发表于 | 分类于

Command方式

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class CommandSubclass : public vtkCommand
{
public:
	vtkTypeMacro(CommandSubclass , vtkCommand);

	static CommandSubclass *New()
	{
		return new CommandSubclass ;
	}

	void Execute(vtkObject *caller, unsigned long eventId,void *callData)
	{
			// 过滤按键信息
		if (eventId == vtkCommand::KeyPressEvent)
		{
			// 过滤ctrl
			vtkRenderWindowInteractor* interactor =
				dynamic_cast<vtkRenderWindowInteractor*>(caller);
			
			if (interactor->GetControlKey())
			{
				std::string key = interactor->GetKeySym();
				if(key == "a")
				cout << "ctrl + a" << endl;
			}
		}	
	}
};

InteractorStyle方式

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class MouseInteractorStyleDoubleClick : public vtkInteractorStyleTrackballCamera
{
public:
	// 先是interactor style进行处理
	// 再是Command进行捕获处理
	static MouseInteractorStyleDoubleClick* New();
	vtkTypeMacro(MouseInteractorStyleDoubleClick, vtkInteractorStyleTrackballCamera);

	MouseInteractorStyleDoubleClick() : NumberOfClicks(0), ResetPixelDistance(5)
	{
		this->PreviousPosition[0] = 0;
		this->PreviousPosition[1] = 0;
	}
	//http://blog.sina.com.cn/s/blog_5e3a1bbb0100esv3.html
	// keydown跟keypress区别
	//virtual void OnKeyPress()
	//{
	//	//// Get the keypress
	//	//vtkRenderWindowInteractor *rwi = this->Interactor;
	//	//std::string key = rwi->GetKeySym();
	//	//if (rwi->GetControlKey())
	//	//{
	//	//	// ctrl+ a
	//	//	if (key == "a")
	//	//	{
	//	//		std::cout << "Ctrl + a" << std::endl;
	//	//	}
	//	//	//rwi->get
	//	//}
	//	//vtkInteractorStyleTrackballCamera::OnKeyPress();
	//}

	virtual void OnLeftButtonUp()
	{
		vtkRenderWindowInteractor *rwi = this->Interactor;
		std::cout << "left mouse" << std::endl;
		if (rwi->GetControlKey())
		{
			std::cout << "Ctrl + left mouse" << std::endl;
		}
		vtkInteractorStyleTrackballCamera::OnLeftButtonUp();
	}


private:
	unsigned int NumberOfClicks;
	int PreviousPosition[2];
	int ResetPixelDistance;
};
vtkStandardNewMacro(MouseInteractorStyleDoubleClick);

发表于 | 分类于 
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#include <modbus.h>
#include <iostream>
#include<stdlib.h>
#include<time.h>

using namespace std;

int main()
{
	modbus_t *ctx;
	srand((unsigned)time(NULL));
	
	// 创建modbus连接
	ctx = modbus_new_tcp("127.0.0.1", 502);

	if (ctx == NULL) 
	{
		// 创建失败
		fprintf(stderr, "Unable to allocate libmodbus context\n");
		return -1;
	}
	
	// 设置调试模式
	modbus_set_debug(ctx,true);

	// 设置连接的slave号，此处为1
	int rc = modbus_set_slave(ctx, 1);
	if (rc == -1)
	{
		fprintf(stderr, "Invalid slave ID\n");
		modbus_free(ctx);
		return -1;
	}

	// 连接
	if (modbus_connect(ctx) == -1) 
	{
		fprintf(stderr, "Connection failed: %s\n", modbus_strerror(errno));
		modbus_free(ctx);
		return -1;
	}

	while(1)
	{
		// 读寄存器
		uint16_t* dest= (uint16_t *) malloc(10 * sizeof(uint16_t));
		memset(dest, 0, 10);
		int rc = modbus_read_registers(ctx, 0, 10, dest);
		if (rc == -1)
		{
			fprintf(stderr, "%s\n", modbus_strerror(errno));
			return -1;
		}
		for(int i = 0; i < 10 ; i++)
		{
			cout << i << "\t:\t" << dest[i] << endl;
		}
		
		Sleep(1000);

		// 写寄存器
		for(int i=0;i<10;i++)
		{
			if (dest[i] == 0)
			{
				dest[i] = rand() %20;
			}
			else
			{
				dest[i] *= 3;
				dest[i] %= 20 ;
			}
		}
		rc = modbus_write_registers(ctx, 0, 10, dest);
		if (rc == -1)
		{
			fprintf(stderr, "%s\n", modbus_strerror(errno));
			return -1;
		}
		Sleep(1000);
	}
	modbus_close(ctx);  
	modbus_free(ctx);  
	return 0;
}

参考：

[1]. http://www.exbob.com/post/blog/2015-05-24
[2]. http://libmodbus.org/docs/v3.1.4/

发表于 | 分类于

官方解决方案要设置pro文件，一般win下开发使用vs-addin，所以不适用。
网上另一种解决方案是修改vs的vcxproj文件，我试了，没有效果。
亲测可用的方案如下：

1.添加ico文件
2.重命名“appnameico.ico”(不确定是不是必须)
3.新建txt文件，命名为appname.rc，添加

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IDI_ICON1 ICON DISCARDABLE "appnameico.ico"

4.重新编译生成appname.res文件
5.链接res文件（属性->配置属性->链接器->输入->嵌入托管资源文件），添加生成的res文件（关键步骤）。

[1]http://stackoverflow.com/questions/2949261/howto-set-icon-to-qt-application-created-with-qt-visual-studio-add-in

发表于 | 分类于

编译Cryptopp

编译过程没有什么特别，需要注意的是，如果使用dll版本的库，只包含IPS认证的算法，而编译静态链接库则包含全部算法，具体参考[1,2]。

  • cryptopp - This builds the DLL. Please note that if you wish to use Crypto++ as a FIPS validated module, you must use a pre-built DLL that has undergone the FIPS validation process instead of building your own.
  • dlltest - This builds a sample application that only uses the DLL.
  • cryptest Non-DLL-Import Configuration - This builds the full static library along with a full test driver.
  • cryptest DLL-Import Configuration - This builds a static library containing only algorithms not in the DLL, along with a full test driver that uses both the DLL and the static library.

本人编译的lib库大小为50多M。

阅读全文 »

发表于 | 分类于

matplotlib基础

1.基本绘图

控制颜色

  • 颜色之间的对应关系为

    b—-blue
    c—-cyan
    g—-green
    k——black
    m—-magenta
    r—-red
    w—-white
    y——yellow

  • 表示颜色的方式:

    a:用全名
    b:16进制如：#FF00FF
    c:RGB或RGBA元组（1,0,1,1）
    d:灰度强度如：‘0.7’

控制线型

  • 符号和线型之间的对应关系
    • 实线
      — 短线
      -. 短点相间线
      ： 虚点线

控制标记风格

  • 标记风格有多种：

    . Point marker
    , Pixel marker
    o Circle marker
    v Triangle down marker
    ^ Triangle up marker
    < Triangle left marker
    > Triangle right marker
    1 Tripod down marker
    2 Tripod up marker
    3 Tripod left marker
    4 Tripod right marker
    s Square marker
    p Pentagon marker
    * Star marker
    h Hexagon marker
    H Rotated hexagon D Diamond marker
    d Thin diamond marker
    | Vertical line (vlinesymbol) marker
    _ Horizontal line (hline symbol) marker
    + Plus marker
    x Cross (x) marker

示例代码

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import pandas as pd
import numpy as np

# 导入matplot库
import matplotlib.pyplot as plt

# 设置输出图像大小
plt.rc('figure', figsize=(10, 5))

# 生成数据
x = np.linspace(0, 2, 10)  				# [0,2]之间均匀产生10个值
plt.plot(x, x, 'o-', label='linear')	# y = x 曲线
plt.plot(x, x ** 2, 'x-', label='quadratic')	# y=x^2曲线
plt.legend(loc='best')
plt.title('Linear vs Quadratic progression')
plt.xlabel('Input')
plt.ylabel('Output');
plt.show()

生成结果

Alt text

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发表于 | 分类于

1. 安装nltk

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sudo pip install nltk

2. 安装nltk_data

  • nltk.downloader自动下载

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    python -m nltk.downloader all

  • 网盘链接: http://pan.baidu.com/s/1mgGozzU 密码: axx8

      > 解压地址：
  > (1). C:\nltk_data (Windows); 
  > (2). /usr/local/share/nltk_data (Mac); 
  > (3). and /usr/share/nltk_data (Unix).

发表于 | 分类于

引例：

NLTK中对于很多自然语言处理应用有着开箱即用的api，但是结果往往让人弄不清楚状况。
下面的例子使用NLTK进行命名实体的识别。第一例中，Apple成功被识别出来，而第二例并未被识别。究竟是什么原因导致这样的结果，接下来一探究竟。

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In [1]: import nltk
In [2]: tokens = nltk.word_tokenize('I am very excited about the next generation of Apple products.')
In [3]: tokens = nltk.pos_tag(tokens)
In [4]: print tokens
[('I', 'PRP'), ('am', 'VBP'), ('very', 'RB'), ('excited', 'JJ'), ('about', 'IN'), ('the', 'DT'), ('next', 'JJ'), ('generation', 'NN'), ('of', 'IN'), ('Apple', 'NNP'), ('products', 'NNS'), ('.', '.')]
In [5]: tree = nltk.ne_chunk(tokens)
In [6]: print tree
(S
  I/PRP
  am/VBP
  very/RB
  excited/JJ
  about/IN
  the/DT
  next/JJ
  generation/NN
  of/IN
  (GPE Apple/NNP)
  products/NNS
  ./.)
In [7]: tokens = nltk.word_tokenize('I bought these Apple products today.')
In [8]: tokens = nltk.pos_tag(tokens)
In [9]: print tokens
['I', 'bought', 'these', 'Apple', 'products', 'today', '.']
In [10]: tree = nltk.ne_chunk(tokens)
In [11]: print tree
(S I/PRP bought/VBD these/DT Apple/NNP products/NNS today/NN ./.)

最大熵算法

注意到在上述两个例子Apple这个词被词性标注为NNP（NNP是宾夕法尼亚大学树图资料库II为专有名词，单数）。另外，这两个单词都以大写字母开始。为什么Apple在1例中被标记为GPE（地缘政治实体），而2例未标记？另外，为什么Apple标记为GPE，而不是ORG（组织机构）？

NLTK的命名实体识别是通过使用的MaxEnt分类器。MaxEnt分类器工作有两个原则：1.总是试图保持均匀分布(即最大化熵)，2.保持其统计概率与经验数据一致。经验数据来源于语料库，通过手动标记，所以大多数标记数据并不是免费的。NLTK不提供其训练命名实体识别器的语料库（训练数据来自ACE（自动内容抽取））。NLTK所提供的是一个pickle文件（在nltk_data/chunkers/目录下），而这个pickle文件，就是训练好的MaxEnt分类器实例。　　 　　

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➜  maxent_ne_chunker  tree
.
├── english_ace_binary.pickle
├── english_ace_multiclass.pickle
└── PY3
    ├── english_ace_binary.pickle
    └── english_ace_multiclass.pickle

要训练良好的监督学习的算法基于良好的特征。在命名实体识别中，特征可能是这个词是否包含一个大写字母。所以NLTK使用的特征有哪些呢？下面我列出他们：

  • 词的形状（是否包含数字/首字母大写/包含符号）　　
  • 词的长度
  • 词的前三个字母
  • 词尾三个字母　
  • 词性标签
  • 词本身　
  • 该词是否存在
  • 该词前面词的词性（前面是否有名词）　　
  • 前词词性
  • 后词词性
  • 前词本身　
  • 后词本身　
  • …

下面的代码可以列出NLTK中所使用的标签

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import nltk

# 载入序列化对象
chunker = nltk.data.load('chunkers/maxent_ne_chunker/english_ace_multiclass.pickle')

# 最大熵分类器
maxEnt = chunker._tagger.classifier()

def maxEnt_report():
    maxEnt = chunker._tagger.classifier()
    print 'These are the labels used by the NLTK\'s NEC...'
    print maxEnt.labels()
    print ''

    print 'These are the most informative features found in the ACE corpora...'
    maxEnt.show_most_informative_features()

def ne_report(sentence, report_all=False):

    # 词性标记
    tokens = nltk.word_tokenize(sentence)
    tokens = nltk.pos_tag(tokens)

    tags = []
    for i in range(0, len(tokens)):
        featureset = chunker._tagger.feature_detector(tokens, i, tags)
        tag = chunker._tagger.choose_tag(tokens, i, tags)
        if tag != 'O' or report_all:
            print '\nExplanation on the why the word \'' + tokens[i][0] + '\' was tagged:'
            featureset = chunker._tagger.feature_detector(tokens, i, tags)
            maxEnt.explain(featureset)
        tags.append(tag)

下面的输出报告中列出了NLTK所使用的标签，”I-“，”B-“, “O”前缀的含义为包含/开始/例外（inside/begin/others）标记。当一块开始，第一个词是前缀“B”来表示这个词是一个块的开始。下一个单词，如果它属于同一块，将以”I-“前缀，表示这是块的一部分，而不是开始。如果一个词不属于一块，贴上“O”，这意味着它是在外面。

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➜  test  python dd.py 
These are the labels used by the NLTK's NEC...
['I-GSP', 'B-LOCATION', 'B-GPE', 'I-ORGANIZATION', 'I-PERSON', 'O', 'I-FACILITY', 'I-LOCATION', 'B-PERSON', 'B-FACILITY', 'B-GSP', 'B-ORGANIZATION', 'I-GPE']

These are the most informative features found in the ACE corpora...
  10.125 bias==True and label is 'O'
   6.631 suffix3=='day' and label is 'O'
  -6.207 bias==True and label is 'I-GSP'
   5.628 prevtag=='O' and label is 'O'
  -4.740 shape=='upcase' and label is 'O'
   4.106 shape+prevtag=='<function shape at 0x8bde0d4>+O' and label is 'O'
  -3.994 shape=='mixedcase' and label is 'O'
   3.992 pos+prevtag=='NNP+B-PERSON' and label is 'I-PERSON'
   3.890 prevtag=='I-ORGANIZATION' and label is 'I-ORGANIZATION'
   3.879 shape+prevtag=='<function shape at 0x8bde0d4>+I-ORGANIZATION' and label is 'I-ORGANIZATION'

Note:

  • GPE is Geo-Political Entity
  • GSP is Geo-Socio-Political group

例1输出：

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Explanation on the why the word 'Apple' was tagged:
  Feature                                            B-GPE       O B-ORGAN   B-GSP
  --------------------------------------------------------------------------------
  prevtag=='O' (1)                                   3.767
  shape=='upcase' (1)                                2.701
  pos+prevtag=='NNP+O' (1)                           2.254
  en-wordlist==False (1)                             2.095
  label is 'B-GPE' (1)                              -2.005
  bias==True (1)                                    -1.975
  prevword=='of' (1)                                 0.742
  pos=='NNP' (1)                                     0.681
  nextpos=='nns' (1)                                 0.661
  prevpos=='IN' (1)                                  0.311
  wordlen==5 (1)                                     0.113
  nextword=='products' (1)                           0.060
  bias==True (1)                                            10.125
  prevtag=='O' (1)                                           5.628
  shape=='upcase' (1)                                       -4.740
  prevpos=='IN' (1)                                         -1.668
  label is 'O' (1)                                          -1.075
  pos=='NNP' (1)                                            -1.024
  suffix3=='ple' (1)                                         0.797
  en-wordlist==False (1)                                     0.698
  wordlen==5 (1)                                            -0.449
  prevword=='of' (1)                                        -0.217
  nextpos=='nns' (1)                                         0.104
  prefix3=='app' (1)                                         0.089
  pos+prevtag=='NNP+O' (1)                                   0.011
  nextword=='products' (1)                                   0.005
  prevtag=='O' (1)                                                   3.389
  pos+prevtag=='NNP+O' (1)                                           1.725
  bias==True (1)                                                     0.955
  en-wordlist==False (1)                                             0.837
  label is 'B-ORGANIZATION' (1)                                      0.718
  nextpos=='nns' (1)                                                 0.365
  wordlen==5 (1)                                                    -0.351
  pos=='NNP' (1)                                                     0.174
  prevpos=='IN' (1)                                                 -0.139
  prevword=='of' (1)                                                 0.131
  prefix3=='app' (1)                                                -0.126
  shape=='upcase' (1)                                               -0.084
  suffix3=='ple' (1)                                                -0.077
  prevtag=='O' (1)                                                           2.925
  pos+prevtag=='NNP+O' (1)                                                   2.213
  shape=='upcase' (1)                                                        0.929
  en-wordlist==False (1)                                                     0.891
  bias==True (1)                                                            -0.592
  label is 'B-GSP' (1)                                                      -0.565
  prevpos=='IN' (1)                                                          0.410
  nextpos=='nns' (1)                                                         0.399
  pos=='NNP' (1)                                                             0.393
  prevword=='of' (1)                                                         0.184
  wordlen==5 (1)                                                             0.177
  ---------------------------------------------------------------------------------
  TOTAL:                                             9.406   8.283   7.515   7.366
  PROBS:                                             0.453   0.208   0.122   0.110

最后一行中列出的概率加起来加起来是0.893，而非1。这是因为只输出概率最大的四类标签。

例2输出：

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Explanation on the why the word 'Apple' was tagged:
  Feature                                                O   B-GPE B-ORGAN B-LOCAT
  --------------------------------------------------------------------------------
  bias==True (1)                                    10.125
  prevtag=='O' (1)                                   5.628
  shape=='upcase' (1)                               -4.740
  label is 'O' (1)                                  -1.075
  pos=='NNP' (1)                                    -1.024
  suffix3=='ple' (1)                                 0.797
  en-wordlist==False (1)                             0.698
  prevpos=='DT' (1)                                  0.585
  wordlen==5 (1)                                    -0.449
  nextpos=='nns' (1)                                 0.104
  prefix3=='app' (1)                                 0.089
  prevword=='these' (1)                             -0.024
  pos+prevtag=='NNP+O' (1)                           0.011
  nextword=='products' (1)                           0.005
  prevtag=='O' (1)                                           3.767
  shape=='upcase' (1)                                        2.701
  pos+prevtag=='NNP+O' (1)                                   2.254
  en-wordlist==False (1)                                     2.095
  label is 'B-GPE' (1)                                      -2.005
  bias==True (1)                                            -1.975
  pos=='NNP' (1)                                             0.681
  nextpos=='nns' (1)                                         0.661
  prevpos=='DT' (1)                                         -0.181
  wordlen==5 (1)                                             0.113
  nextword=='products' (1)                                   0.060
  prevtag=='O' (1)                                                   3.389
  pos+prevtag=='NNP+O' (1)                                           1.725
  bias==True (1)                                                     0.955
  en-wordlist==False (1)                                             0.837
  label is 'B-ORGANIZATION' (1)                                      0.718
  prevpos=='DT' (1)                                                 -0.494
  nextpos=='nns' (1)                                                 0.365
  wordlen==5 (1)                                                    -0.351
  pos=='NNP' (1)                                                     0.174
  prefix3=='app' (1)                                                -0.126
  shape=='upcase' (1)                                               -0.084
  suffix3=='ple' (1)                                                -0.077
  prevword=='these' (1)                                              0.067
  prevtag=='O' (1)                                                           2.682
  label is 'B-LOCATION' (1)                                                 -2.038
  pos+prevtag=='NNP+O' (1)                                                   1.724
  shape=='upcase' (1)                                                        1.275
  prefix3=='app' (1)                                                         1.169
  bias==True (1)                                                             0.747
  prevpos=='DT' (1)                                                          0.745
  pos=='NNP' (1)                                                             0.616
  en-wordlist==False (1)                                                    -0.309
  nextpos=='nns' (1)                                                         0.151
  wordlen==5 (1)                                                             0.041
  ---------------------------------------------------------------------------------
  TOTAL:                                            10.730   8.171   7.095   6.802
  PROBS:                                             0.697   0.118   0.056   0.046

由此：1和2中在GPE识别中唯一的区别在于下面三行：

prevword==’of’ (1) 0.742
prevpos==’IN’ (1) 0.311
prevpos==’DT’ (1) -0.181

可见，1中
1中的Apple被识别为B-GPE，而2中的Apple被识别为O。

引用：

[1] http://www.nltk.org/book/ch07.html
[2] http://spark-public.s3.amazonaws.com/nlp/slides/Information_Extraction_and_Named_Entity_Recognition_v2.pdf
[3] http://www.mattshomepage.com/#/blog/feb2013/liftingthehood

发表于 | 分类于

步骤

1.启动hbase

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➜  hbase-1.1.2  ./bin/start-hbase.sh
starting master, logging to /home/luxh/hbase/hbase-1.1.2/bin/../logs/hbase-luxh-master-ubuntu.out

2.启动gremlin：连接本地hbase时不需要显式加入host

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➜  titan-1.0.0-hadoop1  bin/gremlin.sh


         \,,,/
         (o o)
-----oOOo-(3)-oOOo-----
plugin activated: aurelius.titan
plugin activated: tinkerpop.server
plugin activated: tinkerpop.utilities
SLF4J: Class path contains multiple SLF4J bindings.
SLF4J: Found binding in [jar:file:/home/luxh/titan/titan-1.0.0-hadoop1/lib/slf4j-log4j12-1.7.5.jar!/org/slf4j/impl/StaticLoggerBinder.class]
SLF4J: Found binding in [jar:file:/home/luxh/titan/titan-1.0.0-hadoop1/lib/logback-classic-1.1.2.jar!/org/slf4j/impl/StaticLoggerBinder.class]
SLF4J: See http://www.slf4j.org/codes.html#multiple_bindings for an explanation.
SLF4J: Actual binding is of type [org.slf4j.impl.Log4jLoggerFactory]
15:04:30 INFO  org.apache.tinkerpop.gremlin.hadoop.structure.HadoopGraph  - HADOOP_GREMLIN_LIBS is set to: /home/luxh/titan/titan-1.0.0-hadoop1/lib
plugin activated: tinkerpop.hadoop
plugin activated: tinkerpop.tinkergraph
gremlin> 
gremlin> TitanGraph graph = TitanFactory.build().set("storage.backend", "hbase").open();
==>standardtitangraph[hbase:[127.0.0.1]]
gremlin>

连接远程hbase时，语句如下更改为如下方式

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TitanGraph g = TitanFactory.build().set("storage.backend", "hbase").set("storage.hostname", "77.77.77.77, 77.77.77.78, 77.77.77.79").open();

引用

[1]. http://s3.thinkaurelius.com/docs/titan/1.0.0/hbase.html

发表于 | 分类于

1. Numpy包导入

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>>> import numpy as np

2. 生成矩阵

  • zeros 0矩阵
  • ones 1矩阵
  • identity 单位矩阵
  • eye 对角矩阵
  • random 随机矩阵
  • empty 依据内存生成矩阵
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>>> a = np.array([1, 2, 3])
>>> print a
[1 2 3]
>>> np.zeros(5)
array([ 0.,  0.,  0.,  0.,  0.])
>>> np.ones(shape=(3,4),dtype=np.int32)
array([[1, 1, 1, 1],
       [1, 1, 1, 1],
       [1, 1, 1, 1]], dtype=int32)
>>> print np.identity(3) 
[[ 1.  0.  0.]
 [ 0.  1.  0.]
 [ 0.  0.  1.]]
>>> print np.eye(3,5)
[[ 1.  0.  0.  0.  0.]
 [ 0.  1.  0.  0.  0.]
 [ 0.  0.  1.  0.  0.]]
>>> print np.random.rand(3,4)
[[ 0.40974346  0.09968249  0.32264537  0.78815716]
 [ 0.52959935  0.74650259  0.36604339  0.80450681]
 [ 0.07308854  0.83747867  0.18952243  0.03182399]]
>>> x = np.empty(shape=(3,3),dtype=np.int16)
>>> print x
[[  6088   5994  32645]
 [     0 -16048    630]
 [     0      0     32]]
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