From 669a387c9941b1bc4823f03b7271488087f170a3 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=E9=97=BB=E6=B3=A2?= <813577991@qq.com>
Date: Fri, 19 Aug 2016 03:41:18 +0800
Subject: [PATCH] fix a typo and some code format (#1470)

* fix a typo

* fix a typo and some code format
---
 demo/guide-python/custom_objective.py | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/demo/guide-python/custom_objective.py b/demo/guide-python/custom_objective.py
index d2bd4d9b2..86f69d233 100755
--- a/demo/guide-python/custom_objective.py
+++ b/demo/guide-python/custom_objective.py
@@ -2,9 +2,9 @@
 import numpy as np
 import xgboost as xgb
 ###
-# advanced: cutomsized loss function
+# advanced: customized loss function
 #
-print ('start running example to used cutomized objective function')
+print ('start running example to used customized objective function')
 
 dtrain = xgb.DMatrix('../data/agaricus.txt.train')
 dtest = xgb.DMatrix('../data/agaricus.txt.test')
@@ -12,12 +12,12 @@ dtest = xgb.DMatrix('../data/agaricus.txt.test')
 # note: for customized objective function, we leave objective as default
 # note: what we are getting is margin value in prediction
 # you must know what you are doing
-param = {'max_depth':2, 'eta':1, 'silent':1 }
-watchlist  = [(dtest,'eval'), (dtrain,'train')]
+param = {'max_depth': 2, 'eta': 1, 'silent': 1}
+watchlist = [(dtest, 'eval'), (dtrain, 'train')]
 num_round = 2
 
 # user define objective function, given prediction, return gradient and second order gradient
-# this is loglikelihood loss
+# this is log likelihood loss
 def logregobj(preds, dtrain):
     labels = dtrain.get_label()
     preds = 1.0 / (1.0 + np.exp(-preds))