def __init__(self, L, M, N, R, M_grid, pm, pc, pop_size, max_gen, file_path_name,
Dth, k1, w1, w2, maxdis, GA_step, VF_step, seed_number, alpha1, alpha2, alpha3):
'''
:param L: 场景变长，Km
:param M: 基站总数
:param N: 待选数
:param R: 基站半径
:param M_grid: 栅格数
:param pm: 变异概率
:param pc: 交叉概率
:param pop_size: 种群大小
:param max_gen: 最大迭代次数
:param Dth: 两个点的距离等于这个时既无引力又无斥力
:param k1: 感知距离系数
:param w1: 引力系数
:param w2: 斥力系数
:param maxdis: 单次移动最大距离
:param GA_step: 每GA_step步进行虚拟力引导
:param VF_step: 每VF_step步离散化操作
'''
self.L = L
self.M = M
self.N = N
self.R = R
self.M_grid = M_grid
self.pm = pm
self.pc = pc
self.pop_size = pop_size
self.max_gen = max_gen
self.scence = scence(L, M, N, R, M_grid)
self.Dth = Dth
self.k1 = k1
self.w1 = w1
self.w2 = w2
self.maxdis = maxdis
self.GA_step = GA_step
self.VF_step = VF_step
self.Rs = k1 * R
self.alpha1 = alpha1
self.alpha2 = alpha2
self.alpha3 = alpha3
self.kdtree = kdtree(self.scence.sum_sites)
para_dict = {'L':L, 'M':M, 'N':N, 'R':R, 'M_grid':M_grid, 'pm':pm, 'pc':pc,
'pop_size':pop_size, 'max_gen':max_gen, 'Dth':Dth, 'k1':k1,
'w1':w1, 'w2':w2, 'maxdis':maxdis, 'GA_step':GA_step, 'VF_step':VF_step,
'seed_number':seed_number, 'alpha1':alpha1, 'alpha2':alpha2, 'alpha3':alpha3}
self.record = record(file_path_name)
self.record.write_scence_para(para_dict)
self.pop = []
self.bests = [0] * max_gen
self.g_best = 0
def orderTree(sortedNodes, tree):
global SIZE, LIST
median = ceil(len(sortedNodes) / 2) - 1 # get the root by being in the middle of x-order
tree = kdtree(sortedNodes[median])
orderByLevel(tree.root, sortedNodes[0:median], False, 1) # search in both sides
orderByLevel(tree.root, sortedNodes[median + 1:], True, 1)
#printInorder(tree.root)
return tree
def databaseType(db_type: int, points):
"""
Returns db object depended on db type
"""
if db_type == 0:
return pointcloud(points)
elif db_type == 1:
return eqlcells(points)
elif db_type == 2:
return kdtree(points, False, 10)
def load_favicons(self):
try:
self.favicons = cPickle.load(open(KDTREE_PICKLE))
except IOError:
self.favicons = []
for file in os.listdir(self.favicon_path):
try:
r,g,b = Image.open('%s/%s' % (DIR,file)).resize((1,1), Image.ANTIALIAS).convert('RGB').load()[0,0]
self.favicons.append(kdtree.P(file, self.yuv((r,g,b))))
except IOError:
print 'skipping', file
continue
self.favicons = kdtree.kdtree(self.favicons)
cPickle.dump(self.favicons, open(KDTREE_PICKLE, 'w+'))
def databaseType(db_type: int, points): """ Creates database from points according to id given and points :param points: ndarray, points xyz nx3 :param db_type: 0 = pointcloud, 1 = equalcells, 2 = kdtree :return: Database object """ if db_type == 0: return pointcloud(points) elif db_type == 1: return eqlcells(points) elif db_type == 2: return kdtree(points, False, 10)
def getkdtree(winlist, lay):
# begin "normalize positions"
'''
The kdtree function only accept values between 0 and 1.
'''
tolerance = 0.0
t = tolerance
dy = 10
sw, sh = 2000 * 10, 1000 * 10
normalized = [[(x + t) / sw, (y + dy + t) / sh, (x + w - t) /
sw, (y + dy + h - t) / sh] for x, y, w, h in lay]
# end "normalize positions"
# begin "generate k-d tree"
origin_lay = [[x, y, x + w, y + h] for x, y, w, h in lay]
from kdtree import kdtree
_tree, _map = kdtree(zip(normalized, winlist, origin_lay))
# end "generate k-d tree"
# begin "reload old root"
'''
wmctrl cannot resize some applications precisely, like gnome-terminal. The side effect is that the overall window size decreases.
Reloading old root node position somehow helps prevent these windows shrink too much over time. When the k-d tree is regularized, the size of the root node will be passed to leaves.
'''
p1 = _tree.position
p2 = PERSISTENT_DATA.get('overall_position', None)
if not None == p2:
x0, y0, x1, y1 = p2
p2 = [max(1, x0), max(1, y0), min(x1, int(MaxWidthStr)),
min(y1, int(MaxHeightStr))]
if None == p2:
PERSISTENT_DATA['overall_position'] = [i for i in p1]
elif p1[0] < p2[0] or p1[1] < p2[1] or p1[2] > p2[2] or p1[3] > p2[3]:
PERSISTENT_DATA['overall_position'] = [i for i in p1]
else:
# Root nodes
pass
#_tree.position = [i for i in p2]
#_tree.children[0].position = [i for i in p2]
#_tree.children[0].children[0].position = [i for i in p2]
# end "reload old root"
return _tree, _map
def main():
all_stations = get_stations()
tree = kdtree(all_stations)
sugg_stations_num = int(input())
for _ in range(sugg_stations_num):
sugg_station_input = input().rstrip().split()
sugg_station_name = sugg_station_input[0]
sugg_station_x = int(sugg_station_input[1])
sugg_station_y = int(sugg_station_input[2])
sugg_station = (sugg_station_name, (sugg_station_x, sugg_station_y))
# print('knn ', knn(all_stations, sugg_station))
# print('nn_search ', nn_search(tree, sugg_station))
# print('knn_with_multiprocessing ', knn_with_multiprocessing(all_stations, sugg_station))
nearest_station_name, distance = knn_with_multiprocessing(
all_stations, sugg_station)
print(sugg_station_name, nearest_station_name)
def fp(self, r, elementpair=None):
from kdtree import kdtree
res = 0.01
if elementpair is None:
lcs = self.listOfCartCoorDomain(r)
lc = self.listOfCartCoor()
else:
lcs = self.listOfCartCoorDomain(r, elementpair[0])
lc = self.listOfCartCoor(elementpair[1])
#from math import log
#N = len(lcs); q = len(lc)
#print N, q
# If the number of query, q, is too small
# it does not worth to construct the kdtree, 3 dimensional (k = 3)
# kNlogN > qN -> logN > q/k
#if 3*log(N) > q:
# return self.fp_bf(r)
kd = kdtree(lcs)
tmp = []
for c in lc:
tmp = tmp + kd.rangeSearch(c, r)[1]
tmp = map(lambda a: res*int(a/res), tmp)
return sorted(tmp)
#!/usr/bin/env python2.4
import sys
import kdtree
from math import *
# def dist(p,q):
# """Squared distance between p and q."""
# d = 0
# for i in range(len(p)):
# d += (p[i]-q[i])**2
# return sqrt( d )
inf = float( "inf" )
k = kdtree.kdtree()
points = []
points_to_labels = dict()
wildcard_points = list()
mapping = {}
for line in open( sys.argv[1] ):
fields = line.split()
mapping[ fields[0] ] = fields[1]
for line in sys.stdin:
if line.startswith( '#' ):
continue
fields = line.split()
label = fields[0]
count = fields[1]
def kd_tree():
try:
from kdtree import kdtree
from kdnode import kdnode
print("Create Kd tree")
global xs;
global ys;
global points;
points = []
for i in range(len(xs)):
newpoint = point( xs[i], ys[i] );
points.append(newpoint);
maxbinsz = int( input("Enter max bin size (integer): ") );
emax = 100;
tree = kdtree(maxbinsz, emax);
tree.timer = 0.05;
## Plotting ##
minx, miny, maxx, maxy = -2, -2, 12, 12;
## -------- ##
root = tree.makeTree( points, minx, miny, maxx, maxy);
print("finished")
ch = 0;
prev = 0;
rectangles = 0;
while True:
print("1. for nearest neighbor");
print("0. Exit");
try:
ch = int(input())
except ValueError:
print("That's not an int!");
continue;
if ch == 1:
print("Enter: x y NN");
i = input();
i = i.split(' ');
try:
query = [float(i[0]), float(i[1])];
except ValueError:
print("Could not convert string to float");
continue;
if (prev != 0):
## Plotting ##
prev.set_color('gray');
prev.set_alpha(0.4);
for rect in rectangles:
rect.set_color('black');
rect.set_alpha(0.05);
## -------- ##
prev, rectangles = tree.queuryNNwrap(query, int(i[2]));
print("finished")
elif ch == 0:
plt.ioff();
points = [];
break;
except:
plt.ioff();
plt.close("all")
def kd_tree():
try:
from kdtree import kdtree
from kdnode import kdnode
print("Create Kd tree")
global xs
global ys
global points
points = []
for i in range(len(xs)):
newpoint = point(xs[i], ys[i])
points.append(newpoint)
maxbinsz = int(input("Enter max bin size (integer): "))
emax = 100
tree = kdtree(maxbinsz, emax)
tree.timer = 0.05
## Plotting ##
minx, miny, maxx, maxy = -2, -2, 12, 12
## -------- ##
root = tree.makeTree(points, minx, miny, maxx, maxy)
print("finished")
ch = 0
prev = 0
rectangles = 0
while True:
print("1. for nearest neighbor")
print("0. Exit")
try:
ch = int(input())
except ValueError:
print("That's not an int!")
continue
if ch == 1:
print("Enter: x y NN")
i = input()
i = i.split(' ')
try:
query = [float(i[0]), float(i[1])]
except ValueError:
print("Could not convert string to float")
continue
if (prev != 0):
## Plotting ##
prev.set_color('gray')
prev.set_alpha(0.4)
for rect in rectangles:
rect.set_color('black')
rect.set_alpha(0.05)
## -------- ##
prev, rectangles = tree.queuryNNwrap(query, int(i[2]))
print("finished")
elif ch == 0:
plt.ioff()
points = []
break
except:
plt.ioff()
plt.close("all")
