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How to use status_matrix method in Kiwi
Best Python code snippet using Kiwi_python
graph_extraction.py
Source:graph_extraction.py 
1import networkx as nx2from osgeo import ogr3import numpy as np4import sys5import itertools6from collections import deque7from rtree import Rtree8from tptk.common.douglas_peucker import DouglasPeucker9from tptk.common.spatial_func import SPoint10from tptk.common.mbr import MBR11from tptk.common.grid import Grid12from tptk.common.spatial_func import distance13class CenterNodePixel:14 def __init__(self, node_pixels):15 self.node_pixels = node_pixels16 self.i = sum([node_pixel[0] for node_pixel in node_pixels]) / len(node_pixels)17 self.j = sum([node_pixel[1] for node_pixel in node_pixels]) / len(node_pixels)18 def center_pixel(self):19 return int(self.i), int(self.j)20class GraphExtractor:21 INVALID = -122 BLANK = 023 EDGE = 124 NODE = 225 VISITED_NODE = 326 def __init__(self, epsilon, min_road_dist):27 self.segment_simplifier = DouglasPeucker(epsilon)28 self.min_road_dist = min_road_dist29 def extract(self, skeleton, mbr, target_path):30 assert skeleton.ndim == 2, 'the grey scale skeleton should only have 1 channel'31 # pad zero for safety32 grid = Grid(mbr, skeleton.shape[0], skeleton.shape[1])33 eval_mbr_padded = MBR(mbr.min_lat - 2 * grid.lat_interval, mbr.min_lng - 2 * grid.lng_interval,34 mbr.max_lat + 2 * grid.lat_interval, mbr.max_lng + 2 * grid.lng_interval)35 mbr = eval_mbr_padded36 skeleton_padded = np.zeros((skeleton.shape[0] + 4, skeleton.shape[1] + 4))37 skeleton_padded[2:skeleton.shape[0] + 2, 2:skeleton.shape[1] + 2] = skeleton38 skeleton = skeleton_padded39 nb_rows, nb_cols = skeleton.shape40 yscale = nb_rows / (mbr.max_lat - mbr.min_lat)41 xscale = nb_cols / (mbr.max_lng - mbr.min_lng)42 sys.stdout.write("Identifying road nodes pixels... ")43 sys.stdout.flush()44 binary_skeleton = (skeleton > 0).astype('int32')45 status_matrix = self.identify_node_pixels(binary_skeleton)46 sys.stdout.write("done.\n")47 sys.stdout.flush()48 sys.stdout.write("Detecting road network component... ")49 nodes, segments = self.detect_rn_component(status_matrix)50 sys.stdout.write("done.\n")51 sys.stdout.flush()52 sys.stdout.write("Constructing and saving road network... ")53 sys.stdout.flush()54 self.construct_undirected_rn(nodes, segments, target_path, nb_rows, xscale, yscale, mbr)55 sys.stdout.write("done.\n")56 sys.stdout.flush()57 def identify_node_pixels(self, skeleton):58 """59 22 23 08 09 1060 21 07 00 01 1161 20 06 -1 02 1262 19 05 04 03 1363 18 17 16 15 1464 :param skeleton:65 :return:66 """67 status_matrix = np.copy(skeleton)68 road_pixels = np.where(status_matrix == GraphExtractor.EDGE)69 nb_road_pixels = len(road_pixels[0])70 print('\n# of road pixels:{}'.format(nb_road_pixels))71 cnt = 172 for i, j in zip(road_pixels[0], road_pixels[1]):73 if (cnt % 100 == 0) or (cnt == nb_road_pixels):74 sys.stdout.write("\r" + str(cnt) + "/" + str(nb_road_pixels) + "... ")75 sys.stdout.flush()76 cnt += 177 # skip boundary78 if i < 2 or i >= status_matrix.shape[0] - 2 or j < 2 or j >= status_matrix.shape[1] - 2:79 continue80 p = [skeleton[i - 1][j], skeleton[i - 1][j + 1], skeleton[i][j + 1], skeleton[i + 1][j + 1],81 skeleton[i + 1][j], skeleton[i + 1][j - 1], skeleton[i][j - 1], skeleton[i - 1][j - 1],82 skeleton[i - 2][j], skeleton[i - 2][j + 1], skeleton[i - 2][j + 2], skeleton[i - 1][j + 2],83 skeleton[i][j + 2], skeleton[i + 1][j + 2], skeleton[i + 2][j + 2], skeleton[i + 2][j + 1],84 skeleton[i + 2][j], skeleton[i + 2][j - 1], skeleton[i + 2][j - 2], skeleton[i + 1][j - 2],85 skeleton[i][j - 2], skeleton[i - 1][j - 2], skeleton[i - 2][j - 2], skeleton[i - 2][j - 1]]86 fringe = [bool(p[8] and bool(p[7] or p[0] or p[1])),87 bool(p[9] and bool(p[0] or p[1])),88 bool(p[10] and p[1]),89 bool(p[11] and bool(p[1] or p[2])),90 bool(p[12] and bool(p[1] or p[2] or p[3])),91 bool(p[13] and bool(p[2] or p[3])),92 bool(p[14] and p[3]),93 bool(p[15] and bool(p[3] or p[4])),94 bool(p[16] and bool(p[3] or p[4] or p[5])),95 bool(p[17] and bool(p[4] or p[5])),96 bool(p[18] and p[5]),97 bool(p[19] and bool(p[5] or p[6])),98 bool(p[20] and bool(p[5] or p[6] or p[7])),99 bool(p[21] and bool(p[6] or p[7])),100 bool(p[22] and p[7]),101 bool(p[23] and bool(p[7] or p[0]))]102 connected_component_cnt = 0103 for k in range(0, len(fringe)):104 connected_component_cnt += int(not bool(fringe[k]) and bool(fringe[(k + 1) % len(fringe)]))105 if connected_component_cnt == 0:106 status_matrix[i][j] = GraphExtractor.BLANK107 elif (connected_component_cnt == 1) or (connected_component_cnt > 2):108 status_matrix[i][j] = GraphExtractor.NODE109 # if connected_component_cnt == 2, we think it is a normal internal node110 return status_matrix111 def detect_rn_component(self, status_matrix):112 node_pixels = np.where(status_matrix == GraphExtractor.NODE)113 nb_node_pixels = len(node_pixels[0])114 print('\n# of node pixels:{}'.format(nb_node_pixels))115 neighbor_deltas = [dxdy for dxdy in itertools.product([-1, 0, 1], [-1, 0, 1])116 if dxdy[0] != 0 or dxdy[1] != 0]117 # node pixel -> center node118 nodes = {}119 node_pixel_spatial_index = Rtree()120 # [node pixel sequence (start and end must be node pixel)]121 connected_segments = []122 cnt = 1123 center_nodes = []124 node_pixel_id = 0125 for i, j in zip(node_pixels[0], node_pixels[1]):126 if (cnt % 100 == 0) or (cnt == nb_node_pixels):127 sys.stdout.write("\r" + str(cnt) + "/" + str(nb_node_pixels) + "... ")128 sys.stdout.flush()129 cnt += 1130 if status_matrix[i][j] == GraphExtractor.VISITED_NODE:131 continue132 # region merge neighbor node pixels133 status_matrix[i][j] = GraphExtractor.VISITED_NODE134 candidates = [(i, j)]135 node_pixels = []136 while len(candidates) > 0:137 node_pixel = candidates.pop()138 node_pixels.append(node_pixel)139 m, n = node_pixel140 for dm, dn in neighbor_deltas:141 if status_matrix[m + dm][n + dn] == GraphExtractor.NODE:142 status_matrix[m + dm][n + dn] = GraphExtractor.VISITED_NODE143 candidates.append((m + dm, n + dn))144 center_node = CenterNodePixel(node_pixels)145 center_nodes.append(center_node)146 for node_pixel in node_pixels:147 nodes[node_pixel] = center_node148 node_pixel_spatial_index.insert(node_pixel_id, node_pixel, obj=node_pixel)149 node_pixel_id += 1150 # endregion151 # region find neighbor segments152 # mask current nodes, make sure the edge doesn't return to itself153 for m, n in node_pixels:154 status_matrix[m][n] = GraphExtractor.INVALID155 # find new road segment of the current node in each possible direction156 for node_pixel in node_pixels:157 connected_segment = self.detect_connected_segment(status_matrix, node_pixel)158 if connected_segment is not None:159 connected_segments.append(connected_segment)160 # restore masked nodes161 for m, n in node_pixels:162 status_matrix[m][n] = GraphExtractor.VISITED_NODE163 # endregion164 print('\n# of directly connected segments:{}'.format(len(connected_segments)))165 # there might be few edge pixels left, that should be fine166 nb_unprocessed_edge_pixels = np.sum(status_matrix[status_matrix == GraphExtractor.EDGE])167 print('unprocessed edge pixels:{}'.format(nb_unprocessed_edge_pixels))168 print('# of nodes:{}'.format(len(center_nodes)))169 print('# of segments:{}'.format(len(connected_segments)))170 return nodes, connected_segments171 def detect_connected_segment(self, status_matrix, start_node_pixel):172 """173 find a path ended with node pixel174 :param status_matrix: status175 :param start_node_pixel: start node pixel176 :return: [start_node_pixel, edge_pixel,...,end_node_pixel]177 """178 # for current implementation, we assume edge pixel has only two arcs179 # but it is possible that edge pixel has multiple connected component rather than 2,180 # because crossing are detected using outer pixels181 s = deque()182 neighbor_deltas = [dxdy for dxdy in itertools.product([-1, 0, 1], [-1, 0, 1])183 if dxdy[0] != 0 or dxdy[1] != 0]184 # add candidates to stack185 m, n = start_node_pixel186 for dm, dn in neighbor_deltas:187 if status_matrix[m + dm][n + dn] == GraphExtractor.EDGE:188 s.appendleft(((m + dm, n + dn), [start_node_pixel]))189 while len(s) > 0:190 (m, n), path = s.popleft()191 # end node pixel192 if status_matrix[m][n] == GraphExtractor.NODE or \193 status_matrix[m][n] == GraphExtractor.VISITED_NODE:194 path.append((m, n))195 return path196 # internal edge pixel197 elif status_matrix[m][n] == GraphExtractor.EDGE:198 # mark the edge as visited199 status_matrix[m][n] = GraphExtractor.BLANK200 new_path = path.copy()201 new_path.append((m, n))202 for dm, dn in neighbor_deltas:203 s.appendleft(((m + dm, n + dn), new_path))204 return None205 def construct_undirected_rn(self, nodes, segments, target_path, nb_rows, xscale, yscale, mbr):206 # node pixel -> road node207 road_nodes = {}208 eid = 0209 # we use coordinate tuples as key, consistent with networkx210 # !!! Though we construct DiGraph (compatible with networkx interface, one segment will only add once)211 # loading this data, we should call g.to_undirected()212 rn = nx.DiGraph()213 for segment in segments:214 coords = []215 # start node216 start_node_pixel = nodes[segment[0]].center_pixel()217 if start_node_pixel not in road_nodes:218 lat, lng = self.pixels_to_latlng(start_node_pixel, mbr, nb_rows, xscale, yscale)219 road_node = SPoint(lat, lng)220 geo_pt = ogr.Geometry(ogr.wkbPoint)221 geo_pt.AddPoint(lng, lat)222 rn.add_node((lng, lat))223 road_nodes[start_node_pixel] = road_node224 start_node = road_nodes[start_node_pixel]225 coords.append(start_node)226 start_node_key = (start_node.lng, start_node.lat)227 # internal nodes, we didn't create id for them228 for coord in segment[1:-1]:229 lat, lng = self.pixels_to_latlng(coord, mbr, nb_rows, xscale, yscale)230 coords.append(SPoint(lat, lng))231 # end node232 end_node_pixel = nodes[segment[-1]].center_pixel()233 if end_node_pixel not in road_nodes:234 lat, lng = self.pixels_to_latlng(end_node_pixel, mbr, nb_rows, xscale, yscale)235 road_node = SPoint(lat, lng)236 geo_pt = ogr.Geometry(ogr.wkbPoint)237 geo_pt.AddPoint(lng, lat)238 rn.add_node((lng, lat))239 road_nodes[end_node_pixel] = road_node240 end_node = road_nodes[end_node_pixel]241 coords.append(end_node)242 end_node_key = (end_node.lng, end_node.lat)243 # region add segment244 # skip loop245 if start_node_key == end_node_key:246 continue247 simplified_coords = self.segment_simplifier.simplify(coords)248 # skip too short segment249 if not self.is_valid(simplified_coords):250 continue251 # add forward segment252 geo_line = ogr.Geometry(ogr.wkbLineString)253 for simplified_coord in simplified_coords:254 geo_line.AddPoint(simplified_coord.lng, simplified_coord.lat)255 rn.add_edge(start_node_key, end_node_key, eid=eid, Wkb=geo_line.ExportToWkb(), type='pred')256 eid += 1257 # endregion258 rn.remove_nodes_from(list(nx.isolates(rn)))259 print('\n# of nodes:{}'.format(rn.number_of_nodes()))260 print('# of edges:{}'.format(rn.number_of_edges()))261 nx.write_shp(rn, target_path)262 return rn263 def is_valid(self, coords):264 dist = 0.0265 for i in range(len(coords) - 1):266 dist += distance(coords[i], coords[i + 1])267 return dist > self.min_road_dist268 def pixels_to_latlng(self, pixel, mbr, nb_rows, xscale, yscale):269 i, j = pixel...
hmm_seg.py
Source:hmm_seg.py
1mode_path = '../data/mid_data/hmm.mod'2filter_val = 0.023# 1ãå 载模å4with open(mode_path, 'r', encoding='utf-8') as f:5 pi = eval(f.readline())6 A = eval(f.readline())7 B = eval(f.readline())8# 2ãå®ç°viterbiç®æ³ï¼è·åæä¼è·¯å¾ï¼å¾å°æ¦çæ大çç¶æç»åæ¹å¼9STATUS_NUM = 410# å°è¯è½¬æ¢æå个åçå表:'å¨æè§å'=>['å¨','æ','è§','å']11def get_word_ch(word):12 ch_lst = []13 for ch in word:14 ch_lst.append(ch)15 return ch_lst16# ä¸èèæ¦çç¸åç17def veterbinodiff(sentence, sep=' '):18 ch_lst = get_word_ch(sentence)19 # print(ch_lst)20 # ææ¬åºåçé¿åº¦T21 ch_num = len(ch_lst)22 # åå§åç¶æç©éµ23 status_matrix = [[[0.0, 0] for col in range(ch_num)] for row in range(STATUS_NUM)]24 # ç¶æç§ç±»M个 m*125 for i in range(STATUS_NUM):26 # 第ä¸ä¸ªåçåå°æ¦ç27 if B[i].get(ch_lst[0], -1) != -1:28 cur_B = B[i][ch_lst[0]]29 else:30 cur_B = -100000.031 # Ïå¯ä»¥ç´æ¥ä½¿ç¨32 cur_pi = pi[i]33 status_matrix[i][0][0] = cur_pi + cur_B # åäºlogï¼ç¸ä¹å°±æ¯ç¸å 34 status_matrix[i][0][1] = i35 # M*M*(T-1)36 for i in range(1, ch_num): # 1:T => T-137 for j in range(STATUS_NUM): # M38 max_p = None39 max_status = None40 # ç»è¿jèç¹çåM个èç¹ä¸åå¨æ¦çæ大å¼ï¼åä»ä¸ä¸å±è¿æ¥å°è¿ä¸ªèç¹çç¼å·41 for k in range(STATUS_NUM): # M åä¸å±ç第k个s42 cur_A = A[k][j] # 转移æ¦ç43 cur_p = status_matrix[k][i - 1][0] + cur_A44 if max_p is None or max_p < cur_p:45 max_p = cur_p46 max_status = k47 if B[j].get(ch_lst[i], -1) != -1:48 cur_B = B[j][ch_lst[i]]49 else:50 cur_B = -100000.051 status_matrix[j][i][0] = max_p + cur_B52 status_matrix[j][i][1] = max_status53 # get max prob path54 max_end_p = None55 max_end_status = None56 # æ¾æåä¸å±ä¸æ¦çæ大çï¼ä¹å°±æ¯æç»è·¯å¾æ大ç57 for i in range(STATUS_NUM):58 if max_end_p is None or status_matrix[i][ch_num - 1][0] > max_end_p:59 max_end_p = status_matrix[i][ch_num - 1][0]60 max_end_status = i61 # æ ¹æ®ä¹åè®°å½çå¾åæ¾æä¼è·¯å¾62 best_status_lst = [0 for ch in range(ch_num)]63 best_status_lst[ch_num - 1] = max_end_status64 c = ch_num - 165 cur_best_status = max_end_status66 while c > 0:67 pre_best_status = status_matrix[cur_best_status][c][1]68 best_status_lst[c - 1] = pre_best_status69 cur_best_status = pre_best_status70 c -= 171 # å®ç°åè¯72 rest = ''73 rest += ch_lst[0]74 for i in range(1, ch_num):75 # i-1æ¯EãSæè
iæ¯BãS76 if best_status_lst[i - 1] in {2, 3} or best_status_lst[i] in {0, 3}:77 rest += sep78 rest += ch_lst[i]79 return rest80# æ¦çç¸åæ¶çå¤ç81def veterbisame(sentence, sep=' '):82 ch_lst = get_word_ch(sentence)83 # print(ch_lst)84 # ææ¬åºåçé¿åº¦T85 ch_num = len(ch_lst)86 # åå§åç¶æç©éµ87 status_matrix = [[[0.0, 0] for col in range(ch_num)] for row in range(STATUS_NUM)]88 # ç¶æç§ç±»M个 m*189 for i in range(STATUS_NUM):90 # 第ä¸ä¸ªåçåå°æ¦ç91 if B[i].get(ch_lst[0], -1) != -1:92 cur_B = B[i][ch_lst[0]]93 else:94 cur_B = -100000.095 # Ïå¯ä»¥ç´æ¥ä½¿ç¨96 cur_pi = pi[i]97 status_matrix[i][0][0] = cur_pi + cur_B # åäºlogï¼ç¸ä¹å°±æ¯ç¸å 98 status_matrix[i][0][1] = i99 # M*M*(T-1)100 for i in range(1, ch_num): # 1:T => T-1101 for j in range(STATUS_NUM): # M102 max_p = None103 max_status = None104 # ç»è¿jèç¹çåM个èç¹ä¸åå¨æ¦çæ大å¼ï¼åä»ä¸ä¸å±è¿æ¥å°è¿ä¸ªèç¹çç¼å·105 for k in range(STATUS_NUM): # M åä¸å±ç第k个s106 cur_A = A[k][j] # 转移æ¦ç107 cur_p = status_matrix[k][i - 1][0] + cur_A108 if max_p is None or max_p < cur_p:109 max_p = cur_p110 max_status = k111 if B[j].get(ch_lst[i], -1) != -1:112 cur_B = B[j][ch_lst[i]]113 else:114 cur_B = -100000.0115 status_matrix[j][i][0] = max_p + cur_B116 status_matrix[j][i][1] = max_status117 last_prob_lst = []118 for i in range(STATUS_NUM):119 last_prob_lst.append((status_matrix[i][ch_num - 1][0], status_matrix[i][ch_num - 1][1],i))120 sorted_last_lst = sorted(last_prob_lst, key=lambda x: x[0], reverse=True)121 # print(sorted_last_lst)122 def get_best_seg(max_end_status):123 # æ ¹æ®ä¹åè®°å½çå¾åæ¾æä¼è·¯å¾124 best_status_lst = [0 for ch in range(ch_num)]125 best_status_lst[ch_num - 1] = max_end_status126 c = ch_num - 1127 cur_best_status = max_end_status128 while c > 0:129 pre_best_status = status_matrix[cur_best_status][c][1]130 best_status_lst[c - 1] = pre_best_status131 cur_best_status = pre_best_status132 c -= 1133 # å®ç°åè¯134 rest = ''135 rest += ch_lst[0]136 for i in range(1, ch_num):137 # i-1æ¯EãSæè
iæ¯BãS138 if best_status_lst[i - 1] in {2, 3} or best_status_lst[i] in {0, 3}:139 rest += sep140 rest += ch_lst[i]141 return rest142 i = 0143 res = []144 max_end_p = sorted_last_lst[0][0]145 while (max_end_p - sorted_last_lst[i][0]) < filter_val:146 # è°ç¨è·åæä¼ååæ¹æ³147 max_end_status = sorted_last_lst[i][2]148 res.append(get_best_seg(max_end_status))149 i += 1150 return res151if __name__ == '__main__':152 s = 'å¾å°æ¦çæ大çç¶æç»åæ¹å¼'153 s1 = 'ç»å·´åè¯éä¼ç»åè¯æ§ï¼ç¶æä¼æ´å¤'154 s2 = ''...
linear_svm.py
Source:linear_svm.py
1import numpy as np2from sklearn import svm3from sklearn import preprocessing4from sklearn import utils5original_data_matrix = np.loadtxt("data_simulation.txt")6total_status = len(original_data_matrix)7def mapping(i):8 result = np.zeros(shape=6)9 return result10def status_matrix_transfer(origin, pos_flag):11 index = 012 for i in range(total_status):13 if pos_flag:14 if origin[i][7] > 0.5:15 index += 116 else:17 if origin[i][7] < 0.5:18 index += 119 status_matrix = np.zeros(shape=(index, 6))20 index = 021 for i in range(total_status):22 if pos_flag:23 if origin[i][7] < 0.5:24 continue25 else:26 if origin[i][7] > 0.5:27 continue28 status_matrix[index][0] = origin[i][0]29 status_matrix[index][1] = origin[i][3]30 status_matrix[index][2] = origin[i][1]31 status_matrix[index][3] = origin[i][4]32 status_matrix[index][4] = origin[i][2]33 status_matrix[index][5] = origin[i][5]34 # status_matrix[index][6] = origin[i][7]35 index += 136 return status_matrix37def input_matrix_transfer(origin):38 status_matrix = np.zeros(shape=(total_status, 6))39 for i in range(total_status):40 status_matrix[i][0] = origin[i][0]41 status_matrix[i][1] = origin[i][3]42 status_matrix[i][2] = origin[i][1]43 status_matrix[i][3] = origin[i][4]44 status_matrix[i][4] = origin[i][2]45 status_matrix[i][5] = origin[i][5]46 # status_matrix[index][6] = origin[i][7]47 return status_matrix48def output_matrix_transfer(origin):49 status_matrix = np.zeros(shape=total_status)50 for i in range(total_status):51 status_matrix[i] = origin[i][7]52 # status_matrix[index][6] = origin[i][7]53 return status_matrix54pos = status_matrix_transfer(original_data_matrix, 1)55neg = status_matrix_transfer(original_data_matrix, 0)56X = input_matrix_transfer(original_data_matrix)57Y = output_matrix_transfer(original_data_matrix)58lab_enc = preprocessing.LabelEncoder()59encoded = lab_enc.fit_transform(Y)60clf = svm.SVC()61print("ææ¥")62print(utils.multiclass.type_of_target(encoded))63clf.fit(X, encoded)64print("ææ¥")...
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