def draw_gradient(self, x):
"""
Draws the gradient as an offset vector from the trajectory
Parameters
----------
x : array
the trajectory vector
"""
g = self.objective.objective.gradient(x)
q_init = self.objective.q_init
self.draw(Trajectory(q_init=q_init, x=x),
Trajectory(q_init=q_init, x=-0.01 * g + x))
def hessian(self, x):
if self.viewer is not None:
if self._draw_hessian:
self.draw_gradient(x)
else:
self.draw(Trajectory(q_init=self.objective.q_init, x=x))
return self.objective.objective.hessian(x)
def load_trajectories_from_file(filename='trajectories_1k_small.hdf5'):
""" Load data from an hdf5 file """
data = dict_to_object(load_dictionary_from_file(filename))
print((" -- trajectories * n : {}".format(data.n[0])))
print((" -- trajectories * l : {}".format(len(data.datasets))))
n = data.n[0]
trajectories = [None] * len(data.datasets)
for k, trj in enumerate(data.datasets):
trajectories[k] = Trajectory(q_init=trj[:n], x=trj)
return trajectories
def hessian(self, x):
"""
Calculates the hessian after drawing the trajectory
and draws optionaly the gradient
Parameters
----------
x : array
the trajectory vector
"""
if self.viewer is not None:
if self._draw_hessian:
self.draw_gradient(x)
else:
self.draw(Trajectory(q_init=self.objective.q_init, x=x))
return self.objective.objective.hessian(x)
def draw_gradient(self, x):
g = self.objective.objective.gradient(x)
q_init = self.objective.q_init
self.draw(
Trajectory(q_init=q_init, x=x),
Trajectory(q_init=q_init, x=-0.01 * g + x))