ss1,ss2])), kuyn(np.abs(rdist[s1[0],s2[0]]) ) )
|
||||
dktheta1[ss1,ss2] = np.where( rdist[ss1,ss2]>0 , dkcrtheta1(np.abs(rdist[ss1,ss2])) ,self.varianceU*self.varianceY*(dk1theta1(np.abs(rdist[ss1,ss2]))+dk2theta1(np.abs(rdist[ss1,ss2]))) )
|
||||
dktheta2[ss1,ss2] = np.where( rdist[ss1,ss2]>0 , dkcrtheta2(np.abs(rdist[ss1,ss2])) ,self.varianceU*self.varianceY*(dk1theta2(np.abs(rdist[ss1,ss2]))+dk2theta2(np.abs(rdist[ss1,ss2]))) )
|
||||
dkUdvar[ss1,ss2] = np.where( rdist[ss1,ss2]>0 , kyu3(np.abs(rdist[ss1,ss2])) ,k1(np.abs(rdist[ss1,ss2]))+k2(np.abs(rdist[ss1,ss2])) )
|
||||
dkYdvar[ss1,ss2] = dkUdvar[ss1,ss2]
|
||||
elif i==1 and j==1:
|
||||
#target[ss1,ss2] = kyy(np.abs(rdist[ss1,ss2]))
|
||||
dktheta1[ss1,ss2] = self.varianceU*self.varianceY*(dk1theta1(np.abs(rdist[ss1,ss2]))+dk2theta1(np.abs(rdist[ss1,ss2]))+dk3theta1(np.abs(rdist[ss1,ss2])))
|
||||
dktheta2[ss1,ss2] = self.varianceU*self.varianceY*(dk1theta2(np.abs(rdist[ss1,ss2])) + dk2theta2(np.abs(rdist[ss1,ss2])) +dk3theta2(np.abs(rdist[ss1,ss2])))
|
||||
dkUdvar[ss1,ss2] = (k1(np.abs(rdist[ss1,ss2]))+k2(np.abs(rdist[ss1,ss2]))+k3(np.abs(rdist[ss1,ss2])) )
|
||||
dkYdvar[ss1,ss2] = dkUdvar[ss1,ss2]
|
||||
else:
|
||||
#target[ss1,ss2] = np.where( rdist[ss1,ss2]>0 , kyup(np.abs(rdist[ss1,ss2])), kyun(np.abs(rdist[s1[0],s2[0]]) ) )
|
||||
dktheta1[ss1,ss2] = np.where( rdist[ss1,ss2]>0 ,self.varianceU*self.varianceY*(dk1theta1(np.abs(rdist[ss1,ss2]))+dk2theta1(np.abs(rdist[ss1,ss2]))) , dkcrtheta1(np.abs(rdist[ss1,ss2])) )
|
||||
dktheta2[ss1,ss2] = np.where( rdist[ss1,ss2]>0 ,self.varianceU*self.varianceY*(dk1theta2(np.abs(rdist[ss1,ss2]))+dk2theta2(np.abs(rdist[ss1,ss2]))) , dkcrtheta2(np.abs(rdist[ss1,ss2])) )
|
||||
dkUdvar[ss1,ss2] = np.where( rdist[ss1,ss2]>0 , k1(np.abs(rdist[ss1,ss2]))+k2(np.abs(rdist[ss1,ss2])), kyu3(np.abs(rdist[ss1,ss2])) )
|
||||
dkYdvar[ss1,ss2] = dkUdvar[ss1,ss2]
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
target[0] += np.sum(self.varianceY*dkdvar * dL_dK)
|
||||
target[1] += np.sum(self.varianceU*dkdvar * dL_dK)
|
||||
target[0] += np.sum(self.varianceY*dkUdvar * dL_dK)
|
||||
target[1] += np.sum(self.varianceU*dkYdvar * dL_dK)
|
||||
target[2] += np.sum(dktheta1*(-np.sqrt(3)*self.lengthscaleU**(-2)) * dL_dK)
|
||||
target[3] += np.sum(dktheta2*(-self.lengthscaleY**(-2)) * dL_dK)
|
||||
|
||||
|
|
|
|||
|
|
@ -250,8 +250,11 @@ class Laplace(likelihood):
|
|||
self.W = -self.noise_model.d2logpdf_df2(self.f_hat, self.data, extra_data=self.extra_data)
|
||||
|
||||
if not self.noise_model.log_concave:
|
||||
#print "Under 1e-10: {}".format(np.sum(self.W < 1e-6))
|
||||
self.W[self.W < 1e-6] = 1e-6 # FIXME-HACK: This is a hack since GPy can't handle negative variances which can occur
|
||||
i = self.W < 1e-6
|
||||
if np.any(i):
|
||||
warnings.warn('truncating non log-concave likelihood curvature')
|
||||
# FIXME-HACK: This is a hack since GPy can't handle negative variances which can occur
|
||||
self.W[i] = 1e-6
|
||||
|
||||
self.W12BiW12, self.ln_B_det = self._compute_B_statistics(self.K, self.W, np.eye(self.N))
|
||||
|
||||
|
|
@ -270,14 +273,14 @@ class Laplace(likelihood):
|
|||
:type W: Vector of diagonal values of hessian (1xN)
|
||||
:param a: Matrix to calculate W12BiW12a
|
||||
:type a: Matrix NxN
|
||||
:returns: (W12BiW12, ln_B_det)
|
||||
:returns: (W12BiW12a, ln_B_det)
|
||||
"""
|
||||
if not self.noise_model.log_concave:
|
||||
#print "Under 1e-10: {}".format(np.sum(W < 1e-6))
|
||||
W[W < 1e-6] = 1e-6 # FIXME-HACK: This is a hack since GPy can't handle negative variances which can occur
|
||||
# If the likelihood is non-log-concave. We wan't to say that there is a negative variance
|
||||
# To cause the posterior to become less certain than the prior and likelihood,
|
||||
# This is a property only held by non-log-concave likelihoods
|
||||
W[W < 1e-10] = 1e-10 # FIXME-HACK: This is a hack since GPy can't handle negative variances which can occur
|
||||
# If the likelihood is non-log-concave. We wan't to say that there is a negative variance
|
||||
# To cause the posterior to become less certain than the prior and likelihood,
|
||||
# This is a property only held by non-log-concave likelihoods
|
||||
|
||||
|
||||
#W is diagonal so its sqrt is just the sqrt of the diagonal elements
|
||||
|
|
|
|||
|
|
@ -153,9 +153,11 @@ class NoiseDistribution(object):
|
|||
:param sigma: standard deviation of posterior
|
||||
|
||||
"""
|
||||
#import ipdb; ipdb.set_trace()
|
||||
def int_mean(f,m,v):
|
||||
return self._mean(f)*np.exp(-(0.5/v)*np.square(f - m))
|
||||
scaled_mean = [quad(int_mean, -np.inf, np.inf,args=(mj,s2j))[0] for mj,s2j in zip(mu,variance)]
|
||||
#scaled_mean = [quad(int_mean, -np.inf, np.inf,args=(mj,s2j))[0] for mj,s2j in zip(mu,variance)]
|
||||
scaled_mean = [quad(int_mean, mj-6*np.sqrt(s2j), mj+6*np.sqrt(s2j), args=(mj,s2j))[0] for mj,s2j in zip(mu,variance)]
|
||||
mean = np.array(scaled_mean)[:,None] / np.sqrt(2*np.pi*(variance))
|
||||
|
||||
return mean
|
||||
|
|
@ -172,16 +174,16 @@ class NoiseDistribution(object):
|
|||
:predictive_mean: output's predictive mean, if None _predictive_mean function will be called.
|
||||
|
||||
"""
|
||||
#sigma2 = sigma**2
|
||||
normalizer = np.sqrt(2*np.pi*variance)
|
||||
|
||||
# E( V(Y_star|f_star) )
|
||||
def int_var(f,m,v):
|
||||
return self._variance(f)*np.exp(-(0.5/v)*np.square(f - m))
|
||||
scaled_exp_variance = [quad(int_var, -np.inf, np.inf,args=(mj,s2j))[0] for mj,s2j in zip(mu,variance)]
|
||||
#Most of the weight is within 6 stds and this avoids some negative infinity and infinity problems of taking f^2
|
||||
scaled_exp_variance = [quad(int_var, mj-6*np.sqrt(s2j), mj+6*np.sqrt(s2j), args=(mj,s2j))[0] for mj,s2j in zip(mu,variance)]
|
||||
exp_var = np.array(scaled_exp_variance)[:,None] / normalizer
|
||||
|
||||
#V( E(Y_star|f_star) ) = E( E(Y_star|f_star)**2 ) - E( E(Y_star|f_star) )**2
|
||||
#V( E(Y_star|f_star) ) = E( E(Y_star|f_star)**2 ) - E( E(Y_star|f_star) )**2
|
||||
|
||||
#E( E(Y_star|f_star) )**2
|
||||
if predictive_mean is None:
|
||||
|
|
@ -189,9 +191,9 @@ class NoiseDistribution(object):
|
|||
predictive_mean_sq = predictive_mean**2
|
||||
|
||||
#E( E(Y_star|f_star)**2 )
|
||||
def int_pred_mean_sq(f,m,v,predictive_mean_sq):
|
||||
def int_pred_mean_sq(f,m,v):
|
||||
return self._mean(f)**2*np.exp(-(0.5/v)*np.square(f - m))
|
||||
scaled_exp_exp2 = [quad(int_pred_mean_sq, -np.inf, np.inf,args=(mj,s2j,pm2j))[0] for mj,s2j,pm2j in zip(mu,variance,predictive_mean_sq)]
|
||||
scaled_exp_exp2 = [quad(int_pred_mean_sq, mj-6*np.sqrt(s2j), mj+6*np.sqrt(s2j), args=(mj,s2j))[0] for mj,s2j in zip(mu,variance)]
|
||||
exp_exp2 = np.array(scaled_exp_exp2)[:,None] / normalizer
|
||||
|
||||
var_exp = exp_exp2 - predictive_mean_sq
|
||||
|
|
@ -408,17 +410,16 @@ class NoiseDistribution(object):
|
|||
axis=-1
|
||||
|
||||
#Calculate mean, variance and precentiles from samples
|
||||
print "WARNING: Using sampling to calculate mean, variance and predictive quantiles."
|
||||
warnings.warn("Using sampling to calculate mean, variance and predictive quantiles.")
|
||||
pred_mean = np.mean(samples, axis=axis)[:,None]
|
||||
pred_var = np.var(samples, axis=axis)[:,None]
|
||||
q1 = np.percentile(samples, 2.5, axis=axis)[:,None]
|
||||
q3 = np.percentile(samples, 97.5, axis=axis)[:,None]
|
||||
|
||||
else:
|
||||
|
||||
pred_mean = self.predictive_mean(mu, var)
|
||||
pred_var = self.predictive_variance(mu, var, pred_mean)
|
||||
print "WARNING: Predictive quantiles are only computed when sampling."
|
||||
warnings.warn("Predictive quantiles are only computed when sampling.")
|
||||
q1 = np.repeat(np.nan,pred_mean.size)[:,None]
|
||||
q3 = q1.copy()
|
||||
|
||||
|
|
|
|||
|
|
@ -1,18 +1,20 @@
|
|||
'''
|
||||
GPy Models
|
||||
==========
|
||||
.. module:: GPy.models
|
||||
|
||||
Implementations for common models used in GP regression and classification.
|
||||
The different models can be viewed in :mod:`GPy.models_modules`, which holds
|
||||
detailed explanations for the different models.
|
||||
|
||||
:warning: This module is a convienince module for endusers to use. For developers
|
||||
see :mod:`GPy.models_modules`, which holds the implementions for each model.
|
||||
.. note::
|
||||
This module is a convienince module for endusers to use. For developers
|
||||
see :mod:`GPy.models_modules`, which holds the implementions for each model.:
|
||||
|
||||
.. moduleauthor:: Max Zwiessele <ibinbei@gmail.com>
|
||||
'''
|
||||
|
||||
__updated__ = '2013-11-28'
|
||||
|
||||
from models_modules.bayesian_gplvm import BayesianGPLVM
|
||||
from models_modules.bayesian_gplvm import BayesianGPLVM, BayesianGPLVMWithMissingData
|
||||
from models_modules.gp_regression import GPRegression
|
||||
from models_modules.gp_classification import GPClassification#; _gp_classification = gp_classification ; del gp_classification
|
||||
from models_modules.sparse_gp_regression import SparseGPRegression#; _sparse_gp_regression = sparse_gp_regression ; del sparse_gp_regression
|
||||
|
|
|
|||
|
|
@ -2,17 +2,18 @@
|
|||
# Licensed under the BSD 3-clause license (see LICENSE.txt)
|
||||
|
||||
import numpy as np
|
||||
import itertools
|
||||
from matplotlib import pyplot
|
||||
|
||||
from ..core.sparse_gp import SparseGP
|
||||
from ..likelihoods import Gaussian
|
||||
from .. import kern
|
||||
import itertools
|
||||
from matplotlib.colors import colorConverter
|
||||
from GPy.inference.optimization import SCG
|
||||
from GPy.util import plot_latent, linalg
|
||||
from .gplvm import GPLVM
|
||||
from GPy.util.plot_latent import most_significant_input_dimensions
|
||||
from matplotlib import pyplot
|
||||
from GPy.core.model import Model
|
||||
from ..inference.optimization import SCG
|
||||
from ..util import plot_latent, linalg
|
||||
from .gplvm import GPLVM, initialise_latent
|
||||
from ..util.plot_latent import most_significant_input_dimensions
|
||||
from ..core.model import Model
|
||||
from ..util.subarray_and_sorting import common_subarrays
|
||||
|
||||
class BayesianGPLVM(SparseGP, GPLVM):
|
||||
"""
|
||||
|
|
@ -34,7 +35,7 @@ class BayesianGPLVM(SparseGP, GPLVM):
|
|||
likelihood = likelihood_or_Y
|
||||
|
||||
if X == None:
|
||||
X = self.initialise_latent(init, input_dim, likelihood.Y)
|
||||
X = initialise_latent(init, input_dim, likelihood.Y)
|
||||
self.init = init
|
||||
|
||||
if X_variance is None:
|
||||
|
|
@ -308,14 +309,36 @@ class BayesianGPLVMWithMissingData(Model):
|
|||
:type init: 'PCA' | 'random'
|
||||
"""
|
||||
def __init__(self, likelihood_or_Y, input_dim, X=None, X_variance=None, init='PCA', num_inducing=10,
|
||||
Z=None, kernel=None, missing=np.nan, **kwargs):
|
||||
Z=None, kernel=None, **kwargs):
|
||||
#=======================================================================
|
||||
# Filter Y, such that same missing data is at same positions.
|
||||
# If full rows are missing, delete them entirely!
|
||||
if type(likelihood_or_Y) is np.ndarray:
|
||||
likelihood = Gaussian(likelihood_or_Y)
|
||||
Y = likelihood_or_Y
|
||||
likelihood = Gaussian
|
||||
params = 1.
|
||||
normalize=None
|
||||
else:
|
||||
likelihood = likelihood_or_Y
|
||||
Y = likelihood_or_Y.Y
|
||||
likelihood = likelihood_or_Y.__class__
|
||||
params = likelihood_or_Y._get_params()
|
||||
if isinstance(likelihood_or_Y, Gaussian):
|
||||
normalize = True
|
||||
scale = likelihood_or_Y._scale
|
||||
offset = likelihood_or_Y._offset
|
||||
# Get common subrows
|
||||
filter_ = np.isnan(Y)
|
||||
self.subarray_indices = common_subarrays(filter_,axis=1)
|
||||
likelihoods = [likelihood(Y[~np.array(v,dtype=bool),:][:,ind]) for v,ind in self.subarray_indices.iteritems()]
|
||||
for l in likelihoods:
|
||||
l._set_params(params)
|
||||
if normalize: # get normalization in common
|
||||
l._scale = scale
|
||||
l._offset = offset
|
||||
#=======================================================================
|
||||
|
||||
if X == None:
|
||||
X = self.initialise_latent(init, input_dim, likelihood.Y)
|
||||
X = initialise_latent(init, input_dim, Y[:,np.any(np.isnan(Y),1)])
|
||||
self.init = init
|
||||
|
||||
if X_variance is None:
|
||||
|
|
@ -328,13 +351,52 @@ class BayesianGPLVMWithMissingData(Model):
|
|||
if kernel is None:
|
||||
kernel = kern.rbf(input_dim) # + kern.white(input_dim)
|
||||
|
||||
SparseGP.__init__(self, X, likelihood, kernel, Z=Z, X_variance=X_variance, **kwargs)
|
||||
self.submodels = [BayesianGPLVM(l, input_dim, X, X_variance, init, num_inducing, Z, kernel) for l in likelihoods]
|
||||
self.gref = self.submodels[0]
|
||||
#:type self.gref: BayesianGPLVM
|
||||
self.ensure_default_constraints()
|
||||
|
||||
def log_likelihood(self):
|
||||
ll = -self.gref.KL_divergence()
|
||||
for g in self.submodels:
|
||||
ll += SparseGP.log_likelihood(g)
|
||||
return ll
|
||||
|
||||
def _log_likelihood_gradients(self):
|
||||
dLdmu, dLdS = reduce(lambda a, b: [a[0] + b[0], a[1] + b[1]], (g.dL_dmuS() for g in self.bgplvms))
|
||||
dKLmu, dKLdS = self.gref.dKL_dmuS()
|
||||
dLdmu -= dKLmu
|
||||
dLdS -= dKLdS
|
||||
dLdmuS = np.hstack((dLdmu.flatten(), dLdS.flatten())).flatten()
|
||||
dldzt1 = reduce(lambda a, b: a + b, (SparseGP._log_likelihood_gradients(g)[:self.gref.num_inducing*self.gref.input_dim] for g in self.submodels))
|
||||
|
||||
return np.hstack((dLdmuS,
|
||||
dldzt1,
|
||||
np.hstack([np.hstack([g.dL_dtheta(),
|
||||
g.likelihood._gradients(\
|
||||
partial=g.partial_for_likelihood)]) \
|
||||
for g in self.submodels])))
|
||||
|
||||
def getstate(self):
|
||||
return Model.getstate(self)+[self.submodels,self.subarray_indices]
|
||||
|
||||
def setstate(self, state):
|
||||
self.subarray_indices = state.pop()
|
||||
self.submodels = state.pop()
|
||||
self.gref = self.submodels[0]
|
||||
Model.setstate(self, state)
|
||||
self._set_params(self._get_params())
|
||||
|
||||
def _get_param_names(self):
|
||||
X_names = sum([['X_%i_%i' % (n, q) for q in range(self.input_dim)] for n in range(self.num_data)], [])
|
||||
S_names = sum([['X_variance_%i_%i' % (n, q) for q in range(self.input_dim)] for n in range(self.num_data)], [])
|
||||
return (X_names + S_names + SparseGP._get_param_names(self))
|
||||
return (X_names + S_names + SparseGP._get_param_names(self.gref))
|
||||
|
||||
def _get_params(self):
|
||||
return self.gref._get_params()
|
||||
def _set_params(self, x):
|
||||
[g._set_params(x) for g in self.submodels]
|
||||
|
||||
|
||||
pass
|
||||
|
||||
|
|
|
|||
|
|
@ -10,6 +10,13 @@ from ..core import GP
|
|||
from ..likelihoods import Gaussian
|
||||
from .. import util
|
||||
|
||||
def initialise_latent(init, input_dim, Y):
|
||||
Xr = np.random.randn(Y.shape[0], input_dim)
|
||||
if init == 'pca':
|
||||
from ..util.linalg import pca
|
||||
PC = pca(Y, input_dim)[0]
|
||||
Xr[:PC.shape[0], :PC.shape[1]] = PC
|
||||
return Xr
|
||||
|
||||
class GPLVM(GP):
|
||||
"""
|
||||
|
|
@ -20,12 +27,12 @@ class GPLVM(GP):
|
|||
:param input_dim: latent dimensionality
|
||||
:type input_dim: int
|
||||
:param init: initialisation method for the latent space
|
||||
:type init: 'PCA'|'random'
|
||||
:type init: 'pca'|'random'
|
||||
|
||||
"""
|
||||
def __init__(self, Y, input_dim, init='PCA', X=None, kernel=None, normalize_Y=False):
|
||||
def __init__(self, Y, input_dim, init='pca', X=None, kernel=None, normalize_Y=False):
|
||||
if X is None:
|
||||
X = self.initialise_latent(init, input_dim, Y)
|
||||
X = initialise_latent(init, input_dim, Y)
|
||||
if kernel is None:
|
||||
kernel = kern.rbf(input_dim, ARD=input_dim > 1) + kern.bias(input_dim, np.exp(-2))
|
||||
likelihood = Gaussian(Y, normalize=normalize_Y, variance=np.exp(-2.))
|
||||
|
|
@ -33,14 +40,6 @@ class GPLVM(GP):
|
|||
self.set_prior('.*X', priors.Gaussian(0, 1))
|
||||
self.ensure_default_constraints()
|
||||
|
||||
def initialise_latent(self, init, input_dim, Y):
|
||||
Xr = np.random.randn(Y.shape[0], input_dim)
|
||||
if init == 'PCA':
|
||||
from ..util.linalg import PCA
|
||||
PC = PCA(Y, input_dim)[0]
|
||||
Xr[:PC.shape[0], :PC.shape[1]] = PC
|
||||
return Xr
|
||||
|
||||
def _get_param_names(self):
|
||||
return sum([['X_%i_%i' % (n, q) for q in range(self.input_dim)] for n in range(self.num_data)], []) + GP._get_param_names(self)
|
||||
|
||||
|
|
|
|||
|
|
@ -5,7 +5,7 @@ Created on 10 Apr 2013
|
|||
'''
|
||||
from GPy.core import Model
|
||||
from GPy.core import SparseGP
|
||||
from GPy.util.linalg import PCA
|
||||
from GPy.util.linalg import pca
|
||||
import numpy
|
||||
import itertools
|
||||
import pylab
|
||||
|
|
@ -26,8 +26,8 @@ class MRD(Model):
|
|||
:type input_dim: int
|
||||
:param initx: initialisation method for the latent space :
|
||||
|
||||
* 'concat' - PCA on concatenation of all datasets
|
||||
* 'single' - Concatenation of PCA on datasets, respectively
|
||||
* 'concat' - pca on concatenation of all datasets
|
||||
* 'single' - Concatenation of pca on datasets, respectively
|
||||
* 'random' - Random draw from a normal
|
||||
|
||||
:type initx: ['concat'|'single'|'random']
|
||||
|
|
@ -42,7 +42,7 @@ class MRD(Model):
|
|||
|
||||
"""
|
||||
def __init__(self, likelihood_or_Y_list, input_dim, num_inducing=10, names=None,
|
||||
kernels=None, initx='PCA',
|
||||
kernels=None, initx='pca',
|
||||
initz='permute', _debug=False, **kw):
|
||||
if names is None:
|
||||
self.names = ["{}".format(i) for i in range(len(likelihood_or_Y_list))]
|
||||
|
|
@ -237,7 +237,7 @@ class MRD(Model):
|
|||
partial=g.partial_for_likelihood)]) \
|
||||
for g in self.bgplvms])))
|
||||
|
||||
def _init_X(self, init='PCA', likelihood_list=None):
|
||||
def _init_X(self, init='pca', likelihood_list=None):
|
||||
if likelihood_list is None:
|
||||
likelihood_list = self.likelihood_list
|
||||
Ylist = []
|
||||
|
|
@ -248,11 +248,11 @@ class MRD(Model):
|
|||
Ylist.append(likelihood_or_Y.Y)
|
||||
del likelihood_list
|
||||
if init in "PCA_concat":
|
||||
X = PCA(numpy.hstack(Ylist), self.input_dim)[0]
|
||||
X = pca(numpy.hstack(Ylist), self.input_dim)[0]
|
||||
elif init in "PCA_single":
|
||||
X = numpy.zeros((Ylist[0].shape[0], self.input_dim))
|
||||
for qs, Y in itertools.izip(numpy.array_split(numpy.arange(self.input_dim), len(Ylist)), Ylist):
|
||||
X[:, qs] = PCA(Y, len(qs))[0]
|
||||
X[:, qs] = pca(Y, len(qs))[0]
|
||||
else: # init == 'random':
|
||||
X = numpy.random.randn(Ylist[0].shape[0], self.input_dim)
|
||||
self.X = X
|
||||
|
|
|
|||
|
|
@ -6,7 +6,7 @@ import numpy as np
|
|||
import pylab as pb
|
||||
import sys, pdb
|
||||
from sparse_gp_regression import SparseGPRegression
|
||||
from gplvm import GPLVM
|
||||
from gplvm import GPLVM, initialise_latent
|
||||
# from .. import kern
|
||||
# from ..core import model
|
||||
# from ..util.linalg import pdinv, PCA
|
||||
|
|
@ -24,7 +24,7 @@ class SparseGPLVM(SparseGPRegression, GPLVM):
|
|||
|
||||
"""
|
||||
def __init__(self, Y, input_dim, kernel=None, init='PCA', num_inducing=10):
|
||||
X = self.initialise_latent(init, input_dim, Y)
|
||||
X = initialise_latent(init, input_dim, Y)
|
||||
SparseGPRegression.__init__(self, X, Y, kernel=kernel, num_inducing=num_inducing)
|
||||
self.ensure_default_constraints()
|
||||
|
||||
|
|
|
|||
|
|
@ -19,25 +19,12 @@ class ExamplesTests(unittest.TestCase):
|
|||
def _model_instance(self, Model):
|
||||
self.assertTrue(isinstance(Model, GPy.models))
|
||||
|
||||
"""
|
||||
def model_instance_generator(model):
|
||||
def check_model_returned(self):
|
||||
self._model_instance(model)
|
||||
return check_model_returned
|
||||
|
||||
def checkgrads_generator(model):
|
||||
def model_checkgrads(self):
|
||||
self._checkgrad(model)
|
||||
return model_checkgrads
|
||||
"""
|
||||
|
||||
def model_checkgrads(model):
|
||||
model.randomize()
|
||||
#assert model.checkgrad()
|
||||
return model.checkgrad()
|
||||
#NOTE: Step as 1e-4, this should be acceptable for more peaky models
|
||||
return model.checkgrad(step=1e-4)
|
||||
|
||||
def model_instance(model):
|
||||
#assert isinstance(model, GPy.core.model)
|
||||
return isinstance(model, GPy.core.model.Model)
|
||||
|
||||
def flatten_nested(lst):
|
||||
|
|
@ -49,7 +36,7 @@ def flatten_nested(lst):
|
|||
result.append(element)
|
||||
return result
|
||||
|
||||
#@nottest
|
||||
@nottest
|
||||
def test_models():
|
||||
optimize=False
|
||||
plot=True
|
||||
|
|
@ -66,9 +53,11 @@ def test_models():
|
|||
print "After"
|
||||
print functions
|
||||
for example in functions:
|
||||
#if example[0] in ['oil', 'silhouette', 'GPLVM_oil_100', 'brendan_faces']:
|
||||
#print "SKIPPING"
|
||||
#continue
|
||||
if example[0] in ['epomeo_gpx']:
|
||||
#These are the edge cases that we might want to handle specially
|
||||
if example[0] == 'epomeo_gpx' and not GPy.util.datasets.gpxpy_available:
|
||||
print "Skipping as gpxpy is not available to parse GPS"
|
||||
continue
|
||||
|
||||
print "Testing example: ", example[0]
|
||||
# Generate model
|
||||
|
|
|
|||
|
|
@ -593,7 +593,6 @@ class LaplaceTests(unittest.TestCase):
|
|||
grad.checkgrad(verbose=1)
|
||||
self.assertTrue(grad.checkgrad())
|
||||
|
||||
#@unittest.skip('Not working yet, needs to be checked')
|
||||
def test_laplace_log_likelihood(self):
|
||||
debug = False
|
||||
real_std = 0.1
|
||||
|
|
|
|||
|
|
@ -29,7 +29,8 @@
|
|||
"urls":[
|
||||
"http://staffwww.dcs.shef.ac.uk/people/N.Lawrence/dataset_mirror/ankur_pose_data/"
|
||||
],
|
||||
"details":"Artificially generated data of silhouettes given poses. Note that the data does not display a left/right ambiguity because across the entire data set one of the arms sticks out more the the other, disambiguating the pose as to which way the individual is facing."
|
||||
"details":"Artificially generated data of silhouettes given poses. Note that the data does not display a left/right ambiguity because across the entire data set one of the arms sticks out more the the other, disambiguating the pose as to which way the individual is facing.",
|
||||
"size":1
|
||||
},
|
||||
"osu_accad":{
|
||||
"files":[
|
||||
|
|
@ -316,4 +317,4 @@
|
|||
],
|
||||
"size":2031872
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
|
|||
|
|
@ -26,7 +26,7 @@ def reporthook(a,b,c):
|
|||
# Global variables
|
||||
data_path = os.path.join(os.path.dirname(__file__), 'datasets')
|
||||
default_seed = 10000
|
||||
overide_manual_authorize=False
|
||||
overide_manual_authorize=True
|
||||
neil_url = 'http://staffwww.dcs.shef.ac.uk/people/N.Lawrence/dataset_mirror/'
|
||||
|
||||
# Read data resources from json file.
|
||||
|
|
@ -97,7 +97,7 @@ def download_url(url, store_directory, save_name = None, messages = True, suffix
|
|||
# if we wanted to get more sophisticated maybe we should check the response code here again even for successes.
|
||||
with open(save_name, 'wb') as f:
|
||||
f.write(response.read())
|
||||
|
||||
|
||||
#urllib.urlretrieve(url+suffix, save_name, reporthook)
|
||||
|
||||
def authorize_download(dataset_name=None):
|
||||
|
|
@ -235,7 +235,7 @@ if gpxpy_available:
|
|||
gpx_file.close()
|
||||
return data_details_return({'X' : X, 'info' : 'Data is an array containing time in seconds, latitude, longitude and elevation in that order.'}, data_set)
|
||||
|
||||
del gpxpy_available
|
||||
#del gpxpy_available
|
||||
|
||||
|
||||
|
||||
|
|
|
|||
114
GPy/util/diag.py
Normal file
114
GPy/util/diag.py
Normal file
|
|
@ -0,0 +1,114 @@
|
|||
'''
|
||||
.. module:: GPy.util.diag
|
||||
|
||||
.. moduleauthor:: Max Zwiessele <ibinbei@gmail.com>
|
||||
|
||||
'''
|
||||
__updated__ = '2013-12-03'
|
||||
|
||||
import numpy as np
|
||||
|
||||
def view(A, offset=0):
|
||||
"""
|
||||
Get a view on the diagonal elements of a 2D array.
|
||||
|
||||
This is actually a view (!) on the diagonal of the array, so you can
|
||||
in-place adjust the view.
|
||||
|
||||
:param :class:`ndarray` A: 2 dimensional numpy array
|
||||
:param int offset: view offset to give back (negative entries allowed)
|
||||
:rtype: :class:`ndarray` view of diag(A)
|
||||
|
||||
>>> import numpy as np
|
||||
>>> X = np.arange(9).reshape(3,3)
|
||||
>>> view(X)
|
||||
array([0, 4, 8])
|
||||
>>> d = view(X)
|
||||
>>> d += 2
|
||||
>>> view(X)
|
||||
array([ 2, 6, 10])
|
||||
>>> view(X, offset=-1)
|
||||
array([3, 7])
|
||||
>>> subtract(X, 3, offset=-1)
|
||||
array([[ 2, 1, 2],
|
||||
[ 0, 6, 5],
|
||||
[ 6, 4, 10]])
|
||||
"""
|
||||
from numpy.lib.stride_tricks import as_strided
|
||||
assert A.ndim == 2, "only implemented for 2 dimensions"
|
||||
assert A.shape[0] == A.shape[1], "attempting to get the view of non-square matrix?!"
|
||||
if offset > 0:
|
||||
return as_strided(A[0, offset:], shape=(A.shape[0] - offset, ), strides=((A.shape[0]+1)*A.itemsize, ))
|
||||
elif offset < 0:
|
||||
return as_strided(A[-offset:, 0], shape=(A.shape[0] + offset, ), strides=((A.shape[0]+1)*A.itemsize, ))
|
||||
else:
|
||||
return as_strided(A, shape=(A.shape[0], ), strides=((A.shape[0]+1)*A.itemsize, ))
|
||||
|
||||
def _diag_ufunc(A,b,offset,func):
|
||||
dA = view(A, offset); func(dA,b,dA)
|
||||
return A
|
||||
|
||||
def times(A, b, offset=0):
|
||||
"""
|
||||
Times the view of A with b in place (!).
|
||||
Returns modified A
|
||||
Broadcasting is allowed, thus b can be scalar.
|
||||
|
||||
if offset is not zero, make sure b is of right shape!
|
||||
|
||||
:param ndarray A: 2 dimensional array
|
||||
:param ndarray-like b: either one dimensional or scalar
|
||||
:param int offset: same as in view.
|
||||
:rtype: view of A, which is adjusted inplace
|
||||
"""
|
||||
return _diag_ufunc(A, b, offset, np.multiply)
|
||||
multiply = times
|
||||
|
||||
def divide(A, b, offset=0):
|
||||
"""
|
||||
Divide the view of A by b in place (!).
|
||||
Returns modified A
|
||||
Broadcasting is allowed, thus b can be scalar.
|
||||
|
||||
if offset is not zero, make sure b is of right shape!
|
||||
|
||||
:param ndarray A: 2 dimensional array
|
||||
:param ndarray-like b: either one dimensional or scalar
|
||||
:param int offset: same as in view.
|
||||
:rtype: view of A, which is adjusted inplace
|
||||
"""
|
||||
return _diag_ufunc(A, b, offset, np.divide)
|
||||
|
||||
def add(A, b, offset=0):
|
||||
"""
|
||||
Add b to the view of A in place (!).
|
||||
Returns modified A.
|
||||
Broadcasting is allowed, thus b can be scalar.
|
||||
|
||||
if offset is not zero, make sure b is of right shape!
|
||||
|
||||
:param ndarray A: 2 dimensional array
|
||||
:param ndarray-like b: either one dimensional or scalar
|
||||
:param int offset: same as in view.
|
||||
:rtype: view of A, which is adjusted inplace
|
||||
"""
|
||||
return _diag_ufunc(A, b, offset, np.add)
|
||||
|
||||
def subtract(A, b, offset=0):
|
||||
"""
|
||||
Subtract b from the view of A in place (!).
|
||||
Returns modified A.
|
||||
Broadcasting is allowed, thus b can be scalar.
|
||||
|
||||
if offset is not zero, make sure b is of right shape!
|
||||
|
||||
:param ndarray A: 2 dimensional array
|
||||
:param ndarray-like b: either one dimensional or scalar
|
||||
:param int offset: same as in view.
|
||||
:rtype: view of A, which is adjusted inplace
|
||||
"""
|
||||
return _diag_ufunc(A, b, offset, np.subtract)
|
||||
|
||||
if __name__ == '__main__':
|
||||
import doctest
|
||||
doctest.testmod()
|
||||
|
|
@ -217,7 +217,7 @@ def multiple_pdinv(A):
|
|||
return np.dstack(invs), np.array(halflogdets)
|
||||
|
||||
|
||||
def PCA(Y, input_dim):
|
||||
def pca(Y, input_dim):
|
||||
"""
|
||||
Principal component analysis: maximum likelihood solution by SVD
|
||||
|
||||
|
|
@ -230,7 +230,7 @@ def PCA(Y, input_dim):
|
|||
|
||||
"""
|
||||
if not np.allclose(Y.mean(axis=0), 0.0):
|
||||
print "Y is not zero mean, centering it locally (GPy.util.linalg.PCA)"
|
||||
print "Y is not zero mean, centering it locally (GPy.util.linalg.pca)"
|
||||
|
||||
# Y -= Y.mean(axis=0)
|
||||
|
||||
|
|
@ -241,6 +241,124 @@ def PCA(Y, input_dim):
|
|||
W *= v;
|
||||
return X, W.T
|
||||
|
||||
def ppca(Y, Q, iterations=100):
|
||||
"""
|
||||
EM implementation for probabilistic pca.
|
||||
|
||||
:param array-like Y: Observed Data
|
||||
:param int Q: Dimensionality for reduced array
|
||||
:param int iterations: number of iterations for EM
|
||||
"""
|
||||
from numpy.ma import dot as madot
|
||||
N, D = Y.shape
|
||||
# Initialise W randomly
|
||||
W = np.random.randn(D, Q) * 1e-3
|
||||
Y = np.ma.masked_invalid(Y, copy=0)
|
||||
mu = Y.mean(0)
|
||||
Ycentered = Y - mu
|
||||
try:
|
||||
for _ in range(iterations):
|
||||
exp_x = np.asarray_chkfinite(np.linalg.solve(W.T.dot(W), madot(W.T, Ycentered.T))).T
|
||||
W = np.asarray_chkfinite(np.linalg.solve(exp_x.T.dot(exp_x), madot(exp_x.T, Ycentered))).T
|
||||
except np.linalg.linalg.LinAlgError:
|
||||
#"converged"
|
||||
pass
|
||||
return np.asarray_chkfinite(exp_x), np.asarray_chkfinite(W)
|
||||
|
||||
def ppca_missing_data_at_random(Y, Q, iters=100):
|
||||
"""
|
||||
EM implementation of Probabilistic pca for when there is missing data.
|
||||
|
||||
Taken from <SheffieldML, https://github.com/SheffieldML>
|
||||
|
||||
.. math:
|
||||
\\mathbf{Y} = \mathbf{XW} + \\epsilon \\text{, where}
|
||||
\\epsilon = \\mathcal{N}(0, \\sigma^2 \mathbf{I})
|
||||
|
||||
:returns: X, W, sigma^2
|
||||
"""
|
||||
from numpy.ma import dot as madot
|
||||
import diag
|
||||
from GPy.util.subarray_and_sorting import common_subarrays
|
||||
import time
|
||||
debug = 1
|
||||
# Initialise W randomly
|
||||
N, D = Y.shape
|
||||
W = np.random.randn(Q, D) * 1e-3
|
||||
Y = np.ma.masked_invalid(Y, copy=1)
|
||||
nu = 1.
|
||||
#num_obs_i = 1./Y.count()
|
||||
Ycentered = Y - Y.mean(0)
|
||||
|
||||
X = np.zeros((N,Q))
|
||||
cs = common_subarrays(Y.mask)
|
||||
cr = common_subarrays(Y.mask, 1)
|
||||
Sigma = np.zeros((N, Q, Q))
|
||||
Sigma2 = np.zeros((N, Q, Q))
|
||||
mu = np.zeros(D)
|
||||
if debug:
|
||||
import matplotlib.pyplot as pylab
|
||||
fig = pylab.figure("FIT MISSING DATA");
|
||||
ax = fig.gca()
|
||||
ax.cla()
|
||||
lines = pylab.plot(np.zeros((N,Q)).dot(W))
|
||||
W2 = np.zeros((Q,D))
|
||||
|
||||
for i in range(iters):
|
||||
# Sigma = np.linalg.solve(diag.add(madot(W,W.T), nu), diag.times(np.eye(Q),nu))
|
||||
# exp_x = madot(madot(Ycentered, W.T),Sigma)/nu
|
||||
# Ycentered = (Y - exp_x.dot(W).mean(0))
|
||||
# #import ipdb;ipdb.set_trace()
|
||||
# #Ycentered = mu
|
||||
# W = np.linalg.solve(madot(exp_x.T,exp_x) + Sigma, madot(exp_x.T, Ycentered))
|
||||
# nu = (((Ycentered - madot(exp_x, W))**2).sum(0) + madot(W.T,madot(Sigma,W)).sum(0)).sum()/N
|
||||
for csi, (mask, index) in enumerate(cs.iteritems()):
|
||||
mask = ~np.array(mask)
|
||||
Sigma2[index, :, :] = nu * np.linalg.inv(diag.add(W2[:,mask].dot(W2[:,mask].T), nu))
|
||||
#X[index,:] = madot((Sigma[csi]/nu),madot(W,Ycentered[index].T))[:,0]
|
||||
X2 = ((Sigma2/nu) * (madot(Ycentered,W2.T).base)[:,:,None]).sum(-1)
|
||||
mu2 = (Y - X.dot(W)).mean(0)
|
||||
for n in range(N):
|
||||
Sigma[n] = nu * np.linalg.inv(diag.add(W[:,~Y.mask[n]].dot(W[:,~Y.mask[n]].T), nu))
|
||||
X[n, :] = (Sigma[n]/nu).dot(W[:,~Y.mask[n]].dot(Ycentered[n,~Y.mask[n]].T))
|
||||
for d in range(D):
|
||||
mu[d] = (Y[~Y.mask[:,d], d] - X[~Y.mask[:,d]].dot(W[:, d])).mean()
|
||||
Ycentered = (Y - mu)
|
||||
nu3 = 0.
|
||||
for cri, (mask, index) in enumerate(cr.iteritems()):
|
||||
mask = ~np.array(mask)
|
||||
W2[:,index] = np.linalg.solve(X[mask].T.dot(X[mask]) + Sigma[mask].sum(0), madot(X[mask].T, Ycentered[mask,index]))[:,None]
|
||||
W2[:,index] = np.linalg.solve(X.T.dot(X) + Sigma.sum(0), madot(X.T, Ycentered[:,index]))
|
||||
#nu += (((Ycentered[mask,index] - X[mask].dot(W[:,index]))**2).sum(0) + W[:,index].T.dot(Sigma[mask].sum(0).dot(W[:,index])).sum(0)).sum()
|
||||
nu3 += (((Ycentered[index] - X.dot(W[:,index]))**2).sum(0) + W[:,index].T.dot(Sigma.sum(0).dot(W[:,index])).sum(0)).sum()
|
||||
nu3 /= N
|
||||
nu = 0.
|
||||
nu2 = 0.
|
||||
W = np.zeros((Q,D))
|
||||
for j in range(D):
|
||||
W[:,j] = np.linalg.solve(X[~Y.mask[:,j]].T.dot(X[~Y.mask[:,j]]) + Sigma[~Y.mask[:,j]].sum(0), madot(X[~Y.mask[:,j]].T, Ycentered[~Y.mask[:,j],j]))
|
||||
nu2f = np.tensordot(W[:,j].T, Sigma[~Y.mask[:,j],:,:], [0,1]).dot(W[:,j])
|
||||
nu2s = W[:,j].T.dot(Sigma[~Y.mask[:,j],:,:].sum(0).dot(W[:,j]))
|
||||
nu2 += (((Ycentered[~Y.mask[:,j],j] - X[~Y.mask[:,j],:].dot(W[:,j]))**2) + nu2f).sum()
|
||||
for i in range(N):
|
||||
if not Y.mask[i,j]:
|
||||
nu += ((Ycentered[i,j] - X[i,:].dot(W[:,j]))**2) + W[:,j].T.dot(Sigma[i,:,:].dot(W[:,j]))
|
||||
nu /= N
|
||||
nu2 /= N
|
||||
nu4 = (((Ycentered - X.dot(W))**2).sum(0) + W.T.dot(Sigma.sum(0).dot(W)).sum(0)).sum()/N
|
||||
import ipdb;ipdb.set_trace()
|
||||
if debug:
|
||||
#print Sigma[0]
|
||||
print "nu:", nu, "sum(X):", X.sum()
|
||||
pred_y = X.dot(W)
|
||||
for x, l in zip(pred_y.T, lines):
|
||||
l.set_ydata(x)
|
||||
ax.autoscale_view()
|
||||
ax.set_ylim(pred_y.min(), pred_y.max())
|
||||
fig.canvas.draw()
|
||||
time.sleep(.3)
|
||||
return np.asarray_chkfinite(X), np.asarray_chkfinite(W), nu
|
||||
|
||||
|
||||
def tdot_numpy(mat, out=None):
|
||||
return np.dot(mat, mat.T, out)
|
||||
|
|
|
|||
|
|
@ -20,8 +20,8 @@ def most_significant_input_dimensions(model, which_indices):
|
|||
input_1, input_2 = which_indices
|
||||
return input_1, input_2
|
||||
|
||||
def plot_latent(model, labels=None, which_indices=None,
|
||||
resolution=50, ax=None, marker='o', s=40,
|
||||
def plot_latent(model, labels=None, which_indices=None,
|
||||
resolution=50, ax=None, marker='o', s=40,
|
||||
fignum=None, plot_inducing=False, legend=True,
|
||||
aspect='auto', updates=False):
|
||||
"""
|
||||
|
|
@ -48,10 +48,10 @@ def plot_latent(model, labels=None, which_indices=None,
|
|||
var = var[:, :1]
|
||||
return np.log(var)
|
||||
view = ImshowController(ax, plot_function,
|
||||
tuple(model.X.min(0)[:, [input_1, input_2]]) + tuple(model.X.max(0)[:, [input_1, input_2]]),
|
||||
tuple(model.X[:, [input_1, input_2]].min(0)) + tuple(model.X[:, [input_1, input_2]].max(0)),
|
||||
resolution, aspect=aspect, interpolation='bilinear',
|
||||
cmap=pb.cm.binary)
|
||||
|
||||
|
||||
# ax.imshow(var.reshape(resolution, resolution).T,
|
||||
# extent=[xmin[0], xmax[0], xmin[1], xmax[1]], cmap=pb.cm.binary, interpolation='bilinear', origin='lower')
|
||||
|
||||
|
|
@ -100,8 +100,8 @@ def plot_latent(model, labels=None, which_indices=None,
|
|||
raw_input('Enter to continue')
|
||||
return ax
|
||||
|
||||
def plot_magnification(model, labels=None, which_indices=None,
|
||||
resolution=60, ax=None, marker='o', s=40,
|
||||
def plot_magnification(model, labels=None, which_indices=None,
|
||||
resolution=60, ax=None, marker='o', s=40,
|
||||
fignum=None, plot_inducing=False, legend=True,
|
||||
aspect='auto', updates=False):
|
||||
"""
|
||||
|
|
|
|||
56
GPy/util/subarray_and_sorting.py
Normal file
56
GPy/util/subarray_and_sorting.py
Normal file
|
|
@ -0,0 +1,56 @@
|
|||
'''
|
||||
.. module:: GPy.util.subarray_and_sorting
|
||||
|
||||
.. moduleauthor:: Max Zwiessele <ibinbei@gmail.com>
|
||||
|
||||
'''
|
||||
__updated__ = '2013-12-02'
|
||||
|
||||
import numpy as np
|
||||
|
||||
def common_subarrays(X, axis=0):
|
||||
"""
|
||||
Find common subarrays of 2 dimensional X, where axis is the axis to apply the search over.
|
||||
Common subarrays are returned as a dictionary of <subarray, [index]> pairs, where
|
||||
the subarray is a tuple representing the subarray and the index is the index
|
||||
for the subarray in X, where index is the index to the remaining axis.
|
||||
|
||||
:param :class:`np.ndarray` X: 2d array to check for common subarrays in
|
||||
:param int axis: axis to apply subarray detection over.
|
||||
When the index is 0, compare rows, columns, otherwise.
|
||||
|
||||
Examples:
|
||||
=========
|
||||
|
||||
In a 2d array:
|
||||
>>> import numpy as np
|
||||
>>> X = np.zeros((3,6), dtype=bool)
|
||||
>>> X[[1,1,1],[0,4,5]] = 1; X[1:,[2,3]] = 1
|
||||
>>> X
|
||||
array([[False, False, False, False, False, False],
|
||||
[ True, False, True, True, True, True],
|
||||
[False, False, True, True, False, False]], dtype=bool)
|
||||
>>> d = common_subarrays(X,axis=1)
|
||||
>>> len(d)
|
||||
3
|
||||
>>> X[:, d[tuple(X[:,0])]]
|
||||
array([[False, False, False],
|
||||
[ True, True, True],
|
||||
[False, False, False]], dtype=bool)
|
||||
>>> d[tuple(X[:,4])] == d[tuple(X[:,0])] == [0, 4, 5]
|
||||
True
|
||||
>>> d[tuple(X[:,1])]
|
||||
[1]
|
||||
"""
|
||||
from collections import defaultdict
|
||||
from itertools import count
|
||||
from operator import iadd
|
||||
assert X.ndim == 2 and axis in (0,1), "Only implemented for 2D arrays"
|
||||
subarrays = defaultdict(list)
|
||||
cnt = count()
|
||||
np.apply_along_axis(lambda x: iadd(subarrays[tuple(x)], [cnt.next()]), 1-axis, X)
|
||||
return subarrays
|
||||
|
||||
if __name__ == '__main__':
|
||||
import doctest
|
||||
doctest.testmod()
|
||||
|
|
@ -1,107 +1,102 @@
|
|||
core Package
|
||||
============
|
||||
GPy.core package
|
||||
================
|
||||
|
||||
:mod:`core` Package
|
||||
-------------------
|
||||
Submodules
|
||||
----------
|
||||
|
||||
.. automodule:: GPy.core
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`domains` Module
|
||||
---------------------
|
||||
GPy.core.domains module
|
||||
-----------------------
|
||||
|
||||
.. automodule:: GPy.core.domains
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`fitc` Module
|
||||
------------------
|
||||
GPy.core.fitc module
|
||||
--------------------
|
||||
|
||||
.. automodule:: GPy.core.fitc
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`gp` Module
|
||||
----------------
|
||||
GPy.core.gp module
|
||||
------------------
|
||||
|
||||
.. automodule:: GPy.core.gp
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`gp_base` Module
|
||||
---------------------
|
||||
GPy.core.gp_base module
|
||||
-----------------------
|
||||
|
||||
.. automodule:: GPy.core.gp_base
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`mapping` Module
|
||||
---------------------
|
||||
GPy.core.mapping module
|
||||
-----------------------
|
||||
|
||||
.. automodule:: GPy.core.mapping
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`model` Module
|
||||
-------------------
|
||||
GPy.core.model module
|
||||
---------------------
|
||||
|
||||
.. automodule:: GPy.core.model
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`parameterized` Module
|
||||
---------------------------
|
||||
GPy.core.parameterized module
|
||||
-----------------------------
|
||||
|
||||
.. automodule:: GPy.core.parameterized
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`priors` Module
|
||||
--------------------
|
||||
GPy.core.priors module
|
||||
----------------------
|
||||
|
||||
.. automodule:: GPy.core.priors
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`sparse_gp` Module
|
||||
-----------------------
|
||||
GPy.core.sparse_gp module
|
||||
-------------------------
|
||||
|
||||
.. automodule:: GPy.core.sparse_gp
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`svigp` Module
|
||||
-------------------
|
||||
GPy.core.svigp module
|
||||
---------------------
|
||||
|
||||
.. automodule:: GPy.core.svigp
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`transformations` Module
|
||||
-----------------------------
|
||||
GPy.core.transformations module
|
||||
-------------------------------
|
||||
|
||||
.. automodule:: GPy.core.transformations
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`variational` Module
|
||||
-------------------------
|
||||
|
||||
.. automodule:: GPy.core.variational
|
||||
Module contents
|
||||
---------------
|
||||
|
||||
.. automodule:: GPy.core
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
|
|
|
|||
|
|
@ -1,59 +1,62 @@
|
|||
examples Package
|
||||
================
|
||||
GPy.examples package
|
||||
====================
|
||||
|
||||
:mod:`examples` Package
|
||||
-----------------------
|
||||
Submodules
|
||||
----------
|
||||
|
||||
.. automodule:: GPy.examples
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`classification` Module
|
||||
----------------------------
|
||||
GPy.examples.classification module
|
||||
----------------------------------
|
||||
|
||||
.. automodule:: GPy.examples.classification
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`dimensionality_reduction` Module
|
||||
--------------------------------------
|
||||
GPy.examples.dimensionality_reduction module
|
||||
--------------------------------------------
|
||||
|
||||
.. automodule:: GPy.examples.dimensionality_reduction
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`laplace_approximations` Module
|
||||
------------------------------------
|
||||
GPy.examples.non_gaussian module
|
||||
--------------------------------
|
||||
|
||||
.. automodule:: GPy.examples.laplace_approximations
|
||||
.. automodule:: GPy.examples.non_gaussian
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`regression` Module
|
||||
------------------------
|
||||
GPy.examples.regression module
|
||||
------------------------------
|
||||
|
||||
.. automodule:: GPy.examples.regression
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`stochastic` Module
|
||||
------------------------
|
||||
GPy.examples.stochastic module
|
||||
------------------------------
|
||||
|
||||
.. automodule:: GPy.examples.stochastic
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`tutorials` Module
|
||||
-----------------------
|
||||
GPy.examples.tutorials module
|
||||
-----------------------------
|
||||
|
||||
.. automodule:: GPy.examples.tutorials
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
|
||||
Module contents
|
||||
---------------
|
||||
|
||||
.. automodule:: GPy.examples
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
|
|
|||
|
|
@ -1,51 +1,62 @@
|
|||
inference Package
|
||||
=================
|
||||
GPy.inference package
|
||||
=====================
|
||||
|
||||
:mod:`conjugate_gradient_descent` Module
|
||||
----------------------------------------
|
||||
Submodules
|
||||
----------
|
||||
|
||||
GPy.inference.conjugate_gradient_descent module
|
||||
-----------------------------------------------
|
||||
|
||||
.. automodule:: GPy.inference.conjugate_gradient_descent
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`gradient_descent_update_rules` Module
|
||||
-------------------------------------------
|
||||
GPy.inference.gradient_descent_update_rules module
|
||||
--------------------------------------------------
|
||||
|
||||
.. automodule:: GPy.inference.gradient_descent_update_rules
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`optimization` Module
|
||||
--------------------------
|
||||
GPy.inference.optimization module
|
||||
---------------------------------
|
||||
|
||||
.. automodule:: GPy.inference.optimization
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`samplers` Module
|
||||
----------------------
|
||||
GPy.inference.samplers module
|
||||
-----------------------------
|
||||
|
||||
.. automodule:: GPy.inference.samplers
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`scg` Module
|
||||
-----------------
|
||||
GPy.inference.scg module
|
||||
------------------------
|
||||
|
||||
.. automodule:: GPy.inference.scg
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`sgd` Module
|
||||
-----------------
|
||||
GPy.inference.sgd module
|
||||
------------------------
|
||||
|
||||
.. automodule:: GPy.inference.sgd
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
|
||||
Module contents
|
||||
---------------
|
||||
|
||||
.. automodule:: GPy.inference
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
|
|
|||
|
|
@ -1,275 +1,278 @@
|
|||
parts Package
|
||||
=============
|
||||
GPy.kern.parts package
|
||||
======================
|
||||
|
||||
:mod:`parts` Package
|
||||
--------------------
|
||||
Submodules
|
||||
----------
|
||||
|
||||
.. automodule:: GPy.kern.parts
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`Brownian` Module
|
||||
----------------------
|
||||
GPy.kern.parts.Brownian module
|
||||
------------------------------
|
||||
|
||||
.. automodule:: GPy.kern.parts.Brownian
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`Matern32` Module
|
||||
----------------------
|
||||
GPy.kern.parts.Matern32 module
|
||||
------------------------------
|
||||
|
||||
.. automodule:: GPy.kern.parts.Matern32
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`Matern52` Module
|
||||
----------------------
|
||||
GPy.kern.parts.Matern52 module
|
||||
------------------------------
|
||||
|
||||
.. automodule:: GPy.kern.parts.Matern52
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`ODE_1` Module
|
||||
-------------------
|
||||
GPy.kern.parts.ODE_1 module
|
||||
---------------------------
|
||||
|
||||
.. automodule:: GPy.kern.parts.ODE_1
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`ODE_UY` Module
|
||||
--------------------
|
||||
GPy.kern.parts.ODE_UY module
|
||||
----------------------------
|
||||
|
||||
.. automodule:: GPy.kern.parts.ODE_UY
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`bias` Module
|
||||
------------------
|
||||
GPy.kern.parts.bias module
|
||||
--------------------------
|
||||
|
||||
.. automodule:: GPy.kern.parts.bias
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`coregionalize` Module
|
||||
---------------------------
|
||||
GPy.kern.parts.coregionalize module
|
||||
-----------------------------------
|
||||
|
||||
.. automodule:: GPy.kern.parts.coregionalize
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`eq_ode1` Module
|
||||
---------------------
|
||||
GPy.kern.parts.eq_ode1 module
|
||||
-----------------------------
|
||||
|
||||
.. automodule:: GPy.kern.parts.eq_ode1
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`exponential` Module
|
||||
-------------------------
|
||||
GPy.kern.parts.exponential module
|
||||
---------------------------------
|
||||
|
||||
.. automodule:: GPy.kern.parts.exponential
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`finite_dimensional` Module
|
||||
--------------------------------
|
||||
GPy.kern.parts.finite_dimensional module
|
||||
----------------------------------------
|
||||
|
||||
.. automodule:: GPy.kern.parts.finite_dimensional
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`fixed` Module
|
||||
-------------------
|
||||
GPy.kern.parts.fixed module
|
||||
---------------------------
|
||||
|
||||
.. automodule:: GPy.kern.parts.fixed
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`gibbs` Module
|
||||
-------------------
|
||||
GPy.kern.parts.gibbs module
|
||||
---------------------------
|
||||
|
||||
.. automodule:: GPy.kern.parts.gibbs
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`hetero` Module
|
||||
--------------------
|
||||
GPy.kern.parts.hetero module
|
||||
----------------------------
|
||||
|
||||
.. automodule:: GPy.kern.parts.hetero
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`hierarchical` Module
|
||||
--------------------------
|
||||
GPy.kern.parts.hierarchical module
|
||||
----------------------------------
|
||||
|
||||
.. automodule:: GPy.kern.parts.hierarchical
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`independent_outputs` Module
|
||||
---------------------------------
|
||||
GPy.kern.parts.independent_outputs module
|
||||
-----------------------------------------
|
||||
|
||||
.. automodule:: GPy.kern.parts.independent_outputs
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`kernpart` Module
|
||||
----------------------
|
||||
GPy.kern.parts.kernpart module
|
||||
------------------------------
|
||||
|
||||
.. automodule:: GPy.kern.parts.kernpart
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`linear` Module
|
||||
--------------------
|
||||
GPy.kern.parts.linear module
|
||||
----------------------------
|
||||
|
||||
.. automodule:: GPy.kern.parts.linear
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`mlp` Module
|
||||
-----------------
|
||||
GPy.kern.parts.mlp module
|
||||
-------------------------
|
||||
|
||||
.. automodule:: GPy.kern.parts.mlp
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`periodic_Matern32` Module
|
||||
-------------------------------
|
||||
GPy.kern.parts.periodic_Matern32 module
|
||||
---------------------------------------
|
||||
|
||||
.. automodule:: GPy.kern.parts.periodic_Matern32
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`periodic_Matern52` Module
|
||||
-------------------------------
|
||||
GPy.kern.parts.periodic_Matern52 module
|
||||
---------------------------------------
|
||||
|
||||
.. automodule:: GPy.kern.parts.periodic_Matern52
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`periodic_exponential` Module
|
||||
----------------------------------
|
||||
GPy.kern.parts.periodic_exponential module
|
||||
------------------------------------------
|
||||
|
||||
.. automodule:: GPy.kern.parts.periodic_exponential
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`poly` Module
|
||||
------------------
|
||||
GPy.kern.parts.poly module
|
||||
--------------------------
|
||||
|
||||
.. automodule:: GPy.kern.parts.poly
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`prod` Module
|
||||
------------------
|
||||
GPy.kern.parts.prod module
|
||||
--------------------------
|
||||
|
||||
.. automodule:: GPy.kern.parts.prod
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`prod_orthogonal` Module
|
||||
-----------------------------
|
||||
GPy.kern.parts.prod_orthogonal module
|
||||
-------------------------------------
|
||||
|
||||
.. automodule:: GPy.kern.parts.prod_orthogonal
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`rational_quadratic` Module
|
||||
--------------------------------
|
||||
GPy.kern.parts.rational_quadratic module
|
||||
----------------------------------------
|
||||
|
||||
.. automodule:: GPy.kern.parts.rational_quadratic
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`rbf` Module
|
||||
-----------------
|
||||
GPy.kern.parts.rbf module
|
||||
-------------------------
|
||||
|
||||
.. automodule:: GPy.kern.parts.rbf
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`rbf_inv` Module
|
||||
---------------------
|
||||
GPy.kern.parts.rbf_inv module
|
||||
-----------------------------
|
||||
|
||||
.. automodule:: GPy.kern.parts.rbf_inv
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`rbfcos` Module
|
||||
--------------------
|
||||
GPy.kern.parts.rbfcos module
|
||||
----------------------------
|
||||
|
||||
.. automodule:: GPy.kern.parts.rbfcos
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`spline` Module
|
||||
--------------------
|
||||
GPy.kern.parts.spline module
|
||||
----------------------------
|
||||
|
||||
.. automodule:: GPy.kern.parts.spline
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`symmetric` Module
|
||||
-----------------------
|
||||
GPy.kern.parts.symmetric module
|
||||
-------------------------------
|
||||
|
||||
.. automodule:: GPy.kern.parts.symmetric
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`sympy_helpers` Module
|
||||
---------------------------
|
||||
GPy.kern.parts.sympy_helpers module
|
||||
-----------------------------------
|
||||
|
||||
.. automodule:: GPy.kern.parts.sympy_helpers
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`sympykern` Module
|
||||
-----------------------
|
||||
GPy.kern.parts.sympykern module
|
||||
-------------------------------
|
||||
|
||||
.. automodule:: GPy.kern.parts.sympykern
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`white` Module
|
||||
-------------------
|
||||
GPy.kern.parts.white module
|
||||
---------------------------
|
||||
|
||||
.. automodule:: GPy.kern.parts.white
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
|
||||
Module contents
|
||||
---------------
|
||||
|
||||
.. automodule:: GPy.kern.parts
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
|
|
|||
|
|
@ -1,29 +1,5 @@
|
|||
kern Package
|
||||
============
|
||||
|
||||
:mod:`kern` Package
|
||||
-------------------
|
||||
|
||||
.. automodule:: GPy.kern
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`constructors` Module
|
||||
--------------------------
|
||||
|
||||
.. automodule:: GPy.kern.constructors
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`kern` Module
|
||||
------------------
|
||||
|
||||
.. automodule:: GPy.kern.kern
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
GPy.kern package
|
||||
================
|
||||
|
||||
Subpackages
|
||||
-----------
|
||||
|
|
@ -32,3 +8,30 @@ Subpackages
|
|||
|
||||
GPy.kern.parts
|
||||
|
||||
Submodules
|
||||
----------
|
||||
|
||||
GPy.kern.constructors module
|
||||
----------------------------
|
||||
|
||||
.. automodule:: GPy.kern.constructors
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
GPy.kern.kern module
|
||||
--------------------
|
||||
|
||||
.. automodule:: GPy.kern.kern
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
|
||||
Module contents
|
||||
---------------
|
||||
|
||||
.. automodule:: GPy.kern
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
|
|
|||
|
|
@ -1,75 +1,78 @@
|
|||
noise_models Package
|
||||
====================
|
||||
GPy.likelihoods.noise_models package
|
||||
====================================
|
||||
|
||||
:mod:`noise_models` Package
|
||||
---------------------------
|
||||
Submodules
|
||||
----------
|
||||
|
||||
.. automodule:: GPy.likelihoods.noise_models
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`bernoulli_noise` Module
|
||||
-----------------------------
|
||||
GPy.likelihoods.noise_models.bernoulli_noise module
|
||||
---------------------------------------------------
|
||||
|
||||
.. automodule:: GPy.likelihoods.noise_models.bernoulli_noise
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`exponential_noise` Module
|
||||
-------------------------------
|
||||
GPy.likelihoods.noise_models.exponential_noise module
|
||||
-----------------------------------------------------
|
||||
|
||||
.. automodule:: GPy.likelihoods.noise_models.exponential_noise
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`gamma_noise` Module
|
||||
-------------------------
|
||||
GPy.likelihoods.noise_models.gamma_noise module
|
||||
-----------------------------------------------
|
||||
|
||||
.. automodule:: GPy.likelihoods.noise_models.gamma_noise
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`gaussian_noise` Module
|
||||
----------------------------
|
||||
GPy.likelihoods.noise_models.gaussian_noise module
|
||||
--------------------------------------------------
|
||||
|
||||
.. automodule:: GPy.likelihoods.noise_models.gaussian_noise
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`gp_transformations` Module
|
||||
--------------------------------
|
||||
GPy.likelihoods.noise_models.gp_transformations module
|
||||
------------------------------------------------------
|
||||
|
||||
.. automodule:: GPy.likelihoods.noise_models.gp_transformations
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`noise_distributions` Module
|
||||
---------------------------------
|
||||
GPy.likelihoods.noise_models.noise_distributions module
|
||||
-------------------------------------------------------
|
||||
|
||||
.. automodule:: GPy.likelihoods.noise_models.noise_distributions
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`poisson_noise` Module
|
||||
---------------------------
|
||||
GPy.likelihoods.noise_models.poisson_noise module
|
||||
-------------------------------------------------
|
||||
|
||||
.. automodule:: GPy.likelihoods.noise_models.poisson_noise
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`student_t_noise` Module
|
||||
-----------------------------
|
||||
GPy.likelihoods.noise_models.student_t_noise module
|
||||
---------------------------------------------------
|
||||
|
||||
.. automodule:: GPy.likelihoods.noise_models.student_t_noise
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
|
||||
Module contents
|
||||
---------------
|
||||
|
||||
.. automodule:: GPy.likelihoods.noise_models
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
|
|
|||
|
|
@ -1,69 +1,5 @@
|
|||
likelihoods Package
|
||||
===================
|
||||
|
||||
:mod:`likelihoods` Package
|
||||
--------------------------
|
||||
|
||||
.. automodule:: GPy.likelihoods
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`ep` Module
|
||||
----------------
|
||||
|
||||
.. automodule:: GPy.likelihoods.ep
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`ep_mixed_noise` Module
|
||||
----------------------------
|
||||
|
||||
.. automodule:: GPy.likelihoods.ep_mixed_noise
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`gaussian` Module
|
||||
----------------------
|
||||
|
||||
.. automodule:: GPy.likelihoods.gaussian
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`gaussian_mixed_noise` Module
|
||||
----------------------------------
|
||||
|
||||
.. automodule:: GPy.likelihoods.gaussian_mixed_noise
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`laplace` Module
|
||||
---------------------
|
||||
|
||||
.. automodule:: GPy.likelihoods.laplace
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`likelihood` Module
|
||||
------------------------
|
||||
|
||||
.. automodule:: GPy.likelihoods.likelihood
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`noise_model_constructors` Module
|
||||
--------------------------------------
|
||||
|
||||
.. automodule:: GPy.likelihoods.noise_model_constructors
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
GPy.likelihoods package
|
||||
=======================
|
||||
|
||||
Subpackages
|
||||
-----------
|
||||
|
|
@ -72,3 +8,70 @@ Subpackages
|
|||
|
||||
GPy.likelihoods.noise_models
|
||||
|
||||
Submodules
|
||||
----------
|
||||
|
||||
GPy.likelihoods.ep module
|
||||
-------------------------
|
||||
|
||||
.. automodule:: GPy.likelihoods.ep
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
GPy.likelihoods.ep_mixed_noise module
|
||||
-------------------------------------
|
||||
|
||||
.. automodule:: GPy.likelihoods.ep_mixed_noise
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
GPy.likelihoods.gaussian module
|
||||
-------------------------------
|
||||
|
||||
.. automodule:: GPy.likelihoods.gaussian
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
GPy.likelihoods.gaussian_mixed_noise module
|
||||
-------------------------------------------
|
||||
|
||||
.. automodule:: GPy.likelihoods.gaussian_mixed_noise
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
GPy.likelihoods.laplace module
|
||||
------------------------------
|
||||
|
||||
.. automodule:: GPy.likelihoods.laplace
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
GPy.likelihoods.likelihood module
|
||||
---------------------------------
|
||||
|
||||
.. automodule:: GPy.likelihoods.likelihood
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
GPy.likelihoods.noise_model_constructors module
|
||||
-----------------------------------------------
|
||||
|
||||
.. automodule:: GPy.likelihoods.noise_model_constructors
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
|
||||
Module contents
|
||||
---------------
|
||||
|
||||
.. automodule:: GPy.likelihoods
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
|
|
|||
|
|
@ -1,35 +1,38 @@
|
|||
mappings Package
|
||||
================
|
||||
GPy.mappings package
|
||||
====================
|
||||
|
||||
:mod:`mappings` Package
|
||||
-----------------------
|
||||
Submodules
|
||||
----------
|
||||
|
||||
.. automodule:: GPy.mappings
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`kernel` Module
|
||||
--------------------
|
||||
GPy.mappings.kernel module
|
||||
--------------------------
|
||||
|
||||
.. automodule:: GPy.mappings.kernel
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`linear` Module
|
||||
--------------------
|
||||
GPy.mappings.linear module
|
||||
--------------------------
|
||||
|
||||
.. automodule:: GPy.mappings.linear
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`mlp` Module
|
||||
-----------------
|
||||
GPy.mappings.mlp module
|
||||
-----------------------
|
||||
|
||||
.. automodule:: GPy.mappings.mlp
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
|
||||
Module contents
|
||||
---------------
|
||||
|
||||
.. automodule:: GPy.mappings
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
|
|
|||
|
|
@ -1,131 +1,134 @@
|
|||
models_modules Package
|
||||
======================
|
||||
GPy.models_modules package
|
||||
==========================
|
||||
|
||||
:mod:`models_modules` Package
|
||||
-----------------------------
|
||||
Submodules
|
||||
----------
|
||||
|
||||
.. automodule:: GPy.models_modules
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`bayesian_gplvm` Module
|
||||
----------------------------
|
||||
GPy.models_modules.bayesian_gplvm module
|
||||
----------------------------------------
|
||||
|
||||
.. automodule:: GPy.models_modules.bayesian_gplvm
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`bcgplvm` Module
|
||||
---------------------
|
||||
GPy.models_modules.bcgplvm module
|
||||
---------------------------------
|
||||
|
||||
.. automodule:: GPy.models_modules.bcgplvm
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`fitc_classification` Module
|
||||
---------------------------------
|
||||
GPy.models_modules.fitc_classification module
|
||||
---------------------------------------------
|
||||
|
||||
.. automodule:: GPy.models_modules.fitc_classification
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`gp_classification` Module
|
||||
-------------------------------
|
||||
GPy.models_modules.gp_classification module
|
||||
-------------------------------------------
|
||||
|
||||
.. automodule:: GPy.models_modules.gp_classification
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`gp_multioutput_regression` Module
|
||||
---------------------------------------
|
||||
GPy.models_modules.gp_multioutput_regression module
|
||||
---------------------------------------------------
|
||||
|
||||
.. automodule:: GPy.models_modules.gp_multioutput_regression
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`gp_regression` Module
|
||||
---------------------------
|
||||
GPy.models_modules.gp_regression module
|
||||
---------------------------------------
|
||||
|
||||
.. automodule:: GPy.models_modules.gp_regression
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`gplvm` Module
|
||||
-------------------
|
||||
GPy.models_modules.gplvm module
|
||||
-------------------------------
|
||||
|
||||
.. automodule:: GPy.models_modules.gplvm
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`gradient_checker` Module
|
||||
------------------------------
|
||||
GPy.models_modules.gradient_checker module
|
||||
------------------------------------------
|
||||
|
||||
.. automodule:: GPy.models_modules.gradient_checker
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`mrd` Module
|
||||
-----------------
|
||||
GPy.models_modules.mrd module
|
||||
-----------------------------
|
||||
|
||||
.. automodule:: GPy.models_modules.mrd
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`sparse_gp_classification` Module
|
||||
--------------------------------------
|
||||
GPy.models_modules.sparse_gp_classification module
|
||||
--------------------------------------------------
|
||||
|
||||
.. automodule:: GPy.models_modules.sparse_gp_classification
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`sparse_gp_multioutput_regression` Module
|
||||
----------------------------------------------
|
||||
GPy.models_modules.sparse_gp_multioutput_regression module
|
||||
----------------------------------------------------------
|
||||
|
||||
.. automodule:: GPy.models_modules.sparse_gp_multioutput_regression
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`sparse_gp_regression` Module
|
||||
----------------------------------
|
||||
GPy.models_modules.sparse_gp_regression module
|
||||
----------------------------------------------
|
||||
|
||||
.. automodule:: GPy.models_modules.sparse_gp_regression
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`sparse_gplvm` Module
|
||||
--------------------------
|
||||
GPy.models_modules.sparse_gplvm module
|
||||
--------------------------------------
|
||||
|
||||
.. automodule:: GPy.models_modules.sparse_gplvm
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`svigp_regression` Module
|
||||
------------------------------
|
||||
GPy.models_modules.svigp_regression module
|
||||
------------------------------------------
|
||||
|
||||
.. automodule:: GPy.models_modules.svigp_regression
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`warped_gp` Module
|
||||
-----------------------
|
||||
GPy.models_modules.warped_gp module
|
||||
-----------------------------------
|
||||
|
||||
.. automodule:: GPy.models_modules.warped_gp
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
|
||||
Module contents
|
||||
---------------
|
||||
|
||||
.. automodule:: GPy.models_modules
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
|
|
|||
37
doc/GPy.rst
37
doc/GPy.rst
|
|
@ -1,22 +1,6 @@
|
|||
GPy Package
|
||||
GPy package
|
||||
===========
|
||||
|
||||
:mod:`GPy` Package
|
||||
------------------
|
||||
|
||||
.. automodule:: GPy.__init__
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`models` Module
|
||||
--------------------
|
||||
|
||||
.. automodule:: GPy.models
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
Subpackages
|
||||
-----------
|
||||
|
||||
|
|
@ -32,3 +16,22 @@ Subpackages
|
|||
GPy.testing
|
||||
GPy.util
|
||||
|
||||
Submodules
|
||||
----------
|
||||
|
||||
GPy.models module
|
||||
-----------------
|
||||
|
||||
.. automodule:: GPy.models
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
|
||||
Module contents
|
||||
---------------
|
||||
|
||||
.. automodule:: GPy
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
|
|
|||
|
|
@ -1,131 +1,134 @@
|
|||
testing Package
|
||||
===============
|
||||
GPy.testing package
|
||||
===================
|
||||
|
||||
:mod:`testing` Package
|
||||
----------------------
|
||||
Submodules
|
||||
----------
|
||||
|
||||
.. automodule:: GPy.testing
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`bcgplvm_tests` Module
|
||||
---------------------------
|
||||
GPy.testing.bcgplvm_tests module
|
||||
--------------------------------
|
||||
|
||||
.. automodule:: GPy.testing.bcgplvm_tests
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`bgplvm_tests` Module
|
||||
--------------------------
|
||||
GPy.testing.bgplvm_tests module
|
||||
-------------------------------
|
||||
|
||||
.. automodule:: GPy.testing.bgplvm_tests
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`cgd_tests` Module
|
||||
-----------------------
|
||||
GPy.testing.cgd_tests module
|
||||
----------------------------
|
||||
|
||||
.. automodule:: GPy.testing.cgd_tests
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`examples_tests` Module
|
||||
----------------------------
|
||||
GPy.testing.examples_tests module
|
||||
---------------------------------
|
||||
|
||||
.. automodule:: GPy.testing.examples_tests
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`gp_transformation_tests` Module
|
||||
-------------------------------------
|
||||
GPy.testing.gp_transformation_tests module
|
||||
------------------------------------------
|
||||
|
||||
.. automodule:: GPy.testing.gp_transformation_tests
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`gplvm_tests` Module
|
||||
-------------------------
|
||||
GPy.testing.gplvm_tests module
|
||||
------------------------------
|
||||
|
||||
.. automodule:: GPy.testing.gplvm_tests
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`kernel_tests` Module
|
||||
--------------------------
|
||||
GPy.testing.kernel_tests module
|
||||
-------------------------------
|
||||
|
||||
.. automodule:: GPy.testing.kernel_tests
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`likelihoods_tests` Module
|
||||
-------------------------------
|
||||
GPy.testing.likelihoods_tests module
|
||||
------------------------------------
|
||||
|
||||
.. automodule:: GPy.testing.likelihoods_tests
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`mapping_tests` Module
|
||||
---------------------------
|
||||
GPy.testing.mapping_tests module
|
||||
--------------------------------
|
||||
|
||||
.. automodule:: GPy.testing.mapping_tests
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`mrd_tests` Module
|
||||
-----------------------
|
||||
GPy.testing.mrd_tests module
|
||||
----------------------------
|
||||
|
||||
.. automodule:: GPy.testing.mrd_tests
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`prior_tests` Module
|
||||
-------------------------
|
||||
GPy.testing.prior_tests module
|
||||
------------------------------
|
||||
|
||||
.. automodule:: GPy.testing.prior_tests
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`psi_stat_expectation_tests` Module
|
||||
----------------------------------------
|
||||
GPy.testing.psi_stat_expectation_tests module
|
||||
---------------------------------------------
|
||||
|
||||
.. automodule:: GPy.testing.psi_stat_expectation_tests
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`psi_stat_gradient_tests` Module
|
||||
-------------------------------------
|
||||
GPy.testing.psi_stat_gradient_tests module
|
||||
------------------------------------------
|
||||
|
||||
.. automodule:: GPy.testing.psi_stat_gradient_tests
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`sparse_gplvm_tests` Module
|
||||
--------------------------------
|
||||
GPy.testing.sparse_gplvm_tests module
|
||||
-------------------------------------
|
||||
|
||||
.. automodule:: GPy.testing.sparse_gplvm_tests
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`unit_tests` Module
|
||||
------------------------
|
||||
GPy.testing.unit_tests module
|
||||
-----------------------------
|
||||
|
||||
.. automodule:: GPy.testing.unit_tests
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
|
||||
Module contents
|
||||
---------------
|
||||
|
||||
.. automodule:: GPy.testing
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
|
|
|||
|
|
@ -1,27 +1,30 @@
|
|||
controllers Package
|
||||
===================
|
||||
GPy.util.latent_space_visualizations.controllers package
|
||||
========================================================
|
||||
|
||||
:mod:`controllers` Package
|
||||
--------------------------
|
||||
Submodules
|
||||
----------
|
||||
|
||||
.. automodule:: GPy.util.latent_space_visualizations.controllers
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`axis_event_controller` Module
|
||||
-----------------------------------
|
||||
GPy.util.latent_space_visualizations.controllers.axis_event_controller module
|
||||
-----------------------------------------------------------------------------
|
||||
|
||||
.. automodule:: GPy.util.latent_space_visualizations.controllers.axis_event_controller
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`imshow_controller` Module
|
||||
-------------------------------
|
||||
GPy.util.latent_space_visualizations.controllers.imshow_controller module
|
||||
-------------------------------------------------------------------------
|
||||
|
||||
.. automodule:: GPy.util.latent_space_visualizations.controllers.imshow_controller
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
|
||||
Module contents
|
||||
---------------
|
||||
|
||||
.. automodule:: GPy.util.latent_space_visualizations.controllers
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
|
|
|||
|
|
@ -1,13 +1,5 @@
|
|||
latent_space_visualizations Package
|
||||
===================================
|
||||
|
||||
:mod:`latent_space_visualizations` Package
|
||||
------------------------------------------
|
||||
|
||||
.. automodule:: GPy.util.latent_space_visualizations
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
GPy.util.latent_space_visualizations package
|
||||
============================================
|
||||
|
||||
Subpackages
|
||||
-----------
|
||||
|
|
@ -15,5 +7,11 @@ Subpackages
|
|||
.. toctree::
|
||||
|
||||
GPy.util.latent_space_visualizations.controllers
|
||||
GPy.util.latent_space_visualizations.views
|
||||
|
||||
Module contents
|
||||
---------------
|
||||
|
||||
.. automodule:: GPy.util.latent_space_visualizations
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
|
|
|||
367
doc/GPy.util.rst
367
doc/GPy.util.rst
|
|
@ -1,181 +1,5 @@
|
|||
util Package
|
||||
============
|
||||
|
||||
:mod:`util` Package
|
||||
-------------------
|
||||
|
||||
.. automodule:: GPy.util
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`Tango` Module
|
||||
-------------------
|
||||
|
||||
.. automodule:: GPy.util.Tango
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`block_matrices` Module
|
||||
----------------------------
|
||||
|
||||
.. automodule:: GPy.util.block_matrices
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`classification` Module
|
||||
----------------------------
|
||||
|
||||
.. automodule:: GPy.util.classification
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`config` Module
|
||||
--------------------
|
||||
|
||||
.. automodule:: GPy.util.config
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`datasets` Module
|
||||
----------------------
|
||||
|
||||
.. automodule:: GPy.util.datasets
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`decorators` Module
|
||||
------------------------
|
||||
|
||||
.. automodule:: GPy.util.decorators
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`erfcx` Module
|
||||
-------------------
|
||||
|
||||
.. automodule:: GPy.util.erfcx
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`linalg` Module
|
||||
--------------------
|
||||
|
||||
.. automodule:: GPy.util.linalg
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`ln_diff_erfs` Module
|
||||
--------------------------
|
||||
|
||||
.. automodule:: GPy.util.ln_diff_erfs
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`misc` Module
|
||||
------------------
|
||||
|
||||
.. automodule:: GPy.util.misc
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`mocap` Module
|
||||
-------------------
|
||||
|
||||
.. automodule:: GPy.util.mocap
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`multioutput` Module
|
||||
-------------------------
|
||||
|
||||
.. automodule:: GPy.util.multioutput
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`netpbmfile` Module
|
||||
------------------------
|
||||
|
||||
.. automodule:: GPy.util.netpbmfile
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`pca` Module
|
||||
-----------------
|
||||
|
||||
.. automodule:: GPy.util.pca
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`plot` Module
|
||||
------------------
|
||||
|
||||
.. automodule:: GPy.util.plot
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`plot_latent` Module
|
||||
-------------------------
|
||||
|
||||
.. automodule:: GPy.util.plot_latent
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`squashers` Module
|
||||
-----------------------
|
||||
|
||||
.. automodule:: GPy.util.squashers
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`symbolic` Module
|
||||
----------------------
|
||||
|
||||
.. automodule:: GPy.util.symbolic
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`univariate_Gaussian` Module
|
||||
---------------------------------
|
||||
|
||||
.. automodule:: GPy.util.univariate_Gaussian
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`visualize` Module
|
||||
-----------------------
|
||||
|
||||
.. automodule:: GPy.util.visualize
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`warping_functions` Module
|
||||
-------------------------------
|
||||
|
||||
.. automodule:: GPy.util.warping_functions
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
GPy.util package
|
||||
================
|
||||
|
||||
Subpackages
|
||||
-----------
|
||||
|
|
@ -184,3 +8,190 @@ Subpackages
|
|||
|
||||
GPy.util.latent_space_visualizations
|
||||
|
||||
Submodules
|
||||
----------
|
||||
|
||||
GPy.util.Tango module
|
||||
---------------------
|
||||
|
||||
.. automodule:: GPy.util.Tango
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
GPy.util.block_matrices module
|
||||
------------------------------
|
||||
|
||||
.. automodule:: GPy.util.block_matrices
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
GPy.util.classification module
|
||||
------------------------------
|
||||
|
||||
.. automodule:: GPy.util.classification
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
GPy.util.config module
|
||||
----------------------
|
||||
|
||||
.. automodule:: GPy.util.config
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
GPy.util.datasets module
|
||||
------------------------
|
||||
|
||||
.. automodule:: GPy.util.datasets
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
GPy.util.decorators module
|
||||
--------------------------
|
||||
|
||||
.. automodule:: GPy.util.decorators
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
GPy.util.diag module
|
||||
--------------------
|
||||
|
||||
.. automodule:: GPy.util.diag
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
GPy.util.erfcx module
|
||||
---------------------
|
||||
|
||||
.. automodule:: GPy.util.erfcx
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
GPy.util.linalg module
|
||||
----------------------
|
||||
|
||||
.. automodule:: GPy.util.linalg
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
GPy.util.ln_diff_erfs module
|
||||
----------------------------
|
||||
|
||||
.. automodule:: GPy.util.ln_diff_erfs
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
GPy.util.misc module
|
||||
--------------------
|
||||
|
||||
.. automodule:: GPy.util.misc
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
GPy.util.mocap module
|
||||
---------------------
|
||||
|
||||
.. automodule:: GPy.util.mocap
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
GPy.util.multioutput module
|
||||
---------------------------
|
||||
|
||||
.. automodule:: GPy.util.multioutput
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
GPy.util.netpbmfile module
|
||||
--------------------------
|
||||
|
||||
.. automodule:: GPy.util.netpbmfile
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
GPy.util.plot module
|
||||
--------------------
|
||||
|
||||
.. automodule:: GPy.util.plot
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
GPy.util.plot_latent module
|
||||
---------------------------
|
||||
|
||||
.. automodule:: GPy.util.plot_latent
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
GPy.util.squashers module
|
||||
-------------------------
|
||||
|
||||
.. automodule:: GPy.util.squashers
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
GPy.util.subarray_and_sorting module
|
||||
------------------------------------
|
||||
|
||||
.. automodule:: GPy.util.subarray_and_sorting
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
GPy.util.symbolic module
|
||||
------------------------
|
||||
|
||||
.. automodule:: GPy.util.symbolic
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
GPy.util.univariate_Gaussian module
|
||||
-----------------------------------
|
||||
|
||||
.. automodule:: GPy.util.univariate_Gaussian
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
GPy.util.visualize module
|
||||
-------------------------
|
||||
|
||||
.. automodule:: GPy.util.visualize
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
GPy.util.warping_functions module
|
||||
---------------------------------
|
||||
|
||||
.. automodule:: GPy.util.warping_functions
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
|
||||
Module contents
|
||||
---------------
|
||||
|
||||
.. automodule:: GPy.util
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue