apunkt/GPy
1
0
Fork 0
mirror of https://github.com/SheffieldML/GPy.git synced 2026-06-29 15:59:41 +02:00
Code Issues Projects Releases Packages Wiki Activity Actions

rst "markup"

Browse source
This commit is contained in:
bobturneruk 2020-04-02 15:51:41 +01:00
parent 288d4fc140
commit 680fd29ba4
6 changed files with 51 additions and 42 deletions
Download patch file Download diff file Expand all files Collapse all files

21
GPy/plotting/gpy_plot/latent_plots.py
View file

@ -89,7 +89,8 @@ def plot_latent_scatter(self, labels=None,
Plot a scatter plot of the latent space.
:param array-like labels: a label for each data point (row) of the inputs
:param (int, int) which_indices: which input dimensions to plot against each other
:param which_indices: which input dimensions to plot against each other
:type which_indices: (int, int)
:param bool legend: whether to plot the legend on the figure
:param plot_limits: the plot limits for the plot
:type plot_limits: (xmin, xmax, ymin, ymax) or ((xmin, xmax), (ymin, ymax))
@ -174,7 +175,8 @@ def plot_magnification(self, labels=None, which_indices=None,
density of the GP as a gray scale.
:param array-like labels: a label for each data point (row) of the inputs
:param (int, int) which_indices: which input dimensions to plot against each other
:param which_indices: which input dimensions to plot against each other
:type which_indices: (int, int)
:param int resolution: the resolution at which we predict the magnification factor
:param str marker: markers to use - cycle if more labels then markers are given
:param bool legend: whether to plot the legend on the figure
@ -183,7 +185,8 @@ def plot_magnification(self, labels=None, which_indices=None,
:param bool updates: if possible, make interactive updates using the specific library you are using
:param bool mean: use the mean of the Wishart embedding for the magnification factor
:param bool covariance: use the covariance of the Wishart embedding for the magnification factor
:param :py:class:`~GPy.kern.Kern` kern: the kernel to use for prediction
:param kern: the kernel to use for prediction
:type kern: :py:class:`~GPy.kern.Kern`
:param int num_samples: the number of samples to plot maximally. We do a stratified subsample from the labels, if the number of samples (in X) is higher then num_samples.
:param imshow_kwargs: the kwargs for the imshow (magnification factor)
:param kwargs: the kwargs for the scatter plots
@ -248,13 +251,15 @@ def plot_latent(self, labels=None, which_indices=None,
scatter plot of the input dimemsions selected by which_indices.
:param array-like labels: a label for each data point (row) of the inputs
:param (int, int) which_indices: which input dimensions to plot against each other
:param which_indices: which input dimensions to plot against each other
:type which_indices: (int, int)
:param int resolution: the resolution at which we predict the magnification factor
:param bool legend: whether to plot the legend on the figure
:param plot_limits: the plot limits for the plot
:type plot_limits: (xmin, xmax, ymin, ymax) or ((xmin, xmax), (ymin, ymax))
:param bool updates: if possible, make interactive updates using the specific library you are using
:param :py:class:`~GPy.kern.Kern` kern: the kernel to use for prediction
:param kern: the kernel to use for prediction
:type kern: :py:class:`~GPy.kern.Kern`
:param str marker: markers to use - cycle if more labels then markers are given
:param int num_samples: the number of samples to plot maximally. We do a stratified subsample from the labels, if the number of samples (in X) is higher then num_samples.
:param imshow_kwargs: the kwargs for the imshow (magnification factor)
@ -316,13 +321,15 @@ def plot_steepest_gradient_map(self, output_labels=None, data_labels=None, which
scatter plot of the input dimemsions selected by which_indices.
:param array-like labels: a label for each data point (row) of the inputs
:param (int, int) which_indices: which input dimensions to plot against each other
:param which_indices: which input dimensions to plot against each other
:type which_indices: (int, int)
:param int resolution: the resolution at which we predict the magnification factor
:param bool legend: whether to plot the legend on the figure, if int plot legend columns on legend
:param plot_limits: the plot limits for the plot
:type plot_limits: (xmin, xmax, ymin, ymax) or ((xmin, xmax), (ymin, ymax))
:param bool updates: if possible, make interactive updates using the specific library you are using
:param :py:class:`~GPy.kern.Kern` kern: the kernel to use for prediction
:param kern: the kernel to use for prediction
:type kern: :py:class:`~GPy.kern.Kern`
:param str marker: markers to use - cycle if more labels then markers are given
:param int num_samples: the number of samples to plot maximally. We do a stratified subsample from the labels, if the number of samples (in X) is higher then num_samples.
:param imshow_kwargs: the kwargs for the imshow (magnification factor)