Update demo scripts to use installed python library

demo/guide-python/generalized_linear_model.py

@@ -1,6 +1,4 @@
 #!/usr/bin/python
-import sys
-sys.path.append('../../wrapper')
 import xgboost as xgb
 ##
 #  this script demonstrate how to fit generalized linear model in xgboost
@@ -9,17 +7,17 @@ import xgboost as xgb
 dtrain = xgb.DMatrix('../data/agaricus.txt.train')
 dtest = xgb.DMatrix('../data/agaricus.txt.test')
 # change booster to gblinear, so that we are fitting a linear model
-# alpha is the L1 regularizer 
+# alpha is the L1 regularizer
 # lambda is the L2 regularizer
 # you can also set lambda_bias which is L2 regularizer on the bias term
 param = {'silent':1, 'objective':'binary:logistic', 'booster':'gblinear',
          'alpha': 0.0001, 'lambda': 1 }
 
 # normally, you do not need to set eta (step_size)
-# XGBoost uses a parallel coordinate descent algorithm (shotgun), 
+# XGBoost uses a parallel coordinate descent algorithm (shotgun),
 # there could be affection on convergence with parallelization on certain cases
 # setting eta to be smaller value, e.g 0.5 can make the optimization more stable
-# param['eta'] = 1 
+# param['eta'] = 1
 
 ##
 # the rest of settings are the same