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fixed up plotting in sparse_gp also
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James Hensman
parent
eedeaa4492
commit
a889b0b7b5
1 changed files with 65 additions and 18 deletions
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@ -323,7 +323,10 @@ class SparseGP(GPBase):
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return mean, var, _025pm, _975pm
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def plot_f(self, samples=0, plot_limits=None, which_data='all', which_parts='all', resolution=None, full_cov=False, fignum=None, ax=None):
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def plot_f(self, samples=0, plot_limits=None, which_data_rows='all',
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which_data_cols='all', which_parts='all', resolution=None,
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full_cov=False, fignum=None, ax=None):
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"""
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Plot the GP's view of the world, where the data is normalized and the
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- In one dimension, the function is plotted with a shaded region identifying two standard deviations.
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@ -332,8 +335,8 @@ class SparseGP(GPBase):
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:param samples: the number of a posteriori samples to plot
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:param plot_limits: The limits of the plot. If 1D [xmin,xmax], if 2D [[xmin,ymin],[xmax,ymax]]. Defaluts to data limits
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:param which_data: which if the training data to plot (default all)
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:type which_data: 'all' or a slice object to slice self.X, self.Y
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:param which_data_rows: which if the training data to plot (default all)
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:type which_data_rows: 'all' or a slice object to slice self.X, self.Y
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:param which_parts: which of the kernel functions to plot (additively)
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:type which_parts: 'all', or list of bools
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:param resolution: the number of intervals to sample the GP on. Defaults to 200 in 1D and 50 (a 50x50 grid) in 2D
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@ -353,10 +356,10 @@ class SparseGP(GPBase):
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ax = fig.add_subplot(111)
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if fignum is None and ax is None:
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fignum = fig.num
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if which_data is 'all':
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which_data = slice(None)
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if which_data_rows is 'all':
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which_data_rows = slice(None)
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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)
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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)
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if self.X.shape[1] == 1:
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if self.has_uncertain_inputs:
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@ -371,35 +374,79 @@ class SparseGP(GPBase):
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Zu = self.Z * self._Xscale + self._Xoffset
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ax.plot(Zu[:, 0], Zu[:, 1], 'wo')
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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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def plot(self, samples=0, plot_limits=None, which_data='all', which_parts='all', resolution=None, levels=20, fignum=None, ax=None):
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def plot(self, plot_limits=None, which_data_rows='all',
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which_data_ycols='all', which_parts='all', fixed_inputs=[],
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levels=20, samples=0, fignum=None, ax=None, resolution=None):
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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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- In higher dimensions, use fixed_inputs to plot the GP with some of the inputs fixed.
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Can plot only part of the data and part of the posterior functions
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using which_data_rowsm which_data_ycols and which_parts
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:param plot_limits: The limits of the plot. If 1D [xmin,xmax], if 2D [[xmin,ymin],[xmax,ymax]]. Defaluts to data limits
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:type plot_limits: np.array
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:param which_data_rows: which of the training data to plot (default all)
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:type which_data_rows: 'all' or a slice object to slice self.X, self.Y
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:param which_data_ycols: when the data has several columns (independant outputs), only plot these
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:type which_data_rows: 'all' or a list of integers
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:param which_parts: which of the kernel functions to plot (additively)
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:type which_parts: 'all', or list of bools
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:param fixed_inputs: a list of tuple [(i,v), (i,v)...], specifying that input index i should be set to value v.
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:type fixed_inputs: a list of tuples
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:param resolution: the number of intervals to sample the GP on. Defaults to 200 in 1D and 50 (a 50x50 grid) in 2D
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:type resolution: int
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:param levels: number of levels to plot in a contour plot.
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:type levels: int
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:param samples: the number of a posteriori samples to plot
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:type samples: int
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:param fignum: figure to plot on.
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:type fignum: figure number
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:param ax: axes to plot on.
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:type ax: axes handle
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:type output: integer (first output is 0)
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:param linecol: color of line to plot.
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:type linecol:
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:param fillcol: color of fill
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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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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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if fignum is None and ax is None:
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fignum = fig.num
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if which_data is 'all':
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which_data = slice(None)
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GPBase.plot(self, samples=samples, plot_limits=plot_limits, which_data='all', which_parts='all', resolution=resolution, levels=20, fignum=fignum, ax=ax)
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#work out what the inputs are for plotting (1D or 2D)
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fixed_dims = np.array([i for i,v in fixed_inputs])
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free_dims = np.setdiff1d(np.arange(self.input_dim),fixed_dims)
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if self.X.shape[1] == 1:
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#call the base plotting
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GPBase.plot(self, samples=samples, plot_limits=plot_limits,
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which_data_rows=which_data_rows,
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which_data_ycols=which_data_ycols, fixed_inputs=fixed_inputs,
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which_parts=which_parts, resolution=resolution, levels=20,
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fignum=fignum, ax=ax)
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if len(free_dims) == 1:
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#plot errorbars for the uncertain inputs
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if self.has_uncertain_inputs:
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Xu = self.X * self._Xscale + self._Xoffset # NOTE self.X are the normalized values now
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ax.errorbar(Xu[which_data, 0], self.likelihood.data[which_data, 0],
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xerr=2 * np.sqrt(self.X_variance[which_data, 0]),
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ax.errorbar(Xu[which_data_rows, 0], self.likelihood.data[which_data_rows, 0],
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xerr=2 * np.sqrt(self.X_variance[which_data_rows, 0]),
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ecolor='k', fmt=None, elinewidth=.5, alpha=.5)
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#plot the inducing inputs
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Zu = self.Z * self._Xscale + self._Xoffset
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ax.plot(Zu, np.zeros_like(Zu) + ax.get_ylim()[0], 'r|', mew=1.5, markersize=12)
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elif self.X.shape[1] == 2:
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elif len(free_dims) == 2:
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Zu = self.Z * self._Xscale + self._Xoffset
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ax.plot(Zu[:, 0], Zu[:, 1], 'wo')
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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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