def mean_sdf(self):
"""
Returns an SDF of the mean surface
Params: none
Returns:
SDF: mean surface
"""
return sdf.Sdf2D(self.mean_pred_.reshape(self.dims_))
def _read_2d(self):
'''
Reads a 2d SDF from a CSV file
'''
if not os.path.exists(self.file_name_):
raise IOError('File does not exist')
sdf_data = np.loadtxt(self.file_name_, delimiter=',')
return sdf.Sdf2D(sdf_data)
def _read_2d(self):
"""Reads in a 2D SDF file and returns a Sdf object.
Returns
-------
:obj:`Sdf2D`
A 2DSdf created from the data in the file.
"""
if not os.path.exists(self.filepath_):
return None
sdf_data = np.loadtxt(self.filepath_, delimiter=',')
return sdf.Sdf2D(sdf_data)
def sample_sdfs(self, num_samples=1, full_cov=True):
"""
Samples sdfs from the GPIS
Params:
num_samples: (int) number of samples to generate
full_cov: (bool) whether or not to use the diagonal or entire covariance matrix
Returns:
list of sdf objects
"""
sdf_samples = self.gp_.posterior_samples(self.grid_pts_, num_samples)
sdfs = []
for i in range(num_samples):
sdf_data = sdf_samples[:,i].reshape(self.dims_)
sdfs.append(sdf.Sdf2D(sdf_data, pose = self.pose_))
return sdfs