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[kernel] plotting ard for prod and covariance plots added

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mzwiessele 2015-10-12 13:12:03 +01:00
parent 59ba858aba
commit 12dba962f1
13 changed files with 130 additions and 116 deletions
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29
GPy/plotting/gpy_plot/plot_util.py
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@ -102,19 +102,17 @@ def helper_predict_with_model(self, Xgrid, plot_raw, apply_link, percentiles, wh
fsamples[:, s] = self.likelihood.gp_link.transf(fsamples[:, s])
return retmu, percs, fsamples
def helper_for_plot_data(self, plot_limits, visible_dims, fixed_inputs, resolution):
def helper_for_plot_data(self, X, plot_limits, visible_dims, fixed_inputs, resolution):
"""
Figure out the data, free_dims and create an Xgrid for
the prediction.
This is only implemented for two dimensions for now!
"""
X, Xvar, Y = get_x_y_var(self)
#work out what the inputs are for plotting (1D or 2D)
if fixed_inputs is None:
fixed_inputs = []
fixed_dims = get_fixed_dims(self, fixed_inputs)
fixed_dims = get_fixed_dims(fixed_inputs)
free_dims = get_free_dims(self, visible_dims, fixed_dims)
if len(free_dims) == 1:
@ -129,7 +127,7 @@ def helper_for_plot_data(self, plot_limits, visible_dims, fixed_inputs, resoluti
y = None
elif len(free_dims) == 2:
#define the frame for plotting on
resolution = resolution or 50
resolution = resolution or 35
Xnew, x, y, xmin, xmax = x_frame2D(X[:,free_dims], plot_limits, resolution)
Xgrid = np.zeros((Xnew.shape[0], self.input_dim))
Xgrid[:,free_dims] = Xnew
@ -137,7 +135,7 @@ def helper_for_plot_data(self, plot_limits, visible_dims, fixed_inputs, resoluti
Xgrid[:,i] = v
else:
raise TypeError("calculated free_dims {} from visible_dims {} and fixed_dims {} is neither 1D nor 2D".format(free_dims, visible_dims, fixed_dims))
return X, Xvar, Y, fixed_dims, free_dims, Xgrid, x, y, xmin, xmax, resolution
return fixed_dims, free_dims, Xgrid, x, y, xmin, xmax, resolution
def scatter_label_generator(labels, X, visible_dims, marker=None):
ulabels = []
@ -271,13 +269,16 @@ def update_not_existing_kwargs(to_update, update_from):
def get_x_y_var(model):
"""
The the data from a model as
Either the the data from a model as
X the inputs,
X_variance the variance of the inputs ([default: None])
and Y the outputs
If (X, X_variance, Y) is given, this just returns.
:returns: (X, X_variance, Y)
"""
# model given
if hasattr(model, 'has_uncertain_inputs') and model.has_uncertain_inputs():
X = model.X.mean.values
X_variance = model.X.variance.values
@ -305,7 +306,7 @@ def get_free_dims(model, visible_dims, fixed_dims):
return np.asanyarray([dim for dim in dims if dim is not None])
def get_fixed_dims(model, fixed_inputs):
def get_fixed_dims(fixed_inputs):
"""
Work out the fixed dimensions from the fixed_inputs list of tuples.
"""
@ -339,7 +340,7 @@ def x_frame1D(X,plot_limits=None,resolution=None):
else:
xmin,xmax = X.min(0),X.max(0)
xmin, xmax = xmin-0.25*(xmax-xmin), xmax+0.25*(xmax-xmin)
elif len(plot_limits)==2:
elif len(plot_limits) == 2:
xmin, xmax = plot_limits
else:
raise ValueError("Bad limits for plotting")
@ -355,9 +356,15 @@ def x_frame2D(X,plot_limits=None,resolution=None):
if plot_limits is None:
xmin, xmax = X.min(0),X.max(0)
xmin, xmax = xmin-0.075*(xmax-xmin), xmax+0.075*(xmax-xmin)
elif len(plot_limits)==2:
elif len(plot_limits) == 2:
xmin, xmax = plot_limits
elif len(plot_limits)==4:
try:
xmin = xmin[0], xmin[1]
except:
# only one limit given, copy over to other lim
xmin = [plot_limits[0], plot_limits[0]]
xmax = [plot_limits[1], plot_limits[1]]
elif len(plot_limits) == 4:
xmin, xmax = (plot_limits[0], plot_limits[2]), (plot_limits[1], plot_limits[3])
else:
raise ValueError("Bad limits for plotting")