pca python

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 than the interpretation of original sample ch

://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***********************************

http://blog.csdn.net/jerr__y/article/details/53188573This article mainly refer to the following article, the text of the code is basically the second article of the Code handwritten implementation of a bit.-PCA Explanation: http://www.cnblogs.com/jerrylead/archive/2011/04/18/2020209.html-Python implementation: http://blog.csdn.net/u012162613/article/details/42177327Overall code"" "The Total code. Func: The

features is transformed or the first n features are transformed (the first n features contain most of the data), in short, the PCA is a dimensionality reduction process that maps the data to new features, the new feature being the linear combination of the original features.2. calculation process ( because the insertion formula is troublesome, it is directly used in the way )3.python Implement fromNumPyImp

the data after dimensionality reduction -Pca_svc =linearsvc () the #Learning - Pca_svc.fit (Pca_x_train, Y_train)WuyiPca_y_predict =pca_svc.predict (pca_x_test) the - #4 Model Evaluation Wu Print("accuracy of raw data:", Svc.score (X_test, y_test)) - Print("other ratings: \ n", Classification_report (Y_test, Y_predict, Target_names=np.arange (10). Astype (str ))) About $ Print("data accuracy rate after dimensionality reduction:", Pca_svc.score (Pca_x_test, y_test)) - Print("other ratings: \ n

The following is the process of using PCA to reduce the dimension of data:The Python source code is as follows:1 fromNumPyImport*;2 defLoaddataset (filename,delim='\ t'):3 #Open File4Fr=open (fileName);5 """6 >>> line0=fr.readlines ();7 >>> Type (LINE0)8 9 >>> Line0[0]Ten ' 10.235186\t11.321997\n ' One """ AStringarr=[line.strip (). Split (Delim) forLineinchFr.readlines ()]; - #The map func

a technique of 1.pandas Apply () and applymap () are functions of the Dataframe data type, and map () is a function of the series data type. The action object of the Apply () dataframe a column or row of data, Applymap () is element-wise and is used for each of the dataframe data. Map () is also element-wise, calling a function once for each data in series. 2.PCA decomposition of the German DAX30 index The DAX30 index has 30 stocks, it doesn't sound

Traditional one-dimensional PCA and LDA methods are based on image vectors in image recognition. In these face recognition technologies, 2D face image matrices must be first converted to 1D image vectors, then perform PCA or LDA analysis. The disadvantage is obvious: 1. After being converted to one dimension, the dimension is too large and the calculation workload increases. II. The training of principa

This article is based on two references of the same name.A Tutorial on Principal Component Analysis. PCA, or principal component analysis, is mainly used for dimensionality reduction of features. If the number of features in the data is very large, we can think that only a part of the features are truly interesting and meaningful, while other features or noise, or redundant with other features. The process of finding meaningful features from all featu

EXERCISE:PCA and Whitening No. 0 Step: Data Preparation UFLDL The downloaded file contains the dataset Images_raw, which is a 512*512*10 matrix, which is 10 images of 512*512 (a) data-loading Using the Sampleimagesraw function, extract the numpatches image blocks from the Images_raw, each image block size is patchsize, and the extracted image blocks are stored in columns, respectively, in each column of the matrix patches, That is, patches (:, i) holds all the pixel values of the first image blo

Softmax has been entangled for two days. The reason is that you accidentally changed the main program or pasted the code as usual. If you need it, you can go to the UFLDL tutorial. The effect is the same as that of UFLDL, I won't repeat the textures. ps: the code is matlab, not python's PCA and Whitening: pca_gen.m [python] % ======================================== ============================ x = sampleIM

Source: http://blog.csdn.net/xizhibei ================================== PCA, that is, principalcomponents analysis, is a very good algorithm, according to the book: Find the projection method that best represents the original data in the sense of least square. Then I said: it is mainly used for feature dimensionality reduction. In addition, this algorithm also has a classic application: face recognition. Here, we just need to take each line of the pr

Four machine learning dimensionality reduction algorithms: PCA, LDA, LLE, Laplacian eigenmapsIn the field of machine learning, the so-called dimensionality reduction refers to the mapping of data points in the original high-dimensional space to the low-dimensional space. The essence of dimensionality is to learn a mapping function f:x->y, where x is the expression of the original data point, which is currently used at most in vector representations. Y

The re-sequencing is cheap, and the sequencing and analysis of the population is also growing. The analysis of group structure is the most common analysis content of re-sequencing. The application of group structure analysis is very extensive, first of all, it is the most basic analysis content in the analysis of group evolution, secondly, when conducting GWAS analysis, it is necessary to use the results of PCA or structure analysis as a co-variable t

machine learning algorithm -PCA dimensionality reduction OneIntroductionThe problems we encounter in the actual data analysis problem usually have the characteristics of higher dimensionality, when we carry out the actual data analysis, we will not use all the features for the training of the algorithm, but rather pick out the features that we think may affect the target. For example, in the Titanic Crew survival prediction problem, we will use the na