auth principal

ThinkPHP permission authentication Auth instance details, thinkphpauth This article provides an in-depth analysis of the implementation principles and methods of ThinkPHP permission authentication Auth in the form of instance code. The specific steps are as follows: Some SQL code of mysql database: -- Optimize Table structure for think_auth_group -- -------------------------- drop table if exists 'think _ a

Abstract: PCA (principal component analysis) is a multivariate statistical method. PCA uses linear transformation to select a small number of important variables. It can often effectively obtain the most important elements and structures from overly "rich" data information, remove Data Noise and redundancy, and reduce the original complex data dimension, reveals the simple structure hidden behind complex data. In recent years, PCA has been widely used

This article mainly introduces the ThinkPHP permission authentication Auth instance. if you need it, you can refer to the following example code to thoroughly analyze the implementation principles and methods of ThinkPHP permission authentication Auth. the specific steps are as follows: Some SQL code of mysql database: -- Optimize Table structure for think_auth_group -- -------------------------- drop tab

[Machine Learning Algorithm Implementation] Principal Component Analysis (PCA)-based on python + numpy, pcanumpy[Machine Learning Algorithm Implementation] Principal Component Analysis (PCA)-based on python + numpy @ Author: wepon@ Blog: http://blog.csdn.net/u012162613/article/details/42177327 1. Introduction to PCA Algorithms Principal Component Analysis (PCA) i

. NET implements the identity and principal of custom Contextuser In the traditional. NET, we can pass user.identity.name;//Get user name user.identity.isauthenticated;//judge whether the user has verified user.isinrole ("Admin"); To determine whether a user has a specified role But such a mechanism, in the actual development, it is difficult to meet the development needs. It is difficult to meet the needs simply by User

dimensionality reduction (i)----the source of principal component analysis (PCA)Reduced Dimension Series: dimensionality reduction (i)----the source of principal component analysis (PCA) dimensionality Reduction (ii)----Laplacian Eigenmaps ---------------------Principal component Analysis (PCA) is introduced in many tutorials, but why is the

Module Name: pca.pyPCA principle and the principle of tightening techniques to be mended ...#-*-coding:utf-8-*-" "Created on March 2, 2015 principal component analysis of p-14 image of @author:ayumi Phoenixch01" " fromPILImportImageImportNumPydefPCA (X):"""principal component analysis: input; matrix x each behavior a training data return: Projection matrix (sorted by dimension importance), variance, and mea

Calculation of interest on deposits. There are 1000 yuan, 5 years, the following five kinds of options:(1) 5-year deposit, interest rate R5 = 0.0585(2) First deposit 2 years, after the expiry of the principal and interest to save 3 years, Interest Rate r2 = 0.0468 , R3 = 0.054(3) First deposit 3 years, after the expiry of the principal and interest to save 2 years(4) Deposit 1 years, after expiry the

Laravel container delay loading and auth extension details, laravelauth. Laravel container delay loading and auth extension Details. laravelauth wrote an Auth extension according to the manual tutorial yesterday. according to the packet independence principle, I do not want to add Auth: extend () laravelauth I wrote a

Principal factor analysis, mentioned in the refining into gold course:?A method of dimensionality reduction is the generalization and development of principal component analysis.?is a statistical model used to analyze the effects of factors behind surface phenomena. An attempt to use the least number of non-measurableThe sum of the linear function and the special factor of the common factor to describe each

where P is full, r is monthly, and N is the number of periods.For equal principal and interest, the repayment amount of each month is the same, but the interests are decreasing, the first month is the full one-month interest, the second month is to remove the first month after the principal amount of the remaining one months interest, as follows:Assuming a monthly repayment of X, full m, monthly RA1 = X-m*r

The contents of this lesson: Factor analysis The derivation process of EM step in---factor analysis Principal component analysis: an effective way to reduce dimensions the problem of mixed Gaussian model with factor analysis Next, we discuss the factor analysis model, and before introducing the factor analysis model, we look at another way of writing the Gaussian distribution, whichWriting is the basis

Preface: Because OBJECT-C does not support multiple inheritance, it is often replaced with protocol (protocol). The Protocol (protocol) can only define a common set of interfaces, but cannot provide a specific implementation method. That is, it only tells you what to do, but specifically how to do it, it does not care.When a class is going to use a certain protocol (protocol), it must obey the protocol. For example, there are some necessary ways to implement, you do not implement, then the compi

PCA, principal component analysis　　Principal component analysis is mainly used for dimensionality reduction of data. The dimensions of the data features in the raw data may be many, but these characteristics are not necessarily important, and if we can streamline the data features, we can reduce the storage space and possibly reduce the noise interference in the data.For example: Here is a set of data, as s

Monthly repayment calculation formula of equal and principal interest:Monthly principal and interest amount = (Principal x monthly rate X (1+ month rate) ^ repayment month) ÷ ((1+ monthly rate) ^ Repayment month-1))Reverse Seeking Moon interest rateThe monthly rate is not calculated if it is reversed according to the above formula.Here is a calculation of the spe

Principal component Analysis (principal components ANALYSIS,PCA) is a simple machine learning algorithm, the main idea is to reduce the dimension of high-dimensional data processing, to remove redundant information and noise in the data.Algorithm:Input sample: D={x1,x2,⋯,xm} d=\left \{x_{1},x_{2},\cdots, x_{m}\right \}The dimension of low latitude space Process: •1: All samples are centralized: Xi←xi−1m∑mi=

The Auth class has been around for a long time in the thinkphp code repository, but since it has not been a tutorial, few people know it, it is actually more convenient than RBAC.RBAC is based on the node authentication, if you want to control more than the node finer permissions is a bit difficult, such as the action button on the page, I want to determine the user rights to display this button, if no permissions will not show this button; What to do

Laravel container delay loading and auth extension Yesterday follow the manual tutorial, write a auth extension, according to the principle of package independence, I do not want to auth::extend () This method written in start.php, no doubt, I chose to register the extension driver in the service Provider register () method. However, it backfired ... Discover pr

Laravel5.5 Source code detailed –auth middleware In order to reflect the full picture, the previous Code section did not do too much pruning, focusing on the annotated part of the special addition. The original comments are deleted to reduce the length of the reading. This article focuses on the following process explanations, which are more detailed. If you look at official documents, you often don't know what to do when you run into problems. There

formula for X by Y is as follows: X ' = Aky +mx (2.4) At this time cy = diag (λ1,λ2,..., λk), the mean square error between x and X. can be expressed by the following formula: Λk+1+.λk+2...+λn (2.5) (No Formula editor AH) Above we mentioned that for the eigenvalues λ is from large to small sort, then this time through the equation 2.5 can be shown by selecting K has the largest eigenvalue of the eigenvector to reduce the error. Therefore, the K-L transformation is the best transformatio