我们从Python开源项目中，提取了以下8个代码示例，用于说明如何使用scipy.spatial.distance.cityblock()。
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
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.')
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()
