alyato

Clone the recursive repo for xgboost

git clone --recursive https://github.com/dmlc/xgboost
cd xgboost
make -j4

cd in the folder: xgboost/python-package

学会基本操作后,值得细究的Mode:

CC Mode, 写C/C++代码, 管理缩进,高亮
Cperl Mode, 写Perl代码, 比Perl Mode更强.
Python Mode
eshell, M-x shell RET
Diff Mode / Ediff Mode, 看diff, 做merge操作.
VC, 与版本管理系统相接合,支持CVS, SVN, Git etc.
Hippie Expand, M-/
Dired Mode, 文件, 目录管理. copy, mv, rename. create

Training phase

20000 training instances The number of support vectors is around 15000, which means that most of the training data are near the separating hyper-plane. The training accuracy is around 80%

Test on full data

Accuracy = 52.3734% (662/1264) (classification)
Accuracy = 62.5791% (791/1264) (classification)
Accuracy = 61.0759% (772/1264) (classification)
Accuracy = 60.9968% (771/1264) (classification)

	旧石器时代

	中石器时期

	黄河文明

	长江文明

	新石器时期

	import numpy as np

	from keras.datasets import imdb
	from keras.preprocessing.sequence import pad_sequences
	from keras.models import Sequential
	from keras.layers import containers
	from keras.layers.noise import GaussianNoise
	from keras.layers.core import Dense, AutoEncoder
	from keras.utils import np_utils
	from sklearn.metrics import (precision_score, recall_score,