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Commit 1c774687 authored by Guolin Ke's avatar Guolin Ke
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examples/parallel_learning/binary.train 0 → 100644
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examples/parallel_learning/mlist.txt 0 → 100644
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192.168.1.101 12400
192.168.1.102 12400
examples/parallel_learning/predict.conf 0 → 100644
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task = predict
data = binary.test
input_model= LightGBM_model.txt
examples/parallel_learning/train.conf 0 → 100644
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# task type, support train and predict
task = train
# boosting type, support gbdt for now, alias: boosting, boost
boosting_type = gbdt
# application type, support following application
# regression , regression task
# binary , binary classification task
# lambdarank , lambdarank task
# alias: application, app
objective = binary
# eval metrics, support multi metric, delimite by ',' , support following metrics
# l1
# l2 , default metric for regression
# ndcg , default metric for lambdarank
# auc
# binary_logloss , default metric for binary
# binary_error
metric = binary_logloss,auc
# frequence for metric output
metric_freq = 1
# true if need output metric for training data, alias: tranining_metric, train_metric
is_training_metric = true
# number of bins for feature bucket, 255 is a recommend setting, it can save memories, and also has good accuracy.
max_bin = 255
# training data
# if exsting weight file, should name to "binary.train.weight"
# alias: train_data, train
data = binary.train
# validation data, support multi validation data, separated by ','
# if exsting weight file, should name to "binary.test.weight"
# alias: valid, test, test_data,
valid_data = binary.test
# number of trees(iterations), alias: num_tree, num_iteration, num_iterations, num_round, num_rounds
num_trees = 100
# shrinkage rate , alias: shrinkage_rate
learning_rate = 0.1
# number of leaves for one tree, alias: num_leaf
num_leaves = 63
# type of tree learner, support following types:
# serial , single machine version
# feature , use feature parallel to train
# data , use data parallel to train
# voting , use voting based parallel to train
# alias: tree
tree_learner = feature
# number of threads for multi-threading. One thread will use one CPU, defalut is setted to #cpu.
# num_threads = 8
# feature sub-sample, will random select 80% feature to train on each iteration
# alias: sub_feature
feature_fraction = 0.8
# Support bagging (data sub-sample), will perform bagging every 5 iterations
bagging_freq = 5
# Bagging farction, will random select 80% data on bagging
# alias: sub_row
bagging_fraction = 0.8
# minimal number data for one leaf, use this to deal with over-fit
# alias : min_data_per_leaf, min_data
min_data_in_leaf = 50
# minimal sum hessians for one leaf, use this to deal with over-fit
min_sum_hessian_in_leaf = 5.0
# save memory and faster speed for sparse feature, alias: is_sparse
is_enable_sparse = true
# when data is bigger than memory size, set this to true. otherwise set false will have faster speed
# alias: two_round_loading, two_round
use_two_round_loading = false
# true if need to save data to binary file and application will auto load data from binary file next time
# alias: is_save_binary, save_binary
is_save_binary_file = false
# output model file
output_model = LightGBM_model.txt
# support continuous train from trained gbdt model
# input_model= trained_model.txt
# output prediction file for predict task
# output_result= prediction.txt
# support continuous train from initial score file
# input_init_score= init_score.txt
# number of machines in parallel training, alias: num_machine
num_machines = 2
# local listening port in parallel training, alias: local_port
local_listen_port = 12400
# machines list file for parallel training, alias: mlist
machine_list_file = mlist.txt
examples/regression/README.md 0 → 100644
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Regression Example
=====================
Here is an example for LightGBM to run regression task.
***You should copy executable file to this folder first.***
#### Training
For windows, by running following command in this folder:
```
LightGBM.exe config=train.conf
```
For linux, by running following command in this folder:
```
./LightGBM config=train.conf
```
#### Prediction
You should finish training first.
For windows, by running following command in this folder:
```
LightGBM.exe config=predict.conf
```
For linux, by running following command in this folder:
```
./LightGBM config=predict.conf
```
examples/regression/predict.conf 0 → 100644
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task = predict
data = regression.test
input_model= LightGBM_model.txt
examples/regression/regression.test 0 → 100644
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0 0.828 -1.803 0.342 0.847 -0.162 1.585 -1.128 -0.272 2.173 1.974 0.039 -1.717 0.000 0.900 0.764 -1.741 0.000 1.349 -0.079 1.035 3.102 0.984 0.815 0.985 0.780 1.661 1.403 1.184
1 1.089 -0.350 -0.747 1.472 0.792 1.087 -0.069 -1.192 0.000 0.512 -0.841 -1.284 0.000 2.162 -0.821 0.545 2.548 1.360 2.243 -0.183 0.000 0.977 0.628 1.725 1.168 0.635 0.823 0.822
1 0.444 0.451 -1.332 1.176 -0.247 0.898 0.194 0.007 0.000 1.958 0.576 -1.618 2.215 0.584 1.203 0.268 0.000 0.939 1.033 1.264 3.102 0.829 0.886 0.985 1.265 0.751 1.032 0.948
0 0.629 0.114 1.177 0.917 -1.204 0.845 0.828 -0.088 0.000 0.962 -1.302 0.823 2.215 0.732 0.358 -1.334 2.548 0.538 0.582 1.561 0.000 1.028 0.834 0.988 0.904 1.205 1.039 0.885
1 1.754 -1.259 -0.573 0.959 -1.483 0.358 0.448 -1.452 0.000 0.711 0.313 0.499 2.215 1.482 -0.390 1.474 2.548 1.879 -1.540 0.668 0.000 0.843 0.825 1.313 1.315 0.939 1.048 0.871
1 0.549 0.706 -1.437 0.894 0.891 0.680 -0.762 -1.568 0.000 0.981 0.499 -0.425 2.215 1.332 0.678 0.485 1.274 0.803 0.022 -0.893 0.000 0.793 1.043 0.987 0.761 0.899 0.915 0.794
0 0.475 0.542 -0.987 1.569 0.069 0.551 1.543 -1.488 0.000 0.608 0.301 1.734 2.215 0.277 0.499 -0.522 0.000 1.375 1.212 0.696 3.102 0.652 0.756 0.987 0.828 0.830 0.715 0.679
1 0.723 0.049 -1.153 1.300 0.083 0.723 -0.749 0.630 0.000 1.126 0.412 -0.384 0.000 1.272 1.256 1.358 2.548 3.108 0.777 -1.486 3.102 0.733 1.096 1.206 1.269 0.899 1.015 0.903
1 1.062 0.296 0.725 0.285 -0.531 0.819 1.277 -0.667 0.000 0.687 0.829 -0.092 0.000 1.158 0.447 1.047 2.548 1.444 -0.186 -1.491 3.102 0.863 1.171 0.986 0.769 0.828 0.919 0.840
0 0.572 -0.349 1.396 2.023 0.795 0.577 0.457 -0.533 0.000 1.351 0.701 -1.091 0.000 0.724 -1.012 -0.182 2.548 0.923 -0.012 0.789 3.102 0.936 1.025 0.985 1.002 0.600 0.828 0.909
1 0.563 0.387 0.412 0.553 1.050 0.723 -0.992 -0.447 0.000 0.748 0.948 0.546 2.215 1.761 -0.559 -1.183 0.000 1.114 -0.251 1.192 3.102 0.936 0.912 0.976 0.578 0.722 0.829 0.892
1 1.632 1.577 -0.697 0.708 -1.263 0.863 0.012 1.197 2.173 0.498 0.990 -0.806 0.000 0.627 2.387 -1.283 0.000 0.607 1.290 -0.174 3.102 0.916 1.328 0.986 0.557 0.971 0.935 0.836
1 0.562 -0.360 0.399 0.803 -1.334 1.443 -0.116 1.628 2.173 0.750 0.987 0.135 1.107 0.795 0.298 -0.556 0.000 1.150 -0.113 -0.093 0.000 0.493 1.332 0.985 1.001 1.750 1.013 0.886
1 0.987 0.706 -0.492 0.861 0.607 0.593 0.088 -0.184 0.000 0.802 0.894 1.608 2.215 0.782 -0.471 1.500 2.548 0.521 0.772 -0.960 0.000 0.658 0.893 1.068 0.877 0.664 0.709 0.661
1 1.052 0.883 -0.581 1.566 0.860 0.931 1.515 -0.873 0.000 0.493 0.145 -0.672 0.000 1.133 0.935 1.581 2.548 1.630 0.695 0.923 3.102 1.105 1.087 1.713 0.948 0.590 0.872 0.883
1 2.130 -0.516 -0.291 0.776 -1.230 0.689 -0.257 0.800 2.173 0.730 -0.274 -1.437 0.000 0.615 0.241 1.083 0.000 0.834 0.757 1.613 3.102 0.836 0.806 1.333 1.061 0.730 0.889 0.783
1 0.742 0.797 1.628 0.311 -0.418 0.620 0.685 -1.457 0.000 0.683 1.774 -1.082 0.000 1.700 1.104 0.225 2.548 0.382 -2.184 -1.307 0.000 0.945 1.228 0.984 0.864 0.931 0.988 0.838
0 0.311 -1.249 -0.927 1.272 -1.262 0.642 -1.228 -0.136 0.000 1.220 -0.804 -1.558 2.215 0.950 -0.828 0.495 1.274 2.149 -1.672 0.634 0.000 1.346 0.887 0.981 0.856 1.101 1.001 1.106
0 0.660 -1.834 -0.667 0.601 1.236 0.932 -0.933 -0.135 2.173 1.373 -0.122 1.429 0.000 0.654 -0.034 -0.847 2.548 0.711 0.911 0.703 0.000 1.144 0.942 0.984 0.822 0.739 0.992 0.895
0 3.609 -0.590 0.851 0.615 0.455 1.280 0.003 -0.866 1.087 1.334 0.708 -1.131 0.000 0.669 0.480 0.092 0.000 0.975 0.983 -1.429 3.102 1.301 1.089 0.987 1.476 0.934 1.469 1.352
1 0.905 -0.403 1.567 2.651 0.953 1.194 -0.241 -0.567 1.087 0.308 -0.384 -0.007 0.000 0.608 -0.175 -1.163 2.548 0.379 0.941 1.662 0.000 0.580 0.721 1.126 0.895 0.544 1.097 0.836
1 0.983 0.255 1.093 0.905 -0.874 0.863 0.060 -0.368 0.000 0.824 -0.747 -0.633 0.000 0.614 0.961 1.052 0.000 0.792 -0.260 1.632 3.102 0.874 0.883 1.280 0.663 0.406 0.592 0.645
1 1.160 -1.027 0.274 0.460 0.322 2.085 -1.623 -0.840 0.000 1.634 -1.046 1.182 2.215 0.492 -0.367 1.174 0.000 0.824 -0.998 1.617 0.000 0.943 0.884 1.001 1.209 1.313 1.034 0.866
0 0.299 0.028 -1.372 1.930 -0.661 0.840 -0.979 0.664 1.087 0.535 -2.041 1.434 0.000 1.087 -1.797 0.344 0.000 0.485 -0.560 -1.105 3.102 0.951 0.890 0.980 0.483 0.684 0.730 0.706
0 0.293 1.737 -1.418 2.074 0.794 0.679 1.024 -1.457 0.000 1.034 1.094 -0.168 1.107 0.506 1.680 -0.661 0.000 0.523 -0.042 -1.274 3.102 0.820 0.944 0.987 0.842 0.694 0.761 0.750
0 0.457 -0.393 1.560 0.738 -0.007 0.475 -0.230 0.246 0.000 0.776 -1.264 -0.606 2.215 0.865 -0.731 -1.576 2.548 1.153 0.343 1.436 0.000 1.060 0.883 0.988 0.972 0.703 0.758 0.720
0 0.935 -0.582 0.240 2.401 0.818 1.231 -0.618 -1.289 0.000 0.799 0.544 -0.228 2.215 0.525 -1.494 -0.969 0.000 0.609 -1.123 1.168 3.102 0.871 0.767 1.035 1.154 0.919 0.868 1.006
1 0.902 -0.745 -1.215 1.174 -0.501 1.215 0.167 1.162 0.000 0.896 1.217 -0.976 0.000 0.585 -0.429 1.036 0.000 1.431 -0.416 0.151 3.102 0.524 0.952 0.990 0.707 0.271 0.592 0.826
1 0.653 0.337 -0.320 1.118 -0.934 1.050 0.745 0.529 1.087 1.075 1.742 -1.538 0.000 0.585 1.090 0.973 0.000 1.091 -0.187 1.160 1.551 1.006 1.108 0.978 1.121 0.838 0.947 0.908
0 1.157 1.401 0.340 0.395 -1.218 0.945 1.928 -0.876 0.000 1.384 0.320 1.002 1.107 1.900 1.177 -0.462 2.548 1.122 1.316 1.720 0.000 1.167 1.096 0.989 0.937 1.879 1.307 1.041
0 0.960 0.355 -0.152 0.872 -0.338 0.391 0.348 0.956 1.087 0.469 2.664 1.409 0.000 0.756 -1.561 1.500 0.000 0.525 1.436 1.728 3.102 1.032 0.946 0.996 0.929 0.470 0.698 0.898
1 1.038 0.274 0.825 1.198 0.963 1.078 -0.496 -1.014 2.173 0.739 -0.727 -0.151 2.215 1.035 -0.799 0.398 0.000 1.333 -0.872 -1.498 0.000 0.849 1.033 0.985 0.886 0.936 0.975 0.823
0 0.490 0.277 0.318 1.303 0.694 1.333 -1.620 -0.563 0.000 1.459 -1.326 1.140 0.000 0.779 -0.673 -1.324 2.548 0.860 -1.247 0.043 0.000 0.857 0.932 0.992 0.792 0.278 0.841 1.498
0 1.648 -0.688 -1.386 2.790 0.995 1.087 1.359 -0.687 0.000 1.050 -0.223 -0.261 2.215 0.613 -0.889 1.335 0.000 1.204 0.827 0.309 3.102 0.464 0.973 2.493 1.737 0.827 1.319 1.062
0 1.510 -0.662 1.668 0.860 0.280 0.705 0.974 -1.647 1.087 0.662 -0.393 -0.225 0.000 0.610 -0.996 0.532 2.548 0.464 1.305 0.102 0.000 0.859 1.057 1.498 0.799 1.260 0.946 0.863
1 0.850 -1.185 -0.117 0.943 -0.449 1.142 0.875 -0.030 0.000 2.223 -0.461 1.627 2.215 0.767 -1.761 -1.692 0.000 1.012 -0.727 0.639 3.102 3.649 2.062 0.985 1.478 1.087 1.659 1.358
0 0.933 1.259 0.130 0.326 -0.890 0.306 1.136 1.142 0.000 0.964 0.705 -1.373 2.215 0.546 -0.196 -0.001 0.000 0.578 -1.169 1.004 3.102 0.830 0.836 0.988 0.837 1.031 0.749 0.655
0 0.471 0.697 1.570 1.109 0.201 1.248 0.348 -1.448 0.000 2.103 0.773 0.686 2.215 1.451 -0.087 -0.453 2.548 1.197 -0.045 -1.026 0.000 0.793 1.094 0.987 0.851 1.804 1.378 1.089
1 2.446 -0.701 -1.568 0.059 0.822 1.401 -0.600 -0.044 2.173 0.324 -0.001 1.344 2.215 0.913 -0.818 1.049 0.000 0.442 -1.088 -0.005 0.000 0.611 1.062 0.979 0.562 0.988 0.998 0.806
0 0.619 2.029 0.933 0.528 -0.903 0.974 0.760 -0.311 2.173 0.825 0.658 -1.466 1.107 0.894 1.594 0.370 0.000 0.882 -0.258 1.661 0.000 1.498 1.088 0.987 0.867 1.139 0.900 0.779
1 0.674 -0.131 -0.362 0.518 -1.574 0.876 0.442 0.145 1.087 0.497 -1.526 -1.704 0.000 0.680 2.514 -1.374 0.000 0.792 -0.479 0.773 1.551 0.573 1.198 0.984 0.800 0.667 0.987 0.832
1 1.447 1.145 -0.937 0.307 -1.458 0.478 1.264 0.816 1.087 0.558 1.015 -0.101 2.215 0.937 -0.190 1.177 0.000 0.699 0.954 -1.512 0.000 0.877 0.838 0.990 0.873 0.566 0.646 0.713
1 0.976 0.308 -0.844 0.436 0.610 1.253 0.149 -1.585 2.173 1.415 0.568 0.096 2.215 0.953 -0.855 0.441 0.000 0.867 -0.650 1.643 0.000 0.890 1.234 0.988 0.796 2.002 1.179 0.977
0 0.697 0.401 -0.718 0.920 0.735 0.958 -0.172 0.168 2.173 0.872 -0.097 -1.335 0.000 0.513 -1.192 -1.710 1.274 0.426 -1.637 1.368 0.000 0.997 1.227 1.072 0.800 1.013 0.786 0.749
1 1.305 -2.157 1.740 0.661 -0.912 0.705 -0.516 0.759 2.173 0.989 -0.716 -0.300 2.215 0.627 -1.052 -1.736 0.000 0.467 -2.467 0.568 0.000 0.807 0.964 0.988 1.427 1.012 1.165 0.926
0 1.847 1.663 -0.618 0.280 1.258 1.462 -0.054 1.371 0.000 0.900 0.309 -0.544 0.000 0.331 -2.149 -0.341 0.000 1.091 -0.833 0.710 3.102 1.496 0.931 0.989 1.549 0.115 1.140 1.150
0 0.410 -0.323 1.069 2.160 0.010 0.892 0.942 -1.640 2.173 0.946 0.938 1.314 0.000 1.213 -1.099 -0.794 2.548 0.650 0.053 0.056 0.000 1.041 0.916 1.063 0.985 1.910 1.246 1.107
1 0.576 1.092 -0.088 0.777 -1.579 0.757 0.271 0.109 0.000 0.819 0.827 -1.554 2.215 1.313 2.341 -1.568 0.000 2.827 0.239 -0.338 0.000 0.876 0.759 0.986 0.692 0.457 0.796 0.791
1 0.537 0.925 -1.406 0.306 -0.050 0.906 1.051 0.037 0.000 1.469 -0.177 -1.320 2.215 1.872 0.723 1.158 0.000 1.313 0.227 -0.501 3.102 0.953 0.727 0.978 0.755 0.892 0.932 0.781
0 0.716 -0.065 -0.484 1.313 -1.563 0.596 -0.242 0.678 2.173 0.426 -1.909 0.616 0.000 0.885 -0.406 -1.343 2.548 0.501 -1.327 -0.340 0.000 0.470 0.728 1.109 0.919 0.881 0.665 0.692
1 0.624 -0.389 0.128 1.636 -1.110 1.025 0.573 -0.843 2.173 0.646 -0.697 1.064 0.000 0.632 -1.442 0.961 0.000 0.863 -0.106 1.717 0.000 0.825 0.917 1.257 0.983 0.713 0.890 0.824
0 0.484 2.101 1.714 1.131 -0.823 0.750 0.583 -1.304 1.087 0.894 0.421 0.559 2.215 0.921 -0.063 0.282 0.000 0.463 -0.474 -1.387 0.000 0.742 0.886 0.995 0.993 1.201 0.806 0.754
0 0.570 0.339 -1.478 0.528 0.439 0.978 1.479 -1.411 2.173 0.763 1.541 -0.734 0.000 1.375 0.840 0.903 0.000 0.965 1.599 0.364 0.000 0.887 1.061 0.992 1.322 1.453 1.013 0.969
0 0.940 1.303 1.636 0.851 -1.732 0.803 -0.030 -0.177 0.000 0.480 -0.125 -0.954 0.000 0.944 0.709 0.296 2.548 1.342 -0.418 1.197 3.102 0.853 0.989 0.979 0.873 0.858 0.719 0.786
1 0.599 0.544 -0.238 0.816 1.043 0.857 0.660 1.128 2.173 0.864 -0.624 -0.843 0.000 1.159 0.367 0.174 0.000 1.520 -0.543 -1.508 0.000 0.842 0.828 0.984 0.759 0.895 0.918 0.791
1 1.651 1.897 -0.914 0.423 0.315 0.453 0.619 -1.607 2.173 0.532 -0.424 0.209 1.107 0.369 2.479 0.034 0.000 0.701 0.217 0.984 0.000 0.976 0.951 1.035 0.879 0.825 0.915 0.798
1 0.926 -0.574 -0.763 0.285 1.094 0.672 2.314 1.545 0.000 1.124 0.415 0.809 0.000 1.387 0.270 -0.949 2.548 1.547 -0.631 -0.200 3.102 0.719 0.920 0.986 0.889 0.933 0.797 0.777
0 0.677 1.698 -0.890 0.641 -0.449 0.607 1.754 1.720 0.000 0.776 0.372 0.782 2.215 0.511 1.491 -0.480 0.000 0.547 -0.341 0.853 3.102 0.919 1.026 0.997 0.696 0.242 0.694 0.687
0 1.266 0.602 0.958 0.487 1.256 0.709 0.843 -1.196 0.000 0.893 1.303 -0.594 1.107 1.090 1.320 0.354 0.000 0.797 1.846 1.139 0.000 0.780 0.896 0.986 0.661 0.709 0.790 0.806
1 0.628 -0.616 -0.329 0.764 -1.150 0.477 -0.715 1.187 2.173 1.250 0.607 1.026 2.215 0.983 -0.023 -0.583 0.000 0.377 1.344 -1.015 0.000 0.744 0.954 0.987 0.837 0.841 0.795 0.694
1 1.035 -0.828 -1.358 1.870 -1.060 1.075 0.130 0.448 2.173 0.660 0.697 0.641 0.000 0.425 1.006 -1.035 0.000 0.751 1.055 1.364 3.102 0.826 0.822 0.988 0.967 0.901 1.077 0.906
1 0.830 0.265 -0.150 0.660 1.105 0.592 -0.557 0.908 2.173 0.670 -1.419 -0.671 0.000 1.323 -0.409 1.644 2.548 0.850 -0.033 -0.615 0.000 0.760 0.967 0.984 0.895 0.681 0.747 0.770
1 1.395 1.100 1.167 1.088 0.218 0.400 -0.132 0.024 2.173 0.743 0.530 -1.361 2.215 0.341 -0.691 -0.238 0.000 0.396 -1.426 -0.933 0.000 0.363 0.472 1.287 0.922 0.810 0.792 0.656
1 1.070 1.875 -1.298 1.215 -0.106 0.767 0.795 0.514 1.087 0.401 2.780 1.276 0.000 0.686 1.127 1.721 2.548 0.391 -0.259 -1.167 0.000 1.278 1.113 1.389 0.852 0.824 0.838 0.785
0 1.114 -0.071 1.719 0.399 -1.383 0.849 0.254 0.481 0.000 0.958 -0.579 0.742 0.000 1.190 -0.140 -0.862 2.548 0.479 1.390 0.856 0.000 0.952 0.988 0.985 0.764 0.419 0.835 0.827
0 0.714 0.376 -0.568 1.578 -1.165 0.648 0.141 0.639 2.173 0.472 0.569 1.449 1.107 0.783 1.483 0.361 0.000 0.540 -0.790 0.032 0.000 0.883 0.811 0.982 0.775 0.572 0.760 0.745
0 0.401 -1.731 0.765 0.974 1.648 0.652 -1.024 0.191 0.000 0.544 -0.366 -1.246 2.215 0.627 0.140 1.008 2.548 0.810 0.409 0.429 0.000 0.950 0.934 0.977 0.621 0.580 0.677 0.650
1 0.391 1.679 -1.298 0.605 -0.832 0.549 1.338 0.522 2.173 1.244 0.884 1.070 0.000 1.002 0.846 -1.345 2.548 0.783 -2.464 -0.237 0.000 4.515 2.854 0.981 0.877 0.939 1.942 1.489
1 0.513 -0.220 -0.444 1.699 0.479 1.109 0.181 -0.999 2.173 0.883 -0.335 -1.716 2.215 1.075 -0.380 1.352 0.000 0.857 0.048 0.147 0.000 0.937 0.758 0.986 1.206 0.958 0.949 0.876
0 1.367 -0.388 0.798 1.158 1.078 0.811 -1.024 -1.628 0.000 1.504 0.097 -0.999 2.215 1.652 -0.860 0.054 2.548 0.573 -0.142 -1.401 0.000 0.869 0.833 1.006 1.412 1.641 1.214 1.041
1 1.545 -0.533 -1.517 1.177 1.289 2.331 -0.370 -0.073 0.000 1.295 -0.358 -0.891 2.215 0.476 0.756 0.985 0.000 1.945 -0.016 -1.651 3.102 1.962 1.692 1.073 0.656 0.941 1.312 1.242
0 0.858 0.978 -1.258 0.286 0.161 0.729 1.230 1.087 2.173 0.561 2.670 -0.109 0.000 0.407 2.346 0.938 0.000 1.078 0.729 -0.658 3.102 0.597 0.921 0.982 0.579 0.954 0.733 0.769
1 1.454 -1.384 0.870 0.067 0.394 1.033 -0.673 0.318 0.000 1.166 -0.763 -1.533 2.215 2.848 -0.045 -0.856 2.548 0.697 -0.140 1.134 0.000 0.931 1.293 0.977 1.541 1.326 1.201 1.078
1 0.559 -0.913 0.486 1.104 -0.321 1.073 -0.348 1.345 0.000 0.901 -0.827 -0.842 0.000 0.739 0.047 -0.415 2.548 0.433 -1.132 1.268 0.000 0.797 0.695 0.985 0.868 0.346 0.674 0.623
1 1.333 0.780 -0.964 0.916 1.202 1.822 -0.071 0.742 2.173 1.486 -0.399 -0.824 0.000 0.740 0.568 -0.134 0.000 0.971 -0.070 -1.589 3.102 1.278 0.929 1.421 1.608 1.214 1.215 1.137
1 2.417 0.631 -0.317 0.323 0.581 0.841 1.524 -1.738 0.000 0.543 1.176 -0.325 0.000 0.827 0.700 0.866 0.000 0.834 -0.262 -1.702 3.102 0.932 0.820 0.988 0.646 0.287 0.595 0.589
0 0.955 -1.242 0.938 1.104 0.474 0.798 -0.743 1.535 0.000 1.356 -1.357 -1.080 2.215 1.320 -1.396 -0.132 2.548 0.728 -0.529 -0.633 0.000 0.832 0.841 0.988 0.923 1.077 0.988 0.816
1 1.305 -1.918 0.391 1.161 0.063 0.724 2.593 1.481 0.000 0.592 -1.207 -0.329 0.000 0.886 -0.836 -1.168 2.548 1.067 -1.481 -1.440 0.000 0.916 0.688 0.991 0.969 0.550 0.665 0.638
0 1.201 0.071 -1.123 2.242 -1.533 0.702 -0.256 0.688 0.000 0.967 0.491 1.040 2.215 1.271 -0.558 0.095 0.000 1.504 0.676 -0.383 3.102 0.917 1.006 0.985 1.017 1.057 0.928 1.057
0 0.994 -1.607 1.596 0.774 -1.391 0.625 -0.134 -0.862 2.173 0.746 -0.765 -0.316 2.215 1.131 -0.320 0.869 0.000 0.607 0.826 0.301 0.000 0.798 0.967 0.999 0.880 0.581 0.712 0.774
1 0.482 -0.467 0.729 1.419 1.458 0.824 0.376 -0.242 0.000 1.368 0.023 1.459 2.215 0.826 0.669 -1.079 2.548 0.936 2.215 -0.309 0.000 1.883 1.216 0.997 1.065 0.946 1.224 1.526
1 0.383 1.588 1.611 0.748 1.194 0.866 -0.279 -0.636 0.000 0.707 0.536 0.801 2.215 1.647 -1.155 0.367 0.000 1.292 0.303 -1.681 3.102 2.016 1.581 0.986 0.584 0.684 1.107 0.958
0 0.629 0.203 0.736 0.671 -0.271 1.350 -0.486 0.761 2.173 0.496 -0.805 -1.718 0.000 2.393 0.044 -1.046 1.274 0.651 -0.116 -0.541 0.000 0.697 1.006 0.987 1.069 2.317 1.152 0.902
0 0.905 -0.564 -0.570 0.263 1.096 1.219 -1.397 -1.414 1.087 1.164 -0.533 -0.208 0.000 1.459 1.965 0.784 0.000 2.220 -1.421 0.452 0.000 0.918 1.360 0.993 0.904 0.389 2.118 1.707
1 1.676 1.804 1.171 0.529 1.175 1.664 0.354 -0.530 0.000 1.004 0.691 -1.280 2.215 0.838 0.373 0.626 2.548 1.094 1.774 0.501 0.000 0.806 1.100 0.991 0.769 0.976 0.807 0.740
1 1.364 -1.936 0.020 1.327 0.428 1.021 -1.665 -0.907 2.173 0.818 -2.701 1.303 0.000 0.716 -0.590 -1.629 2.548 0.895 -2.280 -1.602 0.000 1.211 0.849 0.989 1.320 0.864 1.065 0.949
0 0.629 -0.626 0.609 1.828 1.280 0.644 -0.856 -0.873 2.173 0.555 1.066 -0.640 0.000 0.477 -1.364 -1.021 2.548 1.017 0.036 0.380 0.000 0.947 0.941 0.994 1.128 0.241 0.793 0.815
1 1.152 -0.843 0.926 1.802 0.800 2.493 -1.449 -1.127 0.000 1.737 0.833 0.488 0.000 1.026 0.929 -0.990 2.548 1.408 0.689 1.142 3.102 1.171 0.956 0.993 2.009 0.867 1.499 1.474
0 2.204 0.081 0.008 1.021 -0.679 2.676 0.090 1.163 0.000 2.210 -1.686 -1.195 0.000 1.805 0.891 -0.148 2.548 0.450 -0.502 -1.295 3.102 6.959 3.492 1.205 0.908 0.845 2.690 2.183
1 0.957 0.954 1.702 0.043 -0.503 1.113 0.033 -0.308 0.000 0.757 -0.363 -1.129 2.215 1.635 0.068 1.048 1.274 0.415 -2.098 0.061 0.000 1.010 0.979 0.992 0.704 1.125 0.761 0.715
0 1.222 0.418 1.059 1.303 1.442 0.282 -1.499 -1.286 0.000 1.567 0.016 -0.164 2.215 0.451 2.229 -1.229 0.000 0.660 -0.513 -0.296 3.102 2.284 1.340 0.985 1.531 0.314 1.032 1.094
1 0.603 1.675 -0.973 0.703 -1.709 1.023 0.652 1.296 2.173 1.078 0.363 -0.263 0.000 0.734 -0.457 -0.745 1.274 0.561 1.434 -0.042 0.000 0.888 0.771 0.984 0.847 1.234 0.874 0.777
0 0.897 0.949 -0.848 1.115 -0.085 0.522 -1.267 -1.418 0.000 0.684 -0.599 1.474 0.000 1.176 0.922 0.641 2.548 0.470 0.103 0.148 3.102 0.775 0.697 0.984 0.839 0.358 0.847 1.008
1 0.987 1.013 -1.504 0.468 -0.259 1.160 0.476 -0.971 2.173 1.266 0.919 0.780 0.000 0.634 1.695 0.233 0.000 0.487 -0.082 0.719 3.102 0.921 0.641 0.991 0.730 0.828 0.952 0.807
1 0.847 1.581 -1.397 1.629 1.529 1.053 0.816 -0.344 2.173 0.895 0.779 0.332 0.000 0.750 1.311 0.419 2.548 1.604 0.844 1.367 0.000 1.265 0.798 0.989 1.328 0.783 0.930 0.879
1 0.805 1.416 -1.327 0.397 0.589 0.488 0.982 0.843 0.000 0.664 -0.999 0.129 0.000 0.624 0.613 -0.558 0.000 1.431 -0.667 -1.561 3.102 0.959 1.103 0.989 0.590 0.632 0.926 0.798
0 1.220 -0.313 -0.489 1.759 0.201 1.698 -0.220 0.241 2.173 1.294 1.390 -1.682 0.000 1.447 -1.623 -1.296 0.000 1.710 0.872 -1.356 3.102 1.198 0.981 1.184 0.859 2.165 1.807 1.661
0 0.772 -0.611 -0.549 0.465 -1.528 1.103 -0.140 0.001 2.173 0.854 -0.406 1.655 0.000 0.733 -1.250 1.072 0.000 0.883 0.627 -1.132 3.102 0.856 0.927 0.987 1.094 1.013 0.938 0.870
1 1.910 0.771 0.828 0.231 1.267 1.398 1.455 -0.295 2.173 0.837 -2.564 0.770 0.000 0.540 2.189 1.287 0.000 1.345 1.311 -1.151 0.000 0.861 0.869 0.984 1.359 1.562 1.105 0.963
1 0.295 0.832 1.399 1.222 -0.517 2.480 0.013 1.591 0.000 2.289 0.436 0.287 2.215 1.995 -0.367 -0.409 1.274 0.375 1.367 -1.716 0.000 1.356 2.171 0.990 1.467 1.664 1.855 1.705
1 1.228 0.339 -0.575 0.417 1.474 0.480 -1.416 -1.498 2.173 0.614 -0.933 -0.961 0.000 1.189 1.690 1.003 0.000 1.690 -1.065 0.106 3.102 0.963 1.147 0.987 1.086 0.948 0.930 0.866
0 2.877 -1.014 1.440 0.782 0.483 1.134 -0.735 -0.196 2.173 1.123 0.084 -0.596 0.000 1.796 -0.356 1.044 2.548 1.406 1.582 -0.991 0.000 0.939 1.178 1.576 0.996 1.629 1.216 1.280
1 2.178 0.259 1.107 0.256 1.222 0.979 -0.440 -0.538 1.087 0.496 -0.760 -0.049 0.000 1.471 1.683 -1.486 0.000 0.646 0.695 -1.577 3.102 1.093 1.070 0.984 0.608 0.889 0.962 0.866
1 0.604 0.592 1.295 0.964 0.348 1.178 -0.016 0.832 2.173 1.626 -0.420 -0.760 0.000 0.748 0.461 -0.906 0.000 0.728 0.309 -1.269 1.551 0.852 0.604 0.989 0.678 0.949 1.021 0.878
0 0.428 -1.352 -0.912 1.713 0.797 1.894 -1.452 0.191 2.173 2.378 2.113 -1.190 0.000 0.860 2.174 0.949 0.000 1.693 0.759 1.426 3.102 0.885 1.527 1.186 1.090 3.294 4.492 3.676
0 0.473 0.485 0.154 1.433 -1.504 0.766 1.257 -1.302 2.173 0.414 0.119 0.238 0.000 0.805 0.242 -0.691 2.548 0.734 0.749 0.753 0.000 0.430 0.893 1.137 0.686 0.724 0.618 0.608
1 0.763 -0.601 0.876 0.182 -1.678 0.818 0.599 0.481 2.173 0.658 -0.737 -0.553 0.000 0.857 -1.138 -1.435 0.000 1.540 -1.466 -0.447 0.000 0.870 0.566 0.989 0.728 0.658 0.821 0.726
0 0.619 -0.273 -0.143 0.992 -1.267 0.566 0.876 -1.396 2.173 0.515 0.892 0.618 0.000 0.434 -0.902 0.862 2.548 0.490 -0.539 0.549 0.000 0.568 0.794 0.984 0.667 0.867 0.597 0.578
0 0.793 0.970 0.324 0.570 0.816 0.761 -0.550 1.519 2.173 1.150 0.496 -0.447 0.000 0.925 0.724 1.008 1.274 1.135 -0.275 -0.843 0.000 0.829 1.068 0.978 1.603 0.892 1.041 1.059
1 0.480 0.364 -0.067 1.906 -1.582 1.397 1.159 0.140 0.000 0.639 0.398 -1.102 0.000 1.597 -0.668 1.607 2.548 1.306 -0.797 0.288 3.102 0.856 1.259 1.297 1.022 1.032 1.049 0.939
0 0.514 1.304 1.490 1.741 -0.220 0.648 0.155 0.535 0.000 0.562 -1.016 0.837 0.000 0.863 -0.780 -0.815 2.548 1.688 -0.130 -1.545 3.102 0.887 0.980 1.309 1.269 0.654 1.044 1.035
0 1.225 0.333 0.656 0.893 0.859 1.037 -0.876 1.603 1.087 1.769 0.272 -0.227 2.215 1.000 0.579 -1.690 0.000 1.385 0.471 -0.860 0.000 0.884 1.207 0.995 1.097 2.336 1.282 1.145
0 2.044 -1.472 -0.294 0.392 0.369 0.927 0.718 1.492 1.087 1.619 -0.736 0.047 2.215 1.884 -0.101 -1.540 0.000 0.548 -0.441 1.117 0.000 0.798 0.877 0.981 0.750 2.272 1.469 1.276
0 1.037 -0.276 0.735 3.526 1.156 2.498 0.401 -0.590 1.087 0.714 -1.203 1.393 2.215 0.681 0.629 1.534 0.000 0.719 -0.355 -0.706 0.000 0.831 0.857 0.988 2.864 2.633 1.988 1.466
1 0.651 -1.218 -0.791 0.770 -1.449 0.610 -0.535 0.960 2.173 0.380 -1.072 -0.031 2.215 0.415 2.123 -1.100 0.000 0.776 0.217 0.420 0.000 0.986 1.008 1.001 0.853 0.588 0.799 0.776
0 1.586 -0.409 0.085 3.258 0.405 1.647 -0.674 -1.519 0.000 0.640 -1.027 -1.681 0.000 1.452 -0.444 -0.957 2.548 0.927 -0.017 1.215 3.102 0.519 0.866 0.992 0.881 0.847 1.018 1.278
0 0.712 0.092 -0.466 0.688 1.236 0.921 -1.217 -1.022 2.173 2.236 -1.167 0.868 2.215 0.851 -1.892 -0.753 0.000 0.475 -1.216 -0.383 0.000 0.668 0.758 0.988 1.180 2.093 1.157 0.934
0 0.419 0.471 0.974 2.805 0.235 1.473 -0.198 1.255 1.087 0.931 1.083 -0.712 0.000 1.569 1.358 -1.179 2.548 2.506 0.199 -0.842 0.000 0.929 0.991 0.992 1.732 2.367 1.549 1.430
1 0.667 1.003 1.504 0.368 1.061 0.885 -0.318 -0.353 0.000 1.438 -1.939 0.710 0.000 1.851 0.277 -1.460 2.548 1.403 0.517 -0.157 0.000 0.883 1.019 1.000 0.790 0.859 0.938 0.841
1 1.877 -0.492 0.372 0.441 0.955 1.034 -1.220 -0.846 1.087 0.952 -0.320 1.125 0.000 0.542 0.308 -1.261 2.548 1.018 -1.415 -1.547 0.000 1.280 0.932 0.991 1.273 0.878 0.921 0.906
0 1.052 0.901 1.176 1.280 1.517 0.562 -1.150 -0.079 2.173 1.228 -0.308 -0.354 0.000 0.790 -1.492 -0.963 0.000 0.942 -0.672 -1.588 3.102 1.116 0.902 0.988 1.993 0.765 1.375 1.325
1 0.518 -0.254 1.642 0.865 0.725 0.980 0.734 0.023 0.000 1.448 0.780 -1.736 2.215 0.955 0.513 -0.519 0.000 0.365 -0.444 -0.243 3.102 0.833 0.555 0.984 0.827 0.795 0.890 0.786
0 0.870 0.815 -0.506 0.663 -0.518 0.935 0.289 -1.675 2.173 1.188 0.005 0.635 0.000 0.580 0.066 -1.455 2.548 0.580 -0.634 -0.199 0.000 0.852 0.788 0.979 1.283 0.208 0.856 0.950
0 0.628 1.382 0.135 0.683 0.571 1.097 0.564 -0.950 2.173 0.617 -0.326 0.371 0.000 1.093 0.918 1.667 2.548 0.460 1.221 0.708 0.000 0.743 0.861 0.975 1.067 1.007 0.843 0.762
0 4.357 0.816 -1.609 1.845 -1.288 3.292 0.726 0.324 2.173 1.528 0.583 -0.801 2.215 0.605 0.572 1.406 0.000 0.794 -0.791 0.122 0.000 0.967 1.132 1.124 3.602 2.811 2.460 1.861
0 0.677 -1.265 1.559 0.866 -0.618 0.823 0.260 0.185 0.000 1.133 0.337 1.589 2.215 0.563 -0.830 0.510 0.000 0.777 0.117 -0.941 3.102 0.839 0.763 0.986 1.182 0.649 0.796 0.851
0 2.466 -1.838 -1.648 1.717 1.533 1.676 -1.553 -0.109 2.173 0.670 -0.666 0.284 0.000 0.334 -2.480 0.316 0.000 0.366 -0.804 -1.298 3.102 0.875 0.894 0.997 0.548 0.770 1.302 1.079
1 1.403 0.129 -1.307 0.688 0.306 0.579 0.753 0.814 1.087 0.474 0.694 -1.400 0.000 0.520 1.995 0.185 0.000 0.929 -0.504 1.270 3.102 0.972 0.998 1.353 0.948 0.650 0.688 0.724
1 0.351 1.188 -0.360 0.254 -0.346 1.129 0.545 1.691 0.000 0.652 -0.039 -0.258 2.215 1.089 0.655 0.472 2.548 0.554 -0.493 1.366 0.000 0.808 1.045 0.992 0.570 0.649 0.809 0.744
0 1.875 -0.013 -0.128 0.236 1.163 0.902 0.426 0.590 2.173 1.251 -1.210 -0.616 0.000 1.035 1.534 0.912 0.000 1.944 1.789 -1.691 0.000 0.974 1.113 0.990 0.925 1.120 0.956 0.912
0 0.298 0.750 -0.507 1.555 1.463 0.804 1.200 -0.665 0.000 0.439 -0.829 -0.252 1.107 0.770 -1.090 0.947 2.548 1.165 -0.166 -0.763 0.000 1.140 0.997 0.988 1.330 0.555 1.005 1.012
0 0.647 0.342 0.245 4.340 -0.157 2.229 0.068 1.170 2.173 2.133 -0.201 -1.441 0.000 1.467 0.697 -0.532 1.274 1.457 0.583 -1.640 0.000 0.875 1.417 0.976 2.512 2.390 1.794 1.665
1 1.731 -0.803 -1.013 1.492 -0.020 1.646 -0.541 1.121 2.173 0.459 -1.251 -1.495 2.215 0.605 -1.711 -0.232 0.000 0.658 0.634 -0.068 0.000 1.214 0.886 1.738 1.833 1.024 1.192 1.034
0 0.515 1.416 -1.089 1.697 1.426 1.414 0.941 0.027 0.000 1.480 0.133 -1.595 2.215 1.110 0.752 0.760 2.548 1.062 0.697 -0.492 0.000 0.851 0.955 0.994 1.105 1.255 1.175 1.095
0 1.261 0.858 1.465 0.757 0.305 2.310 0.679 1.080 2.173 1.544 2.518 -0.464 0.000 2.326 0.270 -0.841 0.000 2.163 0.839 -0.500 3.102 0.715 0.825 1.170 0.980 2.371 1.527 1.221
1 1.445 1.509 1.471 0.414 -1.285 0.767 0.864 -0.677 2.173 0.524 1.388 0.171 0.000 0.826 0.190 0.121 2.548 0.572 1.691 -1.603 0.000 0.870 0.935 0.994 0.968 0.735 0.783 0.777
1 0.919 -0.264 -1.245 0.681 -1.722 1.022 1.010 0.097 2.173 0.685 0.403 -1.351 0.000 1.357 -0.429 1.262 1.274 0.687 1.021 -0.563 0.000 0.953 0.796 0.991 0.873 1.749 1.056 0.917
1 0.293 -2.258 -1.427 1.191 1.202 0.394 -2.030 1.438 0.000 0.723 0.596 -0.024 2.215 0.525 -1.678 -0.290 0.000 0.788 -0.824 -1.029 3.102 0.821 0.626 0.976 1.080 0.810 0.842 0.771
0 3.286 0.386 1.688 1.619 -1.620 1.392 -0.009 0.280 0.000 1.179 -0.776 -0.110 2.215 1.256 0.248 -1.114 2.548 0.777 0.825 -0.156 0.000 1.026 1.065 0.964 0.909 1.249 1.384 1.395
1 1.075 0.603 0.561 0.656 -0.685 0.985 0.175 0.979 2.173 1.154 0.584 -0.886 0.000 1.084 -0.354 -1.004 2.548 0.865 1.224 1.269 0.000 1.346 1.073 1.048 0.873 1.310 1.003 0.865
1 1.098 -0.091 1.466 1.558 0.915 0.649 1.314 -1.182 2.173 0.791 0.073 0.351 0.000 0.517 0.940 1.195 0.000 1.150 1.187 -0.692 3.102 0.866 0.822 0.980 1.311 0.394 1.119 0.890
1 0.481 -1.042 0.148 1.135 -1.249 1.202 -0.344 0.308 1.087 0.779 -1.431 1.581 0.000 0.860 -0.860 -1.125 0.000 0.785 0.303 1.199 3.102 0.878 0.853 0.988 1.072 0.827 0.936 0.815
0 1.348 0.497 0.318 0.806 0.976 1.393 -0.152 0.632 2.173 2.130 0.515 -1.054 0.000 0.908 0.062 -0.780 0.000 1.185 0.687 1.668 1.551 0.720 0.898 0.985 0.683 1.292 1.320 1.131
0 2.677 -0.420 -1.685 1.828 1.433 2.040 -0.718 -0.039 0.000 0.400 -0.873 0.472 0.000 0.444 0.340 -0.830 2.548 0.431 0.768 -1.417 3.102 0.869 0.917 0.996 0.707 0.193 0.728 1.154
1 1.300 0.586 -0.122 1.306 0.609 0.727 -0.556 -1.652 2.173 0.636 0.720 1.393 2.215 0.328 1.280 -0.390 0.000 0.386 0.752 -0.905 0.000 0.202 0.751 1.106 0.864 0.799 0.928 0.717
0 0.637 -0.176 1.737 1.322 -0.414 0.702 -0.964 -0.680 0.000 1.054 -0.461 0.889 2.215 0.861 -0.267 0.225 0.000 1.910 -1.888 1.027 0.000 0.919 0.899 1.186 0.993 1.109 0.862 0.775
1 0.723 -0.104 1.572 0.428 -0.840 0.655 0.544 1.401 2.173 1.522 -0.154 -0.452 2.215 0.996 0.190 0.273 0.000 1.906 -0.176 0.966 0.000 0.945 0.894 0.990 0.981 1.555 0.988 0.893
0 2.016 -0.570 1.612 0.798 0.441 0.334 0.191 -0.909 0.000 0.939 0.146 0.021 2.215 0.553 -0.444 1.156 2.548 0.781 -1.545 -0.520 0.000 0.922 0.956 1.528 0.722 0.699 0.778 0.901
0 1.352 -0.707 1.284 0.665 0.580 0.694 -1.040 -0.899 2.173 0.692 -2.048 0.029 0.000 0.545 -2.042 1.259 0.000 0.661 -0.808 -1.251 3.102 0.845 0.991 0.979 0.662 0.225 0.685 0.769
1 1.057 -1.561 -0.411 0.952 -0.681 1.236 -1.107 1.045 2.173 1.288 -2.521 -0.521 0.000 1.361 -1.239 1.546 0.000 0.373 -1.540 0.028 0.000 0.794 0.782 0.987 0.889 0.832 0.972 0.828
0 1.118 -0.017 -1.227 1.077 1.256 0.714 0.624 -0.811 0.000 0.800 0.704 0.387 1.107 0.604 0.234 0.986 0.000 1.306 -0.456 0.094 3.102 0.828 0.984 1.195 0.987 0.672 0.774 0.748
1 0.602 2.201 0.212 0.119 0.182 0.474 2.130 1.270 0.000 0.370 2.088 -0.573 0.000 0.780 -0.725 -1.033 0.000 1.642 0.598 0.303 3.102 0.886 0.988 0.985 0.644 0.756 0.651 0.599
0 1.677 -0.844 1.581 0.585 0.887 1.012 -2.315 0.752 0.000 1.077 0.748 -0.195 0.000 0.718 0.832 -1.337 1.274 1.181 -0.557 -1.006 3.102 1.018 1.247 0.988 0.908 0.651 1.311 1.120
1 1.695 0.259 1.224 1.344 1.067 0.718 -1.752 -0.215 0.000 0.473 0.991 -0.993 0.000 0.891 1.285 -1.500 2.548 0.908 -0.131 0.288 0.000 0.945 0.824 0.979 1.009 0.951 0.934 0.833
0 0.793 0.628 0.432 1.707 0.302 0.919 1.045 -0.784 0.000 1.472 0.175 -1.284 2.215 1.569 0.155 0.971 2.548 0.435 0.735 1.625 0.000 0.801 0.907 0.992 0.831 1.446 1.082 1.051
1 0.537 -0.664 -0.244 1.104 1.272 1.154 0.394 1.633 0.000 1.527 0.963 0.559 2.215 1.744 0.650 -0.912 0.000 1.097 0.730 -0.368 3.102 1.953 1.319 1.045 1.309 0.869 1.196 1.126
1 0.585 -1.469 1.005 0.749 -1.060 1.224 -0.717 -0.323 2.173 1.012 -0.201 1.268 0.000 0.359 -0.567 0.476 0.000 1.117 -1.124 1.557 3.102 0.636 1.281 0.986 0.616 1.289 0.890 0.881
1 0.354 -1.517 0.667 2.534 -1.298 1.020 -0.375 1.254 0.000 1.119 -0.060 -1.538 2.215 1.059 -0.395 -0.140 0.000 2.609 0.199 -0.778 1.551 0.957 0.975 1.286 1.666 1.003 1.224 1.135
1 0.691 -1.619 -1.380 0.361 1.727 1.493 -1.093 -0.289 0.000 1.447 -0.640 1.341 0.000 1.453 -0.617 -1.456 1.274 1.061 -1.481 -0.091 0.000 0.744 0.649 0.987 0.596 0.727 0.856 0.797
0 1.336 1.293 -1.359 0.357 0.067 1.110 -0.058 -0.515 0.000 0.976 1.498 1.207 0.000 1.133 0.437 1.053 2.548 0.543 1.374 0.171 0.000 0.764 0.761 0.984 0.827 0.553 0.607 0.612
0 0.417 -1.111 1.661 2.209 -0.683 1.931 -0.642 0.959 1.087 1.514 -2.032 -0.686 0.000 1.521 -0.539 1.344 0.000 0.978 -0.866 0.363 1.551 2.813 1.850 1.140 1.854 0.799 1.600 1.556
0 1.058 0.390 -0.591 0.134 1.149 0.346 -1.550 0.186 0.000 1.108 -0.999 0.843 1.107 1.124 0.415 -1.514 0.000 1.067 -0.426 -1.000 3.102 1.744 1.050 0.985 1.006 1.010 0.883 0.789
1 1.655 0.253 1.216 0.270 1.703 0.500 -0.006 -1.418 2.173 0.690 -0.350 0.170 2.215 1.045 -0.924 -0.774 0.000 0.996 -0.745 -0.123 0.000 0.839 0.820 0.993 0.921 0.869 0.725 0.708
0 1.603 -0.850 0.564 0.829 0.093 1.270 -1.113 -1.155 2.173 0.853 -1.021 1.248 2.215 0.617 -1.270 1.733 0.000 0.935 -0.092 0.136 0.000 1.011 1.074 0.977 0.823 1.269 1.054 0.878
0 1.568 -0.792 1.005 0.545 0.896 0.895 -1.698 -0.988 0.000 0.608 -1.634 1.705 0.000 0.826 0.208 0.618 1.274 2.063 -1.743 -0.520 0.000 0.939 0.986 0.990 0.600 0.435 1.033 1.087
0 0.489 -1.335 -1.102 1.738 1.028 0.628 -0.992 -0.627 0.000 0.652 -0.064 -0.215 0.000 1.072 0.173 -1.251 2.548 1.042 0.057 0.841 3.102 0.823 0.895 1.200 1.164 0.770 0.837 0.846
1 1.876 0.870 1.234 0.556 -1.262 1.764 0.855 -0.467 2.173 1.079 1.351 0.852 0.000 0.773 0.383 0.874 0.000 1.292 0.829 -1.228 3.102 0.707 0.969 1.102 1.601 1.017 1.112 1.028
0 1.033 0.407 -0.374 0.705 -1.254 0.690 -0.231 1.502 2.173 0.433 -2.009 -0.057 0.000 0.861 1.151 0.334 0.000 0.960 -0.839 1.299 3.102 2.411 1.480 0.982 0.995 0.377 1.012 0.994
0 1.092 0.653 -0.801 0.463 0.426 0.529 -1.055 0.040 0.000 0.663 0.999 1.255 1.107 0.749 -1.106 1.185 2.548 0.841 -0.745 -1.029 0.000 0.841 0.743 0.988 0.750 1.028 0.831 0.868
1 0.799 -0.285 -0.011 0.531 1.392 1.063 0.854 0.494 2.173 1.187 -1.065 -0.851 0.000 0.429 -0.296 1.072 0.000 0.942 -1.985 1.172 0.000 0.873 0.693 0.992 0.819 0.689 1.131 0.913
0 0.503 1.973 -0.377 1.515 -1.514 0.708 1.081 -0.313 2.173 1.110 -0.417 0.839 0.000 0.712 -1.153 1.165 0.000 0.675 -0.303 -0.930 1.551 0.709 0.761 1.032 0.986 0.698 0.963 1.291
0 0.690 -0.574 -1.608 1.182 1.118 0.557 -2.243 0.144 0.000 0.969 0.216 -1.383 1.107 1.054 0.888 -0.709 2.548 0.566 1.663 -0.550 0.000 0.752 1.528 0.987 1.408 0.740 1.290 1.123
1 0.890 1.501 0.786 0.779 -0.615 1.126 0.716 1.541 2.173 0.887 0.728 -0.673 2.215 1.216 0.332 -0.020 0.000 0.965 1.828 0.101 0.000 0.827 0.715 1.099 1.088 1.339 0.924 0.878
0 0.566 0.883 0.655 1.600 0.034 1.155 2.028 -1.499 0.000 0.723 -0.871 0.763 0.000 1.286 -0.696 -0.676 2.548 1.134 -0.113 1.207 3.102 4.366 2.493 0.984 0.960 0.962 1.843 1.511
0 1.146 1.086 -0.911 0.838 1.298 0.821 0.127 -0.145 0.000 1.352 0.474 -1.580 2.215 1.619 -0.081 0.675 2.548 1.382 -0.748 0.127 0.000 0.958 0.976 1.239 0.876 1.481 1.116 1.076
0 1.739 -0.326 -1.661 0.420 -1.705 1.193 -0.031 -1.212 2.173 1.783 -0.442 0.522 0.000 1.064 -0.692 0.027 0.000 1.314 0.359 -0.037 3.102 0.968 0.897 0.986 0.907 1.196 1.175 1.112
1 0.669 0.194 -0.703 0.657 -0.260 0.899 -2.511 0.311 0.000 1.482 0.773 0.974 2.215 3.459 0.037 -1.299 1.274 2.113 0.067 1.516 0.000 0.740 0.871 0.979 1.361 2.330 1.322 1.046
1 1.355 -1.033 -1.173 0.552 -0.048 0.899 -0.482 -1.287 2.173 1.422 -1.227 0.390 1.107 1.937 -0.028 0.914 0.000 0.849 -0.230 -1.734 0.000 0.986 1.224 1.017 1.051 1.788 1.150 1.009
1 0.511 -0.202 1.029 0.780 1.154 0.816 0.532 -0.731 0.000 0.757 0.517 0.749 2.215 1.302 0.289 -1.188 0.000 0.584 1.211 -0.350 0.000 0.876 0.943 0.995 0.963 0.256 0.808 0.891
1 1.109 0.572 1.484 0.753 1.543 1.711 -0.145 -0.746 1.087 1.759 0.631 0.845 2.215 0.945 0.542 0.003 0.000 0.378 -1.150 -0.044 0.000 0.764 1.042 0.992 1.045 2.736 1.441 1.140
0 0.712 -0.025 0.553 0.928 -0.711 1.304 0.045 -0.300 0.000 0.477 0.720 0.969 0.000 1.727 -0.474 1.328 1.274 1.282 2.222 1.684 0.000 0.819 0.765 1.023 0.961 0.657 0.799 0.744
1 1.131 -0.302 1.079 0.901 0.236 0.904 -0.249 1.694 2.173 1.507 -0.702 -1.128 0.000 0.774 0.565 0.284 2.548 1.802 1.446 -0.192 0.000 3.720 2.108 0.986 0.930 1.101 1.484 1.238
0 1.392 1.253 0.118 0.864 -1.358 0.922 -0.447 -1.243 1.087 1.969 1.031 0.774 2.215 1.333 -0.359 -0.681 0.000 1.099 -0.257 1.473 0.000 1.246 0.909 1.475 1.234 2.531 1.449 1.306
0 1.374 2.291 -0.479 1.339 -0.243 0.687 2.345 1.310 0.000 0.467 1.081 0.772 0.000 0.656 1.155 -1.636 2.548 0.592 0.536 -1.269 3.102 0.981 0.821 1.010 0.877 0.217 0.638 0.758
1 0.401 -1.516 0.909 2.738 0.519 0.887 0.566 -1.202 0.000 0.909 -0.176 1.682 0.000 2.149 -0.878 -0.514 2.548 0.929 -0.563 -1.555 3.102 1.228 0.803 0.980 1.382 0.884 1.025 1.172
1 0.430 -1.589 1.417 2.158 1.226 1.180 -0.829 -0.781 2.173 0.798 1.400 -0.111 0.000 0.939 -0.878 1.076 2.548 0.576 1.335 -0.826 0.000 0.861 0.970 0.982 1.489 1.308 1.015 0.992
1 1.943 -0.391 -0.840 0.621 -1.613 2.026 1.734 1.025 0.000 0.930 0.573 -0.912 0.000 1.326 0.847 -0.220 1.274 1.181 0.079 0.709 3.102 1.164 1.007 0.987 1.094 0.821 0.857 0.786
1 0.499 0.436 0.887 0.859 1.509 0.733 -0.559 1.111 1.087 1.011 -0.796 0.279 2.215 1.472 -0.510 -0.982 0.000 1.952 0.379 -0.733 0.000 1.076 1.358 0.991 0.589 0.879 1.068 0.922
0 0.998 -0.407 -1.711 0.139 0.652 0.810 -0.331 -0.721 0.000 0.471 -0.533 0.442 0.000 0.531 -1.405 0.120 2.548 0.707 0.098 -1.176 1.551 1.145 0.809 0.988 0.529 0.612 0.562 0.609
1 1.482 0.872 0.638 1.288 0.362 0.856 0.900 -0.511 1.087 1.072 1.061 -1.432 2.215 1.770 -2.292 -1.547 0.000 1.131 1.374 0.783 0.000 6.316 4.381 1.002 1.317 1.048 2.903 2.351
1 2.084 -0.422 1.289 1.125 0.735 1.104 -0.518 -0.326 2.173 0.413 -0.719 -0.699 0.000 0.857 0.108 -1.631 0.000 0.527 0.641 -1.362 3.102 0.791 0.952 1.016 0.776 0.856 0.987 0.836
0 0.464 0.674 0.025 0.430 -1.703 0.982 -1.311 -0.808 2.173 1.875 1.060 0.821 2.215 0.954 -0.480 -1.677 0.000 0.567 0.702 -0.939 0.000 0.781 1.076 0.989 1.256 3.632 1.652 1.252
1 0.457 -1.944 -1.010 1.409 0.931 1.098 -0.742 -0.415 0.000 1.537 -0.834 0.945 2.215 1.752 -0.287 -1.269 2.548 0.692 -1.537 -0.223 0.000 0.801 1.192 1.094 1.006 1.659 1.175 1.122
0 3.260 -0.943 1.737 0.920 1.309 0.946 -0.139 -0.271 2.173 0.994 -0.952 -0.311 0.000 0.563 -0.136 -0.881 0.000 1.236 -0.507 0.906 1.551 0.747 0.869 0.985 1.769 1.034 1.179 1.042
0 0.615 -0.778 0.246 1.861 1.619 0.560 -0.943 -0.204 2.173 0.550 -0.759 -1.342 2.215 0.578 0.076 -0.973 0.000 0.939 0.035 0.680 0.000 0.810 0.747 1.401 0.772 0.702 0.719 0.662
1 2.370 -0.064 -0.237 1.737 0.154 2.319 -1.838 -1.673 0.000 1.053 -1.305 -0.075 0.000 0.925 0.149 0.318 1.274 0.851 -0.922 0.981 3.102 0.919 0.940 0.989 0.612 0.598 1.219 1.626
1 1.486 0.311 -1.262 1.354 -0.847 0.886 -0.158 1.213 2.173 1.160 -0.218 0.239 0.000 1.166 0.494 0.278 2.548 0.575 1.454 -1.701 0.000 0.429 1.129 0.983 1.111 1.049 1.006 0.920
1 1.294 1.587 -0.864 0.487 -0.312 0.828 1.051 -0.031 1.087 2.443 1.216 1.609 2.215 1.167 0.813 0.921 0.000 1.751 -0.415 0.119 0.000 1.015 1.091 0.974 1.357 2.093 1.178 1.059
1 0.984 0.465 -1.661 0.379 -0.554 0.977 0.237 0.365 0.000 0.510 0.143 1.101 0.000 1.099 -0.662 -1.593 2.548 1.104 -0.197 -0.648 3.102 0.925 0.922 0.986 0.642 0.667 0.806 0.722
1 0.930 -0.009 0.047 0.667 1.367 1.065 -0.231 0.815 0.000 1.199 -1.114 -0.877 2.215 0.940 0.824 -1.583 0.000 1.052 -0.407 -0.076 1.551 1.843 1.257 1.013 1.047 0.751 1.158 0.941
0 0.767 -0.011 -0.637 0.341 -1.437 1.438 -0.425 -0.450 2.173 1.073 -0.718 1.341 2.215 0.633 -1.394 0.486 0.000 0.603 -1.945 -1.626 0.000 0.703 0.790 0.984 1.111 1.848 1.129 1.072
1 1.779 0.017 0.432 0.402 1.022 0.959 1.480 1.595 2.173 1.252 1.365 0.006 0.000 1.188 -0.174 -1.107 0.000 1.181 0.518 -0.258 0.000 1.057 0.910 0.991 1.616 0.779 1.158 1.053
0 0.881 0.630 1.029 1.990 0.508 1.102 0.742 -1.298 2.173 1.565 1.085 0.686 2.215 2.691 1.391 -0.904 0.000 0.499 1.388 -1.199 0.000 0.347 0.861 0.997 0.881 1.920 1.233 1.310
0 1.754 -0.266 0.389 0.347 -0.030 0.462 -1.408 -0.957 2.173 0.515 -2.341 -1.700 0.000 0.588 -0.797 1.355 2.548 0.608 0.329 -1.389 0.000 1.406 0.909 0.988 0.760 0.593 0.768 0.847
0 1.087 0.311 -1.447 0.173 0.567 0.854 0.362 0.584 0.000 1.416 -0.716 -1.211 2.215 0.648 -0.358 -0.692 1.274 0.867 -0.513 0.206 0.000 0.803 0.813 0.984 1.110 0.491 0.921 0.873
0 0.279 1.114 -1.190 3.004 -0.738 1.233 0.896 1.092 2.173 0.454 -0.374 0.117 2.215 0.357 0.119 1.270 0.000 0.458 1.343 0.316 0.000 0.495 0.540 0.988 1.715 1.139 1.618 1.183
1 1.773 -0.694 -1.518 2.306 -1.200 3.104 0.749 0.362 0.000 1.871 0.230 -1.686 2.215 0.805 -0.179 -0.871 1.274 0.910 0.607 -0.246 0.000 1.338 1.598 0.984 1.050 0.919 1.678 1.807
0 0.553 0.683 0.827 0.973 -0.706 1.488 0.149 1.140 2.173 1.788 0.447 -0.478 0.000 0.596 1.043 1.607 0.000 0.373 -0.868 -1.308 1.551 1.607 1.026 0.998 1.134 0.808 1.142 0.936
1 0.397 1.101 -1.139 1.688 0.146 0.972 0.541 1.518 0.000 1.549 -0.873 -1.012 0.000 2.282 -0.151 0.314 2.548 1.174 0.033 -1.368 0.000 0.937 0.776 1.039 1.143 0.959 0.986 1.013
1 0.840 1.906 -0.959 0.869 0.576 0.642 0.554 -1.351 0.000 0.756 0.923 -0.823 2.215 1.251 1.130 0.545 2.548 1.513 0.410 1.073 0.000 1.231 0.985 1.163 0.812 0.987 0.816 0.822
1 0.477 1.665 0.814 0.763 -0.382 0.828 -0.008 0.280 2.173 1.213 -0.001 1.560 0.000 1.136 0.311 -1.289 0.000 0.797 1.091 -0.616 3.102 1.026 0.964 0.992 0.772 0.869 0.916 0.803
0 2.655 0.020 0.273 1.464 0.482 1.709 -0.107 -1.456 2.173 0.825 0.141 -0.386 0.000 1.342 -0.592 1.635 1.274 0.859 -0.175 -0.874 0.000 0.829 0.946 1.003 2.179 0.836 1.505 1.176
0 0.771 -1.992 -0.720 0.732 -1.464 0.869 -1.290 0.388 2.173 0.926 -1.072 -1.489 2.215 0.640 -1.232 0.840 0.000 0.528 -2.440 -0.446 0.000 0.811 0.868 0.993 0.995 1.317 0.809 0.714
0 1.357 1.302 0.076 0.283 -1.060 0.783 1.559 -0.994 0.000 0.947 1.212 1.617 0.000 1.127 0.311 0.442 2.548 0.582 -0.052 1.186 1.551 1.330 0.995 0.985 0.846 0.404 0.858 0.815
0 0.442 -0.381 -0.424 1.244 0.591 0.731 0.605 -0.713 2.173 0.629 2.762 1.040 0.000 0.476 2.693 -0.617 0.000 0.399 0.442 1.486 3.102 0.839 0.755 0.988 0.869 0.524 0.877 0.918
0 0.884 0.422 0.055 0.818 0.624 0.950 -0.763 1.624 0.000 0.818 -0.609 -1.166 0.000 1.057 -0.528 1.070 2.548 1.691 -0.124 -0.335 3.102 1.104 0.933 0.985 0.913 1.000 0.863 1.056
0 1.276 0.156 1.714 1.053 -1.189 0.672 -0.464 -0.030 2.173 0.469 -2.483 0.442 0.000 0.564 2.580 -0.253 0.000 0.444 -0.628 1.080 1.551 5.832 2.983 0.985 1.162 0.494 1.809 1.513
0 1.106 -0.556 0.406 0.573 -1.400 0.769 -0.518 1.457 2.173 0.743 -0.352 -0.010 0.000 1.469 -0.550 -0.930 2.548 0.540 1.236 -0.571 0.000 0.962 0.970 1.101 0.805 1.107 0.873 0.773
0 0.539 -0.964 -0.464 1.371 -1.606 0.667 -0.160 0.655 0.000 0.952 0.352 -0.740 2.215 0.952 0.007 1.123 0.000 1.061 -0.505 1.389 3.102 1.063 0.991 1.019 0.633 0.967 0.732 0.799
1 0.533 -0.989 -1.608 0.462 -1.723 1.204 -0.598 -0.098 2.173 1.343 -0.460 1.632 2.215 0.577 0.221 -0.492 0.000 0.628 -0.073 0.472 0.000 0.518 0.880 0.988 1.179 1.874 1.041 0.813
1 1.024 1.075 -0.795 0.286 -1.436 1.365 0.857 -0.309 2.173 0.804 1.532 1.435 0.000 1.511 0.722 1.494 0.000 1.778 0.903 0.753 1.551 0.686 0.810 0.999 0.900 1.360 1.133 0.978
1 2.085 -0.269 -1.423 0.789 1.298 0.281 1.652 0.187 0.000 0.658 -0.760 -0.042 2.215 0.663 0.024 0.120 0.000 0.552 -0.299 -0.428 3.102 0.713 0.811 1.130 0.705 0.218 0.675 0.743
1 0.980 -0.443 0.813 0.785 -1.253 0.719 0.448 -1.458 0.000 1.087 0.595 0.635 1.107 1.428 0.029 -0.995 0.000 1.083 1.562 -0.092 0.000 0.834 0.891 1.165 0.967 0.661 0.880 0.817
1 0.903 -0.733 -0.980 0.634 -0.639 0.780 0.266 -0.287 2.173 1.264 -0.936 1.004 0.000 1.002 -0.056 -1.344 2.548 1.183 -0.098 1.169 0.000 0.733 1.002 0.985 0.711 0.916 0.966 0.875
0 0.734 -0.304 -1.175 2.851 1.674 0.904 -0.634 0.412 2.173 1.363 -1.050 -0.282 0.000 1.476 -1.603 0.103 0.000 2.231 -0.718 1.708 3.102 0.813 0.896 1.088 0.686 1.392 1.033 1.078
1 1.680 0.591 -0.243 0.111 -0.478 0.326 -0.079 -1.555 2.173 0.711 0.714 0.922 2.215 0.355 0.858 1.682 0.000 0.727 1.620 1.360 0.000 0.334 0.526 1.001 0.862 0.633 0.660 0.619
1 1.163 0.225 -0.202 0.501 -0.979 1.609 -0.938 1.424 0.000 1.224 -0.118 -1.274 0.000 2.034 1.241 -0.254 0.000 1.765 0.536 0.237 3.102 0.894 0.838 0.988 0.693 0.579 0.762 0.726
0 1.223 1.232 1.471 0.489 1.728 0.703 -0.111 0.411 0.000 1.367 1.014 -1.294 1.107 1.524 -0.414 -0.164 2.548 1.292 0.833 0.316 0.000 0.861 0.752 0.994 0.836 1.814 1.089 0.950
0 0.816 1.637 -1.557 1.036 -0.342 0.913 1.333 0.949 2.173 0.812 0.756 -0.628 2.215 1.333 0.470 1.495 0.000 1.204 -2.222 -1.675 0.000 1.013 0.924 1.133 0.758 1.304 0.855 0.860
0 0.851 -0.564 -0.691 0.692 1.345 1.219 1.014 0.318 0.000 1.422 -0.262 -1.635 2.215 0.531 1.802 0.008 0.000 0.508 0.515 -1.267 3.102 0.821 0.787 1.026 0.783 0.432 1.149 1.034
0 0.800 -0.599 0.204 0.552 -0.484 0.974 0.413 0.961 2.173 1.269 -0.984 -1.039 2.215 0.380 -1.213 1.371 0.000 0.551 0.332 -0.659 0.000 0.694 0.852 0.984 1.057 2.037 1.096 0.846
0 0.744 -0.071 -0.255 0.638 0.512 1.125 0.407 0.844 2.173 0.860 -0.481 -0.677 0.000 1.102 0.181 -1.194 0.000 1.011 -1.081 -1.713 3.102 0.854 0.862 0.982 1.111 1.372 1.042 0.920
1 0.400 1.049 -0.625 0.880 -0.407 1.040 2.150 -1.359 0.000 0.747 -0.144 0.847 2.215 0.560 -1.829 0.698 0.000 1.663 -0.668 0.267 0.000 0.845 0.964 0.996 0.820 0.789 0.668 0.668
0 1.659 -0.705 -1.057 1.803 -1.436 1.008 0.693 0.005 0.000 0.895 -0.007 0.681 1.107 1.085 0.125 1.476 2.548 1.214 1.068 0.486 0.000 0.867 0.919 0.986 1.069 0.692 1.026 1.313
0 0.829 -0.153 0.861 0.615 -0.548 0.589 1.077 -0.041 2.173 1.056 0.763 -1.737 0.000 0.639 0.970 0.725 0.000 0.955 1.227 -0.799 3.102 1.020 1.024 0.985 0.750 0.525 0.685 0.671
1 0.920 -0.806 -0.840 1.048 0.278 0.973 -0.077 -1.364 2.173 1.029 0.309 0.133 0.000 1.444 1.484 1.618 1.274 1.419 -0.482 0.417 0.000 0.831 1.430 1.151 1.829 1.560 1.343 1.224
1 0.686 0.249 -0.905 0.343 -1.731 0.724 -2.823 -0.901 0.000 0.982 0.303 1.312 1.107 1.016 0.245 0.610 0.000 1.303 -0.557 -0.360 3.102 1.384 1.030 0.984 0.862 1.144 0.866 0.779
0 1.603 0.444 0.508 0.586 0.401 0.610 0.467 -1.735 2.173 0.914 0.626 -1.019 0.000 0.812 0.422 -0.408 2.548 0.902 1.679 1.490 0.000 1.265 0.929 0.990 1.004 0.816 0.753 0.851
1 0.623 0.780 -0.203 0.056 0.015 0.899 0.793 1.326 1.087 0.803 1.478 -1.499 2.215 1.561 1.492 -0.120 0.000 0.904 0.795 0.137 0.000 0.548 1.009 0.850 0.924 0.838 0.914 0.860
0 1.654 -2.032 -1.160 0.859 -1.583 0.689 -1.965 0.891 0.000 0.646 -1.014 -0.288 2.215 0.630 -0.815 0.402 0.000 0.638 0.316 0.655 3.102 0.845 0.879 0.993 1.067 0.625 1.041 0.958
1 0.828 -1.269 -1.203 0.744 -0.213 0.626 -1.017 -0.404 0.000 1.281 -0.931 1.733 2.215 0.699 -0.351 1.287 0.000 1.251 -1.171 0.197 0.000 0.976 1.186 0.987 0.646 0.655 0.733 0.671
1 0.677 0.111 1.090 1.580 1.591 1.560 0.654 -0.341 2.173 0.794 -0.266 0.702 0.000 0.823 0.651 -1.239 2.548 0.730 1.467 -1.530 0.000 1.492 1.023 0.983 1.909 1.022 1.265 1.127
1 0.736 0.882 -1.060 0.589 0.168 1.663 0.781 1.022 2.173 2.025 1.648 -1.292 0.000 1.240 0.924 -0.421 1.274 1.354 0.065 0.501 0.000 0.316 0.925 0.988 0.664 1.736 0.992 0.807
1 1.040 -0.822 1.638 0.974 -0.674 0.393 0.830 0.011 2.173 0.770 -0.140 -0.402 0.000 0.294 -0.133 0.030 0.000 1.220 0.807 0.638 0.000 0.826 1.063 1.216 1.026 0.705 0.934 0.823
1 0.711 0.602 0.048 1.145 0.966 0.934 0.263 -1.589 2.173 0.971 -0.496 -0.421 1.107 0.628 -0.865 0.845 0.000 0.661 -0.008 -0.565 0.000 0.893 0.705 0.988 0.998 1.339 0.908 0.872
1 0.953 -1.651 -0.167 0.885 1.053 1.013 -1.239 0.133 0.000 1.884 -1.122 1.222 2.215 1.906 -0.860 -1.184 1.274 1.413 -0.668 -1.647 0.000 1.873 1.510 1.133 1.050 1.678 1.246 1.061
1 0.986 -0.892 -1.380 0.917 1.134 0.950 -1.162 -0.469 0.000 0.569 -1.393 0.215 0.000 0.320 2.667 1.712 0.000 1.570 -0.375 1.457 3.102 0.925 1.128 1.011 0.598 0.824 0.913 0.833
1 1.067 0.099 1.154 0.527 -0.789 1.085 0.623 -1.602 2.173 1.511 -0.230 0.022 2.215 0.269 -0.377 0.883 0.000 0.571 -0.540 -0.512 0.000 0.414 0.803 1.022 0.959 2.053 1.041 0.780
0 0.825 -2.118 0.217 1.453 -0.493 0.819 0.313 -0.942 0.000 2.098 -0.725 1.096 2.215 0.484 1.336 1.458 0.000 0.482 0.100 1.163 0.000 0.913 0.536 0.990 1.679 0.957 1.095 1.143
1 1.507 0.054 1.120 0.698 -1.340 0.912 0.384 0.015 1.087 0.720 0.247 -0.820 0.000 0.286 0.154 1.578 2.548 0.629 1.582 -0.576 0.000 0.828 0.893 1.136 0.514 0.632 0.699 0.709
1 0.610 1.180 -0.993 0.816 0.301 0.932 0.758 1.539 0.000 0.726 -0.830 0.248 2.215 0.883 0.857 -1.305 0.000 1.338 1.009 -0.252 3.102 0.901 1.074 0.987 0.875 1.159 1.035 0.858
1 1.247 -1.360 1.502 1.525 -1.332 0.618 1.063 0.755 0.000 0.582 -0.155 0.473 2.215 1.214 -0.422 -0.551 2.548 0.838 -1.171 -1.166 0.000 2.051 1.215 1.062 1.091 0.725 0.896 1.091
0 0.373 -0.600 1.291 2.573 0.207 0.765 -0.209 1.667 0.000 0.668 0.724 -1.499 0.000 1.045 -0.338 -0.754 2.548 0.558 -0.469 0.029 3.102 0.868 0.939 1.124 0.519 0.383 0.636 0.838
0 0.791 0.336 -0.307 0.494 1.213 1.158 0.336 1.081 2.173 0.918 1.289 -0.449 0.000 0.735 -0.521 -0.969 0.000 1.052 0.499 -1.188 3.102 0.699 1.013 0.987 0.622 1.050 0.712 0.661
0 1.321 0.856 0.464 0.202 0.901 1.144 0.120 -1.651 0.000 0.803 0.577 -0.509 2.215 0.695 -0.114 0.423 2.548 0.621 1.852 -0.420 0.000 0.697 0.964 0.983 0.527 0.659 0.719 0.729
0 0.563 2.081 0.913 0.982 -0.533 0.549 -0.481 -1.730 0.000 0.962 0.921 0.569 2.215 0.731 1.184 -0.679 1.274 0.918 0.931 -1.432 0.000 1.008 0.919 0.993 0.895 0.819 0.810 0.878
1 1.148 0.345 0.953 0.921 0.617 0.991 1.103 -0.484 0.000 0.970 1.978 1.525 0.000 1.150 0.689 -0.757 2.548 0.517 0.995 1.245 0.000 1.093 1.140 0.998 1.006 0.756 0.864 0.838
1 1.400 0.128 -1.695 1.169 1.070 1.094 -0.345 -0.249 0.000 1.224 0.364 -0.036 2.215 1.178 0.530 -1.544 0.000 1.334 0.933 1.604 0.000 0.560 1.267 1.073 0.716 0.780 0.832 0.792
0 0.330 -2.133 1.403 0.628 0.379 1.686 -0.995 0.030 1.087 2.071 0.127 -0.457 0.000 4.662 -0.855 1.477 0.000 2.072 -0.917 -1.416 3.102 5.403 3.074 0.977 0.936 1.910 2.325 1.702
0 0.989 0.473 0.968 1.970 1.368 0.844 0.574 -0.290 2.173 0.866 -0.345 -1.019 0.000 1.130 0.605 -0.752 0.000 0.956 -0.888 0.870 3.102 0.885 0.886 0.982 1.157 1.201 1.100 1.068
1 0.773 0.418 0.753 1.388 1.070 1.104 -0.378 -0.758 0.000 1.027 0.397 -0.496 2.215 1.234 0.027 1.084 2.548 0.936 0.209 1.677 0.000 1.355 1.020 0.983 0.550 1.206 0.916 0.931
0 0.319 2.015 1.534 0.570 -1.134 0.632 0.124 0.757 0.000 0.477 0.598 -1.109 1.107 0.449 0.438 -0.755 2.548 0.574 -0.659 0.691 0.000 0.440 0.749 0.985 0.517 0.158 0.505 0.522
0 1.215 1.453 -1.386 1.276 1.298 0.643 0.570 -0.196 2.173 0.588 2.104 0.498 0.000 0.617 -0.296 -0.801 2.548 0.452 0.110 0.313 0.000 0.815 0.953 1.141 1.166 0.547 0.892 0.807
1 1.257 -1.869 -0.060 0.265 0.653 1.527 -0.346 1.163 2.173 0.758 -2.119 -0.604 0.000 1.473 -1.133 -1.290 2.548 0.477 -0.428 -0.066 0.000 0.818 0.841 0.984 1.446 1.729 1.211 1.054
1 1.449 0.464 1.585 1.418 -1.488 1.540 0.942 0.087 0.000 0.898 0.402 -0.631 2.215 0.753 0.039 -1.729 0.000 0.859 0.849 -1.054 0.000 0.791 0.677 0.995 0.687 0.527 0.703 0.606
1 1.084 -1.997 0.900 1.333 1.024 0.872 -0.864 -1.500 2.173 1.072 -0.813 -0.421 2.215 0.924 0.478 0.304 0.000 0.992 -0.398 -1.022 0.000 0.741 1.085 0.980 1.221 1.176 1.032 0.961
0 1.712 1.129 0.125 1.120 -1.402 1.749 0.951 -1.575 2.173 1.711 0.445 0.578 0.000 1.114 0.234 -1.011 0.000 1.577 -0.088 0.086 3.102 2.108 1.312 1.882 1.597 2.009 1.441 1.308
0 0.530 0.248 1.622 1.450 -1.012 1.221 -1.154 -0.763 2.173 1.698 -0.586 0.733 0.000 0.889 1.042 1.038 1.274 0.657 0.008 0.701 0.000 0.430 1.005 0.983 0.930 2.264 1.357 1.146
1 0.921 1.735 0.883 0.699 -1.614 0.821 1.463 0.319 1.087 1.099 0.814 -1.600 2.215 1.375 0.702 -0.691 0.000 0.869 1.326 -0.790 0.000 0.980 0.900 0.988 0.832 1.452 0.816 0.709
0 2.485 -0.823 -0.297 0.886 -1.404 0.989 0.835 1.615 2.173 0.382 0.588 -0.224 0.000 1.029 -0.456 1.546 2.548 0.613 -0.359 -0.789 0.000 0.768 0.977 1.726 2.007 0.913 1.338 1.180
1 0.657 -0.069 -0.078 1.107 1.549 0.804 1.335 -1.630 2.173 1.271 0.481 0.153 1.107 1.028 0.144 -0.762 0.000 1.098 0.132 1.570 0.000 0.830 0.979 1.175 1.069 1.624 1.000 0.868
1 2.032 0.329 -1.003 0.493 -0.136 1.159 -0.224 0.750 1.087 0.396 0.546 0.587 0.000 0.620 1.805 0.982 0.000 1.236 0.744 -1.621 0.000 0.930 1.200 0.988 0.482 0.771 0.887 0.779
0 0.524 -1.319 0.634 0.471 1.221 0.599 -0.588 -0.461 0.000 1.230 -1.504 -1.517 1.107 1.436 -0.035 0.104 2.548 0.629 1.997 -1.282 0.000 2.084 1.450 0.984 1.084 1.827 1.547 1.213
1 0.871 0.618 -1.544 0.718 0.186 1.041 -1.180 0.434 2.173 1.133 1.558 -1.301 0.000 0.452 -0.595 0.522 0.000 0.665 0.567 0.130 3.102 1.872 1.114 1.095 1.398 0.979 1.472 1.168
1 3.308 1.037 -0.634 0.690 -0.619 1.975 0.949 1.280 0.000 0.826 0.546 -0.139 2.215 0.635 -0.045 0.427 0.000 1.224 0.112 1.339 3.102 1.756 1.050 0.992 0.738 0.903 0.968 1.238
0 0.588 2.104 -0.872 1.136 1.743 0.842 0.638 0.015 0.000 0.481 0.928 1.000 2.215 0.595 0.125 1.429 0.000 0.951 -1.140 -0.511 3.102 1.031 1.057 0.979 0.673 1.064 1.001 0.891
0 0.289 0.823 0.013 0.615 -1.601 0.177 2.403 -0.015 0.000 0.258 1.151 1.036 2.215 0.694 0.553 -1.326 2.548 0.411 0.366 0.106 0.000 0.482 0.562 0.989 0.670 0.404 0.516 0.561
1 0.294 -0.660 -1.162 1.752 0.384 0.860 0.513 1.119 0.000 2.416 0.107 -1.342 0.000 1.398 0.361 -0.350 2.548 1.126 -0.902 0.040 1.551 0.650 1.125 0.988 0.531 0.843 0.912 0.911
0 0.599 -0.616 1.526 1.381 0.507 0.955 -0.646 -0.085 2.173 0.775 -0.533 1.116 2.215 0.789 -0.136 -1.176 0.000 2.449 1.435 -1.433 0.000 1.692 1.699 1.000 0.869 1.119 1.508 1.303
1 1.100 -1.174 -1.114 1.601 -1.576 1.056 -1.343 0.547 2.173 0.555 0.367 0.592 2.215 0.580 -1.862 -0.914 0.000 0.904 0.508 -0.444 0.000 1.439 1.105 0.986 1.408 1.104 1.190 1.094
1 2.237 -0.701 1.470 0.719 -0.199 0.745 -0.132 -0.737 1.087 0.976 -0.227 0.093 2.215 0.699 0.057 1.133 0.000 0.661 0.573 -0.679 0.000 0.785 0.772 1.752 1.235 0.856 0.990 0.825
1 0.455 -0.880 -1.482 1.260 -0.178 1.499 0.158 1.022 0.000 1.867 -0.435 -0.675 2.215 1.234 0.783 1.586 0.000 0.641 -0.454 -0.409 3.102 1.002 0.964 0.986 0.761 0.240 1.190 0.995
1 1.158 -0.778 -0.159 0.823 1.641 1.341 -0.830 -1.169 2.173 0.840 -1.554 0.934 0.000 0.693 0.488 -1.218 2.548 1.042 1.395 0.276 0.000 0.946 0.785 1.350 1.079 0.893 1.267 1.151
1 0.902 -0.078 -0.055 0.872 -0.012 0.843 1.276 1.739 2.173 0.838 1.492 0.918 0.000 0.626 0.904 -0.648 2.548 0.412 -2.027 -0.883 0.000 2.838 1.664 0.988 1.803 0.768 1.244 1.280
1 0.649 -1.028 -1.521 1.097 0.774 1.216 -0.383 -0.318 2.173 1.643 -0.285 -1.705 0.000 0.911 -0.091 0.341 0.000 0.592 0.537 0.732 3.102 0.911 0.856 1.027 1.160 0.874 0.986 0.893
1 1.192 1.846 -0.781 1.326 -0.747 1.550 1.177 1.366 0.000 1.196 0.151 0.387 2.215 0.527 2.261 -0.190 0.000 0.390 1.474 0.381 0.000 0.986 1.025 1.004 1.392 0.761 0.965 1.043
0 0.438 -0.358 -1.549 0.836 0.436 0.818 0.276 -0.708 2.173 0.707 0.826 0.392 0.000 1.050 1.741 -1.066 0.000 1.276 -1.583 0.842 0.000 1.475 1.273 0.986 0.853 1.593 1.255 1.226
1 1.083 0.142 1.701 0.605 -0.253 1.237 0.791 1.183 2.173 0.842 2.850 -0.082 0.000 0.724 -0.464 -0.694 0.000 1.499 0.456 -0.226 3.102 0.601 0.799 1.102 0.995 1.389 1.013 0.851
0 0.828 1.897 -0.615 0.572 -0.545 0.572 0.461 0.464 2.173 0.393 0.356 1.069 2.215 1.840 0.088 1.500 0.000 0.407 -0.663 -0.787 0.000 0.950 0.965 0.979 0.733 0.363 0.618 0.733
0 0.735 1.438 1.197 1.123 -0.214 0.641 0.949 0.858 0.000 1.162 0.524 -0.896 2.215 0.992 0.454 -1.475 2.548 0.902 1.079 0.019 0.000 0.822 0.917 1.203 1.032 0.569 0.780 0.764
0 0.437 -2.102 0.044 1.779 -1.042 1.231 -0.181 -0.515 1.087 2.666 0.863 1.466 2.215 1.370 0.345 -1.371 0.000 0.906 0.363 1.611 0.000 1.140 1.362 1.013 3.931 3.004 2.724 2.028
1 0.881 1.814 -0.987 0.384 0.800 2.384 1.422 0.640 0.000 1.528 0.292 -0.962 1.107 2.126 -0.371 -1.401 2.548 0.700 0.109 0.203 0.000 0.450 0.813 0.985 0.956 1.013 0.993 0.774
1 0.630 0.408 0.152 0.194 0.316 0.710 -0.824 -0.358 2.173 0.741 0.535 -0.851 2.215 0.933 0.406 1.148 0.000 0.523 -0.479 -0.625 0.000 0.873 0.960 0.988 0.830 0.921 0.711 0.661
1 0.870 -0.448 -1.134 0.616 0.135 0.600 0.649 -0.622 2.173 0.768 0.709 -0.123 0.000 1.308 0.500 1.468 0.000 1.973 -0.286 1.462 3.102 0.909 0.944 0.990 0.835 1.250 0.798 0.776
0 1.290 0.552 1.330 0.615 -1.353 0.661 0.240 -0.393 0.000 0.531 0.053 -1.588 0.000 0.675 0.839 -0.345 1.274 1.597 0.020 0.536 3.102 1.114 0.964 0.987 0.783 0.675 0.662 0.675
1 0.943 0.936 1.068 1.373 0.671 2.170 -2.011 -1.032 0.000 0.640 0.361 -0.806 0.000 2.239 -0.083 0.590 2.548 1.224 0.646 -1.723 0.000 0.879 0.834 0.981 1.436 0.568 0.916 0.931
1 0.431 1.686 -1.053 0.388 1.739 0.457 -0.471 -0.743 2.173 0.786 1.432 -0.547 2.215 0.537 -0.413 1.256 0.000 0.413 2.311 -0.408 0.000 1.355 1.017 0.982 0.689 1.014 0.821 0.715
0 1.620 -0.055 -0.862 1.341 -1.571 0.634 -0.906 0.935 2.173 0.501 -2.198 -0.525 0.000 0.778 -0.708 -0.060 0.000 0.988 -0.621 0.489 3.102 0.870 0.956 1.216 0.992 0.336 0.871 0.889
1 0.549 0.304 -1.443 1.309 -0.312 1.116 0.644 1.519 2.173 1.078 -0.303 -0.736 0.000 1.261 0.387 0.628 2.548 0.945 -0.190 0.090 0.000 0.893 1.043 1.000 1.124 1.077 1.026 0.886
0 0.412 -0.618 -1.486 1.133 -0.665 0.646 0.436 1.520 0.000 0.993 0.976 0.106 2.215 0.832 0.091 0.164 2.548 0.672 -0.650 1.256 0.000 0.695 1.131 0.991 1.017 0.455 1.226 1.087
0 1.183 -0.084 1.644 1.389 0.967 0.843 0.938 -0.670 0.000 0.480 0.256 0.123 2.215 0.437 1.644 0.491 0.000 0.501 -0.416 0.101 3.102 1.060 0.804 1.017 0.775 0.173 0.535 0.760
0 1.629 -1.486 -0.683 2.786 -0.492 1.347 -2.638 1.453 0.000 1.857 0.208 0.873 0.000 0.519 -1.265 -1.602 1.274 0.903 -1.102 -0.329 1.551 6.892 3.522 0.998 0.570 0.477 2.039 2.006
1 2.045 -0.671 -1.235 0.490 -0.952 0.525 -1.252 1.289 0.000 1.088 -0.993 0.648 2.215 0.975 -0.109 -0.254 2.548 0.556 -1.095 -0.194 0.000 0.803 0.861 0.980 1.282 0.945 0.925 0.811
0 0.448 -0.058 -0.974 0.945 -1.633 1.181 -1.139 0.266 2.173 1.118 -0.761 1.502 1.107 1.706 0.585 -0.680 0.000 0.487 -1.951 0.945 0.000 2.347 1.754 0.993 1.161 1.549 1.414 1.176
0 0.551 0.519 0.448 2.183 1.293 1.220 0.628 -0.627 2.173 1.019 -0.002 -0.652 0.000 1.843 -0.386 1.042 2.548 0.400 -1.102 -1.014 0.000 0.648 0.792 1.049 0.888 2.132 1.262 1.096
0 1.624 0.488 1.403 0.760 0.559 0.812 0.777 -1.244 2.173 0.613 0.589 -0.030 2.215 0.692 1.058 0.683 0.000 1.054 1.165 -0.765 0.000 0.915 0.875 1.059 0.821 0.927 0.792 0.721
1 0.774 0.444 1.257 0.515 -0.689 0.515 1.448 -1.271 0.000 0.793 0.118 0.811 1.107 0.679 0.326 -0.426 0.000 1.066 -0.865 -0.049 3.102 0.960 1.046 0.986 0.716 0.772 0.855 0.732
1 2.093 -1.240 1.615 0.918 -1.202 1.412 -0.541 0.640 1.087 2.019 0.872 -0.639 0.000 0.672 -0.936 0.972 0.000 0.896 0.235 0.212 0.000 0.810 0.700 1.090 0.797 0.862 1.049 0.874
1 0.908 1.069 0.283 0.400 1.293 0.609 1.452 -1.136 0.000 0.623 0.417 -0.098 2.215 1.023 0.775 1.054 1.274 0.706 2.346 -1.305 0.000 0.744 1.006 0.991 0.606 0.753 0.796 0.753
0 0.403 -1.328 -0.065 0.901 1.052 0.708 -0.354 -0.718 2.173 0.892 0.633 1.684 2.215 0.999 -1.205 0.941 0.000 0.930 1.072 -0.809 0.000 2.105 1.430 0.989 0.838 1.147 1.042 0.883
0 1.447 0.453 0.118 1.731 0.650 0.771 0.446 -1.564 0.000 0.973 -2.014 0.354 0.000 1.949 -0.643 -1.531 1.274 1.106 -0.334 -1.163 0.000 0.795 0.821 1.013 1.699 0.918 1.118 1.018
1 1.794 0.123 -0.454 0.057 1.489 0.966 -1.190 1.090 1.087 0.539 -0.535 1.035 0.000 1.096 -1.069 -1.236 2.548 0.659 -1.196 -0.283 0.000 0.803 0.756 0.985 1.343 1.109 0.993 0.806
0 1.484 -2.047 0.813 0.591 -0.295 0.923 0.312 -1.164 2.173 0.654 -0.316 0.752 2.215 0.599 1.966 -1.128 0.000 0.626 -0.304 -1.431 0.000 1.112 0.910 1.090 0.986 1.189 1.350 1.472
0 0.417 -2.016 0.849 1.817 0.040 1.201 -1.676 -1.394 0.000 0.792 0.537 0.641 2.215 0.794 -1.222 0.187 0.000 0.825 -0.217 1.334 3.102 1.470 0.931 0.987 1.203 0.525 0.833 0.827
1 0.603 1.009 0.033 0.486 1.225 0.884 -0.617 -1.058 0.000 0.500 -1.407 -0.567 0.000 1.476 -0.876 0.605 2.548 0.970 0.560 1.092 3.102 0.853 1.153 0.988 0.846 0.920 0.944 0.835
1 1.381 -0.326 0.552 0.417 -0.027 1.030 -0.835 -1.287 2.173 0.941 -0.421 1.519 2.215 0.615 -1.650 0.377 0.000 0.606 0.644 0.650 0.000 1.146 0.970 0.990 1.191 0.884 0.897 0.826
1 0.632 1.200 -0.703 0.438 -1.700 0.779 -0.731 0.958 1.087 0.605 0.393 -1.376 0.000 0.670 -0.827 -1.315 2.548 0.626 -0.501 0.417 0.000 0.904 0.903 0.998 0.673 0.803 0.722 0.640
1 1.561 -0.569 1.580 0.329 0.237 1.059 0.731 0.415 2.173 0.454 0.016 -0.828 0.000 0.587 0.008 -0.291 1.274 0.597 1.119 1.191 0.000 0.815 0.908 0.988 0.733 0.690 0.892 0.764
1 2.102 0.087 0.449 1.164 -0.390 1.085 -0.408 -1.116 2.173 0.578 0.197 -0.137 0.000 1.202 0.917 1.523 0.000 0.959 -0.832 1.404 3.102 1.380 1.109 1.486 1.496 0.886 1.066 1.025
1 1.698 -0.489 -0.552 0.976 -1.009 1.620 -0.721 0.648 1.087 1.481 -1.860 -1.354 0.000 1.142 -1.140 1.401 2.548 1.000 -1.274 -0.158 0.000 1.430 1.130 0.987 1.629 1.154 1.303 1.223
1 1.111 -0.249 -1.457 0.421 0.939 0.646 -2.076 0.362 0.000 1.315 0.796 -1.436 2.215 0.780 0.130 0.055 0.000 1.662 -0.834 0.461 0.000 0.920 0.948 0.990 1.046 0.905 1.493 1.169
1 0.945 0.390 -1.159 1.675 0.437 0.356 0.261 0.543 1.087 0.574 0.838 1.599 2.215 0.496 -1.220 -0.022 0.000 0.558 -2.454 1.440 0.000 0.763 0.983 1.728 1.000 0.578 0.922 1.003
1 2.076 0.014 -1.314 0.854 -0.306 3.446 1.341 0.598 0.000 2.086 0.227 -0.747 2.215 1.564 -0.216 1.649 2.548 0.965 -0.857 -1.062 0.000 0.477 0.734 1.456 1.003 1.660 1.001 0.908
1 1.992 0.192 -0.103 0.108 -1.599 0.938 0.595 -1.360 2.173 0.869 -1.012 1.432 0.000 1.302 0.850 0.436 2.548 0.487 1.051 -1.027 0.000 0.502 0.829 0.983 1.110 1.394 0.904 0.836
0 0.460 1.625 1.485 1.331 1.242 0.675 -0.329 -1.039 1.087 0.671 -1.028 -0.514 0.000 1.265 -0.788 0.415 1.274 0.570 -0.683 -1.738 0.000 0.725 0.758 1.004 1.024 1.156 0.944 0.833
0 0.871 0.839 -1.536 0.428 1.198 0.875 -1.256 -0.466 1.087 0.684 -0.768 0.150 0.000 0.556 -1.793 0.389 0.000 0.942 -1.126 1.339 1.551 0.624 0.734 0.986 1.357 0.960 1.474 1.294
1 0.951 1.651 0.576 1.273 1.495 0.834 0.048 -0.578 2.173 0.386 -0.056 -1.448 0.000 0.597 -0.196 0.162 2.548 0.524 1.649 1.625 0.000 0.737 0.901 1.124 1.014 0.556 1.039 0.845
1 1.049 -0.223 0.685 0.256 -1.191 2.506 0.238 -0.359 0.000 1.510 -0.904 1.158 1.107 2.733 -0.902 1.679 2.548 0.407 -0.474 -1.572 0.000 1.513 2.472 0.982 1.238 0.978 1.985 1.510
0 0.455 -0.028 0.265 1.286 1.373 0.459 0.331 -0.922 0.000 0.343 0.634 0.430 0.000 0.279 -0.084 -0.272 0.000 0.475 0.926 -0.123 3.102 0.803 0.495 0.987 0.587 0.211 0.417 0.445
1 2.074 0.388 0.878 1.110 1.557 1.077 -0.226 -0.295 2.173 0.865 -0.319 -1.116 2.215 0.707 -0.835 0.722 0.000 0.632 -0.608 -0.728 0.000 0.715 0.802 1.207 1.190 0.960 1.143 0.926
1 1.390 0.265 1.196 0.919 -1.371 1.858 0.506 0.786 0.000 1.280 -1.367 -0.720 2.215 1.483 -0.441 -0.675 2.548 1.076 0.294 -0.539 0.000 1.126 0.830 1.155 1.551 0.702 1.103 0.933
1 1.014 -0.079 1.597 1.038 -0.281 1.135 -0.722 -0.177 2.173 0.544 -1.475 -1.501 0.000 1.257 -1.315 1.212 0.000 0.496 -0.060 1.180 1.551 0.815 0.611 1.411 1.110 0.792 0.846 0.853
0 0.335 1.267 -1.154 2.011 -0.574 0.753 0.618 1.411 0.000 0.474 0.748 0.681 2.215 0.608 -0.446 -0.354 2.548 0.399 1.295 -0.581 0.000 0.911 0.882 0.975 0.832 0.598 0.580 0.678
1 0.729 -0.189 1.182 0.293 1.310 0.412 0.459 -0.632 0.000 0.869 -1.128 -0.625 2.215 1.173 -0.893 0.478 2.548 0.584 -2.394 -1.727 0.000 2.016 1.272 0.995 1.034 0.905 0.966 1.038
1 1.225 -1.215 -0.088 0.881 -0.237 0.600 -0.976 1.462 2.173 0.876 0.506 1.583 2.215 0.718 1.228 -0.031 0.000 0.653 -1.292 1.216 0.000 0.838 1.108 0.981 1.805 0.890 1.251 1.197
1 2.685 -0.444 0.847 0.253 0.183 0.641 -1.541 -0.873 2.173 0.417 2.874 -0.551 0.000 0.706 -1.431 0.764 0.000 1.390 -0.596 -1.397 0.000 0.894 0.829 0.993 0.789 0.654 0.883 0.746
0 0.638 -0.481 0.683 1.457 -1.024 0.707 -1.338 1.498 0.000 0.980 0.518 0.289 2.215 0.964 -0.531 -0.423 0.000 0.694 -0.654 -1.314 3.102 0.807 1.283 1.335 0.658 0.907 0.797 0.772
1 1.789 -0.765 -0.732 0.421 -0.020 1.142 -1.353 1.439 2.173 0.725 -1.518 -1.261 0.000 0.812 -2.597 -0.463 0.000 1.203 -0.120 1.001 0.000 0.978 0.673 0.985 1.303 1.400 1.078 0.983
1 0.784 -1.431 1.724 0.848 0.559 0.615 -1.643 -1.456 0.000 1.339 -0.513 0.040 2.215 0.394 -2.483 1.304 0.000 0.987 0.889 -0.339 0.000 0.732 0.713 0.987 0.973 0.705 0.875 0.759
1 0.911 1.098 -1.289 0.421 0.823 1.218 -0.503 0.431 0.000 0.775 0.432 -1.680 0.000 0.855 -0.226 -0.460 2.548 0.646 -0.947 -1.243 1.551 2.201 1.349 0.985 0.730 0.451 0.877 0.825
1 0.959 0.372 -0.269 1.255 0.702 1.151 0.097 0.805 2.173 0.993 1.011 0.767 2.215 1.096 0.185 0.381 0.000 1.001 -0.205 0.059 0.000 0.979 0.997 1.168 0.796 0.771 0.839 0.776
0 0.283 -1.864 -1.663 0.219 1.624 0.955 -1.213 0.932 2.173 0.889 0.395 -0.268 0.000 0.597 -1.083 -0.921 2.548 0.584 1.325 -1.072 0.000 0.856 0.927 0.996 0.937 0.936 1.095 0.892
0 2.017 -0.488 -0.466 1.029 -0.870 3.157 0.059 -0.343 2.173 3.881 0.872 1.502 1.107 3.631 1.720 0.963 0.000 0.633 -1.264 -1.734 0.000 4.572 3.339 1.005 1.407 5.590 3.614 3.110
1 1.088 0.414 -0.841 0.485 0.605 0.860 1.110 -0.568 0.000 1.152 -0.325 1.203 2.215 0.324 1.652 -0.104 0.000 0.510 1.095 -1.728 0.000 0.880 0.722 0.989 0.977 0.711 0.888 0.762
0 0.409 -1.717 0.712 0.809 -1.301 0.701 -1.529 -1.411 0.000 1.191 -0.582 0.438 2.215 1.147 0.813 -0.571 2.548 1.039 0.543 0.892 0.000 0.636 0.810 0.986 0.861 1.411 0.907 0.756
1 1.094 1.577 -0.988 0.497 -0.149 0.891 -2.459 1.034 0.000 0.646 0.792 -1.022 0.000 1.573 0.254 -0.053 2.548 1.428 0.190 -1.641 3.102 4.322 2.687 0.985 0.881 1.135 1.907 1.831
1 0.613 1.993 -0.280 0.544 0.931 0.909 1.526 1.559 0.000 0.840 1.473 -0.483 2.215 0.856 0.352 0.408 2.548 1.058 1.733 -1.396 0.000 0.801 1.066 0.984 0.639 0.841 0.871 0.748
0 0.958 -1.202 0.600 0.434 0.170 0.783 -0.214 1.319 0.000 0.835 -0.454 -0.615 2.215 0.658 -1.858 -0.891 0.000 0.640 0.172 -1.204 3.102 1.790 1.086 0.997 0.804 0.403 0.793 0.756
1 1.998 -0.238 0.972 0.058 0.266 0.759 1.576 -0.357 2.173 1.004 -0.349 -0.747 2.215 0.962 0.490 -0.453 0.000 1.592 0.661 -1.405 0.000 0.874 1.086 0.990 1.436 1.527 1.177 0.993
1 0.796 -0.171 -0.818 0.574 -1.625 1.201 -0.737 1.451 2.173 0.651 0.404 -0.452 0.000 1.150 -0.652 -0.120 0.000 1.008 -0.093 0.531 3.102 0.884 0.706 0.979 1.193 0.937 0.943 0.881
1 0.773 1.023 0.527 1.537 -0.201 2.967 -0.574 -1.534 2.173 2.346 -0.307 0.394 2.215 1.393 0.135 -0.027 0.000 3.015 0.187 0.516 0.000 0.819 1.260 0.982 2.552 3.862 2.179 1.786
0 1.823 1.008 -1.489 0.234 -0.962 0.591 0.461 0.996 2.173 0.568 -1.297 -0.410 0.000 0.887 2.157 1.194 0.000 2.079 0.369 -0.085 3.102 0.770 0.945 0.995 1.179 0.971 0.925 0.983
0 0.780 0.640 0.490 0.680 -1.301 0.715 -0.137 0.152 2.173 0.616 -0.831 1.668 0.000 1.958 0.528 -0.982 2.548 0.966 -1.551 0.462 0.000 1.034 1.079 1.008 0.827 1.369 1.152 0.983
1 0.543 0.801 1.543 1.134 -0.772 0.954 -0.849 0.410 1.087 0.851 -1.988 1.686 0.000 0.799 -0.912 -1.156 0.000 0.479 0.097 1.334 0.000 0.923 0.597 0.989 1.231 0.759 0.975 0.867
0 1.241 -0.014 0.129 1.158 0.670 0.445 -0.732 1.739 2.173 0.918 0.659 -1.340 2.215 0.557 2.410 -1.404 0.000 0.966 -1.545 -1.120 0.000 0.874 0.918 0.987 1.001 0.798 0.904 0.937
0 1.751 -0.266 -1.575 0.489 1.292 1.112 1.533 0.137 2.173 1.204 -0.414 -0.928 0.000 0.879 1.237 -0.415 2.548 1.479 1.469 0.913 0.000 2.884 1.747 0.989 1.742 0.600 1.363 1.293
1 1.505 1.208 -1.476 0.995 -0.836 2.800 -1.600 0.111 0.000 2.157 1.241 1.110 2.215 1.076 2.619 -0.913 0.000 1.678 2.204 -1.575 0.000 0.849 1.224 0.990 1.412 0.976 1.271 1.105
0 0.816 0.611 0.779 1.694 0.278 0.575 -0.787 1.592 2.173 1.148 1.076 -0.831 2.215 0.421 1.316 0.632 0.000 0.589 0.452 -1.466 0.000 0.779 0.909 0.990 1.146 1.639 1.236 0.949
1 0.551 -0.808 0.330 1.188 -0.294 0.447 -0.035 -0.993 0.000 0.432 -0.276 -0.481 2.215 1.959 -0.288 1.195 2.548 0.638 0.583 1.107 0.000 0.832 0.924 0.993 0.723 0.976 0.968 0.895
0 1.316 -0.093 0.995 0.860 -0.621 0.593 -0.560 -1.599 2.173 0.524 -0.318 -0.240 2.215 0.566 0.759 -0.368 0.000 0.483 -2.030 -1.104 0.000 1.468 1.041 1.464 0.811 0.778 0.690 0.722
1 1.528 0.067 -0.855 0.959 -1.464 1.143 -0.082 1.023 0.000 0.702 -0.763 -0.244 0.000 0.935 -0.881 0.206 2.548 0.614 -0.831 1.657 3.102 1.680 1.105 0.983 1.078 0.559 0.801 0.809
0 0.558 -0.833 -0.598 1.436 -1.724 1.316 -0.661 1.593 2.173 1.148 -0.503 -0.132 1.107 1.584 -0.125 0.380 0.000 1.110 -1.216 -0.181 0.000 1.258 0.860 1.053 0.790 1.814 1.159 1.007
1 0.819 0.879 1.221 0.598 -1.450 0.754 0.417 -0.369 2.173 0.477 1.199 0.274 0.000 1.073 0.368 0.273 2.548 1.599 2.047 1.690 0.000 0.933 0.984 0.983 0.788 0.613 0.728 0.717
0 0.981 -1.007 0.489 0.923 1.261 0.436 -0.698 -0.506 2.173 0.764 -1.105 -1.241 2.215 0.577 -2.573 -0.036 0.000 0.565 -1.628 1.610 0.000 0.688 0.801 0.991 0.871 0.554 0.691 0.656
0 2.888 0.568 -1.416 1.461 -1.157 1.756 -0.900 0.522 0.000 0.657 0.409 1.076 2.215 1.419 0.672 -0.019 0.000 1.436 -0.184 -0.980 3.102 0.946 0.919 0.995 1.069 0.890 0.834 0.856
1 0.522 1.805 -0.963 1.136 0.418 0.727 -0.195 -1.695 2.173 0.309 2.559 -0.178 0.000 0.521 1.794 0.919 0.000 0.788 0.174 -0.406 3.102 0.555 0.729 1.011 1.385 0.753 0.927 0.832
1 0.793 -0.162 -1.643 0.634 0.337 0.898 -0.633 1.689 0.000 0.806 -0.826 -0.356 2.215 0.890 -0.142 -1.268 0.000 1.293 0.574 0.725 0.000 0.833 1.077 0.988 0.721 0.679 0.867 0.753
0 1.298 1.098 0.280 0.371 -0.373 0.855 -0.306 -1.186 0.000 0.977 -0.421 1.003 0.000 0.978 0.956 -1.249 2.548 0.735 0.577 -0.037 3.102 0.974 1.002 0.992 0.549 0.587 0.725 0.954
1 0.751 -0.520 -1.653 0.168 -0.419 0.878 -1.023 -1.364 2.173 1.310 -0.667 0.863 0.000 1.196 -0.827 0.358 0.000 1.154 -0.165 -0.360 1.551 0.871 0.950 0.983 0.907 0.955 0.959 0.874
0 1.730 0.666 -1.432 0.446 1.302 0.921 -0.203 0.621 0.000 1.171 -0.365 -0.611 1.107 0.585 0.807 1.150 0.000 0.415 -0.843 1.311 0.000 0.968 0.786 0.986 1.059 0.371 0.790 0.848
1 0.596 -1.486 0.690 1.045 -1.344 0.928 0.867 0.820 2.173 0.610 0.999 -1.329 2.215 0.883 -0.001 -0.106 0.000 1.145 2.184 -0.808 0.000 2.019 1.256 1.056 1.751 1.037 1.298 1.518
1 0.656 -1.993 -0.519 1.643 -0.143 0.815 0.256 1.220 1.087 0.399 -1.184 -1.458 0.000 0.738 1.361 -1.443 0.000 0.842 0.033 0.293 0.000 0.910 0.891 0.993 0.668 0.562 0.958 0.787
1 1.127 -0.542 0.645 0.318 -1.496 0.661 -0.640 0.369 2.173 0.992 0.358 1.702 0.000 1.004 0.316 -1.109 0.000 1.616 -0.936 -0.707 1.551 0.875 1.191 0.985 0.651 0.940 0.969 0.834
0 0.916 -1.423 -1.490 1.248 -0.538 0.625 -0.535 -0.174 0.000 0.769 -0.389 1.608 2.215 0.667 -1.138 -1.738 1.274 0.877 -0.019 0.482 0.000 0.696 0.917 1.121 0.678 0.347 0.647 0.722
1 2.756 -0.637 -1.715 1.331 1.124 0.913 -0.296 -0.491 0.000 0.983 -0.831 0.000 2.215 1.180 -0.428 0.742 0.000 1.113 0.005 -1.157 1.551 1.681 1.096 1.462 0.976 0.917 1.009 1.040
0 0.755 1.754 0.701 2.111 0.256 1.243 0.057 -1.502 2.173 0.565 -0.034 -1.078 1.107 0.529 1.696 -1.090 0.000 0.665 0.292 0.107 0.000 0.870 0.780 0.990 2.775 0.465 1.876 1.758
1 0.593 -0.762 1.743 0.908 0.442 0.773 -1.357 -0.768 2.173 0.432 1.421 1.236 0.000 0.579 0.291 -0.403 0.000 0.966 -0.309 1.016 3.102 0.893 0.743 0.989 0.857 1.030 0.943 0.854
1 0.891 -1.151 -1.269 0.504 -0.622 0.893 -0.549 0.700 0.000 0.828 -0.825 0.154 2.215 1.083 0.632 -1.141 0.000 1.059 -0.557 1.526 3.102 2.117 1.281 0.987 0.819 0.802 0.917 0.828
1 2.358 -0.248 0.080 0.747 -0.975 1.019 1.374 1.363 0.000 0.935 0.127 -1.707 2.215 0.312 -0.827 0.017 0.000 0.737 1.059 -0.327 0.000 0.716 0.828 1.495 0.953 0.704 0.880 0.745
0 0.660 -0.017 -1.138 0.453 1.002 0.645 0.518 0.703 2.173 0.751 0.705 -0.592 2.215 0.744 -0.909 -1.596 0.000 0.410 -1.135 0.481 0.000 0.592 0.922 0.989 0.897 0.948 0.777 0.701
1 0.718 0.518 0.225 1.710 -0.022 1.888 -0.424 1.092 0.000 4.134 0.185 -1.366 0.000 1.415 1.293 0.242 2.548 2.351 0.264 -0.057 3.102 0.830 1.630 0.976 1.215 0.890 1.422 1.215
1 1.160 0.203 0.941 0.594 0.212 0.636 -0.556 0.679 2.173 1.089 -0.481 -1.008 1.107 1.245 -0.056 -1.357 0.000 0.587 1.007 0.056 0.000 1.106 0.901 0.987 0.786 1.224 0.914 0.837
1 0.697 0.542 0.619 0.985 1.481 0.745 0.415 1.644 2.173 0.903 0.495 -0.958 2.215 1.165 1.195 0.346 0.000 1.067 -0.881 -0.264 0.000 0.830 1.025 0.987 0.690 0.863 0.894 0.867
0 1.430 0.190 -0.700 0.246 0.518 1.302 0.660 -0.247 2.173 1.185 -0.539 1.504 0.000 1.976 -0.401 1.079 0.000 0.855 -0.958 -1.110 3.102 0.886 0.953 0.993 0.889 1.400 1.376 1.119
1 1.122 -0.795 0.202 0.397 -1.553 0.597 -1.459 -0.734 2.173 0.522 1.044 1.027 2.215 0.783 -1.243 1.701 0.000 0.371 1.737 0.199 0.000 1.719 1.176 0.988 0.723 1.583 1.063 0.914
0 1.153 0.526 1.236 0.266 0.001 1.139 -1.236 -0.585 2.173 1.337 -0.215 -1.356 2.215 1.780 1.129 0.902 0.000 1.608 -0.391 -0.161 0.000 1.441 1.633 0.990 1.838 1.516 1.635 1.373
1 0.760 1.012 0.758 0.937 0.051 0.941 0.687 -1.247 2.173 1.288 -0.743 0.822 0.000 1.552 1.782 -1.533 0.000 0.767 1.349 0.168 0.000 0.716 0.862 0.988 0.595 0.359 0.697 0.623
1 1.756 -1.469 1.395 1.345 -1.595 0.817 0.017 -0.741 2.173 0.483 -0.008 0.293 0.000 1.768 -0.663 0.438 1.274 1.202 -1.387 -0.222 0.000 1.022 1.058 0.992 1.407 1.427 1.356 1.133
0 0.397 0.582 -0.758 1.260 -1.735 0.889 -0.515 1.139 2.173 0.973 1.616 0.460 0.000 1.308 1.001 -0.709 2.548 0.858 0.995 -0.231 0.000 0.749 0.888 0.979 1.487 1.804 1.208 1.079
0 0.515 -0.984 0.425 1.114 -0.439 1.999 0.818 1.561 0.000 1.407 0.009 -0.380 0.000 1.332 0.230 0.397 0.000 1.356 -0.616 -1.057 3.102 0.978 1.017 0.990 1.118 0.862 0.835 0.919
1 1.368 -0.921 -0.866 0.842 -0.598 0.456 -1.176 1.219 1.087 0.419 -1.974 -0.819 0.000 0.791 -1.640 0.881 0.000 1.295 -0.782 0.442 3.102 0.945 0.761 0.974 0.915 0.535 0.733 0.651
0 2.276 0.134 0.399 2.525 0.376 1.111 -1.078 -1.571 0.000 0.657 2.215 -0.900 0.000 1.183 -0.662 -0.508 2.548 1.436 -0.517 0.960 3.102 0.569 0.931 0.993 1.170 0.967 0.879 1.207
0 0.849 0.907 0.124 0.652 1.585 0.715 0.355 -1.200 0.000 0.599 -0.892 1.301 0.000 1.106 1.151 0.582 0.000 1.895 -0.279 -0.568 3.102 0.881 0.945 0.998 0.559 0.649 0.638 0.660
1 2.105 0.248 -0.797 0.530 0.206 1.957 -2.175 0.797 0.000 1.193 0.637 -1.646 2.215 0.881 1.111 -1.046 0.000 0.872 -0.185 1.085 1.551 0.986 1.343 1.151 1.069 0.714 2.063 1.951
1 1.838 1.060 1.637 1.017 1.370 0.913 0.461 -0.609 1.087 0.766 -0.461 0.303 2.215 0.724 -0.061 0.886 0.000 0.941 1.123 -0.745 0.000 0.858 0.847 0.979 1.313 1.083 1.094 0.910
0 0.364 1.274 1.066 1.570 -0.394 0.485 0.012 -1.716 0.000 0.317 -1.233 0.534 2.215 0.548 -2.165 0.762 0.000 0.729 0.169 -0.318 3.102 0.892 0.944 1.013 0.594 0.461 0.688 0.715
1 0.503 1.343 -0.031 1.134 -1.204 0.590 -0.309 0.174 2.173 0.408 2.372 -0.628 0.000 1.850 0.400 1.147 2.548 0.664 -0.458 -0.885 0.000 1.445 1.283 0.989 1.280 1.118 1.127 1.026
0 1.873 0.258 0.103 2.491 0.530 1.678 0.644 -1.738 2.173 1.432 0.848 -1.340 0.000 0.621 1.323 -1.316 0.000 0.628 0.789 -0.206 1.551 0.426 0.802 1.125 0.688 1.079 1.338 1.239
1 0.826 -0.732 1.587 0.582 -1.236 0.495 0.757 -0.741 2.173 0.940 1.474 0.354 2.215 0.474 1.055 -1.657 0.000 0.415 1.758 0.841 0.000 0.451 0.578 0.984 0.757 0.922 0.860 0.696
0 0.935 -1.614 -0.597 0.299 1.223 0.707 -0.853 -1.026 0.000 0.751 0.007 -1.691 0.000 1.062 -0.125 0.976 2.548 0.877 1.275 0.646 0.000 0.962 1.074 0.980 0.608 0.726 0.741 0.662
1 0.643 0.542 -1.285 0.474 -0.366 0.667 -0.446 1.195 2.173 1.076 0.145 -0.126 0.000 0.970 -0.661 0.394 1.274 1.218 -0.184 -1.722 0.000 1.331 1.019 0.985 1.192 0.677 0.973 0.910
0 0.713 0.164 1.080 1.427 -0.460 0.960 -0.152 -0.940 2.173 1.427 -0.901 1.036 1.107 0.440 -1.269 -0.194 0.000 0.452 1.932 -0.532 0.000 1.542 1.210 1.374 1.319 1.818 1.220 1.050
0 0.876 -0.463 -1.224 2.458 -1.689 1.007 -0.752 0.398 0.000 2.456 -1.285 -0.152 1.107 1.641 1.838 1.717 0.000 0.458 0.194 0.488 3.102 4.848 2.463 0.986 1.981 0.974 2.642 2.258
1 0.384 -0.275 0.387 1.403 -0.994 0.620 -1.529 1.685 0.000 1.091 -1.644 1.078 0.000 0.781 -1.311 0.326 2.548 1.228 -0.728 -0.633 1.551 0.920 0.854 0.987 0.646 0.609 0.740 0.884
0 0.318 -1.818 -1.008 0.977 1.268 0.457 2.451 -1.522 0.000 0.881 1.351 0.461 2.215 0.929 0.239 -0.380 2.548 0.382 -0.613 1.330 0.000 1.563 1.193 0.994 0.829 0.874 0.901 1.026
1 0.612 -1.120 1.098 0.402 -0.480 0.818 0.188 1.511 0.000 0.800 -0.253 0.977 0.000 1.175 0.271 -1.289 1.274 2.531 0.226 -0.409 3.102 0.889 0.947 0.979 1.486 0.940 1.152 1.119
1 0.587 -0.737 -0.228 0.970 1.119 0.823 0.184 1.594 0.000 1.104 0.301 -0.818 2.215 0.819 0.712 -0.560 0.000 2.240 -0.419 0.340 3.102 1.445 1.103 0.988 0.715 1.363 1.019 0.926
0 1.030 -0.694 -1.638 0.893 -1.074 1.160 -0.766 0.485 0.000 1.632 -0.698 -1.142 2.215 1.050 -1.092 0.952 0.000 1.475 0.286 0.125 3.102 0.914 1.075 0.982 0.732 1.493 1.219 1.079
1 2.142 0.617 1.517 0.387 -0.862 0.345 1.203 -1.014 2.173 0.609 1.092 0.275 0.000 1.331 0.582 -0.183 2.548 0.557 1.540 -1.642 0.000 0.801 0.737 1.060 0.715 0.626 0.749 0.674
0 1.076 0.240 -0.246 0.871 -1.241 0.496 0.282 0.746 2.173 1.095 -0.648 1.100 2.215 0.446 -1.756 0.764 0.000 0.434 0.788 -0.991 0.000 1.079 0.868 1.047 0.818 0.634 0.795 0.733
0 1.400 0.901 -1.617 0.625 -0.163 0.661 -0.411 -1.616 2.173 0.685 0.524 0.425 0.000 0.881 -0.766 0.312 0.000 0.979 0.255 -0.667 3.102 0.898 1.105 1.253 0.730 0.716 0.738 0.795
0 3.302 1.132 1.051 0.658 0.768 1.308 0.251 -0.374 1.087 1.673 0.015 -0.898 0.000 0.688 -0.535 1.363 1.274 0.871 1.325 -1.583 0.000 1.646 1.249 0.995 1.919 1.288 1.330 1.329
0 1.757 0.202 0.750 0.767 -0.362 0.932 -1.033 -1.366 0.000 1.529 -1.012 -0.771 0.000 1.161 -0.287 0.059 0.000 2.185 1.147 1.099 3.102 0.795 0.529 1.354 1.144 1.491 1.319 1.161
0 1.290 0.905 -1.711 1.017 -0.695 1.008 -1.038 0.693 2.173 1.202 -0.595 0.187 0.000 1.011 0.139 -1.607 0.000 0.789 -0.613 -1.041 3.102 1.304 0.895 1.259 1.866 0.955 1.211 1.200
1 1.125 -0.004 1.694 0.373 0.329 0.978 0.640 -0.391 0.000 1.122 -0.376 1.521 2.215 0.432 2.413 -1.259 0.000 0.969 0.730 0.512 3.102 0.716 0.773 0.991 0.624 0.977 0.981 0.875
0 1.081 0.861 1.252 1.621 1.474 1.293 0.600 0.630 0.000 1.991 -0.090 -0.675 2.215 0.861 1.105 -0.201 0.000 1.135 2.489 -1.659 0.000 1.089 0.657 0.991 2.179 0.412 1.334 1.071
1 0.652 -0.294 1.241 1.034 0.490 1.033 0.551 -0.963 2.173 0.661 1.031 -1.654 2.215 1.376 -0.018 0.843 0.000 0.943 -0.329 -0.269 0.000 1.085 1.067 0.991 1.504 0.773 1.135 0.993
1 1.408 -1.028 -1.018 0.252 -0.242 0.465 -0.364 -0.200 0.000 1.466 0.669 0.739 1.107 1.031 0.415 -1.468 2.548 0.457 -1.091 -1.722 0.000 0.771 0.811 0.979 1.459 1.204 1.041 0.866
1 0.781 -1.143 -0.659 0.961 1.266 1.183 -0.686 0.119 2.173 1.126 -0.064 1.447 0.000 0.730 1.430 -1.535 0.000 1.601 0.513 1.658 0.000 0.871 1.345 1.184 1.058 0.620 1.107 0.978
1 1.300 -0.616 1.032 0.751 -0.731 0.961 -0.716 1.592 0.000 2.079 -1.063 -0.271 2.215 0.475 0.518 1.695 1.274 0.395 -2.204 0.349 0.000 1.350 0.983 1.369 1.265 1.428 1.135 0.982
1 0.833 0.809 1.657 1.637 1.019 0.705 1.077 -0.968 2.173 1.261 0.114 -0.298 1.107 1.032 0.017 0.236 0.000 0.640 -0.026 -1.598 0.000 0.894 0.982 0.981 1.250 1.054 1.018 0.853
1 1.686 -1.090 -0.301 0.890 0.557 1.304 -0.284 -1.393 2.173 0.388 2.118 0.513 0.000 0.514 -0.015 0.891 0.000 0.460 0.547 0.627 3.102 0.942 0.524 1.186 1.528 0.889 1.015 1.122
1 0.551 0.911 0.879 0.379 -0.796 1.154 -0.808 -0.966 0.000 1.168 -0.513 0.355 2.215 0.646 -1.309 0.773 0.000 0.544 -0.283 1.301 3.102 0.847 0.705 0.990 0.772 0.546 0.790 0.719
1 1.597 0.793 -1.119 0.691 -1.455 0.370 0.337 1.354 0.000 0.646 -1.005 0.732 2.215 1.019 0.040 0.209 0.000 0.545 0.958 0.239 3.102 0.962 0.793 0.994 0.719 0.745 0.812 0.739
0 1.033 -1.193 -0.452 0.247 0.970 0.503 -1.424 1.362 0.000 1.062 -0.416 -1.156 2.215 0.935 -0.023 0.555 2.548 0.410 -1.766 0.379 0.000 0.590 0.953 0.991 0.717 1.081 0.763 0.690
1 0.859 -1.004 1.521 0.781 -0.993 0.677 0.643 -0.338 2.173 0.486 0.409 1.283 0.000 0.679 0.110 0.285 0.000 0.715 -0.735 -0.157 1.551 0.702 0.773 0.984 0.627 0.633 0.694 0.643
0 0.612 -1.127 1.074 1.225 -0.426 0.927 -2.141 -0.473 0.000 1.290 -0.927 -1.085 2.215 1.183 1.981 -1.687 0.000 2.176 0.406 -1.581 0.000 0.945 0.651 1.170 0.895 1.604 1.179 1.142
1 0.535 0.321 -1.095 0.281 -0.960 0.876 -0.709 -0.076 0.000 1.563 -0.666 1.536 2.215 0.773 -0.321 0.435 0.000 0.682 -0.801 -0.952 3.102 0.711 0.667 0.985 0.888 0.741 0.872 0.758
1 0.745 1.586 1.578 0.863 -1.423 0.530 1.714 1.085 0.000 1.174 0.679 1.015 0.000 1.158 0.609 -1.186 2.548 1.851 0.832 -0.248 3.102 0.910 1.164 0.983 0.947 0.858 0.928 0.823
0 0.677 -1.014 -1.648 1.455 1.461 0.596 -2.358 0.517 0.000 0.800 0.849 -0.743 2.215 1.024 -0.282 -1.004 0.000 1.846 -0.977 0.378 3.102 2.210 1.423 0.982 1.074 1.623 1.417 1.258
1 0.815 -1.263 0.057 1.018 -0.208 0.339 -0.347 -1.646 2.173 1.223 0.600 -1.658 2.215 1.435 0.042 0.926 0.000 0.777 1.698 -0.698 0.000 1.022 1.058 1.000 0.784 0.477 0.886 0.836
0 3.512 -1.094 -0.220 0.338 -0.328 1.962 -1.099 1.544 1.087 1.461 -1.305 -0.922 2.215 1.219 -1.289 0.400 0.000 0.731 0.155 1.249 0.000 1.173 1.366 0.993 2.259 2.000 1.626 1.349
0 0.904 1.248 0.325 0.317 -1.624 0.685 -0.538 1.665 2.173 0.685 -2.145 -1.106 0.000 0.632 -1.460 1.017 0.000 1.085 -0.182 0.162 3.102 0.885 0.801 0.989 0.930 0.904 1.012 0.961
examples/regression/regression.train 0 → 100644
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examples/regression/train.conf 0 → 100644
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# task type, support train and predict
task = train
# boosting type, support gbdt for now, alias: boosting, boost
boosting_type = gbdt
# application type, support following application
# regression , regression task
# binary , binary classification task
# lambdarank , lambdarank task
# alias: application, app
objective = regression
# eval metrics, support multi metric, delimite by ',' , support following metrics
# l1
# l2 , default metric for regression
# ndcg , default metric for lambdarank
# auc
# binary_logloss , default metric for binary
# binary_error
metric = l2
# frequence for metric output
metric_freq = 1
# true if need output metric for training data, alias: tranining_metric, train_metric
is_training_metric = true
# number of bins for feature bucket, 255 is a recommend setting, it can save memories, and also has good accuracy.
max_bin = 255
# training data
# if exsting weight file, should name to "regression.train.weight"
# alias: train_data, train
data = regression.train
# validation data, support multi validation data, separated by ','
# if exsting weight file, should name to "regression.test.weight"
# alias: valid, test, test_data,
valid_data = regression.test
# number of trees(iterations), alias: num_tree, num_iteration, num_iterations, num_round, num_rounds
num_trees = 100
# shrinkage rate , alias: shrinkage_rate
learning_rate = 0.05
# number of leaves for one tree, alias: num_leaf
num_leaves = 31
# type of tree learner, support following types:
# serial , single machine version
# feature , use feature parallel to train
# data , use data parallel to train
# voting , use voting based parallel to train
# alias: tree
tree_learner = serial
# number of threads for multi-threading. One thread will use one CPU, defalut is setted to #cpu.
# num_threads = 8
# feature sub-sample, will random select 80% feature to train on each iteration
# alias: sub_feature
feature_fraction = 0.9
# Support bagging (data sub-sample), will perform bagging every 5 iterations
bagging_freq = 5
# Bagging farction, will random select 80% data on bagging
# alias: sub_row
bagging_fraction = 0.8
# minimal number data for one leaf, use this to deal with over-fit
# alias : min_data_per_leaf, min_data
min_data_in_leaf = 100
# minimal sum hessians for one leaf, use this to deal with over-fit
min_sum_hessian_in_leaf = 5.0
# save memory and faster speed for sparse feature, alias: is_sparse
is_enable_sparse = true
# when data is bigger than memory size, set this to true. otherwise set false will have faster speed
# alias: two_round_loading, two_round
use_two_round_loading = false
# true if need to save data to binary file and application will auto load data from binary file next time
# alias: is_save_binary, save_binary
is_save_binary_file = false
# output model file
output_model = LightGBM_model.txt
# support continuous train from trained gbdt model
# input_model= trained_model.txt
# output prediction file for predict task
# output_result= prediction.txt
# support continuous train from initial score file
# input_init_score= init_score.txt
# number of machines in parallel training, alias: num_machine
num_machines = 1
# local listening port in parallel training, alias: local_port
local_listen_port = 12400
# machines list file for parallel training, alias: mlist
machine_list_file = mlist.txt
include/LightGBM/application.h 0 → 100644
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#ifndef LIGHTGBM_APPLICATION_H_
#define LIGHTGBM_APPLICATION_H_
#include <LightGBM/meta.h>
#include <LightGBM/config.h>
#include <vector>
namespace LightGBM {
/*! \brief forward declaration */
class Dataset;
class Boosting;
class ObjectiveFunction;
class Metric;
/*!
* \brief The entrance of LightGBM. this application has two tasks:
* Train and Predict.
* Train task will train a new model
* Predict task will predicting the scores of test data then saving the score to local disk
*/
class Application {
public:
Application(int argc, char** argv);
/*! \brief Destructor */
~Application();
/*! \brief To call this funciton to run application*/
inline void Run();
private:
/*!
* \brief Global Sync by minimal, will return minimal of global
* \param local Local data
* \return Global minimal data
*/
template<typename T>
T GlobalSyncUpByMin(T& local);
/*! \brief Load parametes from command line and config file*/
void LoadParameters(int argc, char** argv);
/*! \brief Load data, including training data and validation data*/
void LoadData();
/*! \brief Some initial works before training*/
void InitTrain();
/*! \brief The training logic */
void Train();
/*! \brief Initialize the enviroment needed by prediction */
void InitPredict();
/*! \brief Load model */
void LoadModel();
/*! \brief The prediction logic */
void Predict();
/*! \brief All configs */
OverallConfig config_;
/*! \brief Training data */
Dataset* train_data_;
/*! \brief Validation data */
std::vector<Dataset*> valid_datas_;
/*! \brief Metric for training data */
std::vector<Metric*> train_metric_;
/*! \brief Metrics for validation data */
std::vector<std::vector<Metric*>> valid_metrics_;
/*! \brief Boosting object */
Boosting* boosting_;
/*! \brief Training objective function */
ObjectiveFunction* objective_fun_;
};
inline void Application::Run() {
if (config_.task_type == TaskType::kPredict) {
InitPredict();
Predict();
} else {
InitTrain();
Train();
}
}
} // namespace LightGBM
#endif #endif // LightGBM_APPLICATION_H_
include/LightGBM/bin.h 0 → 100644
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#ifndef LIGHTGBM_BIN_H_
#define LIGHTGBM_BIN_H_
#include <LightGBM/meta.h>
#include <vector>
#include <functional>
namespace LightGBM {
/*! \brief Store data for one histogram bin */
struct HistogramBinEntry {
public:
/*! \brief Sum of gradients on this bin */
score_t sum_gradients = 0.0;
/*! \brief Sum of hessians on this bin */
score_t sum_hessians = 0.0;
/*! \brief Number of data on this bin */
data_size_t cnt = 0;
/*!
* \brief Sum up reduce function for histogram bin
*/
inline static void SumReducer(const char *src, char *dst, int len) {
const int type_size = sizeof(HistogramBinEntry);
int used_size = 0;
const HistogramBinEntry* p1;
HistogramBinEntry* p2;
while (used_size < len) {
// convert
p1 = reinterpret_cast<const HistogramBinEntry*>(src);
p2 = reinterpret_cast<HistogramBinEntry*>(dst);
// add
p2->cnt += p1->cnt;
p2->sum_gradients += p1->sum_gradients;
p2->sum_hessians += p1->sum_hessians;
src += type_size;
dst += type_size;
used_size += type_size;
}
}
};
/*! \brief This class used to convert featrue value to bin,
* and store some meta infomartion for bin*/
class BinMapper {
public:
BinMapper();
BinMapper(const BinMapper& other);
explicit BinMapper(const void* memory);
~BinMapper();
/*! \brief Get number of bins */
inline int num_bin() const { return num_bin_; }
/*! \brief True if bin is trival(only contain one bin) */
inline bool is_trival() const { return is_trival_; }
/*! \brief Sparse rate of this bins( num_zero_bins / num_data ) */
inline double sparse_rate() const { return sparse_rate_; }
/*!
* \brief Save binary data to file
* \param file File want to write
*/
void SaveBinaryToFile(FILE* file) const;
/*!
* \brief Map bin to feature value
* \param bin
* \return Feature value for this bin
*/
inline double BinToValue(unsigned int bin) const {
return bin_upper_bound_[bin];
}
/*!
* \brief Get sizes in byte of this object
*/
size_t SizesInByte() const;
/*!
* \brief Map feature value to bin
* \param value
* \return bin for this feature value
*/
inline unsigned int ValueToBin(double value) const;
/*!
* \brief Construct feature value to bin mapper according feature values
* \param values (Sampled) values of this feature
* \param max_bin The maximal number of bin
*/
void FindBin(std::vector<double>* values, int max_bin);
/*!
* \brief Use specific number of bin to calculate the size of this class
* \param bin The number of bin
* \return Size
*/
static int SizeForSpecificBin(int bin);
/*!
* \brief Copy this object to buffer
* \param buffer The destination
*/
void CopyTo(char* buffer);
/*!
* \brief Restore this object from buffer
* \param buffer The source
*/
void CopyFrom(const char* buffer);
private:
/*! \brief Number of bins */
int num_bin_;
/*! \brief Store upper bound for each bin */
double* bin_upper_bound_;
/*! \brief True if this feature is trival */
bool is_trival_;
/*! \brief Sparse rate of this bins( num_bin0/num_data ) */
double sparse_rate_;
};
/*!
* \brief Interface for ordered bin data. efficient for construct histogram, especally for sparse bin
* There are 2 advantages for using ordered bin.
* 1. group the data by leaf, improve the cache hit.
* 2. only store the non-zero bin, which can speed up the histogram cconsturction for sparse feature.
* But it has a additional cost, it need re-order the bins after leaf split, which will cost much for dense feature.
* So we only use ordered bin for sparse features now.
*/
class OrderedBin {
public:
/*! \brief virtual destructor */
virtual ~OrderedBin() {}
/*!
* \brief Initial logic, call before train one tree.
* \param used_indices If used_indices==nullptr means using all data, otherwise, used_indices[i] != 0 means i-th data is used(for bagging logic)
* \param num_leavas Number of leveas on this iteration
*/
virtual void Init(const char* used_indices, data_size_t num_leavas) = 0;
/*!
* \brief Construct histogram by using this bin
* Note: Unlike Bin, OrderedBin doesn't use ordered gradients and ordered hessians.
* Because it is hard to know the relative index in one leaf for sparse bin, since we skipped zero bins.
* \param leaf Using which leaf's data to construct
* \param gradients Gradients, Note:non-oredered by leaf
* \param hessians Hessians, Note:non-oredered by leaf
* \param out Output Result
*/
virtual void ConstructHistogram(int leaf, const score_t* gradients,
const score_t* hessians, HistogramBinEntry* out) const = 0;
/*!
* \brief Split current bin, and perform re-order by leaf
* \param leaf Using which leaf's to split
* \param right_leaf The new leaf index after perform this split
* \param left_indices left_indices[i] != 0 means the i-th data will be on left leaf after split
*/
virtual void Split(int leaf, int right_leaf, const char* left_indices) = 0;
};
/*! \brief Iterator for one bin column */
class BinIterator {
public:
/*!
* \brief Get bin data on specific row index
* \param idx Index of this data
* \return Bin data
*/
virtual uint32_t Get(data_size_t idx) = 0;
};
/*!
* \brief Interface for bin data. This class will store bin data for one feature.
* unlike OrderedBin, this class will store data by original order.
* Though it may have many cache miss when construct histogram,
* but it doesn't need to re-order operation, So it is still faster than OrderedBin for dense feature
*/
class Bin {
public:
/*! \brief virtual destructor */
virtual ~Bin() {}
/*!
* \brief Push one record
* \pram tid Thread id
* \param idx Index of record
* \param value bin value of record
*/
virtual void Push(int tid, data_size_t idx, uint32_t value) = 0;
/*!
* \brief Get bin interator of this bin
* \param start_idx start index of this
* \return Iterator of this bin
*/
virtual BinIterator* GetIterator(data_size_t start_idx) const = 0;
/*!
* \brief Save binary data to file
* \param file File want to write
*/
virtual void SaveBinaryToFile(FILE* file) const = 0;
/*!
* \brief Load from memory
* \param file File want to write
*/
virtual void LoadFromMemory(const void* memory,
const std::vector<data_size_t>& local_used_indices) = 0;
/*!
* \brief Get sizes in byte of this object
*/
virtual size_t SizesInByte() const = 0;
/*! \brief Number of all data */
virtual data_size_t num_data() const = 0;
/*!
* \brief Construct histogram of this feature,
* Note: here use ordered_gradients and ordered_hessians to improve cache hit chance
* The navie solution is use gradients[data_indices[i]] for data_indices[i] to get gradients, which is not cache friendly, since the access of memory is not continuous.
* ordered_gradients and ordered_hessians are preprocessed, they are re-ordered by data_indices.
* It uses ordered_gradients[i] for data_indices[i]'s gradients (same for ordered_hessians).
* \param data_indices Used data indices in current leaf
* \param num_data Number of used data
* \param ordered_gradients Pointer to gradients, the data_indices[i]-th data's gradient is ordered_gradients[i]
* \param ordered_hessians Pointer to hessians, the data_indices[i]-th data's hessian is ordered_hessians[i]
* \param out Output Result
*/
virtual void ConstructHistogram(
data_size_t* data_indices, data_size_t num_data,
const score_t* ordered_gradients, const score_t* ordered_hessians,
HistogramBinEntry* out) const = 0;
/*!
* \brief Split data according to threshold, if bin <= threshold, will put into left(lte_indices), else put into right(gt_indices)
* \param threshold The split threshold.
* \param data_indices Used data indices. After called this function. The less than or equal data indices will store on this object.
* \param num_data Number of used data
* \param lte_indices After called this function. The less or equal data indices will store on this object.
* \param gt_indices After called this function. The greater data indices will store on this object.
* \return The number of less than or equal data.
*/
virtual data_size_t Split(
unsigned int threshold, data_size_t* data_indices, data_size_t num_data,
data_size_t* lte_indices, data_size_t* gt_indices) const = 0;
/*!
* \brief Create the ordered bin for this bin
* \return Pointer to ordered bin
*/
virtual OrderedBin* CreateOrderedBin() const = 0;
/*!
* \brief After pushed all feature data, should call this to have better refactor for bin data
*/
virtual void FinishLoad() = 0;
/*!
* \brief Create object for bin data of one feature, will call CreateDenseBin or CreateSparseBin according to "is_sparse"
* \param num_data Total number of data
* \param num_bin Number of bin
* \param is_sparse True if this feature is saprese
* \param sparse_rate Sparse rate of this bins( num_bin0/num_data )
* \param is_enable_sparse True if enable sparse feature
* \param is_sparse Will set to true if this bin is sparse
* \return The bin data object
*/
static Bin* CreateBin(data_size_t num_data, int num_bin,
double sparse_rate, bool is_enable_sparse, bool* is_sparse);
/*!
* \brief Create object for bin data of one feature, used for dense feature
* \param num_data Total number of data
* \param num_bin Number of bin
* \return The bin data object
*/
static Bin* CreateDenseBin(data_size_t num_data, int num_bin);
/*!
* \brief Create object for bin data of one feature, used for sparse feature
* \param num_data Total number of data
* \param num_bin Number of bin
* \param sparse_rate Sparse rate of this bins( num_bin0/num_data )
* \return The bin data object
*/
static Bin* CreateSparseBin(data_size_t num_data,
int num_bin);
};
inline unsigned int BinMapper::ValueToBin(double value) const {
// use binary search to find bin
int l = 0;
int r = num_bin_ - 1;
while (l < r) {
int m = (r + l - 1) / 2;
if (value <= bin_upper_bound_[m]) {
r = m;
} else {
l = m + 1;
}
}
return l;
}
} // namespace LightGBM
#endif #endif // LightGBM_BIN_H_
include/LightGBM/boosting.h 0 → 100644
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#ifndef LIGHTGBM_BOOSTING_H_
#define LIGHTGBM_BOOSTING_H_
#include <LightGBM/meta.h>
#include <LightGBM/config.h>
#include <vector>
#include <string>
namespace LightGBM {
/*! \brief forward declaration */
class Dataset;
class ObjectiveFunction;
class Metric;
/*!
* \brief The interface for Boosting
*/
class Boosting {
public:
/*! \brief virtual destructor */
virtual ~Boosting() {}
/*!
* \brief Initial logic
* \param config Config for boosting
* \param train_data Training data
* \param object_function Training objective function
* \param training_metrics Training metric
* \param output_model_filename Filename of output model
*/
virtual void Init(const Dataset* train_data,
const ObjectiveFunction* object_function,
const std::vector<const Metric*>& training_metrics,
const char* output_model_filename) = 0;
/*!
* \brief Add a validation data
* \param valid_data Validation data
* \param valid_metrics Metric for validation data
*/
virtual void AddDataset(const Dataset* valid_data,
const std::vector<const Metric*>& valid_metrics) = 0;
/*! \brief Training logic */
virtual void Train() = 0;
/*!
* \brief Predtion for one record, not sigmoid transform
* \param feature_values Feature value on this record
* \return Prediction result for this record
*/
virtual double PredictRaw(const double * feature_values) const = 0;
/*!
* \brief Predtion for one record, will use sigmoid transform if needed
* \param feature_values Feature value on this record
* \return Prediction result for this record
*/
virtual double Predict(const double * feature_values) const = 0;
/*!
* \brief Serialize models by string
* \return String output of tranined model
*/
virtual std::string ModelsToString() const = 0;
/*!
* \brief Restore from a serialized string
* \param model_str The string of model
*/
virtual void ModelsFromString(const std::string& model_str, int num_used_model) = 0;
/*!
* \brief Get max feature index of this model
* \return Max feature index of this model
*/
virtual int MaxFeatureIdx() const = 0;
/*!
* \brief Get number of weak sub-models
* \return Number of weak sub-models
*/
virtual int NumberOfSubModels() const = 0;
/*!
* \brief Create boosting object
* \param type Type of boosting
* \return The boosting object
*/
static Boosting* CreateBoosting(BoostingType type,
const BoostingConfig* config);
};
} // namespace LightGBM
#endif #endif // LightGBM_BOOSTING_H_
include/LightGBM/config.h 0 → 100644
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#ifndef LIGHTGBM_CONFIG_H_
#define LIGHTGBM_CONFIG_H_
#include <LightGBM/utils/common.h>
#include <LightGBM/utils/log.h>
#include <vector>
#include <string>
#include <unordered_map>
#include <algorithm>
namespace LightGBM {
/*!
* \brief The interface for Config
*/
struct ConfigBase {
public:
/*! \brief virtual destructor */
virtual ~ConfigBase() {}
/*!
* \brief SetLabelAt current config object by params
* \param params Store the key and value for params
*/
virtual void Set(
const std::unordered_map<std::string, std::string>& params) = 0;
/*!
* \brief Get string value by specific name of key
* \param params Store the key and value for params
* \param name Name of key
* \param out Value will asign to out if key exists
* \return True if key exists
*/
inline bool GetString(
const std::unordered_map<std::string, std::string>& params,
const std::string& name, std::string* out);
/*!
* \brief Get int value by specific name of key
* \param params Store the key and value for params
* \param name Name of key
* \param out Value will asign to out if key exists
* \return True if key exists
*/
inline bool GetInt(
const std::unordered_map<std::string, std::string>& params,
const std::string& name, int* out);
/*!
* \brief Get double value by specific name of key
* \param params Store the key and value for params
* \param name Name of key
* \param out Value will asign to out if key exists
* \return True if key exists
*/
inline bool GetDouble(
const std::unordered_map<std::string, std::string>& params,
const std::string& name, double* out);
/*!
* \brief Get bool value by specific name of key
* \param params Store the key and value for params
* \param name Name of key
* \param out Value will asign to out if key exists
* \return True if key exists
*/
inline bool GetBool(
const std::unordered_map<std::string, std::string>& params,
const std::string& name, bool* out);
};
/*! \brief Types of boosting */
enum BoostingType {
kGBDT
};
/*! \brief Types of tasks */
enum TaskType {
kTrain, kPredict
};
/*! \brief Config for input and output files */
struct IOConfig: public ConfigBase {
public:
int max_bin = 255;
int data_random_seed = 1;
std::string data_filename = "";
bool data_has_label = true;
std::vector<std::string> valid_data_filenames;
std::string output_model = "LightGBM_model.txt";
std::string output_result = "LightGBM_predict_result.txt";
std::string input_model = "";
std::string input_init_score = "";
int num_model_predict = -1;
bool is_pre_partition = false;
bool is_enable_sparse = true;
bool use_two_round_loading = false;
bool is_save_binary_file = false;
bool is_sigmoid = true;
void Set(const std::unordered_map<std::string, std::string>& params) override;
};
/*! \brief Config for objective function */
struct ObjectiveConfig: public ConfigBase {
public:
virtual ~ObjectiveConfig() {}
double sigmoid = 1;
// for lambdarank
std::vector<double> label_gain;
// for lambdarank
int max_position = 20;
// for binary
bool is_unbalance = false;
void Set(const std::unordered_map<std::string, std::string>& params) override;
};
/*! \brief Config for metrics interface*/
struct MetricConfig: public ConfigBase {
public:
virtual ~MetricConfig() {}
int output_freq = 1;
double sigmoid = 1;
bool is_provide_training_metric = false;
std::vector<double> label_gain;
std::vector<int> eval_at;
void Set(const std::unordered_map<std::string, std::string>& params) override;
};
/*! \brief Config for tree model */
struct TreeConfig: public ConfigBase {
public:
int min_data_in_leaf = 100;
double min_sum_hessian_in_leaf = 10.0f;
int num_leaves = 127;
int feature_fraction_seed = 2;
double feature_fraction = 1.0;
void Set(const std::unordered_map<std::string, std::string>& params) override;
};
/*! \brief Types of tree learning algorithms */
enum TreeLearnerType {
kSerialTreeLearner, kFeatureParallelTreelearner,
kDataParallelTreeLearner
};
/*! \brief Config for Boosting */
struct BoostingConfig: public ConfigBase {
public:
virtual ~BoostingConfig() {}
int num_iterations = 10;
double learning_rate = 0.1;
double bagging_fraction = 1.0;
int bagging_seed = 3;
int bagging_freq = 0;
void Set(const std::unordered_map<std::string, std::string>& params) override;
};
/*! \brief Config for GBDT */
struct GBDTConfig: public BoostingConfig {
public:
TreeLearnerType tree_learner_type = TreeLearnerType::kSerialTreeLearner;
TreeConfig tree_config;
void Set(const std::unordered_map<std::string, std::string>& params) override;
private:
void GetTreeLearnerType(const std::unordered_map<std::string,
std::string>& params);
};
/*! \brief Config for Network */
struct NetworkConfig: public ConfigBase {
public:
int num_machines = 1;
int local_listen_port = 12400;
int time_out = 120; // in minutes
std::string machine_list_filename = "";
void Set(const std::unordered_map<std::string, std::string>& params) override;
};
/*! \brief Overall config, all configs will put on this class */
struct OverallConfig: public ConfigBase {
public:
TaskType task_type = TaskType::kTrain;
NetworkConfig network_config;
int num_threads = 0;
bool is_parallel = false;
bool is_parallel_find_bin = false;
IOConfig io_config;
BoostingType boosting_type = BoostingType::kGBDT;
BoostingConfig* boosting_config;
std::string objective_type = "regression";
ObjectiveConfig objective_config;
std::vector<std::string> metric_types;
MetricConfig metric_config;
~OverallConfig() {
delete boosting_config;
}
void Set(const std::unordered_map<std::string, std::string>& params) override;
private:
void GetBoostingType(const std::unordered_map<std::string, std::string>& params);
void GetObjectiveType(const std::unordered_map<std::string, std::string>& params);
void GetMetricType(const std::unordered_map<std::string, std::string>& params);
void GetTaskType(const std::unordered_map<std::string, std::string>& params);
void CheckParamConflict();
};
inline bool ConfigBase::GetString(
const std::unordered_map<std::string, std::string>& params,
const std::string& name, std::string* out) {
if (params.count(name) > 0) {
*out = params.at(name);
return true;
}
return false;
}
inline bool ConfigBase::GetInt(
const std::unordered_map<std::string, std::string>& params,
const std::string& name, int* out) {
if (params.count(name) > 0) {
Common::Atoi(params.at(name).c_str(), out);
return true;
}
return false;
}
inline bool ConfigBase::GetDouble(
const std::unordered_map<std::string, std::string>& params,
const std::string& name, double* out) {
if (params.count(name) > 0) {
Common::Atof(params.at(name).c_str(), out);
return true;
}
return false;
}
inline bool ConfigBase::GetBool(
const std::unordered_map<std::string, std::string>& params,
const std::string& name, bool* out) {
if (params.count(name) > 0) {
std::string value = params.at(name);
std::transform(value.begin(), value.end(), value.begin(), ::tolower);
if (value == std::string("false")) {
*out = false;
} else {
*out = true;
}
return true;
}
return false;
}
struct ParameterAlias {
static void KeyAliasTransform(std::unordered_map<std::string, std::string>* params) {
std::unordered_map<std::string, std::string> alias_table(
{
{ "config", "config_file" },
{ "nthread", "num_threads" },
{ "num_thread", "num_threads" },
{ "boosting", "boosting_type" },
{ "boost", "boosting_type" },
{ "application", "objective" },
{ "app", "objective" },
{ "train_data", "data" },
{ "train", "data" },
{ "has_label", "data_has_label" },
{ "is_data_has_label", "data_has_label" },
{ "model_output", "output_model" },
{ "model_out", "output_model" },
{ "model_input", "input_model" },
{ "model_in", "input_model" },
{ "init_score", "input_init_score"},
{ "predict_result", "output_result" },
{ "prediction_result", "output_result" },
{ "valid", "valid_data" },
{ "test_data", "valid_data" },
{ "test", "valid_data" },
{ "is_sparse", "is_enable_sparse" },
{ "tranining_metric", "is_training_metric" },
{ "train_metric", "is_training_metric" },
{ "ndcg_at", "ndcg_eval_at" },
{ "min_data_per_leaf", "min_data_in_leaf" },
{ "min_data", "min_data_in_leaf" },
{ "min_sum_hessian_per_leaf", "min_sum_hessian_in_leaf" },
{ "min_sum_hessian", "min_sum_hessian_in_leaf" },
{ "min_hessian", "min_sum_hessian_in_leaf" },
{ "num_leaf", "num_leaves" },
{ "sub_feature", "feature_fraction" },
{ "num_iteration", "num_iterations" },
{ "num_tree", "num_iterations" },
{ "num_round", "num_iterations" },
{ "num_trees", "num_iterations" },
{ "num_rounds", "num_iterations" },
{ "sub_row", "bagging_fraction" },
{ "shrinkage_rate", "learning_rate" },
{ "tree", "tree_learner" },
{ "num_machine", "num_machines" },
{ "local_port", "local_listen_port" },
{ "two_round_loading", "use_two_round_loading"},
{ "two_round", "use_two_round_loading" },
{ "mlist", "machine_list_file" },
{ "is_save_binary", "is_save_binary_file" },
{ "save_binary", "is_save_binary_file" }
});
std::unordered_map<std::string, std::string> tmp_map;
for (const auto& pair : *params) {
if (alias_table.count(pair.first) > 0) {
tmp_map[alias_table[pair.first]] = pair.second;
}
}
for (const auto& pair : tmp_map) {
if (params->count(pair.first) == 0) {
params->insert(std::make_pair(pair.first, pair.second));
}
}
}
};
} // namespace LightGBM
#endif #endif // LightGBM_CONFIG_H_
include/LightGBM/dataset.h 0 → 100644
View file @ 1c774687
#ifndef LIGHTGBM_DATA_H_
#define LIGHTGBM_DATA_H_
#include <LightGBM/utils/random.h>
#include <LightGBM/utils/text_reader.h>
#include <LightGBM/meta.h>
#include <vector>
#include <utility>
#include <functional>
#include <string>
namespace LightGBM {
/*! \brief forward declaration */
class Feature;
/*!
* \brief This class is used to store some meta(non-feature) data for tranining data,
* e.g. labels, weights, initial scores, qurey level informations.
*
* Some details:
* 1. Label, used for traning.
* 2. Weights, weighs of record, optional
* 3. Query Boundaries, necessary for lambdarank.
* The documents of i-th query is in [ query_boundarise[i], query_boundarise[i+1] )
* 4. Query Weights, auto calculate by weights and query_boundarise(if both of them are existed)
* the weight for i-th query is sum(query_boundarise[i] , .., query_boundarise[i+1]) / (query_boundarise[i + 1] - query_boundarise[i+1])
* 5. Initial score. optional. if exsitng, the model will boost from this score, otherwise will start from 0.
*/
class Metadata {
public:
/*!
* \brief Null costructor
*/
Metadata();
/*!
* \brief Initialize, will load qurey level informations, since it is need for sampling data
* \param data_filename Filename of data
* \param init_score_filename Filename of initial score
* \param is_int_label True if label is int type
*/
void Init(const char* data_filename, const char* init_score_filename);
/*!
* \brief Initialize, only load initial score
* \param init_score_filename Filename of initial score
*/
void Init(const char* init_score_filename);
/*!
* \brief Initial with binary memory
* \param memory Pointer to memory
*/
void LoadFromMemory(const void* memory);
/*! \brief Destructor */
~Metadata();
/*!
* \brief Initial work, will auto load weight, inital scores
* \param num_data Number of training data
*/
void InitLabel(data_size_t num_data);
/*!
* \brief Partition label by used indices
* \param used_indices Indice of local used
*/
void PartitionLabel(const std::vector<data_size_t>& used_indices);
/*!
* \brief Partition meta data according to local used indices if need
* \param num_all_data Number of total training data, including other machines' data on parallel learning
* \param used_data_indices Indices of local used training data
*/
void CheckOrPartition(data_size_t num_all_data,
const std::vector<data_size_t>& used_data_indices);
/*!
* \brief Set initial scores
* \param init_score Initial scores, this class will manage memory for init_score.
*/
void SetInitScore(score_t* init_score);
/*!
* \brief Save binary data to file
* \param file File want to write
*/
void SaveBinaryToFile(FILE* file) const;
/*!
* \brief Get sizes in byte of this object
*/
size_t SizesInByte() const;
/*!
* \brief Get pointer of label
* \return Pointer of label
*/
inline const float* label() const { return label_; }
/*!
* \brief Set label for one record
* \param idx Index of this record
* \param value Label value of this record
*/
inline void SetLabelAt(data_size_t idx, double value)
{
label_[idx] = static_cast<float>(value);
}
/*!
* \brief Get weights, if not exists, will return nullput
* \return Pointer of weights
*/
inline const float* weights()
const { return weights_; }
/*!
* \brief Get data boundaries on queries, if not exists, will return nullput
* we assume data will order by query,
* the interval of [query_boundaris[i], query_boundaris[i+1])
* is the data indices for query i.
* \return Pointer of data boundaries on queries
*/
inline const data_size_t* query_boundaries()
const { return query_boundaries_; }
/*!
* \brief Get Number of queries
* \return Number of queries
*/
inline const data_size_t num_queries() const { return num_queries_; }
/*!
* \brief Get weights for queries, if not exists, will return nullput
* \return Pointer of weights for queries
*/
inline const float* query_weights() const { return query_weights_; }
/*!
* \brief Get initial scores, if not exists, will return nullput
* \return Pointer of initial scores
*/
inline const score_t* init_score() const { return init_score_; }
/*! \brief Load initial scores from file */
void LoadInitialScore();
private:
/*! \brief Load wights from file */
void LoadWeights();
/*! \brief Load query boundaries from file */
void LoadQueryBoundaries();
/*! \brief Load query wights */
void LoadQueryWeights();
/*! \brief Filename of current data */
const char* data_filename_;
/*! \brief Filename of initial scores */
const char* init_score_filename_;
/*! \brief Number of data */
data_size_t num_data_;
/*! \brief Number of weights, used to check correct weight file */
data_size_t num_weights_;
/*! \brief Label data */
float* label_;
/*! \brief Label data, int type */
int16_t* label_int_;
/*! \brief Weights data */
float* weights_;
/*! \brief Query boundaries */
data_size_t* query_boundaries_;
/*! \brief Query weights */
float* query_weights_;
/*! \brief Number of querys */
data_size_t num_queries_;
/*! \brief Number of Initial score, used to check correct weight file */
data_size_t num_init_score_;
/*! \brief Initial score */
score_t* init_score_;
};
/*! \brief Interface for Parser */
class Parser {
public:
/*! \brief virtual destructor */
virtual ~Parser() {}
/*!
* \brief Parse one line with label
* \param str One line record, string format, should end with '\0'
* \param out_features Output features, store in (feature_idx, feature_value)
* \param out_label Output label
*/
virtual void ParseOneLine(const char* str,
std::vector<std::pair<int, double>>* out_features,
double* out_label) const = 0;
/*!
* \brief Parse one line with label
* \param str One line record, string format, should end with '\0'
* \param out_features Output features, store in (feature_idx, feature_value)
* \param out_label Output label
*/
virtual void ParseOneLine(const char* str,
std::vector<std::pair<int, double>>* out_features) const = 0;
/*!
* \brief Create a object of parser, will auto choose the format depend on file
* \param filename One Filename of data
* \return Object of parser
*/
static Parser* CreateParser(const char* filename);
};
using PredictFunction =
std::function<double(const std::vector<std::pair<int, double>>&)>;
/*! \brief The main class of data set,
* which are used to traning or validation
*/
class Dataset {
public:
/*!
* \brief Constructor
* \param data_filename Filename of dataset
* \param init_score_filename Filename of initial score
* \param is_int_label True if label is int type
* \param max_bin The maximal number of bin that feature values will bucket in
* \param random_seed The seed for random generator
* \param is_enable_sparse True for sparse feature
* \param predict_fun Used for initial model, will give a prediction score based on this function, thenn set as initial score
*/
Dataset(const char* data_filename, const char* init_score_filename,
int max_bin, int random_seed, bool is_enable_sparse, const PredictFunction& predict_fun);
/*!
* \brief Constructor
* \param data_filename Filename of dataset
* \param is_int_label True if label is int type
* \param max_bin The maximal number of bin that feature values will bucket in
* \param random_seed The seed for random generator
* \param is_enable_sparse True for sparse feature
* \param predict_fun Used for initial model, will give a prediction score based on this function, thenn set as initial score
*/
Dataset(const char* data_filename,
int max_bin, int random_seed, bool is_enable_sparse,
const PredictFunction& predict_fun)
: Dataset(data_filename, "", max_bin, random_seed,
is_enable_sparse, predict_fun) {
}
/*! \brief Destructor */
~Dataset();
/*!
* \brief Load training data on parallel training
* \param rank Rank of local machine
* \param num_machines Total number of all machines
* \param is_pre_partition True if data file is pre-partitioned
* \param use_two_round_loading True if need to use two round loading
*/
void LoadTrainData(int rank, int num_machines, bool is_pre_partition,
bool use_two_round_loading);
/*!
* \brief Load training data on single machine training
* \param use_two_round_loading True if need to use two round loading
*/
inline void LoadTrainData(bool use_two_round_loading) {
LoadTrainData(0, 1, false, use_two_round_loading);
}
/*!
* \brief Load data and use bin mapper from other data set, general this function is used to extract feature for validation data
* \param train_set Other loaded data set
* \param use_two_round_loading True if need to use two round loading
*/
void LoadValidationData(const Dataset* train_set, bool use_two_round_loading);
/*!
* \brief Save current dataset into binary file, will save to "filename.bin"
*/
void SaveBinaryFile();
/*!
* \brief Get a feature pointer for specific index
* \param i Index for feature
* \return Pointer of feature
*/
inline const Feature* FeatureAt(int i) const { return features_[i]; }
/*!
* \brief Get meta data pointer
* \return Pointer of meta data
*/
inline const Metadata& metadata() const { return metadata_; }
/*! \brief Get Number of used features */
inline int num_features() const { return num_features_; }
/*! \brief Get Number of data */
inline data_size_t num_data() const { return num_data_; }
/*! \brief Disable copy */
Dataset& operator=(const Dataset&) = delete;
/*! \brief Disable copy */
Dataset(const Dataset&) = delete;
private:
/*!
* \brief Load data content on memory. if num_machines > 1 and !is_pre_partition, will partition data
* \param rank Rank of local machine
* \param num_machines Total number of all machines
* \param is_pre_partition True if data file is pre-partitioned
*/
void LoadDataToMemory(int rank, int num_machines, bool is_pre_partition);
/*!
* \brief Sample data from memory, need load data to memory first
* \param out_data Store the sampled data
*/
void SampleDataFromMemory(std::vector<std::string>* out_data);
/*!
* \brief Sample data from file
* \param rank Rank of local machine
* \param num_machines Total number of all machines
* \param is_pre_partition True if data file is pre-partitioned
* \param out_data Store the sampled data
*/
void SampleDataFromFile(int rank, int num_machines,
bool is_pre_partition, std::vector<std::string>* out_data);
/*!
* \brief Get feature bin mapper from sampled data.
* if num_machines > 1, differnt machines will construct bin mapper for different features, then have a global sync up
* \param rank Rank of local machine
* \param num_machines Total number of all machines
*/
void ConstructBinMappers(int rank, int num_machines,
const std::vector<std::string>& sample_data);
/*! \brief Extract local features from memory */
void ExtractFeaturesFromMemory();
/*! \brief Extract local features from file */
void ExtractFeaturesFromFile();
/*! \brief Check can load from binary file */
void CheckCanLoadFromBin();
/*!
* \brief Load data set from binary file
* \param rank Rank of local machine
* \param num_machines Total number of all machines
* \param is_pre_partition True if data file is pre-partitioned
*/
void LoadDataFromBinFile(int rank, int num_machines, bool is_pre_partition);
/*! \brief Check this data set is null or not */
void CheckDataset();
/*! \brief Filename of data */
const char* data_filename_;
/*! \brief A reader class that can read text data */
TextReader<data_size_t>* text_reader_;
/*! \brief A parser class that can parse data */
Parser* parser_;
/*! \brief Store used features */
std::vector<Feature*> features_;
/*! \brief Mapper from real feature index to used index*/
std::vector<int> used_feature_map_;
/*! \brief Number of used features*/
int num_features_;
/*! \brief Number of total data*/
data_size_t num_data_;
/*! \brief Store some label level data*/
Metadata metadata_;
/*! \brief Random generator*/
Random random_;
/*! \brief The maximal number of bin that feature values will bucket in */
int max_bin_;
/*! \brief True if enable sparse */
bool is_enable_sparse_;
/*! \brief True if dataset is loaded from binary file */
bool is_loading_from_binfile_;
/*! \brief Number of global data, used for distributed learning */
size_t global_num_data_ = 0;
// used to local used data indices
std::vector<data_size_t> used_data_indices_;
// prediction function for initial model
const PredictFunction& predict_fun_;
};
} // namespace LightGBM
#endif #endif // LightGBM_DATA_H_
include/LightGBM/feature.h 0 → 100644
View file @ 1c774687
#ifndef LIGHTGBM_FEATURE_H_
#define LIGHTGBM_FEATURE_H_
#include <LightGBM/utils/random.h>
#include <LightGBM/meta.h>
#include <LightGBM/bin.h>
#include <cstdio>
#include <vector>
namespace LightGBM {
/*! \brief Used to store data and provide some operations on one feature*/
class Feature {
public:
/*!
* \brief Constructor
* \param feature_idx Index of this feature
* \param bin_mapper Bin mapper for this feature
* \param num_data Total number of data
* \param is_enable_sparse True if enable sparse feature
*/
Feature(int feature_idx, BinMapper* bin_mapper,
data_size_t num_data, bool is_enable_sparse)
:bin_mapper_(bin_mapper) {
feature_index_ = feature_idx;
bin_data_ = Bin::CreateBin(num_data, bin_mapper_->num_bin(),
bin_mapper_->sparse_rate(), is_enable_sparse, &is_sparse_);
}
/*!
* \brief Constructor from memory
* \param memory Pointer of memory
* \param num_all_data Number of global data
* \param local_used_indices Local used indices, empty means using all data
*/
Feature(const void* memory, data_size_t num_all_data,
const std::vector<data_size_t>& local_used_indices) {
const char* memory_ptr = reinterpret_cast<const char*>(memory);
// get featuer index
feature_index_ = *(reinterpret_cast<const int*>(memory_ptr));
memory_ptr += sizeof(feature_index_);
// get is_sparse
is_sparse_ = *(reinterpret_cast<const bool*>(memory_ptr));
memory_ptr += sizeof(is_sparse_);
// get bin mapper
bin_mapper_ = new BinMapper(memory_ptr);
memory_ptr += bin_mapper_->SizesInByte();
data_size_t num_data = num_all_data;
if (local_used_indices.size() > 0) {
num_data = static_cast<data_size_t>(local_used_indices.size());
}
if (is_sparse_) {
bin_data_ = Bin::CreateSparseBin(num_data, bin_mapper_->num_bin());
} else {
bin_data_ = Bin::CreateDenseBin(num_data, bin_mapper_->num_bin());
}
// get bin data
bin_data_->LoadFromMemory(memory_ptr, local_used_indices);
}
/*! \brief Destructor */
~Feature() {
delete bin_mapper_;
delete bin_data_;
}
/*!
* \brief Push one record, will auto convert to bin and push to bin data
* \param tid Thread id
* \param idx Index of record
* \param value feature value of record
*/
inline void PushData(int tid, data_size_t line_idx, double value) {
unsigned int bin = bin_mapper_->ValueToBin(value);
bin_data_->Push(tid, line_idx, bin);
}
inline void FinishLoad() { bin_data_->FinishLoad(); }
/*! \brief Index of this feature */
inline int feature_index() const { return feature_index_; }
/*! \brief Bin mapper that this feature used */
inline const BinMapper* bin_mapper() const { return bin_mapper_; }
/*! \brief Number of bin of this feature */
inline int num_bin() const { return bin_mapper_->num_bin(); }
/*! \brief Get bin data of this feature */
inline const Bin* bin_data() const { return bin_data_; }
/*!
* \brief From bin to feature value
* \param bin
* \return Feature value of this bin
*/
inline double BinToValue(unsigned int bin)
const { return bin_mapper_->BinToValue(bin); }
/*!
* \brief Save binary data to file
* \param file File want to write
*/
void SaveBinaryToFile(FILE* file) const {
fwrite(&feature_index_, sizeof(feature_index_), 1, file);
fwrite(&is_sparse_, sizeof(is_sparse_), 1, file);
bin_mapper_->SaveBinaryToFile(file);
bin_data_->SaveBinaryToFile(file);
}
/*!
* \brief Get sizes in byte of this object
*/
size_t SizesInByte() const {
return sizeof(feature_index_) + sizeof(is_sparse_) +
bin_mapper_->SizesInByte() + bin_data_->SizesInByte();
}
/*! \brief Disable copy */
Feature& operator=(const Feature&) = delete;
/*! \brief Disable copy */
Feature(const Feature&) = delete;
private:
/*! \brief Index of this feature */
int feature_index_;
/*! \brief Bin mapper that this feature used */
BinMapper* bin_mapper_;
/*! \brief Bin data of this feature */
Bin* bin_data_;
/*! \brief True if this feature is sparse */
bool is_sparse_;
};
} // namespace LightGBM
#endif #endif // LightGBM_FEATURE_H_
include/LightGBM/meta.h 0 → 100644
View file @ 1c774687
#ifndef LIGHTGBM_META_H_
#define LIGHTGBM_META_H_
#include <cstdint>
#include <limits>
#include <vector>
#include <functional>
namespace LightGBM {
/*! \brief Type of data size, it is better to use signed type*/
typedef int32_t data_size_t;
/*! \brief Type of score, and gradients */
typedef double score_t;
const score_t kMinScore = -std::numeric_limits<score_t>::infinity();
const score_t kEpsilon = 1e-15f;
template<typename T>
std::vector<const T*> ConstPtrInVectorWarpper(std::vector<T*> input) {
return std::vector<const T*>(input.begin(), input.end());
}
using ReduceFunction = std::function<void(const char*, char*, int)>;
} // namespace LightGBM
#endif #endif // LightGBM_META_H_
include/LightGBM/metric.h 0 → 100644
View file @ 1c774687
#ifndef LIGHTGBM_METRIC_H_
#define LIGHTGBM_METRIC_H_
#include <LightGBM/meta.h>
#include <LightGBM/config.h>
#include <LightGBM/dataset.h>
#include <vector>
namespace LightGBM {
/*!
* \brief The interface of metric.
* Metric is used to calculate and output metric result on training / validation data.
*/
class Metric {
public:
/*! \brief virtual destructor */
virtual ~Metric() {}
/*!
* \brief Initialize
* \param test_name Specific name for this metric, will output on log
* \param metadata Label data
* \param num_data Number of data
*/
virtual void Init(const char* test_name,
const Metadata& metadata, data_size_t num_data) = 0;
/*!
* \brief Calcalute and print metric result
* \param iter Current iteration
* \param score Current prediction score
*/
virtual void Print(int iter, const score_t* score) const = 0;
/*!
* \brief Create object of metrics
* \param type Specific type of metric
* \param config Config for metric
*/
static Metric* CreateMetric(const std::string& type, const MetricConfig& config);
};
/*!
* \brief Static class, used to calculate DCG score
*/
class DCGCalculator {
public:
/*!
* \brief Initial logic
* \param label_gain Gain for labels, default is 2^i - 1
*/
static void Init(std::vector<double> label_gain);
/*!
* \brief Calculate the DCG score at position k
* \param k The position want to eval at
* \param label Pointer of label
* \param score Pointer of score
* \param num_data Number of data
* \return The DCG score
*/
static double CalDCGAtK(data_size_t k, const float* label,
const score_t* score, data_size_t num_data);
/*!
* \brief Calculate the DCG score at multi position
* \param ks The positions want to eval at
* \param label Pointer of label
* \param score Pointer of score
* \param num_data Number of data
* \param out Output result
*/
static void CalDCG(const std::vector<data_size_t>& ks,
const float* label, const score_t* score,
data_size_t num_data, std::vector<double>* out);
/*!
* \brief Calculate the Max DCG score at position k
* \param k The position want to eval at
* \param label Pointer of label
* \param num_data Number of data
* \return The max DCG score
*/
static double CalMaxDCGAtK(data_size_t k,
const float* label, data_size_t num_data);
/*!
* \brief Calculate the Max DCG score at multi position
* \param ks The positions want to eval at
* \param label Pointer of label
* \param num_data Number of data
* \param out Output result
*/
static void CalMaxDCG(const std::vector<data_size_t>& ks,
const float* label, data_size_t num_data, std::vector<double>* out);
/*!
* \brief Get discount score of position k
* \param k The position
* \return The discount of this position
*/
inline static double GetDiscount(data_size_t k) { return discount_[k]; }
private:
/*! \brief True if inited, avoid init multi times */
static bool is_inited_;
/*! \brief store gains for different label */
static std::vector<double> label_gain_;
/*! \brief store discount score for different position */
static std::vector<double> discount_;
/*! \brief max position for eval */
static const data_size_t kMaxPosition;
};
} // namespace LightGBM
#endif #endif // LightGBM_METRIC_H_
include/LightGBM/network.h 0 → 100644
View file @ 1c774687
#ifndef LIGHTGBM_NETWORK_H_
#define LIGHTGBM_NETWORK_H_
#include <LightGBM/utils/log.h>
#include <LightGBM/meta.h>
#include <LightGBM/config.h>
#include <functional>
#include <vector>
namespace LightGBM {
/*! \brief forward declaration */
class Linkers;
/*! \brief The network structure for all gather */
class BruckMap {
public:
/*! \brief The communication times for one all gather operation */
int k;
/*! \brief in_ranks[i] means the incomming rank on i-th communication */
std::vector<int> in_ranks;
/*! \brief out_ranks[i] means the out rank on i-th communication */
std::vector<int> out_ranks;
BruckMap();
explicit BruckMap(int n);
/*!
* \brief Create the object of bruck map
* \param rank Rank of this machine
* \param num_machines The total number of machines
* \return The object of bruck map
*/
static BruckMap Construct(int rank, int num_machines);
};
/*!
* \brief node type on recursive halving algorithm
* When number of machines is not power of 2, need group maiches into power of 2 group.
* And we can let each group has at most 2 machines.
* if the group only has 1 machine. this machine is the normal node
* if the grou has 2 machines, this group will have two type of nodes, one is the leader.
* leader will represent this group and communication with others.
*/
enum RecursiveHalvingNodeType {
Normal, // normal node, 1 group only have 1 machine
GroupLeader, // leader of group when number of machines in this group is 2.
Other // non-leader machines in group
};
/*! \brief Network structure for recursive halving algorithm */
class RecursiveHalvingMap {
public:
/*! \brief Communication times for one recursize halving algorithm */
int k;
/*! \brief Node type */
RecursiveHalvingNodeType type;
/*! \brief Neighbor, only used for non-normal node*/
int neighbor;
/*! \brief ranks[i] means the machines that will communicate with on i-th communication*/
std::vector<int> ranks;
/*! \brief send_block_start[i] means send block start index at i-th communication*/
std::vector<int> send_block_start;
/*! \brief send_block_start[i] means send block size at i-th communication*/
std::vector<int> send_block_len;
/*! \brief send_block_start[i] means recv block start index at i-th communication*/
std::vector<int> recv_block_start;
/*! \brief send_block_start[i] means recv block size at i-th communication*/
std::vector<int> recv_block_len;
RecursiveHalvingMap();
RecursiveHalvingMap(RecursiveHalvingNodeType _type, int n);
/*!
* \brief Create the object of recursive halving map
* \param rank Rank of this machine
* \param num_machines The total number of machines
* \return The object of recursive halving map
*/
static RecursiveHalvingMap Construct(int rank, int num_machines);
};
/*! \brief A static class that contains some collective communication algorithm */
class Network {
public:
/*!
* \brief Initialize
* \param config Config of network setting
*/
static void Init(NetworkConfig config);
/*! \brief Free this static class */
static void Dispose();
/*! \brief Get rank of this machine */
static inline int rank();
/*! \brief Get total number of machines */
static inline int num_machines();
/*!
* \brief Perform all reduce. if data size is small,
will perform AllreduceByAllGather, else with call ReduceScatter followed allgather
* \param input Input data
* \param input_size The size of input data
* \param type_size The size of one object in the reduce function
* \param output Output result
* \param reducer Reduce function
*/
static void Allreduce(char* input, int input_size, int type_size,
char* output, const ReduceFunction& reducer);
/*!
* \brief Perform all reduce, use all gather. When data is small, can use this to reduce communication times
* \param input Input data
* \param input_size The size of input data
* \param output Output result
* \param reducer Reduce function
*/
static void AllreduceByAllGather(char* input, int input_size, char* output,
const ReduceFunction& reducer);
/*!
* \brief Perform all gather, use bruck algorithm. Communication times is O(log(n)), and communication cost is O(send_size * number_machine)
* if all machine have same input size, can call this function
* \param input Input data
* \param send_size The size of input data
* \param output Output result
*/
static void Allgather(char* input, int send_size, char* output);
/*!
* \brief Perform all gather, use bruck algorithm. Communication times is O(log(n)), and communication cost is O(all_size)
* if all machine have different input size, can call this function
* \param input Input data
* \param all_size The size of input data
* \param block_start The block start for different machines
* \param block_len The block size for different machines
* \param output Output result
*/
static void Allgather(char* input, int all_size, int* block_start,
int* block_len, char* output);
/*!
* \brief Perform reduce scatter, use recursive halving algorithm. Communication times is O(log(n)), and communication cost is O(input_size)
* \param input Input data
* \param input_size The size of input data
* \param block_start The block start for different machines
* \param block_len The block size for different machines
* \param output Output result
* \param reducer Reduce function
*/
static void ReduceScatter(char* input, int input_size,
int* block_start, int* block_len, char* output,
const ReduceFunction& reducer);
private:
/*! \brief Number of all machines */
static int num_machines_;
/*! \brief Rank of local machine */
static int rank_;
/*! \brief The network interface, provide send/recv functions */
static Linkers *linkers_;
/*! \brief Bruck map for all gather algorithm*/
static BruckMap bruck_map_;
/*! \brief Recursive halving map for reduce scatter */
static RecursiveHalvingMap recursive_halving_map_;
/*! \brief Buffer to store block start index */
static int* block_start_;
/*! \brief Buffer to store block size */
static int* block_len_;
/*! \brief Buffer */
static char* buffer_;
/*! \brief Size of buffer_ */
static int buffer_size_;
};
inline int Network::rank() {
return rank_;
}
inline int Network::num_machines() {
return num_machines_;
}
} // namespace LightGBM
#endif #endif // LightGBM_NETWORK_H_
include/LightGBM/objective_function.h 0 → 100644
View file @ 1c774687
#ifndef LIGHTGBM_OBJECTIVE_FUNCTION_H_
#define LIGHTGBM_OBJECTIVE_FUNCTION_H_
#include <LightGBM/meta.h>
#include <LightGBM/config.h>
#include <LightGBM/dataset.h>
namespace LightGBM {
/*!
* \brief The interface of Objective Function.
* Objective function is used to get gradients
*/
class ObjectiveFunction {
public:
/*! \brief virtual destructor */
virtual ~ObjectiveFunction() {}
/*!
* \brief Initialize
* \param metadata Label data
* \param num_data Number of data
*/
virtual void Init(const Metadata& metadata, data_size_t num_data) = 0;
/*!
* \brief calculate first order derivative of loss function
* \param score Current prediction score
* \gradients Output gradients
* \hessians Output hessians
*/
virtual void GetGradients(const score_t* score,
score_t* gradients, score_t* hessians) const = 0;
/*!
* \brief Get sigmoid param for this objective if has.
* This function is used for prediction task, if has sigmoid param, the prediction value will be transform by sigmoid function.
* \return Sigmoid param, if <=0.0 means don't use sigmoid transform on this objective.
*/
virtual double GetSigmoid() const = 0;
/*!
* \brief Create object of objective function
* \param type Specific type of objective function
* \param config Config for objective function
*/
static ObjectiveFunction* CreateObjectiveFunction(const std::string& type,
const ObjectiveConfig& config);
};
} // namespace LightGBM
#endif #endif // LightGBM_OBJECTIVE_FUNCTION_H_
include/LightGBM/tree.h 0 → 100644
View file @ 1c774687
#ifndef LIGHTGBM_TREE_H_
#define LIGHTGBM_TREE_H_
#include <LightGBM/meta.h>
#include <LightGBM/feature.h>
#include <LightGBM/dataset.h>
#include <string>
#include <vector>
namespace LightGBM {
/*!
* \brief Tree model
*/
class Tree {
public:
/*!
* \brief Constructor
* \param max_leaves The number of max leaves
*/
explicit Tree(int max_leaves);
/*!
* \brief Construtor, from a string
* \param str Model string
*/
explicit Tree(const std::string& str);
~Tree();
/*!
* \brief Split a tree leave,
* \param leaf Index of leaf that want to split
* \param feature Index of feature, the converted index after remove useless features
* \param threshold Threshold(bin) of split
* \param real_feature Index of feature, the original index on data
* \param threshold_double Threshold on feature value
* \param left_value Model Left child output
* \param right_value Model Right child output
* \param gain Split gain
* \return The index of new leaf.
*/
int Split(int leaf, int feature, unsigned int threshold, int real_feature,
double threshold_double, score_t left_value,
score_t right_value, double gain);
/*! \brief Get the output of one leave */
inline score_t LeafOutput(int leaf) const { return leaf_value_[leaf]; }
/*!
* \brief Add prediction of this tree model to score
* \param data The dataset
* \param num_data Number of total data
* \param score Will add prediction to score
*/
void AddPredictionToScore(const Dataset* data, data_size_t num_data,
score_t* score) const;
/*!
* \brief Add prediction of this tree model to score
* \param data The dataset
* \param used_data_indices Indices of used data
* \param num_data Number of total data
* \param score Will add prediction to score
*/
void AddPredictionToScore(const Dataset* data,
const data_size_t* used_data_indices,
data_size_t num_data, score_t* score) const;
/*!
* \brief Prediction for one record
* \param feature_values Feature value of this record
* \return Prediction result
*/
inline score_t Predict(const double* feature_values) const;
/*! \brief Get Number of leaves*/
inline int num_leaves() const { return num_leaves_; }
/*!
* \brief Shrinkage for the tree's output
* \param rate The factor of shrinkage
*/
inline void Shrinkage(double rate) {
for (int i = 0; i < num_leaves_; ++i) {
leaf_value_[i] = static_cast<score_t>(leaf_value_[i] * rate);
}
}
/*! \brief Serialize this object by string*/
std::string ToString();
/*! \brief Disable copy */
Tree& operator=(const Tree&) = delete;
/*! \brief Disable copy */
Tree(const Tree&) = delete;
private:
/*!
* \brief Find leaf index that this record belongs
* \param data The dataset
* \param data_idx Index of record
* \return Leaf index
*/
inline int GetLeaf(const std::vector<BinIterator*>& iterators,
data_size_t data_idx) const;
/*!
* \brief Find leaf index that this record belongs
* \param feature_values Feature value of this record
* \return Leaf index
*/
inline int GetLeaf(const double* feature_values) const;
/*! \brief Number of max leaves*/
int max_leaves_;
/*! \brief Number of current levas*/
int num_leaves_;
// following values used for non-leaf node
/*! \brief A non-leaf node's left child */
int* left_child_;
/*! \brief A non-leaf node's right child */
int* right_child_;
/*! \brief A non-leaf node's split feature */
int* split_feature_;
/*! \brief A non-leaf node's split feature, the original index */
int* split_feature_real_;
/*! \brief A non-leaf node's split threshold in bin */
unsigned int* threshold_in_bin_;
/*! \brief A non-leaf node's split threshold in feature value */
double* threshold_;
/*! \brief A non-leaf node's split gain */
double* split_gain_;
// used for leaf node
/*! \brief The parent of leaf */
int* leaf_parent_;
/*! \brief Output of leaves */
score_t* leaf_value_;
};
inline score_t Tree::Predict(const double* feature_values)const {
int leaf = GetLeaf(feature_values);
return LeafOutput(leaf);
}
inline int Tree::GetLeaf(const std::vector<BinIterator*>& iterators,
data_size_t data_idx) const {
int node = 0;
while (node >= 0) {
if (iterators[split_feature_[node]]->Get(data_idx) <=
threshold_in_bin_[node]) {
node = left_child_[node];
} else {
node = right_child_[node];
}
}
return ~node;
}
inline int Tree::GetLeaf(const double* feature_values) const {
int node = 0;
while (node >= 0) {
if (feature_values[split_feature_real_[node]] <= threshold_[node]) {
node = left_child_[node];
} else {
node = right_child_[node];
}
}
return ~node;
}
} // namespace LightGBM
#endif #endif // LightGBM_TREE_H_
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