def f_meshgrid(args):
"""f_meshgrid
create meshgrid
"""
fu_check_required_args(args, ['ranges', 'steps'], 'f_meshgrid')
# FIXME: check_outputs: only one item
# create meshgrid over proprio dimensions
steps = get_input(args, 'steps')
ranges = get_input(args, 'ranges')
ndims = len(ranges)
# dim_axes = [np.linspace(self.environment.conf.m_mins[i], self.environment.conf.m_maxs[i], sweepsteps) for i in range(self.environment.conf.m_ndims)]
# dim_axes = [np.linspace(ranges[i][0], ranges[i][1], steps) for i in range(ndims)]
dim_axes = [np.linspace(min_, max_, steps) for min_, max_ in ranges]
full_axes = np.meshgrid(*tuple(dim_axes), indexing='ij')
# print "dim_axes", dim_axes
# print "full_axes", len(full_axes)
# print "full_axes", full_axes
# for i in range(len(full_axes)):
# print i, full_axes[i].shape
# print i, full_axes[i].flatten()
# return proxy
full_axes_flat = np.vstack(
[full_axes[i].flatten() for i in range(len(full_axes))])
# print "func", full_axes_flat.shape
# print "func", full_axes_flat
return {'meshgrid': full_axes_flat} # .T
def f_random_uniform(args):
fu_check_required_args(args, ['ranges', 'steps'], 'f_random_uniform')
ranges = get_input(args, 'ranges')
steps = get_input(args, 'steps')
meshgrid = np.random.uniform(
ranges[:, [0]], ranges[:, [1]],
(ranges.shape[0], int(steps[0, 0]**ranges.shape[0])))
# print "f_random_uniform meshgrid = %s" % (meshgrid.shape, )
return {'meshgrid': meshgrid}
def f_sum(args):
fu_check_required_args(args, ['x'], 'f_sum')
# lambda x: {'y': np.sum(np.square(x['x']['val']))}
x = get_input(args, 'x')
y = np.atleast_2d(np.sum(x))
# print " f_meansquare x = %s, y = %s" % (x, y)
return {'y': y}
def f_meshgrid_mdl(args):
fu_check_required_args(args, ['ranges', 'steps'], 'f_meshgrid_mdl')
# create meshgrid over proprio dimensions
steps = get_input(args, 'steps')
ranges = get_input(args, 'ranges')
ndims = len(ranges)
# # extero config
# dim_axes = [np.linspace(self.environment.conf.s_mins[i], self.environment.conf.s_maxs[i], steps) for i in range(self.environment.conf.s_ndims)]
# # dim_axes = [np.linspace(self.environment.conf.s_mins[i], self.environment.conf.s_maxs[i], steps) for i in range(self.mdl.idim)]
# print "rh_model_sweep_generate_input_grid: s_ndims = %d, dim_axes = %s" % (self.environment.conf.s_ndims, dim_axes,)
# full_axes = np.meshgrid(*tuple(dim_axes), indexing='ij')
# print "rh_model_sweep_generate_input_grid: full_axes = %s, %s" % (len(full_axes), full_axes,)
dim_axes = [np.linspace(min_, max_, steps) for min_, max_ in ranges]
full_axes = np.meshgrid(*tuple(dim_axes), indexing='ij')
for i in range(len(full_axes)):
print i, full_axes[i].shape
print i, full_axes[i].flatten()
# return proxy
error_grid = np.vstack(
[full_axes[i].flatten() for i in range(len(full_axes))])
print "error_grid", error_grid.shape
# draw state / goal configurations
X_accum = []
states = np.linspace(-1, 1, steps)
# for state in range(1): # steps):
for state in states:
# randomize initial position
# self.M_prop_pred = self.environment.compute_motor_command(np.random.uniform(-1.0, 1.0, (1, self.odim)))
# draw random goal and keep it fixed
# self.goal_prop = self.environment.compute_motor_command(np.random.uniform(-1.0, 1.0, (1, self.odim)))
goal_prop = np.ones((1, ndims)) * state
# self.goal_prop = np.random.uniform(self.environment.conf.m_mins, self.environment.conf.m_maxs, (1, self.odim))
GOALS = np.repeat(goal_prop, error_grid.shape[1],
axis=0) # as many goals as error components
# FIXME: hacks for M1/M2
if ndims == 3:
X = GOALS
elif ndims == 6:
X = np.hstack((GOALS, error_grid.T))
else:
X = np.hstack((GOALS, error_grid.T))
X_accum.append(X)
X_accum = np.array(X_accum)
# don't need this?
# X_accum = X_accum.reshape((X_accum.shape[0] * X_accum.shape[1], X_accum.shape[2]))
# print "X_accum.shape = %s, mdl.idim = %d, mdl.odim = %d" % (X_accum.shape, self.mdl.idim, self.mdl.odim)
# print X_accum
X = X_accum
# X's and pred's indices now mean: slowest: goal, e1, e2, fastest: e3
# self.X_model_sweep = X.copy()
# print "self.X_model_sweep.shape", self.X_model_sweep.shape
# return proxy
full_axes_flat = np.vstack(
[full_axes[i].flatten() for i in range(len(full_axes))])
print "func", full_axes_flat
return {'meshgrid': full_axes_flat} # .T