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Description: ％ EM algorithm for k multidimensional Gaussian mixture estimation
％
％ Inputs:
％ X(n,d) - input data, n=number of observations, d=dimension of variable
％ k - maximum number of Gaussian components allowed
％ ltol - percentage of the log likelihood difference between 2 iterations ([] for none)
％ maxiter - maximum number of iteration allowed ([] for none)
％ pflag - 1 for plotting GM for 1D or 2D cases only, 0 otherwise ([] for none)
％ Init - structure of initial W, M, V: Init.W, Init.M, Init.V ([] for none)
％
％ Ouputs:
％ W(1,k) - estimated weights of GM
％ M(d,k) - estimated mean vectors of GM
％ V(d,d,k) - estimated covariance matrices of GM
％ L - log likelihood of estimates
％
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Size: 3416 |
Author: Shaoqing Yu |
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Description: ％ decode with soft-input viterbi algorithm 硬判决
％ //k=4,r=1/2
％输入数据为软信息，并且数据为均值为1的BPSK调制，如果均值为MEAN,那么62,63,103,104行应做相应修改
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Size: 1831 |
Author: 陈富龙 |
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Description: opencv库编写的运用K－Mean方法的图像分割程序
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Size: 719875 |
Author: csy |
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Description: ClustanGraphics聚类分析工具。提供了11种聚类算法。
Single Linkage (or Minimum Method, Nearest Neighbor)
Complete Linkage (or Maximum Method, Furthest Neighbor)
Average Linkage (UPGMA)
Weighted Average Linkage (WPGMA)
Mean Proximity
Centroid (UPGMC)
Median (WPGMC)
Increase in Sum of Squares (Ward s Method)
Sum of Squares
Flexible (ß space distortion parameter)
Density (or k-linkage, density-seeking mode analysis)
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Size: 56120 |
Author: wangyexin |
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Description: 这是我帮一个本科生做的毕业设计，实现的数据挖掘的k均值和k中心算法，其中包含了我做的两个二维的数据集，感觉要预先知道k的参数值，不是很方便-This is what I do to help an undergraduate graduation Design, Implementation of the Data Mining mean k and k center algorithm, which includes me to do two two-dimensional data sets, feeling to know beforehand the value of the parameter k is not easy
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Size: 157928 |
Author: 孟繁宇 |
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Description: 本软件是由作者经过数字图像处理课程的学习，采用vc++.net将其基本算法实现，其算法主要包括： 1.点运算（灰度直方图，直方图均衡处理，线性运算，二值化，灰度化等） 2.几何运算（旋转，放缩，镜像，平移） 3.几何空间增强（均值，中值滤波器，k近邻均值，中值滤波器，Roberts,sobel,priwitt,laplacian,wallis锐化算子等） 4.频率域增强（基2FFT进行空间域到频率域的转换，高斯，理想，巴特沃斯高低通滤波器） 5.形态学（膨胀，腐蚀，开，闭运算，边缘提取） 6.图像复原（加躁）----- 由于时间关系这部分为完成 7.另外加上一个用于读取24位dib的类。-the software is the author of Digital Image Processing courses of study, using vc. Net to its basic algorithm, the algorithm include : 1. Point Operators (histogram, histogram equalization, linear operation, two values, such as Gray) 2. Geometric Operational (rotation, scaling, mirror and translation) 3. geometric space enhancement (mean, median filter, k neighbors Mean, Median Filter, Roberts, segmentation, priwitt, Laplacian, Wallis Sharpening operator, etc.) 4. frequency Domain Enhancement (radix 2 FFT space frequency domain to the domain conversion, Gaussian, ideals, Butterworth High-Low Pass Filter) 5. Morphology (expansion, corrosion, open and close operations, edge extraction) 6. Image Restoration (plus impatient Hoffmann because of the time this is completed seven. Added to read fo
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Size: 3416064 |
Author: 王晗 |
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Description: 打印所有不超过n(取n<256) 的其平方具有对称性质的数(也称回文数)。
*题目分析与算法设计
对于要判断的数n，计算出其平方后(存于a)，将a的每一位进行分解，再按a的从低到高的顺序将其恢复成一个数k(如n=13，则a=169且k=961)，若a等于k则可判定n为回亠数。
-Print all over n (4 admission; 256) with the square of its symmetrical nature of the (also known palindrome numbers).* Topic analysis and design algorithms for determining the number n, calculated after its square (on a), a one for each of decomposition, according to a from low to high in the order of their resume into a few k (n = 13, a = 169 where k = 961) if a mean k n can be found to back Tou few.
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Size: 1024 |
Author: 姚紫欣 |
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Description: 数字信号处理的应用之一是从含有加性噪声的信号中去除噪声。现有被噪声污染的信号x[k]=s[k]+d[k],式中： 为原始信号d[k]为均匀分布的白噪声。
（1）分别产生50点的序列s[k]和白噪声序列d[k],将二者叠加生成x[k],并在同一张图上绘出x0[k],d[k]和x[k]的序列波形。
（2）均值滤波可以有效去除叠加在低频信号上的噪声。已知3点滑动平均数字滤波器的单位脉冲响应为h[k]=[1,1,1 k=0,1,2],计算y[k]=x[k]*h[k],在同一张图上绘出前50点y[k],s[k]和x[k]的波形，比较序列y[k]和s[k]。
-digital signal processing applications from one containing additive noise signal denoising. Existing noise pollution was a signal x [k] = s [k] d [k], where : of the original signal d [k] uniform distribution of white noise. (1) have 50 points each sequence s [k] and white noise sequence d [k] superposition of the two generation x [k] and the same is likely to draw a map x0 [k] d [k] and x [k] waveform sequence. (2) mean filter can effectively remove superimposed on the low-frequency noise on the signal. 3:00 known moving average digital filter unit for pulse response h [k] = [k = 0,1,2 1,1,1] Calculation y [k] = x [k]* h [k], the same is likely to draw a map before 50 y [k] s [k] and x [k] waveform sequence comparison y [k] s and [k].
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Size: 28672 |
Author: 魏臻 |
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Description: Generate the digital AWGN signal n[k] (sampled n(t)) by generating zero mean
Gaussian random variables independently (separately) for each k MATLAB function random.
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Size: 2048 |
Author: 飞龙 |
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Description: ％ EM algorithm for k multidimensional Gaussian mixture estimation
％
％ Inputs:
％ X(n,d) - input data, n=number of observations, d=dimension of variable
％ k - maximum number of Gaussian components allowed
％ ltol - percentage of the log likelihood difference between 2 iterations ([] for none)
％ maxiter - maximum number of iteration allowed ([] for none)
％ pflag - 1 for plotting GM for 1D or 2D cases only, 0 otherwise ([] for none)
％ Init - structure of initial W, M, V: Init.W, Init.M, Init.V ([] for none)
％
％ Ouputs:
％ W(1,k) - estimated weights of GM
％ M(d,k) - estimated mean vectors of GM
％ V(d,d,k) - estimated covariance matrices of GM
％ L - log likelihood of estimates
％- EM algorithm for k multidimensional Gaussian mixture estimation Inputs: X (n, d)- input data, n = number of observations, d = dimension of variable k- maximum number of Gaussian components allowed ltol- percentage of the log likelihood difference between 2 iterations ([] for none) maxiter- maximum number of iteration allowed ([] for none) pflag- 1 for plotting GM for 1D or 2D cases only, 0 otherwise ([] for none) Init- structure of initial W, M, V: Init.W, Init.M, Init.V ([] for none) Ouputs: W (1, k)- estimated weights of GM M (d, k)- estimated mean vectors of GM V (d, d, k)- estimated covariance matrices of GM L- log likelihood of estimates
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Size: 3072 |
Author: Shaoqing Yu |
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Description: ％ decode with soft-input viterbi algorithm 硬判决
％ //k=4,r=1/2
％输入数据为软信息，并且数据为均值为1的BPSK调制，如果均值为MEAN,那么62,63,103,104行应做相应修改- Decode with soft-input viterbi algorithm hard-decision// k = 4, r = 1/2 input data for the soft information and data for the average one of BPSK modulation, if the average for the MEAN, then the line should 62,63103104 make consequential amendments to
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Size: 2048 |
Author: 陈富龙 |
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Description: k-means 算法的工作过程说明如下：首先从n个数据对象任意选择 k 个对象作为初始聚类中心；而对于所剩下其它对象，则根据它们与这些聚类中心的相似度（距离），分别将它们分配给与其最相似的（聚类中心所代表的）聚类；然后再计算每个所获新聚类的聚类中心（该聚类中所有对象的均值）；不断重复这一过程直到标准测度函数开始收敛为止。-k-means algorithm process as follows: First of all, the object data from the n choose k object as initial cluster centers and the remaining for the other object, then according to their cluster center with those of the similarity (distance) respectively assigned to them with the most similar (represented by cluster center) clustering obtained and then calculated for each new cluster center clustering (all objects in the cluster mean) repeated this process until the standard measure of function until the beginning of convergence.
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Size: 1024 |
Author: lining |
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Description: Here we design the minimum mean-squared error (MMSE) equalizer coefficients {q[k]}assuming that the input symbols {a[n]} and the noise { ˜ w[k]} are white random sequencesthat are uncorrelated with each other.-Here we design the minimum mean-squared error (MMSE) equalizer coefficients {q[k]}assuming that the input symbols {a[n]} and the noise { ˜ w[k]} are white random sequencesthat are uncorrelated with each other.
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Size: 70656 |
Author: Allen |
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Description: 基于PAA的分段线性表示算法:用等宽度窗口分割时间序列，每个窗口内的时间序列用窗口平均值来表示，就得到了时间序列的一种分段线性表示，它的输入参数为分段数，记为K.-PAA-based algorithm for piecewise linear representation: split time series with windows of same width , use the mean of time series in the window to express, it has been a sub-linear time series that it s input parameters as the number of sections, denoted by K.
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Size: 40960 |
Author: zhaozhikai |
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Description: 所给程序中，先给出一副原始图像，在其中添加椒盐噪声，然后用几种方法进行平滑滤波，包括邻域均值法，邻域中值滤波和K邻域均值滤波，其中的K邻域均值滤波所选掩膜为3*3，K值取5，图片选用board.ti。-Given program, the first is given an original image, in which the added salt & pepper noise, and then use several methods of smoothing, including the neighborhood average method, o-field value filtering and K neighborhood mean filter, where K o domain mean filter mask for the selected 3* 3, K value of taking five, picture selection board.ti.
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Size: 136192 |
Author: hanyantao |
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Description: k-means 算法接受输入量 k ；然后将n个数据对象划分为 k个聚类以便使得所获得的聚类满足：同一聚类中的对象相似度较高；而不同聚类中的对象相似度较小。聚类相似度是利用各聚类中对象的均值所获得一个“中心对象”（引力中心）来进行计算的。-In statistics and machine learning, k-means clustering is a method of cluster analysis which aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean. It is similar to the expectation-maximization algorithm for mixtures of Gaussians in that they both attempt to find the centers of natural clusters in the data.
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Size: 1024 |
Author: zhanguo |
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Description: 基于k均值、c均值等聚类算法，应用于数据挖掘-Based on the mean k, c means clustering algorithm, etc., used in data mining
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Size: 503808 |
Author: 闫少华 |
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Description: 原理：以待处理的像素作为中心，取一个nXn的模板，在模板中选择k个与待处理像素的值最接近的像素，将这k个像素的均值替换原来的像素值。
假设n=3，k=5，调用方法：b = KNNMeanFilter(a, 3, 5)-Principle: to be treated as the center pixel, take a nXn template select the template and the pending k-nearest pixel values of pixels, this k pixels mean replacing the original pixel value. Assuming n = 3, k = 5, call the method: b = KNNMeanFilter (a, 3, 5)
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Size: 1024 |
Author: Cathie Wen |
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Description: In statistics and machine learning, k-means clustering is a method of cluster analysis which aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean. It is similar to the expectation-maximization algorithm for mixtures of Gaussians in that they both attempt to find the centers of natural clusters in the data as well as in the iterative refinement approach employed by both algorithms.
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Size: 3072 |
Author: Lee Sangmin |
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Description: 这是C#下编译通过的K均值聚类算法的一个版本，可以应用于遥感图像的非监督分类-K c-mean class two
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Size: 665600 |
Author: 扬扬 |
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