best diagonal cutters

table 1)　　To summarize , there are two aspects:1) The variance of the line vectors is as large as possible, Cov (x,x) Cov (y,y) (variance)2) The covariance between line vectors is 0 Cov (x, y) Cov (y,x)A column vector is a vector of values in the "new axis" projection after the data has been reduced to dimension.We combine these two aspects of a matrix, which is called the covariance matrix, our goal is to make its diagonal other than the covariance

First, the basic concept Dip:density independent pixels, device independent pixels. DP: Just Dip PX: pixels dpi : dots per inch, which is directly the number of pixels in an inch. A common value of 120,160,240. I am generally referred to as pixel density, short density density : Direct translation of the words seems to be called density. Common values are 1.5, 1.0. Proportional to the standard dpi (160PX/INC) Resolution: The number of pixel points in 2 directions, co

after rotating 90 degrees clockwise is:7 4 18 5 29 6 3An equivalent transformation can be summed up:First, each element of the matrix is exchanged along the main diagonal;1 2 3-1) 4 74 5 6-2 5 87 8 9-3) 6 9Then, the elements of each column are flipped symmetrically along the horizontal midline, where the first and third columns are symmetric about the second column, so just swap the one or three column elements to get the result after 90 degrees of r

compressed storage of matrices (sparse matrices)NoteThis article reproduces the address:http://blog.163.com/zhoumhan_0351/blog/static/39954227201001112526244/In order to save storage space and speed up processing, this kind of matrix needs to be compressed, and the principle of compressing storage is that the same elements are not stored repeatedly; 0 value elements are not stored.first, related concepts㈠ Special matricesIn the matrix, most of the values are the same, or not 0, and the distribut

N Queen's questionTime limit:2000/1000 MS (java/others) Memory limit:32768/32768 K (java/others)Total Submission (s): 9579 Accepted Submission (s): 4314Problem DescriptionN-Queens are placed in the N*n's checkered checkerboard, making them not attack each other (that is, any 2 queens are not allowed to be in the same row, in the same column, or in a diagonal line with a 45-angle checkerboard border.)Your task is to find out how many legal placement me

the same column; 2) b position the lattice above the left diagonal diagonal 3) b position at the top right diagonal of the grid; line does not need to detect, because one line can only place a queen, if there is already a queen and B peers, the check will not proceed.Checkerboard data structure and code to create a chessboard#include "stdafx.h"#include using nam

same as N=norm (a) Usage INF returns the largest row in a and, that is, Max (SUM (ABS (A))) Fro ' returns the diagonal and square root of the product of a and a ', that is, sqrt (sum ( Diag (A ' *a))) 2. If a is a vectorNorm (a,p) returns the P-norm of vector A. That is, return sum (ABS (A). ^p) ^ (1/p), to any p greater than 1 less than positive infinity;Norm (a) returns the 2 norm of Vector A, which is equivalent to Norm (a,2), which is sum (ABS

Detailed analysis of phpaddslashes functions. Syntax: stringaddslashes (stringstr); content description this function adds a diagonal line to the quotation marks in the strings to be processed by the database, so that the database query can operate smoothly. This syntax: string addslashes (string str ); Description This function adds a diagonal line to the quotation mark of the string to be processed by the

result was less than 2^31Sample input:4 4 30 2 4 60 2 4 61 13 36 6Sample output8Topic Analysis:The main idea is to enter some lines parallel to the x-axis, the y-axis, and some points to output the number of squares containing a bit of these lines.This is a problem covered by the area. There are two difficulties in the subject: 1. How to judge and count all squares (not rectangles), 2. How to determine if these squares contain points.My idea is to figure out all the squares and then walk throug

want to look for a constant quantity in a change, called a feature. If a constant quantity is changed after a linear change in the direction of the invariance, that is, then called the eigenvector, the corresponding eigenvalues. From the actual example to talk about "characteristics" is not "characteristic", about this part of the content can refer to the Mathematics of machine Learning (5)-powerful matrix singular value decomposition (SVD) and its application, interpretation is very detailed a

Test instructions given 4*4 's chessboard, each position is "B","W" or " ", indicating black, white, or space. The target game is defined as four children with one row, one column, or the same color on a diagonal line. The black and white alternating, the beginning of either party first, the minimum number of steps to achieve the target chess.AnalysisDefine the variables first:Basic Q(1)wt[2][2] record space position (2)int p[4][4] record value, Blac