I want to create a mathematical model for 2d bin packing optimization problem. I am not quite sure if it is bin packing problem it may be called strip packing, anyway let me introduce the problem.
1- There are some group of boxes to be placed on strips (see article 3.)
2- Each group contains a number of boxes which have same width and same hight. For example,
group A
100 boxes with width = 80cm and height = 120cm
group B
250 boxes with width = 150cm and height = 200cm
etc.
3- There are unlimited number of equal sized strips which have fixed width and height, for example
infinite number of Width = 800cm and Height 1400cm
4- The main goal is packing these boxes into minimum number of the strips. However, there are some restrictions to do this job.
5- If we think of the strips as a 2d row and column plane, at each column must has a fixed width of boxes. For example, if (column 0 and row 0) has a box w=100,h=80 then (column 0 and row 1) also has to has a box w=100,h=80. It is not allowed to be in the same column for diferent sized boxes. This rule is not valid for rows. Each row can have different sized boxes, there is no restriction.
6- It is not important to fill the whole strip. We want to fill strips with minimum space between boxes. The heighest column indicates a stop line through other columns and we calculate the loss value (space ratio over the whole strip area).
I tried to implement this optimization problem with GLPK linear programming tool. I have used a mathematical model from the paper (C. Blum, V. Schmid Solving the 2D bin packing problem by means of a hybrid evolutionary algorithm)
C. Blum, V. Schmid Solving the 2D bin packing problem by means of a hybrid evolutionary algorithm
This math model works great in the GLPK. However, it is designed for boxes for packing in x,y coordinates. If you see article 5 we want them in a fixed-width column fashion.
Can you please help me to modify the mathematical model to make possible to implement article 5.
Thank you all,
Related
I have a raster image with multiple polyline feature classes over it. The lines are not overlapping but they are in multiple different orientations. For every pixel in the raster, I want to calculate the length of the line through that pixel so that the result would be a raster with cells assigned a float value of zero to 2^0.5 times the cell size. What's the best way to do this? I'm using ArcPro with an advanced license.
You can have a look at the answers to a similar question here (using R --- but you have a license for that too)
https://gis.stackexchange.com/questions/119993/convert-line-shapefile-to-raster-value-total-length-of-lines-within-cell/120175
Is there any available DM script that can compare two images and know the difference?
I mean the script can compare two or more images, and it can determine the similarity of two images, for example the 95% area of one image is same as another image, then the similarity of these two images is 95%.
The script can compare brightness and contrast distribution of images.
Thanks,
This question is a bit ill-defined, as "similarity" between images depends a lot on what you want.
If by "95% of the area is the same" you mean that 95% of the pixels are of identical value in images A & B, you can simply create a mask and sum() it to count the number of pixels, i.e.:
sum( abs(A-B)==0 ? 1 : 0 )
However, this will utterly fail if the images A & B are shifted with respect to each other even by a single pixel. It will also fail, if A & B are of same contrast but different absolute value.
I guess the intended question was to find similarity of two images in a fuzzy way.
For these, one way is to do crosscorrelation. DM has this function. Like this,
image xcorr= CrossCorrelate(ref,img)
From xcorr, the peak position gives x- and y- shift between the two, the peak intensity gives "similarity" of the two.
If you know there is no shift between the two, you can just do the sum and multiplication,
number similarity1=sum(img1*img2)
Another way to do similarity is calculate Euclidian distance of the two:
number similarity2=sqrt(sum((img1-img2)**2)).
"similarity2" calculates the "pure" similarity. "similarity1" is the pure similarity plus the mean intensity of img1 and img2. The difference is essentially this,
(a-b)**2=a**2+b**2-2*a*b.
The left term is "similarity2", the last term on the right is the "crosscorrelation" or "similarity1".
I think "similarity1" is called cross-correlation, "similarity2" is called correlation coefficient.
In example comparing two diffraction patterns, if you want to compute the degree of similarity, use "similarity2". If you want to compute the degree of similarity plus a certain character of the diffraction pattern, use "similarity1".
how to trace isosurface on a higher dimensional space efficiently
You have a scalar cost function in N dimensions,
f(y0, y1, .., yN) ∊ ℝ, y ∊ ℝ
but sampled only in a regular rectangular grid,
yk = Ψk + ψk xk, constants Ψk ∊ ℝ and ψk ∊ ℝ, and grid coordinates xk ∊ ℕ
and the problem is to locate the isosurface(s) i,
f(y0, y1, .., yN) = Ci
The direct approach would be to just loop over each cell in the grid, and check if the current isosurface intersects the current cell, and if so, describe the part of the isosurface within the current cell. (Marching Cubes is one approach to describing how the isosurface intersects each grid cell.)
The restriction here is to use a neighborhood based search instead of examining every single cell.
OP had a previous question specifically for the 3D case, to which I posted a link to example code, grid.h and grid.c (at Pastebin.com, because they were too long to include inline).
That implementation is completely different to OP's slicing method. Mine is a direct, simple walk over the grid cells intersecting the current isosurface. It caches the grid samples, and uses a separate map (one char per grid cell) to keep track which grid cells have been cached, walked, and/or pushed to a stack to be walked later. This approach is easily extended to more than three dimensions. Although the code is written for exactly three dimensions, the approach itself is not specific to three dimensions at all; all you need to do is to adjust the data structures to accommodate any (sensible) number of dimensions.
The isosurface walk itself is trivial. You start from any grid cell the isosurface intersects, then examine all 2N nearest neighbor cells to see if the isosurface intersects those too. In practice, you use a stack of grid cell locations to be examined, and a map of grid cell flags to avoid re-examining already examined grid cells.
Because the number of grid point samples per grid cell is 2N, my example code is not optimal: a lot of nearby grid points end up being evaluated to see if the neighboring grid cells do intersect the isosurface. (Instead of examining only the grid points delimiting the isosurface, grid points belonging to any grid cells surrounding the isosurface are examined.) This extra work grows exponentially as N increases.
A better approach would be to consider each of the 2N possible (N-1)-faces separately, to avoid examining cells the isosurface does not intersect at all.
In an N-dimensional regular rectangular grid, each cell is an N-dimensional cuboid, defined by the 2N grid points at the vertices (corners). The N-cuboid cells have N(N-1) two-dimensional faces, and 2N (N-1)-dimensional faces.
To examine each (N-1)-face, you need to examine the cost function at the 2N-1 grid points defining that (N-1)-face. If the cost function at those points spans the isosurface value, then the isosurface intersects the (N-1)-face, and the isosurface intersects the next grid cell in that direction also.
There are two (N-1)-faces perpendicular to each axis. If the isosurface intersects the (N-1)-face closer to negative infinity, then the isosurface intersects the next grid cell along that axis towards negative infinity too. Similarly, if the isosurface intersects the (N-1)-face closer to positive infinity, then it also intersects the next grid cell along that axis towards positive infinity too. Thus, the (N-1)-faces are perfect for deciding which neighboring cells should be examined or not. This is true because the (N-1)-face is exactly the set of grid points the two cells share.
I'm very hesitant to provide example C code, because the example code of the same approach for the 3D case does not seem to have helped anyone thus far. I fear a longer explanation with 2- and 3-dimensional example images for illustration would be needed to describe the approach in easily understandable terms; and without a firm grasp of the logic, any example code would just look like gobbledygook.
You are better using a library for 2 dimension you can try the conrec algorithm from Prof. Paul Bourke. It's similar to a marching cube.
I'm trying to highlight some text with a glyph width of 1000 (which corresponds to 1 unit of text space)and font size of 1; the transformation matrix is [50 0 0 50 0 0]. The result is text that is too big. But this is not the case. The text that is being displayed is not big at all; it's a normal size.
Any PDF reader I open the file with has no problems highlighting the word, which means that I'm missing something somewhere.
Currently I'm checking for the default font and the font array in the fonts dictionary, the font size, and the transformation matrix. Is there any other way to scale text in a PDF besides the ones I just mentioned?
This answer combines the comments to the original question:
Currently I'm checking for the default font and the font array in the fonts dictionary, the font size, and the transformation matrix. Is there any other way to scale text in a PDF besides the ones I just mentioned?
A few possibility coming to my mind immediately:
A new transformation matrix (argument to cm) does not replace the old one; instead it is multiplied to it (from the left).
In case of q ... Q you have to consider resets of the current transformation matrix.
(The current transformation matrix, line widths, colors, overprint settings, and much, much more are part of the graphics state. To get an impression, have a look at the entries in tables 57 and 58 of the PDF specification ISO 32000-1. At least all the properties described there are part of the graphics state and, therefore, saved during q and restored during Q.)
Furthermore there is the text matrix to consider.
Finally the UserUnit entry of the page might change the rules.
So there's more to look at than the text positioning operators.
For a good overview have a look at section 9.4.4 Text Space Details of the PDF specification, especially Note 2 therein. (Thanks to #plinth.)
Imagine there is a very very large room in the shape of a hollow cube. There are magic balls hanging in the air at fixed discrete positions of the room. No magic ball has another one exactly above it. If we take an imaginary horizontal plane of infinite area and pass through the cube, how can we be sure that the plane doesn't cut through any of the magic balls ?
The height of a magic ball is given as a function of its position (x and y). The distribution is such a way that some balls are at the same height while other are at different heights.
Let the function be
z = axy + bx + cy
where a,b,c are positive integer constants.
The positions (x-axis and y-axis values) and also the height (z) are discrete values (for simplicity, we can consider them positive integers).
If the ball distribution function was z=10xy+8x+4y, then it is impossible to have a z value of 15 or 21. So a plane at z=15 or z=21 would not cut any of the balls! In fact, in this case, any plane with a height (z = any odd number) would not cut through the balls. It is noticeable that there a some planes with height as even numbers that donot cut through the balls.
We do not want to find the heights of all the magic balls and compare it with the height of the horizontal plane, as that would be like trying all the possible combinations and would take very long time even on a computer.
Our aim is to find a fast method by which we can tell whether a given value of z (height) can be produced by any pair of (x,y) (positions).If a given z cannot be produced, then a plane at that height doesn't cut through any balls!
The question is also similar to finding whether a given number is present in a sequence produced by a function of two variables.
It would a great help if U could give me any suggestions to solve this problem. Thank You.
(I have already tried evolutionary computing like GA,PSO,DE,SA etc. The method needs to be deterministic).
It sounds like you have a lot of balls in the room. The room height is from z=A to z=B. What you're interested in is whether any are at height z. To do that without necessarily iterating through all the balls, you need to start by assuming that the range A to B is empty and iterate through the balls, marking parts of this range as full, until it is either full completely or there are no balls. In the former case, no plane will satisfy, but you haven't iterated through all the balls to know that. In the latter case, you have ranges of z in which there are no balls and you can use these to check more than one plane easily, however at the initial cost of iterating through all balls.