code stringlengths 3 6.57k |
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run_function ({}) |
initialization ({}) |
run_function ({}) |
initialization ({}) |
run_function ({}) |
initialization ({}) |
run_function ({}) |
terminate(self) |
stop(self.pg) |
w(j, p) |
print([w(j, p) |
import (RandomChoiceWithMask) |
__all__.extend(_quant_ops.__all__) |
__all__.sort() |
print_c() |
json.loads(a) |
VersionInfo('mgen') |
semantic_version() |
_v.release_string() |
_v.version_tuple() |
point_distance(self,point) |
sqrt(sum([ (a - b) |
for (a,b) |
zip(point,self.point) |
separator_distance(self,point) |
__repr__(self) |
__init__(self, points, values=None) |
kd_tree(points, values) |
kd.nearest([2,0]) |
kd.nearest_n([2,0],2) |
kd.in_sphere([0.1,0.2], 1.1) |
len(p) |
min(lengths) |
max(lengths) |
ValueError('points must all have the same dimension') |
range(len(points) |
len(points) |
len(values) |
ValueError('points and values must have the same lengths') |
len(points) |
self.__build(zip(points,values) |
__build(self, pv_pairs, depth) |
sorted(pv_pairs, key=lambda x: x[0][axis]) |
len(pv_pairs) |
self.node() |
self.__build(pv_pairs[:mid], depth+1) |
self.__build(pv_pairs[mid+1:], depth+1) |
nearest(self, point, max_dist=float('inf') |
self.nearest_n(point,n=1,max_dist=max_dist) |
len(x) |
in_sphere(self, point, radius, max_points=None) |
float('inf') |
self.nearest_n(point, n=max_points, max_dist=radius) |
nearest_n(self, point, n, max_dist=float('inf') |
integer
(Maximum) |
self.__nearest_n(point, n, max_dist, self.root, heap) |
heap.sort() |
for (neg_dist,node) |
reversed(heap) |
__nearest_n(self,point,n,max_dist,current,heap) |
current.point_distance(point) |
current.separator_distance(point) |
heappush(heap,(-pt_dist,current) |
len(heap) |
heappop(heap) |
len(heap) |
min(-heap[0][0],max_dist) |
self.__nearest_n(point,n,max_dist,current.left_child,heap) |
self.__nearest_n(point,n,max_dist,current.right_child,heap) |
abs(sep_dist) |
self.__nearest_n(point,n,max_dist,current.right_child,heap) |
self.__nearest_n(point,n,max_dist,current.left_child,heap) |
inorder(x) |
inorder(x.left_child) |
inorder(x.right_child) |
Config() |
__init__(self, load=True) |
os.path.exists(self.dir_output) |
os.makedirs(self.dir_output) |
get_logger(self.path_log) |
requested (default) |
self.load() |
load(self) |
load_vocab(self.filename_words) |
load_vocab(self.filename_relation) |
len(self.vocab_words) |
len(self.vocab_relations) |
get_processing_word(self.vocab_words, UNK = "<UNK>") |
get_processing_word(self.vocab_relations, UNK='NA') |
np.load(self.filename_embeddings) |
vocab (created from dataset with build_data.py) |
test_rvec_to_quaterion() |
np.array([math.pi/2.0, 0.0, 0.0]) |
reg._rvec_to_quaternion(rvec) |
math.cos(math.pi/4.0) |
math.sin(math.pi/4.0) |
test_quaterion_to_matrix() |
np.array([math.cos(math.pi/4.0) |
math.sin(math.pi/4.0) |
reg.quaternion_to_matrix(quaternion) |
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