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Merge branch 'devel' of github.com:SheffieldML/GPy into devel

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Max Zwiessele 2015-09-03 09:33:13 +01:00
commit 4dfdce9b80
4 changed files with 266 additions and 85 deletions
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GPy/core/gp.py
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@ -516,8 +516,8 @@ class GP(Model):
def plot(self, plot_limits=None, which_data_rows='all',
which_data_ycols='all', fixed_inputs=[],
levels=20, samples=0, fignum=None, ax=None, resolution=None,
plot_raw=False,
linecol=None,fillcol=None, Y_metadata=None, data_symbol='kx', predict_kw=None):
plot_raw=False, linecol=None,fillcol=None, Y_metadata=None,
data_symbol='kx', predict_kw=None, plot_training_data=True):
"""
Plot the posterior of the GP.
- In one dimension, the function is plotted with a shaded region identifying two standard deviations.
@ -554,6 +554,8 @@ class GP(Model):
:type Y_metadata: dict
:param data_symbol: symbol as used matplotlib, by default this is a black cross ('kx')
:type data_symbol: color either as Tango.colorsHex object or character ('r' is red, 'g' is green) alongside marker type, as is standard in matplotlib.
:param plot_training_data: whether or not to plot the training points
:type plot_training_data: boolean
"""
assert "matplotlib" in sys.modules, "matplotlib package has not been imported."
from ..plotting.matplot_dep import models_plots
@ -566,7 +568,85 @@ class GP(Model):
which_data_ycols, fixed_inputs,
levels, samples, fignum, ax, resolution,
plot_raw=plot_raw, Y_metadata=Y_metadata,
data_symbol=data_symbol, predict_kw=predict_kw, **kw)
data_symbol=data_symbol, predict_kw=predict_kw,
plot_training_data=plot_training_data, **kw)
def plot_data(self, which_data_rows='all',
which_data_ycols='all', visible_dims=None,
fignum=None, ax=None, data_symbol='kx'):
"""
Plot the training data
- For higher dimensions than two, use fixed_inputs to plot the data points with some of the inputs fixed.
Can plot only part of the data
using which_data_rows and which_data_ycols.
: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 model.X, model.Y
:param which_data_ycols: when the data has several columns (independant outputs), only plot these
:type which_data_ycols: 'all' or a list of integers
:param visible_dims: an array specifying the input dimensions to plot (maximum two)
:type visible_dims: a numpy array
: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.
:param levels: for 2D plotting, the number of contour levels to use is ax is None, create a new figure
: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 linecol: color of line to plot [Tango.colorsHex['darkBlue']]
:type linecol: color either as Tango.colorsHex object or character ('r' is red, 'g' is green) as is standard in matplotlib
:param fillcol: color of fill [Tango.colorsHex['lightBlue']]
:type fillcol: color either as Tango.colorsHex object or character ('r' is red, 'g' is green) as is standard in matplotlib
:param data_symbol: symbol as used matplotlib, by default this is a black cross ('kx')
:type data_symbol: color either as Tango.colorsHex object or character ('r' is red, 'g' is green) alongside marker type, as is standard in matplotlib.
"""
assert "matplotlib" in sys.modules, "matplotlib package has not been imported."
from ..plotting.matplot_dep import models_plots
kw = {}
return models_plots.plot_data(self, which_data_rows,
which_data_ycols, visible_dims,
fignum, ax, data_symbol, **kw)
def plot_fit_errorbars(self, which_data_rows='all',
which_data_ycols='all', fixed_inputs=[], fignum=None, ax=None,
linecol=None, data_symbol='kx', predict_kw=None, plot_training_data=True):
"""
Plot the posterior error bars corresponding to the training data
- For higher dimensions than two, use fixed_inputs to plot the data points with some of the inputs fixed.
Can plot only part of the data
using which_data_rows and which_data_ycols.
:param which_data_rows: which of the training data to plot (default all)
:type which_data_rows: 'all' or a slice object to slice model.X, model.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 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 fignum: figure to plot on.
:type fignum: figure number
:param ax: axes to plot on.
:type ax: axes handle
:param plot_training_data: whether or not to plot the training points
:type plot_training_data: boolean
"""
assert "matplotlib" in sys.modules, "matplotlib package has not been imported."
from ..plotting.matplot_dep import models_plots
kw = {}
return models_plots.plot_fit_errorbars(self, which_data_rows, which_data_ycols, fixed_inputs,
fignum, ax, linecol, data_symbol,
predict_kw, plot_training_data, **kw)
def plot_magnification(self, labels=None, which_indices=None,
resolution=50, ax=None, marker='o', s=40,