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Description: 目前流行的WEB控件，即可对页面内容选材打印，也可用JS编码实现复杂输出.
1:套打时最终用户可拖拽微调,自主保存微调结果,是套打的极佳解决方案
2:轻松实现表格的页头页尾打印,快速完成WEB报表，展现报表新思路
3:控件可100 在线或下载安装,适应IE内核类及FireFox系列等所有浏览器
4:曾在大型WEB工程中有出色表现，是国内最大软件公司常用工具
5:下载包仅几百K，含设计界面，专业精致，有大量样例轻松上手
6:输出时可按默认打印，可选机打印，也可固定到某打印机
7:可实现图形、条码、旋转字、图片、URL、表格等打印功能...
-Popular Web controls, you can print the page content selection can also be used JS coding to achieve a complex output.
1: Printing With the end user can drag the fine-tuning, self-saving fine-tune the results, is an excellent solution Taoda
2: page header footer easily form printing, rapid completion of the WEB statements, to show the new ideas of statements
3: control 100 online or download and install, adapt to the IE core classes and FireFox series, all browsers
4: in the large-scale WEB engineering excellence, is the largest software company, a common tool
5: Download the package only a few hundred K, including the design of the interface, professional fine, a large number of sample easy to get started
6: output according to the default print and optional printing machine can also be fixed to a printer
7: graphics, bar code, rotate characters, images, URLs, forms printing capabilities ...
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Size: 1812480 |
Author: tony |
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Description: Apriori核心算法过程如下：
过单趟扫描数据库D计算出各个1项集的支持度，得到频繁1项集的集合。
连接步：为了生成，预先生成,由2个只有一个项不同的属于的频集做一个（k-2）JOIN运算得到的。
剪枝步：由于是的超集，所以可能有些元素不是频繁的。在潜在k项集的某个子集不是中的成员是，则该潜在频繁项集不可能是频繁的可以从中移去。
通过单趟扫描数据库D，计算中各个项集的支持度，将中不满足支持度的项集去掉形成。-Apriori core algorithm process is as follows:
After single trip brain-scan database D calculated each 1 itemsets support and get frequent 1 itemsets collection.
Connect step: in order to create, a predetermined by 2 generate, only one item different belongs to the frequency of the set a (k- 2) JOIN operation get.
The pruning step: because yes superset and may, therefore, some elements are not frequent. In potential k itemsets is a subset of the members of the potential is, the frequent itemsets impossible is the frequent can find removed.
Through a single trip brain-scan database D, calculation of various itemsets support and will not satisfy the support degree itemsets removed form.
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Size: 2048 |
Author: lixiongxi |
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Description: K风是由Kwindsoft自主研发的专业网页搜索引擎系统，拥有先进的智能分析和海量数据检索技术，核心由多线程采集系统、智能分析系统、海量索引系统、全文检索系统四大部分构成。系统采用专业级的搜索引擎系统架构，支持海量数据毫秒级全文检索。主要面向大中型行业搜索引擎、地方搜索引擎、专类信息搜索引擎等应用领域设计的专业全文检索产品，为用户提供海量数据全文检索应用的理想解决方案。
-K Wind is the professional Web search engine system by Kwindsoft independent research and development, advanced intelligent analysis and mass data retrieval techniques constitute the core consists of four major parts of multithreaded acquisition system, intelligent analysis system, mass indexing system, full-text search system. The system uses professional-grade search engine system architecture to support full-text retrieval of massive data millisecond. Professional full-text retrieval products mainly for large and medium-sized industry search engine, local search engines, and class information on search engine applications designed to provide users with massive data retrieval applications ideal solution.
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Size: 3010560 |
Author: sdgfdg508 |
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Description: MMT is a Matlab toolbox implementing the multi-task Lasso models, including: (i) the Lasso (ii) the standard multi-task Lasso, i.e. the group Lasso (iii) the structured input-output multi-task Lasso, a.k.a. the two-graph guided multi-task Lasso proposed in [1]. The last case (iii) subsumes the special cases: tree-guided and the feature-graph guided multi-task Lasso. The core optimization algorithm for solving this model is developed in C to enhance greater computational efficiency. In particular, current scalability of the coefficient matrix that has been tested for MMT is 104*104! The structured input-output multi-task Lasso model is well-suited for addressing the expression quantitative trait loci (eQTL) mapping problems which are of the intrinsic high-dimensional nature. Details can be found in [1].-MMT is a Matlab toolbox implementing the multi-task Lasso models, including: (i) the Lasso (ii) the standard multi-task Lasso, i.e. the group Lasso (iii) the structured input-output multi-task Lasso, a.k.a. the two-graph guided multi-task Lasso proposed in [1]. The last case (iii) subsumes the special cases: tree-guided and the feature-graph guided multi-task Lasso. The core optimization algorithm for solving this model is developed in C to enhance greater computational efficiency. In particular, current scalability of the coefficient matrix that has been tested for MMT is 104*104! The structured input-output multi-task Lasso model is well-suited for addressing the expression quantitative trait loci (eQTL) mapping problems which are of the intrinsic high-dimensional nature. Details can be found in [1].
Platform: |
Size: 478208 |
Author: 莫琳 |
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Description: 自动电阻测试仪程序：自动电阻测量仪设计采用C8051F020单片机作为核心,采用高精度恒流源驱动的伏安法针对不同档位电阻进行测量,选用MAX1241高精度A/D转换器进行模数转换,具有100Ω1、kΩ、10 kΩ1、0 MΩ四个量程,电阻测量精度均达到0.4 。各档位之间均可以实现手动分档或自动档位切换,并能根据键盘输入的电阻值和误差值进行筛选,对于不合格的电阻能够给出语音提示,该系统同时可以自动测量连续变化的电阻值并实时显示及绘制变化的曲线。自动电阻测量仪体积小、精度高、运行稳定可靠,具有一定的实用价值-Automatic Resistance Tester Program: Automatic resistance meter designed using C8051F020 microcontroller as the core, high-precision constant current source driven voltammetry measurement of resistance against different stalls, the choice MAX1241 precision A/D converter analog to digital conversion, has 100惟1, k惟, 10 k惟1, 0 M惟 four ranges, resistance measurement accuracy reaches 0.4 . Both can be achieved between each gear manually sub-file or automatic gear switching, and according to keyboard input resistor values and error values were screened for resistance to give unqualified voice prompts, the system can automatically measure changes continuously while The resistance value and real-time display and rendering the curve. Automatic resistance measuring instrument, small size, high accuracy, stable and reliable, has a certain practical value
Platform: |
Size: 2048 |
Author: weihao |
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Description: 邻近算法，或者说K最近邻(kNN，k-NearestNeighbor)分类算法是数据挖掘分类技术中最简单的方法之一。所谓K最近邻，就是k个最近的邻居的意思，说的是每个样本都可以用它最接近的k个邻居来代表。
kNN算法的核心思想是如果一个样本在特征空间中的k个最相邻的样本中的大多数属于某一个类别，则该样本也属于这个类别，并具有这个类别上样本的特性。该方法在确定分类决策上只依据最邻近的一个或者几个样本的类别来决定待分样本所属的类别。 kNN方法在类别决策时，只与极少量的相邻样本有关。由于kNN方法主要靠周围有限的邻近的样本，而不是靠判别类域的方法来确定所属类别的，因此对于类域的交叉或重叠较多的待分样本集来说，kNN方法较其他方法更为适合。-Nearby algorithm, or K-nearest neighbor (kNN, k-NearestNeighbor) classification algorithm is one of classification data mining technology in the most simple way. The so-called K-nearest neighbor is the k nearest neighbors meant to say is that it can be used for each sample k nearest neighbors to represent. kNN algorithm core idea is that if a sample in feature space is k-nearest neighbor samples most belong to a category, the sample also fall into this category, and the category having the characteristics of the sample. The method in determining the classification decision based solely on the nearest one or several samples to determine the category to be sub-sample belongs to the category. kNN method when category decisions, with only a very small amount of adjacent samples related. Because kNN method is mainly limited by the surrounding adjacent samples, rather than the domain identification method to determine the class belongs to the category, so for class field of overlap or more s
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Size: 2048 |
Author: 黑色地位 |
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Description: 蜡烛图剩余时间，直接在MT4平台显示出来。非常方便外汇交易者注意收盘时间。
储存路径：MT4\MQL4\Indicators-//+ +
//| b-clock.mq4 |
//| Core time code by Nick Bilak |
//| http://metatrader.50webs.com/ beluck[at]gmail.com |
//| modified by adoleh2000 and dwt5 |
//+ +
#property copyright Copyright ?2005, Nick Bilak
#property link http://metatrader.50webs.com/
#property indicator_chart_window
// buffers
double s1[]
//+ +
//| Custom indicator initialization function |
//+ +
int init()
{
}
return(0)
//+ +
//| Custom indicator iteration function |
//+ +
int start()
{
double i
int m,s,k
m=Time[0]+Period()*60-CurTime()
i=m/60.0
s=m
Platform: |
Size: 1024 |
Author: chenxiao |
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Description: 目前擴增實境技術相關應用大部分以使用標記為主，但各式應用需求與日俱增，無標記(markerless)擴增實境技術使用上更具彈性，不必受限於標記的使用，因此應用層面更廣。視覺追蹤技術是擴增實境系統重要底層核心技術之一，但使用視覺追蹤技術在實際應用上易受到追蹤物件本身及外觀變化之影響，因此本文提出適用於無標記擴增實境應用之物件追蹤方法，能有效追蹤各式真實物件。首先框選設定追蹤物件；接著擷取物件特徵值，藉由特徵值比對以持續追蹤物件，並利用金字塔L-K光流法以縮短比對運算時間；最後經由2D-3D座標轉換，將3D虛擬物件疊加至真實場景。經由實驗證明本文所提之方法能有效追蹤各式真實物件，並能適用於無標記擴增實境相關應用。-Augmented Reality technology currently most relevant applications to use mark-based, but the increasing demand for all kinds of applications, without marking (markerless) amplification technology more flexible use of reality, not necessarily limited to the use of tags, therefore the application level broader. Visual tracking technology is one of the important augmented reality system underlying core technology, but the use of visual tracking technology in practical applications susceptible to track changes and appearance of the object itself, and therefore in this paper applies to Amplification unmarked objects of reality application tracing method can effectively track all kinds of real objects. First, set the track object marquee then retrieve objects eigenvalues by eigenvalue than to keep track of objects, and uses of the pyramid LK optical flow method to shorten the computation time than on and finally converted via 2D-3D coordinates of the 3D virtual object superimposed to the r
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Size: 644096 |
Author: 鍾德煥 |
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Description: K-PageSearch是由Kwindsoft自主研发的专业网页搜索引擎系统，拥有先进的智能分析和海量数据检索技术，核心由多线程采集系统、智能分析系统、海量索引系统、全文检索系统四大部分构成。系统采用专业级的搜索引擎系统架构，支持海量数据毫秒级全文检索。主要面向大中型行业搜索引擎、地方搜索引擎、专类信息搜索引擎等应用领域设计的专业全文检索产品，为用户提供海量数据全文检索应用的理想解决方案。-K-PageSearch Kwindsoft independently developed by professional web search engine, advanced intelligent analysis and massive data retri technology, the core by multiple threads acquisition systems, intelligent analysis system, system mass index, full-text retri system composed of four parts. The system uses professional-grade search engine system architecture that supports full-text search massive amounts of data in milliseconds. Mainly for large and medium industry search engines, local search engine, topic information search engines and other applications designed for the professional full-text search products, to provide users with massive amounts of data over full-text search application solutions.
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Size: 3004416 |
Author: 霸天异形 |
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Description: kNN算法(k临近算法)的核心思想是如果一个样本在特征空间中的k个最相邻的样本中的大多数属于某一个类别，则该样本也属于这个类别，并具有这个类别上样本的特性。-The core idea of kNN algorithm is that if a sample in the feature space of k-nearest neighbor samples Most belong to a category, then the sample also fall into this category, and the category having the sample characteristics.
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Size: 2048 |
Author: 李小 |
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Description: 金蝶K/3 ERP面向中小型企业，构建BOS平台之上，帮助企业全面整合内外资源，快速实现个性化需求。金蝶K/3在企业价值创造的各环节，包括采购管理、销售管理、库存管理、生产管理、看板管理等基础业务管理，计划管理、财务管理、人力资源管理、协同办公等企业辅助管理方面，更加注重深入应用，使企业在创造价值过程中的每个环节都得以完美衔接。
应用金蝶K/3 ERP，可以帮助企业打造最佳管理模式，使企业资源配置最优化，提高企业核心竞争力。(Kingdee K/3 ERP for small and medium enterprises, built on the BOS platform, to help enterprises integrate the internal and external resources, and quickly realize the personalized needs. The link to create Kingdee K/3 in the enterprise value, including the basic business management and purchase management, sales management, inventory management, production management, Kanban management, project management, financial management, human resources management, coordination management office and other auxiliary enterprises, pay more attention to deep into the application, so that enterprises in each link in the process of creating value to perfect convergence.
The application of Kingdee K/3 ERP can help enterprises to create the best management model, optimize the allocation of enterprise resources, and improve the core competitiveness of enterprises.)
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Size: 4710400 |
Author: 哈伦
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Description: 自己编写的KNN算法，不用工具包就可实现。kNN算法的核心思想是如果一个样本在特征空间中的k个最相邻的样本中的大多数属于某一个类别，则该样本也属于这个类别，并具有这个类别上样本的特性.(The core idea of the kNN algorithm is that if the majority of the k most neighboring samples of a sample in the feature space belong to a particular category, the sample also belongs to this category and has the characteristics of the sample on that category)
Platform: |
Size: 1024 |
Author: 向日葵葵wxn
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Description: 随着计算机科技的发展，图像检索的应用也越来越成熟，根据检索性质可分为两类：基于文本的图像检索和基于内容的图像检索。本论文通过研究基于内容的图像检索中的几个核心算法，用于聚类分析的K-means算法，通过haar小波变换来提取图像底层视觉特征，以及使用F-范数理论来进行相似性度量，来设计一个离线的图像检索系统。(With the development of computer technology, the application of image retrieval is more and more mature. According to the nature of retrieval, they can be divided into two categories: text based image retrieval and content based image retrieval. This paper studies the core algorithm in content based image retrieval, K-means algorithm for clustering analysis, through Haar wavelet transform to extract image visual features, and using F- norm theory to similarity measure, image retrieval system to design an offline.)
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Size: 3736576 |
Author: 笨笨熊笨笨
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