# Copyright (c) 2012, GPy authors (see AUTHORS.txt).
# Licensed under the BSD 3-clause license (see LICENSE.txt)
import numpy as np
try:
from GPy.plotting import Tango
from matplotlib import pyplot as pb
except:
pass
[docs]def plot_mapping(self, plot_limits=None, which_data='all', which_parts='all', resolution=None, levels=20, samples=0, fignum=None, ax=None, fixed_inputs=[], linecol=Tango.colorsHex['darkBlue']):
"""
Plots the mapping associated with the model.
- In one dimension, the function is plotted.
- In two dimsensions, a contour-plot shows the function
- In higher dimensions, we've not implemented this yet !TODO!
Can plot only part of the data and part of the posterior functions
using which_data and which_functions
: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: 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_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
: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
: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 linecol: color of line to plot.
:type linecol:
:param levels: for 2D plotting, the number of contour levels to use is ax is None, create a new figure
"""
# TODO include samples
if which_data == 'all':
which_data = slice(None)
if ax is None:
fig = pb.figure(num=fignum)
ax = fig.add_subplot(111)
plotdims = self.input_dim - len(fixed_inputs)
from ..gpy_plot.plot_util import x_frame1D, x_frame2D
if plotdims == 1:
Xu = self.X * self._Xscale + self._Xoffset # NOTE self.X are the normalized values now
fixed_dims = np.array([i for i,v in fixed_inputs])
freedim = np.setdiff1d(np.arange(self.input_dim),fixed_dims)
Xnew, xmin, xmax = x_frame1D(Xu[:,freedim], plot_limits=plot_limits)
Xgrid = np.empty((Xnew.shape[0],self.input_dim))
Xgrid[:,freedim] = Xnew
for i,v in fixed_inputs:
Xgrid[:,i] = v
f = self.predict(Xgrid, which_parts=which_parts)
for d in range(y.shape[1]):
ax.plot(Xnew, f[:, d], edgecol=linecol)
elif self.X.shape[1] == 2:
resolution = resolution or 50
Xnew, _, _, xmin, xmax = x_frame2D(self.X, plot_limits, resolution)
x, y = np.linspace(xmin[0], xmax[0], resolution), np.linspace(xmin[1], xmax[1], resolution)
f = self.predict(Xnew, which_parts=which_parts)
m = m.reshape(resolution, resolution).T
ax.contour(x, y, f, levels, vmin=m.min(), vmax=m.max(), cmap=pb.cm.jet) # @UndefinedVariable
ax.set_xlim(xmin[0], xmax[0])
ax.set_ylim(xmin[1], xmax[1])
else:
raise NotImplementedError("Cannot define a frame with more than two input dimensions")