-2 -1 0
-1 1 1
0 1 2
This is 3x3 emboss kernel. How should I write this in 5x5?
As I understand, these filters take directional differences (see the wikipidea page).
We can decompose you filter into directions
0 -1 0 0 0 0 -2 0 0
0 0 0 -1 0 1 0 0 0
0 1 0 0 0 0 0 0 2
So, I think you can expand it over these 3 directions giving emphasis
0 0 -1 0 0 0 0 0 0 0 -2 0 0 0 0
0 0 -1 0 0 0 0 0 0 0 0 -2 0 0 0
0 0 0 0 0 -1 -1 0 1 1 0 0 0 0 0
0 0 1 0 0 0 0 0 0 0 0 0 0 2 0
0 0 1 0 0 0 0 0 0 0 0 0 0 0 2
So, the final kernel would be
-2 0 -1 0 0
0 -2 -1 0 0
-1 -1 1 1 1
0 0 1 2 0
0 0 1 0 2
May be you can also try interpolating filter coefficients marked as x
-2 x -1 0 0
x -2 -1 0 0
-1 -1 1 1 1
0 0 1 2 x
0 0 1 x 2
The simple solution to fitting any lower-dimensional convolution kernel into a higher-dimensional matrix of the same rank is to surround it by zero weights. This is especially true when you're dealing with a concept like embossing, which is arguably more interested in immediate vector of change than the rate at which it is changing. That is, for this embossing matrix,
You could equivalently use this in 5 x 5:
Granted, this will get you a different visual effect than anything with any part of the matrix filled in; but sometimes, especially with edge-detection, immediate clarity is more important. We aren't always displaying it. If this were something like a Guassian blur kernel, having a greater range could improve the effect, but embossing isn't that different conceptually from Sobel-Feldman and it may be better to keep it tight.
Related
I'm stumped here. I have this measure.
Contains = IF(CONTAINS(Flags, Flags[FlagsConcat], SELECTEDVALUE(SlicerFlags[FlagNames])),1,-1)
The concatenated column looks something like this
Flags
id Bco Nat Gur Ga An Sim Oak Ort FlagsConcat
1826 0 0 0 0 0 0 1 1 Oakpoint,Orthoselect
1784 0 0 0 0 0 0 1 1 Oakpoint,Orthoselect
1503 0 0 0 1 0 0 0 0 Guardian
1502 0 0 0 1 0 0 0 0 Guardian
1500 0 0 0 1 0 0 0 0 Guardian
1499 0 0 0 1 0 0 0 0 Guardian
1326 0 0 0 1 0 0 0 0 Guardian
925 0 0 0 1 0 0 0 0 0 Guardian
and here is the values I am grabbing from the selectedvalue()
FlagNames
Benco
National
Guardian-Simply Clear
Guardian
Angel Align
Simply Clear
Oakpoint
Orthoselect
None
If I select Guardian in the slicerFlags table then I get a return value of 1 but if I select either Oakpoint or Orthoselect then I get a -1 even those there are 2 rows in the table that have either word in the FlagsConcat column. I tried putting spaces before and after the comma but that made no difference. Anyone know why the contains() function isn't showing true when looking for Oakpoint or Orthoselect? Thanks in advance.
#Jeroen's code is correct...
Contains = COUNTROWS(FILTER(Flags, CONTAINSSTRING(Flags[FlagsConcat], SELECTEDVALUE(SlicerFlags[FlagNames]))))+0
I have a text file like this:
6.2341 -0.4024 -2.0936 Cl 0 0 0 0 0 0 0 0 0 0 0 0
0.1148 -3.7525 1.0392 S 0 0 0 0 0 0 0 0 0 0 0 0
-2.5441 -0.8745 1.3714 F 0 0 0 0 0 0 0 0 0 0 0 0
The format is: columns 1 to 10, 11 to 20, 21 to 30 are x,y,z coordinates in (10.4) format, i.e. length=10, 4 digits after the decimal point; column 31 is always a space; columns 32 to 32 are the atom type; the remaining columns are not important.
However, for some unknown reason, the atom type field is right-shifted by two columns, like this:
6.2341 -0.4024 -2.0936 Cl 0 0 0 0 0 0 0 0 0 0 0 0
0.1148 -3.7525 1.0392 S 0 0 0 0 0 0 0 0 0 0 0 0
-2.5441 -0.8745 1.3714 F 0 0 0 0 0 0 0 0 0 0 0 0
How to use the sed command and regular expression to match these lines and delete the two extra spaces?
sed -r 's/(.{30}) /\1/' will do the trick.
Group the first 30 characters, match two additional spaces, replace the whole with the grouped characters.
If you don't mind using neither sed nor regular expressions you can just use cut to remove the 2 offending characters:
$ cut --complement -c31,32 file
6.2341 -0.4024 -2.0936 Cl 0 0 0 0 0 0 0 0 0 0 0 0
0.1148 -3.7525 1.0392 S 0 0 0 0 0 0 0 0 0 0 0 0
-2.5441 -0.8745 1.3714 F 0 0 0 0 0 0 0 0 0 0 0 0
This question already has answers here:
srand() — why call it only once?
(7 answers)
Closed 7 years ago.
I am writing a program to simulate Knight's tour randomly. (See wikipedia for what it means: http://en.wikipedia.org/wiki/Knight%27s_tour) First, I create a chess object, which is basically just a 8*8 array with numbers to indicate the position of the knight. I create a chess object and randomly assign a position for the knight. Then, I moved the knight randomly until there is no more legal moves and returns the number of moves performed.
int runTour ()
{
srand (time(NULL));
Chess knight(rand()%8, rand()%8); //Initialize random chess object.
knight.printBoard(); //Prints the board before moving
int moveNumber = 0; //A number from 0 to 7 that dictates how the knight moves
int counter = 0;
while (moveNumber != -1) //A moveNumber of -1 means there is no more legal move
{
moveNumber = knight.findRandMove(knight.getRow(), knight.getColumn()); //findRandMove is a function that returns a legal random move for the knight based on its position. It works perfectly.
knight.move(moveNumber); //move is a function that moves the knight
counter ++;
}
knight.printBoard(); // Returns board when move is exhausted
return counter; //Returns number of moves performed.
}
The interesting thing is that while it runs perfectly randomly from run to run, it keeps outputting the same thing in the same run. For example, this is the main() function:
int main(){
runTour();
runTour();
return 0;
}
And in BOTH runTour() it outputs: (where 0 represents positions not reached, 1 represents the current position of the knight, and 9 positions reached)
0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0
0 0 0 0 0 1 0 0
0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0
0 0 0 0 9 0 0 0
0 9 9 0 0 0 9 0
0 0 0 0 0 9 9 0
9 0 9 9 9 9 0 1
0 0 9 9 9 9 9 9
0 9 9 9 9 0 9 0
9 0 0 0 9 9 9 9
0 0 9 0 9 9 0 9
And when I run it again, BOTH runTour output:
0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 1
0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0
0 0 0 0 0 0 9 9
0 9 0 0 9 9 9 0
0 0 9 9 9 9 9 9
1 0 9 0 9 9 0 9
So the random function is random in different runs, but is the same in each run. Why is this the case? How can I modify the code so that runTour() can have different performances when it is called? Thank you very much for reading this clumsy question.
As you´re using a timestamp as srand seed:
If both runTours are in the same second, what do you think will happen with your code?
...
srand is supposed to be called exactly one time, not one time per function call of runTour
Try moving your srand call to your main function. You should only have to seed the generator one time, rather than each time you call the function.
FAR and FRR are used to express the results of biometric devices. Below is the confusion matrix produced by biometric data produced in weka. I couldn't find any resources explaining the procedure to calculate FAR and FRR using a n*n confusion matrix. Any help explaining the procedure would be of great help. Thanks in advance!
Weka also gives these values, TP Rate, FP Rate, Precision, Recall, F-Measure and ROC Area. Please suggest if the required values can be calculated using these.
=== Confusion Matrix ===
a b c d e f g h i j k l m n o <-- classified as
1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 | a = user1
0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 | b = user2
0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 | c = user3
0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 | d = user4
0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 | e = user5
0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 | f = user6
0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 | g = user7
0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 | h = user9
1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 | i = user10
0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 | j = user11
0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 | k = user14
0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 | l = user15
0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 | m = user16
0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 | n = user17
0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 | o = user19
The accepted answer here by user "chl" has a reference to the Biometrics Literature: https://stats.stackexchange.com/questions/3489/calculating-false-acceptance-rate-for-a-gaussian-distribution-of-scores .
He says,
[the ROC curve] is a plot of (TAR=1-FRR, the false rejection rate) against false
acceptance rate (FAR).
However, commonly the ROC curve happens to be a plot of TP Rate as a function of False Positive Rate (FP Rate).
Seems you can use TP Rate and FP Rate.
I am trying to access the sparse mlf with the keys such as BEpos and BEneg where one key per line. Now the problem is that most commands are not meant to deal with too large input: bin2dec requires clean binary numbers without spaces but the regexp hack fails to too many rows -- and so on.
How to work with sparse keys to access sparse data?
Example
K>> mlf=sparse([],[],[],2^31,1);
BEpos=Cg(pos,:)
BEpos =
(1,1) 1
(2,3) 1
(2,4) 1
K>> mlf(bin2dec(num2str(BEpos)))=1
Error using bin2dec (line 36)
Binary string must be 52 bits or less.
K>> num2str(BEpos)
ans =
1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
K>> bin2dec(num2str('1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0'))
Error using bin2dec (line 36)
Binary string must be 52 bits or less.
K>> regexprep(num2str(BEpos),'[^\w'']','')
Error using regexprep
The 'STRING' input must be a one-dimensional array
of char or cell arrays of strings.
Manually works
K>> mlf(bin2dec('1000000000000000000000000000000'))
ans =
All zero sparse: 1-by-1
Consider a different approach using manual binary to decimal conversions:
pows = pow2(size(BEpos,2)-1 : -1 : 0);
inds = uint32(BEpos*pows.')
I haven't benchmarked this, but it might work faster than bin2dec and cell arrays.
How it works
This is pretty simple: the powers of 2 are calculated and stored in pows (assuming the MSB is in the leftmost position). Then they are multiplied by the bits in the matching positions and summed to produce the corresponding decimal values.
Try to index with this:
inds = uint32( bin2dec(cellstr(num2str(BEpos,'%d'))) );