I am starting to work with a CFD fortran program, and want to update the variables that it writes to an output file.
I want to output several columns, I and J coordinates(IL and JL), Water Surface Elevation (SURFEL), Bottom Elevation of coordinate (BELV), Depth of Water (HP) and finally, and this is where I have the question, the Maximum Water Surface Elevation of the coordinate during the simulation (SURFELMAX). L refers to a specific I,J coordinate, LA is the last coordinate in the simulation
So far I have:
DO L=2,LA
SURFEL=BELV(L)+HP(L)
IF (SURFEL.GT.SURFELMAX)THEN
SURFELMAX=SURFEL
ELSE IF (SURFELMAX.GT.SURFEL) THEN
SURFELMAX=SURFELMAX
WRITE(10,200)IL(L),JL(L),SURFEL,SURFELMAX
ENDIF
ENDDO
Everything works ok other than the SURFELMAX, in which the highest recorded surface elevation that occurred in any coordinate in the whole domain is written for each coordinate, i.e. the column is filled with the same value, the highest experienced in the whole domain during the simulation.
Would I need to first allocate an array for SURFELMAX, and have SURFEL checked against it each time to see if it has increased? If so could somebody point me in the right direction for this?
If I understand the requirements correctly, then you want to calculate SURFELMAX before you start writing out. This could simply be:
SURFELMAX = MAXVAL(BELV(2:LA)+HP(2:LA))
WRITE(10,200) (IL(L), JL(L), BELV(L)+HP(L), SURFELMAX, L=2,LA)
(or even as a single line).
It appears I didn't understand correctly; I'll try again - keeping the above as a warning to others.
It seems that you do indeed want SURFELMAX(2:LA) where each element is the highest in a given cell to date.
do L=2, LA
SURFELMAX(L) = MAX(SURFELMAX(L), BELV(L)+HP(L)) ! Store the historical maximum
WRITE (10,200) IL(L), JL(L), BELV(L)+HP(L), SURFELMAX(L)
end do
where, initially, SURFELMAX has been set to a sufficiently small value. You could also explicitly calculate SURFEL if that is needed.
If this is time dependent, then you will have to define a 2-d array SURFELMAX of size (1:LA,1:T) (T = number of time steps, LA = number of active coordinates).
Then increment the time step (say, the iterator is called I_T) outside of the loop through the domain.
Finally assign the maximum value at each coordinate to the SURFELMAX(I_T,L)
Related
So this question has been asked a few times, but I think my C++ skills are too deficient to really appreciate the answers. What I need is a way to start with an HEVC encoded video and end with CSV that has all the motion vectors. So far, I've compiled and run the reference decoder, everything seems to be working fine. I'm not sure if this matters, but I'm interested in the motion vectors as a convenient way to analyze motion in a video. My plan at first is to average the MVs in each frame to just get a value expressing something about the average amount of movement in that frame.
The discussion here tells me about the TComDataCU class methods I need to interact with to get the MVs and talks about how to iterate over CTUs. But I still don't really understand the following:
1) what information is returned by these MV methods and in what format? With my limited knowledge, I assume that there are going to be something like 7 values associated with the MV: the frame number, an index identifying a macroblock in that frame, the size of the macroblock, the x coordinate of the macroblock (probably the top left corner?), the y coordinate of the macroblock, the x coordinate of the vector, and the y coordinate of the vector.
2) where in the code do I need to put new statements that save the data? I thought there must be some spot in TComDataCU.cpp where I can put lines in that print the data I want to a file, but I'm confused when the values are actually determined and what they are. The variable declarations look like this:
// create motion vector fields
m_pCtuAboveLeft = NULL;
m_pCtuAboveRight = NULL;
m_pCtuAbove = NULL;
m_pCtuLeft = NULL;
But I can't make much sense of those names. AboveLeft, AboveRight, Above, and Left seem like an asymmetric mix of directions?
Any help would be great! I think I would most benefit from seeing some example code. An explanation of the variables I need to pay attention to would also be very helpful.
At TEncSlice.cpp, you can access every CTU in loop
for( UInt ctuTsAddr = startCtuTsAddr; ctuTsAddr < boundingCtuTsAddr; ++ctuTsAddr )
then you can choose exact CTU by using address of CTU.
pCtu(TComDataCU class)->getCtuRsAddr().
After that,
pCtu->getCUMvField()
will return CTU's motion vector field. You can extract MV of CTU in that object.
For example,
TComMvField->getMv(g_auiRasterToZscan[y * 16 + x])->getHor()
returns specific 4x4 block MV's Horizontal element.
You can save these data after m_pcCuEncoder->compressCtu( pCtu ) because compressCtu determines all data of CTU such as CU partition and motion estimation, etc.
I hope this information helps you and other people!
I am learning about Two Dimensional Neuron Network so I am facing many obstacles but I believe it is worth it and I am really enjoying this learning process.
Here's my plan: To make a 2-D NN work on recognizing images of digits. Images are 5 by 3 grids and I prepared 10 images from zero to nine. For Example this would be number 7:
Number 7 has indexes 0,1,2,5,8,11,14 as 1s (or 3,4,6,7,9,10,12,13 as 0s doesn't matter) and so on. Therefore, my input layer will be a 5 by 3 neuron layer and I will be feeding it zeros OR ones only (not in between and the indexes depends on which image I am feeding the layer).
My output layer however will be one dimensional layer of 10 neurons. Depends on which digit was recognized, a certain neuron will fire a value of one and the rest should be zeros (shouldn't fire).
I am done with implementing everything, I have a problem in computing though and I would really appreciate any help. I am getting an extremely high error rate and an extremely low (negative) output values on all output neurons and values (error and output) do not change even on the 10,000th pass.
I would love to go further and post my Backpropagation methods since I believe the problem is in it. However to break down my work I would love to hear some comments first, I want to know if my design is approachable.
Does my plan make sense?
All the posts are speaking about ranges ( 0->1, -1 ->+1, 0.01 -> 0.5 etc ), will it work for either { 0 | .OR. | 1 } on the output layer and not a range? if yes, how can I control that?
I am using TanHyperbolic as my transfer function. Does it make a difference between this and sigmoid, other functions.. etc?
Any ideas/comments/guidance are appreciated and thanks in advance
Well, by the description given above, I think that the design and approach taken it's correct! With respect to the choice of the activation function, remember that those functions help to get the neurons which have the largest activation number, also, their algebraic properties, such as an easy derivative, help with the definition of Backpropagation. Taking this into account, you should not worry about your choice of activation function.
The ranges that you mention above, correspond to a process of scaling of the input, it is better to have your input images in range 0 to 1. This helps to scale the error surface and help with the speed and convergence of the optimization process. Because your input set is composed of images, and each image is composed of pixels, the minimum value and and the maximum value that a pixel can attain is 0 and 255, respectively. To scale your input in this example, it is essential to divide each value by 255.
Now, with respect to the training problems, Have you tried checking if your gradient calculation routine is correct? i.e., by using the cost function, and evaluating the cost function, J? If not, try generating a toy vector theta that contains all the weight matrices involved in your neural network, and evaluate the gradient at each point, by using the definition of gradient, sorry for the Matlab example, but it should be easy to port to C++:
perturb = zeros(size(theta));
e = 1e-4;
for p = 1:numel(theta)
% Set perturbation vector
perturb(p) = e;
loss1 = J(theta - perturb);
loss2 = J(theta + perturb);
% Compute Numerical Gradient
numgrad(p) = (loss2 - loss1) / (2*e);
perturb(p) = 0;
end
After evaluating the function, compare the numerical gradient, with the gradient calculated by using backpropagation. If the difference between each calculation is less than 3e-9, then your implementation shall be correct.
I recommend to checkout the UFLDL tutorials offered by the Stanford Artificial Intelligence Laboratory, there you can find a lot of information related to neural networks and its paradigms, it's worth to take look at it!
http://ufldl.stanford.edu/wiki/index.php/Main_Page
http://ufldl.stanford.edu/tutorial/
I've Googled and found zero answers for "safety wall", so I'm pretty sure that's not the correct term. I'll explain myself:
As I've read, I'm talking about taking a two dimensional array and placing it in a same array with an addition of one cell to each side to make sure staying safe and not getting out the limits I've created.
What is the right term for this technique and how would I use it?
Like others told, you need to search it "sentinel" or something like "sentinel control"..
You can use sentinel control when you dont know size or limits of your program. For example, you are writting a program, which is calculating avarage grade of class. However you dont know how many student are in class. Or you inserting array which you dont know limits. Then you can use sentinel control for this job.
Lets look this example,
int grade;
int totalgrade = 0;
int studentCount = 0;
std::cin >> grade;
while (grade != -1)
{
totalgrade = totalgrade + grade;
studentCount ++;
std::cin >> grade;
} // loop until user enter -1
So if you dont know how many values will be entered from user, you can use sentinel control for this job. You can also read more about sentinel value.
These are usually referred to as "ghost cells", and are often used in numerical simulations or image processing where you are applying a kernel (such as a smoothing or difference operator) to an array. They allow you apply the kernel without special casing the edges.
For example; suppose you want to smooth out an image - you could use a kernel like:
0.0 0.1 0.0
0.1 0.6 0.1
0.0 0.1 0.0
You apply this by taking the source image, and for every pixel, you compute the value of the destination pixel by centering the kernel on the source pixel and adding up the weighted contributions of the 9 covered pixel (0.6 * the value of the source pixel, plus 0.1 times the value of each of the pixels above, below, and to the sides). Do this for every pixel and you'll end up with a smoothed version of your original image.
This works well, but the question is "what do you do at the border cells?" Rather than having complicated if/then logic for the border cases (which can be tricky and can degrade performance), you can just add 1 layer of ghost cells to each side.
Of course, you have to pick values for the cells before you run your algorithm. How you pick their value depends on your algorithm. You might choose to set them all to zero, but in the case of the smoothing kernel, this will darken your image at it's borders, so that's probably not what you want. A better plan would be to fill the ghost cells with the value of the nearest non-ghost cell.
You also need to figure out how many ghost cells you need, which depends on the size of your kernel. For a 3x3 kernel like above, you need 1 layer of ghost cells (to take care of the part of the kernel that might "hang off" the edge). More complicated kernels might require more (a 5x5 kernel would require 2 layers, etc).
You can google "ghost cell computation" to find out more (add 'computation' or you'll get a lot of biology results!)
I am trying to fill a STL file with points in Fortran. I have written a basic code but it is not working.
My method has been to use a random number generator to generate a point. I then normalize this point to the dimensions of the STL bounding box.
I then throw out the "z" coordinate for the the first triangle in the STL. I check if the random point is with the max and min value of the "x" and "y" coordinate of the first triangle. If so I project the random point vertically onto the triangle plane and calculate the "z" value should it intersect with the plane. I then check if the z value of the random point is less than the value of the projected point (Ray casting). If yes I increase a counter, which is initially set to zero, by one.
I do this for every triangle in the STL. If the counter is even the random point is outside the volume, if it is odd the random point is inside the volume and the point is stored.
I then generate a new random point and start again. I have included the important code below. Apologies for the length (lots of comments and blank lines for readability).
! Set inital counter for validated points
k = 1
! Do for all randomly generated points
DO i=1,100000
! Create a random point with coordinates x, y and z.
CALL RANDOM_NUMBER(rand)
! Normalise the random coordinates to the bounding box.
rand(1:3) = (rand(1:3) * (cord(1:3) - cord(4:6))) + cord(4:6)
! Set the initial counter for the vertices
j = 1
! Set the number of intersections with the random point and the triangle
no_insect = 0
! Do for all triangles in STL
DO num = 1, notri
! Get the maximum "x" value for the current triangle
maxtempx = MAXVAL(vertices(1,j:j+2))
! Get the minimum "x" value for the current triangle
mintempx = MINVAL(vertices(1,j:j+2))
! If the random point is within the bounds continue
IF (rand(1)>=mintempx .AND. rand(1)<=maxtempx) THEN
! Get the maximum "y" value for the current triangle
maxtempy = MAXVAL(vertices(2,j:j+2))
! Get the minimum "y" value for the current triangle
mintempy = MINVAL(vertices(2,j:j+2))
! If the random point is within the bounds continue
IF (rand(2)>=mintempy .AND. rand(2)<=maxtempy) THEN
! Find the "z" value of the point as projected onto the triangle plane
tempz = ((norm(1,num)*(rand(1)-vertices(1,j))) &
+(norm(2,num)*(rand(2)-vertices(2,j))) &
- (norm(3,num)*vertices(3,j))) / (-norm(3,num))
! If the "z" value of the randomly generated point goes vertically up
! through the projected point then increase the number of plane intersections
! by one. (Ray casting vertically up, could go down also).
IF (rand(3)<= tempz) THEN
no_insect = no_insect + 1
END IF
END IF
END IF
! Go to the start of the next triangle
j = j + 3
END DO
! If there is an odd number of triangle intersections not
! including 0 intersections then store the point
IF (MOD(no_insect,2)/=0 .AND. no_insect/=0) THEN
point(k,1:3) = rand(1:3)
WRITE(1,"(1X, 3(F10.8, 3X))") point(k,1), point(k,2), point(k,3)
k = k + 1
END IF
END DO
My results have been complete rubbish (see images) Image 1 - Test STL file (ship taken from here). Part of the program (code not shown) reads in binary STL files and stores the surface normals of each triangle and the vertices which make up this triangle. I then wrote the vertices to a text file and call GNUPLOT to connect the vertices of each triangle as show above. This plot is just a test to ensure that the STL files are being read and stored correctly. It does not use the surface normals.
.
Image 2 - This is a plot of the candidate points which were accepted as being inside the STL volume. (Stored in the final if loop shown in code above). These accepted points are then later written to a text file and plotted with GNUPLOT (NOT SHOWN). Had the algorithm worked this plot should be a point cloud of the triangulated mesh shown above. (It also plots the 8 bounding box coordinates to ensure that the random particles are generated in the correct range)
I appreciate that this does not take into account for points generated on vertices or rays which run parallel and intersect with edges. I just wanted to start with a rough code. Could you please advise if there is a problem with my methodology or code? Let me know if the question is too broad and I will delete it and try to be more specific.
I realized my code could be handy for others. I placed it at https://github.com/LadaF/Fortran---CGAL-polyhedra under the GNU GPL v3 license.
You can query, whether a point is inside a point or not. First, you read the file by
cgal_polyhedron_read. You must store the type(c_ptr) :: ptree that is crated and use it in your next calls.
The function cgal_polyhedron_inside returns whether a point is inside a polyhedron, or not. It requires one reference point, which must be known to be outside.
When you are finished call cgal_polyhedron_finalize.
You must have the file as a purely tridiagonal manifold mesh in an OFF file. You can create it from the STL file using http://www.cs.princeton.edu/~min/meshconv/ .
I've run into some nasty problem with my recorder. Some people are still using it with analog tuners, and analog tuners have a tendency to spit out 'snow' if there is no signal present.
The Problem is that when noise is fed into the encoder, it goes completely crazy and first consumes all CPU then ultimately freezes. Since main point od the recorder is to stay up and running no matter what, I have to figure out how to proceed with this, so encoder won't be exposed to the data it can't handle.
So, idea is to create 'entropy detector' - a simple and small routine that will go through the frame buffer data and calculate entropy index i.e. how the data in the picture is actually random.
Result from the routine would be a number, that will be 0 for completely back picture, and 1 for completely random picture - snow, that is.
Routine in itself should be forward scanning only, with few local variables that would fit into registers nicely.
I could use zlib or 7z api for such task, but I would really want to cook something on my own.
Any ideas?
PNG works this way (approximately): For each pixel, replace its value by the value that it had minus the value of the pixel left to it. Do this from right to left.
Then you can calculate the entropy (bits per character) by making a table of how often which value appears now, making relative values out of these absolute ones and adding the results of log2(n)*n for each element.
Oh, and you have to do this for each color channel (r, g, b) seperately.
For the result, take the average of the bits per character for the channels and divide it by 2^8 (assuming that you have 8 bit per color).