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Merge branch 'params' of github.com:SheffieldML/GPy into params
This commit is contained in:
Max Zwiessele
commit
9fa1a40ac3
8 changed files with 65 additions and 35 deletions
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@ -60,6 +60,19 @@ class Param(Constrainable, ObservableArray, Gradcheckable):
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def __init__(self, name, input_array, default_constraint=None, *a, **kw):
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super(Param, self).__init__(name=name, default_constraint=default_constraint, *a, **kw)
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def build_pydot(self,G):
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import pydot
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node = pydot.Node(id(self), shape='record', label=self.name)
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G.add_node(node)
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for o in self._observer_callables_.keys():
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label = o.name if hasattr(o, 'name') else str(o)
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observed_node = pydot.Node(id(o), label=label)
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G.add_node(observed_node)
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edge = pydot.Edge(str(id(self)), str(id(o)), color='darkorange2', arrowhead='vee')
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G.add_edge(edge)
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return node
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def __array_finalize__(self, obj):
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# see InfoArray.__array_finalize__ for comments
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if obj is None: return
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@ -64,6 +64,30 @@ class Parameterized(Parameterizable, Pickleable, Gradcheckable):
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self._connect_parameters()
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del self._in_init_
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def build_pydot(self, G=None):
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import pydot
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iamroot = False
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if G is None:
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G = pydot.Dot(graph_type='digraph')
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iamroot=True
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node = pydot.Node(id(self), shape='record', label=self.name)
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G.add_node(node)
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for child in self._parameters_:
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child_node = child.build_pydot(G)
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G.add_edge(pydot.Edge(node, child_node))
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for o in self._observer_callables_.keys():
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label = o.name if hasattr(o, 'name') else str(o)
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observed_node = pydot.Node(id(o), label=label)
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G.add_node(observed_node)
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edge = pydot.Edge(str(id(self)), str(id(o)), color='darkorange2', arrowhead='vee')
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G.add_edge(edge)
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if iamroot:
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return G
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return node
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def add_parameter(self, param, index=None):
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"""
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:param parameters: the parameters to add
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@ -184,20 +184,12 @@ class Add(Kern):
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target_S += b
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return target_mu, target_S
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def plot(self, *args, **kwargs):
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"""
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See GPy.plotting.matplot_dep.plot
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"""
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assert "matplotlib" in sys.modules, "matplotlib package has not been imported."
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from ..plotting.matplot_dep import kernel_plots
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kernel_plots.plot(self,*args)
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def input_sensitivity(self):
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in_sen = np.zeros((self.num_params, self.input_dim))
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for i, [p, i_s] in enumerate(zip(self._parameters_, self.input_slices)):
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in_sen[i, i_s] = p.input_sensitivity()
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return in_sen
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def _getstate(self):
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"""
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Get the current state of the class,
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@ -60,17 +60,6 @@ class Coregionalize(Kern):
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def K(self, X, X2=None):
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index = np.asarray(X, dtype=np.int)
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#here's the old code (numpy)
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#if index2 is None:
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#index2 = index
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#else:
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#index2 = np.asarray(index2, dtype=np.int)
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#false_target = target.copy()
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#ii, jj = np.meshgrid(index, index2)
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#ii, jj = ii.T, jj.T
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#false_target += self.B[ii, jj]
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if X2 is None:
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target = np.empty((X.shape[0], X.shape[0]), dtype=np.float64)
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code="""
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@ -68,6 +68,14 @@ class Kern(Parameterized):
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"""
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raise NotImplementedError
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def plot(self, *args, **kwargs):
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"""
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See GPy.plotting.matplot_dep.plot
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"""
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assert "matplotlib" in sys.modules, "matplotlib package has not been imported."
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from ..plotting.matplot_dep import kernel_plots
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kernel_plots.plot(self,*args)
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def plot_ARD(self, *args, **kw):
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"""
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See :class:`~GPy.plotting.matplot_dep.kernel_plots`
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@ -179,6 +179,11 @@ class RBF(Stationary):
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return denom, dist, dist_sq, psi1
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#@cache_this(ignore_args=(1,))
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def _Z_distances(self, Z):
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Zhat = 0.5 * (Z[:, None, :] + Z[None, :, :]) # M,M,Q
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Zdist = 0.5 * (Z[:, None, :] - Z[None, :, :]) # M,M,Q
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return Zhat, Zdist
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@Cache_this(limit=1)
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def _psi2computations(self, Z, vp):
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@ -188,8 +193,7 @@ class RBF(Stationary):
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M = Z.shape[0]
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#compute required distances
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Zhat = 0.5 * (Z[:, None, :] + Z[None, :, :]) # M,M,Q
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Zdist = 0.5 * (Z[:, None, :] - Z[None, :, :]) # M,M,Q
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Zhat, Zdist = self._Z_distances(Z)
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Zdist_sq = np.square(Zdist / self.lengthscale) # M,M,Q
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#allocate memory for the things we want to compute
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@ -202,7 +206,7 @@ class RBF(Stationary):
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denom = (2.*S[:,None,None,:] / l2) + 1. # N,Q
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half_log_denom = 0.5 * np.log(denom[:,0,0,:])
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denom_l2 = denom[:,0,0,:]*l2
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variance_sq = float(np.square(self.variance))
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code = """
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double tmp, exponent_tmp;
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@ -238,7 +242,7 @@ class RBF(Stationary):
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}
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}
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"""
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support_code = """
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#include <omp.h>
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#include <math.h>
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@ -18,17 +18,17 @@ def meanplot(x, mu, color=Tango.colorsHex['darkBlue'], ax=None, fignum=None, lin
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_, axes = ax_default(fignum, ax)
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#here's the mean
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return axes.plot(x,mu,color=color,linewidth=linewidth,**kw)
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def gpplot(x,mu,lower,upper,edgecol=Tango.colorsHex['darkBlue'],fillcol=Tango.colorsHex['lightBlue'],ax=None,fignum=None,xlabel='x',ylabel='y',**kwargs):
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_, axes = ax_default(ax, fignum)
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_, axes = ax_default(fignum, ax)
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mu = mu.flatten()
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x = x.flatten()
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lower = lower.flatten()
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upper = upper.flatten()
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plots = []
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#here's the mean
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plots.append(meanplot(x, mu, edgecol, axes))
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@ -39,15 +39,15 @@ def gpplot(x,mu,lower,upper,edgecol=Tango.colorsHex['darkBlue'],fillcol=Tango.co
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plots.append(axes.fill(np.hstack((x,x[::-1])),np.hstack((upper,lower[::-1])),color=fillcol,**kwargs))
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#this is the edge:
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plots.append(meanplot(x, upper,color=edgecol,linewidth=0.2,axes=axes))
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plots.append(meanplot(x, lower,color=edgecol,linewidth=0.2,axes=axes))
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plots.append(meanplot(x, upper,color=edgecol,linewidth=0.2,ax=axes))
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plots.append(meanplot(x, lower,color=edgecol,linewidth=0.2,ax=axes))
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axes.set_xlabel(xlabel)
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axes.set_ylabel(ylabel)
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return plots
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def removeRightTicks(ax=None):
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ax = ax or pb.gca()
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for i, line in enumerate(ax.get_yticklines()):
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@ -86,7 +86,7 @@ def plot_fit(model, plot_limits=None, which_data_rows='all',
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m, v, lower, upper = model.predict(Xgrid)
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Y = Y
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for d in which_data_ycols:
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gpplot(Xnew, m[:, d], lower[:, d], upper[:, d], axes=ax, edgecol=linecol, fillcol=fillcol)
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gpplot(Xnew, m[:, d], lower[:, d], upper[:, d], ax=ax, edgecol=linecol, fillcol=fillcol)
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ax.plot(X[which_data_rows,free_dims], Y[which_data_rows, d], 'kx', mew=1.5)
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#optionally plot some samples
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