正新26x1.95轮胎x 五分之一x=5.95
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时间：2016-07-15 13:34
自行车外胎尺寸26X1.95，指的是什么尺寸？_百度知道
自行车外胎尺寸26X1.95，指的是什么尺寸？
指的是轮子大小的尺寸。1、26是轮子的直径，单位是英寸，1.95指的是轮子外胎的宽度。2、如果需要进行更换，必须要知道外胎尺寸，换个相符的轮胎才能使用。
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提问者采纳
15轮胎的基础上直接换的26×1.95指的是轮子外胎的宽度如果要跟换外胎的话，宽度相近就可以.5的光头外胎26是轮子的直径，单位是英寸（英寸和毫米的换算是1英寸=25.4毫米）1，直径上是一定要26的，可以换更宽或更窄的。我就曾经在原来26×2
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其他1条回答
95的最好是买26X1.95的，因为山地车都是26X1，也没见过换别的尺寸的
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网站备案 : 蜀ICP备号-4项目语言:PYTHON
权限:read-only(如需更高权限请先加入项目)
Index: kernal_ridge_reg.py
===================================================================
--- kernal_ridge_reg.py (revision 0)
+++ kernal_ridge_reg.py (revision 1)
@@ -0,0 +1,61 @@
+import numpy as np
+from numpy import linalg as la
+from numpy import matlib as mt
+def load(filename):
data = np.loadtxt(filename)
train = data[:400,:]
test = data[400:,:]
return (train,test)
+def parse_data(data):
x = data[:,:-1]
y = data[:,-1]
return x,y
+def kernal(x,gamma):
n = x.shape[0]
k = mt.empty((n,n))
for i in range(n):
for j in range(n):
k[i,j] = np.exp(-gamma*la.norm(x[i,:]-x[j,:])**2)
+def lssvm_train(x,y,gamma,lamb):
n = len(y)
K = kernal(x,gamma)
lamb_eye = lamb * mt.eye(n,n)
return (lamb_eye + K).I.dot(y)
+def predictor(model,x):
(betas,gamma,features) = model
x_norm = np.array(map(la.norm,(features - x)))
nf = np.exp(-gamma*x_norm**2)
result = betas.dot(nf)
if result & 0: return 1
else: return -1
+def lssvm_test(x,y,model):
predict = np.array([predictor(model,feat) for feat in x])
label = predict & 0
answer = y & 0
return sum([1 for i in range(len(y)) if label[i] != answer[i]]) / float(len(y))
+if __name__ == '__main__':
(train,test) = load('hw2_lssvm_all.dat')
train_x,train_y = parse_data(train)
test_x,test_y = parse_data(test)
gamma = [32,2,0.125]
lamb = [0.001,1,1000]
e_out = []
for g in gamma:
for l in lamb:
betas = lssvm_train(train_x,train_y,g,l)
e_in.append(lssvm_test(train_x,train_y,(betas,g,train_x)))
e_out.append(lssvm_test(test_x,test_y,(betas,g,train_x)))
print &finish...........gamma: &+str(g)+&
lambda: &+str(l)
print e_in
print e_out
Index: adaboost_decisionstumps.py
===================================================================
--- adaboost_decisionstumps.py (revision 0)
+++ adaboost_decisionstumps.py (revision 1)
@@ -0,0 +1,144 @@
+from math import *
+import numpy as np
+def load_file(file_name):
data = np.loadtxt(file_name)
data_x = np.array([data[:,0],data[:,1]]).T
data_y = data[:,2]
return (data_x,data_y)
+def desion_stump(s,x,threshold):
def sign(x,threshold):
if x &= threshold:return 1
else:return -1
return s * sign(x,threshold)
+def cal_err(i,s,threshold,data_weights,train_y):
return sum([data_weights[j] for j in range(len(train_y))
if desion_stump(s,train_x[j,i],threshold) != train_y[j]]) / sum(data_weights)
+### naively implementation takes O(N^2)
+def get_decision_stump(train_x,train_y,data_weights):
best_err,best_s,best_threshold,best_i = len(train_x[:,0]),0,0,0
for i in range(train_x.shape[1]):
temp = train_x[:,i].copy()
temp.sort()
thresholds = [temp[0]-1] + [(temp[k]+temp[k+1])/2 for k in range(len(temp)-1)]
for s in [-1,1]:
for threshold in thresholds:
err = cal_err(i,s,threshold,data_weights,train_y)
if best_err &= err:
best_err = err
best_s = s
best_threshold = threshold
best_i = i
return (best_s,best_i,best_threshold,best_err)
+### clever way of implementation takes only O(Nlog(N))!!
+def get_decision_stump_clever(train_x,train_y,data_weights):
best_err,best_s,best_threshold,best_i = len(train_x[:,0]),0,0,0
for i in range(train_x.shape[1]):
# create a sorted temp list from ith feature of train_x
# zipped with train_y and data_weights to form a triple list,
# note that this step is extremely important!
temp_w = data_weights.copy()
temp_x = train_x[:,i].copy()
temp_y = train_y.copy()
temp = zip(temp_x,temp_y,temp_w)
temp.sort()
err_base = sum(data_weights[train_y & 0])
error_num = len(data_weights[train_y & 0])
threshold_base = temp[0][0] - 1000 # less than any feature value
thresholds = [(temp[k][0]+temp[k+1][0])/2 for k in range(len(temp)-1)]
errors = [err_base]
rec_num = 0
for j in range(len(thresholds)):
if temp[j][1] == 1: # we made a mistake this time, so punished by its weight accordingly
errors.append(errors[j] + temp[j][2])
errors.append(errors[j] - temp[j][2])
rec_num = rec_num + 1
assert error_num &= rec_num
total_weights = sum(data_weights)
norm_errors = map(lambda x : x / total_weights,errors)
positive_min_err = min(norm_errors)
negative_min_err = 1-max(norm_errors)
if positive_min_err & negative_min_err:
cur_best_err = (positive_min_err,1)
cur_best_err = (negative_min_err,-1)
if best_err & cur_best_err[0]:
(best_err,best_s) = cur_best_err
best_i = i
temp_err = best_err
if best_s == -1:
temp_err = 1 - best_err
thresholds = [threshold_base] + thresholds
best_threshold = thresholds[norm_errors.index(temp_err)]
return (best_s,best_i,best_threshold,best_err)
+def train_adaboost(train_x,train_y,T):
data_weights = np.ones(len(train_y)) * 0.01
sum_weights = [1]
alphas = []
models = []
for t in range(T):
(s,i,theta,err) = get_decision_stump_clever(train_x,train_y,data_weights)
errs.append(err)
err_ratio = sqrt((1-err)/err)
for index in range(len(data_weights)):
if(desion_stump(s,train_x[index,i],theta) != train_y[index]):
data_weights[index] = data_weights[index] * err_ratio
data_weights[index] = data_weights[index] / err_ratio
sum_weights.append(sum(data_weights))
alphas.append(log(err_ratio))
models.append((s,i,theta))
return (models,alphas)
+def test_adaboost(test_x,test_y,models,alphas):
def sign(value):
if value & 0:return 1
else: return -1
for index in range(len(test_y)):
value = sum([alpha * desion_stump(s,test_x[index,i],theta)
for (alpha,(s,i,theta)) in zip(alphas,models)])
if sign(value) != test_y[index]:
err = err + 1
return float(err)/len(test_y)
+def test_decision_stump(test_x,test_y,model):
(s,i,theta) = model
err = sum([1 for j in range(len(test_y)) if desion_stump(s,test_x[j,i],theta) != test_y[j]])
return float(err)/len(test_y)
+if __name__ == '__main__':
(train_x,train_y) = load_file('hw2_adaboost_train.dat')
(models,alphas) = train_adaboost(train_x,train_y,300)
(test_x,test_y) = load_file('hw2_adaboost_test.dat')
print(test_adaboost(test_x,test_y,models,alphas))
print(test_decision_stump(test_x,test_y,models[0]))
Index: hw2_adaboost_train.dat
===================================================================
--- hw2_adaboost_train.dat (revision 0)
+++ hw2_adaboost_train.dat (revision 1)
@@ -0,0 +1,100 @@
+0..50076 -1
+0..43115 -1
+0..90924 -1
+0..59799 1
+0..82293 -1
+0..68299 -1
+0..77369 -1
+0..65833 -1
Index: hw2_adaboost_test.dat
===================================================================
--- hw2_adaboost_test.dat (revision 0)
+++ hw2_adaboost_test.dat (revision 1)
@@ -0,0 +1,1000 @@
+0..56668 1
+0..5639 1
+0..8645 1
+0..62802 -1
+0.6725 -1
+0..2315 -1
+0..38095 1
+0..48784 1
+0..29749 -1
+0..62228 1
+0..63548 -1
+0..67456 -1
+0..80711 -1
+0..63141 1
+0..4751 -1
+0..54639 1
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+0..63122 1
+0..33283 1
+0..96868 -1
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+0..16532 1
+0..26796 1
+0..77697 1
+0..87552 -1
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+0..34167 1
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+0..56206 -1
+0.47877 1
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+0..63859 -1
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+0..23431 1
+0..48991 -1
+0..42871 1
+0..36617 1
+0..61947 -1
+0..87057 -1
+0..99403 -1
+0..39147 1
+0..54697 -1
+0..75973 1
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+0..44017 -1
+0..14355 1
+0..5555 1
+0..15282 1
+0..56916 -1
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+0..947 -1
+0..9081 -1
+0..28806 1
+0..58772 1
+0..18152 1
+0..53794 1
+0..64936 -1
+0..80984 -1
+0..49394 1
+0..31515 1
+0..29146 -1
+0..5995 -1
+0..91632 1
+0..73985 -1
+0..31734 1
+0..21038 1
+0..28045 1
+0..60666 1
+0..79246 -1
+0..59604 1
+0..18986 1
+0..23736 1
+0..42179 1
+0..72881 -1
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+0..88182 -1
+0..83333 -1
+0..82664 -1
+0..6823 1
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+0..69684 -1
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+0..72573 1
+0..36687 -1
+0..2024 -1
+0..47347 1
+0..62246 -1
+0..83112 -1
Index: hw2_lssvm_all.dat
===================================================================
--- hw2_lssvm_all.dat (revision 0)
+++ hw2_lssvm_all.dat (revision 1)
@@ -0,0 +1,500 @@
+ 4.115 5.020 -7.879 -11.780 2.004 -0.353 -0.735 3.561 2.441 -9.822 +1
+ -3.557 0.997 2.932 7.672 5.430 -0.137 1.635 -5.190 -0.394 -7.667 +1
+ 6.417 5.878 5.066 -7.209 -6.953 7.639 -2.937 -1.023 3.963 -11.069 +1
+ -2.247 6.532 6.437 2.293 6.302 2.187 3.429 -3.453 9.172 -4.548 +1
+ 3.708 5.834 3.676 -4.403 -5.296 9.080 -3.110 -3.294 3.189 -8.510 +1
+ -1.586 1.960 -5.506 -8.767 7.871 0.613 -4.693 4.302 -1.219 -8.478 -1
+ -4.181 -6.797 -4.187 8.622 0.771 5.851 -3.893 3.779 4.470 -9.433 +1
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+ 8.124 4.813 3.519 -7.539 -4.723 8.129 -5.165 -3.411 3.552 -9.192 -1
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+ 1.117 -4.978 4.810 0.904 -0.160 1.036 -4.530 -0.242 -9.777 -7.043 -1
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+ 3.821 7.446 0.699 1.459 -6.876 -6.498 -3.382 -7.236 6.155 6.578 -1
+ -2.053 6.469 7.628 0.997 7.178 0.076 7.634 -3.355 8.093 -2.637 +1
+ 2.731 -3.958 4.793 3.704 -1.543 -2.425 -4.486 -0.669 -8.239 -5.808 -1
+ 0.901 -5.275 3.259 2.488 -1.280 -2.095 -2.361 -0.094 -9.157 -5.251 -1
+ -2.324 -5.869 -4.172 6.665 1.345 6.075 -3.961 2.915 2.340 -9.644 +1
+ -3.868 -6.295 -5.345 7.480 2.391 4.763 -4.843 2.769 2.530 -10.609 +1
+ -1.337 6.944 8.291 2.470 7.046 2.190 6.909 -1.755 8.896 -0.916 +1
+ 6.859 4.944 5.298 -5.680 -7.393 9.021 -2.919 -2.478 2.822 -9.414 +1
+ -4.426 -8.947 -3.580 7.756 1.233 5.047 -3.381 2.917 3.072 -10.570 -1
+ 7.414 6.362 4.286 -5.987 -5.187 13.042 -3.300 -1.014 2.268 -11.366 +1
+ 4.024 4.688 1.802 1.577 -7.680 -3.861 -4.391 -6.246 6.351 6.383 -1
+ -4.915 -6.687 -2.742 7.010 1.958 4.891 -4.081 2.878 -0.622 -10.494 +1
+ 5.312 3.225 -7.290 -10.544 1.038 1.133 0.454 3.726 2.000 -5.998 -1
+ -5.886 2.038 -1.136 7.281 5.380 -2.020 1.388 -7.267 -2.814 -8.637 -1
+ -4.278 -6.023 -4.698 7.758 0.745 3.107 -3.645 3.498 1.437 -9.395 +1
+ 5.554 7.105 3.943 -5.502 -4.628 8.411 -4.894 -1.375 3.913 -9.855 +1
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自行车外胎尺寸26X1.95，指的是什么尺寸？
指的是轮子大小的尺寸。1、26是轮子的直径，单位是英寸，1.95指的是轮子外胎的宽度。2、如果需要进行更换，必须要知道外胎尺寸，换个相符的轮胎才能使用。
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提问者采纳
15轮胎的基础上直接换的26×1.95指的是轮子外胎的宽度如果要跟换外胎的话，宽度相近就可以.5的光头外胎26是轮子的直径，单位是英寸（英寸和毫米的换算是1英寸=25.4毫米）1，直径上是一定要26的，可以换更宽或更窄的。我就曾经在原来26×2
提问者评价
其他1条回答
95的最好是买26X1.95的，因为山地车都是26X1，也没见过换别的尺寸的
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出门在外也不愁磁力搜()是一个基于DHT协议的BT磁力搜索引擎，所有资源均来自DHT网络。磁力搜索服务器仅收录磁力链接信息，并不存储任何资源和种子文件。因此经由本站搜索所产生的任何结果皆不代表本站立场，本站不对其真实合法性以及版权负责，亦不承担任何法律责任。
磁力链接()是一种新型的分享形式，每个链接对应一个BT种子文件。您可以通过软件下载磁力链接指向的资源，例如迅雷、BitComet、QQ旋风，也可以使用云点播服务在线观看影视资源，例如迅雷云播、百度云盘等。
网站备案 : 蜀ICP备号-4项目语言:PYTHON
权限:read-only(如需更高权限请先加入项目)
Index: kernal_ridge_reg.py
===================================================================
--- kernal_ridge_reg.py (revision 0)
+++ kernal_ridge_reg.py (revision 1)
@@ -0,0 +1,61 @@
+import numpy as np
+from numpy import linalg as la
+from numpy import matlib as mt
+def load(filename):
data = np.loadtxt(filename)
train = data[:400,:]
test = data[400:,:]
return (train,test)
+def parse_data(data):
x = data[:,:-1]
y = data[:,-1]
return x,y
+def kernal(x,gamma):
n = x.shape[0]
k = mt.empty((n,n))
for i in range(n):
for j in range(n):
k[i,j] = np.exp(-gamma*la.norm(x[i,:]-x[j,:])**2)
+def lssvm_train(x,y,gamma,lamb):
n = len(y)
K = kernal(x,gamma)
lamb_eye = lamb * mt.eye(n,n)
return (lamb_eye + K).I.dot(y)
+def predictor(model,x):
(betas,gamma,features) = model
x_norm = np.array(map(la.norm,(features - x)))
nf = np.exp(-gamma*x_norm**2)
result = betas.dot(nf)
if result & 0: return 1
else: return -1
+def lssvm_test(x,y,model):
predict = np.array([predictor(model,feat) for feat in x])
label = predict & 0
answer = y & 0
return sum([1 for i in range(len(y)) if label[i] != answer[i]]) / float(len(y))
+if __name__ == '__main__':
(train,test) = load('hw2_lssvm_all.dat')
train_x,train_y = parse_data(train)
test_x,test_y = parse_data(test)
gamma = [32,2,0.125]
lamb = [0.001,1,1000]
e_out = []
for g in gamma:
for l in lamb:
betas = lssvm_train(train_x,train_y,g,l)
e_in.append(lssvm_test(train_x,train_y,(betas,g,train_x)))
e_out.append(lssvm_test(test_x,test_y,(betas,g,train_x)))
print &finish...........gamma: &+str(g)+&
lambda: &+str(l)
print e_in
print e_out
Index: adaboost_decisionstumps.py
===================================================================
--- adaboost_decisionstumps.py (revision 0)
+++ adaboost_decisionstumps.py (revision 1)
@@ -0,0 +1,144 @@
+from math import *
+import numpy as np
+def load_file(file_name):
data = np.loadtxt(file_name)
data_x = np.array([data[:,0],data[:,1]]).T
data_y = data[:,2]
return (data_x,data_y)
+def desion_stump(s,x,threshold):
def sign(x,threshold):
if x &= threshold:return 1
else:return -1
return s * sign(x,threshold)
+def cal_err(i,s,threshold,data_weights,train_y):
return sum([data_weights[j] for j in range(len(train_y))
if desion_stump(s,train_x[j,i],threshold) != train_y[j]]) / sum(data_weights)
+### naively implementation takes O(N^2)
+def get_decision_stump(train_x,train_y,data_weights):
best_err,best_s,best_threshold,best_i = len(train_x[:,0]),0,0,0
for i in range(train_x.shape[1]):
temp = train_x[:,i].copy()
temp.sort()
thresholds = [temp[0]-1] + [(temp[k]+temp[k+1])/2 for k in range(len(temp)-1)]
for s in [-1,1]:
for threshold in thresholds:
err = cal_err(i,s,threshold,data_weights,train_y)
if best_err &= err:
best_err = err
best_s = s
best_threshold = threshold
best_i = i
return (best_s,best_i,best_threshold,best_err)
+### clever way of implementation takes only O(Nlog(N))!!
+def get_decision_stump_clever(train_x,train_y,data_weights):
best_err,best_s,best_threshold,best_i = len(train_x[:,0]),0,0,0
for i in range(train_x.shape[1]):
# create a sorted temp list from ith feature of train_x
# zipped with train_y and data_weights to form a triple list,
# note that this step is extremely important!
temp_w = data_weights.copy()
temp_x = train_x[:,i].copy()
temp_y = train_y.copy()
temp = zip(temp_x,temp_y,temp_w)
temp.sort()
err_base = sum(data_weights[train_y & 0])
error_num = len(data_weights[train_y & 0])
threshold_base = temp[0][0] - 1000 # less than any feature value
thresholds = [(temp[k][0]+temp[k+1][0])/2 for k in range(len(temp)-1)]
errors = [err_base]
rec_num = 0
for j in range(len(thresholds)):
if temp[j][1] == 1: # we made a mistake this time, so punished by its weight accordingly
errors.append(errors[j] + temp[j][2])
errors.append(errors[j] - temp[j][2])
rec_num = rec_num + 1
assert error_num &= rec_num
total_weights = sum(data_weights)
norm_errors = map(lambda x : x / total_weights,errors)
positive_min_err = min(norm_errors)
negative_min_err = 1-max(norm_errors)
if positive_min_err & negative_min_err:
cur_best_err = (positive_min_err,1)
cur_best_err = (negative_min_err,-1)
if best_err & cur_best_err[0]:
(best_err,best_s) = cur_best_err
best_i = i
temp_err = best_err
if best_s == -1:
temp_err = 1 - best_err
thresholds = [threshold_base] + thresholds
best_threshold = thresholds[norm_errors.index(temp_err)]
return (best_s,best_i,best_threshold,best_err)
+def train_adaboost(train_x,train_y,T):
data_weights = np.ones(len(train_y)) * 0.01
sum_weights = [1]
alphas = []
models = []
for t in range(T):
(s,i,theta,err) = get_decision_stump_clever(train_x,train_y,data_weights)
errs.append(err)
err_ratio = sqrt((1-err)/err)
for index in range(len(data_weights)):
if(desion_stump(s,train_x[index,i],theta) != train_y[index]):
data_weights[index] = data_weights[index] * err_ratio
data_weights[index] = data_weights[index] / err_ratio
sum_weights.append(sum(data_weights))
alphas.append(log(err_ratio))
models.append((s,i,theta))
return (models,alphas)
+def test_adaboost(test_x,test_y,models,alphas):
def sign(value):
if value & 0:return 1
else: return -1
for index in range(len(test_y)):
value = sum([alpha * desion_stump(s,test_x[index,i],theta)
for (alpha,(s,i,theta)) in zip(alphas,models)])
if sign(value) != test_y[index]:
err = err + 1
return float(err)/len(test_y)
+def test_decision_stump(test_x,test_y,model):
(s,i,theta) = model
err = sum([1 for j in range(len(test_y)) if desion_stump(s,test_x[j,i],theta) != test_y[j]])
return float(err)/len(test_y)
+if __name__ == '__main__':
(train_x,train_y) = load_file('hw2_adaboost_train.dat')
(models,alphas) = train_adaboost(train_x,train_y,300)
(test_x,test_y) = load_file('hw2_adaboost_test.dat')
print(test_adaboost(test_x,test_y,models,alphas))
print(test_decision_stump(test_x,test_y,models[0]))
Index: hw2_adaboost_train.dat
===================================================================
--- hw2_adaboost_train.dat (revision 0)
+++ hw2_adaboost_train.dat (revision 1)
@@ -0,0 +1,100 @@
+0..50076 -1
+0..43115 -1
+0..90924 -1
+0..59799 1
+0..82293 -1
+0..68299 -1
+0..77369 -1
+0..65833 -1
Index: hw2_adaboost_test.dat
===================================================================
--- hw2_adaboost_test.dat (revision 0)
+++ hw2_adaboost_test.dat (revision 1)
@@ -0,0 +1,1000 @@
+0..56668 1
+0..5639 1
+0..8645 1
+0..62802 -1
+0.6725 -1
+0..2315 -1
+0..38095 1
+0..48784 1
+0..29749 -1
+0..62228 1
+0..63548 -1
+0..67456 -1
+0..80711 -1
+0..63141 1
+0..4751 -1
+0..54639 1
+0..41507 -1
+0..63122 1
+0..33283 1
+0..96868 -1
+0..89878 -1
+0..16532 1
+0..26796 1
+0..77697 1
+0..87552 -1
+0..58625 -1
+0..34167 1
+0..51328 -1
+0..56206 -1
+0.47877 1
+0..30652 1
+0..63859 -1
+0..73185 -1
+0..23431 1
+0..48991 -1
+0..42871 1
+0..36617 1
+0..61947 -1
+0..87057 -1
+0..99403 -1
+0..39147 1
+0..54697 -1
+0..75973 1
+0..92312 -1
+0..44017 -1
+0..14355 1
+0..5555 1
+0..15282 1
+0..56916 -1
+0..70981 -1
+0..947 -1
+0..9081 -1
+0..28806 1
+0..58772 1
+0..18152 1
+0..53794 1
+0..64936 -1
+0..80984 -1
+0..49394 1
+0..31515 1
+0..29146 -1
+0..5995 -1
+0..91632 1
+0..73985 -1
+0..31734 1
+0..21038 1
+0..28045 1
+0..60666 1
+0..79246 -1
+0..59604 1
+0..18986 1
+0..23736 1
+0..42179 1
+0..72881 -1
+0..67364 -1
+0..88182 -1
+0..83333 -1
+0..82664 -1
+0..6823 1
+0..36828 1
+0..69684 -1
+0..4744 -1
+0..82055 -1
+0..54545 -1
+0..72573 1
+0..36687 -1
+0..2024 -1
+0..47347 1
+0..62246 -1
+0..83112 -1
Index: hw2_lssvm_all.dat
===================================================================
--- hw2_lssvm_all.dat (revision 0)
+++ hw2_lssvm_all.dat (revision 1)
@@ -0,0 +1,500 @@
+ 4.115 5.020 -7.879 -11.780 2.004 -0.353 -0.735 3.561 2.441 -9.822 +1
+ -3.557 0.997 2.932 7.672 5.430 -0.137 1.635 -5.190 -0.394 -7.667 +1
+ 6.417 5.878 5.066 -7.209 -6.953 7.639 -2.937 -1.023 3.963 -11.069 +1
+ -2.247 6.532 6.437 2.293 6.302 2.187 3.429 -3.453 9.172 -4.548 +1
+ 3.708 5.834 3.676 -4.403 -5.296 9.080 -3.110 -3.294 3.189 -8.510 +1
+ -1.586 1.960 -5.506 -8.767 7.871 0.613 -4.693 4.302 -1.219 -8.478 -1
+ -4.181 -6.797 -4.187 8.622 0.771 5.851 -3.893 3.779 4.470 -9.433 +1
+ -5.589 -7.010 -5.297 7.329 1.872 3.953 -3.425 3.097 1.677 -11.320 +1
+ 8.124 4.813 3.519 -7.539 -4.723 8.129 -5.165 -3.411 3.552 -9.192 -1
+ -7.536 -0.448 -10.633 3.777 0.728 -1.386 -7.756 8.166 -2.979 0.629 -1
+ 2.963 3.801 0.763 4.595 -4.334 -4.267 -0.809 -5.677 6.073 6.625 -1
+ 2.009 4.874 -0.200 3.469 -6.495 -5.364 -1.560 -7.233 6.703 8.024 +1
+ 1.117 -4.978 4.810 0.904 -0.160 1.036 -4.530 -0.242 -9.777 -7.043 -1
+ 2.500 -5.283 2.861 2.062 -1.050 -1.513 -3.469 -0.396 -9.007 -6.550 +1
+ 4.308 5.177 -7.454 -8.630 0.831 1.020 2.347 4.464 3.176 -6.249 +1
+ 2.139 5.880 0.415 2.731 -7.112 -9.191 -2.697 -8.236 6.473 5.862 +1
+ 1.320 -5.006 4.664 2.597 -0.844 -3.269 -4.123 -1.583 -8.507 -6.115 +1
+ 0.900 -4.961 5.714 3.565 -0.242 0.251 -5.140 -1.287 -8.431 -6.103 +1
+ -8.505 -1.866 0.366 3.879 -8.945 -8.063 -0.133 7.214 4.570 -1.740 +1
+ -3.385 -1.143 -5.207 -6.163 9.599 -1.535 -6.582 2.827 -0.883 -6.998 +1
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+ -1.017 5.133 11.423 0.284 6.290 0.421 5.239 -3.398 9.082 -1.691 +1
+ -10.169 -2.113 -2.671 5.976 -6.393 -4.706 1.480 6.266 4.243 -0.733 +1
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