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lmcinnes/umap | umap/spectral.py | multi_component_layout | def multi_component_layout(
data,
graph,
n_components,
component_labels,
dim,
random_state,
metric="euclidean",
metric_kwds={},
):
"""Specialised layout algorithm for dealing with graphs with many connected components.
This will first fid relative positions for the components by ... | python | def multi_component_layout(
data,
graph,
n_components,
component_labels,
dim,
random_state,
metric="euclidean",
metric_kwds={},
):
"""Specialised layout algorithm for dealing with graphs with many connected components.
This will first fid relative positions for the components by ... | [
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lmcinnes/umap | umap/spectral.py | spectral_layout | def spectral_layout(data, graph, dim, random_state, metric="euclidean", metric_kwds={}):
"""Given a graph compute the spectral embedding of the graph. This is
simply the eigenvectors of the laplacian of the graph. Here we use the
normalized laplacian.
Parameters
----------
data: array of shape ... | python | def spectral_layout(data, graph, dim, random_state, metric="euclidean", metric_kwds={}):
"""Given a graph compute the spectral embedding of the graph. This is
simply the eigenvectors of the laplacian of the graph. Here we use the
normalized laplacian.
Parameters
----------
data: array of shape ... | [
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lmcinnes/umap | umap/rp_tree.py | sparse_angular_random_projection_split | def sparse_angular_random_projection_split(inds, indptr, data, indices, rng_state):
"""Given a set of ``indices`` for data points from a sparse data set
presented in csr sparse format as inds, indptr and data, create
a random hyperplane to split the data, returning two arrays indices
that fall on either... | python | def sparse_angular_random_projection_split(inds, indptr, data, indices, rng_state):
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presented in csr sparse format as inds, indptr and data, create
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lmcinnes/umap | umap/rp_tree.py | sparse_euclidean_random_projection_split | def sparse_euclidean_random_projection_split(inds, indptr, data, indices, rng_state):
"""Given a set of ``indices`` for data points from a sparse data set
presented in csr sparse format as inds, indptr and data, create
a random hyperplane to split the data, returning two arrays indices
that fall on eith... | python | def sparse_euclidean_random_projection_split(inds, indptr, data, indices, rng_state):
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lmcinnes/umap | umap/rp_tree.py | make_tree | def make_tree(data, rng_state, leaf_size=30, angular=False):
"""Construct a random projection tree based on ``data`` with leaves
of size at most ``leaf_size``.
Parameters
----------
data: array of shape (n_samples, n_features)
The original data to be split
rng_state: array of int64, shap... | python | def make_tree(data, rng_state, leaf_size=30, angular=False):
"""Construct a random projection tree based on ``data`` with leaves
of size at most ``leaf_size``.
Parameters
----------
data: array of shape (n_samples, n_features)
The original data to be split
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lmcinnes/umap | umap/rp_tree.py | num_nodes | def num_nodes(tree):
"""Determine the number of nodes in a tree"""
if tree.is_leaf:
return 1
else:
return 1 + num_nodes(tree.left_child) + num_nodes(tree.right_child) | python | def num_nodes(tree):
"""Determine the number of nodes in a tree"""
if tree.is_leaf:
return 1
else:
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lmcinnes/umap | umap/rp_tree.py | num_leaves | def num_leaves(tree):
"""Determine the number of leaves in a tree"""
if tree.is_leaf:
return 1
else:
return num_leaves(tree.left_child) + num_leaves(tree.right_child) | python | def num_leaves(tree):
"""Determine the number of leaves in a tree"""
if tree.is_leaf:
return 1
else:
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lmcinnes/umap | umap/rp_tree.py | max_sparse_hyperplane_size | def max_sparse_hyperplane_size(tree):
"""Determine the most number on non zeros in a hyperplane entry"""
if tree.is_leaf:
return 0
else:
return max(
tree.hyperplane.shape[1],
max_sparse_hyperplane_size(tree.left_child),
max_sparse_hyperplane_size(tree.righ... | python | def max_sparse_hyperplane_size(tree):
"""Determine the most number on non zeros in a hyperplane entry"""
if tree.is_leaf:
return 0
else:
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lmcinnes/umap | umap/rp_tree.py | make_forest | def make_forest(data, n_neighbors, n_trees, rng_state, angular=False):
"""Build a random projection forest with ``n_trees``.
Parameters
----------
data
n_neighbors
n_trees
rng_state
angular
Returns
-------
forest: list
A list of random projection trees.
"""
... | python | def make_forest(data, n_neighbors, n_trees, rng_state, angular=False):
"""Build a random projection forest with ``n_trees``.
Parameters
----------
data
n_neighbors
n_trees
rng_state
angular
Returns
-------
forest: list
A list of random projection trees.
"""
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lmcinnes/umap | umap/rp_tree.py | rptree_leaf_array | def rptree_leaf_array(rp_forest):
"""Generate an array of sets of candidate nearest neighbors by
constructing a random projection forest and taking the leaves of all the
trees. Any given tree has leaves that are a set of potential nearest
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lmcinnes/umap | umap/umap_.py | smooth_knn_dist | def smooth_knn_dist(distances, k, n_iter=64, local_connectivity=1.0, bandwidth=1.0):
"""Compute a continuous version of the distance to the kth nearest
neighbor. That is, this is similar to knn-distance but allows continuous
k values rather than requiring an integral k. In esscence we are simply
computi... | python | def smooth_knn_dist(distances, k, n_iter=64, local_connectivity=1.0, bandwidth=1.0):
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lmcinnes/umap | umap/umap_.py | nearest_neighbors | def nearest_neighbors(
X, n_neighbors, metric, metric_kwds, angular, random_state, verbose=False
):
"""Compute the ``n_neighbors`` nearest points for each data point in ``X``
under ``metric``. This may be exact, but more likely is approximated via
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lmcinnes/umap | umap/umap_.py | compute_membership_strengths | def compute_membership_strengths(knn_indices, knn_dists, sigmas, rhos):
"""Construct the membership strength data for the 1-skeleton of each local
fuzzy simplicial set -- this is formed as a sparse matrix where each row is
a local fuzzy simplicial set, with a membership strength for the
1-simplex to eac... | python | def compute_membership_strengths(knn_indices, knn_dists, sigmas, rhos):
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lmcinnes/umap | umap/umap_.py | fuzzy_simplicial_set | def fuzzy_simplicial_set(
X,
n_neighbors,
random_state,
metric,
metric_kwds={},
knn_indices=None,
knn_dists=None,
angular=False,
set_op_mix_ratio=1.0,
local_connectivity=1.0,
verbose=False,
):
"""Given a set of data X, a neighborhood size, and a measure of distance
co... | python | def fuzzy_simplicial_set(
X,
n_neighbors,
random_state,
metric,
metric_kwds={},
knn_indices=None,
knn_dists=None,
angular=False,
set_op_mix_ratio=1.0,
local_connectivity=1.0,
verbose=False,
):
"""Given a set of data X, a neighborhood size, and a measure of distance
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lmcinnes/umap | umap/umap_.py | fast_intersection | def fast_intersection(rows, cols, values, target, unknown_dist=1.0, far_dist=5.0):
"""Under the assumption of categorical distance for the intersecting
simplicial set perform a fast intersection.
Parameters
----------
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An array of the row of each non-zero in the sparse matrix
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lmcinnes/umap | umap/umap_.py | reset_local_connectivity | def reset_local_connectivity(simplicial_set):
"""Reset the local connectivity requirement -- each data sample should
have complete confidence in at least one 1-simplex in the simplicial set.
We can enforce this by locally rescaling confidences, and then remerging the
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"""Reset the local connectivity requirement -- each data sample should
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lmcinnes/umap | umap/umap_.py | categorical_simplicial_set_intersection | def categorical_simplicial_set_intersection(
simplicial_set, target, unknown_dist=1.0, far_dist=5.0
):
"""Combine a fuzzy simplicial set with another fuzzy simplicial set
generated from categorical data using categorical distances. The target
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simplicial_set, target, unknown_dist=1.0, far_dist=5.0
):
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lmcinnes/umap | umap/umap_.py | make_epochs_per_sample | def make_epochs_per_sample(weights, n_epochs):
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epochs per sample for each weight.
Parameters
----------
weights: array of shape (n_1_simplices)
The weights ofhow much we wish to sample each 1-simplex.
n_epochs: int
... | python | def make_epochs_per_sample(weights, n_epochs):
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weights: array of shape (n_1_simplices)
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lmcinnes/umap | umap/umap_.py | rdist | def rdist(x, y):
"""Reduced Euclidean distance.
Parameters
----------
x: array of shape (embedding_dim,)
y: array of shape (embedding_dim,)
Returns
-------
The squared euclidean distance between x and y
"""
result = 0.0
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result += (x[i] - ... | python | def rdist(x, y):
"""Reduced Euclidean distance.
Parameters
----------
x: array of shape (embedding_dim,)
y: array of shape (embedding_dim,)
Returns
-------
The squared euclidean distance between x and y
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lmcinnes/umap | umap/umap_.py | optimize_layout | def optimize_layout(
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tail,
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epochs_per_sample,
a,
b,
rng_state,
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negative_sample_rate=5.0,
verbose=False,
):
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head_embedding,
tail_embedding,
head,
tail,
n_epochs,
n_vertices,
epochs_per_sample,
a,
b,
rng_state,
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lmcinnes/umap | umap/umap_.py | simplicial_set_embedding | def simplicial_set_embedding(
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graph,
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initial_alpha,
a,
b,
gamma,
negative_sample_rate,
n_epochs,
init,
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graph,
n_components,
initial_alpha,
a,
b,
gamma,
negative_sample_rate,
n_epochs,
init,
random_state,
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verbose,
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lmcinnes/umap | umap/umap_.py | init_transform | def init_transform(indices, weights, embedding):
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initialize the positions of new points relative to the
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lmcinnes/umap | umap/umap_.py | find_ab_params | def find_ab_params(spread, min_dist):
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lmcinnes/umap | umap/umap_.py | UMAP.fit | def fit(self, X, y=None):
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lmcinnes/umap | umap/umap_.py | UMAP.fit_transform | def fit_transform(self, X, y=None):
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X : array, shape (n_samples, n_features) or (n_samples, n_samples)
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lmcinnes/umap | umap/umap_.py | UMAP.transform | def transform(self, X):
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X : array, shape (n_samples, n_features)
New data to be transformed.
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X : array, shape (n_samples, n_features)
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lmcinnes/umap | umap/sparse.py | make_sparse_nn_descent | def make_sparse_nn_descent(sparse_dist, dist_args):
"""Create a numba accelerated version of nearest neighbor descent
specialised for the given distance metric and metric arguments on sparse
matrix data provided in CSR ind, indptr and data format. Numba
doesn't support higher order functions directly, b... | python | def make_sparse_nn_descent(sparse_dist, dist_args):
"""Create a numba accelerated version of nearest neighbor descent
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deepmind/pysc2 | pysc2/bin/benchmark_observe.py | interface_options | def interface_options(score=False, raw=False, features=None, rgb=None):
"""Get an InterfaceOptions for the config."""
interface = sc_pb.InterfaceOptions()
interface.score = score
interface.raw = raw
if features:
interface.feature_layer.width = 24
interface.feature_layer.resolution.x = features
int... | python | def interface_options(score=False, raw=False, features=None, rgb=None):
"""Get an InterfaceOptions for the config."""
interface = sc_pb.InterfaceOptions()
interface.score = score
interface.raw = raw
if features:
interface.feature_layer.width = 24
interface.feature_layer.resolution.x = features
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deepmind/pysc2 | pysc2/lib/point_flag.py | DEFINE_point | def DEFINE_point(name, default, help): # pylint: disable=invalid-name,redefined-builtin
"""Registers a flag whose value parses as a point."""
flags.DEFINE(PointParser(), name, default, help) | python | def DEFINE_point(name, default, help): # pylint: disable=invalid-name,redefined-builtin
"""Registers a flag whose value parses as a point."""
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deepmind/pysc2 | pysc2/lib/actions.py | spatial | def spatial(action, action_space):
"""Choose the action space for the action proto."""
if action_space == ActionSpace.FEATURES:
return action.action_feature_layer
elif action_space == ActionSpace.RGB:
return action.action_render
else:
raise ValueError("Unexpected value for action_space: %s" % action... | python | def spatial(action, action_space):
"""Choose the action space for the action proto."""
if action_space == ActionSpace.FEATURES:
return action.action_feature_layer
elif action_space == ActionSpace.RGB:
return action.action_render
else:
raise ValueError("Unexpected value for action_space: %s" % action... | [
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deepmind/pysc2 | pysc2/lib/actions.py | move_camera | def move_camera(action, action_space, minimap):
"""Move the camera."""
minimap.assign_to(spatial(action, action_space).camera_move.center_minimap) | python | def move_camera(action, action_space, minimap):
"""Move the camera."""
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deepmind/pysc2 | pysc2/lib/actions.py | select_point | def select_point(action, action_space, select_point_act, screen):
"""Select a unit at a point."""
select = spatial(action, action_space).unit_selection_point
screen.assign_to(select.selection_screen_coord)
select.type = select_point_act | python | def select_point(action, action_space, select_point_act, screen):
"""Select a unit at a point."""
select = spatial(action, action_space).unit_selection_point
screen.assign_to(select.selection_screen_coord)
select.type = select_point_act | [
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deepmind/pysc2 | pysc2/lib/actions.py | select_rect | def select_rect(action, action_space, select_add, screen, screen2):
"""Select units within a rectangle."""
select = spatial(action, action_space).unit_selection_rect
out_rect = select.selection_screen_coord.add()
screen_rect = point.Rect(screen, screen2)
screen_rect.tl.assign_to(out_rect.p0)
screen_rect.br.... | python | def select_rect(action, action_space, select_add, screen, screen2):
"""Select units within a rectangle."""
select = spatial(action, action_space).unit_selection_rect
out_rect = select.selection_screen_coord.add()
screen_rect = point.Rect(screen, screen2)
screen_rect.tl.assign_to(out_rect.p0)
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deepmind/pysc2 | pysc2/lib/actions.py | select_idle_worker | def select_idle_worker(action, action_space, select_worker):
"""Select an idle worker."""
del action_space
action.action_ui.select_idle_worker.type = select_worker | python | def select_idle_worker(action, action_space, select_worker):
"""Select an idle worker."""
del action_space
action.action_ui.select_idle_worker.type = select_worker | [
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deepmind/pysc2 | pysc2/lib/actions.py | select_army | def select_army(action, action_space, select_add):
"""Select the entire army."""
del action_space
action.action_ui.select_army.selection_add = select_add | python | def select_army(action, action_space, select_add):
"""Select the entire army."""
del action_space
action.action_ui.select_army.selection_add = select_add | [
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deepmind/pysc2 | pysc2/lib/actions.py | select_warp_gates | def select_warp_gates(action, action_space, select_add):
"""Select all warp gates."""
del action_space
action.action_ui.select_warp_gates.selection_add = select_add | python | def select_warp_gates(action, action_space, select_add):
"""Select all warp gates."""
del action_space
action.action_ui.select_warp_gates.selection_add = select_add | [
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deepmind/pysc2 | pysc2/lib/actions.py | select_unit | def select_unit(action, action_space, select_unit_act, select_unit_id):
"""Select a specific unit from the multi-unit selection."""
del action_space
select = action.action_ui.multi_panel
select.type = select_unit_act
select.unit_index = select_unit_id | python | def select_unit(action, action_space, select_unit_act, select_unit_id):
"""Select a specific unit from the multi-unit selection."""
del action_space
select = action.action_ui.multi_panel
select.type = select_unit_act
select.unit_index = select_unit_id | [
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deepmind/pysc2 | pysc2/lib/actions.py | control_group | def control_group(action, action_space, control_group_act, control_group_id):
"""Act on a control group, selecting, setting, etc."""
del action_space
select = action.action_ui.control_group
select.action = control_group_act
select.control_group_index = control_group_id | python | def control_group(action, action_space, control_group_act, control_group_id):
"""Act on a control group, selecting, setting, etc."""
del action_space
select = action.action_ui.control_group
select.action = control_group_act
select.control_group_index = control_group_id | [
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deepmind/pysc2 | pysc2/lib/actions.py | unload | def unload(action, action_space, unload_id):
"""Unload a unit from a transport/bunker/nydus/etc."""
del action_space
action.action_ui.cargo_panel.unit_index = unload_id | python | def unload(action, action_space, unload_id):
"""Unload a unit from a transport/bunker/nydus/etc."""
del action_space
action.action_ui.cargo_panel.unit_index = unload_id | [
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deepmind/pysc2 | pysc2/lib/actions.py | build_queue | def build_queue(action, action_space, build_queue_id):
"""Cancel a unit in the build queue."""
del action_space
action.action_ui.production_panel.unit_index = build_queue_id | python | def build_queue(action, action_space, build_queue_id):
"""Cancel a unit in the build queue."""
del action_space
action.action_ui.production_panel.unit_index = build_queue_id | [
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deepmind/pysc2 | pysc2/lib/actions.py | cmd_quick | def cmd_quick(action, action_space, ability_id, queued):
"""Do a quick command like 'Stop' or 'Stim'."""
action_cmd = spatial(action, action_space).unit_command
action_cmd.ability_id = ability_id
action_cmd.queue_command = queued | python | def cmd_quick(action, action_space, ability_id, queued):
"""Do a quick command like 'Stop' or 'Stim'."""
action_cmd = spatial(action, action_space).unit_command
action_cmd.ability_id = ability_id
action_cmd.queue_command = queued | [
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deepmind/pysc2 | pysc2/lib/actions.py | cmd_screen | def cmd_screen(action, action_space, ability_id, queued, screen):
"""Do a command that needs a point on the screen."""
action_cmd = spatial(action, action_space).unit_command
action_cmd.ability_id = ability_id
action_cmd.queue_command = queued
screen.assign_to(action_cmd.target_screen_coord) | python | def cmd_screen(action, action_space, ability_id, queued, screen):
"""Do a command that needs a point on the screen."""
action_cmd = spatial(action, action_space).unit_command
action_cmd.ability_id = ability_id
action_cmd.queue_command = queued
screen.assign_to(action_cmd.target_screen_coord) | [
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deepmind/pysc2 | pysc2/lib/actions.py | cmd_minimap | def cmd_minimap(action, action_space, ability_id, queued, minimap):
"""Do a command that needs a point on the minimap."""
action_cmd = spatial(action, action_space).unit_command
action_cmd.ability_id = ability_id
action_cmd.queue_command = queued
minimap.assign_to(action_cmd.target_minimap_coord) | python | def cmd_minimap(action, action_space, ability_id, queued, minimap):
"""Do a command that needs a point on the minimap."""
action_cmd = spatial(action, action_space).unit_command
action_cmd.ability_id = ability_id
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deepmind/pysc2 | pysc2/lib/actions.py | autocast | def autocast(action, action_space, ability_id):
"""Toggle autocast."""
del action_space
action.action_ui.toggle_autocast.ability_id = ability_id | python | def autocast(action, action_space, ability_id):
"""Toggle autocast."""
del action_space
action.action_ui.toggle_autocast.ability_id = ability_id | [
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deepmind/pysc2 | pysc2/lib/actions.py | ArgumentType.enum | def enum(cls, options, values):
"""Create an ArgumentType where you choose one of a set of known values."""
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del names # unused
def factory(i, name):
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"""Create an ArgumentType where you choose one of a set of known values."""
names, real = zip(*options)
del names # unused
def factory(i, name):
return cls(i, name, (len(real),), lambda a: real[a[0]], values)
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"""Create an ArgumentType with a single scalar in range(value)."""
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deepmind/pysc2 | pysc2/lib/actions.py | ArgumentType.point | def point(cls): # No range because it's unknown at this time.
"""Create an ArgumentType that is represented by a point.Point."""
def factory(i, name):
return cls(i, name, (0, 0), lambda a: point.Point(*a).floor(), None)
return factory | python | def point(cls): # No range because it's unknown at this time.
"""Create an ArgumentType that is represented by a point.Point."""
def factory(i, name):
return cls(i, name, (0, 0), lambda a: point.Point(*a).floor(), None)
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deepmind/pysc2 | pysc2/lib/actions.py | Arguments.types | def types(cls, **kwargs):
"""Create an Arguments of the possible Types."""
named = {name: factory(Arguments._fields.index(name), name)
for name, factory in six.iteritems(kwargs)}
return cls(**named) | python | def types(cls, **kwargs):
"""Create an Arguments of the possible Types."""
named = {name: factory(Arguments._fields.index(name), name)
for name, factory in six.iteritems(kwargs)}
return cls(**named) | [
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deepmind/pysc2 | pysc2/lib/actions.py | Function.ui_func | def ui_func(cls, id_, name, function_type, avail_fn=always):
"""Define a function representing a ui action."""
return cls(id_, name, 0, 0, function_type, FUNCTION_TYPES[function_type],
avail_fn) | python | def ui_func(cls, id_, name, function_type, avail_fn=always):
"""Define a function representing a ui action."""
return cls(id_, name, 0, 0, function_type, FUNCTION_TYPES[function_type],
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deepmind/pysc2 | pysc2/lib/actions.py | Function.ability | def ability(cls, id_, name, function_type, ability_id, general_id=0):
"""Define a function represented as a game ability."""
assert function_type in ABILITY_FUNCTIONS
return cls(id_, name, ability_id, general_id, function_type,
FUNCTION_TYPES[function_type], None) | python | def ability(cls, id_, name, function_type, ability_id, general_id=0):
"""Define a function represented as a game ability."""
assert function_type in ABILITY_FUNCTIONS
return cls(id_, name, ability_id, general_id, function_type,
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deepmind/pysc2 | pysc2/lib/actions.py | Function.str | def str(self, space=False):
"""String version. Set space=True to line them all up nicely."""
return "%s/%s (%s)" % (str(int(self.id)).rjust(space and 4),
self.name.ljust(space and 50),
"; ".join(str(a) for a in self.args)) | python | def str(self, space=False):
"""String version. Set space=True to line them all up nicely."""
return "%s/%s (%s)" % (str(int(self.id)).rjust(space and 4),
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deepmind/pysc2 | pysc2/lib/actions.py | FunctionCall.init_with_validation | def init_with_validation(cls, function, arguments):
"""Return a `FunctionCall` given some validation for the function and args.
Args:
function: A function name or id, to be converted into a function id enum.
arguments: An iterable of function arguments. Arguments that are enum
types can b... | python | def init_with_validation(cls, function, arguments):
"""Return a `FunctionCall` given some validation for the function and args.
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function: A function name or id, to be converted into a function id enum.
arguments: An iterable of function arguments. Arguments that are enum
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deepmind/pysc2 | pysc2/lib/actions.py | FunctionCall.all_arguments | def all_arguments(cls, function, arguments):
"""Helper function for creating `FunctionCall`s with `Arguments`.
Args:
function: The value to store for the action function.
arguments: The values to store for the arguments of the action. Can either
be an `Arguments` object, a `dict`, or an ite... | python | def all_arguments(cls, function, arguments):
"""Helper function for creating `FunctionCall`s with `Arguments`.
Args:
function: The value to store for the action function.
arguments: The values to store for the arguments of the action. Can either
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deepmind/pysc2 | pysc2/bin/valid_actions.py | main | def main(unused_argv):
"""Print the valid actions."""
feats = features.Features(
# Actually irrelevant whether it's feature or rgb size.
features.AgentInterfaceFormat(
feature_dimensions=features.Dimensions(
screen=FLAGS.screen_size,
minimap=FLAGS.minimap_size)))
... | python | def main(unused_argv):
"""Print the valid actions."""
feats = features.Features(
# Actually irrelevant whether it's feature or rgb size.
features.AgentInterfaceFormat(
feature_dimensions=features.Dimensions(
screen=FLAGS.screen_size,
minimap=FLAGS.minimap_size)))
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deepmind/pysc2 | pysc2/lib/portspicker.py | pick_unused_ports | def pick_unused_ports(num_ports, retry_interval_secs=3, retry_attempts=5):
"""Reserves and returns a list of `num_ports` unused ports."""
ports = set()
for _ in range(retry_attempts):
ports.update(
portpicker.pick_unused_port() for _ in range(num_ports - len(ports)))
ports.discard(None) # portpic... | python | def pick_unused_ports(num_ports, retry_interval_secs=3, retry_attempts=5):
"""Reserves and returns a list of `num_ports` unused ports."""
ports = set()
for _ in range(retry_attempts):
ports.update(
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deepmind/pysc2 | pysc2/lib/portspicker.py | pick_contiguous_unused_ports | def pick_contiguous_unused_ports(
num_ports,
retry_interval_secs=3,
retry_attempts=5):
"""Reserves and returns a list of `num_ports` contiguous unused ports."""
for _ in range(retry_attempts):
start_port = portpicker.pick_unused_port()
if start_port is not None:
ports = [start_port + p for... | python | def pick_contiguous_unused_ports(
num_ports,
retry_interval_secs=3,
retry_attempts=5):
"""Reserves and returns a list of `num_ports` contiguous unused ports."""
for _ in range(retry_attempts):
start_port = portpicker.pick_unused_port()
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deepmind/pysc2 | pysc2/bin/agent.py | run_thread | def run_thread(agent_classes, players, map_name, visualize):
"""Run one thread worth of the environment with agents."""
with sc2_env.SC2Env(
map_name=map_name,
players=players,
agent_interface_format=sc2_env.parse_agent_interface_format(
feature_screen=FLAGS.feature_screen_size,
... | python | def run_thread(agent_classes, players, map_name, visualize):
"""Run one thread worth of the environment with agents."""
with sc2_env.SC2Env(
map_name=map_name,
players=players,
agent_interface_format=sc2_env.parse_agent_interface_format(
feature_screen=FLAGS.feature_screen_size,
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deepmind/pysc2 | pysc2/bin/agent.py | main | def main(unused_argv):
"""Run an agent."""
stopwatch.sw.enabled = FLAGS.profile or FLAGS.trace
stopwatch.sw.trace = FLAGS.trace
map_inst = maps.get(FLAGS.map)
agent_classes = []
players = []
agent_module, agent_name = FLAGS.agent.rsplit(".", 1)
agent_cls = getattr(importlib.import_module(agent_module... | python | def main(unused_argv):
"""Run an agent."""
stopwatch.sw.enabled = FLAGS.profile or FLAGS.trace
stopwatch.sw.trace = FLAGS.trace
map_inst = maps.get(FLAGS.map)
agent_classes = []
players = []
agent_module, agent_name = FLAGS.agent.rsplit(".", 1)
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deepmind/pysc2 | pysc2/lib/remote_controller.py | check_error | def check_error(res, error_enum):
"""Raise if the result has an error, otherwise return the result."""
if res.HasField("error"):
enum_name = error_enum.DESCRIPTOR.full_name
error_name = error_enum.Name(res.error)
details = getattr(res, "error_details", "<none>")
raise RequestError("%s.%s: '%s'" % (e... | python | def check_error(res, error_enum):
"""Raise if the result has an error, otherwise return the result."""
if res.HasField("error"):
enum_name = error_enum.DESCRIPTOR.full_name
error_name = error_enum.Name(res.error)
details = getattr(res, "error_details", "<none>")
raise RequestError("%s.%s: '%s'" % (e... | [
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deepmind/pysc2 | pysc2/lib/remote_controller.py | decorate_check_error | def decorate_check_error(error_enum):
"""Decorator to call `check_error` on the return value."""
def decorator(func):
@functools.wraps(func)
def _check_error(*args, **kwargs):
return check_error(func(*args, **kwargs), error_enum)
return _check_error
return decorator | python | def decorate_check_error(error_enum):
"""Decorator to call `check_error` on the return value."""
def decorator(func):
@functools.wraps(func)
def _check_error(*args, **kwargs):
return check_error(func(*args, **kwargs), error_enum)
return _check_error
return decorator | [
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deepmind/pysc2 | pysc2/lib/remote_controller.py | skip_status | def skip_status(*skipped):
"""Decorator to skip this call if we're in one of the skipped states."""
def decorator(func):
@functools.wraps(func)
def _skip_status(self, *args, **kwargs):
if self.status not in skipped:
return func(self, *args, **kwargs)
return _skip_status
return decorator | python | def skip_status(*skipped):
"""Decorator to skip this call if we're in one of the skipped states."""
def decorator(func):
@functools.wraps(func)
def _skip_status(self, *args, **kwargs):
if self.status not in skipped:
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deepmind/pysc2 | pysc2/lib/remote_controller.py | valid_status | def valid_status(*valid):
"""Decorator to assert that we're in a valid state."""
def decorator(func):
@functools.wraps(func)
def _valid_status(self, *args, **kwargs):
if self.status not in valid:
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"""Decorator to assert that we're in a valid state."""
def decorator(func):
@functools.wraps(func)
def _valid_status(self, *args, **kwargs):
if self.status not in valid:
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deepmind/pysc2 | pysc2/lib/remote_controller.py | catch_game_end | def catch_game_end(func):
"""Decorator to handle 'Game has already ended' exceptions."""
@functools.wraps(func)
def _catch_game_end(self, *args, **kwargs):
"""Decorator to handle 'Game has already ended' exceptions."""
prev_status = self.status
try:
return func(self, *args, **kwargs)
except ... | python | def catch_game_end(func):
"""Decorator to handle 'Game has already ended' exceptions."""
@functools.wraps(func)
def _catch_game_end(self, *args, **kwargs):
"""Decorator to handle 'Game has already ended' exceptions."""
prev_status = self.status
try:
return func(self, *args, **kwargs)
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deepmind/pysc2 | pysc2/lib/remote_controller.py | RemoteController._connect | def _connect(self, host, port, proc, timeout_seconds):
"""Connect to the websocket, retrying as needed. Returns the socket."""
if ":" in host and not host.startswith("["): # Support ipv6 addresses.
host = "[%s]" % host
url = "ws://%s:%s/sc2api" % (host, port)
was_running = False
for i in ran... | python | def _connect(self, host, port, proc, timeout_seconds):
"""Connect to the websocket, retrying as needed. Returns the socket."""
if ":" in host and not host.startswith("["): # Support ipv6 addresses.
host = "[%s]" % host
url = "ws://%s:%s/sc2api" % (host, port)
was_running = False
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deepmind/pysc2 | pysc2/lib/remote_controller.py | RemoteController.save_map | def save_map(self, map_path, map_data):
"""Save a map into temp dir so create game can access it in multiplayer."""
return self._client.send(save_map=sc_pb.RequestSaveMap(
map_path=map_path, map_data=map_data)) | python | def save_map(self, map_path, map_data):
"""Save a map into temp dir so create game can access it in multiplayer."""
return self._client.send(save_map=sc_pb.RequestSaveMap(
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deepmind/pysc2 | pysc2/lib/remote_controller.py | RemoteController.data_raw | def data_raw(self):
"""Get the raw static data for the current game. Prefer `data` instead."""
return self._client.send(data=sc_pb.RequestData(
ability_id=True, unit_type_id=True)) | python | def data_raw(self):
"""Get the raw static data for the current game. Prefer `data` instead."""
return self._client.send(data=sc_pb.RequestData(
ability_id=True, unit_type_id=True)) | [
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deepmind/pysc2 | pysc2/lib/remote_controller.py | RemoteController.step | def step(self, count=1):
"""Step the engine forward by one (or more) step."""
return self._client.send(step=sc_pb.RequestStep(count=count)) | python | def step(self, count=1):
"""Step the engine forward by one (or more) step."""
return self._client.send(step=sc_pb.RequestStep(count=count)) | [
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deepmind/pysc2 | pysc2/lib/remote_controller.py | RemoteController.actions | def actions(self, req_action):
"""Send a `sc_pb.RequestAction`, which may include multiple actions."""
if FLAGS.sc2_log_actions:
for action in req_action.actions:
sys.stderr.write(str(action))
sys.stderr.flush()
return self._client.send(action=req_action) | python | def actions(self, req_action):
"""Send a `sc_pb.RequestAction`, which may include multiple actions."""
if FLAGS.sc2_log_actions:
for action in req_action.actions:
sys.stderr.write(str(action))
sys.stderr.flush()
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deepmind/pysc2 | pysc2/lib/remote_controller.py | RemoteController.act | def act(self, action):
"""Send a single action. This is a shortcut for `actions`."""
if action and action.ListFields(): # Skip no-ops.
return self.actions(sc_pb.RequestAction(actions=[action])) | python | def act(self, action):
"""Send a single action. This is a shortcut for `actions`."""
if action and action.ListFields(): # Skip no-ops.
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deepmind/pysc2 | pysc2/lib/remote_controller.py | RemoteController.chat | def chat(self, message):
"""Send chat message as a broadcast."""
if message:
action_chat = sc_pb.ActionChat(
channel=sc_pb.ActionChat.Broadcast, message=message)
action = sc_pb.Action(action_chat=action_chat)
return self.act(action) | python | def chat(self, message):
"""Send chat message as a broadcast."""
if message:
action_chat = sc_pb.ActionChat(
channel=sc_pb.ActionChat.Broadcast, message=message)
action = sc_pb.Action(action_chat=action_chat)
return self.act(action) | [
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deepmind/pysc2 | pysc2/lib/remote_controller.py | RemoteController.save_replay | def save_replay(self):
"""Save a replay, returning the data."""
res = self._client.send(save_replay=sc_pb.RequestSaveReplay())
return res.data | python | def save_replay(self):
"""Save a replay, returning the data."""
res = self._client.send(save_replay=sc_pb.RequestSaveReplay())
return res.data | [
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deepmind/pysc2 | pysc2/lib/remote_controller.py | RemoteController.debug | def debug(self, debug_commands):
"""Run a debug command."""
if isinstance(debug_commands, sc_debug.DebugCommand):
debug_commands = [debug_commands]
return self._client.send(debug=sc_pb.RequestDebug(debug=debug_commands)) | python | def debug(self, debug_commands):
"""Run a debug command."""
if isinstance(debug_commands, sc_debug.DebugCommand):
debug_commands = [debug_commands]
return self._client.send(debug=sc_pb.RequestDebug(debug=debug_commands)) | [
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deepmind/pysc2 | pysc2/lib/remote_controller.py | RemoteController.quit | def quit(self):
"""Shut down the SC2 process."""
try:
# Don't expect a response.
self._client.write(sc_pb.Request(quit=sc_pb.RequestQuit()))
except protocol.ConnectionError:
pass # It's likely already (shutting) down, so continue as if it worked.
finally:
self.close() | python | def quit(self):
"""Shut down the SC2 process."""
try:
# Don't expect a response.
self._client.write(sc_pb.Request(quit=sc_pb.RequestQuit()))
except protocol.ConnectionError:
pass # It's likely already (shutting) down, so continue as if it worked.
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self.close() | [
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deepmind/pysc2 | pysc2/env/run_loop.py | run_loop | def run_loop(agents, env, max_frames=0, max_episodes=0):
"""A run loop to have agents and an environment interact."""
total_frames = 0
total_episodes = 0
start_time = time.time()
observation_spec = env.observation_spec()
action_spec = env.action_spec()
for agent, obs_spec, act_spec in zip(agents, observa... | python | def run_loop(agents, env, max_frames=0, max_episodes=0):
"""A run loop to have agents and an environment interact."""
total_frames = 0
total_episodes = 0
start_time = time.time()
observation_spec = env.observation_spec()
action_spec = env.action_spec()
for agent, obs_spec, act_spec in zip(agents, observa... | [
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deepmind/pysc2 | pysc2/run_configs/lib.py | RunConfig.map_data | def map_data(self, map_name):
"""Return the map data for a map by name or path."""
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return f.read() | python | def map_data(self, map_name):
"""Return the map data for a map by name or path."""
with gfile.Open(os.path.join(self.data_dir, "Maps", map_name), "rb") as f:
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deepmind/pysc2 | pysc2/run_configs/lib.py | RunConfig.replay_data | def replay_data(self, replay_path):
"""Return the replay data given a path to the replay."""
with gfile.Open(self.abs_replay_path(replay_path), "rb") as f:
return f.read() | python | def replay_data(self, replay_path):
"""Return the replay data given a path to the replay."""
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deepmind/pysc2 | pysc2/run_configs/lib.py | RunConfig.replay_paths | def replay_paths(self, replay_dir):
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replay_dir = self.abs_replay_path(replay_dir)
if replay_dir.lower().endswith(".sc2replay"):
yield replay_dir
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"""A generator yielding the full path to the replays under `replay_dir`."""
replay_dir = self.abs_replay_path(replay_dir)
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deepmind/pysc2 | pysc2/run_configs/lib.py | RunConfig.save_replay | def save_replay(self, replay_data, replay_dir, prefix=None):
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replay_data: The result of controller.save_replay(), ie the binary data.
replay_dir: Where to save the replay. This can be absolute or relative.
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"""Save a replay to a directory, returning the path to the replay.
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replay_data: The result of controller.save_replay(), ie the binary data.
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deepmind/pysc2 | pysc2/run_configs/lib.py | RunConfig.all_subclasses | def all_subclasses(cls):
"""An iterator over all subclasses of `cls`."""
for s in cls.__subclasses__():
yield s
for c in s.all_subclasses():
yield c | python | def all_subclasses(cls):
"""An iterator over all subclasses of `cls`."""
for s in cls.__subclasses__():
yield s
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deepmind/pysc2 | pysc2/lib/named_array.py | NamedNumpyArray._indices | def _indices(self, indices):
"""Turn all string indices into int indices, preserving ellipsis."""
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"""Turn all string indices into int indices, preserving ellipsis."""
if isinstance(indices, tuple):
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deepmind/pysc2 | pysc2/lib/point.py | Point.assign_to | def assign_to(self, obj):
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deepmind/pysc2 | pysc2/lib/point.py | Point.dist | def dist(self, other):
"""Distance to some other point."""
dx = self.x - other.x
dy = self.y - other.y
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"""Distance to some other point."""
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"""Distance squared to some other point."""
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"""Distance squared to some other point."""
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deepmind/pysc2 | pysc2/lib/point.py | Point.round | def round(self):
"""Round `x` and `y` to integers."""
return Point(int(round(self.x)), int(round(self.y))) | python | def round(self):
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deepmind/pysc2 | pysc2/lib/point.py | Point.floor | def floor(self):
"""Round `x` and `y` down to integers."""
return Point(int(math.floor(self.x)), int(math.floor(self.y))) | python | def floor(self):
"""Round `x` and `y` down to integers."""
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deepmind/pysc2 | pysc2/lib/point.py | Point.ceil | def ceil(self):
"""Round `x` and `y` up to integers."""
return Point(int(math.ceil(self.x)), int(math.ceil(self.y))) | python | def ceil(self):
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deepmind/pysc2 | pysc2/lib/point.py | Point.bound | def bound(self, p1, p2=None):
"""Bound this point within the rect defined by (`p1`, `p2`)."""
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"""Bound this point within the rect defined by (`p1`, `p2`)."""
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deepmind/pysc2 | pysc2/lib/point.py | Rect.contains_point | def contains_point(self, pt):
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deepmind/pysc2 | pysc2/lib/point.py | Rect.contains_circle | def contains_circle(self, pt, radius):
"""Is the circle completely inside this rect?"""
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deepmind/pysc2 | pysc2/lib/point.py | Rect.intersects_circle | def intersects_circle(self, pt, radius):
"""Does the circle intersect with this rect?"""
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rect_corner = self.size / 2 # relative to the rect center
circle_center = (pt - self.center).abs() # relative to the rect center
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"""Does the circle intersect with this rect?"""
# How this works: http://stackoverflow.com/a/402010
rect_corner = self.size / 2 # relative to the rect center
circle_center = (pt - self.center).abs() # relative to the rect center
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deepmind/pysc2 | pysc2/run_configs/platforms.py | _read_execute_info | def _read_execute_info(path, parents):
"""Read the ExecuteInfo.txt file and return the base directory."""
path = os.path.join(path, "StarCraft II/ExecuteInfo.txt")
if os.path.exists(path):
with open(path, "rb") as f: # Binary because the game appends a '\0' :(.
for line in f:
parts = [p.strip()... | python | def _read_execute_info(path, parents):
"""Read the ExecuteInfo.txt file and return the base directory."""
path = os.path.join(path, "StarCraft II/ExecuteInfo.txt")
if os.path.exists(path):
with open(path, "rb") as f: # Binary because the game appends a '\0' :(.
for line in f:
parts = [p.strip()... | [
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deepmind/pysc2 | pysc2/run_configs/platforms.py | LocalBase.start | def start(self, version=None, want_rgb=True, **kwargs):
"""Launch the game."""
del want_rgb # Unused
if not os.path.isdir(self.data_dir):
raise sc_process.SC2LaunchError(
"Expected to find StarCraft II installed at '%s'. If it's not "
"installed, do that and run it once so auto-de... | python | def start(self, version=None, want_rgb=True, **kwargs):
"""Launch the game."""
del want_rgb # Unused
if not os.path.isdir(self.data_dir):
raise sc_process.SC2LaunchError(
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deepmind/pysc2 | pysc2/env/host_remote_agent.py | VsAgent.create_game | def create_game(self, map_name):
"""Create a game for the agents to join.
Args:
map_name: The map to use.
"""
map_inst = maps.get(map_name)
map_data = map_inst.data(self._run_config)
if map_name not in self._saved_maps:
for controller in self._controllers:
controller.save_ma... | python | def create_game(self, map_name):
"""Create a game for the agents to join.
Args:
map_name: The map to use.
"""
map_inst = maps.get(map_name)
map_data = map_inst.data(self._run_config)
if map_name not in self._saved_maps:
for controller in self._controllers:
controller.save_ma... | [
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deepmind/pysc2 | pysc2/env/host_remote_agent.py | VsAgent.close | def close(self):
"""Shutdown and free all resources."""
for controller in self._controllers:
controller.quit()
self._controllers = []
for process in self._processes:
process.close()
self._processes = []
portspicker.return_ports(self._lan_ports)
self._lan_ports = [] | python | def close(self):
"""Shutdown and free all resources."""
for controller in self._controllers:
controller.quit()
self._controllers = []
for process in self._processes:
process.close()
self._processes = []
portspicker.return_ports(self._lan_ports)
self._lan_ports = [] | [
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deepmind/pysc2 | pysc2/env/host_remote_agent.py | VsBot.create_game | def create_game(
self,
map_name,
bot_difficulty=sc_pb.VeryEasy,
bot_race=sc_common.Random,
bot_first=False):
"""Create a game, one remote agent vs the specified bot.
Args:
map_name: The map to use.
bot_difficulty: The difficulty of the bot to play against.
bot_ra... | python | def create_game(
self,
map_name,
bot_difficulty=sc_pb.VeryEasy,
bot_race=sc_common.Random,
bot_first=False):
"""Create a game, one remote agent vs the specified bot.
Args:
map_name: The map to use.
bot_difficulty: The difficulty of the bot to play against.
bot_ra... | [
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deepmind/pysc2 | pysc2/env/host_remote_agent.py | VsBot.close | def close(self):
"""Shutdown and free all resources."""
if self._controller is not None:
self._controller.quit()
self._controller = None
if self._process is not None:
self._process.close()
self._process = None | python | def close(self):
"""Shutdown and free all resources."""
if self._controller is not None:
self._controller.quit()
self._controller = None
if self._process is not None:
self._process.close()
self._process = None | [
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deepmind/pysc2 | pysc2/bin/play_vs_agent.py | agent | def agent():
"""Run the agent, connecting to a (remote) host started independently."""
agent_module, agent_name = FLAGS.agent.rsplit(".", 1)
agent_cls = getattr(importlib.import_module(agent_module), agent_name)
logging.info("Starting agent:")
with lan_sc2_env.LanSC2Env(
host=FLAGS.host,
config_p... | python | def agent():
"""Run the agent, connecting to a (remote) host started independently."""
agent_module, agent_name = FLAGS.agent.rsplit(".", 1)
agent_cls = getattr(importlib.import_module(agent_module), agent_name)
logging.info("Starting agent:")
with lan_sc2_env.LanSC2Env(
host=FLAGS.host,
config_p... | [
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deepmind/pysc2 | pysc2/bin/play_vs_agent.py | human | def human():
"""Run a host which expects one player to connect remotely."""
run_config = run_configs.get()
map_inst = maps.get(FLAGS.map)
if not FLAGS.rgb_screen_size or not FLAGS.rgb_minimap_size:
logging.info("Use --rgb_screen_size and --rgb_minimap_size if you want rgb "
"observations.... | python | def human():
"""Run a host which expects one player to connect remotely."""
run_config = run_configs.get()
map_inst = maps.get(FLAGS.map)
if not FLAGS.rgb_screen_size or not FLAGS.rgb_minimap_size:
logging.info("Use --rgb_screen_size and --rgb_minimap_size if you want rgb "
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deepmind/pysc2 | pysc2/env/remote_sc2_env.py | RemoteSC2Env._connect_remote | def _connect_remote(self, host, host_port, lan_ports, race, name, map_inst,
save_map, interface):
"""Make sure this stays synced with bin/agent_remote.py."""
# Connect!
logging.info("Connecting...")
self._controllers = [remote_controller.RemoteController(host, host_port)]
loggi... | python | def _connect_remote(self, host, host_port, lan_ports, race, name, map_inst,
save_map, interface):
"""Make sure this stays synced with bin/agent_remote.py."""
# Connect!
logging.info("Connecting...")
self._controllers = [remote_controller.RemoteController(host, host_port)]
loggi... | [
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