def __init__(self, rad):
"""Constructor
Parameters
----------
rad: radius, in voxels, of the sphere inscribed in the
searchlight cube, not counting the center voxel
"""
super().__init__(rad)
self.mask_ = np.zeros((2*rad+1, 2*rad+1, 2*rad+1), dtype=np.bool)
for r1 in range(2*self.rad+1):
for r2 in range(2*self.rad+1):
for r3 in range(2*self.rad+1):
if(cityblock((r1, r2, r3),
(self.rad, self.rad, self.rad)) <= self.rad):
self.mask_[r1, r2, r3] = True
python类cityblock()的实例源码
def __init__(self, binary_grid=None, heuristic=cityblock):
"""
binary grid: True denoting a valid grid location for the path
heuristic: function handle, which takes two vectors and computes a distance heuristic
"""
if binary_grid is None:
self.grid = Grid(np.load('binary_grid.npy'))
else:
assert len(binary_grid.shape) == 2
self.grid = Grid(binary_grid)
self.heuristic = lambda x, y: heuristic(np.array(x), np.array(y))
def touches(self, otherGroup):
for a in self.points:
for b in otherGroup.points:
if distance.cityblock(a, b) == 1:
return True
return False
def features(self, q1, q2):
q1 = str(q1).lower().split()
q2 = str(q2).lower().split()
q1 = [w for w in q1 if w not in stopwords]
q2 = [w for w in q2 if w not in stopwords]
wmd = min(self.model.wmdistance(q1, q2), 10)
q1vec = self.sent2vec(q1)
q2vec = self.sent2vec(q2)
if q1vec is not None and q2vec is not None:
cos = cosine(q1vec, q2vec)
city = cityblock(q1vec, q2vec)
jacc = jaccard(q1vec, q2vec)
canb = canberra(q1vec, q2vec)
eucl = euclidean(q1vec, q2vec)
mink = minkowski(q1vec, q2vec, 3)
bray = braycurtis(q1vec, q2vec)
q1_skew = skew(q1vec)
q2_skew = skew(q2vec)
q1_kurt = kurtosis(q1vec)
q2_kurt = kurtosis(q2vec)
else:
cos = -1
city = -1
jacc = -1
canb = -1
eucl = -1
mink = -1
bray = -1
q1_skew = 0
q2_skew = 0
q1_kurt = 0
q2_kurt = 0
return wmd, cos, city, jacc, canb, eucl, mink, bray, q1_skew, q2_skew, q1_kurt, q2_kurt
def features(self, q1, q2):
q1 = str(q1).lower().split()
q2 = str(q2).lower().split()
q1 = [w for w in q1 if w not in stopwords]
q2 = [w for w in q2 if w not in stopwords]
wmd = min(self.model.wmdistance(q1, q2), 10)
wmd_norm = min(self.model_norm.wmdistance(q1, q2), 10)
q1vec = self.sent2vec(q1)
q2vec = self.sent2vec(q2)
if q1vec is not None and q2vec is not None:
cos = cosine(q1vec, q2vec)
city = cityblock(q1vec, q2vec)
jacc = jaccard(q1vec, q2vec)
canb = canberra(q1vec, q2vec)
eucl = euclidean(q1vec, q2vec)
mink = minkowski(q1vec, q2vec, 3)
bray = braycurtis(q1vec, q2vec)
q1_skew = skew(q1vec)
q2_skew = skew(q2vec)
q1_kurt = kurtosis(q1vec)
q2_kurt = kurtosis(q2vec)
else:
cos = -1
city = -1
jacc = -1
canb = -1
eucl = -1
mink = -1
bray = -1
q1_skew = 0
q2_skew = 0
q1_kurt = 0
q2_kurt = 0
return wmd, wmd_norm, cos, city, jacc, canb, eucl, mink, bray, q1_skew, q2_skew, q1_kurt, q2_kurt
def score_routing(self, routing, usage_matrix):
"""
For the given layout, and the routing, produce the score of the
routing.
The score is composed of its constituent nets' scores, and the
score of each net is based on the number of violations it has,
the number of vias and pins and the ratio of its actual length
and the lower bound on its length.
layout is the 3D matrix produced by the placer.
"""
alpha = 3
beta = 0.1
gamma = 1
net_scores = {}
net_num_violations = {}
# Score each net segment in the entire net
for net_name, d in routing.iteritems():
net_scores[net_name] = []
net_num_violations[net_name] = []
for i, segment in enumerate(d["segments"]):
routed_net = segment["net"]
# Violations
violation_matrix = segment["violation"]
violations = self.compute_net_violations(violation_matrix, usage_matrix)
net_num_violations[net_name].append(violations)
# Number of vias and pins
vias = 0
num_pins = 2
pins_vias = vias - num_pins
# Lower length bound
coord_a = segment["pins"][0]["route_coord"]
coord_b = segment["pins"][1]["route_coord"]
lower_length_bound = max(1, cityblock(coord_a, coord_b))
length_ratio = len(routed_net) / lower_length_bound
score = (alpha * violations) + (beta * pins_vias) + (gamma * length_ratio)
net_scores[net_name].append(score)
# print(routing)
# print(net_scores)
return net_scores, net_num_violations
def getDistanceFunction(requested_metric):
"""
This function returns a specified distance function.
**PARAMETERS**
:'requested_metric': can be 'hamming', 'eculidean' or any of the functions in https://docs.scipy.org/doc/scipy/reference/spatial.distance.html which only require u and v as input.
**OUTPUT**
returns distance function (as function)
**HISTORY**
:Created: Dec 2016, WHT
:Updated (v0.2.1): Aug 2017, WHT. Changed from distance functions being in misc to using scipy.
"""
distance_options = {
'braycurtis': distance.braycurtis,
'canberra': distance.canberra,
'chebyshev': distance.chebyshev,
'cityblock': distance.cityblock,
'correlation': distance.correlation,
'cosine': distance.cosine,
'euclidean': distance.euclidean,
'sqeuclidean': distance.sqeuclidean,
'dice': distance.dice,
'hamming': distance.hamming,
'jaccard': distance.jaccard,
'kulsinski': distance.kulsinski,
'matching': distance.matching,
'rogerstanimoto': distance.rogerstanimoto,
'russellrao': distance.russellrao,
'sokalmichener': distance.sokalmichener,
'sokalsneath': distance.sokalsneath,
'yule': distance.yule,
}
if requested_metric in distance_options:
return distance_options[requested_metric]
else:
raise ValueError('Distance function cannot be found.')
btm_dis_features.py 文件源码
项目：zhihu-machine-learning-challenge-2017
作者: HouJP
项目源码
文件源码
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def generate(config, argv):
# load valid dataset index
valid_index_fp = '%s/%s.offline.index' % (config.get('DIRECTORY', 'index_pt'),
config.get('TITLE_CONTENT_CNN', 'valid_index_offline_fn'))
valid_index = DataUtil.load_vector(valid_index_fp, 'int')
valid_index = [num - 1 for num in valid_index]
# load topic btm vec
topic_btm_vec = load_topic_btm_vec(config)
# offline / online
data_name = argv[0]
dis_func_names = ["cosine",
"cityblock",
"jaccard",
"canberra",
"euclidean",
"minkowski",
"braycurtis"]
btm_dis_feature_fn = ['vote_fs_btm_dis_%s' % dis_func_name for dis_func_name in dis_func_names]
btm_dis_feature_f = [open('%s/%s.%s.csv' % (config.get('DIRECTORY', 'dataset_pt'),
fn,
data_name), 'w') for fn in btm_dis_feature_fn]
if 'offline' == data_name:
btm_tw_cw_features = load_features_from_file(config, 'fs_btm_tw_cw', data_name, valid_index)
LogUtil.log('INFO', 'load_features_from_file, len=%d' % len(btm_tw_cw_features))
for line_id in range(len(btm_tw_cw_features)):
doc_vec = btm_tw_cw_features[line_id]
for dis_id in range(len(dis_func_names)):
vec = [0.] * 1999
for topic_id in range(1999):
topic_vec = topic_btm_vec[topic_id]
if 'minkowski' == dis_func_names[dis_id]:
vec[topic_id] = eval(dis_func_names[dis_id])(doc_vec, topic_vec, 3)
else:
vec[topic_id] = eval(dis_func_names[dis_id])(doc_vec, topic_vec)
btm_dis_feature_f[dis_id].write('%s\n' % ','.join([str(num) for num in vec]))
else:
btm_vec_fp = '%s/fs_btm_tw_cw.%s.csv' % (config.get('DIRECTORY', 'dataset_pt'), data_name)
btm_vec_f = open(btm_vec_fp, 'r')
for line in btm_vec_f:
doc_vec = np.nan_to_num(parse_feature_vec(line))
for dis_id in range(len(dis_func_names)):
vec = [0.] * 1999
for topic_id in range(1999):
topic_vec = topic_btm_vec[topic_id]
if 'minkowski' == dis_func_names[dis_id]:
vec[topic_id] = eval(dis_func_names[dis_id])(doc_vec, topic_vec, 3)
else:
vec[topic_id] = eval(dis_func_names[dis_id])(doc_vec, topic_vec)
btm_dis_feature_f[dis_id].write('%s\n' % ','.join([str(num) for num in vec]))
for f in btm_dis_feature_f:
f.close()
