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changed nasty whitespace

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James Hensman 2013-11-22 08:58:29 +00:00
parent 98b9dc0163
commit 5b1f700238
1 changed files with 7 additions and 9 deletions
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16
GPy/core/mapping.py
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@ -36,7 +36,6 @@ class Mapping(Parameterized):
def df_dtheta(self, dL_df, X):
"""The gradient of the outputs of the multi-layer perceptron with respect to each of the parameters.
:param dL_df: gradient of the objective with respect to the function.
:type dL_df: ndarray (num_data x output_dim)
:param X: input locations where the function is evaluated.
@ -44,14 +43,13 @@ class Mapping(Parameterized):
:returns: Matrix containing gradients with respect to parameters of each output for each input data.
:rtype: ndarray (num_params length)
"""
raise NotImplementedError
def plot(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']):
"""
Plot the mapping.
Plots the mapping associated with the model.
- In one dimension, the function is plotted.
- In two dimsensions, a contour-plot shows the function
@ -110,7 +108,7 @@ class Mapping(Parameterized):
for d in range(y.shape[1]):
ax.plot(Xnew, f[:, d], edgecol=linecol)
elif self.X.shape[1] == 2:
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)
@ -135,14 +133,14 @@ class Mapping_check_model(Model):
X = np.random.randn(num_samples, mapping.input_dim)
if dL_df==None:
dL_df = np.ones((num_samples, mapping.output_dim))
self.mapping=mapping
self.X = X
self.dL_df = dL_df
self.num_params = self.mapping.num_params
Model.__init__(self)
def _get_params(self):
return self.mapping._get_params()
@ -157,7 +155,7 @@ class Mapping_check_model(Model):
def _log_likelihood_gradients(self):
raise NotImplementedError, "This needs to be implemented to use the Mapping_check_model class."
class Mapping_check_df_dtheta(Mapping_check_model):
"""This class allows gradient checks for the gradient of a mapping with respect to parameters. """
def __init__(self, mapping=None, dL_df=None, X=None):
@ -175,13 +173,13 @@ class Mapping_check_df_dX(Mapping_check_model):
if dL_df==None:
dL_df = np.ones((self.X.shape[0],self.mapping.output_dim))
self.num_params = self.X.shape[0]*self.mapping.input_dim
def _log_likelihood_gradients(self):
return self.mapping.df_dX(self.dL_df, self.X).flatten()
def _get_param_names(self):
return ['X_' +str(i) + ','+str(j) for j in range(self.X.shape[1]) for i in range(self.X.shape[0])]
def _get_params(self):
return self.X.flatten()