matrix

问题 Goal An activity is made to view image, we can pinch zoom or pan the image. The image is centered in the screen in the beginning. Pinch zoom is centered at the center of the image, even after the image is panned somewhere else in the screen. The image for displaying is downloaded from a given URL, and the URL is passed from extra of an intent to start the image viewing activity. Pinch zoom is implemented by postScale() , pan by postTranslate() . Problem After panning the image somewhere, the

问题 I can't seem to find any examples (or much information at all) of what a matrix array looks like for a Johnson graph. Can anyone send me an example of what their arrays look like? 回答1: This directly uses the definition of a Johnson Graph given here Wiki Johnson Graph johnsonmatrix[n_, k_] := Module[{s=Select[Subsets[Range[n]], Length[#]==k&]}, {s, MatrixForm[Table[If[Length[Intersection[s[[i]], s[[j]]]]==k-1, 1, 0], {i, Length[s]}, {j, Length[s]}]]}] and generates the list of subsets which

问题 I have this simple 5x5 button grid: from tkinter import * class App(): def __init__(self, root): self.root = root self.TopFrame = Frame(root) self.BottomFrame = Frame(root) self.TopFrame.grid(row=0) self.BottomFrame.grid(row=6) buttonQ = Button(self.BottomFrame, text="Quit", command=quit) buttonS = Button(self.BottomFrame, text="Save", command=self.saveToFile) buttonS.grid(row=0, column=0, padx=10) buttonQ.grid(row=0, column=1, padx=10) def Function(self): self.grid = [] for i in range(5):

问题 I need to get the indexes of all diagonals in a matrix. The matrix can be non square. The diag function gives the values, but I need the coords. So for instance with this: [1 2 3; 4 5 6; 7 8 9] , I want [1 1; 2 2; 3;3] PLUS [2 6] and 3 because they are the upper diagonals of the matrix, and same for below i.e. [4 8] and 7 . So the full list of indexes is: [1 1; 2 2; 3 3], [1 2; 2 3], [1 3], [2 1], [3 2], [3 1] . And I need this in the other diagonal direction too... And it needs to work for

问题 I tried to find an algorithm for the following problem, but I couldn't. you have a matrix, 10X6 if it matters. (10 on x dimension 6 on y dimension). the algorithm receives 2 point, the opening point and the target point. the array is full of 0's and 1's and it should find the shortest path of 1's between them, and return the first point in this path (the next point in the way to the target). But here's the catch: each point can get the value only of the following points: the point above it.

问题 I've got a simple problem and I can't figure out how to solve it. Here is a matrix: A = np.array([[1,0,3],[0,7,9],[0,0,8]]) . I want to find a quick way to replace all elements of this matrix by their inverses, excluding of course the zero elements. I know, thanks to the search engine of Stackoverflow, how to replace an element by a given value with a condition. On the contrary, I do not figure out how to replace elements by new elements depending on the previous ones (e.g. squared elements,

问题 Say I have a matrix of an image, and I want to do the following: Slide an 8x8 window over the matrix Calculate the mean for each pixel in the matrix How can I do that in matlab , provided that I'm kind of new to coding in matlab. Thanks. 回答1: You could use conv2 with a ones(8) filter, as in I2 = conv2(I, 1.0 / 64.0 * ones(8), 'valid'); . We divide by 64.0 because the "filter" isn't normalized. 回答2: You can also use nlfilter : fun = @(x) mean(x(:)); ans= nlfilter(img,[8 8],fun); But as @s

问题 Say I have a matrix of an image, and I want to do the following: Slide an 8x8 window over the matrix Calculate the mean for each pixel in the matrix How can I do that in matlab , provided that I'm kind of new to coding in matlab. Thanks. 回答1: You could use conv2 with a ones(8) filter, as in I2 = conv2(I, 1.0 / 64.0 * ones(8), 'valid'); . We divide by 64.0 because the "filter" isn't normalized. 回答2: You can also use nlfilter : fun = @(x) mean(x(:)); ans= nlfilter(img,[8 8],fun); But as @s

问题 I am using scipy to generate a sparse finite difference matrix, constructing it initially from block matrices and then editing the diagonal to account for boundary conditions. The resulting sparse matrix is of the BSR type. I have found that if I convert the matrix to a dense matrix and then back to a sparse matrix using the scipy.sparse.BSR_matrix function, I am left with a sparser matrix than before. Here is the code I use to generate the matrix: size = (4,4) xDiff = np.zeros((size[0]+1

问题 I have an array for which I would like to obtain a measure of the similarity between values in each column. By which I mean I wish to compare the rows between pairwise columns of the array and increment a measure when their values match. The resulting measure would then be at a maximum for two columns exactly the same. Essentially my problem is the same as discussed here: R: Compare all the columns pairwise in matrix except that I do not wish empty cells to be counted. With the example data