Jzzhou nlpjoe

\begin{equation}
\left\{
             \begin{array}{lr}
             x=\dfrac{3\pi}{2}(1+2t)\cos(\dfrac{3\pi}{2}(1+2t)), &amp;  \\
             y=s, &amp; 0\leq s\leq L,|t|\leq1.\\
             z=\dfrac{3\pi}{2}(1+2t)\sin(\dfrac{3\pi}{2}(1+2t)), &amp;  
             \end{array}
\right.
\end{equation}

参考文章： https://www.jianshu.com/p/d908ce81017a

https://www.cnblogs.com/algorithmchangeworld/p/7393156.html

cmake -G "Unix Makefiles" -DPYTHON_LIBRARY=~/anaconda3/lib/libpython3.6m.so -DPYTHON_INCLUDE_DIR=~/anaconda3/include -DUSE_PYTHON2=OFF -DUSE_SYSTEM_BOOST=ON -DEXTERNAL_LIBCLANG_PATH=/usr/lib/x86_64-linux-gnu/libclang-3.9.so . ~/.vim/bundle/YouCompleteMe/third_party/ycmd/cpp

pdb调试命令

完整命令	简写命令	描述
args	a	打印当前函数的参数
break	b	设置断点
clear	cl	清除断点
condition	无	设置条件断点
continue	c或者cont	继续运行，知道遇到断点或者脚本结束
disable	无	禁用断点

Resnet 164

      model:add(Convolution(3,nStages[1],3,3,1,1,1,1)) -- one conv at the beginning (spatial size: 32x32)
      model:add(layer(bottleneck, nStages[1], nStages[2], n, 1)) -- Stage 1 (spatial size: 32x32)
      model:add(layer(bottleneck, nStages[2], nStages[3], n, 2)) -- Stage 2 (spatial size: 16x16)
      model:add(layer(bottleneck, nStages[3], nStages[4], n, 2)) -- Stage 3 (spatial size: 8x8)

tensorboard使用

JZTrainCategory.py训练策略

import keras.backend as K
import keras 
import numpy as np
import warnings


class JZTrainCategory(keras.callbacks.Callback):

ss

导出

jupyter nbconvert --to html EDA.ipynb

只导出图表和markdown

import IPython.core.display as di 
di.display_html('<script>jQuery(function() {if (jQuery("body.notebook_app").length == 0) { jQuery(".input_area").toggle(); jQuery(".prompt").toggle();}});</script>', raw=True)


	def lccs(a, b):
	flag = True
	if len(a) > len(b):
	a, b = b, a
	flag = False
	ret = ''
	sigma = 0
	last_position = 0
	dp = [[0]*(len(b)+1) for i in range(len(a)+1)]

	"""Kaggle competition: Predicting a Biological Response.

	Blending {RandomForests, ExtraTrees, GradientBoosting} + stretching to
	[0,1]. The blending scheme is related to the idea Jose H. Solorzano
	presented here:
	http://www.kaggle.com/c/bioresponse/forums/t/1889/question-about-the-process-of-ensemble-learning/10950#post10950
	'''You can try this: In one of the 5 folds, train the models, then use
	the results of the models as 'variables' in logistic regression over
	the validation data of that fold'''. Or at least this is the
	implementation of my understanding of that idea :-)

	# -- coding: utf-8 --
	"""
	# 作者：wanglei5205
	# 邮箱：[email protected]
	# 博客：http://cnblogs.com/wanglei5205
	# github：http://github.com/wanglei5205
	"""
	### 导入模块
	import numpy as np
	import pandas as pd