alyato

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)

alyato

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,

alyato

旧石器时代

中石器时期

黄河文明

长江文明

新石器时期

alyato

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

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