diff --git a/data/mllib/sample_libsvm_data_with_unique_column.txt b/data/mllib/sample_libsvm_data_with_unique_column.txt
new file mode 100644
index 000000000000..5c378e5a1447
--- /dev/null
+++ b/data/mllib/sample_libsvm_data_with_unique_column.txt
@@ -0,0 +1,270 @@
+1 1:0.708333 2:1 3:1 4:-0.320755 5:-0.105023 6:-1 7:1 8:-0.419847 9:-1 10:-0.225806 12:1 13:-1 14:1
+0 1:0.583333 2:-1 3:0.333333 4:-0.603774 5:1 6:-1 7:1 8:0.358779 9:-1 10:-0.483871 12:-1 13:1 14:1
+1 1:0.166667 2:1 3:-0.333333 4:-0.433962 5:-0.383562 6:-1 7:-1 8:0.0687023 9:-1 10:-0.903226 11:-1 12:-1 13:1 14:1
+0 1:0.458333 2:1 3:1 4:-0.358491 5:-0.374429 6:-1 7:-1 8:-0.480916 9:1 10:-0.935484 12:-0.333333 13:1 14:1
+0 1:0.875 2:-1 3:-0.333333 4:-0.509434 5:-0.347032 6:-1 7:1 8:-0.236641 9:1 10:-0.935484 11:-1 12:-0.333333 13:-1 14:1
+0 1:0.5 2:1 3:1 4:-0.509434 5:-0.767123 6:-1 7:-1 8:0.0534351 9:-1 10:-0.870968 11:-1 12:-1 13:1 14:1
+1 1:0.125 2:1 3:0.333333 4:-0.320755 5:-0.406393 6:1 7:1 8:0.0839695 9:1 10:-0.806452 12:-0.333333 13:0.5 14:1
+1 1:0.25 2:1 3:1 4:-0.698113 5:-0.484018 6:-1 7:1 8:0.0839695 9:1 10:-0.612903 12:-0.333333 13:1 14:1
+1 1:0.291667 2:1 3:1 4:-0.132075 5:-0.237443 6:-1 7:1 8:0.51145 9:-1 10:-0.612903 12:0.333333 13:1 14:1
+1 1:0.416667 2:-1 3:1 4:0.0566038 5:0.283105 6:-1 7:1 8:0.267176 9:-1 10:0.290323 12:1 13:1 14:1
+0 1:0.25 2:1 3:1 4:-0.226415 5:-0.506849 6:-1 7:-1 8:0.374046 9:-1 10:-0.83871 12:-1 13:1 14:1
+0 2:1 3:1 4:-0.0943396 5:-0.543379 6:-1 7:1 8:-0.389313 9:1 10:-1 11:-1 12:-1 13:1 14:1
+0 1:-0.375 2:1 3:0.333333 4:-0.132075 5:-0.502283 6:-1 7:1 8:0.664122 9:-1 10:-1 11:-1 12:-1 13:-1 14:1
+1 1:0.333333 2:1 3:-1 4:-0.245283 5:-0.506849 6:-1 7:-1 8:0.129771 9:-1 10:-0.16129 12:0.333333 13:-1 14:1
+0 1:0.166667 2:-1 3:1 4:-0.358491 5:-0.191781 6:-1 7:1 8:0.343511 9:-1 10:-1 11:-1 12:-0.333333 13:-1 14:1
+0 1:0.75 2:-1 3:1 4:-0.660377 5:-0.894977 6:-1 7:-1 8:-0.175573 9:-1 10:-0.483871 12:-1 13:-1 14:1
+1 1:-0.291667 2:1 3:1 4:-0.132075 5:-0.155251 6:-1 7:-1 8:-0.251908 9:1 10:-0.419355 12:0.333333 13:1 14:1
+1 2:1 3:1 4:-0.132075 5:-0.648402 6:1 7:1 8:0.282443 9:1 11:1 12:-1 13:1 14:1
+0 1:0.458333 2:1 3:-1 4:-0.698113 5:-0.611872 6:-1 7:1 8:0.114504 9:1 10:-0.419355 12:-1 13:-1 14:1
+0 1:-0.541667 2:1 3:-1 4:-0.132075 5:-0.666667 6:-1 7:-1 8:0.633588 9:1 10:-0.548387 11:-1 12:-1 13:1 14:1
+1 1:0.583333 2:1 3:1 4:-0.509434 5:-0.52968 6:-1 7:1 8:-0.114504 9:1 10:-0.16129 12:0.333333 13:1 14:1
+0 1:-0.208333 2:1 3:-0.333333 4:-0.320755 5:-0.456621 6:-1 7:1 8:0.664122 9:-1 10:-0.935484 12:-1 13:-1 14:1
+0 1:-0.416667 2:1 3:1 4:-0.603774 5:-0.191781 6:-1 7:-1 8:0.679389 9:-1 10:-0.612903 12:-1 13:-1 14:1
+0 1:-0.25 2:1 3:1 4:-0.660377 5:-0.643836 6:-1 7:-1 8:0.0992366 9:-1 10:-0.967742 11:-1 12:-1 13:-1 14:1
+0 1:0.0416667 2:-1 3:-0.333333 4:-0.283019 5:-0.260274 6:1 7:1 8:0.343511 9:1 10:-1 11:-1 12:-0.333333 13:-1 14:1
+0 1:-0.208333 2:-1 3:0.333333 4:-0.320755 5:-0.319635 6:-1 7:-1 8:0.0381679 9:-1 10:-0.935484 11:-1 12:-1 13:-1 14:1
+0 1:-0.291667 2:-1 3:1 4:-0.169811 5:-0.465753 6:-1 7:1 8:0.236641 9:1 10:-1 12:-1 13:-1 14:1
+0 1:-0.0833333 2:-1 3:0.333333 4:-0.509434 5:-0.228311 6:-1 7:1 8:0.312977 9:-1 10:-0.806452 11:-1 12:-1 13:-1 14:1
+1 1:0.208333 2:1 3:0.333333 4:-0.660377 5:-0.525114 6:-1 7:1 8:0.435115 9:-1 10:-0.193548 12:-0.333333 13:1 14:1
+0 1:0.75 2:-1 3:0.333333 4:-0.698113 5:-0.365297 6:1 7:1 8:-0.0992366 9:-1 10:-1 11:-1 12:-0.333333 13:-1 14:1
+1 1:0.166667 2:1 3:0.333333 4:-0.358491 5:-0.52968 6:-1 7:1 8:0.206107 9:-1 10:-0.870968 12:-0.333333 13:1 14:1
+0 1:0.541667 2:1 3:1 4:0.245283 5:-0.534247 6:-1 7:1 8:0.0229008 9:-1 10:-0.258065 11:-1 12:-1 13:0.5 14:1
+0 1:-0.666667 2:-1 3:0.333333 4:-0.509434 5:-0.593607 6:-1 7:-1 8:0.51145 9:-1 10:-1 11:-1 12:-1 13:-1 14:1
+1 1:0.25 2:1 3:1 4:0.433962 5:-0.086758 6:-1 7:1 8:0.0534351 9:1 10:0.0967742 11:1 12:-1 13:1 14:1
+1 1:-0.125 2:1 3:1 4:-0.0566038 5:-0.6621 6:-1 7:1 8:-0.160305 9:1 10:-0.709677 12:-1 13:1 14:1
+1 1:-0.208333 2:1 3:1 4:-0.320755 5:-0.406393 6:1 7:1 8:0.206107 9:1 10:-1 11:-1 12:0.333333 13:1 14:1
+1 1:0.333333 2:1 3:1 4:-0.132075 5:-0.630137 6:-1 7:1 8:0.0229008 9:1 10:-0.387097 11:-1 12:-0.333333 13:1 14:1
+1 1:0.25 2:1 3:-1 4:0.245283 5:-0.328767 6:-1 7:1 8:-0.175573 9:-1 10:-1 11:-1 12:-1 13:-1 14:1
+0 1:-0.458333 2:1 3:0.333333 4:-0.320755 5:-0.753425 6:-1 7:-1 8:0.206107 9:-1 10:-1 11:-1 12:-1 13:-1 14:1
+0 1:-0.208333 2:1 3:1 4:-0.471698 5:-0.561644 6:-1 7:1 8:0.755725 9:-1 10:-1 11:-1 12:-1 13:-1 14:1
+1 1:-0.541667 2:1 3:1 4:0.0943396 5:-0.557078 6:-1 7:-1 8:0.679389 9:-1 10:-1 11:-1 12:-1 13:1 14:1
+0 1:0.375 2:-1 3:1 4:-0.433962 5:-0.621005 6:-1 7:-1 8:0.40458 9:-1 10:-1 11:-1 12:-1 13:-1 14:1
+0 1:-0.375 2:1 3:0.333333 4:-0.320755 5:-0.511416 6:-1 7:-1 8:0.648855 9:1 10:-0.870968 11:-1 12:-1 13:-1 14:1
+0 1:-0.291667 2:1 3:-0.333333 4:-0.867925 5:-0.675799 6:1 7:-1 8:0.29771 9:-1 10:-1 11:-1 12:-1 13:1 14:1
+1 1:0.25 2:1 3:0.333333 4:-0.396226 5:-0.579909 6:1 7:-1 8:-0.0381679 9:-1 10:-0.290323 12:-0.333333 13:0.5 14:1
+0 1:0.208333 2:1 3:0.333333 4:-0.132075 5:-0.611872 6:1 7:1 8:0.435115 9:-1 10:-1 11:-1 12:-1 13:-1 14:1
+1 1:-0.166667 2:1 3:0.333333 4:-0.54717 5:-0.894977 6:-1 7:1 8:-0.160305 9:-1 10:-0.741935 11:-1 12:1 13:-1 14:1
+1 1:-0.375 2:1 3:1 4:-0.698113 5:-0.675799 6:-1 7:1 8:0.618321 9:-1 10:-1 11:-1 12:-0.333333 13:-1 14:1
+1 1:0.541667 2:1 3:-0.333333 4:0.245283 5:-0.452055 6:-1 7:-1 8:-0.251908 9:1 10:-1 12:1 13:0.5 14:1
+1 1:0.5 2:-1 3:1 4:0.0566038 5:-0.547945 6:-1 7:1 8:-0.343511 9:-1 10:-0.677419 12:1 13:1 14:1
+1 1:-0.458333 2:1 3:1 4:-0.207547 5:-0.136986 6:-1 7:-1 8:-0.175573 9:1 10:-0.419355 12:-1 13:0.5 14:1
+0 1:-0.0416667 2:1 3:-0.333333 4:-0.358491 5:-0.639269 6:1 7:-1 8:0.725191 9:-1 10:-1 11:-1 12:-1 13:-1 14:1
+0 1:0.5 2:-1 3:0.333333 4:-0.132075 5:0.328767 6:1 7:1 8:0.312977 9:-1 10:-0.741935 11:-1 12:-0.333333 13:-1 14:1
+0 1:0.416667 2:-1 3:-0.333333 4:-0.132075 5:-0.684932 6:-1 7:-1 8:0.648855 9:-1 10:-1 11:-1 12:0.333333 13:-1 14:1
+0 1:-0.333333 2:-1 3:-0.333333 4:-0.320755 5:-0.506849 6:-1 7:1 8:0.587786 9:-1 10:-0.806452 12:-1 13:-1 14:1
+0 1:-0.5 2:-1 3:-0.333333 4:-0.792453 5:-0.671233 6:-1 7:-1 8:0.480916 9:-1 10:-1 11:-1 12:-0.333333 13:-1 14:1
+1 1:0.333333 2:1 3:1 4:-0.169811 5:-0.817352 6:-1 7:1 8:-0.175573 9:1 10:0.16129 12:-0.333333 13:-1 14:1
+0 1:0.291667 2:-1 3:0.333333 4:-0.509434 5:-0.762557 6:1 7:-1 8:-0.618321 9:-1 10:-1 11:-1 12:-1 13:-1 14:1
+1 1:0.25 2:-1 3:1 4:0.509434 5:-0.438356 6:-1 7:-1 8:0.0992366 9:1 10:-1 12:-1 13:-1 14:1
+1 1:0.375 2:1 3:-0.333333 4:-0.509434 5:-0.292237 6:-1 7:1 8:-0.51145 9:-1 10:-0.548387 12:-0.333333 13:1 14:1
+0 1:0.166667 2:1 3:0.333333 4:0.0566038 5:-1 6:1 7:-1 8:0.557252 9:-1 10:-0.935484 11:-1 12:-0.333333 13:1 14:1
+1 1:-0.0833333 2:-1 3:1 4:-0.320755 5:-0.182648 6:-1 7:-1 8:0.0839695 9:1 10:-0.612903 12:-1 13:1 14:1
+0 1:-0.375 2:1 3:0.333333 4:-0.509434 5:-0.543379 6:-1 7:-1 8:0.496183 9:-1 10:-1 11:-1 12:-1 13:-1 14:1
+0 1:0.291667 2:-1 3:-1 4:0.0566038 5:-0.479452 6:-1 7:-1 8:0.526718 9:-1 10:-0.709677 11:-1 12:-1 13:-1 14:1
+0 1:0.416667 2:1 3:-1 4:-0.0377358 5:-0.511416 6:1 7:1 8:0.206107 9:-1 10:-0.258065 11:1 12:-1 13:0.5 14:1
+1 1:0.166667 2:1 3:1 4:0.0566038 5:-0.315068 6:-1 7:1 8:-0.374046 9:1 10:-0.806452 12:-0.333333 13:0.5 14:1
+0 1:-0.0833333 2:1 3:1 4:-0.132075 5:-0.383562 6:-1 7:1 8:0.755725 9:1 10:-1 11:-1 12:-1 13:-1 14:1
+1 1:0.208333 2:-1 3:-0.333333 4:-0.207547 5:-0.118721 6:1 7:1 8:0.236641 9:-1 10:-1 11:-1 12:0.333333 13:-1 14:1
+0 1:-0.375 2:-1 3:0.333333 4:-0.54717 5:-0.47032 6:-1 7:-1 8:0.19084 9:-1 10:-0.903226 12:-0.333333 13:-1 14:1
+1 1:-0.25 2:1 3:0.333333 4:-0.735849 5:-0.465753 6:-1 7:-1 8:0.236641 9:-1 10:-1 11:-1 12:-1 13:-1 14:1
+1 1:0.333333 2:1 3:1 4:-0.509434 5:-0.388128 6:-1 7:-1 8:0.0534351 9:1 10:0.16129 12:-0.333333 13:1 14:1
+0 1:0.166667 2:-1 3:1 4:-0.509434 5:0.0410959 6:-1 7:-1 8:0.40458 9:1 10:-0.806452 11:-1 12:-1 13:-1 14:1
+0 1:0.708333 2:1 3:-0.333333 4:0.169811 5:-0.456621 6:-1 7:1 8:0.0992366 9:-1 10:-1 11:-1 12:-1 13:-1 14:1
+0 1:0.958333 2:-1 3:0.333333 4:-0.132075 5:-0.675799 6:-1 8:-0.312977 9:-1 10:-0.645161 12:-1 13:-1 14:1
+0 1:0.583333 2:-1 3:1 4:-0.773585 5:-0.557078 6:-1 7:-1 8:0.0839695 9:-1 10:-0.903226 11:-1 12:0.333333 13:-1 14:1
+1 1:-0.333333 2:1 3:1 4:-0.0943396 5:-0.164384 6:-1 7:1 8:0.160305 9:1 10:-1 12:1 13:1 14:1
+0 1:-0.333333 2:1 3:1 4:-0.811321 5:-0.625571 6:-1 7:1 8:0.175573 9:1 10:-0.0322581 12:-1 13:-1 14:1
+0 1:-0.583333 2:-1 3:0.333333 4:-1 5:-0.666667 6:-1 7:-1 8:0.648855 9:-1 10:-1 11:-1 12:-1 13:-1 14:1
+0 1:-0.458333 2:-1 3:0.333333 4:-0.509434 5:-0.621005 6:-1 7:-1 8:0.557252 9:-1 10:-1 12:-1 13:-1 14:1
+0 1:0.125 2:1 3:-0.333333 4:-0.509434 5:-0.497717 6:-1 7:-1 8:0.633588 9:-1 10:-0.741935 11:-1 12:-1 13:-1 14:1
+1 1:0.208333 2:1 3:1 4:-0.0188679 5:-0.579909 6:-1 7:-1 8:-0.480916 9:-1 10:-0.354839 12:-0.333333 13:1 14:1
+1 1:-0.75 2:1 3:1 4:-0.509434 5:-0.671233 6:-1 7:-1 8:-0.0992366 9:1 10:-0.483871 12:-1 13:1 14:1
+1 1:0.208333 2:1 3:1 4:0.0566038 5:-0.342466 6:-1 7:1 8:-0.389313 9:1 10:-0.741935 11:-1 12:-1 13:1 14:1
+0 1:-0.5 2:1 3:0.333333 4:-0.320755 5:-0.598174 6:-1 7:1 8:0.480916 9:-1 10:-0.354839 12:-1 13:-1 14:1
+0 1:0.166667 2:1 3:1 4:-0.698113 5:-0.657534 6:-1 7:-1 8:-0.160305 9:1 10:-0.516129 12:-1 13:0.5 14:1
+0 1:-0.458333 2:1 3:-1 4:0.0188679 5:-0.461187 6:-1 7:1 8:0.633588 9:-1 10:-0.741935 11:-1 12:0.333333 13:-1 14:1
+0 1:0.375 2:1 3:-0.333333 4:-0.358491 5:-0.625571 6:1 7:1 8:0.0534351 9:-1 10:-1 11:-1 12:-1 13:-1 14:1
+0 1:0.25 2:1 3:-1 4:0.584906 5:-0.342466 6:-1 7:1 8:0.129771 9:-1 10:0.354839 11:1 12:-1 13:1 14:1
+0 1:-0.5 2:-1 3:-0.333333 4:-0.396226 5:-0.178082 6:-1 7:-1 8:0.40458 9:-1 10:-1 11:-1 12:-1 13:-1 14:1
+1 1:-0.125 2:1 3:1 4:0.0566038 5:-0.465753 6:-1 7:1 8:-0.129771 9:-1 10:-0.16129 12:-1 13:1 14:1
+0 1:0.25 2:1 3:-0.333333 4:-0.132075 5:-0.56621 6:-1 7:-1 8:0.419847 9:1 10:-1 11:-1 12:-1 13:-1 14:1
+1 1:0.333333 2:-1 3:1 4:-0.320755 5:-0.0684932 6:-1 7:1 8:0.496183 9:-1 10:-1 11:-1 12:-1 13:-1 14:1
+1 1:0.0416667 2:1 3:1 4:-0.433962 5:-0.360731 6:-1 7:1 8:-0.419847 9:1 10:-0.290323 12:-0.333333 13:1 14:1
+1 1:0.0416667 2:1 3:1 4:-0.698113 5:-0.634703 6:-1 7:1 8:-0.435115 9:1 10:-1 12:-0.333333 13:-1 14:1
+1 1:-0.0416667 2:1 3:1 4:-0.415094 5:-0.607306 6:-1 7:-1 8:0.480916 9:-1 10:-0.677419 11:-1 12:0.333333 13:1 14:1
+1 1:-0.25 2:1 3:1 4:-0.698113 5:-0.319635 6:-1 7:1 8:-0.282443 9:1 10:-0.677419 12:-0.333333 13:-1 14:1
+0 1:0.541667 2:1 3:1 4:-0.509434 5:-0.196347 6:-1 7:1 8:0.221374 9:-1 10:-0.870968 12:-1 13:-1 14:1
+1 1:0.208333 2:1 3:1 4:-0.886792 5:-0.506849 6:-1 7:-1 8:0.29771 9:-1 10:-0.967742 11:-1 12:-0.333333 13:1 14:1
+0 1:0.458333 2:-1 3:0.333333 4:-0.132075 5:-0.146119 6:-1 7:-1 8:-0.0534351 9:-1 10:-0.935484 11:-1 12:-1 13:1 14:1
+0 1:-0.125 2:-1 3:-0.333333 4:-0.509434 5:-0.461187 6:-1 7:-1 8:0.389313 9:-1 10:-0.645161 11:-1 12:-1 13:-1 14:1
+0 1:-0.375 2:-1 3:0.333333 4:-0.735849 5:-0.931507 6:-1 7:-1 8:0.587786 9:-1 10:-0.806452 12:-1 13:-1 14:1
+1 1:0.583333 2:1 3:1 4:-0.509434 5:-0.493151 6:-1 7:-1 8:-1 9:-1 10:-0.677419 12:-1 13:-1 14:1
+0 1:-0.166667 2:-1 3:1 4:-0.320755 5:-0.347032 6:-1 7:-1 8:0.40458 9:-1 10:-1 11:-1 12:-1 13:-1 14:1
+1 1:0.166667 2:1 3:1 4:0.339623 5:-0.255708 6:1 7:1 8:-0.19084 9:-1 10:-0.677419 12:1 13:1 14:1
+1 1:0.416667 2:1 3:1 4:-0.320755 5:-0.415525 6:-1 7:1 8:0.160305 9:-1 10:-0.548387 12:-0.333333 13:1 14:1
+1 1:-0.208333 2:1 3:1 4:-0.433962 5:-0.324201 6:-1 7:1 8:0.450382 9:-1 10:-0.83871 12:-1 13:1 14:1
+0 1:-0.0833333 2:1 3:0.333333 4:-0.886792 5:-0.561644 6:-1 7:-1 8:0.0992366 9:1 10:-0.612903 12:-1 13:-1 14:1
+1 1:0.291667 2:-1 3:1 4:0.0566038 5:-0.39726 6:-1 7:1 8:0.312977 9:-1 10:-0.16129 12:0.333333 13:1 14:1
+1 1:0.25 2:1 3:1 4:-0.132075 5:-0.767123 6:-1 7:-1 8:0.389313 9:1 10:-1 11:-1 12:-0.333333 13:1 14:1
+0 1:-0.333333 2:-1 3:-0.333333 4:-0.660377 5:-0.844749 6:-1 7:-1 8:0.0229008 9:-1 10:-1 12:-1 13:-1 14:1
+1 1:0.0833333 2:-1 3:1 4:0.622642 5:-0.0821918 6:-1 8:-0.29771 9:1 10:0.0967742 12:-1 13:-1 14:1
+0 1:-0.5 2:1 3:-0.333333 4:-0.698113 5:-0.502283 6:-1 7:-1 8:0.251908 9:-1 10:-1 11:-1 12:-1 13:-1 14:1
+1 1:0.291667 2:-1 3:1 4:0.207547 5:-0.182648 6:-1 7:1 8:0.374046 9:-1 10:-1 11:-1 12:-1 13:-1 14:1
+0 1:0.0416667 2:-1 3:0.333333 4:-0.226415 5:-0.187215 6:1 7:-1 8:0.51145 9:-1 10:-1 11:-1 12:-1 13:-1 14:1
+0 1:-0.458333 2:1 3:-0.333333 4:-0.509434 5:-0.228311 6:-1 7:-1 8:0.389313 9:-1 10:-1 11:-1 12:-1 13:-1 14:1
+0 1:-0.166667 2:-1 3:-0.333333 4:-0.245283 5:-0.3379 6:-1 7:-1 8:0.389313 9:-1 10:-1 12:-1 13:-1 14:1
+1 1:-0.291667 2:1 3:1 4:-0.509434 5:-0.438356 6:-1 7:1 8:0.114504 9:-1 10:-0.741935 11:-1 12:-1 13:1 14:1
+1 1:0.125 2:-1 3:1 4:1 5:-0.260274 6:1 7:1 8:-0.0534351 9:1 10:0.290323 11:1 12:0.333333 13:1 14:1
+0 1:0.541667 2:-1 3:-1 4:0.0566038 5:-0.543379 6:-1 7:-1 8:-0.343511 9:-1 10:-0.16129 11:1 12:-1 13:-1 14:1
+1 1:0.125 2:1 3:1 4:-0.320755 5:-0.283105 6:1 7:1 8:-0.51145 9:1 10:-0.483871 11:1 12:-1 13:1 14:1
+1 1:-0.166667 2:1 3:0.333333 4:-0.509434 5:-0.716895 6:-1 7:-1 8:0.0381679 9:-1 10:-0.354839 12:1 13:1 14:1
+1 1:0.0416667 2:1 3:1 4:-0.471698 5:-0.269406 6:-1 7:1 8:-0.312977 9:1 10:0.0322581 12:0.333333 13:-1 14:1
+1 1:0.166667 2:1 3:1 4:0.0943396 5:-0.324201 6:-1 7:-1 8:-0.740458 9:1 10:-0.612903 12:-0.333333 13:1 14:1
+0 1:0.5 2:-1 3:0.333333 4:0.245283 5:0.0684932 6:-1 7:1 8:0.221374 9:-1 10:-0.741935 11:-1 12:-1 13:-1 14:1
+0 1:0.0416667 2:1 3:0.333333 4:-0.415094 5:-0.328767 6:-1 7:1 8:0.236641 9:-1 10:-0.83871 11:1 12:-0.333333 13:-1 14:1
+0 1:0.0416667 2:-1 3:0.333333 4:0.245283 5:-0.657534 6:-1 7:-1 8:0.40458 9:-1 10:-1 11:-1 12:-0.333333 13:-1 14:1
+1 1:0.375 2:1 3:1 4:-0.509434 5:-0.356164 6:-1 7:-1 8:-0.572519 9:1 10:-0.419355 12:0.333333 13:1 14:1
+0 1:-0.0416667 2:-1 3:0.333333 4:-0.207547 5:-0.680365 6:-1 7:1 8:0.496183 9:-1 10:-0.967742 12:-1 13:-1 14:1
+0 1:-0.0416667 2:1 3:-0.333333 4:-0.245283 5:-0.657534 6:-1 7:-1 8:0.328244 9:-1 10:-0.741935 11:-1 12:-0.333333 13:-1 14:1
+1 1:0.291667 2:1 3:1 4:-0.566038 5:-0.525114 6:1 7:-1 8:0.358779 9:1 10:-0.548387 11:-1 12:0.333333 13:1 14:1
+1 1:0.416667 2:-1 3:1 4:-0.735849 5:-0.347032 6:-1 7:-1 8:0.496183 9:1 10:-0.419355 12:0.333333 13:-1 14:1
+1 1:0.541667 2:1 3:1 4:-0.660377 5:-0.607306 6:-1 7:1 8:-0.0687023 9:1 10:-0.967742 11:-1 12:-0.333333 13:-1 14:1
+0 1:-0.458333 2:1 3:1 4:-0.132075 5:-0.543379 6:-1 7:-1 8:0.633588 9:-1 10:-1 11:-1 12:-1 13:-1 14:1
+1 1:0.458333 2:1 3:1 4:-0.509434 5:-0.452055 6:-1 7:1 8:-0.618321 9:1 10:-0.290323 11:1 12:-0.333333 13:-1 14:1
+0 1:0.0416667 2:1 3:0.333333 4:0.0566038 5:-0.515982 6:-1 7:1 8:0.435115 9:-1 10:-0.483871 11:-1 12:-1 13:1 14:1
+0 1:-0.291667 2:-1 3:0.333333 4:-0.0943396 5:-0.767123 6:-1 7:1 8:0.358779 9:1 10:-0.548387 11:1 12:-1 13:-1 14:1
+0 1:0.583333 2:-1 3:0.333333 4:0.0943396 5:-0.310502 6:-1 7:-1 8:0.541985 9:-1 10:-1 11:-1 12:-0.333333 13:-1 14:1
+1 1:0.125 2:1 3:1 4:-0.415094 5:-0.438356 6:1 7:1 8:0.114504 9:1 10:-0.612903 12:-0.333333 13:-1 14:1
+0 1:-0.791667 2:-1 3:-0.333333 4:-0.54717 5:-0.616438 6:-1 7:-1 8:0.847328 9:-1 10:-0.774194 11:-1 12:-1 13:-1 14:1
+0 1:0.166667 2:1 3:1 4:-0.283019 5:-0.630137 6:-1 7:-1 8:0.480916 9:1 10:-1 11:-1 12:-1 13:1 14:1
+1 1:0.458333 2:1 3:1 4:-0.0377358 5:-0.607306 6:-1 7:1 8:-0.0687023 9:-1 10:-0.354839 12:0.333333 13:0.5 14:1
+0 1:0.25 2:1 3:1 4:-0.169811 5:-0.3379 6:-1 7:1 8:0.694656 9:-1 10:-1 11:-1 12:-1 13:-1 14:1
+1 1:-0.125 2:1 3:0.333333 4:-0.132075 5:-0.511416 6:-1 7:-1 8:0.40458 9:-1 10:-0.806452 12:-0.333333 13:1 14:1
+0 1:-0.0833333 2:1 3:-1 4:-0.415094 5:-0.60274 6:-1 7:1 8:-0.175573 9:1 10:-0.548387 11:-1 12:-0.333333 13:-1 14:1
+1 1:0.0416667 2:1 3:-0.333333 4:0.849057 5:-0.283105 6:-1 7:1 8:0.89313 9:-1 10:-1 11:-1 12:-0.333333 13:1 14:1
+1 2:1 3:1 4:-0.45283 5:-0.287671 6:-1 7:-1 8:-0.633588 9:1 10:-0.354839 12:0.333333 13:1 14:1
+1 1:-0.0416667 2:1 3:1 4:-0.660377 5:-0.525114 6:-1 7:-1 8:0.358779 9:-1 10:-1 11:-1 12:-0.333333 13:-1 14:1
+1 1:-0.541667 2:1 3:1 4:-0.698113 5:-0.812785 6:-1 7:1 8:-0.343511 9:1 10:-0.354839 12:-1 13:1 14:1
+1 1:0.208333 2:1 3:0.333333 4:-0.283019 5:-0.552511 6:-1 7:1 8:0.557252 9:-1 10:0.0322581 11:-1 12:0.333333 13:1 14:1
+0 1:-0.5 2:-1 3:0.333333 4:-0.660377 5:-0.351598 6:-1 7:1 8:0.541985 9:1 10:-1 11:-1 12:-1 13:-1 14:1
+0 1:-0.5 2:1 3:0.333333 4:-0.660377 5:-0.43379 6:-1 7:-1 8:0.648855 9:-1 10:-1 11:-1 12:-1 13:-1 14:1
+0 1:-0.125 2:-1 3:0.333333 4:-0.509434 5:-0.575342 6:-1 7:-1 8:0.328244 9:-1 10:-0.483871 12:-1 13:-1 14:1
+0 1:0.0416667 2:-1 3:0.333333 4:-0.735849 5:-0.356164 6:-1 7:1 8:0.465649 9:-1 10:-1 11:-1 12:-1 13:-1 14:1
+0 1:0.458333 2:-1 3:1 4:-0.320755 5:-0.191781 6:-1 7:-1 8:-0.221374 9:-1 10:-0.354839 12:0.333333 13:-1 14:1
+0 1:-0.0833333 2:-1 3:0.333333 4:-0.320755 5:-0.406393 6:-1 7:1 8:0.19084 9:-1 10:-0.83871 11:-1 12:-1 13:-1 14:1
+0 1:-0.291667 2:-1 3:-0.333333 4:-0.792453 5:-0.643836 6:-1 7:-1 8:0.541985 9:-1 10:-1 11:-1 12:-1 13:-1 14:1
+1 1:0.0833333 2:1 3:1 4:-0.132075 5:-0.584475 6:-1 7:-1 8:-0.389313 9:1 10:0.806452 11:1 12:-1 13:1 14:1
+0 1:-0.333333 2:1 3:-0.333333 4:-0.358491 5:-0.16895 6:-1 7:1 8:0.51145 9:-1 10:-1 11:-1 12:-1 13:-1 14:1
+0 1:0.125 2:1 3:-1 4:-0.509434 5:-0.694064 6:-1 7:1 8:0.389313 9:-1 10:-0.387097 12:-1 13:1 14:1
+1 1:0.541667 2:-1 3:1 4:0.584906 5:-0.534247 6:1 7:-1 8:0.435115 9:1 10:-0.677419 12:0.333333 13:1 14:1
+1 1:-0.625 2:1 3:-1 4:-0.509434 5:-0.520548 6:-1 7:-1 8:0.694656 9:1 10:0.225806 12:-1 13:1 14:1
+1 1:0.375 2:-1 3:1 4:0.0566038 5:-0.461187 6:-1 7:-1 8:0.267176 9:1 10:-0.548387 12:-1 13:-1 14:1
+0 1:0.0833333 2:1 3:-0.333333 4:-0.320755 5:-0.378995 6:-1 7:-1 8:0.282443 9:-1 10:-1 11:-1 12:-1 13:-1 14:1
+1 1:0.208333 2:1 3:1 4:-0.358491 5:-0.392694 6:-1 7:1 8:-0.0992366 9:1 10:-0.0322581 12:0.333333 13:1 14:1
+0 1:-0.416667 2:1 3:1 4:-0.698113 5:-0.611872 6:-1 7:-1 8:0.374046 9:-1 10:-1 11:-1 12:-1 13:1 14:1
+0 1:0.458333 2:-1 3:1 4:0.622642 5:-0.0913242 6:-1 7:-1 8:0.267176 9:1 10:-1 11:-1 12:-1 13:-1 14:1
+0 1:-0.125 2:-1 3:1 4:-0.698113 5:-0.415525 6:-1 7:1 8:0.343511 9:-1 10:-1 11:-1 12:-1 13:-1 14:1
+0 2:1 3:0.333333 4:-0.320755 5:-0.675799 6:1 7:1 8:0.236641 9:-1 10:-0.612903 11:1 12:-1 13:-1 14:1
+0 1:-0.333333 2:-1 3:1 4:-0.169811 5:-0.497717 6:-1 7:1 8:0.236641 9:1 10:-0.935484 12:-1 13:-1 14:1
+1 1:0.5 2:1 3:-1 4:-0.169811 5:-0.287671 6:1 7:1 8:0.572519 9:-1 10:-0.548387 12:-0.333333 13:-1 14:1
+0 1:0.666667 2:1 3:-1 4:0.245283 5:-0.506849 6:1 7:1 8:-0.0839695 9:-1 10:-0.967742 12:-0.333333 13:-1 14:1
+1 1:0.666667 2:1 3:0.333333 4:-0.132075 5:-0.415525 6:-1 7:1 8:0.145038 9:-1 10:-0.354839 12:1 13:1 14:1
+1 1:0.583333 2:1 3:1 4:-0.886792 5:-0.210046 6:-1 7:1 8:-0.175573 9:1 10:-0.709677 12:0.333333 13:-1 14:1
+0 1:0.625 2:-1 3:0.333333 4:-0.509434 5:-0.611872 6:-1 7:1 8:-0.328244 9:-1 10:-0.516129 12:-1 13:-1 14:1
+0 1:-0.791667 2:1 3:-1 4:-0.54717 5:-0.744292 6:-1 7:1 8:0.572519 9:-1 10:-1 11:-1 12:-1 13:-1 14:1
+1 1:0.375 2:-1 3:1 4:-0.169811 5:-0.232877 6:1 7:-1 8:-0.465649 9:-1 10:-0.387097 12:1 13:-1 14:1
+1 1:-0.0833333 2:1 3:1 4:-0.132075 5:-0.214612 6:-1 7:-1 8:-0.221374 9:1 10:0.354839 12:1 13:1 14:1
+1 1:-0.291667 2:1 3:0.333333 4:0.0566038 5:-0.520548 6:-1 7:-1 8:0.160305 9:-1 10:0.16129 12:-1 13:-1 14:1
+1 1:0.583333 2:1 3:1 4:-0.415094 5:-0.415525 6:1 7:-1 8:0.40458 9:-1 10:-0.935484 12:0.333333 13:1 14:1
+0 1:-0.125 2:1 3:0.333333 4:-0.339623 5:-0.680365 6:-1 7:-1 8:0.40458 9:-1 10:-1 11:-1 12:-1 13:-1 14:1
+0 1:-0.458333 2:1 3:0.333333 4:-0.509434 5:-0.479452 6:1 7:-1 8:0.877863 9:-1 10:-0.741935 11:1 12:-1 13:1 14:1
+1 1:0.125 2:-1 3:1 4:-0.245283 5:0.292237 6:-1 7:1 8:0.206107 9:1 10:-0.387097 12:0.333333 13:1 14:1
+1 1:-0.5 2:1 3:1 4:-0.698113 5:-0.789954 6:-1 7:1 8:0.328244 9:-1 10:-1 11:-1 12:-1 13:1 14:1
+0 1:-0.458333 2:-1 3:1 4:-0.849057 5:-0.365297 6:-1 7:1 8:-0.221374 9:-1 10:-0.806452 12:-1 13:-1 14:1
+0 2:1 3:0.333333 4:-0.320755 5:-0.452055 6:1 7:1 8:0.557252 9:-1 10:-1 11:-1 12:1 13:-1 14:1
+0 1:-0.416667 2:1 3:0.333333 4:-0.320755 5:-0.136986 6:-1 7:-1 8:0.389313 9:-1 10:-0.387097 11:-1 12:-0.333333 13:-1 14:1
+1 1:0.125 2:1 3:1 4:-0.283019 5:-0.73516 6:-1 7:1 8:-0.480916 9:1 10:-0.322581 12:-0.333333 13:0.5 14:1
+0 1:-0.0416667 2:1 3:1 4:-0.735849 5:-0.511416 6:1 7:-1 8:0.160305 9:-1 10:-0.967742 11:-1 12:1 13:1 14:1
+0 1:0.375 2:-1 3:1 4:-0.132075 5:0.223744 6:-1 7:1 8:0.312977 9:-1 10:-0.612903 12:-1 13:-1 14:1
+1 1:0.708333 2:1 3:0.333333 4:0.245283 5:-0.347032 6:-1 7:-1 8:-0.374046 9:1 10:-0.0645161 12:-0.333333 13:1 14:1
+0 1:0.0416667 2:1 3:1 4:-0.132075 5:-0.484018 6:-1 7:-1 8:0.358779 9:-1 10:-0.612903 11:-1 12:-1 13:-1 14:1
+1 1:0.708333 2:1 3:1 4:-0.0377358 5:-0.780822 6:-1 7:-1 8:-0.175573 9:1 10:-0.16129 11:1 12:-1 13:1 14:1
+0 1:0.0416667 2:1 3:-0.333333 4:-0.735849 5:-0.164384 6:-1 7:-1 8:0.29771 9:-1 10:-1 11:-1 12:-1 13:1 14:1
+1 1:-0.75 2:1 3:1 4:-0.396226 5:-0.287671 6:-1 7:1 8:0.29771 9:1 10:-1 11:-1 12:-1 13:1 14:1
+0 1:-0.208333 2:1 3:0.333333 4:-0.433962 5:-0.410959 6:1 7:-1 8:0.587786 9:-1 10:-1 11:-1 12:0.333333 13:-1 14:1
+0 1:0.0833333 2:-1 3:-0.333333 4:-0.226415 5:-0.43379 6:-1 7:1 8:0.374046 9:-1 10:-0.548387 12:-1 13:-1 14:1
+0 1:0.208333 2:-1 3:1 4:-0.886792 5:-0.442922 6:-1 7:1 8:-0.221374 9:-1 10:-0.677419 12:-1 13:-1 14:1
+0 1:0.0416667 2:-1 3:0.333333 4:-0.698113 5:-0.598174 6:-1 7:-1 8:0.328244 9:-1 10:-0.483871 12:-1 13:-1 14:1
+0 1:0.666667 2:-1 3:-1 4:-0.132075 5:-0.484018 6:-1 7:-1 8:0.221374 9:-1 10:-0.419355 11:-1 12:0.333333 13:-1 14:1
+1 1:1 2:1 3:1 4:-0.415094 5:-0.187215 6:-1 7:1 8:0.389313 9:1 10:-1 11:-1 12:1 13:-1 14:1
+0 1:0.625 2:1 3:0.333333 4:-0.54717 5:-0.310502 6:-1 7:-1 8:0.221374 9:-1 10:-0.677419 11:-1 12:-0.333333 13:1 14:1
+1 1:0.208333 2:1 3:1 4:-0.415094 5:-0.205479 6:-1 7:1 8:0.526718 9:-1 10:-1 11:-1 12:0.333333 13:1 14:1
+1 1:0.291667 2:1 3:1 4:-0.415094 5:-0.39726 6:-1 7:1 8:0.0687023 9:1 10:-0.0967742 12:-0.333333 13:1 14:1
+1 1:-0.0833333 2:1 3:1 4:-0.132075 5:-0.210046 6:-1 7:-1 8:0.557252 9:1 10:-0.483871 11:-1 12:-1 13:1 14:1
+1 1:0.0833333 2:1 3:1 4:0.245283 5:-0.255708 6:-1 7:1 8:0.129771 9:1 10:-0.741935 12:-0.333333 13:1 14:1
+0 1:-0.0416667 2:1 3:-1 4:0.0943396 5:-0.214612 6:1 7:-1 8:0.633588 9:-1 10:-0.612903 12:-1 13:1 14:1
+0 1:0.291667 2:-1 3:0.333333 4:-0.849057 5:-0.123288 6:-1 7:-1 8:0.358779 9:-1 10:-1 11:-1 12:-0.333333 13:-1 14:1
+0 1:0.208333 2:1 3:0.333333 4:-0.792453 5:-0.479452 6:-1 7:1 8:0.267176 9:1 10:-0.806452 12:-1 13:1 14:1
+1 1:0.458333 2:1 3:0.333333 4:-0.415094 5:-0.164384 6:-1 7:-1 8:-0.0839695 9:1 10:-0.419355 12:-1 13:1 14:1
+0 1:-0.666667 2:1 3:0.333333 4:-0.320755 5:-0.43379 6:-1 7:-1 8:0.770992 9:-1 10:0.129032 11:1 12:-1 13:-1 14:1
+1 1:0.25 2:1 3:-1 4:0.433962 5:-0.260274 6:-1 7:1 8:0.343511 9:-1 10:-0.935484 12:-1 13:1 14:1
+0 1:-0.0833333 2:1 3:0.333333 4:-0.415094 5:-0.456621 6:1 7:1 8:0.450382 9:-1 10:-0.225806 12:-1 13:-1 14:1
+0 1:-0.416667 2:-1 3:0.333333 4:-0.471698 5:-0.60274 6:-1 7:-1 8:0.435115 9:-1 10:-0.935484 12:-1 13:-1 14:1
+1 1:0.208333 2:1 3:1 4:-0.358491 5:-0.589041 6:-1 7:1 8:-0.0839695 9:1 10:-0.290323 12:1 13:1 14:1
+0 1:-1 2:1 3:-0.333333 4:-0.320755 5:-0.643836 6:-1 7:1 8:1 9:-1 10:-1 11:-1 12:-1 13:-1 14:1
+0 1:-0.5 2:-1 3:-0.333333 4:-0.320755 5:-0.643836 6:-1 7:1 8:0.541985 9:-1 10:-0.548387 11:-1 12:-1 13:-1 14:1
+0 1:0.416667 2:-1 3:0.333333 4:-0.226415 5:-0.424658 6:-1 7:1 8:0.541985 9:-1 10:-1 11:-1 12:-1 13:-1 14:1
+0 1:-0.0833333 2:1 3:0.333333 4:-1 5:-0.538813 6:-1 7:-1 8:0.267176 9:1 10:-1 11:-1 12:-0.333333 13:1 14:1
+0 1:0.0416667 2:1 3:0.333333 4:-0.509434 5:-0.39726 6:-1 7:1 8:0.160305 9:-1 10:-0.870968 12:-1 13:1 14:1
+0 1:-0.375 2:1 3:-0.333333 4:-0.509434 5:-0.570776 6:-1 7:-1 8:0.51145 9:-1 10:-1 11:-1 12:-1 13:-1 14:1
+1 1:0.0416667 2:1 3:1 4:-0.698113 5:-0.484018 6:-1 7:-1 8:-0.160305 9:1 10:-0.0967742 12:-0.333333 13:1 14:1
+1 1:0.5 2:1 3:1 4:-0.226415 5:-0.415525 6:-1 7:1 8:-0.145038 9:-1 10:-0.0967742 12:-0.333333 13:1 14:1
+0 1:0.166667 2:1 3:0.333333 4:0.0566038 5:-0.808219 6:-1 7:-1 8:0.572519 9:-1 10:-0.483871 11:-1 12:-1 13:-1 14:1
+1 1:0.416667 2:1 3:1 4:-0.320755 5:-0.0684932 6:1 7:1 8:-0.0687023 9:1 10:-0.419355 11:-1 12:1 13:1 14:1
+0 1:-0.75 2:-1 3:1 4:-0.169811 5:-0.739726 6:-1 7:-1 8:0.694656 9:-1 10:-0.548387 11:-1 12:-1 13:-1 14:1
+0 1:-0.5 2:1 3:-0.333333 4:-0.226415 5:-0.648402 6:-1 7:-1 8:-0.0687023 9:-1 10:-1 12:-1 13:0.5 14:1
+1 1:0.375 2:-1 3:0.333333 4:-0.320755 5:-0.374429 6:-1 7:-1 8:-0.603053 9:-1 10:-0.612903 12:-0.333333 13:1 14:1
+1 1:-0.416667 2:-1 3:1 4:-0.283019 5:-0.0182648 6:1 7:1 8:-0.00763359 9:1 10:-0.0322581 12:-1 13:1 14:1
+0 1:0.208333 2:-1 3:-1 4:0.0566038 5:-0.283105 6:1 7:1 8:0.389313 9:-1 10:-0.677419 11:-1 12:-1 13:-1 14:1
+0 1:-0.0416667 2:1 3:-1 4:-0.54717 5:-0.726027 6:-1 7:1 8:0.816794 9:-1 10:-1 12:-1 13:0.5 14:1
+1 1:0.333333 2:-1 3:1 4:-0.0377358 5:-0.173516 6:-1 7:1 8:0.145038 9:1 10:-0.677419 12:-1 13:1 14:1
+1 1:-0.583333 2:1 3:1 4:-0.54717 5:-0.575342 6:-1 7:-1 8:0.0534351 9:-1 10:-0.612903 12:-1 13:1 14:1
+0 1:-0.333333 2:1 3:1 4:-0.603774 5:-0.388128 6:-1 7:1 8:0.740458 9:-1 10:-1 11:-1 12:-1 13:-1 14:1
+1 1:-0.0416667 2:1 3:1 4:-0.358491 5:-0.410959 6:-1 7:-1 8:0.374046 9:1 10:-1 11:-1 12:-0.333333 13:1 14:1
+0 1:0.375 2:1 3:0.333333 4:-0.320755 5:-0.520548 6:-1 7:-1 8:0.145038 9:-1 10:-0.419355 12:1 13:1 14:1
+1 1:0.375 2:-1 3:1 4:0.245283 5:-0.826484 6:-1 7:1 8:0.129771 9:-1 10:1 11:1 12:1 13:1 14:1
+0 2:-1 3:1 4:-0.169811 5:-0.506849 6:-1 7:1 8:0.358779 9:-1 10:-1 11:-1 12:-1 13:-1 14:1
+1 1:-0.416667 2:1 3:1 4:-0.509434 5:-0.767123 6:-1 7:1 8:-0.251908 9:1 10:-0.193548 12:-1 13:1 14:1
+0 1:-0.25 2:1 3:0.333333 4:-0.169811 5:-0.401826 6:-1 7:1 8:0.29771 9:-1 10:-1 11:-1 12:-1 13:-1 14:1
+0 1:-0.0416667 2:1 3:-0.333333 4:-0.509434 5:-0.0913242 6:-1 7:-1 8:0.541985 9:-1 10:-0.935484 11:-1 12:-1 13:-1 14:1
+1 1:0.625 2:1 3:0.333333 4:0.622642 5:-0.324201 6:1 7:1 8:0.206107 9:1 10:-0.483871 12:-1 13:1 14:1
+0 1:-0.583333 2:1 3:0.333333 4:-0.132075 5:-0.109589 6:-1 7:1 8:0.694656 9:-1 10:-1 11:-1 12:-1 13:-1 14:1
+0 2:-1 3:1 4:-0.320755 5:-0.369863 6:-1 7:1 8:0.0992366 9:-1 10:-0.870968 12:-1 13:-1 14:1
+1 1:0.375 2:-1 3:1 4:-0.132075 5:-0.351598 6:-1 7:1 8:0.358779 9:-1 10:0.16129 11:1 12:0.333333 13:-1 14:1
+0 1:-0.0833333 2:-1 3:0.333333 4:-0.132075 5:-0.16895 6:-1 7:1 8:0.0839695 9:-1 10:-0.516129 11:-1 12:-0.333333 13:-1 14:1
+1 1:0.291667 2:1 3:1 4:-0.320755 5:-0.420091 6:-1 7:-1 8:0.114504 9:1 10:-0.548387 11:-1 12:-0.333333 13:1 14:1
+1 1:0.5 2:1 3:1 4:-0.698113 5:-0.442922 6:-1 7:1 8:0.328244 9:-1 10:-0.806452 11:-1 12:0.333333 13:0.5 14:1
+0 1:0.5 2:-1 3:0.333333 4:0.150943 5:-0.347032 6:-1 7:-1 8:0.175573 9:-1 10:-0.741935 11:-1 12:-1 13:-1 14:1
+1 1:0.291667 2:1 3:0.333333 4:-0.132075 5:-0.730594 6:-1 7:1 8:0.282443 9:-1 10:-0.0322581 12:-1 13:-1 14:1
+1 1:0.291667 2:1 3:1 4:-0.0377358 5:-0.287671 6:-1 7:1 8:0.0839695 9:1 10:-0.0967742 12:0.333333 13:1 14:1
+1 1:0.0416667 2:1 3:1 4:-0.509434 5:-0.716895 6:-1 7:-1 8:-0.358779 9:-1 10:-0.548387 12:-0.333333 13:1 14:1
+0 1:-0.375 2:1 3:-0.333333 4:-0.320755 5:-0.575342 6:-1 7:1 8:0.78626 9:-1 10:-1 11:-1 12:-1 13:-1 14:1
+1 1:-0.375 2:1 3:1 4:-0.660377 5:-0.251142 6:-1 7:1 8:0.251908 9:-1 10:-1 11:-1 12:-0.333333 13:-1 14:1
+0 1:-0.0833333 2:1 3:0.333333 4:-0.698113 5:-0.776256 6:-1 7:-1 8:-0.206107 9:-1 10:-0.806452 11:-1 12:-1 13:-1 14:1
+0 1:0.25 2:1 3:0.333333 4:0.0566038 5:-0.607306 6:1 7:-1 8:0.312977 9:-1 10:-0.483871 11:-1 12:-1 13:-1 14:1
+0 1:0.75 2:-1 3:-0.333333 4:0.245283 5:-0.196347 6:-1 7:-1 8:0.389313 9:-1 10:-0.870968 11:-1 12:0.333333 13:-1 14:1
+0 1:0.333333 2:1 3:0.333333 4:0.0566038 5:-0.465753 6:1 7:-1 8:0.00763359 9:1 10:-0.677419 12:-1 13:-1 14:1
+1 1:0.0833333 2:1 3:1 4:-0.283019 5:0.0365297 6:-1 7:-1 8:-0.0687023 9:1 10:-0.612903 12:-0.333333 13:1 14:1
+1 1:0.458333 2:1 3:0.333333 4:-0.132075 5:-0.0456621 6:-1 7:-1 8:0.328244 9:-1 10:-1 11:-1 12:-1 13:-1 14:1
+0 1:-0.416667 2:1 3:1 4:0.0566038 5:-0.447489 6:-1 7:-1 8:0.526718 9:-1 10:-0.516129 11:-1 12:-1 13:-1 14:1
+0 1:0.208333 2:-1 3:0.333333 4:-0.509434 5:-0.0228311 6:-1 7:-1 8:0.541985 9:-1 10:-1 11:-1 12:-1 13:-1 14:1
+1 1:0.291667 2:1 3:1 4:-0.320755 5:-0.634703 6:-1 7:1 8:-0.0687023 9:1 10:-0.225806 12:0.333333 13:1 14:1
+1 1:0.208333 2:1 3:-0.333333 4:-0.509434 5:-0.278539 6:-1 7:1 8:0.358779 9:-1 10:-0.419355 12:-1 13:-1 14:1
+0 1:-0.166667 2:1 3:-0.333333 4:-0.320755 5:-0.360731 6:-1 7:-1 8:0.526718 9:-1 10:-0.806452 11:-1 12:-1 13:-1 14:1
+1 1:-0.208333 2:1 3:-0.333333 4:-0.698113 5:-0.52968 6:-1 7:-1 8:0.480916 9:-1 10:-0.677419 11:1 12:-1 13:1 14:1
+0 1:-0.0416667 2:1 3:0.333333 4:0.471698 5:-0.666667 6:1 7:-1 8:0.389313 9:-1 10:-0.83871 11:-1 12:-1 13:1 14:1
+0 1:-0.375 2:1 3:-0.333333 4:-0.509434 5:-0.374429 6:-1 7:-1 8:0.557252 9:-1 10:-1 11:-1 12:-1 13:1 14:1
+0 1:0.125 2:-1 3:-0.333333 4:-0.132075 5:-0.232877 6:-1 7:1 8:0.251908 9:-1 10:-0.580645 12:-1 13:-1 14:1
+0 1:0.166667 2:1 3:1 4:-0.132075 5:-0.69863 6:-1 7:-1 8:0.175573 9:-1 10:-0.870968 12:-1 13:0.5 14:1
+1 1:0.583333 2:1 3:1 4:0.245283 5:-0.269406 6:-1 7:1 8:-0.435115 9:1 10:-0.516129 12:1 13:-1 14:1
diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala
index 9b2340a1f16f..6ef38cde8e58 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala
@@ -341,11 +341,11 @@ class LogisticRegression @Since("1.2.0") (
                 regParamL1
               } else {
                 // If `standardization` is false, we still standardize the data
-                // to improve the rate of convergence; as a result, we have to
-                // perform this reverse standardization by penalizing each component
-                // differently to get effectively the same objective function when
+                // to improve the rate of convergence unless the standard deviation is zero;
+                // as a result, we have to perform this reverse standardization by penalizing
+                // each component differently to get effectively the same objective function when
                 // the training dataset is not standardized.
-                if (featuresStd(index) != 0.0) regParamL1 / featuresStd(index) else 0.0
+                if (featuresStd(index) != 0.0) regParamL1 / featuresStd(index) else regParamL1
               }
             }
           }
@@ -417,7 +417,7 @@ class LogisticRegression @Since("1.2.0") (
         val rawCoefficients = state.x.toArray.clone()
         var i = 0
         while (i < numFeatures) {
-          rawCoefficients(i) *= { if (featuresStd(i) != 0.0) 1.0 / featuresStd(i) else 0.0 }
+          rawCoefficients(i) *= { if (featuresStd(i) != 0.0) 1.0 / featuresStd(i) else 1.0 }
           i += 1
         }
 
@@ -971,8 +971,12 @@ private class LogisticAggregator(
           val margin = - {
             var sum = 0.0
             features.foreachActive { (index, value) =>
-              if (featuresStd(index) != 0.0 && value != 0.0) {
-                sum += localCoefficientsArray(index) * (value / featuresStd(index))
+              if (value != 0.0) {
+                if (featuresStd(index) != 0.0) {
+                  sum += localCoefficientsArray(index) * (value / featuresStd(index))
+                } else {
+                  sum += localCoefficientsArray(index) * value
+                }
               }
             }
             sum + {
@@ -983,8 +987,12 @@ private class LogisticAggregator(
           val multiplier = weight * (1.0 / (1.0 + math.exp(margin)) - label)
 
           features.foreachActive { (index, value) =>
-            if (featuresStd(index) != 0.0 && value != 0.0) {
-              localGradientSumArray(index) += multiplier * (value / featuresStd(index))
+            if (value != 0.0) {
+              if (featuresStd(index) != 0.0) {
+                localGradientSumArray(index) += multiplier * (value / featuresStd(index))
+              } else {
+                localGradientSumArray(index) += multiplier * value
+              }
             }
           }
 
@@ -1106,7 +1114,8 @@ private class LogisticCostFun(
                 totalGradientArray(index) += regParamL2 * temp
                 value * temp
               } else {
-                0.0
+                totalGradientArray(index) += regParamL2 * value
+                value * value
               }
             }
           }
diff --git a/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala
index 972c0868a454..f8b3651f064a 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala
@@ -607,6 +607,73 @@ class LogisticRegressionSuite
     assert(model2.coefficients ~= coefficientsR2 relTol 1E-2)
   }
 
+
+  test("an extra large example for review only") {
+    val trainer1 = (new LogisticRegression).setFitIntercept(false)
+      .setElasticNetParam(0.0)
+      .setRegParam(1)
+      .setStandardization(false)
+      .setMaxIter(1000)
+      .setTol(1e-9)
+
+    val binaryDatasetWithUniqueColumn = sqlContext.read
+      .format("libsvm")
+      .load("../data/mllib/sample_libsvm_data_with_unique_column.txt")
+
+    val model1 = trainer1.fit(binaryDatasetWithUniqueColumn)
+
+    val interceptR1 = 0.0
+    val coefficientsR1 = Vectors.dense(0.0301002746509743, 0.0906099616129797, 0.0954855492088332,
+      0.0243782420594917, 0.0174024017667667, -0.0006549273929309,
+      0.0637250665085166, -0.0589532651377124, 0.1383368129434264,
+      0.0665749825701113, 0.0799386779781182, 0.1198682685242071,
+      0.1802933312643371, -0.0124797701753129)
+
+    assert(model1.intercept ~== interceptR1 absTol 1E-3)
+    assert(model1.coefficients ~= coefficientsR1 relTol 1E-2)
+  }
+
+  test("binary logistic regression without intercept with L2 regularizationon " +
+    "data with a constant column without intercept") {
+    /*
+        Use the following scikit-learn Python code to get a reference result:
+
+        import numpy as np
+        from sklearn.datasets import load_svmlight_file
+        from sklearn.linear_model import LogisticRegression
+        x_train = np.array([[1, 1], [0, 1]])
+        y_train = np.array([1, 0])
+        model = LogisticRegression(tol=1e-9, C=0.5, max_iter=1000, fit_intercept=False) \
+            .fit(x_train, y_train)
+        print model.coef_
+   */
+
+    val trainer = (new LogisticRegression).setFitIntercept(false)
+      .setElasticNetParam(0.0)
+      .setRegParam(1)
+      .setStandardization(false)
+      .setMaxIter(1000)
+      .setTol(1e-9)
+
+    val binaryDatasetWithUniqueColumn = sqlContext.createDataFrame(
+      sc.parallelize(
+        Array(
+          LabeledPoint(label = 1.0, features = Vectors.dense(1, 1)),
+          LabeledPoint(label = 0.0, features = Vectors.dense(0, 1))
+        )
+      )
+    )
+
+    val model = trainer.fit(binaryDatasetWithUniqueColumn)
+
+    val interceptR = 0.0
+    val coefficientsR = Vectors.dense(0.22478867, -0.02241016)
+
+    assert(model.intercept ~== interceptR absTol 1E-3)
+    assert(model.coefficients ~= coefficientsR relTol 1E-2)
+  }
+
+
   test("binary logistic regression with intercept with ElasticNet regularization") {
     val trainer1 = (new LogisticRegression).setFitIntercept(true)
       .setElasticNetParam(0.38).setRegParam(0.21).setStandardization(true)