mars

在人们的生产活动和社会活动中常常会遇到这样的问题：经过一段时间之后，需要对具有相同类型的部门或单位（称为决策单元）进行评价，其评价的依据是决策单元的“输入”数据和“输出”数据，输入数据是指决策单元在某种活动中需要消耗的某些量，例如投入的资金总额，投入的总劳动力数，占地面积等等；输出数据是决策单元经过一定的输入之后，产生的表明该活动成效的某些信息量，例如不同类型的产品数量，产品的质量，经济效益等等．再具体些说，譬如在评价某城市的高等学校时，输入可以是学校的全年的资金，教职员工的总人数，教学用房的总面积，各类职称的教师人数等等；输出可以是培养博士研究生的人数，硕士研究生的人数，大学生的人数，学生的质量（德，智，体），教师的教学工作量，学校的科研成果（数量与质量)等等．根据输入数据和输出数据来评价决策单元的优劣，即所谓评价部门（或单位）间的相对有效性．(In people s productive activities and social activities often encounter such a problem: After a period of time, need to have the same type of departments or units (referred to as the decision-making unit) to evaluate the basis for his assessment in decision-making unit "input "Data and" output "data, input data refers to the decision-making unit in certain activities that need to consume certain amount of, for example, the total funds invested, the investment of the total workforce number, area, etc. output data through the decision-making unit After a certain input to produce the show the effectiveness of the activity of certain amount of information, such as different types of product quantity, product quality, economic efficiency and so on. Be more specific, said, for example, in evaluating a city s institutions of higher learning, the input can be the year the school funds, the total number of faculty, teaching a total area of space, all kinds of titles the number of teachers, etc)

说明：  使用matlab中的simulink工具对电路进行仿真、分析，这里提供了一些源代码。(Matlab simulink tools used in the circuit simulation, analysis, here are some source code.)

Robust Non-negative Dictionary Learning for Visual Tracking The provided codes could be either embedded into the benchmark framework of paper Online Object Tracking: A Benchmark (CVPR2013) (You can find details here: http://visual-tracking.net/) or run on individual sequence. To run the benchmark, just put the entire folder into the /trackers folder in the benchmark code base, and modify the configTrackers.m in util folder. DLT gets an AUC of 0.436, which ranks 5th among 26 in the benchmark by 19/03/2014. We don t tune parameters for single sequence in this case, all the parameters are stored in trackparam_DLT.m. To run on individual video, you need to modify the dataPath and title in run_individual.m. If you run MATLAB version after 2012, and have a CUDA compatible GPU installed, you may enjoy the fast computation speed by GPU, just set useGPU to true in trackparam_DLT.m and run_individual.m!

A genetic implimentation for 2 class input classification

说明：   在MATLAB中开发的最速梯度法算例(in MATLAB development of the steepest gradient method counting cases)

LEACH protocol for adhoc network using MATLAB

hex_counter old project, please let me know if need any help

Business Cycle Uncertainty and Economic Welfare

Demonstration of Quantization Process (matlab)

0_Linear：线性回归练习 1_LogistRegression：逻辑回归练习 2_boston：波士顿房价预测 3_regularization：正则化 5_stock：股票价格预测 6_CTR：点击率预测 7_all_model：回归与分类模型 8_muticlass：多分类练习 9_regression_implement：不同回归算法tf实现(0_Linear. 1_LogistRegression. 2_boston. 3_regularization. 5_stock. 6_CTR. 7_all_model. 8_muticlass. 9_regression_implement.)