diff --git a/GPy/core/sparse_gp.py b/GPy/core/sparse_gp.py index 8c8df30c..e02da768 100644 --- a/GPy/core/sparse_gp.py +++ b/GPy/core/sparse_gp.py @@ -323,7 +323,10 @@ class SparseGP(GPBase): return mean, var, _025pm, _975pm - def plot_f(self, samples=0, plot_limits=None, which_data='all', which_parts='all', resolution=None, full_cov=False, fignum=None, ax=None): + def plot_f(self, samples=0, plot_limits=None, which_data_rows='all', + which_data_cols='all', which_parts='all', resolution=None, + full_cov=False, fignum=None, ax=None): + """ Plot the GP's view of the world, where the data is normalized and the - In one dimension, the function is plotted with a shaded region identifying two standard deviations. @@ -332,8 +335,8 @@ class SparseGP(GPBase): :param samples: the number of a posteriori samples to plot :param plot_limits: The limits of the plot. If 1D [xmin,xmax], if 2D [[xmin,ymin],[xmax,ymax]]. Defaluts to data limits - :param which_data: which if the training data to plot (default all) - :type which_data: 'all' or a slice object to slice self.X, self.Y + :param which_data_rows: which if the training data to plot (default all) + :type which_data_rows: 'all' or a slice object to slice self.X, self.Y :param which_parts: which of the kernel functions to plot (additively) :type which_parts: 'all', or list of bools :param resolution: the number of intervals to sample the GP on. Defaults to 200 in 1D and 50 (a 50x50 grid) in 2D @@ -353,10 +356,10 @@ class SparseGP(GPBase): ax = fig.add_subplot(111) if fignum is None and ax is None: fignum = fig.num - if which_data is 'all': - which_data = slice(None) + if which_data_rows is 'all': + which_data_rows = slice(None) - GPBase.plot_f(self, samples=samples, plot_limits=plot_limits, which_data='all', which_parts='all', resolution=resolution, full_cov=full_cov, fignum=fignum, ax=ax) + GPBase.plot_f(self, samples=samples, plot_limits=plot_limits, which_data_rows=which_data_rows, which_data_ycols=which_data_ycols, which_parts=which_parts, resolution=resolution, full_cov=full_cov, fignum=fignum, ax=ax) if self.X.shape[1] == 1: if self.has_uncertain_inputs: @@ -371,35 +374,79 @@ class SparseGP(GPBase): Zu = self.Z * self._Xscale + self._Xoffset ax.plot(Zu[:, 0], Zu[:, 1], 'wo') - else: raise NotImplementedError, "Cannot define a frame with more than two input dimensions" - def plot(self, samples=0, plot_limits=None, which_data='all', which_parts='all', resolution=None, levels=20, fignum=None, ax=None): + def plot(self, plot_limits=None, which_data_rows='all', + which_data_ycols='all', which_parts='all', fixed_inputs=[], + levels=20, samples=0, fignum=None, ax=None, resolution=None): + """ + Plot the posterior of the sparse GP. + - In one dimension, the function is plotted with a shaded region identifying two standard deviations. + - In two dimsensions, a contour-plot shows the mean predicted function + - In higher dimensions, use fixed_inputs to plot the GP with some of the inputs fixed. + + Can plot only part of the data and part of the posterior functions + using which_data_rowsm which_data_ycols and which_parts + + :param plot_limits: The limits of the plot. If 1D [xmin,xmax], if 2D [[xmin,ymin],[xmax,ymax]]. Defaluts to data limits + :type plot_limits: np.array + :param which_data_rows: which of the training data to plot (default all) + :type which_data_rows: 'all' or a slice object to slice self.X, self.Y + :param which_data_ycols: when the data has several columns (independant outputs), only plot these + :type which_data_rows: 'all' or a list of integers + :param which_parts: which of the kernel functions to plot (additively) + :type which_parts: 'all', or list of bools + :param fixed_inputs: a list of tuple [(i,v), (i,v)...], specifying that input index i should be set to value v. + :type fixed_inputs: a list of tuples + :param resolution: the number of intervals to sample the GP on. Defaults to 200 in 1D and 50 (a 50x50 grid) in 2D + :type resolution: int + :param levels: number of levels to plot in a contour plot. + :type levels: int + :param samples: the number of a posteriori samples to plot + :type samples: int + :param fignum: figure to plot on. + :type fignum: figure number + :param ax: axes to plot on. + :type ax: axes handle + :type output: integer (first output is 0) + :param linecol: color of line to plot. + :type linecol: + :param fillcol: color of fill + :param levels: for 2D plotting, the number of contour levels to use is ax is None, create a new figure + """ + #deal work out which ax to plot on if ax is None: fig = pb.figure(num=fignum) ax = fig.add_subplot(111) - if fignum is None and ax is None: - fignum = fig.num - if which_data is 'all': - which_data = slice(None) - GPBase.plot(self, samples=samples, plot_limits=plot_limits, which_data='all', which_parts='all', resolution=resolution, levels=20, fignum=fignum, ax=ax) + #work out what the inputs are for plotting (1D or 2D) + fixed_dims = np.array([i for i,v in fixed_inputs]) + free_dims = np.setdiff1d(np.arange(self.input_dim),fixed_dims) - if self.X.shape[1] == 1: + #call the base plotting + GPBase.plot(self, samples=samples, plot_limits=plot_limits, + which_data_rows=which_data_rows, + which_data_ycols=which_data_ycols, fixed_inputs=fixed_inputs, + which_parts=which_parts, resolution=resolution, levels=20, + fignum=fignum, ax=ax) + + if len(free_dims) == 1: + #plot errorbars for the uncertain inputs if self.has_uncertain_inputs: Xu = self.X * self._Xscale + self._Xoffset # NOTE self.X are the normalized values now - ax.errorbar(Xu[which_data, 0], self.likelihood.data[which_data, 0], - xerr=2 * np.sqrt(self.X_variance[which_data, 0]), + ax.errorbar(Xu[which_data_rows, 0], self.likelihood.data[which_data_rows, 0], + xerr=2 * np.sqrt(self.X_variance[which_data_rows, 0]), ecolor='k', fmt=None, elinewidth=.5, alpha=.5) + + #plot the inducing inputs Zu = self.Z * self._Xscale + self._Xoffset ax.plot(Zu, np.zeros_like(Zu) + ax.get_ylim()[0], 'r|', mew=1.5, markersize=12) - elif self.X.shape[1] == 2: + elif len(free_dims) == 2: Zu = self.Z * self._Xscale + self._Xoffset ax.plot(Zu[:, 0], Zu[:, 1], 'wo') - else: raise NotImplementedError, "Cannot define a frame with more than two input dimensions"