principal component analysis python

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[Mathematical model] python Implementation of principal component analysis and python Analysis Def pca (dataMat, topNfeat = 9999999): # data matrix. The top feat meanVals = mean (dataMat, axis = 0) is output) # calculate the aver

main component from the largest contribution rate, until the cumulative contribution rate to meet the requirements;Then define the principal component load (loadings, which is called the factor load in the factor analysis):That is, the correlation coefficients of the first princi

components in a lower proportion of PCA dimensionality, it uses a number of random algorithms to accelerate SVD. Full is the traditional SVD, using the corresponding implementation of the SCIPY library. Arpack and randomized similar to the applicable scenario, the difference is that randomized uses scikit-learn own SVD implementation, and Arpack directly uses scipy the library sparse SVD implementation. The default is auto, that is, the PCA class will go through the three alg

factors other than the data set.2) orthogonal between the main components, can eliminate the interaction between the original data components of the factors.3) Calculation method is simple, the main operation is eigenvalue decomposition, easy to achieve.The main drawbacks of PCA algorithms are:1) The meaning of each characteristic dimension of principal component has certain fuzziness, which is not better

[Machine Learning Algorithm Implementation] Principal Component Analysis (PCA)-based on python + numpy, pcanumpy[Machine Learning Algorithm Implementation] Principal Component Analysis

The person who picks the projectSource: http://blog.csdn.net/Dream_angel_Z/article/details/50760130GitHub Source code: Https://github.com/csuldw/MachineLearning/tree/master/PCAPCA (principle component analysis). Principal component analysis is mainly used to reduce the dimen

Principle of principal component analysis and its Python implementation preface:This article mainly refers to Andrew Ng's machine learning course handout, I translated, and with a Python demo demo to deepen understanding.This paper mainly introduces a dimensionality reductio

://matplotlib.org/downloads.html(3) Dateutil and pyparsing modules: required when installing the configuration Matplotlib package. installation files for Win32: http://www.lfd.uci.edu/~gohlke/pythonlibs/3. The compilation encountered a problem:(1) Hint "No module name six", copy six.py Six.pyc six.pyo three files from \python27\lib\site-packages\scipy\lib to \python27\lib\ Site-packages directory.(2) Hint "Importerror:six 1.3 or later is required; You have 1.2.0 ", stating that the six.py versio

references: The reference is the low-dimensional matrix returned. corresponding to the input parameters of two.The number of references two corresponds to the matrix after the axis is moved.The previous picture. Green is the raw data. Red is a 2-dimensional feature of extraction.3. Code Download:Please click on my/********************************* This article from the blog "Bo Li Garvin"* Reprint Please indicate the source : Http://blog.csdn.net/buptgshengod***********************************

[Mathematical model] python Implementation of principal component analysis Def pca (dataMat, topNfeat = 9999999): # data matrix. The top feat meanVals = mean (dataMat, axis = 0) is output) # calculate the average meanRemoved = dataMat-meanVals covMat = cov (meanRemoved, rowvar = 0) # Calculate the covariance matrix ei

def PCA (Datamat, topnfeat=9999999): #数据矩阵, output before Topnfeat feat meanvals = Mean (Datamat, axis=0) # Calculate Average meanremoved = datamat-meanvals Covmat = CoV (meanremoved, rowvar=0) #计算协方差矩阵 eigvals,eigvects = Linalg.eig (Mat (Covmat)) #特征值, eigvalind = Argsort (eigvals) #排序, to find the eigenvalues. In fact, the most inconsistent with other changes Eigvalind = eigvalind[:-( topnfeat+1): -1] #反转 redeigvects = eigvects[

)#Display OutputFigure () Gray () forIinchRange (File_len): Subplot (3,4,i + 1) pic= Immatrix[i].reshape (180,360) pic= Pic[::-1]#picshow = rot90 (pic,4)imshow (pic) Colorbar () show ()#Convert to sample populationX =Immatrix. T#get to this sizeM,n = X.shape[0:2]#obtain the average of each sampleMeanval = mean (X,axis =0)#Tempmean = Tile (Meanval, (64800,1))#Sample matrix de -CentralizedX = X-tile (Meanval, (64800,1))#Calculate covarianceS = dot (x.t,x)/(m-1)#calculate eigenvalues eg and eigenve

principal component Analysis ( Principal Component Analysis , PCA is a multivariate statistical analysis method that transforms multiple variables through a linear transformation to sel

Principle analysis of PCA algorithm for principal component analysesDiscussion on the understanding of principal component Analysis (PCA) algorithmPrincipal component

Http://matlabdatamining.blogspot.com/2010/02/principal-components-analysis.htmlEnglish principal components Analysis of the blog, write very good, worried after not open, full text reproduced.Principal Components AnalysisIntroductionReal-world data sets usually exhibit relationships among their variables. These relationships is often linear, or at least approxima

The factor analysis is based on the probabilistic model, and the parameters are estimated by using the iterative method of EM algorithm. The principal component analysis (Principal, PCA) only passes the linear change, and uses a few prin

Principal component Analysis (Principal)-Minimum squared error interpretation connected to the previous article3.2 Minimum squared error theoryAssuming that there are two-dimensional sample points (red dots), we are looking at a line so that the variance of the sample points projected onto the line is the largest. The

the straight line, the direction vector of the line, and is the unit vector.4) The mean value of each dimension feature of the sample point (sample) is equal to the mean of the sample point projected onto U.The best projection vector u can make the sample point variance maximum after projection.In this case, the known mean value is 0, so the variance isTherefore, λ is the eigenvalues of Σ, and U is the eigenvector. The best projection line is the characteristic vector that corresponds to the ma

Principal Component analysis (PCA)Also called:Principal Component Analysis and Principal Component Regression AnalysisDirectory 1. What is princ

related to his "achievement", using the method of mutual information.Many of the features here are related to class labels, but there is noise or redundancy. In this case, a feature reduction method is required to reduce the number of features, reduce noise and redundancy, and reduce the likelihood of overfitting.A method called Principal component Analysis (PCA