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1.1
- 标准grove算法与pi/2相移算法的成功概率对比程序 标准grove算法与三种改进算法的成功概率对比程序-Standard grove algorithm and pi/2 phase shift algorithm is the probability of success compared with the three process standards grove algorithm improved algorithm compared the probability of pro
nqueen
- 用概率算法求解N皇后问题。1、拉斯维加斯概率算法 每一行得到了几个摆放位置时，不是按顺序进行摆放，而是随机摆放的。因此程序每次运行的时间都不一样。用拉斯维加斯算法除非找不到解，如果找到，答案就一定是正确的。-Probabilistic algorithm with N queens problem. 1, Las Vegas probabilistic algorithms have several display each line position, not by placing the
multi-ctp1
- 一个基于阈值的粒子比较准则，用于处理多目标约束优化问题,该准则可以保留一部分序值较小且约束违反度在允许范围内的不可行解微粒,从而达到由不可行解向可行解进化的目的；一个新的拥挤度函数,使得位于稀疏区域和Pareto前沿边界附近的点有较大的拥挤度函数值,从而被选择上的概率也较大 从而构成解决多目标约束优化问题的混合粒子群算法。-A comparison based on the threshold criteria for the particle to handle multi-objective
elevator
- 实际的场景，若电梯的最大载客量m=10，设电梯中已有的 客人服从0-10 之间的均匀分布，且电梯中的任意一人在任意一层下的 概率相同，若你在第三层需要乘电梯到第七层，电梯处在第一层，共 8 层。且在每一层等电梯到达他们的目的楼层的客人服从0-3 的均匀分 布此时我们对电梯的运行加一些限 制，即电梯中若有客人未达目的地，电梯不会改变运行方向，求直到 你到达第七层为止，，电梯需停次数的数学期望，并进行计算机模拟验 证。-The actual scene, if the m
QAM
- QAM调制下信道容量的计算，字符错误概率和符号错误概率曲线-QAM modulation in the calculation of channel capacity, character error rate and symbol error probability curve
Introductiontoprobability_MIT2000
- 概率导论，计算机需要的 学习AI的更需要-Introduction to probability, the computer AI needs more study needs
buffonexperiment
- 实现了布丰投针实验的演示,有简单的动画演示,能在用户界面上输入平行线间距,投针的针长,投针次数,并能输出针与任一平行线相交的概率,以及根据此概率和平行线间距,针长计算出的pi值-Buffon needle experiment realized investment presentation, there is a simple animation, can be entered in the user interface on the parallel line spacing, cast a
MC
- 此代码是用概率算法来求素数，输入n值便可以得到n以内的所有素数，概率算法相比于确定性算法效率快乐很多，代码中用到了二个经典函数int MillRab(int n) int RepeatMillRab(int n,int k) 计算机面试时考官常问有关这二个函数的问题。-This code is to use probabilistic algorithms to find prime numbers, enter the n value will be able to get all prime
matlabprobabilitydensity
- 用matlab实现概率方面的编程，包括随机数的生成函数方面知识-Probability with matlab implementation aspects of programming, including random number generation function of knowledge
gailvsuanfa
- 这是中国数学建模中编程交流上概率算法内容，里面提供了很多概率算法的例子-This is the exchange of mathematical modeling in the programming content on a probability algorithm, which provides many examples of probabilistic algorithms
Queen_lv
- 这个代码是用visual c++6.0编写的用概率算法求n皇后问题，当输入n具体值时便会出现失败和成功探测的步数，以及耗时等信息，很好地实现了经典的n皇后问题，即棋盘上放置皇后要有一定的规则，比如不同行列等。-This code is written in visual c++6.0 using probabilistic algorithms find n queens problem, the exact value of the input n there will be failures
CRY1
- 实现加密字符串技术，程序根据概率统计自动绘制一张加密表，然后对输入字符串进行加密和自动解密工作，不过为了节省时间，只能对小写字母进行加密-Technology to achieve the encrypted string, the program automatically based on probability and statistics to draw an encrypted form, and then the input string to encrypt and decrypt
cv_pdaf
- CV模型，利用概率数据关联算法和最近邻算法对其进行跟踪滤波，保证正确-CV model, the probabilistic data association algorithm and the nearest neighbor filter algorithm to track and ensure the correct
AdapGA
- Srinvivas提出的自适应遗传算法，交叉概率和变异概率随适应度自动改变-Srinvivas proposed genetic algorithm, crossover probability and mutation probability automatically change with fitness
GMGA
- 大变异遗传算法以一个远大于通常的变异概率执行一次编译操作，从而使整体总群脱离“早熟”。-Genetic algorithm with a large variation is much larger than the mutation probability is usually compiled once operation, so that the overall total group from the " premature."
classicalprobabilityandstatisticscalculations
- 主要介绍MATLAB概率统计计算经典30例，非常实用。-Introduces MATLAB 30 cases of classical probability and statistics calculations, very practical.
PR
- 采用身高和体重数据作为特征，分别假设二者相关或不相关，在正态分布假设下估计概率密度，建立最小错误率Bayes分类器，写出得到的决策规则，将该分类器应用到训练/测试样本-Bayes classifier is the most classic! Minimize the classification error. With the matlab language, with examples
PR1
- 采用身高和体重数据作为特征，在正态分布假设下估计概率密度，建立最小错误率Bayes分类器，写出得到的决策规则.-Height and weight data used as the feature, under the assumption of normal distribution probability density estimation, establish Bayes minimum error rate classifier, written by the decision-mak
ppbGauss
- 基于概率块权重计算的非局部图像去噪的程序-denoise
huffmannn
- 实现霍夫曼编码，根据随机概率编程霍夫曼码，有详细解释。-Huffman coding to achieve, according to random probability programming Huffman code, a detailed explanation.