Description: 进行多元统计分析基类代码及接口的实现
元统计分析 基类 声明
提供矩阵数据格式 及矩阵的 加减乘 运算及多元统计中常用的求 样本均值 协方差 方法-Multivariate statistical analysis for the base class code and the interface element statistical analysis of base class declaration data format and the matrix matrix addition and subtraction, multiplication and Multivariate Statistics used in order to sample mean covariance method Platform: |
Size: 43008 |
Author:胡子春 |
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Description: 用来产生多变量高斯过程的MATLAB源程序。-MULTI_GP generates a multivariate Gaussian random process with mean vector m (column vector) and covariance matrix C。 Platform: |
Size: 1024 |
Author:selen32 |
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Description: C++实现的自动聚类系统KlustaKwik源代码\KlustaKwik-R1-7\KlustaKwik-ks for any type of data. We needed a program that
would:
1) Fit a mixture of Gaussians with unconstrained covariance matrices
2) Automatically choose the number of mixture components
3) Be robust against noise
4) Reduce the problem of local minima
5) Run fast on large data sets (up to 100000 points, 48 dimensions)
Speed in particular was essential. KlustaKwik is based on the CEM algorithm of
Celeux and Govaert (which is faster than the standard EM algorithm Platform: |
Size: 409600 |
Author:大家 |
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Description: Evaluate the multi-variate density with mean vector m and covariance
matrix C for the input vector x.-Evaluate the multi-variate density with mean vector m and covariance
matrix C for the input vector x. Platform: |
Size: 769024 |
Author:bassoum |
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Description: Evaluate the multi-variate density with mean vector m and covariance
matrix C for the input vector x.-Evaluate the multi-variate density with mean vector m and covariance
matrix C for the input vector x. Platform: |
Size: 53248 |
Author:bassoum |
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Description: C++与FORTRAN混编程序。考虑各向异性的克里金法及序贯高斯法。在地质，油藏建模中很有用处
Author--Jeff Boisvert and Clayton V. Deutsch-for incorporating locally varying anisotropy in kriging or sequential Gaussian simulation is based on modifying how locations in space are related. Normally, the straight line path is used however, when nonlinear features exist the appropriate path between locations follows along the features. The Dijkstra algorithm is used to determine the shortest path/distance between locations and a conventional covariance or variogram function is used. This nonlinear path is a non-Euclidean distance metric and positive definiteness of the resulting kriging system of equations is not guaranteed. Multidimensional scaling (landmark isometric mapping) is used to ensure positive definiteness. Platform: |
Size: 467968 |
Author:张开 |
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Description: When you need answers for programming with C# 5.0, this practical and tightly focused book tells you exactly what you need to know—without long introductions or bloated samples. Easy to browse, it’s ideal as quick reference or as a guide to get you rapidly up to speed if you already know Java, C++, or an earlier version of C#.
Written by the authors of C# 5.0 in a Nutshell, this book covers the entire C# 5.0 language, including:
All of C#’s fundamentals
Advanced topics such as operator overloading, type constraints, covariance & contravariance, iterators, nullable types, operator lifting, lambda expressions & closures
LINQ, starting with sequences, lazy execution and standard query operators, and finishing with a complete reference to query expressions
Dynamic binding and C# 5.0’s new asynchronous functions
Unsafe code & pointers, custom attributes, preprocessor directives, and XML documentation Platform: |
Size: 3406848 |
Author:kaplan |
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Description: 两个代码一个是demo, demo是它的小样例子, 另外一个是它的源代码.
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This is the matlab implementation of following noise level estimation:
Xinhao Liu, Masayuki Tanaka and Masatoshi Okutomi
Noise Level Estimation Using Weak Textured Patches of a Single Noisy Image
IEEE International Conference on Image Processing (ICIP), 2012.
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Copyright (C) 2012 Masayuki Tanaka. All rights reserved.
mtanaka@ctrl.titech.ac.jp
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Contents
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* NoiseLevel.m
The main code of the noise level estimation.
You can show the description by
> help NoiseLevel
demo.m also includes simple usage.
This algorithm is implemented with only single m file.
* demo.m
Demonstration example.
* lena.png
Sample image.
* README.txt
This file.
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Note
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We used the maximum eigenvalue of the gradient covariance matrix Platform: |
Size: 3072 |
Author:马欢 |
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Description: Overview
The Simulink model shows an example how the Kalman Filter can be
implemented in Simulink. The model itself is configured with a Gaussian
process connected with a Kalman Filter. To directly use this model, one
only needs to provide model prarameters including parameters of the
Gaussian process, which are state space matrices, A, B, C, and D, initial
state, x0, and covariance matrices, Q and R and similar parameters for
the Kalman Filter, which can be in different values to mimic the model
mismatch, plus the state covariance, P. The following examples show how
this model can be used.
The Kalman Filter can also be used as a standard model block to be
connected with any other systems- Overview
The Simulink model shows an example how the Kalman Filter can be
implemented in Simulink. The model itself is configured with a Gaussian
process connected with a Kalman Filter. To directly use this model, one
only needs to provide model prarameters including parameters of the
Gaussian process, which are state space matrices, A, B, C, and D, initial
state, x0, and covariance matrices, Q and R and similar parameters for
the Kalman Filter, which can be in different values to mimic the model
mismatch, plus the state covariance, P. The following examples show how
this model can be used.
The Kalman Filter can also be used as a standard model block to be
connected with any other systems Platform: |
Size: 11264 |
Author:amir2 |
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Description: 编写MATLAB程序，产生协方差函数为C(τ)=9??^(?10|??| )的零均值平稳高斯过程，产生一条样本函数.测量所产生样本的时间自相关函数，将结果与理论值比较。(Procedures for the preparation of MATLAB produced C covariance function (tau) =9^ (- 10||) zero mean stationary Gauss process to produce a sample function. Measuring the resulting sample time autocorrelation function, the results will be compared with the theoretical value.) Platform: |
Size: 1039360 |
Author:Selena_01
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Description: （a）产生两个都具有200个二维向量的数据集和（注意：在生成数据集之前最好使用命令randn(‘seed’,0)初始化高斯随机生成器为0（或任意给定数值），这对结果的可重复性很重要）。向量的前半部分来自均值向量的正态分布，并且协方差矩阵。向量的后半部分来自均值向量的正态分布，并且协方差矩阵。其中是一个2*2的单位矩阵。
（b）在上述数据集上运用感知器算法，并且使用不同的初始向量初始化参数向量。
（c）测试每一次算法在和上的性能。
（d）画出数据集和，以及分类面。((a) Generate the sum of two datasets with 200 two-dimensional vectors (Note: before generating the dataset, it is better to initialize the Gaussian random generator to 0 (or any given value) with the command randn ("seed", 0), which is important for the repeatability of the results). The first half of the vector comes from the normal distribution of the mean vector and the covariance matrix. The second half of the vector comes from the normal distribution of the mean vector and the covariance matrix. Where is a 2 * 2 identity matrix.
(b) The perceptron algorithm is used on the above data set, and different initial vectors are used to initialize the parameter vector.
(c) Test the performance of each algorithm on and.
(d) Draw the data set and, as well as the classification surface.) Platform: |
Size: 1024 |
Author:zilong1999 |
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Search - covariance with C