concurrent programming algorithms principles and foundations

Java Concurrency Programming Practice DirectoryConcurrent Programming 01--concurrenthashmapConcurrent programming 02--blocking queues and producer-consumer patternsConcurrent programming 03--latching countdownlatch and fence CyclicbarrierConcurrent programming 04--callable a

distributed RESTful Service video tutorial First set:billions of traffic e-Commerce Details page System combat-cache Architecture + high-Availability service architecture + MicroServices Architecture (first edition)Second set:billions of traffic e-Commerce Details page System combat-cache Architecture + high-Availability service architecture + MicroServices Architecture (second edition)The third set:Elaticsearch Video Two sets-full version (core and advanced step)Elasticsearch Top Master Series

Java concurrent Programming-various locks Security and activity are often mutually constrained. We use locks to keep threads safe, but abusing locks can cause lock-order deadlocks. Similarly, we use thread pools and semaphores to constrain the use of resources, But the lack of knowledge of which areas of activity may form a deadlock of resources. Java applications cannot recover from deadlocks, so it is va

. 7.5 910.5 . 13.5]]# n Powers of each element of the matrix: n=2mymatrix1 = Mat ([[[1,2,3],[4,5,6],[7,8,9]])print power (mymatrix1,2 1 4 9] [[49 6481]]# matrix multiplied by matrix mymatrix1 = Mat ([[1,2,3],[4,5,6],[7,8,9 = Mat ([[[1],[2],[3]])print mymatrix1*mymatrix2 output: [[[][+][50]]# Transpose of the matrix mymatrix1 = Mat ([[[1,2,3],[4,5,6],[7,8,9]])print mymatrix1. The transpose of the # Matrix to the transpose of the T # Matrix print mymatrix1 output results as follow

(First chapter above)1.2.5 Linalg Linear Algebra LibraryBased on the basic operation of matrices, the Linalg Library of NumPy can satisfy most linear algebra operations.. determinant of matrices. Inverse of the Matrix. Symmetry of matrices. The rank of the matrix. The reversible matrix solves the linear equation1. Determinant of matrices from Import * in[#N-order matrix determinant operation in [6]: A = Mat ([[[1,2,3],[4,5,6],[7,8,9]]) in [print]det (A):"6.66133814775e-162. Inverse of the Matrix

]) *double (Dy[i])#Sqx = double (Dx[i]) **2Sumxy= VDOT (Dx,dy)#returns the point multiplication of two vectors multiplySQX = SUM (Power (dx,2))#Square of the vector: (x-meanx) ^2#calculate slope and interceptA = sumxy/SQXB= meany-a*MeanxPrintA, b#Draw a graphicPlotscatter (XMAT,YMAT,A,B,PLT)7.1.4 Normal Equation Group methodCode implementation of 7.1.5 normal equation set#data Matrix, category labelsXarr,yarr = Loaddataset ("Regdataset.txt")#Importing Data Filesm= Len (Xarr)#generate x-coordinat