本文整理匯總了Python中numpy.PINF屬性的典型用法代碼示例。如果您正苦於以下問題:Python numpy.PINF屬性的具體用法?Python numpy.PINF怎麽用?Python numpy.PINF使用的例子?那麽, 這裏精選的屬性代碼示例或許可以為您提供幫助。您也可以進一步了解該屬性所在類numpy
的用法示例。
在下文中一共展示了numpy.PINF屬性的13個代碼示例,這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚,您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: ComputeEnabledAABB
# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import PINF [as 別名]
def ComputeEnabledAABB(kinbody):
"""
Returns the AABB of the enabled links of a KinBody.
@param kinbody: an OpenRAVE KinBody
@returns: AABB of the enabled links of the KinBody
"""
from numpy import NINF, PINF
from openravepy import AABB
min_corner = numpy.array([PINF] * 3)
max_corner = numpy.array([NINF] * 3)
for link in kinbody.GetLinks():
if link.IsEnabled():
link_aabb = link.ComputeAABB()
center = link_aabb.pos()
half_extents = link_aabb.extents()
min_corner = numpy.minimum(center - half_extents, min_corner)
max_corner = numpy.maximum(center + half_extents, max_corner)
center = (min_corner + max_corner) / 2.
half_extents = (max_corner - min_corner) / 2.
return AABB(center, half_extents)
示例2: test_constants
# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import PINF [as 別名]
def test_constants():
assert chainerx.Inf is numpy.Inf
assert chainerx.Infinity is numpy.Infinity
assert chainerx.NAN is numpy.NAN
assert chainerx.NINF is numpy.NINF
assert chainerx.NZERO is numpy.NZERO
assert chainerx.NaN is numpy.NaN
assert chainerx.PINF is numpy.PINF
assert chainerx.PZERO is numpy.PZERO
assert chainerx.e is numpy.e
assert chainerx.euler_gamma is numpy.euler_gamma
assert chainerx.inf is numpy.inf
assert chainerx.infty is numpy.infty
assert chainerx.nan is numpy.nan
assert chainerx.newaxis is numpy.newaxis
assert chainerx.pi is numpy.pi
示例3: normalize
# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import PINF [as 別名]
def normalize(v):
if isinstance(v, numpy.bool_):
return bool(v)
elif isinstance(v, numpy.ndarray):
return [normalize(item) for item in v]
elif v == numpy.NaN:
return "NaN"
elif v == numpy.NINF:
return "-Infinity"
elif v == numpy.PINF:
return "Infinity"
elif isinstance(v, numpy.float):
return float(v)
elif isinstance(v, tuple):
return list(v)
else:
return v
示例4: _get_proposal_function
# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import PINF [as 別名]
def _get_proposal_function(self, model, space):
# Define proposal function for multi-fidelity
ei = ExpectedImprovement(model)
def proposal_func(x):
x_ = x[None, :]
# Map to highest fidelity
idx = np.ones((x_.shape[0], 1)) * self.high_fidelity
x_ = np.insert(x_, self.target_fidelity_index, idx, axis=1)
if space.check_points_in_domain(x_):
val = np.log(np.clip(ei.evaluate(x_)[0], 0., np.PINF))
if np.any(np.isnan(val)):
return np.array([np.NINF])
else:
return val
else:
return np.array([np.NINF])
return proposal_func
示例5: _get_proposal_function
# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import PINF [as 別名]
def _get_proposal_function(self, model, space):
# Define proposal function for multi-fidelity
ei = ExpectedImprovement(model)
def proposal_func(x):
x_ = x[None, :]
# Add information source parameter into array
idx = np.ones((x_.shape[0], 1)) * self.target_information_source_index
x_ = np.insert(x_, self.source_idx, idx, axis=1)
if space.check_points_in_domain(x_):
val = np.log(np.clip(ei.evaluate(x_)[0], 0., np.PINF))
if np.any(np.isnan(val)):
return np.array([np.NINF])
else:
return val
else:
return np.array([np.NINF])
return proposal_func
示例6: get_ground_clearance
# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import PINF [as 別名]
def get_ground_clearance(data):
"""
Extracts the minimum value of for_pos[2] and returns that value and the
timestep it happened at.
"""
structure = data.structure
min_clear = np.PINF
ts_min_clear = None
for ts, tstep in enumerate(structure.timestep_info):
try:
tstep.mb_dict['constraint_00']
continue
except KeyError:
pass
if tstep.for_pos[2] < min_clear:
min_clear = tstep.for_pos[2]
ts_min_clear = ts
return min_clear, ts_min_clear
示例7: init
# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import PINF [as 別名]
def init(self, **options):
self.__dict__.update(options)
self.lower = asarray(self.lower)
self.lower = where(self.lower == numpy.NINF, -_double_max, self.lower)
self.upper = asarray(self.upper)
self.upper = where(self.upper == numpy.PINF, _double_max, self.upper)
self.k = 0
self.accepted = 0
self.feval = 0
self.tests = 0
示例8: _get_weight_map
# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import PINF [as 別名]
def _get_weight_map(self, ann, inst_list):
if len(inst_list) <= 1: # 1 instance only
return np.zeros(ann.shape[:2])
stacked_inst_bgd_dst = np.zeros(ann.shape[:2] +(len(inst_list),))
for idx, inst_id in enumerate(inst_list):
inst_bgd_map = np.array(ann != inst_id , np.uint8)
inst_bgd_dst = distance_transform_edt(inst_bgd_map)
stacked_inst_bgd_dst[...,idx] = inst_bgd_dst
near1_dst = np.amin(stacked_inst_bgd_dst, axis=2)
near2_dst = np.expand_dims(near1_dst ,axis=2)
near2_dst = stacked_inst_bgd_dst - near2_dst
near2_dst[near2_dst == 0] = np.PINF # very large
near2_dst = np.amin(near2_dst, axis=2)
near2_dst[ann > 0] = 0 # the instances
near2_dst = near2_dst + near1_dst
# to fix pixel where near1 == near2
near2_eve = np.expand_dims(near1_dst ,axis=2)
# to avoide the warning of a / 0
near2_eve = (1.0 + stacked_inst_bgd_dst) / (1.0 + near2_eve)
near2_eve[near2_eve != 1] = 0
near2_eve = np.sum(near2_eve, axis=2)
near2_dst[near2_eve > 1] = near1_dst[near2_eve > 1]
#
pix_dst = near1_dst + near2_dst
pen_map = pix_dst / self.sigma
pen_map = self.w0 * np.exp(- pen_map**2 / 2)
pen_map[ann > 0] = 0 # inner instances zero
return pen_map
示例9: test_normalize_json
# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import PINF [as 別名]
def test_normalize_json():
doc = {"foo": {numpy.bool_(True): "value"},
"what": numpy.bool_(False),
"this": numpy.PINF}
normalized_doc = normalize_json(doc)
assert isinstance(normalized_doc['what'], bool)
assert isinstance(list(normalized_doc['foo'].keys())[0], bool)
assert normalized_doc['this'] == "Infinity"
示例10: best_h
# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import PINF [as 別名]
def best_h(self, lambda_vec):
"""Solve the best-response problem.
Returns the classifier that solves the best-response problem for
the vector of Lagrange multipliers `lambda_vec`.
"""
classifier = self._call_oracle(lambda_vec)
def h(X): return classifier.predict(X)
h_error = self.obj.gamma(h)[0]
h_gamma = self.constraints.gamma(h)
h_value = h_error + h_gamma.dot(lambda_vec)
if not self.hs.empty:
values = self.errors + self.gammas.transpose().dot(lambda_vec)
best_idx = values.idxmin()
best_value = values[best_idx]
else:
best_idx = -1
best_value = np.PINF
if h_value < best_value - _PRECISION:
logger.debug("%sbest_h: val improvement %f", _LINE, best_value - h_value)
h_idx = len(self.hs)
self.hs.at[h_idx] = h
self.predictors.at[h_idx] = classifier
self.errors.at[h_idx] = h_error
self.gammas[h_idx] = h_gamma
self.lambdas[h_idx] = lambda_vec.copy()
best_idx = h_idx
return self.hs[best_idx], best_idx
示例11: __init__
# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import PINF [as 別名]
def __init__(self,
position,
nzis=None,
shape=None,
hitpoints=np.PINF,
is_entity=True,
color=(128, 128, 128),
visible=True,
indirect_collision_effects=True):
self._position = np.array(position)
self.hitpoints = hitpoints
self.is_entity = is_entity
self.color = color
self.visible = visible
self.is_rectangular = True
self.indirect_collision_effects = indirect_collision_effects
assert self.hitpoints > 0
assert ((nzis is None and shape is not None) or
(nzis is not None and shape is None))
# Set non-zero indices of the object mask's
if nzis is None:
self._nzis = shape_to_nzis(shape)
else:
self._nzis = np.array(nzis)
if is_entity:
self.entity_id = BreakoutObject.unique_entity_id
BreakoutObject.unique_entity_id += MAX_NZIS_PER_ENTITY
assert len(self._nzis) <= MAX_NZIS_PER_ENTITY
else:
self.entity_id = None
self.object_id = BreakoutObject.unique_object_id
BreakoutObject.unique_object_id += 1
BreakoutObject.register_color(self.color)
# Sets up slots for memoization
self.reset_cache()
示例12: play_game
# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import PINF [as 別名]
def play_game(environment_class,
cheat_mode=DEFAULT_CHEAT_MODE,
debug=DEFAULT_DEBUG,
fps=30):
"""
Interactively play an environment.
Parameters
----------
environment_class : type
A subclass of schema_games.breakout.core.BreakoutEngine that represents
a game. A convenient list is included in schema_games.breakout.games.
cheat_mode : bool
If True, player has an infinite amount of lives.
debug : bool
If True, print debugging messages and perform additional sanity checks.
fps : int
Frame rate per second at which to display the game.
"""
print blue("-" * 80)
print blue("Starting interactive game. "
"Press <ESC> at any moment to terminate.")
print blue("-" * 80)
env_args = {
'return_state_as_image': True,
'debugging': debug,
}
if cheat_mode:
env_args['num_lives'] = np.PINF
env = environment_class(**env_args)
keys_to_action = defaultdict(lambda: env.NOOP, {
(pygame.K_LEFT,): env.LEFT,
(pygame.K_RIGHT,): env.RIGHT,
})
play(env, fps=fps, keys_to_action=keys_to_action, zoom=ZOOM_FACTOR)
示例13: PlanToEndEffectorPose
# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import PINF [as 別名]
def PlanToEndEffectorPose(self, robot, goal_pose, timelimit=5.0,
pose_error_tol=0.01,
integration_interval=10.0,
**kw_args):
"""
Plan to an end effector pose by following a geodesic loss function
in SE(3) via an optimized Jacobian.
@param robot
@param goal_pose desired end-effector pose
@param timelimit time limit before giving up
@param pose_error_tol in meters
@param integration_interval The time interval to integrate over
@return traj
"""
manip = robot.GetActiveManipulator()
def vf_geodesic():
"""
Define a joint-space vector field, that moves along the
geodesic (shortest path) from the start pose to the goal pose.
"""
twist = util.GeodesicTwist(manip.GetEndEffectorTransform(),
goal_pose)
dqout, tout = util.ComputeJointVelocityFromTwist(
robot, twist, joint_velocity_limits=numpy.PINF)
# Go as fast as possible
vlimits = robot.GetDOFVelocityLimits(robot.GetActiveDOFIndices())
return min(abs(vlimits[i] / dqout[i])
if dqout[i] != 0. else 1.
for i in xrange(vlimits.shape[0])) * dqout
def CloseEnough():
"""
The termination condition.
At each integration step, the geodesic error between the
start and goal poses is compared. If within threshold,
the integration will terminate.
"""
pose_error = util.GetGeodesicDistanceBetweenTransforms(
manip.GetEndEffectorTransform(), goal_pose)
if pose_error < pose_error_tol:
return Status.TERMINATE
return Status.CONTINUE
traj = self.FollowVectorField(robot, vf_geodesic, CloseEnough,
integration_interval,
timelimit,
**kw_args)
# Flag this trajectory as unconstrained. This overwrites the
# constrained flag set by FollowVectorField.
util.SetTrajectoryTags(traj, {Tags.CONSTRAINED: False}, append=True)
return traj