#!/usr/bin/python
import xgboost as xgb
import numpy as np

xgb.rabit.init()

X = [
  [15.00,28.90,29.00,3143.70,0.00,0.10,69.90,90.00,13726.07,0.00,2299.70,0.00,0.05,
   4327.03,0.00,24.00,0.18,3.00,0.41,3.77,0.00,0.00,4.00,0.00,150.92,0.00,2.00,0.00,
   0.01,138.00,1.00,0.02,69.90,0.00,0.83,5.00,0.01,0.12,47.30,0.00,296.00,0.16,0.00,
   0.00,27.70,7.00,7.25,4406.16,1.00,0.54,245.28,3.00,0.06,306.50,5143.00,29.00,23.74,
   548.00,2.00,68.00,70.90,25.45,0.39,0.00,0.01,497.11,0.00,42.00,83.00,4.00,0.00,1.00,
   0.00,104.35,94.12,0.03,79.23,237.69,1.00,0.04,0.01,0.02,2.00,108.81,7.00,12.00,0.46,
   31.00,0.00,0.15,74.59,0.00,19.50,0.00,0.75,0.06,0.08,118.00,35.90,0.01,0.07,1.00,
   0.03,81.18,13.33,0.00,0.00,0.00,0.00,0.00,0.41,0.00,0.15,57.00,0.00,22.00,449.68,
   0.00,0.00,2.00,195.26,51.58,306.50,0.10,1.00,0.00,258.00,21.00,0.43,3.00,16.00,0.00,
   0.00,0.00,0.00,1.00,74.51,4.00,0.02,35.90,30.00,8.69,0.00,0.36,5.00,2.00,3.00,0.26,
   9.50,8.00,11.00,11918.15,0.00,258.00,13.00,9.04,0.14,604.65,0.92,74.59,0.00,0.00,
   72.76,1.00,0.22,64.00,2.00,0.00,0.00,0.02,0.00,305.50,27.70,0.02,0.00,177.00,14.00,
   0.00,0.05,90.00,0.03,0.00,1.00,0.43,4.00,0.05,0.09,431.00,0.00,2.00,0.00,0.00,1.00,
   0.25,0.17,0.00,0.00,21.00,94.12,0.17,0.00,0.00,0.00,548.00,0.00,68.00,0.00,0.00,9.50,
   25.45,1390.31,7.00,0.00,2.00,310.70,0.00,0.01,0.01,0.03,81.40,1.00,0.02,0.00,9.00,
   6.00,0.00,175.76,36.00,0.00,20.75,2.00,0.00,0.00,0.00,0.22,74.16,0.10,56.81,0.00,
   2197.03,0.00,197.66,0.00,55.00,20.00,367.18,22.00,0.00,0.01,1510.26,0.24,0.00,0.01,
   0.00,11.00,278.10,61.70,278.10,0.00,0.08,0.57,1.00,0.65,255.60,0.00,0.86,0.25,70.95,
   2299.70,0.23,0.05,92.70,1.00,38.00,0.00,0.00,56.81,21.85,0.00,23.74,0.00,2.00,0.03,
   2.00,0.00,347.58,30.00,243.55,109.00,0.00,296.00,6.00,6.00,0.00,0.00,109.00,2299.70,
   0.00,0.01,0.08,1.00,4745.09,4.00,0.18,0.00,0.17,0.02,0.00,1.00,147.13,71.07,2115.16,
   0.00,0.26,0.00,43.00,604.90,49.44,4327.03,0.68,0.75,0.10,86.36,52.98,0.20,0.00,22.50,
   305.50,0.00,1.00,0.00,7.00,0.78,0.00,296.00,22.50,0.00,5.00,2979.54,1.00,14.00,51.00,
   0.42,0.11,0.00,1.00,0.00,0.00,70.90,37.84,0.02,548.40,0.00,46.35,5.00,1.66,0.29,0.00,
   0.02,2255.69,160.53,790.64,6775.15,0.68,19.50,2299.70,79.87,6.00,0.00,60.00,0.27,
   233.77,10.00,0.00,0.00,23.00,82.27,1.00,0.00,1.00,0.42,1.00,0.01,0.40,0.41,9.50,2299.70,
   46.30,0.00,0.00,2299.70,3.00,0.00,0.00,83.00,1.00],
  [48.00,80.89,69.90,11570.00,26.00,0.40,468.00,0.00,5739.46,0.00,1480.00,90.89,0.00,
   14042.09,3600.08,120.00,0.09,31.00,0.25,2.36,0.00,7.00,22.00,0.00,257.59,0.00,6.00,
   260.00,0.05,313.00,1.00,0.07,468.00,0.00,0.67,11.00,0.02,0.32,0.00,0.00,1387.61,0.34,
   0.00,0.00,158.04,6.00,13.98,12380.05,0.00,0.16,122.74,3.00,0.18,291.33,7517.79,124.00,
   45.08,900.00,1.00,0.00,577.25,79.75,0.39,0.00,0.00,244.62,0.00,57.00,178.00,19.00,
   0.00,1.00,386.10,103.51,480.00,0.06,129.41,334.31,1.00,0.06,0.00,0.06,3.00,125.55,
   0.00,76.00,0.14,30.00,0.00,0.03,411.29,791.33,55.00,0.12,3.80,0.07,0.01,188.00,221.11,
   0.01,0.15,1.00,0.18,144.32,15.00,0.00,0.05,0.00,3.00,0.00,0.20,0.00,0.14,62.00,0.06,
   55.00,239.35,0.00,0.00,2.00,534.20,747.50,400.57,0.40,0.00,0.00,219.98,30.00,0.25,
   1.00,70.00,0.02,0.04,0.00,0.00,7.00,747.50,8.67,0.06,271.01,28.00,5.63,75.39,0.46,
   11.00,3.00,19.00,0.38,131.74,23.00,39.00,30249.41,0.00,202.68,2.00,64.94,0.03,2787.68,
   0.54,35.00,0.02,106.03,25.00,1.00,0.10,45.00,2.00,0.00,0.00,0.00,0.00,449.27,172.38,
   0.05,0.00,550.00,130.00,2006.55,0.07,0.00,0.03,0.00,5.00,0.21,22.00,0.05,0.01,1011.40,
   0.00,4.00,3600.08,0.00,1.00,1.00,1.00,0.00,3.00,9.00,270.00,0.12,0.03,0.00,0.00,820.00,
   1827.50,0.00,100.33,0.00,131.74,53.16,9557.97,7.00,0.00,11.00,180.81,0.00,0.01,0.04,
   0.02,1480.00,0.92,0.05,0.00,15.00,6.00,0.00,161.42,28.00,169.00,35.60,4.00,0.12,0.00,
   0.00,0.27,230.56,0.42,171.90,0.00,28407.51,1.00,883.10,0.00,261.00,9.00,1031.67,38.00,
   0.00,0.04,1607.68,0.32,791.33,0.04,1403.00,2.00,2260.50,88.08,2260.50,0.00,0.12,0.75,
   3.00,0.00,1231.68,0.07,0.60,0.24,0.00,0.00,0.15,0.14,753.50,1.00,95.00,7.00,0.26,
   77.63,38.45,0.00,42.65,0.00,14.00,0.07,6.00,0.00,1911.59,43.00,386.77,1324.80,0.00,
   518.00,10.00,10.00,0.11,0.00,1324.80,0.00,0.00,0.02,0.16,1.00,10492.12,5.00,0.94,
   5.00,0.08,0.10,1.00,0.92,3731.49,105.81,6931.39,0.00,0.43,0.00,118.00,5323.71,81.66,
   14042.09,0.08,0.20,0.40,96.64,0.00,0.08,4.00,1028.82,353.00,0.00,2.00,32.00,43.00,
   5.16,75.39,900.00,232.10,3.00,5.00,6049.88,1.00,126.00,46.00,0.59,0.15,0.00,8.00,
   7.00,0.00,577.25,0.00,0.07,2415.10,0.00,83.72,9.00,1.76,0.20,0.00,0.17,3278.65,155.26,
   4415.50,22731.62,1.00,55.00,0.00,499.94,22.00,0.58,67.00,0.21,341.72,16.00,0.00,965.07,
   17.00,138.41,0.00,0.00,1.00,0.14,1.00,0.02,0.35,1.69,369.00,1300.00,25.00,0.00,0.01,
   0.00,0.00,0.00,0.00,52.00,8.00]]
X = np.array(X)
y = [1, 0]

dtrain = xgb.DMatrix(X, label=y)

param = {'max_depth': 2, 'eta': 1, 'objective': 'binary:logistic' }
watchlist  = [(dtrain,'train')]
num_round = 2
bst = xgb.train(param, dtrain, num_round, watchlist)

if xgb.rabit.get_rank() == 0:
  bst.save_model("test_issue3402.model")
  xgb.rabit.tracker_print("Finished training\n")

# Notify the tracker all training has been successful
# This is only needed in distributed training.
xgb.rabit.finalize()