├── README.md
├── code
    ├── readme.txt
    ├── bpNetwork.pyc
    ├── rbfNetwork.pyc
    ├── executeBpNetwork.py
    ├── svm.py
    ├── executeRbfNetwork.py
    ├── bpNetwork.py
    ├── rbfNetwork.py
    └── data.txt
└── result
    ├── 学习速率.png
    ├── cross.png
    ├── kMeans.png
    ├── 神经元个数.png
    ├── predict1.png
    ├── predict2.png
    └── cross_norm.png


/README.md:
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1 | # MachineLearning_Ass2
2 | 神经网络的实现，包括bp神经网络和rbf神经网络
3 | 神经网络的实现，包括bp神经网络和rbf神经网络
4 | 


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/code/readme.txt:
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https://raw.githubusercontent.com/wangtuntun/MachineLearning_Ass2/HEAD/code/readme.txt


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/result/学习速率.png:
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https://raw.githubusercontent.com/wangtuntun/MachineLearning_Ass2/HEAD/result/学习速率.png


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/code/bpNetwork.pyc:
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https://raw.githubusercontent.com/wangtuntun/MachineLearning_Ass2/HEAD/code/bpNetwork.pyc


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/result/cross.png:
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https://raw.githubusercontent.com/wangtuntun/MachineLearning_Ass2/HEAD/result/cross.png


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/result/kMeans.png:
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https://raw.githubusercontent.com/wangtuntun/MachineLearning_Ass2/HEAD/result/kMeans.png


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/result/神经元个数.png:
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https://raw.githubusercontent.com/wangtuntun/MachineLearning_Ass2/HEAD/result/神经元个数.png


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/code/rbfNetwork.pyc:
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https://raw.githubusercontent.com/wangtuntun/MachineLearning_Ass2/HEAD/code/rbfNetwork.pyc


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/result/predict1.png:
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https://raw.githubusercontent.com/wangtuntun/MachineLearning_Ass2/HEAD/result/predict1.png


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/result/predict2.png:
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https://raw.githubusercontent.com/wangtuntun/MachineLearning_Ass2/HEAD/result/predict2.png


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/result/cross_norm.png:
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https://raw.githubusercontent.com/wangtuntun/MachineLearning_Ass2/HEAD/result/cross_norm.png


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/code/executeBpNetwork.py:
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 1 | #coding=utf-8
 2 | '''
 3 | Created on 2016��5��17��
 4 | 
 5 | @author: pangb
 6 | '''
 7 | from bpNetwork import *
 8 | import matplotlib.pyplot as plt
 9 | import sys
10 | def testBestLearningRate():
11 |     rate = [0.001, 0.005, 0.01, 0.05, 0.1, 0.5, 1]
12 |     errNum = []
13 |     for i in range(len(rate)):
14 |         print 'test' + str(i)
15 |         xArr, yArr = loadData('data.txt')
16 |         n = NN(2, 7, 1)
17 |     # 用一些模式训练它
18 |         n.train(xArr[500:2999][:], yArr[500:2999], 500, rate[i])
19 |         errNum.append(n.test(xArr[500:2999][:], yArr[500:2999]))
20 |     
21 |     fig = plt.figure()
22 |     ax = fig.add_subplot(111)
23 |     x1 = range(7)
24 |     ax.plot(x1, errNum)
25 |     plt.show()
26 | 
27 | def testBestNumOfNeural():
28 |     nums = [3,4,5,6,7,8,9,10,11,12]
29 |     errNum = []
30 |     for i in range(len(nums)):
31 |          print 'test' + str(i)
32 |          xArr, yArr = loadData('data.txt')
33 |          n = NN(2, nums[i], 1)
34 |          n.train(xArr[500:2999][:], yArr[500:2999],500)
35 |          errNum.append(n.test(xArr[500:2999][:], yArr[500:2999]))
36 |     fig = plt.figure()
37 |     ax = fig.add_subplot(111)
38 |     ax.plot(nums, errNum)
39 |     plt.show()
40 | 
41 | def testNorm():    
42 |     xArr, yArr = loadData('data.txt')
43 |     # print xArr
44 |     # print xArr
45 |     xArr = regularize(xArr).tolist()
46 |     n = NN(2, 7, 1)
47 |     # 用一些模式训练它
48 |     n.train(xArr[500:2999][:], yArr[500:2999], 500, 0.01)
49 |     print n.test(xArr[500:2999][:], yArr[500:2999])
50 | 
51 | def testNoNorm():
52 |     xArr, yArr = loadData('data.txt')
53 |     n = NN(2, 7, 1)
54 |     # 用一些模式训练它
55 |     n.train(xArr[500:2999][:], yArr[500:2999], 500, 0.01)
56 |     print n.test(xArr[500:2999][:], yArr[500:2999])
57 | 
58 | def testPredict():
59 |     xArr, yArr = loadData('data.txt')
60 |     n = NN(2, 7, 1)
61 |     # 用一些模式训练它
62 |     n.train(xArr[500:2999][:], yArr[500:2999], 500, 0.01)
63 |     count, predict = n.testP(xArr[0:500][:], yArr[0:500])
64 |     print count
65 |     x1 = range(500)
66 |     fig = plt.figure()
67 |     ax = fig.add_subplot(111)
68 |     ax.scatter(x1, predict)
69 |     plt.show()
70 |     
71 | if __name__ == '__main__':
72 | 	for arg in sys.argv:
73 | 		if arg == '-rate':
74 | 			testBestLearningRate()
75 | 		if arg == '-num':
76 | 			testBestNumOfNeural()
77 | 		if arg == '-predict':
78 | 			testPredict()
79 | 		if arg == '-norm':
80 | 			testNorm()
81 | 		if arg == '-nonorm':
82 | 			testNoNorm()
83 |     #testPredict()
84 |     


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/code/svm.py:
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 1 | #coding=utf-8
 2 | '''
 3 | Created on 2016��5��17��
 4 | 
 5 | @author: pangb
 6 | '''
 7 | from sklearn import svm
 8 | import numpy
 9 | def regularize(xMat):
10 |     #inMat = xMat.copy()
11 |     inMeans = numpy.mean(xMat, 0)
12 |     inVar = numpy.var(xMat, 0)
13 |     xMat = (xMat - inMeans) / inVar
14 | 
15 |     return xMat
16 | def loadData(fileName):
17 |     numFeat = len(open(fileName).readline().strip().split(' ')) - 1
18 |     dataMat = []
19 |     labelMat = []
20 |     fr = open(fileName)
21 |     for line in fr.readlines():
22 |         lineArr = []
23 |         curLine = line.strip().split(' ')
24 |         for i in range(numFeat):
25 |             lineArr.append(float(curLine[i]))
26 |         dataMat.append(lineArr)
27 |         labelMat.append(float(curLine[-1]))
28 |     return dataMat, labelMat
29 | def diff(y1, y2):
30 |     count = 0
31 |     for i in range(len(y1)):
32 |         if abs(y1[i] - y2[i]) < 1:
33 |             count += 1
34 |     return count
35 |             
36 | def linearSVM():
37 |     xArr, yArr = loadData('data.txt')
38 |     xArr = regularize(xArr).tolist()
39 |     xArrTrain = xArr[0:2500][:]
40 |     yArrTrain = yArr[0:2500]
41 |     xArrTest = xArr[2500:3000]
42 |     yArrTest = yArr[2500:3000]
43 |     linear_svc = svm.SVC(kernel='linear')
44 |     linear_svc.fit(xArrTrain, yArrTrain)
45 |     yPredict = linear_svc.predict(xArrTest)
46 |     #print yPredict
47 |     print '正确的个数:'
48 |     num = diff(yPredict, yArrTest)
49 |     print num
50 |     print '正确率'
51 |     print float(num) / len(yArrTest)
52 | 
53 | def rbfSVM():
54 |     xArr, yArr = loadData('data.txt')
55 |     #xArr = regularize(xArr).tolist()
56 |     xArrTrain = xArr[0:2500][:]
57 |     yArrTrain = yArr[0:2500]
58 |     xArrTest = xArr[2500:3000]
59 |     yArrTest = yArr[2500:3000]
60 |     linear_svc = svm.SVC(kernel='rbf')
61 |     linear_svc.fit(xArrTrain, yArrTrain)
62 |     yPredict = linear_svc.predict(xArrTest)
63 |     #print yPredict
64 |     print '正确的个数:'
65 |     num = diff(yPredict, yArrTest)
66 |     print num
67 |     print '正确率'
68 |     print float(num) / len(yArrTest)
69 | 
70 | def sigMoidSVM():
71 |     xArr, yArr = loadData('data.txt')
72 |     xArr = regularize(xArr).tolist()
73 |     xArrTrain = xArr[0:2500][:]
74 |     yArrTrain = yArr[0:2500]
75 |     xArrTest = xArr[2500:3000]
76 |     yArrTest = yArr[2500:3000]
77 |     linear_svc = svm.SVC(kernel='sigmoid')
78 |     linear_svc.fit(xArrTrain, yArrTrain)
79 |     yPredict = linear_svc.predict(xArrTest)
80 |     #print yPredict
81 |     print '正确的个数:'
82 |     num = diff(yPredict, yArrTest)
83 |     print num
84 |     print '正确率'
85 |     print float(num) / len(yArrTest)
86 | 
87 | if __name__ == '__main__':
88 |     print 'rbf:'
89 |     rbfSVM()
90 |     print 'sigmoid:'
91 |     sigMoidSVM()
92 |     print 'linear:'
93 |     linearSVM()
94 | 


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/code/executeRbfNetwork.py:
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 1 | #coding=utf-8
 2 | from rbfNetwork import *
 3 | import matplotlib.pyplot as plt
 4 | from sklearn.cluster import KMeans
 5 | from numpy import *
 6 | import sys
 7 | '''
 8 | Created on 2016��5��17��
 9 | 
10 | @author: pangb
11 | '''
12 | #运行交叉校验求的较优的k值
13 | def crossKNormal():
14 |     xArr, yArr= RBFNN.loadData('data.txt')
15 |     xArr = regularize(mat(xArr)).tolist()
16 |     errlist = []
17 |     for i in range(10):
18 |         n=RBFNN(2, i+3, 1)
19 |         err = n.rbfCross(xArr[0:2500][:], yArr[0:2500])
20 |         print err
21 |         errlist.append(err)
22 |     
23 |     x1 = [3,4,5,6,7,8,9,10,11,12]
24 |     fig = plt.figure()
25 |     ax = fig.add_subplot(111)
26 |     ax.plot(x1, errlist)
27 |     plt.show()
28 | 
29 | def crossKNoNormal():
30 |     xArr, yArr= RBFNN.loadData('data.txt')
31 |     #xArr = regularize(mat(xArr)).tolist()
32 |     errlist = []
33 |     for i in range(10):
34 |         n=RBFNN(2, i+3, 1)
35 |         err = n.rbfCross(xArr[0:2500][:], yArr[0:2500])
36 |         print err
37 |         errlist.append(err)
38 |     
39 |     x1 = [3,4,5,6,7,8,9,10,11,12]
40 |     fig = plt.figure()
41 |     ax = fig.add_subplot(111)
42 |     ax.plot(x1, errlist)
43 |     plt.show()
44 |     
45 | def drawKmeans():
46 |     xArr, yArr= RBFNN.loadData('data.txt')
47 |     clf = KMeans(n_clusters=12)
48 |     xArr1 = xArr[0:2500][:]
49 |     xArr1 = mat(xArr1)
50 |     s = clf.fit(xArr1)
51 |     cents = clf.cluster_centers_
52 |     labels = clf.labels_
53 |     colors = ['or', 'ob', 'og', 'ok', '^r', '+r', 'sr', 'dr', '<y', 'pr', '+b','oy']
54 |     for i in range(12):
55 |         index = nonzero(labels==i)[0]
56 |         x0 = xArr1[index,0]
57 |         x1 = xArr1[index,1]
58 |         for j in range(len(x0)):
59 |             plt.plot(x0[j],x1[j],colors[i],markersize = 12)
60 |         plt.plot(cents[i, 0], cents[i, 1], colors[i],markersize = 12)
61 |     plt.show()
62 | 
63 | def testPredict():
64 |     xArr, yArr= RBFNN.loadData('data.txt')
65 |     xArrTrain = xArr[0:2500][:]
66 |     yArrTrain = yArr[0:2500]
67 |     xArrTest = xArr[2500:3000][:]
68 |     yArrTest = yArr[2500:3000]
69 |     rbf = RBFNN(2,12,1)
70 |     yPredict = rbf.rbfPredict(xArrTrain, yArrTrain, xArrTest)
71 |     print rbf.calculateDiff(yPredict.tolist(), yArrTest)
72 |     yPredictArr = yPredict.tolist()
73 |     x1 = range(500)
74 |     fig = plt.figure()
75 |     ax = fig.add_subplot(111)
76 |     ax.scatter(x1, yPredictArr[0])
77 |     plt.show()
78 | if __name__ == '__main__':
79 |     for arg in sys.argv:
80 |         if arg == '-crossNormal':
81 |             crossKNormal()
82 |         if arg == '-crossNoNormal':
83 |             crossKNoNormal()
84 |         if arg == '-drawKmeans':
85 |             drawKmeans()
86 |         if arg == '-predict':
87 |             testPredict()
88 | 
89 |     #crossKNoNormal()
90 |     #drawKmeans()
91 |     #crossKNormal()
92 |     #testPredict()
93 |     


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/code/bpNetwork.py:
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  1 | # coding=utf8
  2 | import math
  3 | import random
  4 | import string
  5 | import numpy
  6 | import sys
  7 | 
  8 | random.seed(0)
  9 | 
 10 | def regularize(xMat):
 11 |     #inMat = xMat.copy()
 12 |     inMeans = numpy.mean(xMat, 0)
 13 |     inVar = numpy.var(xMat, 0)
 14 |     xMat = (xMat - inMeans) / inVar
 15 | 
 16 |     return xMat
 17 | 
 18 | def loadData(fileName):
 19 |     numFeat = len(open(fileName).readline().strip().split(' ')) - 1
 20 |     dataMat = []
 21 |     labelMat = []
 22 |     fr = open(fileName)
 23 |     for line in fr.readlines():
 24 |         lineArr = []
 25 |         curLine = line.strip().split(' ')
 26 |         for i in range(numFeat):
 27 |             lineArr.append(float(curLine[i]))
 28 |         dataMat.append(lineArr)
 29 |         labelMat.append(float(curLine[-1]))
 30 |     return dataMat, labelMat
 31 |  # 生成区间[a, b)内的随机数
 32 | 
 33 | 
 34 | def rand(a, b):
 35 |     return (b - a) * random.random() + a
 36 | 
 37 | def sigmoid(x):
 38 |     a = 1 / (1 + numpy.exp(-x))
 39 |     return a
 40 |     #return math.tanh(x)
 41 | 
 42 |  # 函数 sigmoid 的派生函数, 为了得到输出 (即：y)
 43 | 
 44 | 
 45 | def dsigmoid(y):
 46 |     #return 1.0 - y**2
 47 |     return y * (1-y)
 48 | 
 49 | 
 50 | class NN:
 51 |     def __init__(self, ni, nh, no):
 52 |         # 输入层、隐藏层、输出层的节点（数）
 53 |         self.input_num = ni + 1  # 增加一个偏差节点
 54 |         self.hide_num = nh
 55 |         self.output_num = no
 56 | 
 57 |         # 激活神经网络的所有节点（向量）
 58 |         self.input_arr = [1.0] * self.input_num
 59 |         self.hide_arr = [1.0] * self.hide_num
 60 |         self.output_arr = [1.0] * self.output_num
 61 | 
 62 |         # 建立权重（矩阵）
 63 |         self.inHide_weight = numpy.ones((self.input_num, self.hide_num)).tolist()
 64 |         self.hideOut_weight = numpy.ones((self.hide_num, self.output_num)).tolist()
 65 |         # 设为随机值
 66 |         for i in range(self.input_num):
 67 |             for j in range(self.hide_num):
 68 |                 self.inHide_weight[i][j] = rand(-1.0, 1.0)
 69 |         for j in range(self.hide_num):
 70 |             for k in range(self.output_num):
 71 |                 self.hideOut_weight[j][k] = rand(-1.0, 1.0)
 72 |     def update(self, inputs):
 73 | 
 74 |         # 激活输入层
 75 |         for i in range(self.input_num - 1):
 76 |             self.input_arr[i] = inputs[i]
 77 | 
 78 |         # 激活隐藏层
 79 |         for j in range(self.hide_num):
 80 |             sum1 = 0.0
 81 |             for i in range(self.input_num):
 82 |                 sum1 = sum1 + self.input_arr[i] * self.inHide_weight[i][j]
 83 |             self.hide_arr[j] = sigmoid(sum1)
 84 | 
 85 |         # 激活输出层
 86 |         for k in range(self.output_num):
 87 |             sum2 = 0.0
 88 |             for j in range(self.hide_num):
 89 |                 sum2 = sum2 + self.hide_arr[j] * self.hideOut_weight[j][k]
 90 |             self.output_arr[k] = sigmoid(sum2)
 91 | 
 92 |         return self.output_arr[:]
 93 | 
 94 |     def backPropagate(self, targets, N):
 95 |         ''' 反向传播 '''
 96 | 
 97 |         # 计算输出层的误差
 98 |         errorlist = [0.0] * self.output_num
 99 |         for k in range(self.output_num):
100 |             error = targets[k] - self.output_arr[k]
101 |             errorlist[k] = dsigmoid(self.output_arr[k]) * error
102 | 
103 |         # 计算隐藏层的误差
104 |         hide_errlist = [0.0] * self.hide_num
105 |         for j in range(self.hide_num):
106 |             error = 0.0
107 |             for k in range(self.output_num):
108 |                 error = error + errorlist[k] * self.hideOut_weight[j][k]
109 |             hide_errlist[j] = dsigmoid(self.hide_arr[j]) * error
110 | 
111 |         # 更新输出层权重
112 |         for j in range(self.hide_num):
113 |             for k in range(self.output_num):
114 |                 change = errorlist[k] * self.hide_arr[j]
115 |                 self.hideOut_weight[j][k] = self.hideOut_weight[j][k] + N * change
116 |         # 更新输入层权重
117 |         for i in range(self.input_num):
118 |             for j in range(self.hide_num):
119 |                 change = hide_errlist[j] * self.input_arr[i]
120 |                 self.inHide_weight[i][j] = self.inHide_weight[i][j] + N * change
121 | 
122 |         # 计算误差
123 |         error = 0.0
124 |         for k in range(len(targets)):
125 |             error = error + 0.5 * (targets[k] - self.output_arr[k])**2
126 |         return error
127 | 
128 |     def test(self, xArr, yArr):
129 |         count = 0
130 |         for i in range(len(yArr)):
131 |             y1 = self.update(xArr[i])[0]
132 |             y2 = yArr[i]
133 |             print(y2, '->', y1)
134 |            
135 | 
136 |             if (y1 >= 0.5) and (y2 >= 0.0):
137 |                 count += 1
138 |             if (y1 < 0.5) and (y2 < 0.0):
139 |                 count += 1
140 |             
141 |             #if abs(y1 - y2) < 1:
142 |              #   count += 1;
143 |                 
144 |         return count
145 |     
146 |     #返回预测的y与正确的count值
147 |     def testP(self, xArr, yArr):
148 |         count = 0
149 |         predict = []
150 |         for i in range(len(yArr)):
151 |             y1 = self.update(xArr[i])[0]
152 |             y2 = yArr[i]
153 |             print(y2, '->', y1)
154 |            
155 |             predict.append(y1)
156 |             if (y1 >= 0.5) and (y2 >= 0.0):
157 |                 count += 1
158 |             if (y1 < 0.5) and (y2 < 0.0):
159 |                 count += 1
160 |             
161 |             #if abs(y1 - y2) < 1:
162 |              #   count += 1;
163 |                 
164 |         return count,predict
165 | 
166 | 
167 | 
168 |     def train(self, xArr, yArr, iterations=1000, N=0.01):
169 |         # N: 学习速率(learning rate)
170 |         for i in range(iterations):
171 |             error = 0.0
172 |             for j in range(len(yArr)):
173 |                 inputs = xArr[j]
174 | 
175 |                 targets = []
176 |                 targets.append(yArr[j])
177 | 
178 |                 self.update(inputs)
179 |                 error = error + self.backPropagate(targets, N)
180 |             if i % 100 == 0:
181 |                 print('err %-.5f' % error)
182 | 
183 | 
184 | def testMain():
185 |     xArr, yArr = loadData('data.txt')
186 |     # print xArr
187 |     # print xArr
188 |     #xArr = regularize(xArr).tolist()
189 |     n = NN(2, 10, 1)
190 |     # 用一些模式训练它
191 |     n.train(xArr[500:2999][:], yArr[500:2999])
192 |     # 测试训练的成果（不要吃惊哦）
193 |     print n.test(xArr[0:499][:], yArr[0:499])
194 |     # 看看训练好的权重（当然可以考虑把训练好的权重持久化）
195 |     # n.weights()
196 | 
197 | 
198 | if __name__ == '__main__':
199 |     testMain()
200 | 


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/code/rbfNetwork.py:
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  1 | #coding=utf-8
  2 | '''
  3 | Created on 2016年5月13日
  4 | 
  5 | @author: pangb
  6 | '''
  7 | from sklearn.cluster import KMeans
  8 | from numpy import *
  9 | import math
 10 | 
 11 | def regularize(xMat):
 12 |     #inMat = xMat.copy()
 13 |     inMeans = mean(xMat, 0)
 14 |     inVar = var(xMat, 0)
 15 |     xMat = (xMat - inMeans) / inVar
 16 | 
 17 |     return xMat
 18 | class RBFNN:
 19 | 
 20 |     # 从文件中读取数据
 21 |     @staticmethod
 22 |     def loadData(fileName):
 23 |         numFeat = len(open(fileName).readline().strip().split(' ')) - 1
 24 |         dataMat = []
 25 |         labelMat = []
 26 |         fr = open(fileName)
 27 |         for line in fr.readlines():
 28 |             lineArr = []
 29 |             curLine = line.strip().split(' ')
 30 |             for i in range(numFeat):
 31 |                 lineArr.append(float(curLine[i]))
 32 |             dataMat.append(lineArr)
 33 |             labelMat.append(float(curLine[-1]))
 34 |         return dataMat, labelMat
 35 | 
 36 |     # 计算两个向量之间的距离
 37 |     @staticmethod
 38 |     def distCal(vecA, vecB):
 39 |         return sqrt(sum(power(vecA - vecB, 2)))
 40 | 
 41 |     # 为给定数据集构建一个包含k个随机质心的集合
 42 |     @staticmethod
 43 |     def randCentral(dataSet, k):
 44 |         n = shape(dataSet)[1]
 45 |         cents = mat(zeros((k, n)))
 46 |         for i in range(n):
 47 |             minJ = min(dataSet[:, i])
 48 |             rangeJ = float(max(dataSet[:, i]) - minJ)
 49 |             cents[:, i] = mat(minJ + rangeJ * random.rand(k, 1))
 50 |         return cents
 51 | 
 52 |     # 求k均值聚类
 53 |     @staticmethod
 54 |     def kMeans(self, dataSet, k):
 55 |         m = shape(dataSet)[0]
 56 |         clusterAssMent = mat(zeros((m, 2)))
 57 |         cents = self.randCentral(dataSet, k)
 58 |         clusterChanged = True
 59 |         while clusterChanged:
 60 |             clusterChanged = False
 61 |             for i in range(m):
 62 |                 minDist = inf; minIndex = -1
 63 |                 for j in range(k):
 64 |                     distJI = self.distCal(cents[j, :], dataSet[i, :])
 65 |                     if distJI < minDist:
 66 |                         minDist = distJI; minIndex = j
 67 |                 if clusterAssMent[i, 0] != minIndex:clusterChanged = True
 68 |                 clusterAssMent[i, :] = minIndex, minDist ** 2
 69 |             for cent in range(k):
 70 |                 ptsInClust = dataSet[nonzero(clusterAssMent[:, 0].A == cent)[0]]
 71 |                 cents[cent, :] = mean(ptsInClust, axis=0)
 72 |         return cents, clusterAssMent
 73 |         # 初始化
 74 |         # @i,j,k分别为rbf网络的输入层，隐含层，输出层的节点个数
 75 |     def __init__(self, i, j, k):
 76 |         self.input_num = i
 77 |         self.hide_num = j
 78 |         self.output_num = k
 79 | 
 80 |         self.weight = ones((self.hide_num, self.output_num))
 81 | 
 82 |     # 高斯函数
 83 |     @staticmethod
 84 |     def gaussian(self, x, y, fai):
 85 |         r = sum(power(x - y, 2))
 86 |         return exp(-r / (2 *fai))
 87 | 
 88 |     # 矩阵的逆求解theta
 89 | 
 90 |     @staticmethod
 91 |     def standRegres(xArr, yArr):
 92 |         xMat = mat(xArr)
 93 |         yMat = mat(yArr).T
 94 |         xTx = xMat.T * xMat
 95 |         if linalg.det(xTx) == 0.0:
 96 |             print "该矩阵没有逆矩阵，无法求解"
 97 |             return
 98 |         ws = xTx.I * (xMat.T * yMat)
 99 |         return ws
100 |     
101 |     def lingSolution(self, xArr, yArr, lam):
102 |         xMat = mat(xArr)
103 |         yMat = mat(yArr).T
104 |         xTx  = xMat.T*xMat
105 |         xTemp = xTx + eye(shape(xMat)[1]) * lam
106 |         if linalg.det(xTemp) == 0.0:
107 |             print "该矩阵没有逆矩阵，无法求解"
108 |             return
109 |         ws = xTemp.I * (xMat.T*yMat)
110 |         return ws
111 |     
112 |     # 求中心点之间的最大距离
113 |     def calculateRange(self, cents):
114 |         row = len(cents)
115 |         maxrange = []
116 |         for i in range(row):
117 |             nowRow = cents[i, :]
118 |             tempMat = tile(nowRow, (row, 1))
119 |             diff = (tempMat - cents)
120 |             diff = square(diff)
121 |             
122 |             diffsum = diff.sum(axis=1)
123 |             diffsum = diffsum.tolist()
124 |             diffsum.sort()
125 |             # if(maxrange < sortedDiff[row - 1]):
126 |                 # maxrange = sortedDiff[row - 1]
127 |                 
128 |             maxrange.append(diffsum[1])
129 | 
130 |         return maxrange
131 |         
132 |          
133 |     def regularize(self, xMat):
134 |     #inMat = xMat.copy()
135 |         inMeans = mean(xMat, 0)
136 |         inVar = var(xMat, 0)
137 |     
138 |     
139 |         xMat = (xMat - inMeans) / inVar
140 | 
141 |         return xMat
142 |     
143 |     def rand(self, a, b):
144 |         return (b - a) * random.random() + a
145 |     def calculateWeight(self, basis, yArr, IntegerNum = 1000, rate = 0.01):
146 |         n = len(basis)
147 |         weight = [1.0] * (self.hide_num+1)
148 |         for i in range(self.hide_num):
149 |             weight[i] = self.rand(-1.0, 1.0)
150 |         
151 |         for j in range(IntegerNum):
152 |             for k in range(n):
153 |                 yPre = weight * basis[k,:]
154 |                 fY = 1 / (1 + exp(-yPre))
155 |                 df = fY * (1 - fY)
156 |                 err = yArr[k] - df
157 |                 weight = weight + rate * err * df * basis[k,:].T
158 |         
159 |         return weight
160 | 
161 |     def calculateDiff(self, y1, y2):
162 |         count = 0
163 |         for i in range(len(y2)):
164 |             if y1[0][i] > 0.0 and y2[i] > 0.0:
165 |                 count += 1
166 |             if y1[0][i] < 0.0 and y2[i] < 0.0:
167 |                 count += 1
168 |         return count
169 |     
170 |     def isnan(self, cents):
171 |         tempCents = cents.tolist()
172 |         for i in range(len(tempCents)):
173 |             if isnan(tempCents[i][0]) or isnan(tempCents[i][1]):
174 |                 return True
175 |         
176 |         return False
177 |     # 交叉校验k的值
178 |     def rbfCross(self, xArr, yArr, k=5):
179 |         row = len(yArr)
180 |         foldSize = int(math.floor(row / k))
181 |         xArr = self.regularize(mat(xArr)).tolist()
182 |         num1 = 0
183 |         error = 0
184 |         # 交叉校验k次
185 |         for i in range(k):
186 |             xTrain = []
187 |             yTrain = []
188 |             xTest = []
189 |             yTest = []
190 |             # 交叉校验分组
191 |             for j in range(k):
192 |                 for m in range(foldSize):
193 |                     if j == i:
194 |                         if(j * foldSize + m >= row):
195 |                             break
196 |                         xTest.append(xArr[j * foldSize + m])
197 |                         yTest.append(yArr[j * foldSize + m])
198 |                     else:
199 |                         if(j * foldSize + m >= row):
200 |                             break
201 |                         xTrain.append(xArr[j * foldSize + m])
202 |                         yTrain.append(yArr[j * foldSize + m])
203 | 
204 |             # k聚类xTest集合
205 |             clf = KMeans(n_clusters=self.hide_num)
206 |             s = clf.fit(xTest)
207 |             cents = clf.cluster_centers_
208 |             #cents ,clus=  self.kMeans(self, mat(xTest), self.hide_num)
209 |             if self.isnan(cents):
210 |                 continue
211 |             
212 |             num1 += 1
213 |             maxRange = self.calculateRange(cents)
214 |             # 得到权重
215 |             basis = ones((len(xTrain), len(cents)+1))
216 |             # for n in range(len(xTrain)):
217 |             for n in range(len(xTrain)):
218 |                 for m in range(len(cents)):
219 |                     basis[n, m+1]=self.gaussian(self, xTrain[n], cents[m], maxRange[m])
220 | 
221 |             
222 |             ws=self.lingSolution(basis, yTrain, 0.001)
223 |             
224 |             #ws = self.calculateWeight(basis, yTrain)
225 |             # 交叉校验误差
226 |             vBasis=ones((len(xTest), len(cents)+1))
227 |             for n in range(len(xTest)):
228 |                 for m in range(len(cents)):
229 |                     vBasis[n, m+1]=self.gaussian(self, xTest[n], cents[m], maxRange[m])
230 | 
231 |             yPredict=vBasis * ws
232 |             #ws = mat(ws)
233 |             #shape1 = shape(ws)
234 |             #yPredict = vBasis * ws.T
235 |             #fY = 1 / (1 + exp(-yPredict))
236 |             yPredict = yPredict.T
237 |             print self.calculateDiff(yPredict.tolist(), yTest)
238 |             matYtest = mat(yTest)
239 |             diff=(yPredict - matYtest)
240 |             diff = square(diff)
241 |             errlist=diff.sum()
242 |             error += errlist
243 |         return error/num1
244 |     
245 |     #测试集合
246 |     def rbfPredict(self, xArr, yArr, xTest):
247 |         row = len(xArr)
248 |             # k聚类xTest集合
249 |         clf = KMeans(n_clusters=self.hide_num)
250 |         s = clf.fit(xArr)
251 |         cents = clf.cluster_centers_
252 |             #cents ,clus=  self.kMeans(self, mat(xTest), self.hide_num)
253 |             
254 |         maxRange = self.calculateRange(cents)
255 |             # 得到权重
256 |         basis = ones((len(xArr), len(cents)+1))
257 |             # for n in range(len(xTrain)):
258 |         for n in range(len(xArr)):
259 |             for m in range(len(cents)):
260 |                 basis[n, m+1]=self.gaussian(self, xArr[n], cents[m], maxRange[m])
261 | 
262 |         #岭回归求得权重 
263 |         ws=self.lingSolution(basis, yArr, 0.001)
264 |             
265 |             #ws = self.calculateWeight(basis, yTrain)
266 |             # 交叉校验误差
267 |         vBasis=ones((len(xTest), len(cents)+1))
268 |         for n in range(len(xTest)):
269 |             for m in range(len(cents)):
270 |                 vBasis[n, m+1]=self.gaussian(self, xTest[n], cents[m], maxRange[m])
271 | 
272 |         yPredict=vBasis * ws
273 |         return yPredict.T
274 |     
275 | def TestCross():
276 | 
277 |     xArr, yArr= RBFNN.loadData('data.txt')
278 |     xArr = regularize(mat(xArr)).tolist()
279 |     n=RBFNN(2, 10, 1)
280 |     print n.rbfCross(xArr[0:2500][:], yArr[0:2500])
281 | 
282 | if __name__ == '__main__':
283 |     TestCross()
284 | 


--------------------------------------------------------------------------------
/code/data.txt:
--------------------------------------------------------------------------------
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   2 | 11.8985 -13.7178 -1
   3 | 15.9559 6.57125 1
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  13 | 18.4031 1.77285 1
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1447 | 5.09911 4.89243 -1
1448 | 10.8536 -8.52178 -1
1449 | 0.696306 4.97327 -1
1450 | -8.15656 13.5948 1
1451 | 40.573 -5.88446 -1
1452 | 16.4859 -18.9964 -1
1453 | 19.466 0.858785 1
1454 | 6.00662 -7.15133 -1
1455 | 12.7807 10.5732 1
1456 | -21.1147 10.0248 1
1457 | 7.66119 11.2784 1
1458 | 14.3072 -13.4815 -1
1459 | 2.60755 -3.60008 -1
1460 | 13.5643 17.1734 1
1461 | 7.31039 24.0097 1
1462 | -14.0336 10.8441 1
1463 | 19.248 -8.43736 -1
1464 | 14.9166 7.13469 1
1465 | -21.8078 4.86597 1
1466 | -19.1775 14.0891 1
1467 | 23.92 -9.00077 -1
1468 | -14.8171 16.453 1
1469 | -7.28977 24.6678 1
1470 | -14.2681 2.80334 1
1471 | 35.3773 0.996155 -1
1472 | 8.38673 10.6788 1
1473 | -18.4145 0.362442 1
1474 | -0.355614 24.5193 1
1475 | -16.9153 9.71112 1
1476 | 16.2693 -14.1354 -1
1477 | 1.8348 15.0885 1
1478 | 12.7198 7.52487 1
1479 | 12.0103 -5.77952 -1
1480 | 40.9024 -8.87537 -1
1481 | 16.1357 16.564 1
1482 | 21.1764 -9.54367 -1
1483 | 16.5806 11.5091 1
1484 | 32.5109 0.716061 -1
1485 | 0.284561 19.7666 1
1486 | 5.34803 -10.9373 -1
1487 | -12.2787 6.56973 1
1488 | -18.427 8.21743 1
1489 | -1.38252 22.0446 1
1490 | 26.8499 -6.43889 -1
1491 | -16.0186 4.50575 1
1492 | 5.60941 -11.7535 -1
1493 | -4.42459 21.281 1
1494 | 29.5971 -11.1458 -1
1495 | 27.1685 -12.2781 -1
1496 | 35.8118 2.88253 -1
1497 | 38.139 3.21177 -1
1498 | 5.70154 -3.66449 -1
1499 | 29.6668 -13.6248 -1
1500 | 33.0352 -14.7255 -1
1501 | -1.35685 3.49892 -1
1502 | -17.3944 1.89577 1
1503 | 22.0471 -17.2511 -1
1504 | 41.6154 2.86991 -1
1505 | 2.0091 17.5784 1
1506 | 8.98457 -1.34988 -1
1507 | 27.1656 -5.06104 -1
1508 | 22.3368 -10.9017 -1
1509 | 2.6928 -11.2479 -1
1510 | -22.2075 3.10894 1
1511 | 15.1555 4.96031 1
1512 | -17.0436 14.713 1
1513 | 10.8676 12.5004 1
1514 | -19.7241 12.1182 1
1515 | -6.78601 21.0065 1
1516 | 19.1473 15.1694 1
1517 | 7.96455 -15.6951 -1
1518 | 18.7687 13.6324 1
1519 | 8.34097 -11.5806 -1
1520 | 45.2158 6.97104 -1
1521 | 21.6154 4.13009 1
1522 | 35.4672 -13.8193 -1
1523 | -6.39994 16.7396 1
1524 | 12.0036 -7.5284 -1
1525 | 10.8081 -13.6291 -1
1526 | -2.23127 4.05737 -1
1527 | -6.19847 12.0385 1
1528 | 35.9925 -1.98087 -1
1529 | 23.2257 1.08725 1
1530 | 24.8149 4.18464 1
1531 | 23.7501 3.54366 1
1532 | 25.2183 1.00677 1
1533 | 12.8428 11.7604 1
1534 | 34.5661 -14.8283 -1
1535 | -14.6385 1.003 1
1536 | 37.9707 -6.08476 -1
1537 | -6.20088 23.9913 1
1538 | -22.6657 10.6824 1
1539 | 2.77358 -3.87413 -1
1540 | 14.0636 15.4828 1
1541 | 18.6838 9.87767 1
1542 | 6.24679 -1.87748 -1
1543 | -9.19002 21.6204 1
1544 | 2.01415 -10.1949 -1
1545 | 3.95248 1.55631 -1
1546 | 4.71263 -14.1953 -1
1547 | 4.14036 1.98807 -1
1548 | 2.55017 21.2445 1
1549 | 4.9881 1.08931 -1
1550 | 37.5006 -11.6217 -1
1551 | -18.9958 13.3267 1
1552 | 33.1063 -5.09957 -1
1553 | 13.6749 1.26737 1
1554 | -7.19354 19.6579 1
1555 | 37.9298 2.3756 -1
1556 | -10.3164 13.8733 1
1557 | 23.6116 -7.85561 -1
1558 | 20.5449 5.53061 1
1559 | 14.4122 0.710318 1
1560 | 13.051 19.1946 1
1561 | -10.4872 9.76568 1
1562 | 4.4445 -1.55086 -1
1563 | 7.44945 -2.46203 -1
1564 | 3.38153 3.38769 -1
1565 | 11.8492 9.48297 1
1566 | 41.4207 5.18242 -1
1567 | 5.94767 -7.25921 -1
1568 | 29.8701 -12.9993 -1
1569 | 16.0421 4.14943 1
1570 | 3.55728 16.3849 1
1571 | 39.7144 6.61305 -1
1572 | 34.9093 -11.6309 -1
1573 | 16.2139 1.93667 1
1574 | 30.9549 -8.78244 -1
1575 | 16.4132 8.31064 1
1576 | 6.22762 -9.56878 -1
1577 | -1.22701 2.74278 -1
1578 | 7.91016 -10.8027 -1
1579 | 6.73138 3.95878 -1
1580 | 15.3556 5.85409 1
1581 | 28.3209 -8.39836 -1
1582 | -1.77243 15.6559 1
1583 | 6.29464 18.3398 1
1584 | 13.4673 4.67097 1
1585 | 22.5343 -8.7763 -1
1586 | 9.12472 -9.2936 -1
1587 | -15.0172 1.01406 1
1588 | 39.6992 -2.73216 -1
1589 | -12.9902 9.33037 1
1590 | 14.418 14.8786 1
1591 | 20.9231 15.1637 1
1592 | 7.06849 14.8928 1
1593 | 27.6612 -4.27836 -1
1594 | 35.4786 -0.209398 -1
1595 | 38.6355 0.280894 -1
1596 | 5.6939 0.318431 -1
1597 | 29.7778 -16.3031 -1
1598 | 12.538 19.5367 1
1599 | 34.6302 1.46781 -1
1600 | -20.0264 2.74091 1
1601 | 23.4087 -11.3711 -1
1602 | 25.2675 6.10778 1
1603 | -14.7755 5.14379 1
1604 | 22.1642 -14.595 -1
1605 | -7.99642 14.5284 1
1606 | -18.7104 8.03855 1
1607 | 10.9901 -8.8599 -1
1608 | 12.7438 16.7516 1
1609 | -21.0027 8.42989 1
1610 | -7.39396 13.3773 1
1611 | -14.6033 10.9793 1
1612 | 23.7105 -17.9178 -1
1613 | -14.326 12.8907 1
1614 | 25.3555 -7.08347 -1
1615 | 33.9259 -7.3896 -1
1616 | 32.9307 -3.70376 -1
1617 | 26.0022 -7.28476 -1
1618 | 4.49227 19.4566 1
1619 | 12.9566 7.55157 1
1620 | 19.5923 -14.2349 -1
1621 | 33.0281 -0.842349 -1
1622 | -23.3531 5.04216 1
1623 | 24.3837 -10.3917 -1
1624 | 1.48622 21.0805 1
1625 | 13.9702 -6.84726 -1
1626 | 34.4878 -11.2211 -1
1627 | 1.58175 -1.15867 -1
1628 | 20.1921 -10.2338 -1
1629 | 4.0226 16.4965 1
1630 | 3.73062 -13.594 -1
1631 | 15.4633 3.05826 1
1632 | 26.1485 -11.1261 -1
1633 | 16.2811 20.4539 1
1634 | 4.45493 -1.81998 -1
1635 | 17.426 19.3931 1
1636 | 8.21903 -1.96157 -1
1637 | 27.4699 -5.05496 -1
1638 | 14.0907 3.58739 1
1639 | 12.6746 6.8877 1
1640 | 0.0926282 -7.65918 -1
1641 | 9.92572 14.7151 1
1642 | -10.7039 14.9047 1
1643 | 13.3772 18.8168 1
1644 | 34.1159 5.03289 -1
1645 | 44.4279 0.200465 -1
1646 | 19.412 -9.69385 -1
1647 | -15.515 0.288461 1
1648 | 8.74995 20.166 1
1649 | 20.9024 15.8754 1
1650 | 10.7471 21.2048 1
1651 | 4.76848 -6.16979 -1
1652 | 41.017 2.30146 -1
1653 | 2.67837 21.8255 1
1654 | 7.31309 1.35332 -1
1655 | 17.7495 3.12012 1
1656 | -14.7987 6.42882 1
1657 | 3.3696 18.0779 1
1658 | 18.1428 5.77973 1
1659 | 5.36151 5.997 -1
1660 | 21.2486 4.3213 1
1661 | 11.3597 -17.7006 -1
1662 | 35.645 -8.86422 -1
1663 | -13.6485 17.8404 1
1664 | -0.600107 -4.1641 -1
1665 | -10.05 12.1493 1
1666 | 44.4775 5.85624 -1
1667 | -20.4428 6.59986 1
1668 | 36.4451 -7.67241 -1
1669 | -6.99825 16.8056 1
1670 | 8.00214 -12.8026 -1
1671 | -21.7099 8.04074 1
1672 | 34.034 -0.886334 -1
1673 | 39.1473 -8.16941 -1
1674 | -25.3477 5.64641 1
1675 | -24.0631 5.69278 1
1676 | 13.6741 20.5768 1
1677 | -0.835477 6.58863 -1
1678 | 2.42901 19.4755 1
1679 | 29.5748 -16.7519 -1
1680 | 7.20292 13.1806 1
1681 | 20.9249 7.65175 1
1682 | 10.4345 -4.18763 -1
1683 | -18.6364 5.73829 1
1684 | 2.33682 17.9017 1
1685 | 22.8249 7.89813 1
1686 | 27.049 -5.62192 -1
1687 | -0.493861 20.2312 1
1688 | -12.2962 12.6748 1
1689 | 17.1928 9.51137 1
1690 | -16.864 8.38845 1
1691 | 37.0306 2.05608 -1
1692 | 12.3865 -9.82128 -1
1693 | -18.232 13.2611 1
1694 | 44.7521 2.40365 -1
1695 | 13.3082 -10.8127 -1
1696 | 33.3751 -11.0236 -1
1697 | 1.08738 -7.89645 -1
1698 | 18.9866 8.01161 1
1699 | -18.1466 16.6612 1
1700 | 42.8249 -0.898134 -1
1701 | 32.6746 0.112296 -1
1702 | -9.43018 21.7774 1
1703 | -12.2757 12.093 1
1704 | 30.4875 -8.49472 -1
1705 | 16.2688 8.29034 1
1706 | -25.5427 2.43065 1
1707 | -13.7754 15.5273 1
1708 | 16.7774 -11.6456 -1
1709 | 27.3332 -13.7412 -1
1710 | 6.98411 -8.9939 -1
1711 | 36.2954 -1.45257 -1
1712 | 13.3538 -10.9971 -1
1713 | 37.4379 -4.90957 -1
1714 | 24.7238 -16.8472 -1
1715 | 39.0843 -9.72959 -1
1716 | -5.00052 1.22335 -1
1717 | -16.1208 5.46582 1
1718 | 35.5918 -9.58987 -1
1719 | 19.057 -9.69711 -1
1720 | 13.2896 6.57519 1
1721 | 23.361 11.2314 1
1722 | 21.1803 -10.0873 -1
1723 | -16.0429 6.41173 1
1724 | -9.16184 16.751 1
1725 | 14.5429 5.60466 1
1726 | 29.5875 -9.27253 -1
1727 | 35.7919 2.29047 -1
1728 | 15.2391 5.46777 1
1729 | 20.7875 5.18642 1
1730 | 36.3681 -10.9042 -1
1731 | -5.85399 12.9665 1
1732 | 12.9592 -7.51518 -1
1733 | 12.6697 14.4714 1
1734 | 6.26112 2.55224 -1
1735 | -19.4603 13.462 1
1736 | -0.371359 15.9975 1
1737 | 15.9182 19.1967 1
1738 | 16.7547 17.1564 1
1739 | 14.4577 -10.874 -1
1740 | 5.39477 -9.11895 -1
1741 | 5.19615 25.8027 1
1742 | 41.8163 -2.68952 -1
1743 | -13.4064 21.9919 1
1744 | 35.2072 1.02697 -1
1745 | -21.227 4.25722 1
1746 | 17.6972 8.84101 1
1747 | -16.1208 9.43953 1
1748 | -0.726548 -2.54178 -1
1749 | 10.243 10.9976 1
1750 | -0.0264172 4.25909 -1
1751 | -2.64555 23.0429 1
1752 | 6.73877 -11.2246 -1
1753 | 36.1512 -1.48789 -1
1754 | -8.44406 14.9874 1
1755 | -12.466 21.7109 1
1756 | 23.4457 -6.29974 -1
1757 | 15.2568 -12.023 -1
1758 | 16.4451 14.6724 1
1759 | 37.6972 -1.84101 -1
1760 | 28.5463 -9.13033 -1
1761 | -10.6763 12.9675 1
1762 | 25.944 -11.222 -1
1763 | 12.011 11.0875 1
1764 | -14.0473 16.4258 1
1765 | 29.8518 -14.9105 -1
1766 | -9.14638 15.5218 1
1767 | 8.97583 -12.8927 -1
1768 | 6.83442 3.57876 -1
1769 | 31.158 -0.762969 -1
1770 | 24.128 -9.86323 -1
1771 | -7.2605 19.5784 1
1772 | -20.2214 16.3409 1
1773 | 19.5061 -13.2312 -1
1774 | 25.4158 -14.264 -1
1775 | 18.055 -12.9563 -1
1776 | -12.159 20.6124 1
1777 | 14.3994 0.45912 1
1778 | 38.8257 -0.0270328 -1
1779 | 27.9967 -5.07555 -1
1780 | 39.7889 3.80744 -1
1781 | 7.06855 -1.48082 -1
1782 | -17.7911 13.4574 1
1783 | 36.983 5.20514 -1
1784 | 8.85351 16.1518 1
1785 | 13.2196 -5.50975 -1
1786 | 20.9914 -8.44551 -1
1787 | -0.407707 21.2349 1
1788 | 0.565325 25.2857 1
1789 | -22.9218 11.7308 1
1790 | 11.1134 -10.5264 -1
1791 | -4.79292 16.1806 1
1792 | -20.9398 0.410393 1
1793 | 24.7521 4.59635 1
1794 | -1.54948 16.1505 1
1795 | 33.1626 -15.361 -1
1796 | 13.9259 14.3896 1
1797 | 19.7144 0.386948 1
1798 | 2.32214 4.39026 -1
1799 | -9.76031 20.5168 1
1800 | 33.099 3.00849 -1
1801 | 43.0954 -5.53193 -1
1802 | 21.8348 -8.08854 -1
1803 | 19.1179 -18.9274 -1
1804 | 14.4481 1.46043 1
1805 | 11.173 16.9558 1
1806 | 18.7112 -11.98 -1
1807 | -14.8863 7.95866 1
1808 | -15.0672 4.5197 1
1809 | 1.75609 20.3625 1
1810 | 15.0145 7.04566 1
1811 | 10.9549 15.7824 1
1812 | 7.76678 14.8138 1
1813 | -6.07822 21.4194 1
1814 | -0.717342 -4.71646 -1
1815 | 14.9093 18.6309 1
1816 | -1.34724 15.5702 1
1817 | -1.94704 1.91252 -1
1818 | -4.08305 3.78055 -1
1819 | 10.9381 10.0255 1
1820 | 9.51275 -2.76568 -1
1821 | 4.72382 23.8472 1
1822 | -11.659 18.5806 1
1823 | -19.3012 14.7029 1
1824 | -10.2389 12.353 1
1825 | 28.2684 -5.07785 -1
1826 | 3.67446 -4.60935 -1
1827 | 18.0221 -10.2673 -1
1828 | 10.7013 13.2645 1
1829 | -8.64028 24.7006 1
1830 | -15.2315 13.1698 1
1831 | 18.5365 12.2643 1
1832 | 21.6781 10.8718 1
1833 | -11.1893 20.7868 1
1834 | 4.45794 17.6989 1
1835 | 12.3036 7.23448 1
1836 | 3.44573 13.2997 1
1837 | 11.3277 13.2184 1
1838 | -20.4641 7.2242 1
1839 | 18.2023 -16.3703 -1
1840 | 36.734 -5.32669 -1
1841 | 3.40872 18.3711 1
1842 | 8.5542 -13.5051 -1
1843 | 4.47972 3.72442 -1
1844 | 33.9876 -9.3584 -1
1845 | 8.54626 16.1303 1
1846 | -9.31034 14.6272 1
1847 | 9.6615 -13.8491 -1
1848 | 12.8065 -12.6579 -1
1849 | 3.32466 4.81849 -1
1850 | -13.1383 7.20027 1
1851 | 13.2931 -11.7794 -1
1852 | 11.87 8.63637 1
1853 | -6.33425 14.0082 1
1854 | 18.1681 -12.8949 -1
1855 | 8.47791 -5.00914 -1
1856 | 24.1583 -11.1495 -1
1857 | -13.1656 3.42124 1
1858 | 14.2295 5.3941 1
1859 | 25.3768 -9.5532 -1
1860 | 11.5118 -12.0308 -1
1861 | 2.21466 -6.30196 -1
1862 | 35.2912 -5.68647 -1
1863 | -9.15073 15.9828 1
1864 | 35.2732 0.519983 -1
1865 | 39.2128 -0.583918 -1
1866 | 21.1358 2.43259 1
1867 | -12.8319 10.3031 1
1868 | 16.7261 -12.1945 -1
1869 | -23.0146 4.99417 1
1870 | 8.21433 23.6553 1
1871 | 17.9156 1.53757 1
1872 | 25.3399 -17.8478 -1
1873 | 12.433 16.9838 1
1874 | 18.9704 1.75032 1
1875 | 9.26288 17.9976 1
1876 | 17.334 12.4431 1
1877 | -13.1912 20.3286 1
1878 | -5.76764 1.46094 -1
1879 | -2.20752 3.89106 -1
1880 | -19.9737 4.14586 1
1881 | 33.0369 -8.38589 -1
1882 | 9.41192 10.3268 1
1883 | 19.9157 3.35959 1
1884 | 15.0092 -9.82892 -1
1885 | 18.3192 -6.87607 -1
1886 | -26.1373 3.52611 1
1887 | 9.59794 -5.44625 -1
1888 | -16.0475 5.44369 1
1889 | 9.19668 -6.19522 -1
1890 | 44.934 -0.753608 -1
1891 | 35.8973 1.69418 -1
1892 | 7.00978 -2.33037 -1
1893 | 2.84084 -10.3305 -1
1894 | 19.7762 15.3045 1
1895 | 19.8231 10.968 1
1896 | 0.297852 15.6623 1
1897 | 32.0453 -7.58762 -1
1898 | -12.7607 4.45842 1
1899 | -3.04819 3.37576 -1
1900 | 37.4324 1.90823 -1
1901 | -23.6404 6.79969 1
1902 | 44.1846 6.20198 -1
1903 | 31.5007 -14.2489 -1
1904 | -13.686 2.25742 1
1905 | 2.74954 19.1741 1
1906 | 7.52254 20.4514 1
1907 | 9.57479 23.7519 1
1908 | 12.0412 -9.23334 -1
1909 | 29.3774 -7.21599 -1
1910 | 0.0946575 2.57041 -1
1911 | 19.7716 12.5368 1
1912 | 25.5774 -6.52476 -1
1913 | 35.2391 1.53223 -1
1914 | 42.8503 4.13532 -1
1915 | -10.072 9.27147 1
1916 | -2.0284 20.8157 1
1917 | -15.2162 5.93458 1
1918 | 0.983937 21.3571 1
1919 | 15.3773 6.00384 1
1920 | 32.804 -2.33124 -1
1921 | 21.8139 4.15054 1
1922 | -10.0185 13.1715 1
1923 | 11.873 16.3542 1
1924 | 36.4698 -11.4311 -1
1925 | 3.13598 -1.38845 -1
1926 | -3.38002 20.5169 1
1927 | 4.98623 6.51736 -1
1928 | -18.3576 0.87365 1
1929 | 17.0698 -10.3306 -1
1930 | -22.8587 4.02476 1
1931 | -10.4091 11.04 1
1932 | 3.80966 17.2949 1
1933 | -1.61784 -1.83461 -1
1934 | -5.81419 16.5809 1
1935 | 39.297 0.158046 -1
1936 | -15.117 2.26003 1
1937 | -17.2124 11.1127 1
1938 | -6.19028 24.5579 1
1939 | 45.5046 3.75805 -1
1940 | -2.34611 15.0003 1
1941 | 39.4428 -5.44378 -1
1942 | -11.2446 9.95554 1
1943 | 23.1536 2.43209 1
1944 | -18.3939 9.67703 1
1945 | 1.60609 -2.67703 -1
1946 | -17.3072 18.2479 1
1947 | -21.7336 12.5606 1
1948 | 9.40288 11.4468 1
1949 | 17.2349 19.4967 1
1950 | 14.1729 -15.4336 -1
1951 | 27.2029 -6.18057 -1
1952 | 37.7805 3.97196 -1
1953 | -14.324 14.9039 1
1954 | 37.6235 -3.82379 -1
1955 | 11.9589 18.9287 1
1956 | -5.33776 13.2521 1
1957 | 6.59365 -14.9919 -1
1958 | 41.5506 0.350943 -1
1959 | 13.0666 -15.8769 -1
1960 | -3.27613 20.3422 1
1961 | 28.8175 -3.3267 -1
1962 | 2.34995 -0.295755 -1
1963 | -7.98973 12.7795 1
1964 | -13.1951 8.93551 1
1965 | 30.6307 -5.24859 -1
1966 | -16.9364 14.1038 1
1967 | -3.01464 2.00583 -1
1968 | 23.3492 -9.33248 -1
1969 | -20.7714 4.0368 1
1970 | 11.7198 -6.4061 -1
1971 | 30.8246 -1.39882 -1
1972 | 32.8428 -4.76043 -1
1973 | -4.24507 19.6616 1
1974 | -0.456395 1.16251 -1
1975 | -14.1159 0.239055 1
1976 | -9.03374 10.3559 1
1977 | -14.2879 1.535 1
1978 | 17.056 -8.59454 -1
1979 | -26.3497 2.30346 1
1980 | -2.74695 -5.87514 -1
1981 | 14.1612 2.70248 1
1982 | 8.5473 -13.6179 -1
1983 | 24.3867 -9.82472 -1
1984 | 13.8097 -17.5579 -1
1985 | 21.502 10.2477 1
1986 | 17.8246 -15.3566 -1
1987 | -7.41919 21.0007 1
1988 | 27.7781 -15.4262 -1
1989 | -10.6681 10.7537 1
1990 | 20.3946 3.32596 1
1991 | -0.35283 -9.89305 -1
1992 | 8.3193 14.6748 1
1993 | 3.93421 25.0108 1
1994 | 11.7001 -6.3457 -1
1995 | -22.9683 13.1628 1
1996 | 11.8434 -6.59477 -1
1997 | 16.324 0.437138 1
1998 | 30.8479 -13.2306 -1
1999 | 4.61451 -5.65671 -1
2000 | -17.9802 13.841 1
2001 | 30.6947 -1.76781 -1
2002 | 37.7976 -7.17053 -1
2003 | -4.18966 4.79277 -1
2004 | -6.26909 6.3405 -1
2005 | 4.98242 -3.9126 -1
2006 | 9.78647 -2.63471 -1
2007 | 8.68849 -11.1416 -1
2008 | 14.2557 10.7257 1
2009 | -6.78039 12.5098 1
2010 | 10.81 -14.6204 -1
2011 | 29.1465 -15.8253 -1
2012 | 7.30071 22.8448 1
2013 | -1.62464 23.5954 1
2014 | 19.8016 2.49329 1
2015 | 12.9437 -13.1194 -1
2016 | -13.7048 9.3056 1
2017 | -3.33455 19.9831 1
2018 | 21.9944 10.209 1
2019 | -1.41482 25.7564 1
2020 | -0.374403 -1.71477 -1
2021 | 14.6622 -6.25207 -1
2022 | 12.2329 23.4207 1
2023 | 7.65456 11.3702 1
2024 | 35.4271 -6.64882 -1
2025 | 0.165652 15.1893 1
2026 | 41.264 5.40138 -1
2027 | 16.983 1.79486 1
2028 | 33.5594 4.11007 -1
2029 | 34.6361 -2.75789 -1
2030 | 10.1939 14.5595 1
2031 | 35.7885 -1.38856 -1
2032 | -12.1268 8.47564 1
2033 | 16.0001 -6.91409 -1
2034 | -0.942993 16.6971 1
2035 | 9.59712 18.1458 1
2036 | -1.11474 -3.02479 -1
2037 | 2.80806 -1.73187 -1
2038 | 7.7681 17.3237 1
2039 | 17.6477 -14.5296 -1
2040 | 16.6581 -10.9095 -1
2041 | 20.3501 -8.95126 -1
2042 | -3.27388 19.1945 1
2043 | 35.5497 6.85743 -1
2044 | 5.57739 13.5248 1
2045 | 27.09 -7.88991 -1
2046 | -8.61715 18.2897 1
2047 | 9.29885 -16.1998 -1
2048 | 35.1155 -5.48601 -1
2049 | 13.3174 -6.57809 -1
2050 | -12.8613 17.8956 1
2051 | -5.50315 18.1286 1
2052 | 21.3389 -15.7922 -1
2053 | -0.813329 -1.17763 -1
2054 | 15.864 -8.89845 -1
2055 | 38.5365 -5.26432 -1
2056 | 25.454 6.9161 1
2057 | -0.15383 -4.43406 -1
2058 | -1.47149 -6.53547 -1
2059 | 16.7659 11.493 1
2060 | -6.64624 17.9971 1
2061 | 40.5449 1.46939 -1
2062 | 1.72498 -0.65357 -1
2063 | 28.9432 -4.48899 -1
2064 | 27.3104 -17.0097 -1
2065 | 3.98882 4.80233 -1
2066 | -12.2762 12.4991 1
2067 | -1.70973 20.3621 1
2068 | -11.0242 19.8927 1
2069 | 30.9589 -16.1798 -1
2070 | 26.0256 -8.37063 -1
2071 | 14.6302 5.53219 1
2072 | 32.5252 1.6447 -1
2073 | -17.7751 2.24742 1
2074 | 27.3007 -15.8448 -1
2075 | -19.333 3.40681 1
2076 | 10.8382 -9.75095 -1
2077 | 24.9954 1.87632 1
2078 | 15.8973 5.30582 1
2079 | 17.7708 2.74633 1
2080 | -9.88151 13.9441 1
2081 | 33.3772 -11.8168 -1
2082 | 27.9113 -4.57528 -1
2083 | 35.4844 -12.4294 -1
2084 | 37.5419 -10.934 -1
2085 | 7.72382 -5.4991 -1
2086 | -16.3458 3.40339 1
2087 | 39.4075 -5.77049 -1
2088 | -15.8596 5.01193 1
2089 | 2.6989 -3.58034 -1
2090 | 11.8982 -10.5835 -1
2091 | 8.09476 -4.47252 -1
2092 | -18.1891 4.28828 1
2093 | -1.73358 -5.56061 -1
2094 | 27.1419 -17.8981 -1
2095 | 6.14854 18.1261 1
2096 | -0.58915 -0.183327 -1
2097 | 15.0954 2.01151 1
2098 | 4.17322 -9.88348 -1
2099 | 3.73407 5.8057 -1
2100 | 24.1379 -17.3159 -1
2101 | 11.4239 17.5377 1
2102 | -7.93249 22.2063 1
2103 | -9.99455 13.3684 1
2104 | -7.04076 14.5152 1
2105 | 13.1101 -9.48924 -1
2106 | 25.5508 2.56751 1
2107 | 27.0423 -5.36434 -1
2108 | 33.0594 2.09154 -1
2109 | 8.61313 -6.96533 -1
2110 | 5.71212 5.465 -1
2111 | -4.99256 14.2342 1
2112 | 5.87532 24.984 1
2113 | 21.7799 -7.35904 -1
2114 | 37.537 6.5754 -1
2115 | 30.1326 -16.0327 -1
2116 | 23.4003 -9.23592 -1
2117 | 3.32482 -1.04303 -1
2118 | 5.41061 -2.73928 -1
2119 | 36.1396 5.68798 -1
2120 | 7.17431 12.4407 1
2121 | 45.9922 6.33204 -1
2122 | 1.04952 16.2918 1
2123 | 0.509177 18.4886 1
2124 | 29.1872 -3.33192 -1
2125 | 39.1616 5.24475 -1
2126 | 4.91903 20.6923 1
2127 | 8.0618 14.3838 1
2128 | 33.3737 3.70825 -1
2129 | 39.8309 5.0494 -1
2130 | 3.91996 16.0008 1
2131 | 13.9218 -14.4194 -1
2132 | 20.4168 6.26724 1
2133 | 35.9514 -13.1565 -1
2134 | -15.8268 16.8835 1
2135 | -6.23944 17.6533 1
2136 | 33.2241 -8.49305 -1
2137 | 15.4844 19.4294 1
2138 | 4.64271 1.80964 -1
2139 | -13.7783 6.57278 1
2140 | -20.7173 11.7165 1
2141 | -3.92927 18.2748 1
2142 | -17.3925 10.6001 1
2143 | 9.25353 14.942 1
2144 | 22.7092 -10.0934 -1
2145 | 14.1755 18.0636 1
2146 | -2.9288 24.3169 1
2147 | -21.3781 3.12083 1
2148 | 7.77814 22.4262 1
2149 | 8.27977 20.4733 1
2150 | 4.20555 13.8654 1
2151 | -10.1775 9.70084 1
2152 | 35.9559 0.428754 -1
2153 | 4.88304 4.73997 -1
2154 | 18.653 -18.7382 -1
2155 | -7.86251 12.9189 1
2156 | 25.0511 5.12587 1
2157 | -17.5538 5.96489 1
2158 | 40.069 0.356147 -1
2159 | 13.3625 11.3382 1
2160 | 13.1626 22.361 1
2161 | 2.97652 24.7874 1
2162 | 1.48827 13.4578 1
2163 | 21.4207 1.81758 1
2164 | 45.454 0.083899 -1
2165 | 36.7379 5.7152 -1
2166 | 38.5395 -10.9396 -1
2167 | 20.3246 -7.32055 -1
2168 | 24.9179 -16.1939 -1
2169 | 30.9126 -8.90514 -1
2170 | -18.5601 4.41529 1
2171 | 0.703844 -6.03771 -1
2172 | -3.3287 2.31091 -1
2173 | 14.4878 18.2211 1
2174 | 22.6834 -11.147 -1
2175 | 31.4503 -15.5962 -1
2176 | 5.08554 -2.63172 -1
2177 | 3.29095 21.8262 1
2178 | 24.1278 8.59254 1
2179 | 33.2896 0.424808 -1
2180 | -20.8133 8.17763 1
2181 | 0.591633 -9.26845 -1
2182 | -2.744 14.506 1
2183 | 18.4688 1.5222 1
2184 | 21.5473 2.0457 1
2185 | -15.5451 8.81998 1
2186 | 10.2397 -13.5168 -1
2187 | 40.0699 -4.85216 -1
2188 | 21.0306 -19.3421 -1
2189 | 10.8479 20.2306 1
2190 | -16.3255 11.6093 1
2191 | -19.142 16.2935 1
2192 | 19.8309 1.9506 1
2193 | 19.2947 0.729154 1
2194 | 3.70104 4.57199 -1
2195 | -17.9188 11.1799 1
2196 | 41.5982 -3.56639 -1
2197 | 22.9019 -16.8439 -1
2198 | -16.6636 19.8177 1
2199 | -20.2602 16.5965 1
2200 | -3.48144 22.4738 1
2201 | 16.5186 -15.4738 -1
2202 | -15.3243 3.4193 1
2203 | 2.15102 6.156 -1
2204 | -2.99562 23.2623 1
2205 | 19.1616 1.75525 1
2206 | 15.5497 0.142568 1
2207 | -7.08682 17.7263 1
2208 | 9.89926 -15.7218 -1
2209 | 43.0814 5.99452 -1
2210 | 26.985 -11.9358 -1
2211 | 15.0834 -9.37872 -1
2212 | 2.60555 5.10423 -1
2213 | 20.7252 12.5274 1
2214 | 12.3287 -16.5799 -1
2215 | 13.1901 18.1133 1
2216 | 39.215 2.38073 -1
2217 | 43.6397 -3.49396 -1
2218 | 4.3606 13.2462 1
2219 | -0.935839 16.3282 1
2220 | 8.56804 -2.01369 -1
2221 | 44.1366 -0.925009 -1
2222 | -14.1593 17.4924 1
2223 | 32.6916 -0.39497 -1
2224 | -15.9684 18.4258 1
2225 | 1.00417 -6.32667 -1
2226 | 15.8118 4.11747 1
2227 | 12.609 -14.9474 -1
2228 | 34.6155 2.53246 -1
2229 | 27.7463 -12.2443 -1
2230 | 13.6657 -7.00819 -1
2231 | 17.6703 -7.80878 -1
2232 | 11.5905 7.80283 1
2233 | 4.08125 4.14585 -1
2234 | 17.649 5.3223 1
2235 | 5.27342 -0.0566172 -1
2236 | -11.2372 20.9659 1
2237 | 23.599 2.14986 1
2238 | 1.57299 -1.21743 -1
2239 | -8.90891 10.292 1
2240 | -17.5754 12.7933 1
2241 | 5.69719 20.9666 1
2242 | 36.0366 -6.84958 -1
2243 | 28.0912 -16.8868 -1
2244 | 36.0496 -0.852752 -1
2245 | 8.81074 -13.7868 -1
2246 | 17.1414 -16.575 -1
2247 | -14.6052 16.1189 1
2248 | 5.58081 17.204 1
2249 | 23.0002 -15.0959 -1
2250 | -18.6207 2.04439 1
2251 | 9.83248 15.4476 1
2252 | 14.146 -5.96648 -1
2253 | 35.1527 -9.66531 -1
2254 | 21.8163 9.68952 1
2255 | 40.3335 -0.403403 -1
2256 | -14.9148 2.2736 1
2257 | 38.7715 -6.02299 -1
2258 | -12.6869 5.64668 1
2259 | -17.7853 7.3172 1
2260 | -13.5947 4.98993 1
2261 | 21.028 15.3583 1
2262 | 30.634 -3.71323 -1
2263 | 36.9669 1.76495 -1
2264 | -9.72367 18.475 1
2265 | 1.54019 2.28163 -1
2266 | -12.8869 14.7409 1
2267 | -0.771382 2.9632 -1
2268 | 3.60762 21.3584 1
2269 | -4.74316 19.023 1
2270 | 4.41662 15.436 1
2271 | 13.3733 -18.2348 -1
2272 | 30.8676 -5.50045 -1
2273 | 7.16814 -3.30314 -1
2274 | 11.8217 13.0687 1
2275 | 29.9177 -3.92641 -1
2276 | 5.81006 -6.41896 -1
2277 | 14.349 -6.78821 -1
2278 | 3.22671 4.96056 -1
2279 | -1.94496 19.9563 1
2280 | 34.9142 3.96015 -1
2281 | 18.2903 -13.3621 -1
2282 | 10.3993 19.501 1
2283 | -3.03271 18.9399 1
2284 | 0.698764 -7.70293 -1
2285 | -7.5426 12.7572 1
2286 | 28.3386 -4.54992 -1
2287 | 17.1361 -19.1494 -1
2288 | 0.642197 17.1947 1
2289 | 23.5276 1.5089 1
2290 | -18.2202 9.56306 1
2291 | 6.23722 -7.03694 -1
2292 | 23.7409 -6.62535 -1
2293 | -7.3674 21.4854 1
2294 | -0.712499 23.5031 1
2295 | 9.92301 -11.6608 -1
2296 | 1.77993 14.359 1
2297 | 8.58781 -1.17141 -1
2298 | 16.5342 -17.1522 -1
2299 | -2.86385 26.1494 1
2300 | -13.7628 14.0369 1
2301 | -14.1899 13.419 1
2302 | -20.7268 3.02628 1
2303 | 19.4413 -6.48959 -1
2304 | 29.2535 -7.94198 -1
2305 | 33.963 -13.0559 -1
2306 | -2.85871 2.97524 -1
2307 | 7.04234 12.3643 1
2308 | -10.0672 18.8041 1
2309 | 0.357232 -2.39246 -1
2310 | 35.4633 3.94174 -1
2311 | 5.20132 0.571179 -1
2312 | 7.72134 0.430266 -1
2313 | -23.2355 0.135541 1
2314 | 9.18718 10.3319 1
2315 | -10.1011 18.1436 1
2316 | 29.1547 -3.56486 -1
2317 | -23.3287 4.68909 1
2318 | -3.35308 1.95784 -1
2319 | 44.8826 5.06675 -1
2320 | 0.380656 2.53105 -1
2321 | -9.37485 11.7975 1
2322 | 41.0167 4.08849 -1
2323 | 17.9707 13.0848 1
2324 | 7.49887 14.855 1
2325 | 39.2643 5.93304 -1
2326 | 3.69223 20.2692 1
2327 | 2.29403 25.1643 1
2328 | 4.52895 21.4256 1
2329 | 36.0099 -5.90898 -1
2330 | 38.5325 -10.8588 -1
2331 | 11.9159 20.3542 1
2332 | 27.7668 -7.81376 -1
2333 | -20.3528 16.8931 1
2334 | 18.3754 -16.5954 -1
2335 | 16.0707 -11.2748 -1
2336 | 41.2486 2.6787 -1
2337 | 4.98283 5.98594 -1
2338 | 10.774 20.2282 1
2339 | 7.15456 24.7894 1
2340 | 18.5619 -15.4183 -1
2341 | 33.5781 -8.83066 -1
2342 | -17.1592 17.3305 1
2343 | -1.80777 2.13403 -1
2344 | 6.84994 13.4389 1
2345 | 10.0656 -11.7982 -1
2346 | 35.7413 -6.20178 -1
2347 | 36.2139 5.06333 -1
2348 | 13.0395 7.24079 1
2349 | 18.4505 -9.1505 -1
2350 | -13.4043 16.2647 1
2351 | -20.7265 9.54178 1
2352 | 4.91615 -5.52296 -1
2353 | 40.8493 5.03038 -1
2354 | 12.1228 -16.3557 -1
2355 | 20.099 6.59108 1
2356 | 11.9893 6.69985 1
2357 | 23.9357 -16.6824 -1
2358 | -19.0706 2.33099 1
2359 | 11.208 16.087 1
2360 | 34.7538 -14.5913 -1
2361 | 18.2912 -6.45162 -1
2362 | 5.41578 21.264 1
2363 | 2.90186 23.8439 1
2364 | 45.2183 5.99323 -1
2365 | 10.8246 8.39882 1
2366 | 14.7147 -18.569 -1
2367 | 8.76277 -13.9659 -1
2368 | 3.88835 5.17863 -1
2369 | 31.2166 -14.5711 -1
2370 | -1.79771 23.3703 1
2371 | -11.9438 17.6188 1
2372 | 9.6836 -6.87326 -1
2373 | -7.72144 16.0368 1
2374 | 40.099 0.408917 -1
2375 | 2.77943 22.4247 1
2376 | 4.1656 2.3558 -1
2377 | 39.4568 -9.41513 -1
2378 | -16.3671 15.424 1
2379 | -13.7816 12.2344 1
2380 | -14.0523 14.2592 1
2381 | 34.3672 -14.6839 -1
2382 | -16.7003 10.556 1
2383 | 21.5982 10.5664 1
2384 | 32.3917 -13.8001 -1
2385 | 4.07958 22.4845 1
2386 | 19.297 6.84195 1
2387 | 3.06356 -7.10383 -1
2388 | -4.55721 4.32906 -1
2389 | 16.6106 14.6579 1
2390 | 12.4118 -9.58894 -1
2391 | 1.1764 16.5437 1
2392 | -15.2552 8.88382 1
2393 | -9.63605 19.4274 1
2394 | -15.027 2.54858 1
2395 | -10.8033 13.1952 1
2396 | 29.4119 -3.32678 -1
2397 | -6.70735 24.1774 1
2398 | 11.5559 -7.98742 -1
2399 | 12.2786 -9.0368 -1
2400 | -1.97791 17.2673 1
2401 | -7.58816 16.5889 1
2402 | -11.4513 8.08415 1
2403 | 1.79484 2.0055 -1
2404 | 12.9307 10.7038 1
2405 | 38.0195 -6.48395 -1
2406 | 17.4379 11.9096 1
2407 | 19.7961 15.8275 1
2408 | -0.754425 1.08518 -1
2409 | 27.5865 -8.46468 -1
2410 | 31.2548 -5.23416 -1
2411 | 20.9839 -14.3571 -1
2412 | -1.51492 0.836154 -1
2413 | -14.7788 21.7785 1
2414 | 15.2781 1.47596 1
2415 | 24.934 7.75361 1
2416 | -16.1668 4.10474 1
2417 | -9.00988 15.8599 1
2418 | -19.9074 14.6592 1
2419 | 16.7433 -13.44 -1
2420 | -16.7733 2.03944 1
2421 | 33.5643 -10.1734 -1
2422 | 26.9319 -15.8947 -1
2423 | 23.3696 -11.0779 -1
2424 | 34.4481 5.53957 -1
2425 | 17.6539 -8.00035 -1
2426 | 39.9157 3.64041 -1
2427 | 36.98 3.85986 -1
2428 | -23.1148 3.83695 1
2429 | 37.0514 -4.93238 -1
2430 | -20.7544 5.91482 1
2431 | 3.98142 2.49425 -1
2432 | 1.72654 17.7547 1
2433 | 14.8242 7.24858 1
2434 | -2.06749 -3.95074 -1
2435 | -24.1788 10.796 1
2436 | 1.4547 14.5154 1
2437 | -11.9224 19.4701 1
2438 | 40.1201 3.37786 -1
2439 | -16.2694 20.594 1
2440 | 24.571 -12.0214 -1
2441 | 4.57098 19.0214 1
2442 | 27.7121 -6.39374 -1
2443 | 29.9602 -10.6953 -1
2444 | 35.2781 5.52404 -1
2445 | -20.4927 4.04135 1
2446 | -13.4067 7.79824 1
2447 | 34.2557 -3.7257 -1
2448 | 42.928 -4.42157 -1
2449 | 5.52112 -2.00388 -1
2450 | 4.23846 5.07015 -1
2451 | -4.59359 -0.713106 -1
2452 | 11.1997 -7.23086 -1
2453 | 9.14198 12.7794 1
2454 | 10.3772 12.1864 1
2455 | 10.1344 -6.07433 -1
2456 | 15.7885 8.38856 1
2457 | 17.3105 12.453 1
2458 | 21.9793 6.34431 1
2459 | -2.79967 -5.25019 -1
2460 | -2.35233 21.5296 1
2461 | 24.4222 7.23514 1
2462 | 21.778 5.14537 1
2463 | 19.6286 -8.99749 -1
2464 | 6.09514 17.5553 1
2465 | 15.5918 16.5899 1
2466 | 44.3437 -3.45975 -1
2467 | 38.4567 -0.359287 -1
2468 | 36.324 6.56286 -1
2469 | 12.2701 -13.5206 -1
2470 | -17.849 0.844003 1
2471 | 6.2103 2.73463 -1
2472 | -7.48436 11.4807 1
2473 | 15.9514 20.1565 1
2474 | 40.9698 4.0229 -1
2475 | 0.583378 -3.20437 -1
2476 | -4.71286 20.67 1
2477 | 13.9057 4.84831 1
2478 | 16.7239 -13.3422 -1
2479 | 1.43989 2.58471 -1
2480 | -18.0296 17.1184 1
2481 | 19.0753 15.3157 1
2482 | -22.8375 0.335528 1
2483 | 18.5578 -14.0216 -1
2484 | -13.0944 17.592 1
2485 | 34.1612 4.29752 -1
2486 | 19.6444 -17.5193 -1
2487 | 10.5308 13.7251 1
2488 | 35.155 5.30103 -1
2489 | 5.9664 -3.84415 -1
2490 | 20.2119 -15.6551 -1
2491 | 13.4609 14.5312 1
2492 | 2.16218 5.54145 -1
2493 | 8.4368 -9.48504 -1
2494 | 19.4075 12.7705 1
2495 | -5.01698 4.79854 -1
2496 | 20.3536 -12.4356 -1
2497 | 5.77818 24.2925 1
2498 | -3.44222 6.73948 -1
2499 | 3.24498 2.9427 -1
2500 | 6.29665 -2.27567 -1
2501 | 15.7848 2.51798 1
2502 | 26.9626 -11.9363 -1
2503 | 34.197 -4.99286 -1
2504 | 2.40024 6.66915 -1
2505 | 23.933 -10.4497 -1
2506 | 18.4631 10.1936 1
2507 | 8.44169 -7.13262 -1
2508 | 35.6049 -9.31799 -1
2509 | 1.21523 -1.6421 -1
2510 | -8.69356 11.4972 1
2511 | -12.0354 22.6951 1
2512 | 20.8493 1.96962 1
2513 | 39.1926 6.32636 -1
2514 | 12.1867 -12.5265 -1
2515 | 0.47261 24.2691 1
2516 | 35.767 5.98705 -1
2517 | 39.091 -5.50932 -1
2518 | -3.02108 13.4219 1
2519 | 6.2068 17.554 1
2520 | 11.4491 9.97442 1
2521 | 25.3919 -15.9904 -1
2522 | -18.9208 3.4641 1
2523 | 8.0912 23.8868 1
2524 | -19.6428 9.39246 1
2525 | 4.06948 -6.03771 -1
2526 | 22.0091 -10.5784 -1
2527 | 12.804 9.33124 1
2528 | 39.6389 3.76906 -1
2529 | 12.606 -6.37725 -1
2530 | 4.12802 16.8632 1
2531 | 6.56936 16.4122 1
2532 | 11.5174 -3.96902 -1
2533 | -14.9145 9.63172 1
2534 | 35.903 0.388221 -1
2535 | 22.2788 -13.1343 -1
2536 | 37.8581 -4.67113 -1
2537 | 33.1408 -3.00506 -1
2538 | 6.04728 -10.4679 -1
2539 | 9.96015 17.6953 1
2540 | 41.6226 4.84931 -1
2541 | -1.37813 3.87917 -1
2542 | 39.466 6.14122 -1
2543 | 41.028 -8.35833 -1
2544 | 28.7405 -17.594 -1
2545 | -7.01212 14.2724 1
2546 | 6.33173 -3.76275 -1
2547 | 31.4239 -10.5377 -1
2548 | 12.3917 20.8001 1
2549 | 17.3545 -16.0429 -1
2550 | 19.215 4.61927 1
2551 | 11.7647 -16.7545 -1
2552 | 42.9142 -1.88313 -1
2553 | 25.2799 1.32596 1
2554 | 22.8526 5.94906 1
2555 | -0.824795 4.33601 -1
2556 | -7.67127 23.5799 1
2557 | -21.2749 11.9874 1
2558 | -1.43808 22.4183 1
2559 | 20.9045 11.5432 1
2560 | 21.0688 10.5533 1
2561 | 20.3792 12.0862 1
2562 | 15.6186 8.29584 1
2563 | 25.5808 -10.204 -1
2564 | 10.2988 21.6104 1
2565 | 13.099 3.99151 1
2566 | -1.96065 -5.05825 -1
2567 | 40.7711 -7.47977 -1
2568 | -4.136 15.8985 1
2569 | -15.8344 4.6442 1
2570 | -11.5221 12.0091 1
2571 | 5.9527 -9.42583 -1
2572 | 42.5814 -3.8447 -1
2573 | 26.9039 -8.2589 -1
2574 | 45.0511 1.87413 -1
2575 | -0.155791 -7.00401 -1
2576 | -13.7724 16.5688 1
2577 | 16.1396 1.31202 1
2578 | 14.4561 1.40913 1
2579 | -15.9692 5.73919 1
2580 | 10.1326 23.0327 1
2581 | 6.59332 -0.798235 -1
2582 | -2.16294 13.6448 1
2583 | 17.0306 4.94392 1
2584 | 13.0017 -9.8056 -1
2585 | 3.49729 15.2179 1
2586 | 24.3437 10.4598 1
2587 | 23.5002 -9.63306 -1
2588 | 31.9589 -11.9287 -1
2589 | 25.4678 -14.7018 -1
2590 | 20.2979 -8.66229 -1
2591 | -13.6146 0.169764 1
2592 | 33.5973 -15.4496 -1
2593 | -19.0227 17.599 1
2594 | -15.004 18.003 1
2595 | 31.3277 -6.21844 -1
2596 | 19.5657 3.98604 1
2597 | 10.9589 23.1798 1
2598 | 10.8493 -8.98278 -1
2599 | -19.4166 10.2044 1
2600 | -4.81706 13.6724 1
2601 | 19.9083 7.23604 1
2602 | -0.964445 5.81991 -1
2603 | 27.0685 -7.89282 -1
2604 | 30.929 -14.0747 -1
2605 | -19.6652 3.23146 1
2606 | 0.717396 18.6659 1
2607 | 4.20068 23.8677 1
2608 | -13.1309 14.9539 1
2609 | 10.5521 21.5575 1
2610 | -0.882071 25.9274 1
2611 | -2.74002 19.1582 1
2612 | -4.71201 22.9884 1
2613 | -5.12953 25.0931 1
2614 | -14.241 3.33206 1
2615 | 13.8912 -11.9137 -1
2616 | 35.7356 6.78759 -1
2617 | -3.50949 1.4707 -1
2618 | -13.5601 12.0726 1
2619 | 7.16563 12.061 1
2620 | -18.9418 9.92362 1
2621 | 32.538 -12.5367 -1
2622 | 21.7265 -10.7547 -1
2623 | 14.3326 4.11787 1
2624 | -23.5095 5.5293 1
2625 | -11.3019 12.048 1
2626 | 16.7767 11.0561 1
2627 | 30.2474 -14.8557 -1
2628 | 5.29168 24.8377 1
2629 | -21.257 10.5864 1
2630 | -0.122095 -7.81252 -1
2631 | -26.3334 0.557614 1
2632 | 5.46784 21.7018 1
2633 | -1.21408 17.6817 1
2634 | 44.1278 -1.59254 -1
2635 | -1.26961 4.96141 -1
2636 | 15.155 1.69897 1
2637 | 21.0167 2.91151 1
2638 | -19.6971 16.06 1
2639 | 27.5225 -13.4514 -1
2640 | -7.39177 17.3605 1
2641 | -19.0858 10.6365 1
2642 | 25.6972 -13.9666 -1
2643 | -17.5998 0.330854 1
2644 | 27.5674 -5.16649 -1
2645 | 8.85241 13.7279 1
2646 | 19.85 3.90465 1
2647 | 4.15825 18.1495 1
2648 | 3.61159 14.8556 1
2649 | 10.6923 13.9686 1
2650 | 36.7679 2.17307 -1
2651 | 8.2684 12.0778 1
2652 | 24.0796 -15.4845 -1
2653 | -3.82136 22.0725 1
2654 | 19.1443 9.7983 1
2655 | 10.3544 11.6222 1
2656 | 14.3672 21.6839 1
2657 | -14.4539 16.7992 1
2658 | 6.80488 -1.93551 -1
2659 | 8.32094 15.3984 1
2660 | 0.575097 18.5555 1
2661 | 6.86906 -7.95393 -1
2662 | 1.36363 1.26171 -1
2663 | 3.5299 0.818759 -1
2664 | -23.0555 10.3377 1
2665 | -10.2339 12.8802 1
2666 | 16.2954 8.45257 1
2667 | 7.47154 21.356 1
2668 | 5.33991 24.8478 1
2669 | 4.6757 3.5807 -1
2670 | -3.27052 14.421 1
2671 | 32.7438 -9.75162 -1
2672 | 7.77718 0.0540344 -1
2673 | 36.7672 -11.1763 -1
2674 | 1.25649 17.2682 1
2675 | -3.64044 0.200313 -1
2676 | 15.2698 -11.8098 -1
2677 | 14.5661 21.8283 1
2678 | 16.7295 -7.42102 -1
2679 | -20.9644 1.18009 1
2680 | 20.8187 5.44817 1
2681 | 8.21047 12.9746 1
2682 | 22.1751 -11.6602 -1
2683 | 1.03059 26.3421 1
2684 | 4.67694 -6.58875 -1
2685 | 12.6082 -10.3605 -1
2686 | 6.00218 14.2848 1
2687 | 34.0481 -4.78269 -1
2688 | 4.93285 2.4803 -1
2689 | 8.31136 23.038 1
2690 | -14.5418 10.5599 1
2691 | -16.9146 18.1448 1
2692 | -19.2962 13.0377 1
2693 | 16.1786 -15.0725 -1
2694 | 12.0508 -10.089 -1
2695 | -21.6832 5.97398 1
2696 | -7.94917 17.089 1
2697 | 14.0481 11.7827 1
2698 | 9.76369 -11.1141 -1
2699 | 13.1063 12.0996 1
2700 | 35.4835 -9.49271 -1
2701 | 18.5499 -7.31418 -1
2702 | 17.3589 1.26861 1
2703 | 1.37933 4.95561 -1
2704 | -3.14938 -4.38558 -1
2705 | 16.4698 18.4311 1
2706 | 5.76134 6.73229 -1
2707 | 41.0688 -3.55325 -1
2708 | -2.47642 -3.57446 -1
2709 | 0.5397 -6.46202 -1
2710 | -5.2853 25.569 1
2711 | 9.77782 23.3031 1
2712 | -13.2686 3.04122 1
2713 | 32.7316 -7.20067 -1
2714 | -9.50604 21.9237 1
2715 | -15.9305 13.0377 1
2716 | -12.7893 9.38709 1
2717 | 11.5007 21.2489 1
2718 | 4.17579 -4.89783 -1
2719 | -15.3573 5.19036 1
2720 | -0.142987 20.3191 1
2721 | 11.4482 8.05402 1
2722 | 10.8755 22.2263 1
2723 | -7.3151 18.9275 1
2724 | 13.2927 -17.1774 -1
2725 | 26.5694 -9.41218 -1
2726 | -24.5407 1.18492 1
2727 | -2.32972 14.8088 1
2728 | 5.06696 13.2889 1
2729 | 10.6825 -16.9545 -1
2730 | 8.45341 11.8083 1
2731 | 10.1315 -16.892 -1
2732 | 3.91485 23.4231 1
2733 | 2.65069 16.2219 1
2734 | 7.23926 2.54158 -1
2735 | 2.37073 2.71498 -1
2736 | 21.529 3.25547 1
2737 | 18.81 -8.64099 -1
2738 | -5.89552 6.07389 -1
2739 | 3.87918 -2.43953 -1
2740 | -9.75314 15.9957 1
2741 | 0.230748 -1.95988 -1
2742 | -7.39629 12.29 1
2743 | 2.93299 -0.166943 -1
2744 | 6.03421 18.2327 1
2745 | 10.3639 -12.4274 -1
2746 | 36.0824 1.39475 -1
2747 | 10.5257 -14.949 -1
2748 | -20.7822 4.9059 1
2749 | 23.9342 -18.0108 -1
2750 | 5.11366 -0.958658 -1
2751 | 16.384 19.0736 1
2752 | -7.1703 23.393 1
2753 | -10.2363 18.1141 1
2754 | 12.2388 -8.31871 -1
2755 | 40.5947 3.79494 -1
2756 | -18.5446 14.5595 1
2757 | 17.0835 -17.2841 -1
2758 | -23.4422 0.26052 1
2759 | 14.4538 -18.5218 -1
2760 | 34.0636 -8.4828 -1
2761 | -5.69283 20.4815 1
2762 | 2.85183 2.23199 -1
2763 | 4.48499 6.71154 -1
2764 | 18.5572 -9.97507 -1
2765 | 13.561 -7.99183 -1
2766 | 36.3616 -8.68994 -1
2767 | 13.175 10.012 1
2768 | 13.7735 -11.1858 -1
2769 | -3.91816 19.9003 1
2770 | 39.5175 2.07887 -1
2771 | 16.3071 -7.12883 -1
2772 | 40.5216 -3.44747 -1
2773 | 25.0256 2.03708 1
2774 | 32.7807 -3.57316 -1
2775 | 9.37744 14.216 1
2776 | 2.95547 16.7987 1
2777 | 36.5394 -11.5277 -1
2778 | 15.942 -7.25109 -1
2779 | 24.2007 -16.8677 -1
2780 | 0.172274 -2.59502 -1
2781 | 44.9954 5.12368 -1
2782 | 22.7475 -10.5223 -1
2783 | 36.7659 -4.493 -1
2784 | 35.0145 -0.0456576 -1
2785 | 46.2915 5.59458 -1
2786 | 34.5762 -1.67448 -1
2787 | 40.3792 -5.08619 -1
2788 | -12.8337 23.1641 1
2789 | 36.7008 -9.35596 -1
2790 | -3.9999 13.9141 1
2791 | -17.2264 10.8741 1
2792 | -15.3855 12.6567 1
2793 | -4.91662 16.3787 1
2794 | 5.18881 2.53529 -1
2795 | 38.6109 -7.64526 -1
2796 | 22.6716 -16.1897 -1
2797 | 7.84102 -13.6124 -1
2798 | 3.14313 -7.10795 -1
2799 | -8.48264 10.969 1
2800 | 28.75 -13.166 -1
2801 | 17.9298 4.6244 1
2802 | 0.211946 22.6551 1
2803 | 3.9571 0.588265 -1
2804 | 5.35552 14.0835 1
2805 | 30.5521 -14.5575 -1
2806 | 5.75903 3.66794 -1
2807 | 28.2143 -16.6553 -1
2808 | 16.9422 7.71024 1
2809 | 6.55652 -4.1127 -1
2810 | 16.0818 -12.9003 -1
2811 | 31.1778 -6.61804 -1
2812 | 36.9897 1.8763 -1
2813 | -20.009 0.329442 1
2814 | 13.8964 2.46393 1
2815 | 23.8097 -10.2949 -1
2816 | -3.11475 3.16305 -1
2817 | 18.139 3.78823 1
2818 | 16.1512 8.48789 1
2819 | 3.93297 17.4497 1
2820 | 9.14655 22.8253 1
2821 | 6.19167 15.492 1
2822 | 35.6555 5.89395 -1
2823 | 30.7657 -2.49367 -1
2824 | 4.26837 -8.44256 -1
2825 | 39.4212 0.167052 -1
2826 | 40.6206 6.38323 -1
2827 | 27.5616 -6.74187 -1
2828 | -8.23856 19.3253 1
2829 | 9.85177 21.9105 1
2830 | -11.3304 15.216 1
2831 | -10.3423 12.1183 1
2832 | 30.3544 -4.62221 -1
2833 | 22.4162 -18.3455 -1
2834 | -0.213567 21.045 1
2835 | -15.9187 2.85415 1
2836 | -13.5261 5.51183 1
2837 | 41.2176 5.4633 -1
2838 | 15.4786 7.2094 1
2839 | 19.5856 7.86789 1
2840 | 41.8139 2.84946 -1
2841 | -3.99641 3.5123 -1
2842 | 6.3498 0.211105 -1
2843 | 0.192102 17.2338 1
2844 | -1.19 15.641 1
2845 | 23.5919 1.35662 1
2846 | -11.933 7.02483 1
2847 | 16.5918 5.45492 1
2848 | -11.4122 8.17141 1
2849 | 12.5156 -4.48065 -1
2850 | 3.46715 1.7926 -1
2851 | -0.039262 15.905 1
2852 | -25.4166 4.11118 1
2853 | 2.74748 17.5223 1
2854 | 15.3624 20.2977 1
2855 | 39.3212 -5.08078 -1
2856 | 9.91772 10.9264 1
2857 | 25.7782 -17.2925 -1
2858 | 36.0421 2.85057 -1
2859 | -15.6029 9.59133 1
2860 | 14.6361 9.75789 1
2861 | -9.5655 11.1876 1
2862 | -13.6502 6.7889 1
2863 | -3.76595 18.8418 1
2864 | -2.15888 -0.495258 -1
2865 | -0.782181 2.0941 -1
2866 | 24.4166 -8.436 -1
2867 | 13.4739 -9.65774 -1
2868 | -14.2985 10.6645 1
2869 | 19.4568 16.4151 1
2870 | 21.6226 2.15069 1
2871 | 21.7561 -13.3625 -1
2872 | 8.54868 -1.08415 -1
2873 | 16.1755 1.00546 1
2874 | 36.4819 -1.1705 -1
2875 | -6.69183 17.8127 1
2876 | 16.7672 18.1763 1
2877 | 1.58553 6.63756 -1
2878 | -13.7897 4.26537 1
2879 | 19.7903 -16.9626 -1
2880 | 33.4673 2.32903 -1
2881 | -1.42778 23.2597 1
2882 | 27.7681 -10.3237 -1
2883 | 4.13795 24.3159 1
2884 | 19.7889 3.19256 1
2885 | -13.0142 12.4423 1
2886 | 2.0198 -6.84097 -1
2887 | 0.828081 15.3187 1
2888 | 29.6101 -16.4547 -1
2889 | 13.0594 4.90846 1
2890 | 34.418 -7.87862 -1
2891 | 29.8218 -11.8482 -1
2892 | 24.2055 -6.86536 -1
2893 | 35.3624 -13.2977 -1
2894 | 19.0642 -9.32822 -1
2895 | 9.66685 20.6248 1
2896 | 12.4574 -5.75721 -1
2897 | 5.37675 16.5532 1
2898 | -16.8569 14.108 1
2899 | 20.2218 -18.1264 -1
2900 | 7.98336 19.6202 1
2901 | -15.2463 0.067316 1
2902 | 15.4835 16.4927 1
2903 | -11.4458 20.5051 1
2904 | 9.19399 -1.81162 -1
2905 | 31.873 -9.35419 -1
2906 | 32.0111 -0.795502 -1
2907 | -20.4564 5.83749 1
2908 | 21.547 15.0258 1
2909 | 31.6224 -1.8428 -1
2910 | 1.24437 15.7512 1
2911 | 45.5508 4.43249 -1
2912 | 39.9083 -0.236043 -1
2913 | -7.19906 21.2657 1
2914 | 7.56742 12.1665 1
2915 | 7.45863 22.8475 1
2916 | 9.32367 -5.96754 -1
2917 | 28.4534 -4.80828 -1
2918 | 1.14594 2.11288 -1
2919 | 25.5729 -17.8134 -1
2920 | 15.008 10.2736 1
2921 | 27.914 -15.99 -1
2922 | -12.8676 13.9219 1
2923 | 19.7864 -14.045 -1
2924 | -3.25673 20.44 1
2925 | 4.22799 0.900619 -1
2926 | 25.9922 0.667961 1
2927 | 20.3977 14.9401 1
2928 | 5.82337 -7.32614 -1
2929 | 31.8281 -6.92362 -1
2930 | 20.5947 3.20506 1
2931 | -20.4564 0.589288 1
2932 | -12.1901 6.74977 1
2933 | 20.7174 -11.6659 -1
2934 | 17.4324 5.09177 1
2935 | 40.733 -5.7165 -1
2936 | -17.1482 4.76801 1
2937 | -4.17876 -3.79597 -1
2938 | 8.0776 -12.4701 -1
2939 | -3.7307 21.1354 1
2940 | -13.2612 18.2246 1
2941 | -17.9219 4.27035 1
2942 | -1.70994 -1.04074 -1
2943 | 14.1091 -10.5622 -1
2944 | -17.5174 18.7539 1
2945 | 13.1408 10.0051 1
2946 | -16.0717 5.57864 1
2947 | 10.5501 12.8864 1
2948 | 2.67162 23.1897 1
2949 | 22.928 11.4216 1
2950 | 3.64519 -0.728829 -1
2951 | -17.7365 7.32824 1
2952 | 15.7919 4.70953 1
2953 | 13.5668 1.85696 1
2954 | 21.0166 -15.3054 -1
2955 | 12.8297 -16.393 -1
2956 | -12.2228 6.94597 1
2957 | 43.6644 3.23374 -1
2958 | 17.26 -12.1582 -1
2959 | 20.8961 -13.0743 -1
2960 | 11.4503 22.5962 1
2961 | 33.6956 -6.6929 -1
2962 | -4.06314 1.30722 -1
2963 | 32.3434 -8.67394 -1
2964 | -15.772 6.09938 1
2965 | 13.5973 22.4496 1
2966 | 20.7781 9.70134 1
2967 | 19.6534 3.6518 1
2968 | -12.2336 18.5899 1
2969 | 30.3053 -4.60146 -1
2970 | 13.1491 3.2956 1
2971 | 0.858038 -9.29352 -1
2972 | 14.6729 17.1732 1
2973 | -3.05548 -3.33771 -1
2974 | 26.5642 -16.1559 -1
2975 | 25.2484 6.08279 1
2976 | 10.6307 12.2486 1
2977 | 17.0712 -17.3169 -1
2978 | 39.8231 -3.96797 -1
2979 | -1.52284 16.0231 1
2980 | 2.20894 -6.45743 -1
2981 | 45.7157 6.97893 -1
2982 | -7.33746 21.405 1
2983 | 15.4667 -17.1518 -1
2984 | 5.45816 -3.55986 -1
2985 | 3.50018 16.6331 1
2986 | -7.91974 19.604 1
2987 | 13.0281 7.84235 1
2988 | -16.299 2.42801 1
2989 | -19.445 4.5037 1
2990 | 30.5593 -3.94602 -1
2991 | 17.762 14.2208 1
2992 | -7.95882 16.2333 1
2993 | -4.5917 23.0215 1
2994 | -4.38526 19.5416 1
2995 | 12.4387 -8.87826 -1
2996 | 2.44615 1.03511 -1
2997 | 26.7448 -13.0181 -1
2998 | -17.6293 4.28502 1
2999 | 41.3819 6.03199 -1
3000 | 28.8535 -9.15178 -1
3001 | 


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