matlab data analysis book

Matlab provides a series of functions for clustering analysis, summed up the specific methods are as follows: method One: direct clustering, using Clusterdata function to cluster the sample data, its disadvantage is that the user can choose a narrow surface, can not change the calculation method of distance, the user of this method does not need to understand t

, and the vertical axis is plotted as a discrete pointX3=1:10;y3=-0.5;Fh=figure (' Numbertitle ', ' off ', ' name ', ' PLOT usability Demo ');% Create Figure objectah=axes;% Creating Axes objectsH=plot (...% return all curve handlesAh,...% Specifies the coordinate system, which can be omitted at this time by default GCAX1,y1,...% coordinate data'-.^ ',...% curve attribute, can be omitted or partially omitted, at this time automatically selectedX2,y2,.

a vector, and the vertical axis is plotted as a discrete pointX3=1:10;y3=-0.5;Fh=figure (' Numbertitle ', ' off ', ' name ', ' PLOT usability Demo ');% Create Figure objectah=axes;% Creating Axes objectsH=plot (...% return all curve handlesAh,...% Specifies the coordinate system, which can be omitted at this time by default GCAX1,y1,...% coordinate data'-.^ ',...% curve attribute, can be omitted or partially omitted, at this time automatically select

One: Cause(1) Recently has been dealing with big data, from MB----> GB changes, is a qualitative leap, the corresponding tools are also changing from widows to Linux, from single-machine to Hadoop multi-node computing(2) The problem is, in the face of huge amounts of data, how to tap into practical information or to find potential phenomena, visual tools may be essential;(3) Visualization tool can say Baidu

Http://blog.sina.com.cn/s/blog_68f3a4510100qvp1.htmlNOTE: Reprint please indicate source--by author. We know that Fourier analysis is a very important technology in signal processing, MATLAB provides a powerful signal processing capability, but there are some details that need our attention. The starting time of the signal F (t) is T_start, the terminating time is t_end, the sampling period is t_s, th

MATLAB provides a series of functions for clustering analysis, summed up the specific methods are as follows: Method One: Direct clustering, using Clusterdata function to cluster the sample data, its disadvantage is that the user can choose a narrow face, can not change the distance calculation method, the method users do not need to understand the principle and

converted from continuous variable observation data, no matter the total distribution of two variables and the size of sample capacity, can be used spearman correlation coefficient to study.Using MATLAB to calculate spearman correlation coefficients is relatively simple and also uses the Corr function, as follows:r= Corr (x, Y, ' type ', ' Spearman ');For the above example, you can calculate the r=1.Note:

Simple principal component analysis. The first time I saw PCA, my understanding was to try to describe the data in less dimensions to achieve the desired (though not the best, but ' cost-effective ' highest) effect.clear;% parameter initialization inputfile = ' F:\Techonolgoy\MATLAB\file\MTALAB data

(' Predictive error ') Preddata (1)%{Scatter (I, Error,3); Figurescatter (I, data,3) Figurescatter (I, Preddata (1,:),3)%}EndThe effect is as follows: The prediction result is: 155.7493, with the actual result 157.72 only 1.9 error, you can see that the Kalman filter algorithm for a small amount of data prediction effect is quite good. Of course, we also got the prediction speed at the same time we predict

Http://wang-yg.diandian.com/post/2011-03-12/40028916801 Note: For reprint, please indicate the source -- by author. We know that Fourier analysis is an important technology in signal processing. MATLAB provides powerful signal processing capabilities, but we need to pay attention to some details.The start time of the recording signal f (t) is t_start, the end time is t_end, the sampling period is t_s, and t

One: Cause(1) Recently has been dealing with big data, from MB----> GB changes, is a qualitative leap, the corresponding tools are also changing from widows to Linux, from single-machine to Hadoop multi-node computing(2) The problem is, in the face of huge amounts of data, how to tap into practical information or to find potential phenomena, visual tools may be essential;(3) Visualization tool can say Baidu

In this course of machine learning, Andrew first mentioned regression analysis under supervised learning. The programming job is to use MATLAB to implement regression. It mainly includes two aspects: computing cost and gradient descent. The calculation cost can be described in the following formula: Htheta (x) is the predicted value, and Y is the actual value. The objective is to minimize the gap

LDA stands for latent Dirichlet allocation. For more information about Lda, see Wikipedia. Here we will explain the LDA source code analysis (MatLab)Original code Author: Daichi mochihashiSource code: http://download.csdn.net/detail/nuptboyzhb/53051451. lda source code execution in the MATLAB environment1. Environment ConfigurationSwitch the working directory of

Application Scenario of MATLAB Simulation and Analysis of Doppler distortion signal re-sampling Underwater acoustic communication refers to the use of acoustic signals to transmit data in the water. Relatively speaking, the electromagnetic signal absorption in the water seriously degrades too fast, and the optical signal is affected by the suspension of particl

New book Unix/Linux Log Analysis and traffic monitoring is coming soon The new book "Unix/Linux Log Analysis and traffic monitoring" is about to release the 0.75 million-word book created in three years. It has been approved by the publishing house today and will be publishe

Description: If you want to use MATLAB to do Kmeans clustering analysis, directly using the function Kmeans can be. How to use: Kmeans (input matrix, number of categories K).Reprint One:MATLAB provides two methods for cluster analysis:1, the use of clusterdata function of the data samples of a cluster, the method is si

Add two KMV Model Documents Http://www.business.uiuc.edu/gpennacc/MoodysKMV.pdf Http://www.prmia.org/Chapter_Pages/Data/Chicago/Kurbat_Paper.PDF Risk management KMV model Matlab computing-instance analysis % Test KMV% R: risk-free rateR = 0.0425; % T: Time to expirationT = 1; % enter the number of months % DP: defaut point% SD: Short deb

using MATLAB to analyze the data collected by Chipscope, we hope to be useful to everyone. 1, the first as usual with chipscope data sampling. However, in order to facilitate the introduction of MATLAB to view, here we recommend to see the sampling signal to use bus bus, this is not much to say, should be. 2, click

label of the neighbor node with greater influence on this node.But these shortcomings do not hinder LPA's often used as a benchmark-contrast algorithm for paper, and this idea can be used in machine learning fieldHere should be a small case support, first look at someone else'sThe point is that the main function needs to load the classic data of the football club we used to use, unfortunately it can't be attached, and later I'll write a blog about

In-depth analysis of C # Wonderful book reviews Detailed information page address: http://www.china-pub.com/196689 This is a pure C # language book. It has little to do with. NET Framework and has little to do with CLR. As the preface of this book says, the author's intention is to explain the C # language so that ever