Tag：component

Extension tecdatpython uses sparse, Gaussian random projection and principal component analysis PCA to reduce the dimension of MNIST handwritten digital data
Original link:http://tecdat.cn/?p=23599 Dimensionality reduction is used when we deal with large datasets that contain too many characteristic data, improve the calculation speed, reduce the size of the model, and visualize the huge datasets in a better way. The purpose of this method is to retain the most important data and delete most of the feature […]

R language finite mixed model clustering FMM, generalized linear regression model GLM mixed application analysis whisky market and research patent applications, expenditure data
Original link:http://tecdat.cn/?p=24742 abstract Finite mixture model is a popular method to model or approximate general distribution function of unobserved heterogeneity. They are used in many different fields, such as astronomy, biology, medicine or marketing. This paper gives an overview of these models and many application examples. introduce Finite mixture model is a popular method to […]

Combination mode of design mode
Composite mode is also called composite (part whole) mode, which belongs to structural mode. The composite pattern organizes objects into a tree structure, which can be used to describe the relationship between the whole and parts, and enable the client to treat simple elements and composite elements equally. Tree structure has played a great role […]

23 design patterns java version Part 4
PS: This article is a reprint of the article. You can get the source code by reading the original text. There is a link to the original text at the end of the article PS: This article is about bridging mode, combination mode and sharing mode 1. Bridging mode The abstract part is independent of […]

R language matrix eigenvalue decomposition (spectral decomposition) and singular value decomposition (SVD) eigenvector analysis of securities data
Original link: http://tecdat.cn/?p=23973 R language is a very convenient data analysis language, which has builtin many methods of processing matrix. As a part of data analysis, we need to do some work on the operation of securities matrix, which only needs a few lines of code. The securities data matrix is here D=read.table(“securite.txt”,header=TRUE) M=marix(D\[,2:10\]) […]

Method of registering 32bit COM components in 64 bit operating system
When registering COM components in the 64 bit operating system, the Regsvr32 command is used to register successfully, but the component creation in the VBS file fails, prompting that the relevant components cannot be found, err Munber=429。 according to http://support.microsoft.com/kb/249873 Tips for: The 64bit version is %systemroot%\System32\regsvr32.exe. The 32bit version is %systemroot%\SysWoW64\regsvr32. exe. Looking at […]

Python uses tsne nonlinear dimensionality reduction technology to fit and visualize highdimensional data iris iris and MNIST data
Original link: http://tecdat.cn/?p=24002 Tdistributed stochastic neighbor embedding (tsne) is a tool for visualizing highdimensional data. Based on random neighborhood embedding, tsne is a nonlinear dimensionality reduction technology, which is used to visualize data in twodimensional or threedimensional space. Python API provides tsne methods to visualize data. In this tutorial, we will briefly learn how to […]

Understand the relationship between serviceoriented architecture (SOA) and microservices
**SOA is a software application architecture method. It is based on objectoriented, but not objectoriented. On the whole, it is a serviceoriented architecture. SOA is composed of precise service definition, loose component services, and business process invocation.Does this sound a little dizzy? Let’s read it carefully** Architecture Idea of SOA (I)SOA architecture is serviceoriented, just […]

R language principal component regression (PCR) and multiple linear regression feature dimensionality reduction analysis vehicle fuel consumption, design and performance data and spectral data
Original link:http://tecdat.cn/?p=24152 What is PCR? （PCR = PCA + MLR） • PCR is a regression technique that processes many x variables• given y and X data:• PCA on X matrix– score: new principal variable• in multivariate linearity_ Return_ (_MLR_) Some of these new variables are used to model / predict y• y may be univariate […]

[mindspire: let’s learn machine with little Mi!] How to achieve dimensionality reduction?
I haven’t seen you for a week. I miss you very much! Today, little Mi takes you to learn how to reduce dimension, which is the second type of unsupervised learning problem we encounter! No more nonsense. Let’s start~ 1 dimension reduction exampleFirst, what is dimensionality reduction? This problem should be clarified first. Since data […]

R language principal component analysis PCA spectrum decomposition, singular value decomposition SVD prediction analysis of athlete performance data and dimensionality reduction visualization
Original link:http://tecdat.cn/?p=25067 This article describes how to useRimplementprincipal component analysis ( PCA)。 You will learn how to} use PCA_ Forecast_ New individual and variable coordinates. We will also provide_ PCA results_ The theory behind it. There are two general methods to perform PCA in R: _ Spectral decomposition_ , check the covariance / correlation between variables […]