├── LICENSE
├── README.md
├── data-cg
├── 100Homophily-0Heterophily.matrix
├── 20Homophily-80Heterophily.matrix
├── 80Homophily-20Heterophily.matrix
├── 80ToBlue-20ToRed.matrix
├── 80ToRed-20ToBlue.matrix
├── 90Homophily-10Heterophily.matrix
├── ToBlueOnly.matrix
├── ToRedOnly.matrix
├── data.matrix
├── noise-1.0.matrix
├── noise-10.0.matrix
├── noise-100.0.matrix
├── noise-1000.0.matrix
├── noise-5.0.matrix
└── uniform-0.02.matrix
├── data-rg
├── Barabassi-m49.matrix
├── Erdos-Renyi-p0.5.matrix
├── Erdos-Renyi-p1.0.matrix
├── Geometric-r1.0.matrix
├── data.matrix
├── noise-1.0.matrix
├── noise-10.0.matrix
├── noise-100.0.matrix
├── noise-1000.0.matrix
├── noise-5.0.matrix
└── uniform-0.02.matrix
├── data
├── advice.dat
├── crudematerials.dat
├── diplomatic.dat
├── foods.dat
├── friendship.dat
├── info.dat
├── manufacturedgoods.dat
├── minerals.dat
└── money.dat
├── example_biasedwalker.py
├── example_countries.py
├── example_countries_timing_permutations.py
├── example_diplomaticexchange.py
├── example_friendadvice.py
├── example_infomoney.py
├── example_materialsgoods.py
├── example_randomgraphs.py
├── example_synthetic_timing_netsize_nodes_ernos_renyi.py
└── libs
├── __init__.py
├── mrqap.py
├── profiling.py
├── qap.py
└── utils.py
/LICENSE:
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2 |
3 | Statement of Purpose
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/README.md:
--------------------------------------------------------------------------------
1 | # MRQAP Implementation in Python 2
2 | Multiple Regression Quadratic Assignment Procedure
3 |
4 | - This project contains 5 examples:
5 | - 2 MRQAP (more than 1 dependent variables):
6 | - example_countries.py
7 | - example_diplomaticexchange.py
8 | - 3 QAP (only 1 dependent variable)
9 | - example_friendadvice.py
10 | - example_infomoney.py
11 | - example_materialsgoods.py
12 | - All 14 different datasets are in folder "data".
13 | - 9 of them (.dat) are already in a matrix text format
14 | - 5 of them (.txt) contain relationship values (e.g., nodeA nodeB valueAB)
15 |
16 |
17 | ### Other versions:
18 | - Python3 [here](https://github.com/lisette-espin/mrqap-python/tree/p3).
19 | - R [here](https://github.com/lisette-espin/mrqap-r "MRQAP in R").
20 |
--------------------------------------------------------------------------------
/data-cg/ToBlueOnly.matrix:
--------------------------------------------------------------------------------
1 | 0.000,1.000,0.000,1.000,0.000,1.000,1.000,1.000,1.000,0.000,1.000,0.000,1.000,1.000,1.000,0.000,1.000,0.000,1.000,0.000,0.000,1.000,1.000,0.000,1.000,1.000,0.000,1.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,1.000,0.000,0.000,1.000,0.000,0.000,0.000,1.000,1.000,0.000,0.000,0.000,0.000,0.000
2 | 1.000,0.000,0.000,1.000,0.000,1.000,1.000,1.000,1.000,0.000,1.000,0.000,1.000,1.000,1.000,0.000,1.000,0.000,1.000,0.000,0.000,1.000,1.000,0.000,1.000,1.000,0.000,1.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,1.000,0.000,0.000,1.000,0.000,0.000,0.000,1.000,1.000,0.000,0.000,0.000,0.000,0.000
3 | 1.000,1.000,0.000,1.000,0.000,1.000,1.000,1.000,1.000,0.000,1.000,0.000,1.000,1.000,1.000,0.000,1.000,0.000,1.000,0.000,0.000,1.000,1.000,0.000,1.000,1.000,0.000,1.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,1.000,0.000,0.000,1.000,0.000,0.000,0.000,1.000,1.000,0.000,0.000,0.000,0.000,0.000
4 | 1.000,1.000,0.000,0.000,0.000,1.000,1.000,1.000,1.000,0.000,1.000,0.000,1.000,1.000,1.000,0.000,1.000,0.000,1.000,0.000,0.000,1.000,1.000,0.000,1.000,1.000,0.000,1.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,1.000,0.000,0.000,1.000,0.000,0.000,0.000,1.000,1.000,0.000,0.000,0.000,0.000,0.000
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8 | 1.000,1.000,0.000,1.000,0.000,1.000,1.000,0.000,1.000,0.000,1.000,0.000,1.000,1.000,1.000,0.000,1.000,0.000,1.000,0.000,0.000,1.000,1.000,0.000,1.000,1.000,0.000,1.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,1.000,0.000,0.000,1.000,0.000,0.000,0.000,1.000,1.000,0.000,0.000,0.000,0.000,0.000
9 | 1.000,1.000,0.000,1.000,0.000,1.000,1.000,1.000,0.000,0.000,1.000,0.000,1.000,1.000,1.000,0.000,1.000,0.000,1.000,0.000,0.000,1.000,1.000,0.000,1.000,1.000,0.000,1.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,1.000,0.000,0.000,1.000,0.000,0.000,0.000,1.000,1.000,0.000,0.000,0.000,0.000,0.000
10 | 1.000,1.000,0.000,1.000,0.000,1.000,1.000,1.000,1.000,0.000,1.000,0.000,1.000,1.000,1.000,0.000,1.000,0.000,1.000,0.000,0.000,1.000,1.000,0.000,1.000,1.000,0.000,1.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,1.000,0.000,0.000,1.000,0.000,0.000,0.000,1.000,1.000,0.000,0.000,0.000,0.000,0.000
11 | 1.000,1.000,0.000,1.000,0.000,1.000,1.000,1.000,1.000,0.000,0.000,0.000,1.000,1.000,1.000,0.000,1.000,0.000,1.000,0.000,0.000,1.000,1.000,0.000,1.000,1.000,0.000,1.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,1.000,0.000,0.000,1.000,0.000,0.000,0.000,1.000,1.000,0.000,0.000,0.000,0.000,0.000
12 | 1.000,1.000,0.000,1.000,0.000,1.000,1.000,1.000,1.000,0.000,1.000,0.000,1.000,1.000,1.000,0.000,1.000,0.000,1.000,0.000,0.000,1.000,1.000,0.000,1.000,1.000,0.000,1.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,1.000,0.000,0.000,1.000,0.000,0.000,0.000,1.000,1.000,0.000,0.000,0.000,0.000,0.000
13 | 1.000,1.000,0.000,1.000,0.000,1.000,1.000,1.000,1.000,0.000,1.000,0.000,0.000,1.000,1.000,0.000,1.000,0.000,1.000,0.000,0.000,1.000,1.000,0.000,1.000,1.000,0.000,1.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,1.000,0.000,0.000,1.000,0.000,0.000,0.000,1.000,1.000,0.000,0.000,0.000,0.000,0.000
14 | 1.000,1.000,0.000,1.000,0.000,1.000,1.000,1.000,1.000,0.000,1.000,0.000,1.000,0.000,1.000,0.000,1.000,0.000,1.000,0.000,0.000,1.000,1.000,0.000,1.000,1.000,0.000,1.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,1.000,0.000,0.000,1.000,0.000,0.000,0.000,1.000,1.000,0.000,0.000,0.000,0.000,0.000
15 | 1.000,1.000,0.000,1.000,0.000,1.000,1.000,1.000,1.000,0.000,1.000,0.000,1.000,1.000,0.000,0.000,1.000,0.000,1.000,0.000,0.000,1.000,1.000,0.000,1.000,1.000,0.000,1.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,1.000,0.000,0.000,1.000,0.000,0.000,0.000,1.000,1.000,0.000,0.000,0.000,0.000,0.000
16 | 1.000,1.000,0.000,1.000,0.000,1.000,1.000,1.000,1.000,0.000,1.000,0.000,1.000,1.000,1.000,0.000,1.000,0.000,1.000,0.000,0.000,1.000,1.000,0.000,1.000,1.000,0.000,1.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,1.000,0.000,0.000,1.000,0.000,0.000,0.000,1.000,1.000,0.000,0.000,0.000,0.000,0.000
17 | 1.000,1.000,0.000,1.000,0.000,1.000,1.000,1.000,1.000,0.000,1.000,0.000,1.000,1.000,1.000,0.000,0.000,0.000,1.000,0.000,0.000,1.000,1.000,0.000,1.000,1.000,0.000,1.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,1.000,0.000,0.000,1.000,0.000,0.000,0.000,1.000,1.000,0.000,0.000,0.000,0.000,0.000
18 | 1.000,1.000,0.000,1.000,0.000,1.000,1.000,1.000,1.000,0.000,1.000,0.000,1.000,1.000,1.000,0.000,1.000,0.000,1.000,0.000,0.000,1.000,1.000,0.000,1.000,1.000,0.000,1.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,1.000,0.000,0.000,1.000,0.000,0.000,0.000,1.000,1.000,0.000,0.000,0.000,0.000,0.000
19 | 1.000,1.000,0.000,1.000,0.000,1.000,1.000,1.000,1.000,0.000,1.000,0.000,1.000,1.000,1.000,0.000,1.000,0.000,0.000,0.000,0.000,1.000,1.000,0.000,1.000,1.000,0.000,1.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,1.000,0.000,0.000,1.000,0.000,0.000,0.000,1.000,1.000,0.000,0.000,0.000,0.000,0.000
20 | 1.000,1.000,0.000,1.000,0.000,1.000,1.000,1.000,1.000,0.000,1.000,0.000,1.000,1.000,1.000,0.000,1.000,0.000,1.000,0.000,0.000,1.000,1.000,0.000,1.000,1.000,0.000,1.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,1.000,0.000,0.000,1.000,0.000,0.000,0.000,1.000,1.000,0.000,0.000,0.000,0.000,0.000
21 | 1.000,1.000,0.000,1.000,0.000,1.000,1.000,1.000,1.000,0.000,1.000,0.000,1.000,1.000,1.000,0.000,1.000,0.000,1.000,0.000,0.000,1.000,1.000,0.000,1.000,1.000,0.000,1.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,1.000,0.000,0.000,1.000,0.000,0.000,0.000,1.000,1.000,0.000,0.000,0.000,0.000,0.000
22 | 1.000,1.000,0.000,1.000,0.000,1.000,1.000,1.000,1.000,0.000,1.000,0.000,1.000,1.000,1.000,0.000,1.000,0.000,1.000,0.000,0.000,0.000,1.000,0.000,1.000,1.000,0.000,1.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,1.000,0.000,0.000,1.000,0.000,0.000,0.000,1.000,1.000,0.000,0.000,0.000,0.000,0.000
23 | 1.000,1.000,0.000,1.000,0.000,1.000,1.000,1.000,1.000,0.000,1.000,0.000,1.000,1.000,1.000,0.000,1.000,0.000,1.000,0.000,0.000,1.000,0.000,0.000,1.000,1.000,0.000,1.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,1.000,0.000,0.000,1.000,0.000,0.000,0.000,1.000,1.000,0.000,0.000,0.000,0.000,0.000
24 | 1.000,1.000,0.000,1.000,0.000,1.000,1.000,1.000,1.000,0.000,1.000,0.000,1.000,1.000,1.000,0.000,1.000,0.000,1.000,0.000,0.000,1.000,1.000,0.000,1.000,1.000,0.000,1.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,1.000,0.000,0.000,1.000,0.000,0.000,0.000,1.000,1.000,0.000,0.000,0.000,0.000,0.000
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51 |
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1 | 0.000,6.000,5.000,8.000,0.000,7.000,7.000,9.000,8.000,1.000,8.000,2.000,10.000,7.000,9.000,2.000,8.000,0.000,6.000,3.000,1.000,8.000,7.000,2.000,9.000,7.000,1.000,7.000,2.000,3.000,2.000,3.000,1.000,1.000,1.000,1.000,10.000,1.000,1.000,10.000,2.000,1.000,2.000,9.000,9.000,1.000,1.000,2.000,2.000,3.000
2 | 10.000,0.000,2.000,9.000,2.000,7.000,8.000,9.000,10.000,4.000,7.000,1.000,7.000,9.000,9.000,5.000,9.000,3.000,7.000,1.000,0.000,8.000,6.000,3.000,8.000,9.000,2.000,5.000,0.000,5.000,1.000,1.000,2.000,0.000,2.000,0.000,8.000,1.000,2.000,8.000,2.000,3.000,2.000,10.000,8.000,2.000,1.000,4.000,4.000,1.000
3 | 2.000,3.000,0.000,2.000,7.000,1.000,1.000,1.000,1.000,10.000,2.000,7.000,3.000,3.000,3.000,9.000,0.000,8.000,5.000,8.000,6.000,1.000,2.000,9.000,2.000,2.000,8.000,3.000,8.000,6.000,9.000,7.000,9.000,8.000,8.000,10.000,1.000,7.000,10.000,3.000,8.000,9.000,7.000,4.000,3.000,8.000,6.000,8.000,9.000,7.000
4 | 8.000,8.000,1.000,0.000,1.000,9.000,6.000,10.000,8.000,0.000,6.000,4.000,9.000,8.000,10.000,2.000,7.000,3.000,8.000,1.000,0.000,9.000,8.000,2.000,7.000,10.000,0.000,10.000,2.000,1.000,1.000,0.000,1.000,2.000,4.000,2.000,8.000,3.000,2.000,7.000,1.000,2.000,0.000,7.000,7.000,2.000,0.000,2.000,3.000,2.000
5 | 2.000,2.000,9.000,0.000,0.000,2.000,2.000,2.000,2.000,8.000,0.000,6.000,2.000,3.000,3.000,8.000,2.000,9.000,1.000,6.000,8.000,2.000,1.000,10.000,3.000,4.000,9.000,3.000,8.000,9.000,9.000,10.000,9.000,6.000,10.000,9.000,1.000,9.000,8.000,2.000,7.000,8.000,10.000,0.000,2.000,9.000,9.000,8.000,8.000,9.000
6 | 9.000,8.000,2.000,9.000,2.000,0.000,6.000,8.000,5.000,2.000,9.000,1.000,7.000,9.000,7.000,2.000,7.000,2.000,9.000,0.000,2.000,8.000,8.000,2.000,8.000,8.000,2.000,9.000,1.000,1.000,1.000,3.000,0.000,3.000,3.000,1.000,8.000,2.000,2.000,9.000,1.000,3.000,1.000,9.000,6.000,0.000,3.000,4.000,1.000,0.000
7 | 7.000,7.000,1.000,6.000,1.000,9.000,0.000,6.000,6.000,2.000,8.000,3.000,9.000,6.000,5.000,4.000,8.000,2.000,9.000,0.000,1.000,8.000,8.000,1.000,7.000,8.000,1.000,9.000,3.000,2.000,2.000,1.000,2.000,4.000,1.000,1.000,9.000,0.000,3.000,7.000,1.000,3.000,2.000,9.000,10.000,1.000,3.000,2.000,3.000,0.000
8 | 8.000,5.000,4.000,9.000,3.000,8.000,9.000,0.000,9.000,1.000,9.000,3.000,8.000,7.000,7.000,3.000,10.000,3.000,8.000,1.000,2.000,8.000,8.000,3.000,8.000,8.000,1.000,9.000,2.000,0.000,2.000,4.000,3.000,1.000,3.000,2.000,8.000,1.000,2.000,8.000,3.000,2.000,1.000,7.000,5.000,1.000,1.000,2.000,3.000,2.000
9 | 10.000,8.000,5.000,8.000,1.000,7.000,9.000,9.000,0.000,1.000,4.000,1.000,5.000,8.000,10.000,3.000,6.000,4.000,8.000,3.000,2.000,9.000,9.000,4.000,7.000,6.000,2.000,4.000,1.000,3.000,1.000,2.000,3.000,2.000,1.000,2.000,9.000,3.000,0.000,7.000,1.000,1.000,1.000,9.000,6.000,5.000,2.000,2.000,4.000,4.000
10 | 1.000,2.000,8.000,4.000,9.000,2.000,2.000,1.000,0.000,0.000,2.000,7.000,2.000,2.000,1.000,7.000,1.000,6.000,2.000,7.000,9.000,2.000,2.000,10.000,3.000,2.000,6.000,1.000,7.000,8.000,6.000,6.000,8.000,8.000,8.000,8.000,1.000,9.000,8.000,2.000,8.000,8.000,10.000,2.000,1.000,9.000,8.000,5.000,10.000,8.000
11 | 8.000,9.000,3.000,9.000,3.000,8.000,8.000,9.000,10.000,2.000,0.000,3.000,7.000,9.000,6.000,0.000,9.000,1.000,9.000,2.000,3.000,10.000,8.000,2.000,5.000,8.000,4.000,7.000,2.000,2.000,2.000,1.000,5.000,3.000,4.000,1.000,7.000,3.000,3.000,8.000,3.000,1.000,0.000,8.000,8.000,1.000,2.000,2.000,1.000,2.000
12 | 2.000,3.000,8.000,4.000,7.000,4.000,3.000,3.000,2.000,9.000,3.000,0.000,3.000,2.000,2.000,9.000,1.000,9.000,6.000,9.000,9.000,0.000,4.000,7.000,1.000,1.000,9.000,1.000,9.000,8.000,8.000,5.000,8.000,8.000,8.000,6.000,2.000,9.000,9.000,0.000,10.000,7.000,8.000,0.000,0.000,8.000,7.000,8.000,10.000,7.000
13 | 6.000,8.000,1.000,8.000,1.000,8.000,9.000,8.000,9.000,3.000,8.000,4.000,0.000,9.000,7.000,3.000,9.000,2.000,8.000,1.000,3.000,9.000,8.000,1.000,7.000,8.000,1.000,10.000,1.000,1.000,0.000,1.000,1.000,1.000,1.000,2.000,7.000,3.000,3.000,9.000,1.000,3.000,1.000,8.000,9.000,3.000,0.000,3.000,2.000,1.000
14 | 7.000,8.000,1.000,7.000,0.000,9.000,7.000,9.000,9.000,1.000,7.000,5.000,8.000,0.000,9.000,0.000,8.000,1.000,8.000,2.000,3.000,9.000,7.000,3.000,8.000,5.000,0.000,7.000,2.000,1.000,3.000,2.000,1.000,0.000,2.000,0.000,8.000,4.000,1.000,9.000,2.000,2.000,2.000,9.000,9.000,4.000,1.000,0.000,3.000,1.000
15 | 5.000,8.000,2.000,7.000,3.000,7.000,9.000,8.000,6.000,2.000,10.000,1.000,7.000,6.000,0.000,4.000,7.000,3.000,9.000,1.000,2.000,9.000,8.000,2.000,9.000,9.000,3.000,8.000,3.000,3.000,1.000,3.000,1.000,2.000,2.000,1.000,10.000,3.000,1.000,7.000,1.000,0.000,3.000,10.000,10.000,2.000,1.000,1.000,3.000,3.000
16 | 2.000,3.000,10.000,2.000,10.000,3.000,5.000,3.000,2.000,8.000,0.000,8.000,1.000,2.000,0.000,0.000,2.000,10.000,2.000,8.000,6.000,4.000,3.000,7.000,2.000,4.000,9.000,2.000,10.000,8.000,8.000,6.000,8.000,9.000,9.000,8.000,2.000,9.000,9.000,0.000,9.000,9.000,8.000,1.000,2.000,7.000,8.000,9.000,8.000,10.000
17 | 8.000,7.000,6.000,9.000,3.000,9.000,7.000,9.000,6.000,4.000,7.000,4.000,8.000,10.000,9.000,1.000,0.000,2.000,8.000,2.000,4.000,9.000,8.000,3.000,8.000,5.000,3.000,9.000,1.000,2.000,0.000,0.000,1.000,4.000,1.000,2.000,7.000,2.000,0.000,10.000,1.000,3.000,1.000,8.000,9.000,2.000,4.000,1.000,2.000,1.000
18 | 1.000,1.000,7.000,3.000,6.000,1.000,3.000,1.000,1.000,6.000,2.000,10.000,1.000,1.000,2.000,8.000,3.000,0.000,1.000,9.000,8.000,1.000,1.000,9.000,4.000,3.000,9.000,2.000,9.000,7.000,9.000,10.000,9.000,7.000,8.000,6.000,1.000,9.000,9.000,1.000,9.000,9.000,7.000,3.000,2.000,10.000,7.000,9.000,9.000,7.000
19 | 8.000,7.000,3.000,7.000,2.000,5.000,9.000,9.000,7.000,2.000,7.000,1.000,7.000,8.000,9.000,3.000,8.000,3.000,0.000,2.000,4.000,6.000,8.000,1.000,7.000,9.000,2.000,9.000,2.000,5.000,3.000,2.000,2.000,4.000,2.000,2.000,9.000,0.000,4.000,10.000,2.000,3.000,4.000,7.000,6.000,2.000,0.000,1.000,1.000,0.000
20 | 3.000,1.000,9.000,2.000,10.000,1.000,1.000,3.000,2.000,10.000,2.000,8.000,4.000,4.000,4.000,10.000,2.000,8.000,3.000,0.000,7.000,1.000,3.000,5.000,0.000,1.000,8.000,3.000,8.000,7.000,8.000,9.000,8.000,7.000,10.000,8.000,1.000,10.000,9.000,1.000,8.000,9.000,10.000,5.000,1.000,8.000,9.000,10.000,7.000,7.000
21 | 2.000,1.000,7.000,2.000,10.000,0.000,4.000,2.000,1.000,9.000,2.000,10.000,0.000,1.000,2.000,9.000,1.000,9.000,5.000,9.000,0.000,2.000,2.000,7.000,2.000,0.000,9.000,2.000,9.000,8.000,8.000,8.000,9.000,9.000,9.000,7.000,1.000,6.000,8.000,4.000,7.000,7.000,8.000,3.000,2.000,9.000,6.000,7.000,7.000,10.000
22 | 8.000,10.000,1.000,7.000,2.000,7.000,10.000,9.000,8.000,1.000,6.000,4.000,8.000,8.000,9.000,5.000,6.000,3.000,6.000,0.000,1.000,0.000,9.000,2.000,7.000,10.000,4.000,9.000,2.000,0.000,1.000,1.000,2.000,5.000,0.000,3.000,6.000,1.000,1.000,8.000,3.000,1.000,1.000,8.000,8.000,3.000,1.000,3.000,2.000,2.000
23 | 7.000,8.000,3.000,6.000,2.000,9.000,10.000,9.000,8.000,5.000,9.000,4.000,9.000,8.000,8.000,2.000,8.000,4.000,10.000,6.000,4.000,6.000,0.000,2.000,8.000,8.000,2.000,6.000,2.000,0.000,3.000,2.000,2.000,4.000,1.000,3.000,8.000,1.000,1.000,7.000,3.000,0.000,3.000,7.000,7.000,2.000,0.000,3.000,3.000,1.000
24 | 1.000,2.000,9.000,0.000,6.000,1.000,1.000,3.000,1.000,5.000,0.000,9.000,2.000,1.000,1.000,10.000,2.000,8.000,1.000,8.000,6.000,2.000,1.000,0.000,0.000,2.000,9.000,3.000,8.000,8.000,9.000,9.000,5.000,7.000,8.000,6.000,0.000,7.000,9.000,3.000,5.000,7.000,6.000,3.000,2.000,8.000,10.000,6.000,10.000,6.000
25 | 9.000,8.000,4.000,9.000,2.000,8.000,8.000,7.000,10.000,2.000,7.000,0.000,8.000,10.000,7.000,3.000,8.000,3.000,8.000,1.000,1.000,9.000,9.000,1.000,0.000,7.000,0.000,10.000,3.000,1.000,3.000,2.000,1.000,0.000,2.000,3.000,8.000,3.000,4.000,8.000,3.000,0.000,1.000,7.000,9.000,2.000,3.000,2.000,4.000,0.000
26 | 7.000,7.000,3.000,9.000,4.000,7.000,6.000,9.000,8.000,3.000,7.000,2.000,9.000,8.000,9.000,2.000,7.000,2.000,9.000,2.000,0.000,9.000,9.000,2.000,5.000,0.000,1.000,9.000,1.000,2.000,2.000,1.000,2.000,2.000,2.000,4.000,6.000,0.000,3.000,8.000,0.000,1.000,2.000,9.000,10.000,1.000,3.000,2.000,2.000,2.000
27 | 3.000,3.000,9.000,1.000,10.000,3.000,1.000,1.000,2.000,7.000,2.000,7.000,1.000,1.000,1.000,9.000,3.000,5.000,2.000,7.000,9.000,3.000,2.000,9.000,0.000,2.000,0.000,1.000,8.000,8.000,8.000,9.000,9.000,10.000,6.000,6.000,2.000,10.000,6.000,1.000,7.000,10.000,8.000,5.000,2.000,8.000,7.000,8.000,10.000,8.000
28 | 10.000,9.000,2.000,7.000,2.000,9.000,9.000,8.000,7.000,5.000,9.000,0.000,10.000,6.000,9.000,6.000,8.000,2.000,7.000,0.000,2.000,10.000,7.000,1.000,9.000,6.000,4.000,0.000,1.000,4.000,1.000,1.000,4.000,3.000,0.000,3.000,8.000,2.000,2.000,8.000,1.000,1.000,3.000,8.000,6.000,3.000,2.000,1.000,1.000,3.000
29 | 2.000,4.000,8.000,3.000,6.000,4.000,3.000,2.000,2.000,8.000,3.000,8.000,0.000,1.000,1.000,8.000,3.000,9.000,1.000,9.000,9.000,1.000,2.000,9.000,0.000,1.000,8.000,1.000,0.000,10.000,8.000,8.000,10.000,8.000,8.000,6.000,2.000,9.000,9.000,0.000,9.000,8.000,9.000,1.000,2.000,7.000,8.000,8.000,9.000,8.000
30 | 2.000,2.000,9.000,1.000,9.000,0.000,1.000,1.000,2.000,7.000,3.000,6.000,2.000,4.000,6.000,8.000,2.000,9.000,3.000,6.000,8.000,0.000,2.000,5.000,0.000,1.000,10.000,2.000,10.000,0.000,7.000,5.000,10.000,10.000,7.000,8.000,0.000,9.000,8.000,1.000,10.000,7.000,7.000,2.000,1.000,10.000,8.000,8.000,7.000,9.000
31 | 1.000,2.000,9.000,3.000,5.000,1.000,3.000,0.000,4.000,8.000,1.000,8.000,3.000,3.000,2.000,9.000,3.000,10.000,0.000,9.000,8.000,1.000,2.000,7.000,0.000,0.000,10.000,3.000,8.000,9.000,0.000,9.000,7.000,8.000,8.000,6.000,1.000,9.000,7.000,2.000,6.000,7.000,8.000,0.000,0.000,8.000,5.000,8.000,8.000,10.000
32 | 1.000,3.000,9.000,0.000,9.000,1.000,4.000,2.000,3.000,9.000,1.000,9.000,1.000,4.000,3.000,8.000,5.000,7.000,0.000,8.000,9.000,5.000,2.000,10.000,4.000,2.000,10.000,0.000,7.000,7.000,4.000,0.000,9.000,6.000,8.000,8.000,1.000,8.000,8.000,2.000,9.000,9.000,9.000,2.000,1.000,10.000,7.000,8.000,5.000,7.000
33 | 0.000,1.000,8.000,1.000,9.000,2.000,1.000,2.000,1.000,9.000,1.000,10.000,4.000,3.000,4.000,6.000,3.000,8.000,3.000,7.000,9.000,2.000,1.000,8.000,2.000,3.000,8.000,2.000,9.000,5.000,7.000,6.000,0.000,7.000,7.000,9.000,0.000,9.000,7.000,2.000,9.000,8.000,8.000,1.000,1.000,8.000,9.000,8.000,8.000,7.000
34 | 0.000,2.000,9.000,1.000,9.000,0.000,1.000,1.000,2.000,9.000,2.000,6.000,2.000,2.000,1.000,10.000,0.000,8.000,3.000,9.000,9.000,2.000,1.000,10.000,2.000,1.000,8.000,4.000,9.000,9.000,5.000,10.000,6.000,0.000,9.000,8.000,3.000,9.000,8.000,2.000,8.000,9.000,8.000,4.000,2.000,8.000,6.000,7.000,7.000,9.000
35 | 1.000,1.000,7.000,3.000,6.000,3.000,2.000,0.000,0.000,9.000,2.000,7.000,3.000,1.000,1.000,9.000,3.000,4.000,2.000,8.000,6.000,1.000,1.000,9.000,3.000,2.000,6.000,1.000,9.000,8.000,7.000,9.000,6.000,7.000,0.000,9.000,1.000,7.000,7.000,3.000,7.000,6.000,9.000,2.000,3.000,9.000,8.000,7.000,9.000,9.000
36 | 1.000,0.000,6.000,1.000,7.000,2.000,5.000,1.000,2.000,10.000,2.000,7.000,1.000,0.000,3.000,10.000,3.000,7.000,2.000,8.000,8.000,4.000,1.000,6.000,3.000,5.000,7.000,2.000,10.000,8.000,8.000,7.000,8.000,9.000,8.000,0.000,4.000,7.000,6.000,1.000,6.000,7.000,7.000,3.000,2.000,6.000,10.000,7.000,8.000,10.000
37 | 8.000,8.000,0.000,9.000,3.000,9.000,6.000,9.000,8.000,4.000,9.000,3.000,8.000,7.000,8.000,4.000,7.000,3.000,9.000,1.000,2.000,9.000,7.000,1.000,9.000,7.000,4.000,8.000,5.000,0.000,3.000,3.000,2.000,3.000,4.000,1.000,0.000,3.000,2.000,10.000,0.000,4.000,2.000,9.000,8.000,2.000,2.000,3.000,1.000,3.000
38 | 3.000,0.000,7.000,3.000,9.000,2.000,1.000,0.000,0.000,7.000,3.000,8.000,2.000,0.000,3.000,7.000,1.000,8.000,1.000,10.000,7.000,3.000,4.000,6.000,1.000,0.000,9.000,3.000,9.000,8.000,7.000,8.000,9.000,7.000,7.000,8.000,1.000,0.000,7.000,2.000,8.000,6.000,9.000,1.000,4.000,6.000,8.000,8.000,7.000,7.000
39 | 3.000,2.000,7.000,1.000,8.000,1.000,3.000,2.000,3.000,8.000,3.000,8.000,3.000,0.000,1.000,6.000,4.000,9.000,5.000,8.000,7.000,1.000,2.000,8.000,2.000,2.000,5.000,2.000,8.000,6.000,9.000,8.000,9.000,9.000,7.000,9.000,2.000,8.000,0.000,1.000,10.000,8.000,7.000,2.000,1.000,7.000,3.000,5.000,10.000,9.000
40 | 10.000,7.000,2.000,7.000,2.000,5.000,8.000,9.000,8.000,2.000,9.000,3.000,8.000,6.000,7.000,1.000,8.000,2.000,8.000,4.000,1.000,6.000,10.000,2.000,8.000,7.000,2.000,6.000,3.000,1.000,2.000,2.000,1.000,0.000,1.000,2.000,8.000,2.000,1.000,0.000,3.000,2.000,1.000,5.000,6.000,2.000,5.000,1.000,3.000,1.000
41 | 2.000,2.000,8.000,2.000,10.000,1.000,2.000,1.000,4.000,8.000,0.000,7.000,3.000,4.000,1.000,8.000,2.000,7.000,0.000,7.000,8.000,3.000,1.000,8.000,4.000,4.000,8.000,2.000,9.000,7.000,9.000,8.000,7.000,8.000,8.000,9.000,3.000,8.000,10.000,1.000,0.000,10.000,10.000,3.000,4.000,8.000,8.000,8.000,9.000,9.000
42 | 1.000,2.000,8.000,1.000,10.000,2.000,2.000,1.000,2.000,6.000,3.000,9.000,1.000,2.000,1.000,8.000,0.000,10.000,1.000,9.000,6.000,0.000,1.000,10.000,3.000,2.000,7.000,3.000,6.000,7.000,8.000,10.000,7.000,10.000,9.000,10.000,2.000,9.000,6.000,3.000,10.000,0.000,8.000,1.000,2.000,8.000,6.000,10.000,9.000,8.000
43 | 3.000,0.000,6.000,1.000,9.000,1.000,3.000,2.000,2.000,7.000,0.000,7.000,3.000,1.000,3.000,6.000,1.000,9.000,1.000,7.000,9.000,2.000,3.000,9.000,2.000,3.000,8.000,1.000,10.000,7.000,8.000,7.000,8.000,7.000,9.000,9.000,2.000,9.000,9.000,0.000,10.000,9.000,0.000,3.000,2.000,6.000,8.000,8.000,6.000,7.000
44 | 9.000,6.000,0.000,7.000,2.000,10.000,8.000,7.000,8.000,2.000,8.000,1.000,6.000,8.000,8.000,0.000,7.000,3.000,6.000,3.000,3.000,9.000,7.000,3.000,9.000,7.000,4.000,5.000,2.000,2.000,0.000,2.000,1.000,1.000,3.000,4.000,6.000,1.000,1.000,7.000,2.000,3.000,1.000,0.000,10.000,2.000,2.000,1.000,1.000,3.000
45 | 6.000,9.000,2.000,7.000,1.000,9.000,8.000,10.000,8.000,3.000,6.000,3.000,8.000,8.000,7.000,3.000,8.000,2.000,9.000,2.000,2.000,8.000,6.000,3.000,9.000,9.000,1.000,9.000,2.000,1.000,1.000,4.000,0.000,2.000,0.000,1.000,6.000,1.000,1.000,5.000,1.000,5.000,1.000,7.000,0.000,2.000,2.000,2.000,1.000,0.000
46 | 4.000,1.000,9.000,2.000,10.000,3.000,3.000,1.000,2.000,10.000,3.000,8.000,1.000,1.000,2.000,7.000,1.000,9.000,3.000,6.000,9.000,1.000,4.000,10.000,2.000,3.000,8.000,1.000,9.000,7.000,7.000,10.000,7.000,8.000,7.000,9.000,4.000,9.000,10.000,1.000,8.000,6.000,10.000,3.000,2.000,0.000,7.000,9.000,6.000,6.000
47 | 2.000,4.000,10.000,4.000,7.000,3.000,1.000,4.000,3.000,10.000,1.000,6.000,2.000,0.000,4.000,9.000,0.000,9.000,1.000,9.000,9.000,2.000,1.000,9.000,4.000,0.000,9.000,3.000,10.000,8.000,7.000,8.000,10.000,10.000,8.000,10.000,1.000,9.000,8.000,5.000,9.000,7.000,7.000,0.000,1.000,8.000,0.000,8.000,9.000,8.000
48 | 3.000,1.000,8.000,0.000,8.000,3.000,2.000,5.000,3.000,6.000,3.000,9.000,2.000,2.000,1.000,8.000,0.000,9.000,1.000,10.000,7.000,4.000,0.000,9.000,3.000,1.000,8.000,2.000,7.000,10.000,7.000,7.000,8.000,9.000,6.000,8.000,6.000,7.000,8.000,3.000,8.000,8.000,5.000,3.000,1.000,7.000,10.000,0.000,9.000,10.000
49 | 0.000,3.000,9.000,0.000,7.000,3.000,3.000,2.000,1.000,6.000,1.000,6.000,2.000,3.000,2.000,8.000,4.000,9.000,2.000,8.000,10.000,4.000,1.000,8.000,3.000,2.000,7.000,3.000,9.000,8.000,7.000,8.000,9.000,8.000,10.000,9.000,1.000,6.000,7.000,1.000,8.000,5.000,6.000,4.000,2.000,8.000,6.000,9.000,0.000,6.000
50 | 3.000,1.000,8.000,1.000,8.000,3.000,1.000,3.000,3.000,8.000,2.000,8.000,1.000,4.000,3.000,6.000,3.000,9.000,4.000,10.000,7.000,4.000,1.000,9.000,1.000,4.000,7.000,4.000,8.000,10.000,7.000,9.000,7.000,7.000,6.000,8.000,1.000,10.000,10.000,2.000,8.000,9.000,8.000,1.000,2.000,6.000,7.000,8.000,6.000,0.000
51 |
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1 | 0.000,5.000,6.000,7.000,0.000,7.000,6.000,9.000,7.000,2.000,8.000,3.000,10.000,6.000,10.000,3.000,7.000,0.000,7.000,2.000,0.000,9.000,8.000,2.000,10.000,8.000,1.000,8.000,2.000,2.000,3.000,4.000,0.000,1.000,1.000,1.000,10.000,1.000,2.000,10.000,3.000,0.000,3.000,10.000,10.000,0.000,1.000,3.000,2.000,4.000
2 | 11.000,0.000,3.000,9.000,2.000,8.000,8.000,8.000,9.000,4.000,7.000,1.000,8.000,10.000,9.000,5.000,9.000,3.000,7.000,2.000,0.000,8.000,6.000,3.000,7.000,10.000,1.000,5.000,0.000,5.000,0.000,1.000,2.000,0.000,2.000,0.000,8.000,1.000,2.000,8.000,2.000,2.000,3.000,9.000,9.000,2.000,0.000,4.000,5.000,1.000
3 | 2.000,4.000,0.000,2.000,7.000,0.000,1.000,1.000,1.000,11.000,3.000,6.000,3.000,4.000,4.000,8.000,0.000,7.000,6.000,8.000,7.000,0.000,2.000,9.000,2.000,3.000,7.000,4.000,7.000,5.000,8.000,7.000,9.000,8.000,8.000,10.000,0.000,7.000,11.000,4.000,8.000,9.000,8.000,4.000,2.000,9.000,6.000,8.000,9.000,7.000
4 | 7.000,7.000,1.000,0.000,2.000,9.000,7.000,10.000,7.000,0.000,6.000,3.000,9.000,8.000,9.000,1.000,7.000,4.000,7.000,1.000,0.000,10.000,7.000,2.000,6.000,10.000,0.000,9.000,3.000,0.000,1.000,0.000,0.000,2.000,5.000,3.000,8.000,4.000,2.000,7.000,1.000,2.000,0.000,7.000,6.000,3.000,0.000,2.000,4.000,1.000
5 | 1.000,2.000,9.000,0.000,0.000,2.000,3.000,2.000,2.000,9.000,0.000,5.000,3.000,2.000,4.000,9.000,2.000,9.000,1.000,5.000,8.000,2.000,1.000,10.000,3.000,3.000,9.000,4.000,9.000,9.000,10.000,10.000,10.000,5.000,11.000,8.000,1.000,9.000,8.000,1.000,6.000,8.000,10.000,0.000,2.000,9.000,10.000,8.000,8.000,9.000
6 | 10.000,8.000,3.000,10.000,3.000,0.000,5.000,9.000,5.000,2.000,9.000,0.000,7.000,9.000,7.000,1.000,7.000,1.000,9.000,0.000,2.000,7.000,8.000,1.000,8.000,9.000,2.000,10.000,0.000,1.000,1.000,3.000,0.000,3.000,3.000,0.000,8.000,2.000,1.000,9.000,2.000,3.000,2.000,9.000,5.000,0.000,3.000,3.000,2.000,0.000
7 | 8.000,7.000,1.000,7.000,1.000,9.000,0.000,7.000,5.000,2.000,8.000,3.000,9.000,7.000,4.000,4.000,9.000,1.000,9.000,0.000,1.000,8.000,8.000,1.000,8.000,7.000,0.000,8.000,3.000,3.000,3.000,2.000,3.000,5.000,2.000,2.000,9.000,0.000,2.000,7.000,0.000,4.000,2.000,9.000,9.000,1.000,2.000,2.000,3.000,0.000
8 | 7.000,4.000,4.000,8.000,4.000,9.000,10.000,0.000,10.000,1.000,10.000,4.000,9.000,6.000,7.000,2.000,10.000,3.000,9.000,0.000,2.000,7.000,8.000,4.000,9.000,9.000,1.000,10.000,2.000,0.000,1.000,4.000,3.000,0.000,2.000,2.000,9.000,1.000,2.000,8.000,4.000,2.000,1.000,7.000,6.000,1.000,1.000,1.000,2.000,1.000
9 | 11.000,7.000,5.000,9.000,2.000,6.000,8.000,9.000,0.000,1.000,4.000,2.000,6.000,8.000,10.000,3.000,6.000,3.000,8.000,3.000,3.000,9.000,10.000,3.000,8.000,6.000,3.000,4.000,2.000,3.000,0.000,1.000,3.000,3.000,1.000,2.000,8.000,3.000,0.000,6.000,1.000,1.000,1.000,10.000,5.000,4.000,3.000,1.000,3.000,3.000
10 | 0.000,2.000,9.000,5.000,10.000,2.000,2.000,1.000,0.000,0.000,2.000,8.000,2.000,2.000,2.000,8.000,0.000,5.000,1.000,8.000,9.000,1.000,3.000,10.000,3.000,1.000,5.000,1.000,7.000,8.000,6.000,5.000,8.000,7.000,8.000,7.000,1.000,8.000,8.000,3.000,8.000,8.000,9.000,1.000,1.000,8.000,8.000,5.000,10.000,8.000
11 | 9.000,9.000,2.000,10.000,3.000,8.000,9.000,9.000,10.000,2.000,0.000,3.000,6.000,9.000,6.000,0.000,9.000,0.000,9.000,3.000,3.000,10.000,8.000,3.000,5.000,7.000,4.000,8.000,2.000,1.000,2.000,2.000,6.000,2.000,4.000,2.000,6.000,2.000,2.000,7.000,2.000,0.000,0.000,7.000,9.000,0.000,2.000,3.000,2.000,1.000
12 | 2.000,3.000,7.000,4.000,7.000,4.000,4.000,2.000,2.000,9.000,4.000,0.000,3.000,1.000,2.000,10.000,1.000,10.000,6.000,9.000,9.000,0.000,3.000,6.000,1.000,0.000,10.000,2.000,9.000,9.000,7.000,4.000,8.000,9.000,8.000,6.000,2.000,9.000,9.000,0.000,11.000,8.000,7.000,0.000,0.000,7.000,8.000,9.000,9.000,8.000
13 | 6.000,7.000,1.000,8.000,2.000,9.000,9.000,9.000,9.000,3.000,9.000,4.000,0.000,10.000,6.000,3.000,10.000,2.000,9.000,2.000,3.000,8.000,7.000,2.000,8.000,7.000,0.000,10.000,1.000,0.000,0.000,1.000,1.000,1.000,2.000,1.000,7.000,2.000,3.000,9.000,1.000,2.000,0.000,8.000,9.000,3.000,0.000,2.000,3.000,1.000
14 | 8.000,7.000,2.000,7.000,0.000,9.000,8.000,8.000,8.000,1.000,7.000,5.000,8.000,0.000,9.000,0.000,8.000,1.000,7.000,1.000,2.000,9.000,7.000,3.000,8.000,5.000,0.000,7.000,3.000,2.000,3.000,2.000,0.000,0.000,2.000,0.000,8.000,5.000,2.000,9.000,1.000,1.000,2.000,10.000,9.000,4.000,0.000,0.000,4.000,0.000
15 | 6.000,8.000,1.000,8.000,2.000,7.000,10.000,7.000,7.000,2.000,10.000,1.000,7.000,6.000,0.000,5.000,7.000,2.000,9.000,1.000,2.000,10.000,8.000,1.000,9.000,9.000,4.000,9.000,3.000,4.000,1.000,3.000,2.000,2.000,3.000,1.000,9.000,4.000,1.000,7.000,1.000,0.000,3.000,10.000,10.000,1.000,1.000,2.000,3.000,2.000
16 | 2.000,4.000,10.000,3.000,10.000,4.000,5.000,2.000,3.000,9.000,0.000,8.000,0.000,2.000,0.000,0.000,3.000,10.000,3.000,8.000,6.000,5.000,4.000,7.000,3.000,5.000,10.000,2.000,11.000,9.000,9.000,5.000,7.000,9.000,9.000,8.000,1.000,8.000,10.000,0.000,8.000,10.000,8.000,0.000,2.000,7.000,9.000,9.000,9.000,10.000
17 | 8.000,6.000,7.000,10.000,2.000,10.000,6.000,8.000,6.000,5.000,8.000,3.000,8.000,11.000,9.000,1.000,0.000,2.000,7.000,1.000,4.000,9.000,8.000,3.000,8.000,4.000,2.000,8.000,0.000,2.000,0.000,0.000,2.000,4.000,0.000,2.000,8.000,2.000,0.000,9.000,0.000,2.000,2.000,9.000,9.000,2.000,3.000,1.000,2.000,0.000
18 | 0.000,0.000,7.000,3.000,6.000,2.000,3.000,2.000,1.000,6.000,2.000,10.000,1.000,2.000,2.000,9.000,3.000,0.000,0.000,10.000,8.000,1.000,1.000,9.000,5.000,2.000,9.000,3.000,10.000,7.000,9.000,11.000,9.000,6.000,8.000,5.000,2.000,9.000,9.000,2.000,8.000,9.000,7.000,3.000,1.000,10.000,7.000,10.000,9.000,7.000
19 | 9.000,6.000,3.000,7.000,3.000,5.000,8.000,10.000,8.000,3.000,7.000,1.000,7.000,7.000,9.000,3.000,9.000,3.000,0.000,2.000,3.000,6.000,8.000,2.000,7.000,9.000,1.000,8.000,2.000,5.000,4.000,3.000,3.000,5.000,1.000,1.000,9.000,0.000,4.000,9.000,1.000,3.000,4.000,7.000,6.000,2.000,0.000,0.000,1.000,0.000
20 | 4.000,1.000,9.000,2.000,9.000,0.000,2.000,3.000,3.000,9.000,3.000,7.000,4.000,4.000,4.000,10.000,2.000,8.000,3.000,0.000,8.000,0.000,3.000,5.000,0.000,1.000,8.000,2.000,8.000,6.000,8.000,10.000,8.000,8.000,11.000,9.000,1.000,11.000,9.000,2.000,8.000,9.000,10.000,5.000,1.000,7.000,10.000,9.000,6.000,7.000
21 | 2.000,2.000,7.000,3.000,10.000,0.000,3.000,2.000,1.000,8.000,2.000,9.000,0.000,0.000,1.000,9.000,1.000,10.000,6.000,9.000,0.000,2.000,1.000,7.000,2.000,0.000,9.000,3.000,10.000,9.000,8.000,7.000,9.000,9.000,8.000,7.000,2.000,6.000,8.000,4.000,8.000,8.000,9.000,4.000,1.000,9.000,7.000,7.000,7.000,11.000
22 | 8.000,11.000,1.000,7.000,1.000,8.000,11.000,8.000,8.000,0.000,5.000,3.000,7.000,8.000,9.000,6.000,6.000,4.000,5.000,0.000,2.000,0.000,8.000,3.000,7.000,9.000,4.000,9.000,2.000,0.000,1.000,2.000,3.000,5.000,0.000,4.000,6.000,1.000,1.000,9.000,4.000,1.000,1.000,7.000,8.000,3.000,0.000,3.000,2.000,3.000
23 | 6.000,8.000,3.000,5.000,1.000,9.000,11.000,8.000,7.000,5.000,10.000,3.000,10.000,7.000,7.000,2.000,9.000,4.000,9.000,5.000,4.000,5.000,0.000,1.000,9.000,8.000,2.000,5.000,2.000,0.000,2.000,3.000,2.000,4.000,1.000,4.000,8.000,2.000,1.000,8.000,4.000,0.000,4.000,8.000,7.000,1.000,0.000,2.000,3.000,2.000
24 | 0.000,2.000,8.000,0.000,7.000,0.000,1.000,4.000,1.000,4.000,0.000,9.000,1.000,1.000,1.000,10.000,3.000,9.000,1.000,8.000,5.000,3.000,2.000,0.000,0.000,2.000,9.000,3.000,8.000,8.000,9.000,9.000,5.000,7.000,8.000,6.000,0.000,6.000,8.000,4.000,6.000,7.000,7.000,3.000,2.000,8.000,10.000,6.000,9.000,6.000
25 | 9.000,7.000,4.000,9.000,2.000,7.000,7.000,8.000,10.000,2.000,7.000,0.000,8.000,9.000,7.000,2.000,8.000,3.000,7.000,1.000,1.000,9.000,8.000,1.000,0.000,7.000,0.000,9.000,4.000,1.000,4.000,3.000,2.000,0.000,3.000,3.000,9.000,4.000,4.000,9.000,2.000,0.000,1.000,7.000,9.000,3.000,2.000,2.000,5.000,0.000
26 | 6.000,7.000,4.000,9.000,4.000,7.000,6.000,9.000,9.000,2.000,7.000,3.000,9.000,8.000,10.000,2.000,6.000,1.000,8.000,1.000,0.000,8.000,10.000,1.000,5.000,0.000,0.000,9.000,2.000,2.000,2.000,2.000,3.000,1.000,2.000,5.000,6.000,0.000,3.000,8.000,0.000,1.000,2.000,10.000,10.000,1.000,3.000,3.000,3.000,2.000
27 | 3.000,3.000,8.000,2.000,10.000,3.000,1.000,1.000,1.000,7.000,3.000,8.000,1.000,1.000,1.000,9.000,2.000,5.000,2.000,7.000,8.000,4.000,2.000,10.000,0.000,2.000,0.000,1.000,7.000,9.000,8.000,9.000,9.000,9.000,6.000,5.000,1.000,10.000,5.000,0.000,8.000,10.000,9.000,5.000,1.000,9.000,8.000,8.000,9.000,9.000
28 | 11.000,8.000,2.000,7.000,2.000,8.000,9.000,8.000,7.000,5.000,9.000,0.000,10.000,5.000,9.000,5.000,7.000,1.000,7.000,0.000,1.000,11.000,8.000,0.000,8.000,7.000,4.000,0.000,1.000,4.000,2.000,1.000,5.000,3.000,0.000,3.000,9.000,2.000,1.000,9.000,1.000,1.000,3.000,8.000,6.000,3.000,2.000,0.000,2.000,2.000
29 | 2.000,4.000,8.000,3.000,7.000,3.000,3.000,1.000,2.000,9.000,4.000,8.000,0.000,1.000,2.000,7.000,3.000,10.000,0.000,8.000,9.000,1.000,3.000,8.000,0.000,2.000,8.000,1.000,0.000,10.000,7.000,8.000,11.000,8.000,8.000,6.000,1.000,10.000,9.000,0.000,8.000,8.000,9.000,0.000,2.000,8.000,7.000,7.000,9.000,8.000
30 | 2.000,2.000,9.000,0.000,10.000,0.000,2.000,0.000,2.000,7.000,2.000,5.000,1.000,5.000,7.000,9.000,2.000,8.000,2.000,5.000,8.000,0.000,2.000,4.000,0.000,0.000,10.000,3.000,10.000,0.000,6.000,6.000,11.000,10.000,8.000,8.000,0.000,8.000,7.000,2.000,10.000,6.000,7.000,1.000,1.000,10.000,8.000,9.000,6.000,9.000
31 | 2.000,3.000,10.000,3.000,4.000,1.000,2.000,0.000,3.000,8.000,2.000,8.000,4.000,3.000,2.000,9.000,3.000,10.000,0.000,10.000,8.000,1.000,2.000,8.000,0.000,0.000,11.000,3.000,9.000,9.000,0.000,10.000,6.000,8.000,8.000,5.000,1.000,10.000,7.000,2.000,5.000,7.000,9.000,0.000,0.000,8.000,6.000,7.000,9.000,11.000
32 | 0.000,3.000,8.000,0.000,9.000,0.000,4.000,1.000,3.000,9.000,1.000,9.000,1.000,3.000,3.000,8.000,5.000,7.000,0.000,9.000,10.000,5.000,2.000,10.000,3.000,1.000,10.000,0.000,8.000,6.000,3.000,0.000,9.000,5.000,9.000,7.000,1.000,9.000,8.000,2.000,10.000,8.000,9.000,2.000,1.000,10.000,7.000,8.000,5.000,7.000
33 | 0.000,2.000,8.000,1.000,8.000,2.000,1.000,2.000,1.000,8.000,2.000,9.000,4.000,3.000,4.000,6.000,3.000,8.000,2.000,7.000,10.000,1.000,1.000,8.000,1.000,4.000,8.000,1.000,10.000,4.000,7.000,6.000,0.000,8.000,7.000,10.000,0.000,9.000,7.000,1.000,9.000,7.000,8.000,1.000,2.000,8.000,10.000,7.000,7.000,6.000
34 | 0.000,1.000,9.000,0.000,8.000,0.000,2.000,1.000,2.000,9.000,2.000,5.000,3.000,2.000,1.000,9.000,0.000,7.000,3.000,8.000,9.000,3.000,2.000,11.000,1.000,1.000,8.000,4.000,10.000,8.000,5.000,9.000,7.000,0.000,8.000,8.000,4.000,9.000,9.000,2.000,9.000,10.000,7.000,4.000,2.000,7.000,6.000,7.000,7.000,8.000
35 | 0.000,1.000,7.000,3.000,6.000,2.000,2.000,0.000,0.000,9.000,3.000,6.000,4.000,0.000,0.000,9.000,3.000,5.000,3.000,9.000,6.000,2.000,1.000,8.000,2.000,2.000,6.000,1.000,8.000,8.000,7.000,9.000,5.000,8.000,0.000,10.000,0.000,7.000,7.000,4.000,7.000,5.000,8.000,2.000,3.000,9.000,8.000,7.000,10.000,10.000
36 | 1.000,0.000,5.000,0.000,7.000,2.000,5.000,1.000,3.000,11.000,2.000,7.000,2.000,0.000,3.000,9.000,3.000,7.000,1.000,9.000,9.000,4.000,1.000,6.000,2.000,5.000,7.000,1.000,10.000,9.000,9.000,6.000,9.000,9.000,8.000,0.000,3.000,8.000,6.000,0.000,6.000,7.000,8.000,3.000,3.000,7.000,10.000,6.000,7.000,10.000
37 | 8.000,8.000,0.000,9.000,3.000,9.000,5.000,8.000,9.000,3.000,9.000,2.000,9.000,7.000,9.000,5.000,7.000,3.000,10.000,1.000,1.000,8.000,6.000,2.000,9.000,6.000,4.000,7.000,5.000,0.000,4.000,3.000,2.000,3.000,3.000,2.000,0.000,3.000,1.000,10.000,0.000,5.000,3.000,8.000,8.000,1.000,2.000,2.000,2.000,3.000
38 | 3.000,0.000,7.000,3.000,9.000,2.000,1.000,0.000,0.000,6.000,2.000,9.000,1.000,0.000,3.000,6.000,1.000,8.000,2.000,10.000,6.000,3.000,4.000,6.000,0.000,0.000,10.000,4.000,9.000,9.000,7.000,8.000,9.000,6.000,6.000,8.000,2.000,0.000,7.000,1.000,7.000,6.000,10.000,1.000,4.000,5.000,9.000,7.000,7.000,8.000
39 | 2.000,2.000,6.000,1.000,7.000,1.000,2.000,3.000,2.000,8.000,3.000,8.000,2.000,0.000,0.000,6.000,3.000,9.000,6.000,8.000,7.000,1.000,3.000,7.000,1.000,2.000,4.000,1.000,9.000,7.000,9.000,7.000,10.000,9.000,8.000,8.000,3.000,8.000,0.000,1.000,9.000,9.000,8.000,1.000,2.000,6.000,4.000,4.000,10.000,10.000
40 | 9.000,6.000,2.000,8.000,3.000,5.000,9.000,9.000,8.000,1.000,8.000,4.000,9.000,5.000,7.000,0.000,7.000,3.000,8.000,4.000,1.000,5.000,11.000,3.000,8.000,8.000,2.000,7.000,3.000,1.000,3.000,3.000,2.000,0.000,1.000,1.000,9.000,1.000,2.000,0.000,2.000,3.000,1.000,4.000,5.000,1.000,5.000,1.000,3.000,0.000
41 | 2.000,2.000,9.000,2.000,11.000,0.000,1.000,0.000,3.000,8.000,0.000,7.000,3.000,5.000,1.000,7.000,1.000,7.000,0.000,7.000,8.000,2.000,2.000,8.000,4.000,5.000,8.000,3.000,10.000,6.000,8.000,8.000,8.000,7.000,8.000,9.000,4.000,8.000,9.000,0.000,0.000,10.000,9.000,3.000,4.000,7.000,8.000,8.000,9.000,9.000
42 | 1.000,3.000,8.000,2.000,10.000,3.000,3.000,1.000,3.000,6.000,3.000,8.000,0.000,3.000,2.000,7.000,0.000,11.000,2.000,8.000,7.000,0.000,1.000,11.000,4.000,3.000,7.000,3.000,6.000,7.000,8.000,10.000,6.000,11.000,10.000,10.000,2.000,9.000,6.000,2.000,9.000,0.000,8.000,2.000,2.000,8.000,6.000,10.000,9.000,8.000
43 | 4.000,0.000,6.000,0.000,9.000,1.000,4.000,2.000,2.000,6.000,0.000,6.000,4.000,1.000,3.000,6.000,1.000,10.000,1.000,7.000,9.000,2.000,2.000,8.000,3.000,2.000,8.000,1.000,9.000,6.000,9.000,8.000,7.000,6.000,9.000,9.000,2.000,9.000,9.000,0.000,10.000,9.000,0.000,2.000,2.000,5.000,8.000,8.000,6.000,7.000
44 | 9.000,5.000,0.000,7.000,1.000,9.000,7.000,8.000,8.000,1.000,7.000,1.000,6.000,8.000,8.000,0.000,7.000,4.000,5.000,2.000,3.000,9.000,6.000,3.000,9.000,6.000,5.000,4.000,1.000,2.000,0.000,2.000,2.000,2.000,4.000,3.000,6.000,1.000,2.000,7.000,3.000,3.000,1.000,0.000,10.000,2.000,3.000,0.000,1.000,3.000
45 | 6.000,9.000,2.000,7.000,1.000,8.000,8.000,10.000,7.000,3.000,6.000,2.000,8.000,8.000,6.000,4.000,8.000,1.000,8.000,3.000,2.000,8.000,6.000,3.000,8.000,8.000,2.000,9.000,3.000,0.000,1.000,5.000,0.000,2.000,0.000,2.000,6.000,2.000,0.000,5.000,2.000,5.000,1.000,6.000,0.000,2.000,3.000,3.000,2.000,0.000
46 | 3.000,1.000,10.000,2.000,9.000,3.000,3.000,1.000,1.000,9.000,3.000,7.000,1.000,1.000,2.000,8.000,0.000,9.000,3.000,7.000,8.000,0.000,3.000,11.000,2.000,3.000,8.000,1.000,10.000,6.000,6.000,9.000,7.000,8.000,6.000,8.000,4.000,9.000,10.000,1.000,8.000,7.000,10.000,4.000,2.000,0.000,7.000,10.000,5.000,5.000
47 | 1.000,4.000,10.000,3.000,6.000,3.000,1.000,5.000,3.000,9.000,0.000,7.000,3.000,0.000,4.000,9.000,0.000,8.000,1.000,10.000,8.000,1.000,1.000,8.000,4.000,0.000,9.000,3.000,11.000,8.000,7.000,8.000,9.000,11.000,8.000,11.000,2.000,10.000,8.000,5.000,10.000,7.000,7.000,0.000,1.000,8.000,0.000,9.000,8.000,9.000
48 | 2.000,0.000,7.000,0.000,8.000,2.000,3.000,5.000,4.000,7.000,4.000,10.000,1.000,2.000,2.000,7.000,0.000,8.000,0.000,11.000,8.000,4.000,0.000,9.000,2.000,1.000,9.000,1.000,7.000,10.000,6.000,7.000,8.000,8.000,6.000,8.000,5.000,7.000,9.000,3.000,8.000,8.000,5.000,4.000,1.000,6.000,10.000,0.000,10.000,9.000
49 | 0.000,3.000,9.000,0.000,7.000,3.000,3.000,2.000,0.000,6.000,2.000,5.000,2.000,2.000,1.000,8.000,4.000,8.000,2.000,7.000,11.000,3.000,2.000,8.000,3.000,3.000,8.000,4.000,10.000,8.000,6.000,8.000,9.000,7.000,10.000,9.000,1.000,7.000,7.000,1.000,8.000,4.000,7.000,4.000,2.000,8.000,6.000,9.000,0.000,6.000
50 | 3.000,1.000,7.000,1.000,8.000,4.000,1.000,3.000,4.000,8.000,3.000,7.000,1.000,5.000,2.000,6.000,4.000,8.000,4.000,11.000,8.000,4.000,1.000,9.000,2.000,5.000,7.000,5.000,8.000,11.000,8.000,8.000,7.000,7.000,7.000,9.000,0.000,9.000,11.000,2.000,9.000,8.000,8.000,1.000,1.000,7.000,7.000,8.000,7.000,0.000
51 |
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1 | 0.000,0.000,6.000,12.000,0.000,1.000,4.000,9.000,9.000,5.000,4.000,5.000,13.000,10.000,6.000,1.000,5.000,0.000,0.000,0.000,2.000,7.000,1.000,0.000,5.000,6.000,0.000,10.000,10.000,0.000,0.000,0.000,6.000,1.000,4.000,0.000,20.000,6.000,4.000,15.000,0.000,9.000,9.000,12.000,5.000,4.000,0.000,9.000,5.000,11.000
2 | 0.000,0.000,0.000,6.000,0.000,0.000,0.000,17.000,15.000,2.000,8.000,0.000,8.000,16.000,10.000,10.000,18.000,4.000,15.000,6.000,0.000,9.000,3.000,4.000,6.000,14.000,11.000,3.000,0.000,1.000,8.000,0.000,2.000,0.000,4.000,0.000,15.000,0.000,5.000,4.000,10.000,0.000,3.000,14.000,1.000,12.000,0.000,0.000,5.000,5.000
3 | 5.000,5.000,0.000,12.000,4.000,3.000,2.000,0.000,0.000,12.000,0.000,6.000,0.000,0.000,6.000,19.000,0.000,8.000,10.000,7.000,0.000,0.000,0.000,17.000,2.000,0.000,4.000,8.000,9.000,1.000,2.000,7.000,11.000,15.000,0.000,15.000,9.000,3.000,15.000,8.000,14.000,4.000,2.000,1.000,0.000,8.000,3.000,7.000,1.000,5.000
4 | 17.000,2.000,6.000,0.000,6.000,17.000,12.000,3.000,0.000,0.000,6.000,0.000,5.000,11.000,19.000,0.000,8.000,7.000,11.000,0.000,0.000,3.000,10.000,5.000,8.000,10.000,0.000,5.000,2.000,7.000,0.000,0.000,0.000,0.000,14.000,2.000,0.000,0.000,2.000,0.000,11.000,7.000,0.000,5.000,15.000,3.000,0.000,3.000,0.000,7.000
5 | 0.000,8.000,16.000,0.000,0.000,0.000,0.000,0.000,9.000,16.000,0.000,10.000,1.000,0.000,0.000,16.000,3.000,19.000,10.000,16.000,0.000,0.000,8.000,13.000,0.000,0.000,17.000,11.000,0.000,17.000,6.000,19.000,2.000,13.000,3.000,10.000,8.000,16.000,11.000,5.000,16.000,15.000,14.000,0.000,4.000,6.000,9.000,16.000,11.000,6.000
6 | 16.000,16.000,10.000,5.000,2.000,0.000,0.000,3.000,13.000,0.000,12.000,2.000,11.000,2.000,2.000,0.000,13.000,11.000,16.000,0.000,0.000,13.000,2.000,2.000,4.000,17.000,4.000,12.000,0.000,0.000,11.000,11.000,0.000,5.000,4.000,0.000,16.000,0.000,6.000,12.000,7.000,0.000,8.000,8.000,8.000,0.000,0.000,3.000,0.000,0.000
7 | 7.000,0.000,5.000,0.000,0.000,15.000,0.000,0.000,5.000,5.000,4.000,0.000,12.000,7.000,12.000,0.000,6.000,0.000,18.000,0.000,4.000,14.000,10.000,2.000,2.000,4.000,0.000,13.000,3.000,2.000,0.000,10.000,0.000,5.000,11.000,0.000,6.000,0.000,11.000,13.000,10.000,12.000,0.000,11.000,20.000,0.000,11.000,0.000,0.000,0.000
8 | 17.000,15.000,0.000,16.000,0.000,6.000,4.000,0.000,1.000,0.000,7.000,0.000,7.000,2.000,2.000,0.000,0.000,0.000,5.000,8.000,0.000,0.000,2.000,7.000,8.000,10.000,6.000,6.000,0.000,0.000,3.000,6.000,0.000,0.000,13.000,0.000,8.000,0.000,0.000,10.000,6.000,0.000,0.000,0.000,3.000,0.000,8.000,4.000,11.000,0.000
9 | 2.000,10.000,0.000,0.000,0.000,2.000,2.000,8.000,0.000,1.000,3.000,0.000,0.000,2.000,6.000,4.000,8.000,10.000,9.000,3.000,0.000,4.000,16.000,0.000,5.000,0.000,0.000,0.000,2.000,9.000,0.000,10.000,8.000,0.000,0.000,5.000,16.000,4.000,0.000,11.000,4.000,10.000,7.000,8.000,0.000,14.000,0.000,0.000,10.000,0.000
10 | 0.000,3.000,13.000,0.000,3.000,0.000,0.000,4.000,0.000,0.000,0.000,5.000,10.000,11.000,0.000,16.000,6.000,6.000,7.000,10.000,5.000,10.000,0.000,12.000,0.000,8.000,9.000,3.000,9.000,7.000,14.000,3.000,1.000,10.000,5.000,14.000,0.000,16.000,13.000,0.000,9.000,4.000,1.000,2.000,0.000,17.000,14.000,3.000,9.000,2.000
11 | 8.000,0.000,1.000,0.000,11.000,12.000,11.000,11.000,3.000,0.000,0.000,5.000,1.000,5.000,10.000,0.000,16.000,2.000,2.000,0.000,3.000,9.000,0.000,9.000,11.000,7.000,2.000,0.000,0.000,5.000,12.000,5.000,14.000,6.000,0.000,0.000,6.000,0.000,10.000,12.000,2.000,0.000,0.000,3.000,2.000,0.000,2.000,3.000,0.000,1.000
12 | 0.000,2.000,12.000,13.000,16.000,13.000,6.000,11.000,10.000,7.000,2.000,0.000,0.000,8.000,0.000,3.000,0.000,7.000,0.000,17.000,0.000,0.000,0.000,8.000,0.000,0.000,8.000,0.000,16.000,1.000,8.000,0.000,5.000,11.000,5.000,15.000,0.000,0.000,6.000,0.000,6.000,5.000,5.000,0.000,0.000,13.000,15.000,3.000,8.000,0.000
13 | 12.000,0.000,0.000,0.000,0.000,13.000,11.000,16.000,10.000,0.000,1.000,0.000,0.000,18.000,0.000,4.000,2.000,0.000,16.000,4.000,7.000,17.000,17.000,1.000,4.000,16.000,0.000,2.000,0.000,0.000,0.000,4.000,0.000,0.000,4.000,0.000,15.000,12.000,0.000,7.000,0.000,0.000,0.000,16.000,12.000,0.000,0.000,0.000,0.000,2.000
14 | 12.000,5.000,8.000,9.000,0.000,8.000,13.000,15.000,12.000,9.000,0.000,0.000,7.000,0.000,4.000,0.000,0.000,2.000,17.000,4.000,10.000,13.000,16.000,12.000,0.000,3.000,0.000,15.000,2.000,0.000,2.000,0.000,4.000,0.000,1.000,0.000,3.000,4.000,5.000,2.000,0.000,0.000,0.000,11.000,3.000,10.000,0.000,0.000,6.000,1.000
15 | 0.000,3.000,9.000,0.000,0.000,16.000,18.000,17.000,11.000,0.000,4.000,0.000,0.000,0.000,0.000,6.000,14.000,12.000,0.000,2.000,6.000,0.000,7.000,8.000,11.000,13.000,0.000,15.000,12.000,0.000,0.000,0.000,0.000,3.000,6.000,0.000,8.000,5.000,0.000,13.000,6.000,0.000,2.000,17.000,5.000,10.000,0.000,10.000,2.000,5.000
16 | 1.000,1.000,12.000,12.000,3.000,0.000,1.000,6.000,0.000,12.000,0.000,9.000,5.000,3.000,0.000,0.000,3.000,17.000,2.000,1.000,5.000,12.000,10.000,10.000,4.000,0.000,8.000,10.000,0.000,5.000,5.000,0.000,0.000,3.000,16.000,14.000,0.000,16.000,16.000,0.000,18.000,0.000,0.000,0.000,0.000,13.000,0.000,19.000,17.000,10.000
17 | 13.000,17.000,10.000,4.000,0.000,14.000,1.000,12.000,0.000,0.000,1.000,2.000,0.000,18.000,0.000,3.000,0.000,0.000,9.000,0.000,13.000,1.000,8.000,8.000,10.000,6.000,0.000,9.000,2.000,0.000,0.000,0.000,0.000,12.000,0.000,12.000,13.000,7.000,0.000,10.000,1.000,6.000,0.000,12.000,19.000,2.000,2.000,0.000,0.000,11.000
18 | 6.000,0.000,4.000,0.000,10.000,4.000,7.000,0.000,0.000,0.000,11.000,15.000,0.000,0.000,1.000,0.000,0.000,0.000,3.000,12.000,13.000,0.000,0.000,5.000,0.000,3.000,2.000,5.000,3.000,0.000,8.000,7.000,6.000,0.000,2.000,13.000,0.000,16.000,17.000,7.000,11.000,17.000,12.000,0.000,0.000,2.000,8.000,10.000,0.000,7.000
19 | 0.000,6.000,4.000,0.000,8.000,0.000,2.000,12.000,6.000,2.000,11.000,0.000,9.000,8.000,3.000,0.000,10.000,2.000,0.000,5.000,4.000,6.000,0.000,2.000,14.000,3.000,7.000,7.000,0.000,4.000,3.000,0.000,7.000,1.000,0.000,0.000,10.000,0.000,9.000,15.000,0.000,7.000,5.000,7.000,12.000,7.000,0.000,2.000,0.000,0.000
20 | 0.000,8.000,10.000,5.000,7.000,11.000,0.000,3.000,9.000,11.000,0.000,14.000,7.000,8.000,0.000,2.000,7.000,0.000,3.000,0.000,7.000,9.000,0.000,0.000,0.000,0.000,9.000,12.000,5.000,8.000,2.000,5.000,6.000,8.000,20.000,18.000,0.000,17.000,1.000,2.000,13.000,4.000,11.000,0.000,8.000,9.000,3.000,12.000,0.000,16.000
21 | 5.000,4.000,1.000,0.000,17.000,0.000,0.000,0.000,10.000,0.000,5.000,7.000,0.000,0.000,10.000,1.000,0.000,16.000,9.000,9.000,0.000,3.000,0.000,4.000,6.000,0.000,17.000,11.000,10.000,3.000,5.000,5.000,13.000,8.000,3.000,15.000,0.000,15.000,17.000,9.000,12.000,7.000,2.000,1.000,4.000,9.000,8.000,8.000,15.000,10.000
22 | 17.000,4.000,0.000,2.000,0.000,8.000,10.000,6.000,8.000,0.000,8.000,11.000,17.000,8.000,7.000,13.000,0.000,0.000,3.000,0.000,10.000,0.000,2.000,0.000,7.000,17.000,9.000,17.000,9.000,0.000,9.000,5.000,6.000,14.000,0.000,0.000,5.000,0.000,0.000,2.000,0.000,0.000,9.000,5.000,12.000,9.000,0.000,0.000,8.000,0.000
23 | 3.000,2.000,6.000,0.000,0.000,17.000,19.000,12.000,1.000,9.000,3.000,6.000,17.000,16.000,7.000,1.000,3.000,12.000,10.000,0.000,10.000,9.000,0.000,11.000,8.000,11.000,4.000,4.000,2.000,0.000,0.000,7.000,12.000,9.000,0.000,12.000,0.000,0.000,0.000,5.000,7.000,0.000,5.000,4.000,1.000,10.000,0.000,4.000,0.000,8.000
24 | 9.000,8.000,18.000,0.000,0.000,10.000,0.000,11.000,3.000,7.000,0.000,3.000,0.000,0.000,0.000,9.000,7.000,0.000,0.000,10.000,5.000,0.000,5.000,0.000,0.000,0.000,14.000,11.000,2.000,5.000,8.000,14.000,11.000,11.000,14.000,1.000,0.000,1.000,17.000,3.000,7.000,5.000,4.000,0.000,4.000,11.000,18.000,4.000,7.000,10.000
25 | 0.000,0.000,4.000,5.000,12.000,16.000,6.000,8.000,3.000,0.000,2.000,0.000,8.000,14.000,6.000,11.000,15.000,7.000,10.000,5.000,8.000,4.000,3.000,9.000,0.000,12.000,0.000,2.000,7.000,1.000,0.000,9.000,0.000,0.000,4.000,0.000,15.000,7.000,2.000,15.000,5.000,0.000,9.000,11.000,3.000,4.000,1.000,0.000,4.000,0.000
26 | 15.000,7.000,12.000,11.000,11.000,8.000,1.000,14.000,6.000,1.000,2.000,4.000,5.000,17.000,3.000,9.000,8.000,4.000,16.000,0.000,0.000,5.000,15.000,9.000,0.000,0.000,1.000,5.000,7.000,0.000,10.000,0.000,6.000,8.000,5.000,2.000,13.000,0.000,0.000,13.000,0.000,6.000,1.000,10.000,1.000,0.000,0.000,7.000,3.000,5.000
27 | 9.000,0.000,14.000,1.000,19.000,6.000,2.000,8.000,1.000,16.000,0.000,11.000,0.000,1.000,0.000,11.000,4.000,14.000,7.000,9.000,16.000,0.000,8.000,4.000,0.000,2.000,0.000,0.000,11.000,2.000,12.000,0.000,11.000,0.000,12.000,1.000,0.000,15.000,8.000,0.000,2.000,17.000,0.000,0.000,5.000,14.000,0.000,6.000,11.000,10.000
28 | 10.000,1.000,10.000,2.000,0.000,17.000,15.000,6.000,2.000,9.000,13.000,0.000,4.000,12.000,10.000,9.000,12.000,9.000,9.000,0.000,0.000,5.000,16.000,0.000,12.000,1.000,4.000,0.000,5.000,2.000,7.000,1.000,0.000,0.000,0.000,5.000,11.000,11.000,5.000,2.000,0.000,6.000,0.000,0.000,9.000,3.000,0.000,0.000,0.000,8.000
29 | 0.000,13.000,0.000,7.000,14.000,10.000,0.000,0.000,6.000,8.000,1.000,2.000,0.000,0.000,0.000,3.000,0.000,11.000,11.000,6.000,4.000,0.000,7.000,14.000,0.000,0.000,14.000,10.000,0.000,5.000,16.000,12.000,7.000,14.000,14.000,14.000,8.000,8.000,12.000,0.000,3.000,2.000,12.000,0.000,0.000,0.000,4.000,13.000,1.000,11.000
30 | 2.000,1.000,3.000,4.000,4.000,0.000,0.000,0.000,0.000,5.000,0.000,12.000,0.000,10.000,1.000,6.000,9.000,18.000,0.000,6.000,13.000,0.000,0.000,0.000,0.000,0.000,12.000,0.000,8.000,0.000,1.000,0.000,11.000,7.000,8.000,2.000,0.000,2.000,15.000,9.000,14.000,1.000,16.000,9.000,0.000,8.000,2.000,14.000,4.000,0.000
31 | 2.000,2.000,10.000,0.000,12.000,0.000,2.000,0.000,5.000,12.000,1.000,15.000,13.000,9.000,0.000,16.000,4.000,18.000,0.000,11.000,10.000,0.000,7.000,14.000,0.000,0.000,12.000,0.000,0.000,10.000,0.000,19.000,14.000,8.000,7.000,11.000,4.000,14.000,8.000,10.000,7.000,4.000,13.000,0.000,0.000,3.000,14.000,13.000,5.000,20.000
32 | 6.000,6.000,1.000,0.000,8.000,1.000,13.000,7.000,0.000,2.000,5.000,0.000,5.000,8.000,6.000,5.000,14.000,4.000,0.000,5.000,8.000,0.000,0.000,13.000,10.000,6.000,13.000,0.000,8.000,4.000,10.000,0.000,15.000,1.000,0.000,7.000,0.000,1.000,2.000,0.000,6.000,12.000,19.000,12.000,10.000,13.000,8.000,14.000,5.000,3.000
33 | 0.000,0.000,7.000,0.000,0.000,12.000,0.000,9.000,7.000,8.000,6.000,11.000,1.000,0.000,4.000,4.000,5.000,0.000,7.000,0.000,1.000,0.000,4.000,10.000,0.000,0.000,15.000,0.000,6.000,10.000,3.000,0.000,0.000,13.000,2.000,8.000,0.000,9.000,0.000,5.000,13.000,7.000,15.000,2.000,3.000,17.000,5.000,5.000,8.000,8.000
34 | 0.000,6.000,19.000,0.000,0.000,0.000,0.000,4.000,4.000,3.000,6.000,16.000,0.000,0.000,0.000,14.000,0.000,3.000,4.000,11.000,16.000,0.000,1.000,8.000,2.000,4.000,10.000,7.000,1.000,14.000,11.000,18.000,8.000,0.000,18.000,14.000,10.000,17.000,14.000,1.000,0.000,18.000,8.000,0.000,0.000,16.000,4.000,13.000,6.000,3.000
35 | 3.000,0.000,10.000,7.000,14.000,7.000,0.000,0.000,0.000,7.000,8.000,0.000,0.000,0.000,0.000,3.000,9.000,6.000,0.000,11.000,15.000,7.000,6.000,9.000,0.000,0.000,9.000,1.000,13.000,12.000,4.000,8.000,2.000,11.000,0.000,11.000,1.000,14.000,16.000,3.000,0.000,3.000,5.000,7.000,0.000,4.000,4.000,10.000,5.000,0.000
36 | 7.000,0.000,5.000,0.000,0.000,0.000,9.000,6.000,3.000,5.000,6.000,11.000,8.000,0.000,12.000,13.000,0.000,3.000,0.000,8.000,10.000,0.000,3.000,14.000,6.000,6.000,12.000,5.000,1.000,9.000,7.000,17.000,7.000,9.000,9.000,0.000,8.000,5.000,0.000,3.000,9.000,16.000,9.000,10.000,2.000,11.000,1.000,11.000,15.000,10.000
37 | 11.000,15.000,0.000,18.000,7.000,18.000,15.000,2.000,16.000,0.000,9.000,0.000,15.000,7.000,13.000,0.000,15.000,3.000,2.000,0.000,5.000,0.000,1.000,0.000,2.000,16.000,14.000,0.000,14.000,0.000,0.000,4.000,0.000,0.000,5.000,1.000,0.000,0.000,4.000,5.000,0.000,13.000,2.000,0.000,12.000,0.000,2.000,9.000,0.000,12.000
38 | 9.000,0.000,7.000,3.000,12.000,2.000,10.000,0.000,0.000,6.000,2.000,3.000,9.000,0.000,1.000,16.000,6.000,11.000,0.000,19.000,11.000,9.000,12.000,0.000,0.000,0.000,6.000,0.000,10.000,14.000,0.000,4.000,12.000,14.000,11.000,10.000,0.000,0.000,7.000,11.000,13.000,0.000,1.000,7.000,14.000,1.000,12.000,11.000,4.000,13.000
39 | 10.000,0.000,15.000,0.000,17.000,2.000,0.000,0.000,0.000,15.000,6.000,3.000,4.000,0.000,0.000,2.000,12.000,0.000,1.000,8.000,14.000,1.000,7.000,11.000,0.000,0.000,8.000,0.000,12.000,5.000,18.000,4.000,14.000,15.000,5.000,7.000,3.000,6.000,0.000,6.000,12.000,0.000,3.000,2.000,0.000,15.000,9.000,0.000,7.000,3.000
40 | 16.000,0.000,0.000,0.000,6.000,11.000,2.000,11.000,13.000,0.000,7.000,0.000,16.000,0.000,15.000,0.000,3.000,0.000,2.000,0.000,0.000,4.000,16.000,0.000,10.000,2.000,8.000,5.000,7.000,5.000,2.000,0.000,8.000,0.000,9.000,4.000,1.000,0.000,6.000,0.000,9.000,0.000,6.000,0.000,2.000,10.000,1.000,7.000,5.000,5.000
41 | 10.000,0.000,9.000,0.000,11.000,0.000,0.000,3.000,2.000,9.000,0.000,6.000,0.000,14.000,2.000,14.000,0.000,4.000,0.000,0.000,3.000,2.000,7.000,11.000,3.000,0.000,8.000,2.000,13.000,0.000,17.000,7.000,17.000,4.000,14.000,17.000,2.000,0.000,7.000,0.000,0.000,10.000,1.000,0.000,0.000,11.000,2.000,4.000,11.000,1.000
42 | 10.000,0.000,6.000,2.000,15.000,0.000,0.000,0.000,6.000,0.000,0.000,0.000,2.000,7.000,0.000,15.000,0.000,2.000,10.000,10.000,10.000,0.000,10.000,8.000,2.000,0.000,0.000,5.000,2.000,7.000,4.000,16.000,10.000,18.000,13.000,3.000,2.000,6.000,10.000,6.000,5.000,0.000,12.000,2.000,0.000,12.000,11.000,6.000,7.000,2.000
43 | 0.000,0.000,9.000,0.000,16.000,9.000,7.000,8.000,8.000,0.000,0.000,13.000,0.000,0.000,0.000,12.000,5.000,14.000,8.000,2.000,14.000,7.000,0.000,18.000,6.000,3.000,15.000,0.000,13.000,16.000,0.000,2.000,0.000,11.000,7.000,15.000,2.000,14.000,19.000,0.000,2.000,10.000,0.000,0.000,3.000,6.000,0.000,14.000,8.000,0.000
44 | 15.000,3.000,0.000,5.000,0.000,20.000,0.000,0.000,10.000,11.000,16.000,0.000,3.000,4.000,0.000,0.000,11.000,8.000,14.000,7.000,3.000,4.000,17.000,0.000,8.000,5.000,4.000,0.000,1.000,0.000,0.000,0.000,8.000,3.000,5.000,0.000,8.000,0.000,0.000,9.000,8.000,0.000,0.000,0.000,11.000,0.000,0.000,0.000,0.000,0.000
45 | 10.000,10.000,0.000,4.000,6.000,7.000,10.000,2.000,18.000,13.000,2.000,4.000,15.000,11.000,12.000,3.000,2.000,0.000,2.000,7.000,9.000,0.000,0.000,0.000,12.000,13.000,7.000,8.000,0.000,0.000,0.000,4.000,0.000,2.000,0.000,0.000,15.000,0.000,0.000,13.000,1.000,2.000,0.000,0.000,0.000,0.000,7.000,5.000,2.000,0.000
46 | 0.000,0.000,14.000,9.000,16.000,1.000,0.000,0.000,9.000,4.000,9.000,2.000,6.000,8.000,1.000,12.000,9.000,13.000,5.000,0.000,15.000,0.000,0.000,3.000,2.000,0.000,6.000,0.000,10.000,15.000,0.000,4.000,7.000,8.000,2.000,9.000,14.000,8.000,8.000,1.000,11.000,8.000,12.000,0.000,0.000,0.000,3.000,14.000,9.000,0.000
47 | 0.000,11.000,4.000,0.000,4.000,4.000,1.000,12.000,8.000,7.000,0.000,8.000,0.000,0.000,0.000,13.000,0.000,6.000,0.000,6.000,12.000,0.000,10.000,13.000,8.000,0.000,13.000,11.000,15.000,16.000,3.000,0.000,11.000,2.000,1.000,17.000,2.000,9.000,11.000,3.000,0.000,11.000,10.000,0.000,4.000,14.000,0.000,3.000,2.000,0.000
48 | 0.000,0.000,16.000,0.000,2.000,8.000,0.000,14.000,10.000,15.000,12.000,5.000,0.000,0.000,6.000,10.000,0.000,7.000,0.000,18.000,0.000,12.000,0.000,8.000,0.000,6.000,14.000,0.000,16.000,13.000,9.000,16.000,0.000,0.000,10.000,9.000,4.000,11.000,15.000,2.000,4.000,10.000,14.000,3.000,5.000,13.000,9.000,0.000,14.000,5.000
49 | 0.000,2.000,15.000,0.000,5.000,5.000,0.000,0.000,3.000,0.000,0.000,8.000,1.000,6.000,0.000,17.000,11.000,16.000,11.000,9.000,1.000,4.000,7.000,11.000,5.000,1.000,0.000,0.000,8.000,0.000,12.000,14.000,14.000,3.000,17.000,18.000,0.000,4.000,8.000,0.000,11.000,13.000,11.000,6.000,8.000,4.000,6.000,2.000,0.000,0.000
50 | 0.000,0.000,0.000,8.000,15.000,0.000,5.000,0.000,9.000,3.000,7.000,10.000,9.000,7.000,8.000,0.000,11.000,4.000,10.000,12.000,4.000,8.000,0.000,11.000,0.000,12.000,15.000,5.000,5.000,14.000,14.000,4.000,2.000,8.000,11.000,17.000,0.000,4.000,15.000,0.000,5.000,6.000,15.000,9.000,2.000,5.000,11.000,13.000,0.000,0.000
51 |
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3 | 0.000,7.000,0.000,2.000,6.000,5.000,0.000,3.000,0.000,13.000,0.000,7.000,0.000,0.000,4.000,12.000,0.000,3.000,6.000,12.000,8.000,0.000,6.000,12.000,6.000,6.000,11.000,3.000,8.000,5.000,14.000,2.000,5.000,5.000,10.000,8.000,4.000,5.000,7.000,1.000,4.000,6.000,7.000,3.000,2.000,4.000,7.000,8.000,5.000,9.000
4 | 13.000,11.000,0.000,0.000,0.000,7.000,2.000,9.000,7.000,0.000,5.000,6.000,8.000,7.000,11.000,2.000,3.000,0.000,4.000,5.000,0.000,5.000,12.000,5.000,10.000,9.000,0.000,11.000,1.000,1.000,0.000,0.000,3.000,7.000,6.000,0.000,7.000,3.000,0.000,10.000,0.000,3.000,0.000,11.000,12.000,1.000,0.000,1.000,2.000,3.000
5 | 0.000,1.000,11.000,0.000,0.000,5.000,1.000,0.000,2.000,8.000,0.000,8.000,3.000,3.000,7.000,11.000,1.000,6.000,4.000,4.000,7.000,0.000,0.000,14.000,3.000,2.000,13.000,6.000,7.000,13.000,8.000,5.000,7.000,4.000,10.000,11.000,0.000,6.000,4.000,4.000,10.000,12.000,14.000,0.000,0.000,11.000,6.000,7.000,5.000,5.000
6 | 6.000,5.000,3.000,12.000,1.000,0.000,2.000,5.000,5.000,2.000,6.000,0.000,7.000,10.000,12.000,0.000,12.000,6.000,12.000,0.000,5.000,6.000,3.000,2.000,6.000,12.000,5.000,8.000,0.000,0.000,1.000,0.000,0.000,1.000,3.000,2.000,12.000,4.000,0.000,5.000,0.000,6.000,0.000,5.000,6.000,0.000,4.000,1.000,3.000,0.000
7 | 6.000,8.000,0.000,3.000,0.000,8.000,0.000,3.000,2.000,6.000,8.000,1.000,8.000,7.000,10.000,7.000,4.000,3.000,8.000,0.000,5.000,7.000,8.000,0.000,10.000,8.000,0.000,13.000,5.000,2.000,6.000,0.000,3.000,5.000,3.000,0.000,12.000,0.000,1.000,7.000,1.000,2.000,0.000,6.000,7.000,0.000,6.000,4.000,6.000,0.000
8 | 12.000,10.000,9.000,11.000,1.000,4.000,5.000,0.000,11.000,0.000,4.000,1.000,10.000,9.000,10.000,0.000,6.000,4.000,5.000,0.000,6.000,7.000,10.000,4.000,5.000,10.000,0.000,6.000,5.000,0.000,0.000,6.000,0.000,5.000,7.000,0.000,10.000,3.000,5.000,8.000,7.000,5.000,1.000,11.000,6.000,0.000,1.000,0.000,1.000,5.000
9 | 9.000,12.000,6.000,8.000,0.000,5.000,6.000,11.000,0.000,0.000,2.000,0.000,3.000,6.000,13.000,2.000,8.000,7.000,13.000,1.000,0.000,13.000,13.000,8.000,2.000,10.000,0.000,2.000,2.000,3.000,0.000,0.000,0.000,4.000,0.000,2.000,10.000,5.000,0.000,9.000,0.000,4.000,0.000,13.000,7.000,1.000,4.000,2.000,0.000,0.000
10 | 0.000,4.000,8.000,4.000,4.000,3.000,3.000,0.000,0.000,0.000,1.000,4.000,6.000,4.000,0.000,11.000,0.000,8.000,2.000,5.000,12.000,0.000,0.000,13.000,2.000,0.000,7.000,0.000,11.000,4.000,1.000,8.000,12.000,5.000,6.000,10.000,5.000,6.000,8.000,0.000,13.000,6.000,7.000,5.000,1.000,11.000,10.000,3.000,14.000,11.000
11 | 4.000,13.000,5.000,13.000,0.000,8.000,12.000,11.000,10.000,0.000,0.000,4.000,6.000,13.000,7.000,0.000,5.000,3.000,7.000,4.000,2.000,9.000,10.000,5.000,10.000,8.000,5.000,10.000,0.000,3.000,4.000,0.000,6.000,3.000,6.000,0.000,10.000,5.000,6.000,13.000,2.000,0.000,0.000,9.000,6.000,3.000,4.000,6.000,0.000,1.000
12 | 0.000,8.000,5.000,7.000,7.000,3.000,2.000,2.000,6.000,12.000,1.000,0.000,2.000,5.000,2.000,12.000,3.000,13.000,4.000,10.000,10.000,0.000,6.000,9.000,0.000,3.000,12.000,0.000,4.000,12.000,4.000,2.000,12.000,13.000,12.000,10.000,0.000,12.000,7.000,0.000,8.000,7.000,8.000,0.000,0.000,6.000,9.000,5.000,7.000,11.000
13 | 9.000,10.000,1.000,7.000,1.000,12.000,11.000,11.000,11.000,1.000,7.000,6.000,0.000,12.000,4.000,0.000,11.000,0.000,9.000,0.000,7.000,10.000,10.000,0.000,8.000,6.000,0.000,8.000,1.000,0.000,0.000,4.000,0.000,0.000,1.000,0.000,10.000,3.000,0.000,8.000,6.000,0.000,5.000,8.000,10.000,3.000,0.000,7.000,3.000,2.000
14 | 6.000,6.000,0.000,9.000,0.000,6.000,6.000,14.000,4.000,2.000,10.000,8.000,6.000,0.000,13.000,0.000,8.000,2.000,3.000,7.000,0.000,9.000,9.000,1.000,9.000,7.000,0.000,4.000,1.000,0.000,0.000,0.000,3.000,0.000,5.000,0.000,5.000,5.000,0.000,9.000,0.000,2.000,6.000,5.000,13.000,0.000,0.000,0.000,7.000,5.000
15 | 7.000,4.000,0.000,5.000,1.000,5.000,11.000,5.000,5.000,4.000,9.000,4.000,7.000,1.000,0.000,5.000,11.000,0.000,4.000,0.000,1.000,11.000,5.000,1.000,7.000,8.000,0.000,8.000,0.000,4.000,4.000,2.000,0.000,5.000,6.000,4.000,12.000,0.000,2.000,7.000,5.000,0.000,3.000,5.000,14.000,4.000,0.000,0.000,0.000,0.000
16 | 0.000,2.000,13.000,0.000,5.000,0.000,5.000,5.000,3.000,11.000,0.000,12.000,4.000,3.000,0.000,0.000,6.000,11.000,7.000,10.000,3.000,3.000,0.000,11.000,0.000,1.000,13.000,5.000,15.000,11.000,8.000,3.000,10.000,9.000,9.000,6.000,1.000,6.000,12.000,0.000,12.000,6.000,4.000,0.000,5.000,5.000,10.000,13.000,5.000,9.000
17 | 12.000,11.000,3.000,9.000,0.000,10.000,2.000,13.000,10.000,2.000,4.000,3.000,4.000,11.000,13.000,0.000,0.000,7.000,8.000,0.000,5.000,12.000,5.000,2.000,4.000,8.000,0.000,6.000,6.000,4.000,0.000,0.000,4.000,2.000,4.000,0.000,10.000,1.000,0.000,10.000,5.000,1.000,0.000,5.000,5.000,0.000,4.000,0.000,7.000,0.000
18 | 2.000,0.000,3.000,6.000,10.000,0.000,1.000,0.000,0.000,5.000,1.000,8.000,0.000,0.000,7.000,6.000,7.000,0.000,2.000,4.000,7.000,0.000,1.000,7.000,2.000,2.000,11.000,4.000,5.000,8.000,5.000,8.000,5.000,11.000,12.000,8.000,0.000,7.000,7.000,0.000,6.000,11.000,12.000,7.000,3.000,8.000,8.000,9.000,7.000,8.000
19 | 4.000,5.000,5.000,2.000,0.000,1.000,5.000,11.000,6.000,2.000,2.000,5.000,4.000,6.000,11.000,4.000,6.000,0.000,0.000,0.000,8.000,8.000,4.000,0.000,10.000,9.000,0.000,5.000,5.000,7.000,2.000,7.000,2.000,4.000,6.000,0.000,9.000,0.000,3.000,11.000,0.000,0.000,3.000,5.000,5.000,0.000,0.000,0.000,4.000,0.000
20 | 2.000,5.000,12.000,0.000,9.000,0.000,5.000,4.000,5.000,9.000,0.000,10.000,0.000,8.000,3.000,14.000,7.000,7.000,0.000,0.000,10.000,0.000,0.000,7.000,0.000,0.000,5.000,1.000,11.000,11.000,8.000,11.000,11.000,4.000,14.000,12.000,1.000,13.000,4.000,4.000,6.000,12.000,8.000,8.000,0.000,8.000,11.000,7.000,2.000,9.000
21 | 0.000,0.000,6.000,5.000,11.000,0.000,1.000,5.000,0.000,6.000,0.000,10.000,0.000,1.000,0.000,7.000,2.000,5.000,3.000,13.000,0.000,0.000,0.000,4.000,3.000,0.000,12.000,5.000,9.000,7.000,12.000,5.000,14.000,11.000,10.000,5.000,0.000,5.000,4.000,0.000,4.000,10.000,5.000,5.000,0.000,13.000,8.000,7.000,4.000,12.000
22 | 9.000,15.000,3.000,7.000,3.000,7.000,14.000,9.000,11.000,0.000,4.000,4.000,6.000,12.000,13.000,1.000,7.000,2.000,8.000,0.000,3.000,0.000,13.000,6.000,11.000,11.000,8.000,11.000,5.000,0.000,0.000,0.000,2.000,5.000,0.000,6.000,10.000,4.000,0.000,12.000,0.000,2.000,6.000,6.000,10.000,1.000,0.000,5.000,6.000,3.000
23 | 10.000,9.000,7.000,6.000,3.000,10.000,14.000,8.000,4.000,5.000,5.000,3.000,12.000,3.000,4.000,1.000,5.000,1.000,10.000,6.000,8.000,4.000,0.000,2.000,7.000,12.000,2.000,7.000,6.000,0.000,7.000,1.000,6.000,5.000,0.000,7.000,12.000,0.000,0.000,8.000,0.000,0.000,1.000,9.000,7.000,5.000,0.000,1.000,4.000,6.000
24 | 4.000,0.000,8.000,0.000,7.000,0.000,0.000,4.000,0.000,0.000,0.000,9.000,3.000,5.000,5.000,13.000,2.000,13.000,1.000,8.000,3.000,5.000,5.000,0.000,0.000,0.000,9.000,4.000,4.000,13.000,11.000,7.000,2.000,9.000,10.000,10.000,0.000,5.000,11.000,1.000,3.000,9.000,11.000,4.000,4.000,8.000,6.000,2.000,7.000,7.000
25 | 5.000,6.000,5.000,11.000,3.000,10.000,4.000,5.000,5.000,0.000,4.000,0.000,13.000,5.000,10.000,0.000,9.000,8.000,10.000,0.000,4.000,12.000,13.000,1.000,0.000,9.000,0.000,13.000,0.000,0.000,2.000,2.000,0.000,0.000,2.000,7.000,7.000,0.000,2.000,9.000,0.000,0.000,5.000,8.000,5.000,4.000,0.000,0.000,8.000,0.000
26 | 5.000,2.000,8.000,11.000,8.000,6.000,4.000,14.000,7.000,7.000,6.000,1.000,13.000,6.000,7.000,4.000,9.000,0.000,8.000,1.000,0.000,11.000,12.000,0.000,2.000,0.000,0.000,8.000,0.000,5.000,5.000,2.000,1.000,5.000,0.000,1.000,3.000,0.000,0.000,3.000,0.000,0.000,4.000,11.000,6.000,2.000,5.000,2.000,2.000,0.000
27 | 0.000,4.000,13.000,2.000,13.000,7.000,4.000,3.000,6.000,5.000,4.000,4.000,0.000,5.000,0.000,8.000,6.000,6.000,2.000,11.000,7.000,5.000,3.000,13.000,0.000,1.000,0.000,5.000,12.000,7.000,4.000,9.000,6.000,14.000,8.000,3.000,1.000,8.000,7.000,0.000,11.000,15.000,10.000,3.000,0.000,4.000,2.000,9.000,8.000,11.000
28 | 8.000,9.000,0.000,5.000,0.000,13.000,10.000,4.000,6.000,4.000,7.000,0.000,7.000,9.000,11.000,4.000,4.000,0.000,6.000,0.000,3.000,6.000,4.000,3.000,6.000,5.000,5.000,0.000,0.000,7.000,4.000,5.000,8.000,0.000,0.000,1.000,9.000,3.000,5.000,10.000,0.000,2.000,5.000,12.000,3.000,8.000,6.000,0.000,3.000,0.000
29 | 4.000,2.000,8.000,7.000,4.000,0.000,1.000,0.000,0.000,5.000,7.000,3.000,0.000,3.000,0.000,4.000,5.000,7.000,2.000,13.000,10.000,0.000,0.000,8.000,0.000,2.000,13.000,0.000,0.000,12.000,3.000,4.000,7.000,12.000,10.000,8.000,0.000,11.000,9.000,0.000,13.000,8.000,8.000,0.000,0.000,9.000,8.000,10.000,5.000,7.000
30 | 0.000,6.000,12.000,0.000,8.000,0.000,3.000,0.000,5.000,10.000,4.000,4.000,4.000,5.000,9.000,13.000,1.000,6.000,3.000,6.000,4.000,0.000,4.000,4.000,0.000,0.000,7.000,3.000,13.000,0.000,5.000,6.000,11.000,13.000,6.000,10.000,0.000,12.000,10.000,0.000,8.000,3.000,12.000,2.000,0.000,12.000,12.000,5.000,6.000,7.000
31 | 0.000,6.000,9.000,6.000,9.000,0.000,4.000,0.000,2.000,10.000,2.000,4.000,3.000,0.000,0.000,9.000,3.000,5.000,0.000,10.000,4.000,4.000,6.000,11.000,0.000,0.000,13.000,0.000,9.000,12.000,0.000,5.000,12.000,12.000,12.000,3.000,3.000,11.000,4.000,3.000,10.000,3.000,10.000,0.000,0.000,4.000,1.000,12.000,8.000,5.000
32 | 6.000,2.000,9.000,0.000,10.000,0.000,9.000,4.000,6.000,5.000,5.000,14.000,0.000,4.000,5.000,5.000,2.000,12.000,0.000,8.000,9.000,0.000,0.000,7.000,4.000,1.000,13.000,0.000,7.000,8.000,0.000,0.000,9.000,9.000,5.000,12.000,6.000,11.000,10.000,1.000,13.000,6.000,12.000,0.000,3.000,9.000,5.000,3.000,0.000,6.000
33 | 0.000,2.000,8.000,0.000,10.000,6.000,4.000,0.000,1.000,13.000,0.000,13.000,8.000,2.000,1.000,8.000,1.000,4.000,6.000,5.000,13.000,7.000,4.000,3.000,2.000,0.000,4.000,0.000,4.000,3.000,7.000,3.000,0.000,8.000,9.000,10.000,0.000,7.000,9.000,3.000,10.000,10.000,11.000,0.000,0.000,7.000,14.000,4.000,11.000,7.000
34 | 0.000,0.000,11.000,2.000,6.000,0.000,5.000,0.000,0.000,5.000,0.000,3.000,4.000,3.000,0.000,6.000,0.000,4.000,1.000,6.000,6.000,0.000,5.000,6.000,0.000,3.000,5.000,0.000,9.000,10.000,6.000,13.000,2.000,0.000,9.000,4.000,0.000,13.000,11.000,1.000,10.000,8.000,6.000,5.000,0.000,6.000,3.000,3.000,6.000,10.000
35 | 0.000,2.000,6.000,1.000,9.000,1.000,1.000,0.000,0.000,8.000,4.000,7.000,6.000,5.000,0.000,6.000,3.000,6.000,6.000,8.000,9.000,0.000,1.000,6.000,4.000,1.000,8.000,3.000,9.000,4.000,5.000,13.000,1.000,8.000,0.000,13.000,2.000,4.000,4.000,8.000,6.000,3.000,6.000,1.000,7.000,8.000,5.000,10.000,6.000,10.000
36 | 2.000,0.000,9.000,5.000,11.000,0.000,5.000,0.000,3.000,10.000,4.000,7.000,4.000,0.000,3.000,10.000,0.000,5.000,4.000,7.000,12.000,0.000,3.000,9.000,2.000,4.000,3.000,0.000,14.000,9.000,8.000,11.000,7.000,12.000,9.000,0.000,0.000,5.000,3.000,4.000,7.000,4.000,4.000,0.000,2.000,3.000,8.000,10.000,11.000,12.000
37 | 4.000,4.000,0.000,11.000,4.000,7.000,7.000,5.000,6.000,1.000,6.000,0.000,5.000,5.000,8.000,0.000,2.000,3.000,8.000,3.000,7.000,11.000,9.000,5.000,14.000,3.000,5.000,5.000,8.000,0.000,1.000,5.000,0.000,5.000,2.000,0.000,0.000,5.000,5.000,10.000,0.000,1.000,2.000,10.000,11.000,1.000,1.000,5.000,5.000,7.000
38 | 0.000,0.000,11.000,1.000,6.000,6.000,3.000,0.000,0.000,11.000,0.000,6.000,0.000,0.000,4.000,6.000,0.000,10.000,1.000,12.000,8.000,6.000,3.000,10.000,5.000,0.000,6.000,0.000,10.000,13.000,7.000,9.000,6.000,11.000,9.000,12.000,0.000,0.000,3.000,6.000,9.000,6.000,6.000,0.000,5.000,6.000,13.000,10.000,2.000,8.000
39 | 0.000,2.000,9.000,0.000,5.000,2.000,2.000,1.000,2.000,10.000,8.000,9.000,4.000,0.000,2.000,9.000,6.000,5.000,0.000,10.000,11.000,2.000,4.000,8.000,7.000,3.000,6.000,6.000,9.000,7.000,7.000,7.000,8.000,7.000,7.000,9.000,7.000,9.000,0.000,1.000,10.000,6.000,11.000,3.000,2.000,10.000,6.000,10.000,8.000,13.000
40 | 14.000,4.000,4.000,7.000,0.000,6.000,12.000,8.000,12.000,6.000,9.000,0.000,7.000,6.000,5.000,0.000,6.000,0.000,7.000,1.000,4.000,10.000,12.000,0.000,12.000,3.000,1.000,2.000,3.000,2.000,0.000,0.000,0.000,0.000,0.000,3.000,8.000,0.000,0.000,0.000,5.000,0.000,0.000,1.000,4.000,6.000,5.000,3.000,0.000,1.000
41 | 5.000,0.000,13.000,0.000,11.000,5.000,3.000,0.000,1.000,5.000,0.000,8.000,2.000,0.000,6.000,10.000,3.000,3.000,0.000,4.000,10.000,0.000,2.000,6.000,7.000,3.000,8.000,3.000,5.000,11.000,6.000,13.000,9.000,5.000,4.000,9.000,2.000,9.000,15.000,3.000,0.000,10.000,6.000,7.000,5.000,9.000,4.000,8.000,6.000,14.000
42 | 1.000,5.000,10.000,0.000,11.000,0.000,0.000,0.000,5.000,5.000,1.000,7.000,4.000,2.000,0.000,10.000,0.000,13.000,0.000,10.000,10.000,0.000,5.000,13.000,2.000,2.000,5.000,0.000,11.000,11.000,6.000,11.000,11.000,6.000,12.000,7.000,2.000,9.000,9.000,2.000,6.000,0.000,6.000,0.000,4.000,12.000,7.000,6.000,14.000,7.000
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25 | 0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,66.000
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34 | 0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,61.000
35 | 0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,37.000
36 | 0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,21.000
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42 | 0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,72.000
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45 | 0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,29.000
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47 | 0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,42.000
48 | 0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,20.000
49 | 0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,22.000
50 | 58.000,81.000,28.000,15.000,70.000,71.000,93.000,44.000,39.000,75.000,35.000,26.000,89.000,62.000,28.000,64.000,43.000,23.000,25.000,12.000,40.000,54.000,32.000,95.000,66.000,52.000,22.000,16.000,14.000,65.000,74.000,75.000,70.000,61.000,37.000,21.000,25.000,64.000,72.000,25.000,68.000,72.000,49.000,3.000,29.000,99.000,42.000,20.000,22.000,0.000
51 |
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5 | 0.000,0.000,66.000,0.000,0.000,0.000,69.000,97.000,0.000,62.000,0.000,0.000,80.000,79.000,0.000,0.000,0.000,86.000,0.000,0.000,0.000,0.000,73.000,25.000,87.000,70.000,53.000,80.000,0.000,0.000,0.000,21.000,28.000,0.000,0.000,0.000,53.000,0.000,0.000,0.000,25.000,76.000,2.000,6.000,0.000,0.000,0.000,5.000,0.000,0.000
6 | 0.000,55.000,33.000,0.000,66.000,0.000,0.000,0.000,36.000,0.000,32.000,49.000,0.000,0.000,0.000,0.000,0.000,81.000,0.000,87.000,100.000,0.000,61.000,85.000,0.000,0.000,89.000,5.000,0.000,0.000,0.000,84.000,0.000,0.000,6.000,0.000,0.000,0.000,0.000,68.000,0.000,92.000,0.000,0.000,39.000,86.000,0.000,68.000,76.000,2.000
7 | 47.000,0.000,0.000,38.000,0.000,14.000,0.000,85.000,41.000,0.000,47.000,0.000,0.000,95.000,85.000,0.000,0.000,60.000,0.000,0.000,0.000,0.000,62.000,56.000,0.000,72.000,0.000,61.000,0.000,16.000,38.000,0.000,95.000,80.000,16.000,0.000,22.000,0.000,69.000,0.000,0.000,23.000,0.000,0.000,34.000,0.000,0.000,12.000,0.000,0.000
8 | 0.000,34.000,54.000,0.000,34.000,96.000,22.000,0.000,77.000,72.000,90.000,0.000,0.000,98.000,67.000,0.000,0.000,0.000,80.000,66.000,10.000,7.000,0.000,0.000,0.000,0.000,0.000,63.000,0.000,85.000,0.000,6.000,0.000,51.000,57.000,0.000,0.000,1.000,89.000,85.000,0.000,0.000,0.000,8.000,0.000,0.000,0.000,0.000,53.000,0.000
9 | 93.000,0.000,0.000,0.000,36.000,83.000,0.000,0.000,0.000,100.000,0.000,0.000,74.000,0.000,36.000,10.000,0.000,0.000,0.000,100.000,25.000,54.000,0.000,2.000,46.000,0.000,30.000,1.000,9.000,0.000,0.000,89.000,67.000,97.000,20.000,60.000,1.000,66.000,94.000,21.000,0.000,0.000,92.000,0.000,52.000,60.000,52.000,0.000,0.000,0.000
10 | 99.000,29.000,0.000,66.000,0.000,27.000,16.000,94.000,0.000,0.000,36.000,0.000,64.000,41.000,30.000,0.000,42.000,0.000,0.000,48.000,0.000,76.000,69.000,0.000,37.000,0.000,11.000,0.000,60.000,48.000,0.000,24.000,0.000,0.000,27.000,0.000,0.000,0.000,11.000,0.000,0.000,81.000,81.000,0.000,63.000,84.000,75.000,62.000,69.000,72.000
11 | 0.000,0.000,0.000,0.000,0.000,0.000,81.000,47.000,0.000,98.000,0.000,70.000,87.000,88.000,0.000,48.000,0.000,0.000,0.000,55.000,73.000,0.000,61.000,27.000,0.000,0.000,44.000,54.000,13.000,0.000,0.000,0.000,0.000,94.000,77.000,0.000,0.000,0.000,32.000,0.000,36.000,49.000,26.000,61.000,0.000,15.000,76.000,54.000,0.000,0.000
12 | 19.000,0.000,14.000,42.000,0.000,37.000,21.000,96.000,0.000,64.000,0.000,0.000,36.000,22.000,0.000,53.000,72.000,0.000,0.000,71.000,14.000,0.000,30.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,6.000,0.000,81.000,0.000,23.000,0.000,82.000,14.000,9.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,18.000,0.000,99.000
13 | 25.000,0.000,65.000,99.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,86.000,0.000,9.000,44.000,0.000,65.000,66.000,49.000,0.000,87.000,61.000,83.000,0.000,0.000,0.000,72.000,84.000,62.000,0.000,0.000,4.000,22.000,52.000,74.000,0.000,85.000,62.000,0.000,63.000,0.000,46.000,0.000,0.000,79.000,0.000,57.000,0.000,0.000,62.000
14 | 74.000,26.000,55.000,93.000,0.000,0.000,0.000,72.000,32.000,0.000,0.000,95.000,0.000,0.000,86.000,38.000,83.000,55.000,0.000,4.000,0.000,53.000,0.000,11.000,0.000,0.000,64.000,0.000,0.000,0.000,9.000,0.000,0.000,17.000,0.000,0.000,0.000,51.000,0.000,0.000,0.000,29.000,45.000,0.000,53.000,0.000,34.000,47.000,2.000,38.000
15 | 0.000,0.000,33.000,28.000,0.000,0.000,0.000,8.000,0.000,14.000,61.000,0.000,88.000,0.000,0.000,0.000,0.000,0.000,0.000,68.000,59.000,41.000,79.000,0.000,70.000,22.000,92.000,0.000,84.000,14.000,0.000,0.000,0.000,5.000,0.000,0.000,0.000,0.000,0.000,17.000,6.000,84.000,71.000,50.000,0.000,61.000,22.000,0.000,9.000,25.000
16 | 61.000,93.000,0.000,33.000,14.000,0.000,0.000,0.000,0.000,57.000,77.000,0.000,23.000,94.000,0.000,0.000,0.000,91.000,0.000,0.000,15.000,0.000,0.000,0.000,0.000,0.000,25.000,39.000,0.000,99.000,31.000,0.000,0.000,34.000,97.000,34.000,13.000,0.000,0.000,15.000,0.000,0.000,0.000,83.000,0.000,34.000,0.000,39.000,6.000,0.000
17 | 23.000,0.000,0.000,78.000,80.000,0.000,0.000,85.000,34.000,29.000,22.000,0.000,0.000,13.000,0.000,69.000,0.000,19.000,0.000,0.000,0.000,0.000,29.000,81.000,45.000,73.000,55.000,23.000,0.000,38.000,0.000,22.000,0.000,0.000,0.000,0.000,21.000,0.000,0.000,39.000,0.000,0.000,18.000,25.000,11.000,0.000,0.000,70.000,0.000,0.000
18 | 14.000,0.000,99.000,32.000,0.000,10.000,0.000,85.000,0.000,62.000,14.000,48.000,0.000,34.000,2.000,9.000,0.000,0.000,31.000,0.000,16.000,0.000,84.000,0.000,10.000,0.000,25.000,0.000,0.000,0.000,11.000,22.000,0.000,0.000,51.000,18.000,0.000,0.000,0.000,0.000,0.000,44.000,0.000,0.000,0.000,0.000,87.000,67.000,74.000,0.000
19 | 0.000,0.000,0.000,58.000,0.000,0.000,93.000,33.000,0.000,0.000,0.000,95.000,0.000,66.000,0.000,0.000,0.000,67.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,96.000,0.000,17.000,67.000,96.000,1.000,0.000,55.000,34.000,0.000,0.000,92.000,77.000,24.000,0.000,74.000,0.000,0.000,0.000,0.000,26.000,0.000,36.000,0.000,20.000
20 | 0.000,42.000,0.000,42.000,31.000,92.000,18.000,11.000,31.000,0.000,10.000,0.000,100.000,66.000,0.000,0.000,51.000,0.000,34.000,0.000,74.000,0.000,32.000,66.000,91.000,19.000,87.000,72.000,0.000,86.000,0.000,78.000,76.000,63.000,0.000,87.000,0.000,0.000,66.000,75.000,0.000,3.000,0.000,0.000,90.000,0.000,0.000,0.000,24.000,0.000
21 | 55.000,14.000,4.000,68.000,39.000,62.000,21.000,0.000,0.000,0.000,0.000,92.000,87.000,0.000,0.000,34.000,67.000,0.000,0.000,67.000,0.000,0.000,63.000,89.000,0.000,0.000,6.000,72.000,0.000,88.000,0.000,0.000,64.000,44.000,12.000,92.000,5.000,0.000,59.000,0.000,0.000,0.000,0.000,0.000,36.000,0.000,72.000,71.000,70.000,0.000
22 | 0.000,0.000,16.000,76.000,0.000,98.000,13.000,0.000,18.000,1.000,29.000,0.000,23.000,59.000,20.000,15.000,99.000,0.000,43.000,11.000,76.000,0.000,0.000,0.000,0.000,77.000,0.000,85.000,73.000,87.000,25.000,37.000,0.000,60.000,13.000,0.000,58.000,0.000,0.000,0.000,0.000,0.000,48.000,87.000,0.000,0.000,0.000,17.000,0.000,62.000
23 | 0.000,42.000,85.000,0.000,37.000,0.000,0.000,41.000,86.000,57.000,0.000,0.000,0.000,46.000,0.000,0.000,74.000,90.000,14.000,43.000,0.000,0.000,0.000,0.000,9.000,6.000,0.000,93.000,0.000,0.000,0.000,16.000,62.000,33.000,0.000,23.000,17.000,84.000,0.000,0.000,0.000,0.000,72.000,12.000,94.000,60.000,0.000,31.000,0.000,0.000
24 | 35.000,50.000,15.000,41.000,13.000,0.000,0.000,0.000,56.000,29.000,0.000,5.000,30.000,54.000,28.000,0.000,61.000,0.000,0.000,0.000,0.000,0.000,45.000,0.000,0.000,91.000,0.000,0.000,0.000,0.000,23.000,0.000,21.000,75.000,70.000,0.000,60.000,0.000,81.000,0.000,20.000,91.000,0.000,84.000,0.000,0.000,0.000,0.000,30.000,83.000
25 | 69.000,0.000,75.000,0.000,18.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,87.000,79.000,0.000,66.000,69.000,15.000,0.000,25.000,51.000,0.000,0.000,48.000,0.000,0.000,73.000,0.000,0.000,4.000,35.000,81.000,24.000,0.000,47.000,0.000,67.000,0.000,0.000,18.000,54.000,0.000,91.000,0.000,0.000,0.000,0.000,37.000,100.000,21.000
26 | 97.000,89.000,0.000,0.000,0.000,79.000,0.000,32.000,0.000,0.000,69.000,0.000,0.000,89.000,0.000,83.000,2.000,0.000,0.000,90.000,0.000,81.000,9.000,50.000,0.000,0.000,4.000,0.000,0.000,65.000,57.000,13.000,27.000,0.000,0.000,74.000,0.000,83.000,0.000,71.000,0.000,0.000,36.000,0.000,98.000,0.000,0.000,0.000,0.000,62.000
27 | 90.000,0.000,19.000,0.000,0.000,17.000,8.000,96.000,99.000,0.000,0.000,0.000,0.000,0.000,9.000,2.000,14.000,0.000,0.000,82.000,93.000,0.000,0.000,7.000,80.000,0.000,0.000,0.000,0.000,60.000,0.000,74.000,0.000,8.000,43.000,89.000,84.000,0.000,0.000,94.000,0.000,0.000,1.000,0.000,68.000,0.000,90.000,0.000,0.000,0.000
28 | 34.000,88.000,11.000,32.000,0.000,0.000,0.000,92.000,49.000,0.000,0.000,0.000,58.000,0.000,50.000,29.000,0.000,7.000,41.000,32.000,43.000,49.000,71.000,0.000,86.000,0.000,0.000,0.000,49.000,50.000,44.000,65.000,0.000,0.000,0.000,0.000,0.000,36.000,0.000,0.000,93.000,0.000,94.000,0.000,45.000,0.000,50.000,0.000,0.000,70.000
29 | 52.000,94.000,0.000,0.000,0.000,0.000,0.000,46.000,0.000,46.000,6.000,18.000,23.000,0.000,0.000,0.000,23.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,79.000,6.000,32.000,0.000,0.000,10.000,37.000,35.000,56.000,93.000,0.000,92.000,76.000,0.000,29.000,0.000,0.000,66.000,0.000,0.000,13.000,47.000,98.000,54.000,48.000,0.000
30 | 81.000,31.000,0.000,1.000,75.000,0.000,0.000,0.000,91.000,0.000,0.000,62.000,65.000,0.000,0.000,0.000,77.000,83.000,95.000,4.000,0.000,0.000,0.000,0.000,0.000,60.000,0.000,46.000,12.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,75.000,37.000,0.000,59.000,0.000,89.000,0.000,0.000,0.000,54.000,18.000,0.000,55.000,26.000
31 | 0.000,0.000,23.000,34.000,32.000,0.000,0.000,0.000,18.000,39.000,0.000,0.000,3.000,0.000,0.000,0.000,12.000,0.000,0.000,0.000,0.000,62.000,73.000,0.000,0.000,65.000,0.000,99.000,58.000,25.000,0.000,3.000,55.000,91.000,89.000,0.000,0.000,0.000,0.000,51.000,0.000,0.000,0.000,0.000,100.000,94.000,0.000,12.000,33.000,70.000
32 | 30.000,0.000,42.000,0.000,0.000,0.000,46.000,0.000,0.000,0.000,24.000,0.000,0.000,21.000,0.000,47.000,0.000,0.000,26.000,0.000,0.000,0.000,85.000,79.000,36.000,0.000,87.000,67.000,0.000,28.000,0.000,0.000,0.000,0.000,84.000,0.000,57.000,91.000,96.000,0.000,0.000,0.000,82.000,0.000,100.000,98.000,14.000,0.000,0.000,29.000
33 | 0.000,47.000,7.000,85.000,0.000,45.000,0.000,37.000,0.000,0.000,0.000,0.000,97.000,0.000,20.000,0.000,76.000,0.000,96.000,0.000,0.000,72.000,0.000,0.000,0.000,93.000,0.000,0.000,0.000,0.000,0.000,86.000,0.000,91.000,27.000,0.000,90.000,0.000,0.000,23.000,62.000,82.000,13.000,5.000,0.000,19.000,0.000,16.000,55.000,30.000
34 | 0.000,21.000,49.000,0.000,72.000,58.000,0.000,0.000,0.000,0.000,20.000,0.000,0.000,0.000,0.000,3.000,0.000,0.000,27.000,0.000,0.000,0.000,8.000,1.000,0.000,77.000,0.000,89.000,0.000,0.000,25.000,0.000,25.000,0.000,0.000,0.000,58.000,0.000,82.000,0.000,66.000,0.000,47.000,0.000,0.000,31.000,59.000,51.000,0.000,0.000
35 | 0.000,38.000,55.000,48.000,29.000,85.000,0.000,0.000,0.000,70.000,0.000,0.000,66.000,63.000,35.000,69.000,15.000,76.000,90.000,0.000,67.000,0.000,3.000,4.000,32.000,50.000,0.000,81.000,17.000,19.000,0.000,5.000,0.000,76.000,0.000,42.000,0.000,0.000,0.000,0.000,0.000,33.000,93.000,0.000,0.000,0.000,0.000,74.000,0.000,19.000
36 | 24.000,0.000,0.000,76.000,63.000,5.000,0.000,0.000,0.000,75.000,99.000,58.000,24.000,97.000,0.000,0.000,0.000,0.000,0.000,0.000,84.000,69.000,0.000,0.000,0.000,33.000,0.000,13.000,0.000,81.000,0.000,91.000,79.000,26.000,0.000,0.000,0.000,80.000,43.000,95.000,44.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,64.000,0.000
37 | 71.000,34.000,74.000,0.000,19.000,87.000,0.000,0.000,92.000,0.000,48.000,67.000,0.000,67.000,88.000,93.000,0.000,0.000,92.000,55.000,88.000,0.000,27.000,0.000,0.000,0.000,0.000,90.000,0.000,0.000,0.000,0.000,0.000,71.000,82.000,0.000,0.000,0.000,54.000,0.000,0.000,0.000,0.000,0.000,99.000,82.000,0.000,90.000,0.000,0.000
38 | 0.000,58.000,74.000,1.000,33.000,0.000,0.000,0.000,85.000,0.000,0.000,0.000,0.000,0.000,0.000,87.000,12.000,0.000,0.000,0.000,0.000,0.000,23.000,0.000,83.000,0.000,0.000,87.000,0.000,0.000,87.000,0.000,70.000,53.000,0.000,97.000,0.000,0.000,64.000,0.000,0.000,0.000,32.000,0.000,0.000,0.000,78.000,0.000,26.000,44.000
39 | 54.000,0.000,0.000,98.000,0.000,0.000,29.000,97.000,13.000,0.000,89.000,0.000,0.000,0.000,95.000,61.000,31.000,5.000,90.000,13.000,82.000,11.000,42.000,0.000,0.000,35.000,0.000,82.000,31.000,0.000,0.000,50.000,0.000,3.000,0.000,0.000,0.000,35.000,0.000,51.000,83.000,68.000,37.000,0.000,0.000,0.000,51.000,27.000,12.000,40.000
40 | 0.000,25.000,0.000,84.000,22.000,13.000,0.000,92.000,0.000,47.000,93.000,0.000,0.000,59.000,0.000,13.000,0.000,65.000,0.000,69.000,61.000,0.000,38.000,0.000,0.000,0.000,8.000,17.000,0.000,49.000,0.000,89.000,70.000,48.000,66.000,0.000,0.000,0.000,19.000,0.000,0.000,0.000,56.000,0.000,0.000,48.000,75.000,86.000,100.000,0.000
41 | 0.000,0.000,51.000,0.000,0.000,50.000,0.000,0.000,22.000,0.000,2.000,0.000,60.000,0.000,27.000,66.000,28.000,0.000,95.000,75.000,0.000,42.000,0.000,0.000,98.000,0.000,0.000,77.000,14.000,36.000,0.000,0.000,0.000,77.000,87.000,0.000,0.000,5.000,0.000,80.000,0.000,12.000,0.000,51.000,0.000,0.000,0.000,21.000,0.000,35.000
42 | 0.000,85.000,0.000,0.000,29.000,4.000,0.000,0.000,0.000,0.000,28.000,26.000,67.000,0.000,17.000,0.000,57.000,0.000,4.000,93.000,0.000,100.000,24.000,0.000,0.000,60.000,63.000,0.000,0.000,73.000,0.000,43.000,6.000,0.000,92.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,43.000,0.000,0.000,76.000,44.000,78.000,0.000
43 | 7.000,31.000,63.000,94.000,56.000,0.000,16.000,52.000,42.000,0.000,93.000,0.000,77.000,25.000,88.000,71.000,23.000,25.000,19.000,29.000,0.000,0.000,84.000,0.000,50.000,0.000,0.000,36.000,5.000,46.000,80.000,66.000,0.000,0.000,0.000,0.000,62.000,99.000,0.000,63.000,0.000,0.000,0.000,0.000,0.000,0.000,70.000,0.000,0.000,0.000
44 | 21.000,0.000,0.000,66.000,0.000,0.000,0.000,50.000,0.000,99.000,84.000,4.000,0.000,0.000,0.000,95.000,0.000,93.000,11.000,0.000,12.000,0.000,0.000,97.000,0.000,59.000,17.000,0.000,91.000,0.000,26.000,0.000,0.000,74.000,0.000,0.000,88.000,0.000,79.000,0.000,72.000,0.000,0.000,0.000,86.000,0.000,0.000,0.000,0.000,0.000
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49 | 2.000,52.000,0.000,0.000,0.000,0.000,97.000,77.000,44.000,46.000,23.000,0.000,89.000,80.000,74.000,34.000,55.000,60.000,0.000,0.000,7.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,16.000,58.000,0.000,87.000,43.000,73.000,0.000,13.000,4.000,0.000,5.000,0.000,22.000,0.000,28.000,37.000,98.000,0.000,15.000,50.000,0.000,14.000
50 | 0.000,0.000,0.000,7.000,0.000,8.000,0.000,0.000,87.000,0.000,70.000,38.000,43.000,45.000,14.000,99.000,53.000,0.000,16.000,14.000,0.000,3.000,93.000,46.000,73.000,49.000,52.000,0.000,0.000,68.000,0.000,18.000,74.000,0.000,0.000,0.000,99.000,0.000,0.000,0.000,36.000,74.000,0.000,0.000,15.000,0.000,0.000,0.000,0.000,0.000
51 |
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9 | 93.000,0.000,0.000,0.000,36.000,82.000,0.000,0.000,0.000,100.000,0.000,0.000,74.000,0.000,37.000,10.000,0.000,0.000,0.000,101.000,24.000,53.000,0.000,2.000,45.000,0.000,31.000,0.000,9.000,0.000,0.000,88.000,67.000,97.000,20.000,59.000,1.000,66.000,94.000,21.000,0.000,0.000,92.000,0.000,52.000,61.000,53.000,0.000,0.000,0.000
10 | 98.000,28.000,0.000,65.000,0.000,27.000,17.000,94.000,0.000,0.000,36.000,0.000,65.000,40.000,31.000,0.000,43.000,0.000,0.000,48.000,0.000,77.000,70.000,0.000,36.000,0.000,10.000,0.000,61.000,47.000,0.000,24.000,0.000,0.000,27.000,0.000,0.000,0.000,12.000,0.000,0.000,81.000,81.000,0.000,63.000,84.000,76.000,63.000,70.000,72.000
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18 | 14.000,0.000,100.000,33.000,0.000,9.000,0.000,84.000,0.000,62.000,14.000,47.000,0.000,34.000,3.000,9.000,0.000,0.000,31.000,0.000,17.000,0.000,85.000,0.000,9.000,0.000,25.000,0.000,0.000,0.000,11.000,21.000,0.000,0.000,50.000,18.000,0.000,0.000,0.000,0.000,0.000,45.000,0.000,0.000,0.000,0.000,88.000,68.000,75.000,0.000
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20 | 0.000,42.000,0.000,41.000,32.000,91.000,18.000,12.000,31.000,0.000,10.000,0.000,99.000,67.000,0.000,0.000,51.000,0.000,34.000,0.000,75.000,0.000,33.000,66.000,91.000,18.000,86.000,72.000,0.000,86.000,0.000,79.000,76.000,63.000,0.000,88.000,0.000,0.000,65.000,76.000,0.000,2.000,0.000,0.000,89.000,0.000,0.000,0.000,25.000,0.000
21 | 54.000,14.000,5.000,68.000,39.000,61.000,20.000,0.000,0.000,0.000,0.000,93.000,87.000,0.000,0.000,35.000,68.000,0.000,0.000,67.000,0.000,0.000,63.000,89.000,0.000,0.000,6.000,72.000,0.000,88.000,0.000,0.000,64.000,44.000,12.000,91.000,6.000,0.000,59.000,0.000,0.000,0.000,0.000,0.000,36.000,0.000,72.000,71.000,71.000,0.000
22 | 0.000,0.000,17.000,76.000,0.000,97.000,12.000,0.000,17.000,0.000,29.000,0.000,24.000,59.000,20.000,15.000,99.000,0.000,42.000,10.000,77.000,0.000,0.000,0.000,0.000,76.000,0.000,85.000,73.000,87.000,26.000,38.000,0.000,61.000,12.000,0.000,59.000,0.000,0.000,0.000,0.000,0.000,48.000,86.000,0.000,0.000,0.000,16.000,0.000,63.000
23 | 0.000,43.000,85.000,0.000,37.000,0.000,0.000,42.000,85.000,58.000,0.000,0.000,0.000,45.000,0.000,0.000,73.000,90.000,14.000,43.000,0.000,0.000,0.000,0.000,9.000,6.000,0.000,93.000,0.000,0.000,0.000,16.000,61.000,32.000,0.000,24.000,17.000,83.000,0.000,0.000,0.000,0.000,71.000,12.000,95.000,61.000,0.000,31.000,0.000,0.000
24 | 34.000,49.000,15.000,41.000,13.000,0.000,0.000,0.000,56.000,30.000,0.000,4.000,29.000,55.000,27.000,0.000,62.000,0.000,0.000,0.000,0.000,0.000,45.000,0.000,0.000,92.000,0.000,0.000,0.000,0.000,24.000,0.000,21.000,76.000,69.000,0.000,60.000,0.000,81.000,0.000,19.000,90.000,0.000,83.000,0.000,0.000,0.000,0.000,30.000,84.000
25 | 68.000,0.000,74.000,0.000,19.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,87.000,79.000,0.000,66.000,70.000,14.000,0.000,26.000,50.000,0.000,0.000,49.000,0.000,0.000,73.000,0.000,0.000,4.000,35.000,82.000,25.000,0.000,46.000,0.000,68.000,0.000,0.000,18.000,53.000,0.000,92.000,0.000,0.000,0.000,0.000,38.000,100.000,21.000
26 | 97.000,90.000,0.000,0.000,0.000,79.000,0.000,33.000,0.000,0.000,68.000,0.000,0.000,89.000,0.000,82.000,1.000,0.000,0.000,89.000,0.000,81.000,8.000,50.000,0.000,0.000,4.000,0.000,0.000,65.000,57.000,13.000,27.000,0.000,0.000,75.000,0.000,84.000,0.000,70.000,0.000,0.000,36.000,0.000,97.000,0.000,0.000,0.000,0.000,63.000
27 | 90.000,0.000,18.000,0.000,0.000,17.000,8.000,96.000,99.000,0.000,0.000,0.000,0.000,0.000,8.000,2.000,14.000,0.000,0.000,83.000,93.000,0.000,0.000,7.000,80.000,0.000,0.000,0.000,0.000,60.000,0.000,73.000,0.000,8.000,43.000,90.000,83.000,0.000,0.000,94.000,0.000,0.000,1.000,0.000,69.000,0.000,90.000,0.000,0.000,0.000
28 | 34.000,88.000,10.000,32.000,0.000,0.000,0.000,92.000,48.000,0.000,0.000,0.000,58.000,0.000,50.000,29.000,0.000,8.000,42.000,32.000,42.000,49.000,72.000,0.000,87.000,0.000,0.000,0.000,50.000,50.000,44.000,64.000,0.000,0.000,0.000,0.000,0.000,36.000,0.000,0.000,93.000,0.000,94.000,0.000,44.000,0.000,49.000,0.000,0.000,71.000
29 | 52.000,94.000,0.000,0.000,0.000,0.000,0.000,46.000,0.000,45.000,6.000,18.000,23.000,0.000,0.000,0.000,24.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,79.000,6.000,33.000,0.000,0.000,10.000,38.000,34.000,56.000,93.000,0.000,92.000,76.000,0.000,29.000,0.000,0.000,66.000,0.000,0.000,13.000,48.000,97.000,53.000,49.000,0.000
30 | 82.000,30.000,0.000,0.000,74.000,0.000,0.000,0.000,92.000,0.000,0.000,61.000,66.000,0.000,0.000,0.000,77.000,82.000,96.000,3.000,0.000,0.000,0.000,0.000,0.000,60.000,0.000,46.000,11.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,74.000,37.000,0.000,58.000,0.000,88.000,0.000,0.000,0.000,54.000,18.000,0.000,55.000,27.000
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7 | 1 0 0 0 1 0 0 0 1 0 1 1 0 0 0 0 0 0 1 0 0 1 1 0
8 | 0 0 0 0 1 0 0 0 1 0 0 1 1 0 0 0 0 1 1 0 0 1 1 0
9 | 1 0 0 0 1 0 1 0 0 0 1 1 1 0 0 0 0 1 1 1 1 1 1 1
10 | 0 0 0 0 1 0 1 0 1 0 0 1 1 0 0 0 0 1 1 1 0 1 1 1
11 | 1 0 0 1 1 0 1 0 1 0 0 0 1 0 0 1 1 1 1 0 1 1 1 1
12 | 0 1 1 0 0 0 1 0 1 0 0 0 1 0 0 1 0 1 1 0 1 1 1 1
13 | 0 0 0 1 1 0 1 0 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 0
14 | 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 1 0
15 | 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 1 1 0 0 0 1 0
16 | 1 0 0 1 0 1 1 0 1 1 1 0 1 0 0 0 1 1 1 1 1 1 1 0
17 | 1 0 0 0 1 0 0 0 1 0 1 0 1 0 0 1 0 0 1 0 0 1 1 0
18 | 1 1 1 1 1 1 1 0 1 1 1 1 1 1 0 1 0 0 1 1 1 1 1 1
19 | 1 1 1 1 1 0 1 1 1 0 1 1 1 1 1 1 1 1 0 1 1 1 1 1
20 | 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0
21 | 1 0 0 1 1 1 1 0 1 0 1 1 1 0 1 1 1 1 1 1 0 1 1 0
22 | 1 0 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1
23 | 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1
24 | 1 0 1 1 1 0 1 0 1 0 0 1 1 0 0 0 0 1 1 1 0 1 1 0
--------------------------------------------------------------------------------
/data/friendship.dat:
--------------------------------------------------------------------------------
1 | 0 1 0 0 0
2 | 1 0 1 0 0
3 | 0 1 0 1 1
4 | 0 0 1 0 1
5 | 0 0 1 1 0
--------------------------------------------------------------------------------
/data/info.dat:
--------------------------------------------------------------------------------
1 | 0 1 0 0 1 0 1 0 1 0
2 | 1 0 1 1 1 0 1 1 1 0
3 | 0 1 0 1 1 1 1 0 0 1
4 | 1 1 0 0 1 0 1 0 0 0
5 | 1 1 1 1 0 0 1 1 1 1
6 | 0 0 1 0 0 0 1 0 1 0
7 | 0 1 0 1 1 0 0 0 0 0
8 | 1 1 0 1 1 0 1 0 1 0
9 | 0 1 0 0 1 0 1 0 0 0
10 | 1 1 1 0 1 0 1 0 0 0
--------------------------------------------------------------------------------
/data/manufacturedgoods.dat:
--------------------------------------------------------------------------------
1 | 0 0 0 1 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1
2 | 1 0 1 1 0 1 0 0 1 0 1 1 1 0 0 0 1 1 1 0 1 0 1 0
3 | 1 1 0 1 1 1 1 0 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1
4 | 1 1 1 0 1 0 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1
5 | 1 1 1 1 0 1 1 1 1 1 1 0 1 1 0 1 1 1 1 1 1 1 1 1
6 | 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
7 | 0 0 0 0 1 0 0 1 1 0 0 0 1 0 0 0 0 1 1 0 0 1 1 1
8 | 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0
9 | 1 1 1 1 1 1 1 1 0 1 1 1 1 0 0 1 1 1 1 1 1 1 1 1
10 | 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
11 | 1 0 0 1 1 0 1 0 1 0 0 0 1 0 0 1 1 1 1 0 1 1 1 1
12 | 0 1 0 0 0 0 0 1 1 0 0 0 1 0 0 1 0 1 1 0 1 1 1 1
13 | 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1
14 | 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
15 | 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0
16 | 1 0 0 1 0 0 1 0 0 0 1 0 1 0 0 0 1 1 0 0 1 1 1 1
17 | 0 0 0 1 1 0 0 0 1 0 1 0 1 1 0 1 0 1 1 1 1 1 1 0
18 | 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1
19 | 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1
20 | 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
21 | 0 0 1 1 0 0 0 0 1 0 1 1 1 0 0 1 1 1 1 1 0 1 1 1
22 | 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1
23 | 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1
24 | 1 1 0 1 1 0 1 1 1 0 1 1 1 0 0 1 1 1 1 1 1 1 1 0
--------------------------------------------------------------------------------
/data/minerals.dat:
--------------------------------------------------------------------------------
1 | 0 0 1 0 0 0 1 0 0 0 0 0 1 0 0 0 0 1 1 1 0 1 1 1
2 | 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0
3 | 1 1 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 1 0
4 | 0 0 1 0 0 0 1 0 1 0 1 0 1 0 1 1 1 1 0 0 1 0 1 0
5 | 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1
6 | 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
7 | 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 1 1 1
8 | 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
9 | 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 1 1 0
10 | 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
11 | 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 1 0 0 1 1 1 0
12 | 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
13 | 1 1 1 1 0 1 1 0 0 1 1 0 0 0 0 1 1 1 0 0 1 1 1 0
14 | 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
15 | 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
16 | 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
17 | 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
18 | 1 1 0 0 0 0 1 0 1 0 0 0 0 1 1 0 1 0 0 1 0 1 1 1
19 | 0 0 0 0 1 0 0 0 0 0 1 1 0 1 0 0 0 0 0 0 0 1 0 1
20 | 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0
21 | 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0
22 | 1 0 1 1 1 1 1 1 1 0 1 1 0 1 0 1 1 1 1 1 1 0 1 1
23 | 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1
24 | 1 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 1 1 1 0 0 1 0
--------------------------------------------------------------------------------
/data/money.dat:
--------------------------------------------------------------------------------
1 | 0 0 1 0 1 0 0 1 1 1
2 | 0 0 1 0 0 0 0 0 0 0
3 | 0 0 0 0 0 0 0 1 0 0
4 | 0 1 1 0 0 0 1 1 1 0
5 | 0 1 1 0 0 0 0 1 1 0
6 | 0 0 0 0 0 0 0 0 0 0
7 | 0 1 0 0 0 0 0 1 0 0
8 | 0 0 0 0 0 0 0 0 1 1
9 | 0 0 1 0 0 0 0 1 0 0
10 | 0 0 0 0 0 0 0 0 0 0
--------------------------------------------------------------------------------
/example_biasedwalker.py:
--------------------------------------------------------------------------------
1 | __author__ = 'lisette.espin'
2 |
3 | #######################################################################
4 | # Dependencies
5 | #######################################################################
6 | import numpy as np
7 | from libs.mrqap import MRQAP
8 | import time
9 | from pandas.core.series import Series
10 |
11 | #######################################################################
12 | # Constants
13 | #######################################################################
14 | N = 50
15 | DIRECTED = True
16 | NPERMUTATIONS = 2000
17 |
18 | #######################################################################
19 | # Data Matrices
20 | #######################################################################
21 | Y = np.loadtxt('data-cg/data.matrix',delimiter=',')
22 | X1 = np.loadtxt('data-cg/noise-1.0.matrix',delimiter=',')
23 | X2 = np.loadtxt('data-cg/noise-5.0.matrix',delimiter=',')
24 | X3 = np.loadtxt('data-cg/noise-10.0.matrix',delimiter=',')
25 | X4 = np.loadtxt('data-cg/noise-100.0.matrix',delimiter=',')
26 | X5 = np.loadtxt('data-cg/noise-1000.0.matrix',delimiter=',')
27 | X6 = np.loadtxt('data-cg/20Homophily-80Heterophily.matrix',delimiter=',')
28 | X7 = np.loadtxt('data-cg/80Homophily-20Heterophily.matrix',delimiter=',')
29 | X8 = np.loadtxt('data-cg/80ToBlue-20ToRed.matrix',delimiter=',')
30 | X9 = np.loadtxt('data-cg/80ToRed-20ToBlue.matrix',delimiter=',')
31 | X10 = np.loadtxt('data-cg/90Homophily-10Heterophily.matrix',delimiter=',')
32 | X11 = np.loadtxt('data-cg/100Homophily-0Heterophily.matrix',delimiter=',')
33 | X12 = np.loadtxt('data-cg/ToBlueOnly.matrix',delimiter=',')
34 | X13 = np.loadtxt('data-cg/ToRedOnly.matrix',delimiter=',')
35 | X14 = np.loadtxt('data-cg/uniform-0.02.matrix',delimiter=',')
36 |
37 | X = {'NOISE1':X1, 'NOISE5':X2, 'NOISE10':X3, 'NOISE100':X4, 'NOISE1000':X5,
38 | 'Hom20Het80':X6, 'Hom80Het20':X7, 'Blue80Red20':X8, 'Red80Blue20':X9,
39 | 'Hom90Het10':X10,'Hom100Het0':X11, 'ToBlue':X12, 'ToRed':X13, 'UNIFORM':X14}
40 | Y = {'DATA':Y}
41 | np.random.seed(1)
42 |
43 | #######################################################################
44 | # QAP
45 | #######################################################################
46 | start_time = time.time()
47 | mrqap = MRQAP(Y=Y, X=X, npermutations=NPERMUTATIONS, diagonal=False, directed=DIRECTED, standarized=True)
48 | mrqap.mrqap()
49 | mrqap.summary()
50 | print("--- {}, {}: {} seconds ---".format('directed' if DIRECTED else 'undirected', NPERMUTATIONS, time.time() - start_time))
51 | mrqap.plot('betas','results-cg/betas.pdf')
52 | mrqap.plot('tvalues','results-cg/tvalues.pdf')
53 |
--------------------------------------------------------------------------------
/example_countries.py:
--------------------------------------------------------------------------------
1 | __author__ = 'lisette.espin'
2 |
3 | #######################################################################
4 | # Dependencies
5 | #######################################################################
6 | import numpy as np
7 | from libs.mrqap import MRQAP
8 | import time
9 |
10 | #######################################################################
11 | # Constants
12 | #######################################################################
13 | NCOUNTRIES = 249
14 | DIRECTED = True
15 | NPERMUTATIONS = 2000
16 |
17 | #######################################################################
18 | # Functions
19 | #######################################################################
20 | def getMatrix(path, directed=False, log1p=False):
21 | matrix = np.zeros(shape=(NCOUNTRIES,NCOUNTRIES))
22 | with open(path, 'rb') as f:
23 | for line in f:
24 | data = line.split(' ')
25 | c1 = int(data[0])-1
26 | c2 = int(data[1])-1
27 | v = np.log1p(float(data[2])) if log1p else float(data[2])
28 | matrix[c1][c2] = v # real data from file
29 | if not DIRECTED:
30 | matrix[c2][c1] = v # symmetry
31 | print '{} loaded as a matrix!'.format(path)
32 | return matrix
33 |
34 | #######################################################################
35 | # Data Matrices
36 | #######################################################################
37 | X1 = getMatrix('data/country_trade_index.txt',DIRECTED,True)
38 | X2 = getMatrix('data/country_distance_index.txt',DIRECTED,True)
39 | X3 = getMatrix('data/country_colonial_index.txt',DIRECTED)
40 | Y = getMatrix('data/country_lang_index.txt',DIRECTED)
41 | X = {'TRADE':X1, 'DISTANCE':X2, 'COLONIAL':X3}
42 | Y = {'LANG':Y}
43 | np.random.seed(1)
44 |
45 | #######################################################################
46 | # QAP
47 | #######################################################################
48 | start_time = time.time()
49 | mrqap = MRQAP(Y=Y, X=X, npermutations=NPERMUTATIONS, diagonal=False, directed=True)
50 | mrqap.mrqap()
51 | mrqap.summary()
52 | print("--- {}, {}: {} seconds ---".format('directed' if DIRECTED else 'undirected', NPERMUTATIONS, time.time() - start_time))
53 | mrqap.plot('betas')
54 | mrqap.plot('tvalues')
55 |
--------------------------------------------------------------------------------
/example_countries_timing_permutations.py:
--------------------------------------------------------------------------------
1 | __author__ = 'lisette.espin'
2 |
3 | #######################################################################
4 | # Dependencies
5 | #######################################################################
6 | import numpy as np
7 | from libs.mrqap import MRQAP
8 | import time
9 | from libs import utils
10 | from libs.profiling import Profiling
11 | import sys
12 |
13 |
14 | #######################################################################
15 | # Functions
16 | #######################################################################
17 | NCOUNTRIES = 249
18 | def getMatrix(path, directed=False, log1p=False):
19 | matrix = np.zeros(shape=(NCOUNTRIES,NCOUNTRIES))
20 | with open(path, 'rb') as f:
21 | for line in f:
22 | data = line.split(' ')
23 | c1 = int(data[0])-1
24 | c2 = int(data[1])-1
25 | v = np.log1p(float(data[2])) if log1p else float(data[2])
26 | matrix[c1][c2] = v # real data from file
27 | if not directed:
28 | matrix[c2][c1] = v # symmetry
29 | print '{} loaded as a matrix!'.format(path)
30 | return matrix
31 |
32 | #######################################################################
33 | # Main
34 | ######################################################################
35 | @profile
36 | def main(directed):
37 |
38 | logfile = 'results-permutations/timigs-{}.txt'.format('directed' if directed else 'undirected')
39 | memory = Profiling('Permutations {}'.format('directed' if directed else 'undirected'), 'results-permutations/python-profiling-nperm-nodes{}-{}.png'.format(NCOUNTRIES,'directed' if directed else 'undirected'), True)
40 | memory.check_memory('init-{}'.format('d' if directed else 'i'))
41 |
42 | #######################################################################
43 | # Data Matrices
44 | #######################################################################
45 | X1 = getMatrix('data-permutations/country_trade_index.txt',directed,True)
46 | memory.check_memory('X1-{}'.format('d' if directed else 'i'))
47 | X2 = getMatrix('data-permutations/country_distance_index.txt',directed,True)
48 | memory.check_memory('X2-{}'.format('d' if directed else 'i'))
49 | X3 = getMatrix('data-permutations/country_colonial_index.txt',directed)
50 | Y = getMatrix('data-permutations/country_lang_index.txt',directed)
51 | memory.check_memory('Y-{}'.format('d' if directed else 'i'))
52 | X = {'TRADE':X1, 'DISTANCE':X2, 'COLONIAL':X3}
53 | Y = {'LANG':Y}
54 | np.random.seed(1)
55 |
56 | #######################################################################
57 | # QAP
58 | #######################################################################
59 | perms = np.logspace(1,7,num=7-1, endpoint=False)
60 | for nperm in perms:
61 | start_time = time.time()
62 | mrqap = MRQAP(Y=Y, X=X, npermutations=int(nperm), diagonal=False, directed=directed, logfile=logfile, memory=memory)
63 | mrqap.mrqap()
64 |
65 | utils.printf("--- {}, nperm {}: {} seconds ---".format('directed' if directed else 'undirected', nperm, time.time() - start_time), logfile)
66 | mrqap.summary()
67 |
68 | fn = 'results-permutations/python-nperm{}-{}-.png'.format(nperm,'directed' if directed else 'undirected')
69 | mrqap.plot('betas', fn.replace('','betas'))
70 | mrqap.plot('tvalues', fn.replace('','tvalues'))
71 |
72 | utils.printf('******************************************************************************\n\n', logfile)
73 | del(mrqap)
74 | return
75 |
76 |
77 | if __name__ == '__main__':
78 | directed = sys.argv[1] == '1'
79 | main(directed)
80 | sys.exit(0)
81 |
--------------------------------------------------------------------------------
/example_diplomaticexchange.py:
--------------------------------------------------------------------------------
1 | __author__ = 'lisette.espin'
2 |
3 | #######################################################################
4 | # References
5 | # - http://www.albany.edu/faculty/kretheme/PAD637/ClassNotes/Spring%202013/Lab8.pdf
6 | #######################################################################
7 |
8 | #######################################################################
9 | # Dependencies
10 | #######################################################################
11 | import numpy as np
12 | from libs.mrqap import MRQAP
13 |
14 | #######################################################################
15 | # Data
16 | # Source: http://vlado.fmf.uni-lj.si/pub/networks/data/ucinet/ucidata.htm
17 | #######################################################################
18 | X1 = np.loadtxt('data/crudematerials.dat')
19 | X2 = np.loadtxt('data/foods.dat')
20 | X3 = np.loadtxt('data/manufacturedgoods.dat')
21 | X4 = np.loadtxt('data/minerals.dat')
22 | Y = np.loadtxt('data/diplomatic.dat')
23 | X = {'CRUDEMATERIALS':X1, 'FOODS':X2, 'MANUFACTUREDGOODS':X3, 'MINERALS':X4}
24 | Y = {'DIPLOMATIC':Y}
25 | np.random.seed(473)
26 |
27 | #######################################################################
28 | # QAP
29 | #######################################################################
30 | mrqap = MRQAP(Y=Y, X=X, npermutations=2000, diagonal=False, directed=True)
31 | mrqap.mrqap()
32 | mrqap.summary()
33 | mrqap.plot('betas')
34 | mrqap.plot('tvalues')
35 |
36 |
--------------------------------------------------------------------------------
/example_friendadvice.py:
--------------------------------------------------------------------------------
1 | __author__ = 'lisette.espin'
2 |
3 | #######################################################################
4 | # References
5 | # - http://www.albany.edu/faculty/kretheme/PAD637/ClassNotes/Spring%202013/Lab8.pdf
6 | #######################################################################
7 |
8 | #######################################################################
9 | # Dependencies
10 | #######################################################################
11 | import numpy as np
12 |
13 | from libs import utils
14 | from libs.qap import QAP
15 |
16 |
17 | #######################################################################
18 | # Data
19 | # Source: http://vlado.fmf.uni-lj.si/pub/networks/data/ucinet/ucidata.htm
20 | #######################################################################
21 | X = minfo = np.loadtxt('data/friendship.dat')
22 | Y = np.loadtxt('data/advice.dat')
23 | utils.printf('Friendship: \n{}'.format(X))
24 | utils.printf('Advise: \n{}'.format(Y))
25 | np.random.seed(831)
26 |
27 | #######################################################################
28 | # QAP
29 | #######################################################################
30 | qap = QAP(Y, X, 2000)
31 | qap.qap()
32 | qap.summary()
33 | qap.plot()
34 |
--------------------------------------------------------------------------------
/example_infomoney.py:
--------------------------------------------------------------------------------
1 | __author__ = 'lisette.espin'
2 |
3 | #######################################################################
4 | # References
5 | # - http://www.albany.edu/faculty/kretheme/PAD637/ClassNotes/Spring%202013/Lab8.pdf
6 | #######################################################################
7 |
8 | #######################################################################
9 | # Dependencies
10 | #######################################################################
11 | import numpy as np
12 | from libs import utils
13 | from libs.qap import QAP
14 |
15 | #######################################################################
16 | # Data
17 | # Source: http://vlado.fmf.uni-lj.si/pub/networks/data/ucinet/ucidata.htm
18 | #######################################################################
19 | minfo = minfo = np.loadtxt('data/info.dat')
20 | mmoney = np.loadtxt('data/money.dat')
21 | utils.printf('Information: \n{}'.format(minfo))
22 | utils.printf('Money Exchange: \n{}'.format(mmoney))
23 |
24 | #######################################################################
25 | # QAP
26 | #######################################################################
27 | qap = QAP(minfo, mmoney, 5000)
28 | qap.qap()
29 | qap.summary()
30 |
--------------------------------------------------------------------------------
/example_materialsgoods.py:
--------------------------------------------------------------------------------
1 | __author__ = 'lisette.espin'
2 |
3 | #######################################################################
4 | # References
5 | # - http://www.albany.edu/faculty/kretheme/PAD637/ClassNotes/Spring%202013/Lab8.pdf
6 | #######################################################################
7 |
8 | #######################################################################
9 | # Dependencies
10 | #######################################################################
11 | import numpy as np
12 | from libs import utils
13 | from libs.qap import QAP
14 |
15 | #######################################################################
16 | # Data
17 | # Source: http://vlado.fmf.uni-lj.si/pub/networks/data/ucinet/ucidata.htm
18 | #######################################################################
19 | X = np.loadtxt('data/crudematerials.dat')
20 | Y = np.loadtxt('data/manufacturedgoods.dat')
21 | utils.printf('Crude Materials: \n{}'.format(X))
22 | utils.printf('Manufactured Goods: \n{}'.format(Y))
23 | np.random.seed(15843)
24 |
25 | #######################################################################
26 | # QAP
27 | #######################################################################
28 | qap = QAP(Y, X, 5000)
29 | qap.qap()
30 | qap.summary()
31 | qap.plot()
--------------------------------------------------------------------------------
/example_randomgraphs.py:
--------------------------------------------------------------------------------
1 | __author__ = 'lisette.espin'
2 |
3 | #######################################################################
4 | # Dependencies
5 | #######################################################################
6 | import numpy as np
7 | from libs.mrqap import MRQAP
8 | import time
9 | from pandas.core.series import Series
10 |
11 | #######################################################################
12 | # Constants
13 | #######################################################################
14 | N = 50
15 | DIRECTED = True
16 | NPERMUTATIONS = 2000
17 |
18 | #######################################################################
19 | # Data Matrices
20 | #######################################################################
21 | Y = np.loadtxt('data-rg/data.matrix',delimiter=',')
22 | X1 = np.loadtxt('data-rg/noise-1.0.matrix',delimiter=',')
23 | X2 = np.loadtxt('data-rg/noise-5.0.matrix',delimiter=',')
24 | X3 = np.loadtxt('data-rg/noise-10.0.matrix',delimiter=',')
25 | X4 = np.loadtxt('data-rg/noise-100.0.matrix',delimiter=',')
26 | X5 = np.loadtxt('data-rg/noise-1000.0.matrix',delimiter=',')
27 | X6 = np.loadtxt('data-rg/Erdos-Renyi-p0.5.matrix',delimiter=',')
28 | X7 = np.loadtxt('data-rg/Erdos-Renyi-p1.0.matrix',delimiter=',')
29 | X8 = np.loadtxt('data-rg/Geometric-r1.0.matrix',delimiter=',')
30 | X9 = np.loadtxt('data-rg/Barabassi-m49.matrix',delimiter=',')
31 | X10 = np.loadtxt('data-rg/uniform-0.02.matrix',delimiter=',')
32 |
33 | X = {'NOISE1':X1, 'NOISE5':X2, 'NOISE10':X3, 'NOISE100':X4, 'NOISE1000':X5,'ERDOS05':X6, 'ERDOS1':X7, 'GEOMETRIC1':X8, 'BARABASI49':X9, 'UNIFORM':X10}
34 | Y = {'DATA':Y}
35 | np.random.seed(1)
36 |
37 | #######################################################################
38 | # QAP
39 | #######################################################################
40 | start_time = time.time()
41 | mrqap = MRQAP(Y=Y, X=X, npermutations=NPERMUTATIONS, diagonal=False, directed=DIRECTED, standarized=False)
42 | mrqap.mrqap()
43 | mrqap.summary()
44 | print("--- {}, {}: {} seconds ---".format('directed' if DIRECTED else 'undirected', NPERMUTATIONS, time.time() - start_time))
45 | mrqap.plot('betas','results-rg/betas.pdf')
46 | mrqap.plot('tvalues','results-rg/tvalues.pdf')
47 |
--------------------------------------------------------------------------------
/example_synthetic_timing_netsize_nodes_ernos_renyi.py:
--------------------------------------------------------------------------------
1 | __author__ = 'lisette.espin'
2 |
3 | #######################################################################
4 | # Dependencies
5 | #######################################################################
6 | import numpy as np
7 | from libs.mrqap import MRQAP
8 | import time
9 | import networkx as nx
10 | from libs import utils
11 | from libs.profiling import Profiling
12 | import threading
13 | import time
14 | import sys
15 | from libs.mtTkinter import *
16 | import os
17 |
18 | #######################################################################
19 | # Constants
20 | #######################################################################
21 | NPERMUTATIONS = 1000
22 | EDGEPROB = 0.1
23 | SEED = 1
24 | np.random.seed(SEED)
25 |
26 | #######################################################################
27 | # Global
28 | #######################################################################
29 |
30 |
31 | #######################################################################
32 | # Functions
33 | #######################################################################
34 | def generateGraph(nnodes, edgeprob, directed, pathtosave):
35 | if os.path.exists(pathtosave):
36 | matrix = np.loadtxt(pathtosave)
37 | else:
38 | shape = (nnodes,nnodes)
39 | G = nx.fast_gnp_random_graph(n=nnodes, p=edgeprob, directed=directed)
40 | matrix = nx.adjacency_matrix(G)
41 |
42 | if pathtosave is not None:
43 | np.savetxt(pathtosave, matrix.toarray(), fmt='%d',)
44 |
45 | print nx.info(G)
46 | matrix = matrix.toarray()
47 |
48 | return matrix
49 |
50 | #######################################################################
51 | # Main
52 | #######################################################################
53 | @profile
54 | def main(directed):
55 |
56 | logfile = 'results-synthetic-ernos-renyi/timigs-{}.txt'.format('directed' if directed else 'undirected')
57 | memory = Profiling('Nodes {}'.format('directed' if directed else 'undirected'), 'results-synthetic-ernos-renyi/python-profiling-netsize-edgeprob{}-nperm{}-{}.png'.format(EDGEPROB, NPERMUTATIONS,'directed' if directed else 'undirected'), False)
58 | memory.check_memory('init-{}'.format('d' if directed else 'i'))
59 |
60 | #######################################################################
61 | # Data Matrices
62 | #######################################################################
63 | #nnodes = np.logspace(1,7,num=7-1, endpoint=False)
64 | nnodes = np.logspace(1,5,num=5-1, endpoint=False)
65 | for n in nnodes:
66 | n = int(n)
67 | fn = 'data-synthetic-ernos-renyi/nodes{}_edgeprob{}_.dat'.format(n,EDGEPROB)
68 | memory.check_memory('nodes-{}'.format(n))
69 | X1 = generateGraph(n,EDGEPROB,directed, fn.replace('','X1'))
70 | memory.check_memory('X1-{}'.format(n))
71 | X2 = generateGraph(n,EDGEPROB,directed, fn.replace('','X2'))
72 | memory.check_memory('X2-{}'.format(n))
73 | X3 = generateGraph(n,EDGEPROB,directed, fn.replace('','X3'))
74 | memory.check_memory('X3-{}'.format(n))
75 | Y = generateGraph(n,EDGEPROB,directed, fn.replace('','Y'))
76 | memory.check_memory('Y-{}'.format(n))
77 | X = {'X1':X1, 'X2':X2, 'X3':X3}
78 | Y = {'Y':Y}
79 |
80 | #######################################################################
81 | # QAP
82 | #######################################################################
83 | start_time = time.time()
84 | mrqap = MRQAP(Y=Y, X=X, npermutations=int(NPERMUTATIONS), diagonal=False, directed=directed, logfile=logfile, memory=memory)
85 | mrqap.mrqap()
86 |
87 | utils.printf("\n--- {}, nodes {}: {} seconds ---".format('directed' if directed else 'undirected', n, time.time() - start_time), logfile)
88 | mrqap.summary()
89 |
90 | fn = 'results-synthetic-ernos-renyi/python-nodes{}-edgeprob{}-nperm{}-{}-.png'.format(n, EDGEPROB, NPERMUTATIONS,'directed' if directed else 'undirected')
91 | mrqap.plot('betas',fn.replace('','betas'))
92 | mrqap.plot('tvalues',fn.replace('','tvalues'))
93 |
94 | utils.printf('******************************************************************************\n\n', logfile)
95 | del(mrqap)
96 | return
97 |
98 |
99 | if __name__ == '__main__':
100 | directed = sys.argv[1] == '1'
101 | main(directed)
102 | sys.exit(0)
103 |
--------------------------------------------------------------------------------
/libs/__init__.py:
--------------------------------------------------------------------------------
1 | __author__ = 'espin'
2 |
--------------------------------------------------------------------------------
/libs/mrqap.py:
--------------------------------------------------------------------------------
1 | __author__ = 'espin'
2 |
3 | #######################################################################
4 | # Dependencies
5 | #######################################################################
6 | import sys
7 | import collections
8 | import numpy as np
9 | import pandas
10 | import matplotlib
11 | matplotlib.use('Agg')
12 | import matplotlib.pyplot as plt
13 | from statsmodels.formula.api import ols
14 | from libs import utils
15 | from libs.profiling import Profiling
16 | import time
17 | import gc
18 | from scipy import stats
19 | from scipy.stats.mstats import zscore
20 |
21 | #######################################################################
22 | # MRQAP
23 | #######################################################################
24 | INTERCEPT = 'Intercept'
25 |
26 | class MRQAP():
27 |
28 | #####################################################################################
29 | # Constructor and Init
30 | #####################################################################################
31 |
32 | def __init__(self, Y=None, X=None, npermutations=-1, diagonal=False, directed=False, logfile=None, memory=None, standarized=False):
33 | '''
34 | Initialization of variables
35 | :param Y: numpy array depended variable
36 | :param X: dictionary of numpy array independed variables
37 | :param npermutations: int number of permutations
38 | :param diagonal: boolean, False to delete diagonal from the OLS model
39 | :return:
40 | '''
41 | self.X = X # independent variables: dictionary of numpy.array
42 | self.Y = Y # dependent variable: dictionary numpy.array
43 | self.n = Y.values()[0].shape[0] # number of nodes
44 | self.npermutations = npermutations # number of permutations
45 | self.diagonal = diagonal # False then diagonal is removed
46 | self.directed = directed # directed True, undirected False
47 | self.data = None # Pandas DataFrame
48 | self.model = None # OLS Model y ~ x1 + x2 + x3 (original)
49 | self.v = collections.OrderedDict() # vectorized matrices, flatten variables with no diagonal
50 | self.betas = collections.OrderedDict() # betas distribution
51 | self.tvalues = collections.OrderedDict() # t-test values
52 | self.logfile = logfile # logfile path name
53 | self.standarized = standarized
54 | self.memory = memory if memory is not None else Profiling() # to track memory usage
55 |
56 | def init(self):
57 | '''
58 | Generating the original OLS model. Y and Xs are flattened.
59 | Also, the betas and tvalues dictionaries are initialized (key:independent variables, value:[])
60 | :return:
61 | '''
62 | self.v[self.Y.keys()[0]] = self._getFlatten(self.Y.values()[0])
63 | self._initCoefficients(INTERCEPT)
64 | for k,x in self.X.items():
65 | if k == self.Y.keys()[0]:
66 | utils.printf('ERROR: Idependent variable cannot be named \'[}\''.format(self.Y.keys()[0]), self.logfile)
67 | sys.exit(0)
68 | self.v[k] = self._getFlatten(x)
69 | self._initCoefficients(k)
70 | self.data = pandas.DataFrame(self.v)
71 | self.model = self._fit(self.v.keys(), self.data)
72 | del(self.X)
73 |
74 | def profiling(self, key):
75 | self.memory.check_memory(key)
76 |
77 | #####################################################################################
78 | # Core QAP methods
79 | #####################################################################################
80 |
81 | def mrqap(self):
82 | '''
83 | MultipleRegression Quadratic Assignment Procedure
84 | :return:
85 | '''
86 | directed = 'd' if self.directed else 'i'
87 | key = self.npermutations if self.memory.perm else self.n
88 | self.profiling('init-{}-{}'.format(directed, key))
89 | self.init()
90 | self.profiling('shuffle-{}-{}'.format(directed, key))
91 | self._shuffle()
92 | self.profiling('end-{}-{}'.format(directed, key))
93 |
94 | def _shuffle(self):
95 | '''
96 | Shuffling rows and columns npermutations times.
97 | beta coefficients and tvalues are stored.
98 | :return:
99 | '''
100 | for p in range(self.npermutations):
101 | self.Ymod = self.Y.values()[0].copy()
102 | self._rmperm()
103 | model = self._newfit()
104 | self._update_betas(model._results.params)
105 | self._update_tvalues(model.tvalues)
106 | self.Ymod = None
107 | gc.collect()
108 |
109 |
110 | def _newfit(self):
111 | '''
112 | Generates a new OLS fit model
113 | :return:
114 | '''
115 | newv = collections.OrderedDict()
116 | newv[self.Y.keys()[0]] = self._getFlatten(self.Ymod)
117 | for k,x in self.v.items():
118 | if k != self.Y.keys()[0]:
119 | newv[k] = x
120 | newdata = pandas.DataFrame(newv)
121 | newfit = self._fit(newv.keys(), newdata)
122 | del(newdata)
123 | del(newv)
124 | return newfit
125 |
126 |
127 | #####################################################################################
128 | # Handlers
129 | #####################################################################################
130 |
131 | def _fit(self, keys, data):
132 | '''
133 | Fitting OLS model
134 | v a dictionary with all variables.
135 | :return:
136 | '''
137 | if self.standarized:
138 | data = data.apply(lambda x: (x - np.mean(x)) / (np.std(x)), axis=0) #axis: 0 to each column, 1 to each row
139 |
140 | formula = '{} ~ {}'.format(self.Y.keys()[0], ' + '.join([k for k in keys if k != self.Y.keys()[0]]))
141 | return ols(formula, data).fit()
142 |
143 | def _initCoefficients(self, key):
144 | self.betas[key] = []
145 | self.tvalues[key] = []
146 |
147 | def _rmperm(self, duplicates=True):
148 | shuffle = np.random.permutation(self.Ymod.shape[0])
149 | np.take(self.Ymod,shuffle,axis=0,out=self.Ymod)
150 | np.take(self.Ymod,shuffle,axis=1,out=self.Ymod)
151 | del(shuffle)
152 |
153 | def _update_betas(self, betas):
154 | for idx,k in enumerate(self.betas.keys()):
155 | self.betas[k].append(round(betas[idx],6))
156 |
157 | def _update_tvalues(self, tvalues):
158 | for k in self.tvalues.keys():
159 | self.tvalues[k].append(round(tvalues[k],6))
160 |
161 | def _getFlatten(self, original):
162 | return self._deleteDiagonalFlatten(original)
163 |
164 |
165 | def _deleteDiagonalFlatten(self, original):
166 | tmp = original.flatten()
167 | if not self.diagonal:
168 | tmp = np.delete(tmp, [i*(original.shape[0]+1)for i in range(original.shape[0])])
169 | return tmp
170 |
171 | def _zeroDiagonalFlatten(self, original):
172 | tmp = original.copy()
173 | if not self.diagonal:
174 | np.fill_diagonal(tmp,0)
175 | f = tmp.flatten()
176 | del(tmp)
177 | return f
178 |
179 |
180 | #####################################################################################
181 | # Prints
182 | #####################################################################################
183 |
184 | def summary(self):
185 | '''
186 | Prints the OLS original summary and beta and tvalue summary.
187 | :return:
188 | '''
189 | self._summary_ols()
190 | self._summary_betas()
191 | self._summary_tvalues()
192 | self._ttest()
193 |
194 | def _summary_ols(self):
195 | '''
196 | Print the OLS summary
197 | :return:
198 | '''
199 | utils.printf('', self.logfile)
200 | utils.printf('=== Summary OLS (original) ===\n{}'.format(self.model.summary()), self.logfile)
201 | utils.printf('', self.logfile)
202 | utils.printf('# of Permutations: {}'.format(self.npermutations), self.logfile)
203 |
204 | def _summary_betas(self):
205 | '''
206 | Summary of beta coefficients
207 | :return:
208 | '''
209 | utils.printf('', self.logfile)
210 | utils.printf('=== Summary beta coefficients ===', self.logfile)
211 | utils.printf('{:20s}{:>10s}{:>10s}{:>10s}{:>10s}{:>12s}{:>12s}{:>12s}{:>12s}{:>12s}'.format('INDEPENDENT VAR.','MIN','MEDIAN','MEAN','MAX','STD. DEV.','B.COEFF.','As Large', 'As Small', 'P-VALUE'), self.logfile)
212 | for k,v in self.betas.items():
213 | beta = self.model.params[k]
214 | pstats = self.model.pvalues[k]
215 | aslarge = sum([1 for c in v if c >= beta]) / float(len(v))
216 | assmall = sum([1 for c in v if c <= beta]) / float(len(v))
217 | utils.printf('{:20s}{:10f}{:10f}{:10f}{:10f}{:12f}{:12f}{:12f}{:12f}{:12f}'.format(k,min(v),sorted(v)[len(v)/2],sum(v)/len(v),max(v),round(np.std(v),6),beta,aslarge,assmall,round(float(pstats),2)), self.logfile)
218 |
219 | def _summary_tvalues(self):
220 | '''
221 | Summary t-values
222 | :return:
223 | '''
224 | utils.printf('', self.logfile)
225 | utils.printf('=== Summary T-Values ===', self.logfile)
226 | utils.printf('{:20s}{:>10s}{:>10s}{:>10s}{:>10s}{:>12s}{:>12s}{:>12s}{:>12s}'.format('INDEPENDENT VAR.','MIN','MEDIAN','MEAN','MAX','STD. DEV.','T-TEST','As Large', 'As Small'), self.logfile)
227 | for k,v in self.tvalues.items():
228 | tstats = self.model.tvalues[k]
229 | aslarge = sum([1 for c in v if c >= tstats]) / float(len(v))
230 | assmall = sum([1 for c in v if c <= tstats]) / float(len(v))
231 | utils.printf('{:20s}{:10f}{:10f}{:10f}{:10f}{:12f}{:12f}{:12f}{:12f}'.format(k,min(v),sorted(v)[len(v)/2],sum(v)/len(v),max(v),round(np.std(v),6),round(float(tstats),2),aslarge,assmall), self.logfile)
232 |
233 | def _ttest(self):
234 | utils.printf('')
235 | utils.printf('========== T-TEST ==========')
236 | utils.printf('{:25s} {:25s} {:25s} {:25s}'.format('IND. VAR.','COEF.','T-STAT','P-VALUE'))
237 |
238 | ts = {}
239 | lines = {}
240 | for k,vlist in self.betas.items():
241 | t = stats.ttest_1samp(vlist,self.model.params[k])
242 | ts[k] = abs(round(float(t[0]),6))
243 | lines[k] = '{:20s} {:25f} {:25f} {:25f}'.format(k,self.model.params[k],round(float(t[0]),6),round(float(t[1]),6))
244 |
245 | ts = utils.sortDictByValue(ts,True)
246 | for t in ts:
247 | utils.printf(lines[t[0]])
248 |
249 |
250 | #####################################################################################
251 | # Plots
252 | #####################################################################################
253 |
254 | def plot(self,coef='betas',fn=None):
255 | '''
256 | Plots frequency of pearson's correlation values
257 | :param coef: string \in {betas, tvalues}
258 | :return:
259 | '''
260 | ncols = 3
261 | m = len(self.betas.keys())
262 | ranges = range(ncols, m, ncols)
263 | i = np.searchsorted(ranges, m, 'left')
264 | nrows = len(ranges)
265 |
266 | if i == nrows:
267 | ranges.append((i+1)*ncols)
268 | nrows += 1
269 |
270 | fig = plt.figure(figsize=(8,3*i))
271 | for idx,k in enumerate(self.betas.keys()):
272 | plt.subplot(nrows,ncols,idx+1)
273 |
274 | if coef == 'betas':
275 | plt.hist(self.betas[k])
276 | elif coef == 'tvalues':
277 | plt.hist(self.tvalues[k])
278 |
279 | plt.xlabel('regression coefficients', fontsize=8)
280 | plt.ylabel('frequency', fontsize=8)
281 | plt.title(k)
282 | plt.grid(True)
283 |
284 | for ax in fig.get_axes():
285 | ax.tick_params(axis='x', labelsize=5)
286 | ax.tick_params(axis='y', labelsize=5)
287 |
288 | plt.tight_layout()
289 | plt.savefig(fn)
290 | plt.close()
291 |
292 |
293 |
294 |
295 |
296 |
297 |
--------------------------------------------------------------------------------
/libs/profiling.py:
--------------------------------------------------------------------------------
1 | __author__ = 'espin'
2 |
3 | #######################################################################################
4 | ### Dependences
5 | ### Reference:
6 | ### http://fa.bianp.net/blog/2013/different-ways-to-get-memory-consumption-or-lessons-learned-from-memory_profiler/
7 | #######################################################################################
8 | import resource
9 | import psutil
10 | import sys
11 | import os
12 | import matplotlib
13 | matplotlib.use('Agg')
14 | import matplotlib.pyplot as plt
15 | from libs import utils
16 | import copy
17 | from threading import Thread
18 | import time
19 | from collections import OrderedDict
20 |
21 | #######################################################################################
22 | # FUNCTIONS
23 | #######################################################################################
24 |
25 | class Profiling():
26 |
27 | def __init__(self, title=None, fn=None, perm=False):
28 | self.mem_resource = OrderedDict()
29 | self.mem_psutil = OrderedDict()
30 | self.cpu_usage = OrderedDict()
31 | self.virtual_memory_usage = OrderedDict()
32 | self.swap_memory_usage = OrderedDict()
33 | self.title = title
34 | self.fn = fn
35 | self.perm = perm
36 |
37 | def check_memory(self, key):
38 | self.memory_usage_resource(key)
39 | self.memory_usage_psutil(key)
40 | self.memory_cpu_usage(key)
41 | self.plot()
42 |
43 | def kill_if_necessary(self, vm, sm):
44 | if vm.percent >= 85. or sm.percent >= 85.:
45 | print('FULL MEMORY: \n- Virtual Memory: {}\n- Swap Memory: {}'.format(vm, sm))
46 |
47 | def memory_cpu_usage(self, key):
48 | cpu = psutil.cpu_percent(interval=None)
49 | vm = psutil.virtual_memory()
50 | sm = psutil.swap_memory()
51 |
52 | self.cpu_usage[key] = int(255 * cpu / 100)
53 | self.virtual_memory_usage[key] = vm.percent
54 | self.swap_memory_usage[key] = sm.percent
55 | self.kill_if_necessary(vm, sm)
56 |
57 |
58 | def memory_usage_psutil(self, key):
59 | # return the memory usage in MB
60 | process = psutil.Process(os.getpid())
61 | self.mem_psutil[key] = process.memory_info()[0] / float(2 ** 20)
62 |
63 | def memory_usage_resource(self, key):
64 | rusage_denom = 1024.
65 | if sys.platform == 'darwin':
66 | # ... it seems that in OSX the output is different units ...
67 | rusage_denom = rusage_denom * rusage_denom
68 | self.mem_resource[key] = resource.getrusage(resource.RUSAGE_SELF).ru_maxrss / rusage_denom
69 |
70 | def copy(self, mem):
71 | self.mem_resource = mem.mem_resource.deepcopy()
72 | self.mem_psutil = mem.mem_psutil.deepcopy()
73 | self.cpu_usage = mem.cpu_usage.deepcopy()
74 | self.virtual_memory_usage = mem.virtual_memory_usage.deepcopy()
75 | self.swap_memory_usage = mem.swap_memory_usage.deepcopy()
76 |
77 | def plot(self):
78 | '''
79 | Plots the Memory usage in MB
80 | :return:
81 | '''
82 | if self.fn is not None and self.title is not None:
83 | labels = self.mem_resource.keys()
84 | x = range(len(labels))
85 |
86 | plt.figure(1)
87 | ax1 = plt.subplot(211)
88 | ax1.plot(x, self.mem_resource.values(), color='red', marker='o', label='mem_resource')
89 | ax1.plot(x, self.mem_psutil.values(), color='blue', marker='o', label='mem_psutil')
90 | ax1.set_ylabel('Memory usage in MB')
91 | ax1.grid(True)
92 | ax1.set_xticks(x)
93 | ax1.set_xticklabels(labels, rotation=20, fontsize=7)
94 | box = ax1.get_position()
95 | ax1.set_position([box.x0, box.y0, box.width * 0.8, box.height])
96 | ax1.legend(loc='center left', bbox_to_anchor=(1, 0.5), prop={'size':10})
97 |
98 | ax2 = plt.subplot(212)
99 | ax2.plot(x, self.cpu_usage.values(), color='black', marker='o', label='cpu_usage')
100 | ax2.plot(x, self.virtual_memory_usage.values(), color='orange', marker='o', label='virtual_memory')
101 | ax2.plot(x, self.swap_memory_usage.values(), color='green', marker='o', label='swap_memory')
102 | #ax2.set_xlabel(self.xlabel)
103 | ax2.set_ylabel('Percentage (usage)')
104 | ax2.grid(True)
105 | ax2.set_xticks(x)
106 | ax2.set_xticklabels(labels, rotation=20, fontsize=7)
107 | box = ax2.get_position()
108 | ax2.set_position([box.x0, box.y0, box.width * 0.8, box.height])
109 | ax2.legend(loc='center left', prop={'size':10}) # bbox_to_anchor=(1, 0.5),
110 |
111 | plt.suptitle('Profiling - {}'.format(self.title))
112 | plt.tight_layout()
113 | plt.savefig(self.fn)
114 | plt.close()
115 |
--------------------------------------------------------------------------------
/libs/qap.py:
--------------------------------------------------------------------------------
1 | __author__ = 'lisette.espin'
2 |
3 | #######################################################################
4 | # Dependencies
5 | #######################################################################
6 | import numpy as np
7 | from scipy.stats.stats import pearsonr
8 | import statsmodels.api as sm
9 | from statsmodels.stats.weightstats import ztest
10 | import matplotlib.pyplot as plt
11 | from scipy.stats import ttest_ind
12 | from libs import utils
13 |
14 |
15 | #######################################################################
16 | # QAP
17 | #######################################################################
18 | class QAP():
19 |
20 | #####################################################################################
21 | # Constructor and Init
22 | #####################################################################################
23 |
24 | def __init__(self, Y=None, X=None, npermutations=-1, diagonal=False):
25 | '''
26 | Initialization of variables
27 | :param Y: numpy array depended variable
28 | :param X: numpy array independed variable
29 | :return:
30 | '''
31 | self.Y = Y
32 | self.X = X
33 | self.npermutations = npermutations
34 | self.diagonal = diagonal
35 | self.beta = None
36 | self.Ymod = None
37 | self.betas = []
38 |
39 | def init(self):
40 | '''
41 | Shows the correlation of the initial/original variables (no shuffeling)
42 | :return:
43 | '''
44 | self.beta = self.correlation(self.X, self.Y)
45 | self.stats(self.X, self.Y)
46 |
47 | #####################################################################################
48 | # Core QAP methods
49 | #####################################################################################
50 |
51 | def qap(self):
52 | '''
53 | Quadratic Assignment Procedure
54 | :param npermutations:
55 | :return:
56 | '''
57 | self.init()
58 | self._shuffle()
59 |
60 | def _shuffle(self):
61 | self.Ymod = self.Y.copy()
62 | for t in range(self.npermutations):
63 | self._rmperm()
64 | self._addBeta(self.correlation(self.X, self.Ymod, False))
65 |
66 | def correlation(self, x, y, show=True):
67 | '''
68 | Computes Pearson's correlation value of variables x and y.
69 | Diagonal values are removed.
70 | :param x: numpy array independent variable
71 | :param y: numpu array dependent variable
72 | :param show: if True then shows pearson's correlation and p-value.
73 | :return:
74 | '''
75 | if not self.diagonal:
76 | xflatten = np.delete(x, [i*(x.shape[0]+1)for i in range(x.shape[0])])
77 | yflatten = np.delete(y, [i*(y.shape[0]+1)for i in range(y.shape[0])])
78 | pc = pearsonr(xflatten, yflatten)
79 | else:
80 | pc = pearsonr(x.flatten(), y.flatten())
81 | if show:
82 | utils.printf('Pearson Correlation: {}'.format(pc[0]))
83 | utils.printf('p-value: {}'.format(pc[1]))
84 | return pc
85 |
86 | #####################################################################################
87 | # Handlers
88 | #####################################################################################
89 |
90 | def _addBeta(self, p):
91 | '''
92 | frequency dictionary of pearson's correlation values
93 | :param p: person's correlation value
94 | :return:
95 | '''
96 | p = round(p[0],6)
97 | self.betas.append(p)
98 |
99 | def _rmperm(self):
100 | shuffle = np.random.permutation(self.Ymod.shape[0])
101 | np.take(self.Ymod,shuffle,axis=0,out=self.Ymod)
102 | np.take(self.Ymod,shuffle,axis=1,out=self.Ymod)
103 |
104 |
105 | #####################################################################################
106 | # Plots & Prints
107 | #####################################################################################
108 |
109 | def summary(self):
110 | utils.printf('')
111 | utils.printf('# Permutations: {}'.format(self.npermutations))
112 | utils.printf('Correlation coefficients: Obs. Value({}), Significance({})'.format(self.beta[0], self.beta[1]))
113 | utils.printf('')
114 | utils.printf('- Sum all betas: {}'.format(sum(self.betas)))
115 | utils.printf('- Min betas: {}'.format(min(self.betas)))
116 | utils.printf('- Max betas: {}'.format(max(self.betas)))
117 | utils.printf('- Average betas: {}'.format(np.average(self.betas)))
118 | utils.printf('- Std. Dev. betas: {}'.format(np.std(self.betas)))
119 | utils.printf('')
120 | utils.printf('prop >= {}: {}'.format(self.beta[0], sum([1 for b in self.betas if b >= self.beta[0] ])/float(len(self.betas))))
121 | utils.printf('prop <= {}: {} (proportion of randomly generated correlations that were as {} as the observed)'.format(self.beta[0], sum([1 for b in self.betas if b <= self.beta[0] ])/float(len(self.betas)), 'large' if self.beta[0] >= 0 else 'small'))
122 | utils.printf('')
123 |
124 | def plot(self):
125 | '''
126 | Plots frequency of pearson's correlation values
127 | :return:
128 | '''
129 | plt.hist(self.betas)
130 | plt.xlabel('regression coefficients')
131 | plt.ylabel('frequency')
132 | plt.title('QAP')
133 | plt.grid(True)
134 | plt.show()
135 | plt.close()
136 |
137 | #####################################################################################
138 | # Others
139 | #####################################################################################
140 |
141 | def stats(self, x, y):
142 | if not self.diagonal:
143 | xflatten = np.delete(x, [i*(x.shape[0]+1)for i in range(x.shape[0])])
144 | yflatten = np.delete(y, [i*(y.shape[0]+1)for i in range(y.shape[0])])
145 | p = np.corrcoef(xflatten,yflatten)
146 | utils.printf('Pearson\'s correlation:\n{}'.format(p))
147 | utils.printf('Z-Test:{}'.format(ztest(xflatten, yflatten)))
148 | utils.printf('T-Test:{}'.format(ttest_ind(xflatten, yflatten)))
149 | else:
150 | p = np.corrcoef(x, y)
151 | utils.printf('Pearson\'s correlation:\n{}'.format(p))
152 | utils.printf('Z-Test:{}'.format(ztest(x, y)))
153 | utils.printf('T-Test:{}'.format(ttest_ind(x, y)))
154 |
155 | def ols(self, x, y):
156 | xflatten = np.delete(x, [i*(x.shape[0]+1)for i in range(x.shape[0])])
157 | yflatten = np.delete(y, [i*(y.shape[0]+1)for i in range(y.shape[0])])
158 | xflatten = sm.add_constant(xflatten)
159 | model = sm.OLS(yflatten,xflatten)
160 | results = model.fit()
161 | print results.summary()
162 |
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/libs/utils.py:
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1 | __author__ = 'lisette.espin'
2 |
3 | ######################################################################################################################
4 | # SYSTEM DEPENDENCES
5 | ######################################################################################################################
6 | from datetime import datetime
7 | import operator
8 | import sys, os
9 |
10 | ######################################################################################################################
11 | # FUNCTIONS
12 | ######################################################################################################################
13 | def printf(msg, logfile=None):
14 | strtowrite = "[{}] {}".format(datetime.now(), msg)
15 | print(strtowrite)
16 | if logfile is not None:
17 | with open(logfile, 'a') as f:
18 | f.write('{}\n'.format(strtowrite))
19 |
20 | def sortDictByValue(x,desc):
21 | sorted_x = sorted(x.items(), key=operator.itemgetter(1),reverse=desc)
22 | return sorted_x
23 |
24 | def sortDictByKey(x,desc):
25 | sorted_x = sorted(x.items(), key=operator.itemgetter(0),reverse=desc)
26 | return sorted_x
27 |
28 | def _swap_cols(arr, frm, to):
29 | arr[:,[frm, to]] = arr[:,[to, frm]]
30 |
31 | def _swap_rows(arr, frm, to):
32 | arr[[frm, to],:] = arr[[to, frm],:]
33 |
34 | def appendToFile(str, path):
35 | with open(path,'a') as f:
36 | f.write(str)
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