I'm adding (summing) and array of floats in perl, and I was trying to speed it up. When I tried, I started getting weird results.
#!/usr/bin/perl
my $total = 0;
my $sum = 0;
# Compute $sum (adds from index 0 forward)
my #y = #{$$self{"closing"}}[-$periods..-1];
my #x = map {${$_}{$what}} #y;
# map { $sum += $_ } #x;
$sum += $_ for #x;
# Compute $total (adds from index -1 backward)
for (my $i = -1; $i >= -$periods; $i--) {
$total += ${${$$self{"closing"}}[$i]}{$what};
}
if($total != $sum) {
printf("SMA($what, $periods) total ($total) != sum ($sum) %g\n",
($total - $sum));
}
# Example output:
# SMA(close, 20) total (941.03) != sum (941.03) -2.27374e-13
I seem to get different answers when I compute $sum and $total.
The only thing I can think of is that one method adds forward through the array, and the other backward.
Would this cause them to overflow differently? I would expect so, but it never occurred to me that I would get different answers. Notice that the difference is small (-2.27374e-13).
Is this what's going on, or is my code busted?
This is perl 5, version 16, subversion 3 (v5.16.3) built for x86_64-linux-thread-multi
As Eric mentioned in the comments, floating point arithmetic is not associative; so the order you do the operations will impact the answer.
While "add smaller values first" is good advice, it is important to emphasize that you can have differences even with just regular "small" values. Here's one example:
x = 1.004028
y = 3.0039678
z = 4.000855
If these are taken to be IEEE-754 single-precision floats (i.e., 32-bit binary format), then we get:
x + (y+z) = 8.008851
(x+y) + z = 8.00885
Infinitely precise result is 8.0088508. So neither are very good! And the error isn't insignificant for scientific computations and it accumulates.
This is a rich field with many numerical algorithms to ensure precision. While which one you pick entirely depends on your problem domain and particular needs and resources you have available, one of the best-known algorithms is Kahan's summation algorithm, see: https://en.wikipedia.org/wiki/Kahan_summation_algorithm. You can easily adopt it to your problem for (hopefully) better results.
I am randomly populating a grid where the cartesian coordinates are normalized from 0 to 100 (100x100x100 grid) and the "intensity" of each data point within is normalized from 0 to 256. Here is an excerpt from my code in perl:
open(FILE,$file);
while(sysread(FILE,$data,16)) {
#row=unpack("f>4",$data); # input file is binary so I had to convert here
$x=int((($row[0] - $xmin)/($xmax - $xmin)*10) + 0.5); # max and min variables
$y=int((($row[1] - $ymin)/($ymax - $ymin)*10) + 0.5); # are previously defined
$z=int((($row[2] - $zmin)/($zmax - $zmin)*10) + 0.5);
$i=int(($row[3]/62*256) + 0.5);
$i=255 if ($i>255);
$out[$x][$y][$z]=$i; # simply assigns an intensity for each data
# point (in random order), only 1 point can be
# added to each 1x1x1 cell
}
Some points are too close together and are being placed in the same 1x1x1 cell. When this happens, each intensity added overwrites the previous one. How can I count the number of times that more than one point is placed in a cell?
Thanks in advance!
You can do this pretty easily with another hash, just join all of your keys ($x,$y,$z) together into a single key and set the hash value to true whenever you insert a value.
my %visited_points;
open(FILE,$file);
while(sysread(FILE,$data,16)) {
#row=unpack("f>4",$data); # input file is binary so I had to convert here
$x=int((($row[0] - $xmin)/($xmax - $xmin)*10) + 0.5); # max and min variables
$y=int((($row[1] - $ymin)/($ymax - $ymin)*10) + 0.5); # are
$z=int((($row[2] - $zmin)/($zmax - $zmin)*10) + 0.5);
$i=int(($row[3]/62*256) + 0.5);
$i=255 if ($i>255);
my $key = "$x$y$z";
# check if something already occupies this cell
if( exists( $visited_points{$key} ) ) {
# take some other action
}
$out[$x][$y][$z]=$i; # simply assigns an intensity for each data
# point (in random order), only 1 point can be
# added to each 1x1x1 cell
# mark that there is something in this cell
$visited_points{$key} = 1;
}
If you wanted to count you count easily just increment the value as well.
To make it more hpc(high performance computing)-friendly, I found that instead of the $key and if-loop, simply putting in a count like this works too.
open(FILE,$file);
while(sysread(FILE,$data,16)) {
#row=unpack("f>4",$data); # input file is binary so I had to convert here
$x=int((($row[0] - $xmin)/($xmax - $xmin)*10) + 0.5); # max and min variables
$y=int((($row[1] - $ymin)/($ymax - $ymin)*10) + 0.5); # are previously defined
$z=int((($row[2] - $zmin)/($zmax - $zmin)*10) + 0.5);
$i=int(($row[3]/62*256) + 0.5);
$i=255 if ($i>255);
$count[$x][$y][$z]+=1;
$out[$x][$y][$z]=$i; # simply assigns an intensity for each data
# point (in random order), only 1 point can be
# added to each 1x1x1 cell
}
Then, if $count[$x][$y][$z] is greater than 1, it means more than one point was put in that bin. If it equals 1, only one point was put there, and if it is less than one, then the bin is empty.
Another version of Hunter's solution replaces a hash (with encoded key); with an array (with an encoded index).
Pros: possibly very slightly improving performance. More likely than not, not by a great enough margin to matter, in all honesty, but do your own benchmarking to be sure.
Cons: sacrifice of memory. If your grid is populated sparsely - say 1000 points out of 1 million - you would store 1000 elements in a hash but 1,000,000 elements in the array.
# my #visited_points;
my $key = $x * 10000 + $y * 100 + $z;
# Mark as visited
$visited_points[$key]++;
# Check if visited:
if (defined $visited_points[$key]) {
# Bail out?
}
# Check how many times visited?
# Use trinary ?: operator to gracefully convert undef to 0
my $count = $visited_points[$key] ? $visited_points[$key] : 0;
Problem statement:
We have equal number of men and women. Each man has a preference score toward each woman. So do the woman for each man. Each of the men and women have certain interests. Based on the interest, we calculate the preference scores.
So initially, we have an input in a file having x columns. The first column is the person (man/woman) id. Ids are nothing but numbers from 0 ... n. (First half are men and next half women). The remaining x-1 columns will have the interests. These are integers too.
Now, using this n by x-1 matrix, we have come up with an n by n/2 matrix. The new matrix has all men and woman as their rows and scores for opposite sex in columns.
We have to sort the scores in descending order, also we need to know the id of person related to the scores after sorting.
So, here I wanted to use hash table.
Once we get the scores we need to make up pairs, for which we need to follow some rules.
My trouble is with the second matrix of n by n/2 that needs to give information of which man/woman has how much preference on a woman/man. I need these scores sorted so that I know who is the first preferred woman/man, 2nd preferred and so on for a man/woman.
I hope to get good suggestions on the data structures I use. I prefer PHP or Perl.
NB:
This is not homework. This is a little modified version of stable marriage algorithm. I have a working solution. I am only working on optimizing my code.
It is very similar to stable marriage problem but here we need to calculate the scores based on the interests they share. So, I have implemented it as the way you see in the wiki page http://en.wikipedia.org/wiki/Stable_marriage_problem.
My problem is not solving the problem. I solved it and can run it. I am just trying to have a better solution. So I am asking suggestions on the type of data structure to use.
Conceptually I tried using an array of hashes. where the array index give the person id and the hash in it gives the ids <=> scores in sorted manner. I initially start with an array of hashes. Now, I sort the hashes on values, but I could not store the sorted hashes back in an array. So just stored the keys after sorting and used these to get the values from my initial unsorted hashes.
Can we store the hashes after sorting?
Can you suggest a better structure?
I think the following implements the Gale-Shapley algorithm where each person's preference ordering is given as an array of scores over the members of the opposite sex.
As an aside, I just found out that David Gale passed away (see his Wikipedia entry — he will be missed).
The code is wordy, I just quickly transcribed the algorithm as described on Wikipedia and did not check original sources, but it should give you an idea of how to use appropriate Perl data structures. If the dimensions of the problem grow, profile first before trying to optimize.
I am not going to try to address the specific issues in your problem. In particular, you did not fully flesh out the idea of computing a match score based on interests and trying to guess is bound to be frustrating.
#!/usr/bin/perl
use strict; use warnings;
use YAML;
my (%pref, %people, %proposed_by);
while ( my $line = <DATA> ) {
my ($sex, $id, #pref) = split ' ', $line;
last unless $sex and ($sex) =~ /^(m|w)\z/;
$pref{$sex}{$id} = [ map 0 + $_, #pref ];
$people{$sex}{$id} = undef;
}
while ( defined( my $man = bachelor($people{m}) ) ) {
my #women = eligible_women($people{w}, $proposed_by{$man});
next unless #women;
my $woman = argmax($pref{m}{$man}, \#women);
$proposed_by{$man}{$woman} = 1;
if ( defined ( my $jilted = $people{w}{$woman}{m} ) ) {
my $proposal_score = $pref{w}{$woman}[$man];
my $jilted_score = $pref{w}{$woman}[$jilted];
next if $proposal_score < $jilted_score;
$people{m}{$jilted}{w} = undef;
}
$people{m}{$man}{w} = $woman;
$people{w}{$woman}{m} = $man;
}
print Dump \%people;
sub argmax {
my ($pref, $candidates) = #_;
my ($ret) = sort { $pref->[$b] <=> $pref->[$a] } #$candidates;
return $ret;
}
sub bachelor {
my ($men) = #_;
my ($bachelor) = grep { not defined $men->{$_}{w} } keys %$men;
return $bachelor;
}
sub eligible_women {
my ($women, $proposed_to) = #_;
return grep { not defined $proposed_to->{$_} } keys %$women;
}
__DATA__
m 0 10 20 30 40 50
m 1 50 30 40 20 10
m 2 30 40 50 10 20
m 3 10 10 10 10 10
m 4 50 40 30 20 10
w 0 50 40 30 20 10
w 1 40 30 20 10 50
w 2 30 20 10 50 40
w 3 20 10 50 40 30
w 4 10 50 40 30 20
I have a matrix that I want to randomize a couple of thousand times, while keeping the row and column totals the same:
1 2 3
A 0 0 1
B 1 1 0
C 1 0 0
An example of a valid random matrix would be:
1 2 3
A 1 0 0
B 1 1 0
C 0 0 1
My actual matrix is a lot bigger (about 600x600 items), so I really need an approach that is computationally efficient.
My initial (inefficient) approach consisted of shuffling arrays using the Perl Cookbook shuffle
I pasted my current code below. I've got extra code in place to start with a new shuffled list of numbers, if no solution is found in the while loop. The algorithm works fine for a small matrix, but as soon as I start scaling up it takes forever to find a random matrix that fits the requirements.
Is there a more efficient way to accomplish what I'm searching for?
Thanks a lot!
#!/usr/bin/perl -w
use strict;
my %matrix = ( 'A' => {'3' => 1 },
'B' => {'1' => 1,
'2' => 1 },
'C' => {'1' => 1 }
);
my #letters = ();
my #numbers = ();
foreach my $letter (keys %matrix){
foreach my $number (keys %{$matrix{$letter}}){
push (#letters, $letter);
push (#numbers, $number);
}
}
my %random_matrix = ();
&shuffle(\#numbers);
foreach my $letter (#letters){
while (exists($random_matrix{$letter}{$numbers[0]})){
&shuffle (\#numbers);
}
my $chosen_number = shift (#numbers);
$random_matrix{$letter}{$chosen_number} = 1;
}
sub shuffle {
my $array = shift;
my $i = scalar(#$array);
my $j;
foreach my $item (#$array )
{
--$i;
$j = int rand ($i+1);
next if $i == $j;
#$array [$i,$j] = #$array[$j,$i];
}
return #$array;
}
The problem with your current algorithm is that you are trying to shuffle your way out of dead ends -- specifically, when your #letters and #numbers arrays (after the initial shuffle of #numbers) yield the same cell more than once. That approach works when the matrix is small, because it doesn't take too many tries to find a viable re-shuffle. However, it's a killer when the lists are big. Even if you could hunt for alternatives more efficiently -- for example, trying permutations rather than random shuffling -- the approach is probably doomed.
Rather than shuffling entire lists, you might tackle the problem by making small modifications to an existing matrix.
For example, let's start with your example matrix (call it M1). Randomly pick one cell to change (say, A1). At this point the matrix is in an illegal state. Our goal will be to fix it in the minimum number of edits -- specifically 3 more edits. You implement these 3 additional edits by "walking" around the matrix, with each repair of a row or column yielding another problem to be solved, until you have walked full circle (err ... full rectangle).
For example, after changing A1 from 0 to 1, there are 3 ways to walk for the next repair: A3, B1, and C1. Let's decide that the 1st edit should fix rows. So we pick A3. On the second edit, we will fix the column, so we have choices: B3 or C3 (say, C3). The final repair offers only one choice (C1), because we need to return to the column of our original edit. The end result is a new, valid matrix.
Orig Change A1 Change A3 Change C3 Change C1
M1 M2
1 2 3 1 2 3 1 2 3 1 2 3 1 2 3
----- ----- ----- ----- -----
A | 0 0 1 1 0 1 1 0 0 1 0 0 1 0 0
B | 1 1 0 1 1 0 1 1 0 1 1 0 1 1 0
C | 1 0 0 1 0 0 1 0 0 1 0 1 0 0 1
If an editing path leads to a dead end, you backtrack. If all of the repair paths fail, the initial edit can be rejected.
This approach will generate new, valid matrixes quickly. It will not necessarily produce random outcomes: M1 and M2 will still be highly correlated with each other, a point that will become more directly evident as the size of the matrix grows.
How do you increase the randomness? You mentioned that most cells (99% or more) are zeros. One idea would be to proceed like this: for each 1 in the matrix, set its value to 0 and then repair the matrix using the 4-edit method outlined above. In effect, you would be moving all of the ones to new, random locations.
Here is an illustration. There are probably further speed optimizations in here, but this approach yielded 10 new 600x600 matrixes, at 0.5% density, in 30 seconds or so on my Windows box. Don't know if that's fast enough.
use strict;
use warnings;
# Args: N rows, N columns, density, N iterations.
main(#ARGV);
sub main {
my $n_iter = pop;
my $matrix = init_matrix(#_);
print_matrix($matrix);
for my $n (1 .. $n_iter){
warn $n, "\n"; # Show progress.
edit_matrix($matrix);
print_matrix($matrix);
}
}
sub init_matrix {
# Generate initial matrix, given N of rows, N of cols, and density.
my ($rows, $cols, $density) = #_;
my #matrix;
for my $r (1 .. $rows){
push #matrix, [ map { rand() < $density ? 1 : 0 } 1 .. $cols ];
}
return \#matrix;
}
sub print_matrix {
# Dump out a matrix for checking.
my $matrix = shift;
print "\n";
for my $row (#$matrix){
my #vals = map { $_ ? 1 : ''} #$row;
print join("\t", #vals), "\n";
}
}
sub edit_matrix {
# Takes a matrix and moves all of the non-empty cells somewhere else.
my $matrix = shift;
my $move_these = cells_to_move($matrix);
for my $cell (#$move_these){
my ($i, $j) = #$cell;
# Move the cell, provided that the cell hasn't been moved
# already and the subsequent edits don't lead to a dead end.
$matrix->[$i][$j] = 0
if $matrix->[$i][$j]
and other_edits($matrix, $cell, 0, $j);
}
}
sub cells_to_move {
# Returns a list of non-empty cells.
my $matrix = shift;
my $i = -1;
my #cells = ();
for my $row (#$matrix){
$i ++;
for my $j (0 .. #$row - 1){
push #cells, [$i, $j] if $matrix->[$i][$j];
}
}
return \#cells;
}
sub other_edits {
my ($matrix, $cell, $step, $last_j) = #_;
# We have succeeded if we've already made 3 edits.
$step ++;
return 1 if $step > 3;
# Determine the roster of next edits to fix the row or
# column total upset by our prior edit.
my ($i, $j) = #$cell;
my #fixes;
if ($step == 1){
#fixes =
map { [$i, $_] }
grep { $_ != $j and not $matrix->[$i][$_] }
0 .. #{$matrix->[0]} - 1
;
shuffle(\#fixes);
}
elsif ($step == 2) {
#fixes =
map { [$_, $j] }
grep { $_ != $i and $matrix->[$_][$j] }
0 .. #$matrix - 1
;
shuffle(\#fixes);
}
else {
# On the last edit, the column of the fix must be
# the same as the column of the initial edit.
#fixes = ([$i, $last_j]) unless $matrix->[$i][$last_j];
}
for my $f (#fixes){
# If all subsequent fixes succeed, we are golden: make
# the current fix and return true.
if ( other_edits($matrix, [#$f], $step, $last_j) ){
$matrix->[$f->[0]][$f->[1]] = $step == 2 ? 0 : 1;
return 1;
}
}
# Failure if we get here.
return;
}
sub shuffle {
my $array = shift;
my $i = scalar(#$array);
my $j;
for (#$array ){
$i --;
$j = int rand($i + 1);
#$array[$i, $j] = #$array[$j, $i] unless $i == $j;
}
}
Step 1: First I would initialize the matrix to zeros and calculate the required row and column totals.
Step 2: Now pick a random row, weighted by the count of 1s that must be in that row (so a row with count 300 is more likely to be picked than a row with weight 5).
Step 3: For this row, pick a random column, weighted by the count of 1s in that column (except ignore any cells that may already contain a 1 - more on this later).
Step 4: Place a one in this cell and reduce both the row and column count for the appropriate row and column.
Step 5: Go back to step 2 until no rows have non-zero count.
The problem though is that this algorithm can fail to terminate because you may have a row where you need to place a one, and a column that needs a one, but you've already placed a one in that cell, so you get 'stuck'. I'm not sure how likely this is to happen, but I wouldn't be surprised if it happened very frequently - enough to make the algorithm unusable. If this is a problem I can think of two ways to fix it:
a) Construct the above algorithm recursively and allow backtracking on failure.
b) Allow a cell to contain a value greater than 1 if there is no other option and keep going. Then at the end you have a correct row and column count but some cells may contain numbers greater than 1. You can fix this by finding a grouping that looks like this:
2 . . . . 0
. . . . . .
. . . . . .
0 . . . . 1
and changing it to:
1 . . . . 1
. . . . . .
. . . . . .
1 . . . . 0
It should be easy to find such a grouping if you have many zeros. I think b) is likely to be faster.
I'm not sure it's the best way, but it's probably faster than shuffling arrays. I'll be tracking this question to see what other people come up with.
I'm not a mathematician, but I figure that if you need to keep the same column and row totals, then random versions of the matrix will have the same quantity of ones and zeros.
Correct me if I'm wrong, but that would mean that making subsequent versions of the matrix would only require you to shuffle around the rows and columns.
Randomly shuffling columns won't change your totals for rows and columns, and randomly shuffling rows won't either. So, what I would do, is first shuffle rows, and then shuffle columns.
That should be pretty fast.
Not sure if it will help, but you can try going from one corner and for each column and row you should track the total and actual sum. Instead of trying to hit a good matrix, try to see the total as amount and split it. For each element, find the smaller number of row total - actual row total and column total - actual column total. Now you have the upper bound for your random number.
Is it clear? Sorry I don't know Perl, so I cannot show any code.
Like #Gabriel I'm not a Perl programmer so it's possible that this is what your code already does ...
You've only posted one example. It's not clear whether you want a random matrix which has the same number of 1s in each row and column as your start matrix, or one which has the same rows and columns but shuffled. If the latter is good enough you could create an array of row (or column, it doesn't matter) indexes and randomly permute that. You can then read your original array in the order specified by the randomised index. No need to modify the original array or create a copy.
Of course, this might not meet aspects of your requirements which are not explicit.
Thank the Perl code of FMc. Based on this solution, I rewrite it in Python (for my own use and share here for more clarity) as shown below:
matrix = numpy.array(
[[0, 0, 1],
[1, 1, 0],
[1, 0, 0]]
)
def shuffle(array):
i = len(array)
j = 0
for _ in (array):
i -= 1;
j = random.randrange(0, i+1) #int rand($i + 1);
#print('arrary:', array)
#print(f'len(array)={len(array)}, (i, j)=({i}, {j})')
if i != j:
tmp = array[i]
array[i] = array[j]
array[j] = tmp
return array
def other_edits(matrix, cell, step, last_j):
# We have succeeded if we've already made 3 edits.
step += 1
if step > 3:
return True
# Determine the roster of next edits to fix the row or
# column total upset by our prior edit.
(i, j) = cell
fixes = []
if (step == 1):
fixes = [[i, x] for x in range(len(matrix[0])) if x != j and not matrix[i][x] ]
fixes = shuffle(fixes)
elif (step == 2):
fixes = [[x, j] for x in range(len(matrix)) if x != i and matrix[x][j]]
fixes = shuffle(fixes)
else:
# On the last edit, the column of the fix must be
# the same as the column of the initial edit.
if not matrix[i][last_j]: fixes = [[i, last_j]]
for f in (fixes):
# If all subsequent fixes succeed, we are golden: make
# the current fix and return true.
if ( other_edits(matrix, f, step, last_j) ):
matrix[f[0]][f[1]] = 0 if step == 2 else 1
return True
# Failure if we get here.
return False # return False
def cells_to_move(matrix):
# Returns a list of non-empty cells.
i = -1
cells = []
for row in matrix:
i += 1;
for j in range(len(row)):
if matrix[i][j]: cells.append([i, j])
return cells
def edit_matrix(matrix):
# Takes a matrix and moves all of the non-empty cells somewhere else.
move_these = cells_to_move(matrix)
for cell in move_these:
(i, j) = cell
# Move the cell, provided that the cell hasn't been moved
# already and the subsequent edits don't lead to a dead end.
if matrix[i][j] and other_edits(matrix, cell, 0, j):
matrix[i][j] = 0
return matrix
def Shuffle_Matrix(matrix, N, M, n_iter):
for n in range(n_iter):
print(f'iteration: {n+1}') # Show progress.
matrix = edit_matrix(matrix)
#print('matrix:\n', matrix)
return matrix
print(matrix.shape[0], matrix.shape[1])
# Args: N rows, N columns, N iterations.
matrix2 = Shuffle_Matrix(matrix, matrix.shape[0], matrix.shape[1], 1)
print("The resulting matrix:\n", matrix2)
I want split integers into their factors. For example, if the total number of records is:
169 - ( 13 x 13 times)
146 - ( 73 x 2 times)
150 - ( 50 x 3 times)
175 - ( 25 x 7 times)
168 - ( 84 x 2 )
160 - ( 80 x 2 times)
When it's more than 10k - I want everything on 1000
When it's more than 100k - I want everything on 10k
In this way I want to factor the number. How to achieve this? Is there any Perl module available for these kinds of number operations?
Suppose total number of records is 10k. It should be split by 1000x10 times only; not by 100 or 10s.
I can use sqrt function. But it's not always what I am expecting. If I give the input 146, I have to get (73, 2).
You can use the same algorithms you find for other languages in Perl. There isn't any Perl special magic in the ideas. It's just the implementation, and for something like this problem, it's probably going to look very similar to the implementation in any language.
What problem are you trying to solve? Maybe we can point you at the right algorithm if we know what you are trying to do:
Why must numbers over 10,000 use the 1,000 factor? Most numbers won't have a 1,000 factor.
Do you want all the factors, or just the largest and its companion?
What do you mean that the sqrt function doesn't work as you expect? If you're following the common algorithm, you just need to iterate up to the floor of the square root to test for factors. Most integers don't have an integral square root.
If the number is not a prime you can use a factoring algorithm.
There is an example of such a function here: http://www.classhelper.org/articles/perl-by-example-factoring-numbers/factoring-numbers-with-perl.shtml
Loop through some common numbers in an acceptable range (say, 9 to 15), compute the remainder modulo your test number, and choose the lowest.
sub compute_width {
my ($total_records) = #_;
my %remainders;
for(my $width = 9; $width <= 15; $width += 1) {
my $remainder = $total_records % $width;
$remainders{$width} = $remainder;
}
my #widths = sort {
$remainders{$a} <=> $remainders{$b} ||
$a <=> $b
} keys %remainders;
return $widths[0];
}