不知道大家有没有用过，淘宝电影客户端（淘票票）买过电影票，纵观各类在线选座app的在线选座功能 淘宝在线选座功能用户体验最好，用起来最顺手，夸张点说已经到了炉火纯青的地步，下面我们看一下效果： 效果分析： 整个控件分成几个部分，座位图区域、座位缩略图区域、行号区域、屏幕区域 1、座位图可以自由的移动缩放，放大缩小移动后会自动回弹到合适的位置，选中座位会自动放大到合适比例。 2、行号部分跟着座位图缩放以及上下移动，屏幕区域跟着座位图左右移动缩放。 3、当手指按下的时候会出现缩略图，缩略图上有个红色的方框表示，当前能看到的区域，并且跟随缩略图的移动。 涉及到的知识点： view的绘制原理、事件分

该LED显示模块可与两种尺寸（大和小）的7色LED矩阵以及7段x 4位数字一起使用。

用python写了一个简单版本的textrank，实现提取关键词的功能。 import numpy as np import jieba import jieba.posseg as pseg class TextRank(object): def __init__(self, sentence, window, alpha, iternum): self.sentence = sentence self.window = window self.alpha = alpha self.edge_dict = {} #记录节点的边连接字典 sel

label matrix 8.002 注册机

布朗大学Philip Klein教授授课，讲述矩阵分析的应用，用python进行编程。

关于矩阵完全的程序，能够快速实现矩阵完全的c源码程序

用Array Vector 实现Matrix 加减乘法

MahNMF Manhattan Non-negative Matrix Factorization code % Manhattan Non-negative Matrix Factorization. % ManhNMF: Matlab Code for Efficient Robust Manhattan NMF Solver % Reference % [1] N. Guan, D. Tao, Z. Luo, and J. Shawe-taylor, "MahNMF: Manhattan % Non-negative Matrix Factorization," arXiv:1207.3438v1, 2012. % [2] N. Guan, D. Tao, Z. Luo, and J. Shawe-taylor, "MahNMF: Manhattan % Non-negative Matrix Factorization," Submitted to Journal of Machine Learning Research, 2013. % The model is X \approx W^TH, where X, W, and H are defined as follows: % X (m x n): data matrix including n samples in m-dimensional space; % W (r x m): basis matrix including r bases in m-dimensional space; % H (r x n): coefficients matrix includeing n encodings in r-dimensional space. % Written by Naiyang Guan (ny.guan@gmail.com) % Copyright 2012-2014 by Naiyang Guan and Dacheng Tao % Modified at Jan. 28 2013 % % X : Input data matrix (m x n) % r : Target low-rank % % (Below are optional arguments: can be set by providing name-value pairs) % MAX_ITER : Maximum number of iterations. Default is 1,000. % MIN_ITER : Minimum number of iterations. Default is 10. % MAX_TIME : Maximum amount of time in seconds. Default is 100,000. % W_INIT : (m x r) initial value for W. % H_INIT : (r x n) initial value for H. % LAM_INIT : initial value of smoothness parameter. Default is 1. % MDL_TYPE : Model type (Default is 'PLAIN'), % 'PLAIN' - MahNMF (min{||X-W^T*H||_1,s.t.,W >= 0 and H >= 0}.), % 'BXC' - Box Constrained MahNMF (min{||X-W^T*H||_1,s.t.,1 >= W >= 0 and 1 >= H >= 0}.), % 'MNR' - Manifold Regularized MahNMF % (min{||X-W^T*H||_1+.5*beta*TR(H*Lp*H^T),s.t.,W >= 0 and H >= 0}.), % 'GSP' - Group Sparse MahNMF % (min{||X-W^T*H||_1+.5*beta*\sum_{g\in G}||W^[g]||_{1,p},s.t.,W >= 0 and H >= 0}.), % 'SYM' - Symmetric MahNMF (min{||X-H*H^T||_1,s.t., H >= 0}.). % ALG_TYPE : Algorithm type (Default is 'AGD'), % 'AGD' - Accelerated Gradient Descent, % 'RRI' - Rank-one Residue Iteration. % BETA : Tradeoff parameter over regularization term. Default is 1e-3. % SIM_MTX : Similarity matrix constructed by 'constructW'. % GPP_MTX : Group pattern for boundary of all groups. % TOL_INNR : Stopping tolerance of inner iterations. Default is 1e-2. % TOL_OUTR : Stopping tolerance of outer iterations. Default is 1e-3. % If you want to obtain a more accurate solution, decrease TOL_INNR or TOL_OUTR and increase MAX_ITER at the same time. % VB_OUTR : 0 (default) - No debugging information is collected. % 1 (debugging purpose) - History of computation is returned by 'HIS' variable. % 2 (debugging purpose) - History of computation is additionally printed on screen. % VB_INNR : 0 (default) - No debugging information is collected. % 1 (debugging purpose) - History of computation is returned by 'HIS' variable. % 2 (debugging purpose) - History of computation is additionally printed on screen. % % W : Obtained basis matrix (r x m). % H : Obtained coefficients matrix (r x n). % iter : Number of iterations. % elapse : CPU time in seconds. % HIS : (debugging purpose) History of computation, % niter - total iteration number spent for Nesterov's optimal % gradient method, % cpus - CPU seconds at iteration rounds, % objf - objective function values at iteration rounds, % dlta - stopping criteria of block coordinate descent. % % % >>X=rand(1000,500); % >>ManhNMF(X,10); % >>ManhNMF(X,20,'verbose',1); % >>ManhNMF(X,30,'verbose',2,'w_init',rand(r,m)); % >>ManhNMF(X,5,'verbose',2,'tol_outr',1e-5); % Note: other files 'GetStopCriterion.m', 'ApproxFunC.m', and 'wmedianf.mexw32' should be included under the same % directory as this code.

一种可扩展的工具，用于可视化邻接矩阵，其中边、方向上的值以及作为节点大小的值的总和。 如果您发现这对您的工作有用，请考虑引用以下参考资料： @文章{yang2018learning, title={大型网络中的学习功能需要模块化并产生多代理动态}， 作者={Yang, CH and Ooi, Rise and Hiscock, Tom and Eguiluz, Victor and Tegner, Jesper}, 期刊={ICML, Workshop of Computational Biology 2018, arXiv preprint arXiv:1807.03001}, 年= {2018} }

matrix-computation 上科大2020秋矩阵计算作业 作业题传上来的都是自己写的或是网上找的答案，大部分没放标准答案，懒得再回去找，因此写的不一定全对。