I'm still confused why am not able to know the results of this small algorithm of my array. the array has almost 1000 number 1-D. am trying to find the peak and the index of each peak. I did found the peaks, but I can't find the index of them. Could you please help me out. I want to plot all my values regardless the indexes.
%clear all
%close all
%clc
%// not generally appreciated
%-----------------------------------
%message1.txt.
%-----------------------------------
% t=linspace(0,tmax,length(x)); %get all numbers
% t1_n=0:0.05:tmax;
x=load('ww.txt');
tmax= length(x) ;
tt= 0:tmax -1;
x4 = x(1:5:end);
t1_n = 1:5:tt;
x1_n_ref=0;
k=0;
for i=1:length(x4)
if x4(i)>170
if x1_n_ref-x4(i)<0
x1_n_ref=x4(i);
alpha=1;
elseif alpha==1 && x1_n_ref-x4(i)>0
k=k+1;
peak(k)=x1_n_ref; // This is my peak value. but I also want to know the index of it. which will represent the time.
%peak_time(k) = t1_n(i); // this is my issue.
alpha=2;
end
else
x1_n_ref=0;
end
end
%----------------------
figure(1)
% plot(t,x,'k','linewidth',2)
hold on
% subplot(2,1,1)
grid
plot( x4,'b'); % ,tt,x,'k'
legend('down-sampling by 5');
Here is you error:
tmax= length(x) ;
tt= 0:tmax -1;
x4 = x(1:5:end);
t1_n = 1:5:tt; % <---
tt is an array containing numbers 0 through tmax-1. Defining t1_n as t1_n = 1:5:tt will not create an array, but an empty matrix. Why? Expression t1_n = 1:5:tt will use only the first value of array tt, hence reduce to t1_n = 1:5:tt = 1:5:0 = <empty matrix>. Naturally, when you later on try to access t1_n as if it were an array (peak_time(k) = t1_n(i)), you'll get an error.
You probably want to exchange t1_n = 1:5:tt with
t1_n = 1:5:tmax;
You need to index the tt array correctly.
you can use
t1_n = tt(1:5:end); % note that this will give a zero based index, rather than a 1 based index, due to t1_n starting at 0. you can use t1_n = 1:tmax if you want 1 based (matlab style)
you can also cut down the code a little, there are some variables that dont seem to be used, or may not be necessary -- including the t1_n variable:
x=load('ww.txt');
tmax= length(x);
x4 = x(1:5:end);
xmin = 170
% now change the code
maxnopeaks = round(tmax/2);
peaks(maxnopeaks)=0; % preallocate the peaks for speed
index(maxnopeaks)=0; % preallocate index for speed
i = 0;
for n = 2 : tmax-1
if x(n) > xmin
if x(n) >= x(n-1) & x(n) >= x(n+1)
i = i+1;
peaks(i) = t(n);
index(i) = n;
end
end
end
% now trim the excess values (if any)
peaks = peaks(1:i);
index = index(1:i);
Related
N = 500;
pattern = zeros(N,N);
grid on
plot(pattern)
% gets coordinates of modified cells
[x,y] = ginput;
% convert coordinates to integers
X = uint8(x);
Y = uint8(y);
% convert (X,Y) into linear indices
indx = sub2ind([N,N],x,y);
% switch desired cells on (value of 1)
pattern(indx) = 1;
I'm trying to assign several elements of a zeros array the value of 1. Basically I want to create an interactive plot where the user decides what cells he wants to turn on and then save his drawing as a matrix. In Python it's very simple to use the on_click with Matplotlib, but Matlab is weird and I can't find a clear answer. What's annoying is you can't see where you clicked until you save your changes and check the final matrix. You also can't erase a point if you made a mistake.
Moreover I get the following error : Error using sub2ind Out of range subscript. Error in createPattern (line 12) indx = sub2ind([N,N],X,Y);
Any idea how to fix it?
function CreatePattern
hFigure = figure;
hAxes = axes;
axis equal;
axis off;
hold on;
N = 3; % for line width
M = 20; % board size
squareEdgeSize = 5;
% create the board of patch objects
hPatchObjects = zeros(M,M);
for j = M:-1:1
for k = 1:M
hPatchObjects(M - j+ 1, k) = rectangle('Position', [k*squareEdgeSize,j*squareEdgeSize,squareEdgeSize,squareEdgeSize], 'FaceColor', [0 0 0],...
'EdgeColor', 'w', 'LineWidth', N, 'HitTest', 'on', 'ButtonDownFcn', {#OnPatchPressedCallback, M - j+ 1, k});
end
end
Board = zeros(M,M);
playerColours = [1 1 1; 0 0 0];
xlim([squareEdgeSize M*squareEdgeSize]);
ylim([squareEdgeSize M*squareEdgeSize]);
function OnPatchPressedCallback(hObject, eventdata, rowIndex, colIndex)
% change FaceColor to player colour
value = Board(rowIndex,colIndex);
if value == 1
set(hObject, 'FaceColor', playerColours(2, :));
Board(rowIndex,colIndex) = 0; % update board
else
set(hObject, 'FaceColor', playerColours(1, :));
Board(rowIndex,colIndex) = 1; % update board
end
end
end
I found this link and modified the code to be able to expand the board and also select cells that have been turned on already to switch them off.
Now I need a way to extract that board value to save the array.
Given n pairs of integers. Split into two subsets A and B to minimize sum(maximum difference among first values of A, maximum difference among second values of B).
Example : n = 4
{0, 0}; {5;5}; {1; 1}; {3; 4}
A = {{0; 0}; {1; 1}}
B = {{5; 5}; {3; 4}}
(maximum difference among first values of A, maximum difference among second values of B).
(maximum difference among first values of A) = fA_max - fA_min = 1 - 0 = 1
(maximum difference among second values of B) = sB_max - sB_min = 5 - 4 = 1
Therefore, the answer if 1 + 1 = 2. And this is the best way.
Obviously, maximum difference among the values equals to (maximum value - minimum value). Hence, what we need to do is find the minimum of (fA_max - fA_min) + (sB_max - sB_min)
Suppose the given array is arr[], first value if arr[].first and second value is arr[].second.
I think it is quite easy to solve this in quadratic complexity. You just need to sort the array by the first value. Then all the elements in subset A should be picked consecutively in the sorted array. So, you can loop for all ranges [L;R] of the sorted. Each range, try to add all elements in that range into subset A and add all the remains into subset B.
For more detail, this is my C++ code
int calc(pair<int, int> a[], int n){
int m = 1e9, M = -1e9, res = 2e9; //m and M are min and max of all the first values in subset A
for (int l = 1; l <= n; l++){
int g = m, G = M; //g and G are min and max of all the second values in subset B
for(int r = n; r >= l; r--) {
if (r - l + 1 < n){
res = min(res, a[r].first - a[l].first + G - g);
}
g = min(g, a[r].second);
G = max(G, a[r].second);
}
m = min(m, a[l].second);
M = max(M, a[l].second);
}
return res;
}
Now, I want to improve my algorithm down to loglinear complexity. Of course, sort the array by the first value. After that, if I fixed fA_min = a[i].first, then if the index i increase, the fA_max will increase while the (sB_max - sB_min) decrease.
But now I am still stuck here, is there any ways to solve this problem in loglinear complexity?
The following approach is an attempt to escape the n^2, using an argmin list for the second element of the tuples (lets say the y-part). Where the points are sorted regarding x.
One Observation is that there is an optimum solution where A includes index argmin[0] or argmin[n-1] or both.
in get_best_interval_min_max we focus once on including argmin[0] and the next smallest element on y and so one. The we do the same from the max element.
We get two dictionaries {(i,j):(profit, idx)}, telling us how much we gain in y when including points[i:j+1] in A, towards min or max on y. idx is the idx in the argmin array.
calculate the objective for each dict assuming max/min or y is not in A.
combine the results of both dictionaries, : (i1,j1): (v1, idx1) and (i2,j2): (v2, idx2). result : j2 - i1 + max_y - min_y - v1 - v2.
Constraint: idx1 < idx2. Because the indices in the argmin array can not intersect, otherwise some profit in y might be counted twice.
On average the dictionaries (dmin,dmax) are smaller than n, but in the worst case when x and y correlate [(i,i) for i in range(n)] they are exactly n, and we do not win any time. Anyhow on random instances this approach is much faster. Maybe someone can improve upon this.
import numpy as np
from random import randrange
import time
def get_best_interval_min_max(points):# sorted input according to x dim
L = len(points)
argmin_b = np.argsort([p[1] for p in points])
b_min,b_max = points[argmin_b[0]][1], points[argmin_b[L-1]][1]
arg = [argmin_b[0],argmin_b[0]]
res_min = dict()
for i in range(1,L):
res_min[tuple(arg)] = points[argmin_b[i]][1] - points[argmin_b[0]][1],i # the profit in b towards min
if arg[0] > argmin_b[i]: arg[0]=argmin_b[i]
elif arg[1] < argmin_b[i]: arg[1]=argmin_b[i]
arg = [argmin_b[L-1],argmin_b[L-1]]
res_max = dict()
for i in range(L-2,-1,-1):
res_max[tuple(arg)] = points[argmin_b[L-1]][1]-points[argmin_b[i]][1],i # the profit in b towards max
if arg[0]>argmin_b[i]: arg[0]=argmin_b[i]
elif arg[1]<argmin_b[i]: arg[1]=argmin_b[i]
# return the two dicts, difference along y,
return res_min, res_max, b_max-b_min
def argmin_algo(points):
# return the objective value, sets A and B, and the interval for A in points.
points.sort()
# get the profits for different intervals on the sorted array for max and min
dmin, dmax, y_diff = get_best_interval_min_max(points)
key = [None,None]
res_min = 2e9
# the best result when only the min/max b value is includes in A
for d in [dmin,dmax]:
for k,(v,i) in d.items():
res = points[k[1]][0]-points[k[0]][0] + y_diff - v
if res < res_min:
key = k
res_min = res
# combine the results for max and min.
for k1,(v1,i) in dmin.items():
for k2,(v2,j) in dmax.items():
if i > j: break # their argmin_b indices can not intersect!
idx_l, idx_h = min(k1[0], k2[0]), max(k1[1],k2[1]) # get index low and idx hight for combination
res = points[idx_h][0]-points[idx_l][0] -v1 -v2 + y_diff
if res < res_min:
key = (idx_l, idx_h) # new merged interval
res_min = res
return res_min, points[key[0]:key[1]+1], points[:key[0]]+points[key[1]+1:], key
def quadratic_algorithm(points):
points.sort()
m, M, res = 1e9, -1e9, 2e9
idx = (0,0)
for l in range(len(points)):
g, G = m, M
for r in range(len(points)-1,l-1,-1):
if r-l+1 < len(points):
res_n = points[r][0] - points[l][0] + G - g
if res_n < res:
res = res_n
idx = (l,r)
g = min(g, points[r][1])
G = max(G, points[r][1])
m = min(m, points[l][1])
M = max(M, points[l][1])
return res, points[idx[0]:idx[1]+1], points[:idx[0]]+points[idx[1]+1:], idx
# let's try it and compare running times to the quadratic_algorithm
# get some "random" points
c1=0
c2=0
for i in range(100):
points = [(randrange(100), randrange(100)) for i in range(1,200)]
points.sort() # sorted for x dimention
s = time.time()
r1 = argmin_algo(points)
e1 = time.time()
r2 = quadratic_algorithm(points)
e2 = time.time()
c1 += (e1-s)
c2 += (e2-e1)
if not r1[0] == r2[0]:
print(r1,r2)
raise Exception("Error, results are not equal")
print("time of argmin_algo", c1, "time of quadratic_algorithm",c2)
UPDATE: #Luka proved the algorithm described in this answer is not exact. But I will keep it here because it's a good performance heuristics and opens the way to many probabilistic methods.
I will describe a loglinear algorithm. I couldn't find a counter example. But I also couldn't find a proof :/
Let set A be ordered by first element and set B be ordered by second element. They are initially empty. Take floor(n/2) random points of your set of points and put in set A. Put the remaining points in set B. Define this as a partition.
Let's call a partition stable if you can't take an element of set A, put it in B and decrease the objective function and if you can't take an element of set B, put it in A and decrease the objective function. Otherwise, let's call the partition unstable.
For an unstable partition, the only moves that are interesting are the ones that take the first or the last element of A and move to B or take the first or the last element of B and move to A. So, we can find all interesting moves for a given unstable partition in O(1). If an interesting move decreases the objective function, do it. Go like that until the partition becomes stable. I conjecture that it takes at most O(n) moves for the partition to become stable. I also conjecture that at the moment the partition becomes stable, you will have a solution.
I have a matrix A of dimension m-by-n composed of zeros and ones, and a matrix J of dimension m-by-1 reporting some integers from [1,...,n].
I want to construct a matrix B of dimension m-by-n such that for i = 1,...,m
B(i,j) = A(i,j) for j=1,...,n-1
B(i,n) = abs(A(i,n)-1)
If sum(B(i,:)) is odd then B(i,J(i)) = abs(B(i,J(i))-1)
This code does what I want:
m = 4;
n = 5;
A = [1 1 1 1 1; ...
0 0 1 0 0; ...
1 0 1 0 1; ...
0 1 0 0 1];
J = [1;2;1;4];
B = zeros(m,n);
for i = 1:m
B(i,n) = abs(A(i,n)-1);
for j = 1:n-1
B(i,j) = A(i,j);
end
if mod(sum(B(i,:)),2)~=0
B(i,J(i)) = abs(B(i,J(i))-1);
end
end
Can you suggest more efficient algorithms, that do not use the nested loop?
No for loops are required for your question. It just needs an effective use of the colon operator and logical-indexing as follows:
% First initialize B to all zeros
B = zeros(size(A));
% Assign all but last columns of A to B
B(:, 1:end-1) = A(:, 1:end-1);
% Assign the last column of B based on the last column of A
B(:, end) = abs(A(:, end) - 1);
% Set all cells to required value
% Original code which does not work: B(oddRow, J(oddRow)) = abs(B(oddRow, J(oddRow)) - 1);
% Correct code:
% Find all rows in B with an odd sum
oddRow = find(mod(sum(B, 2), 2) ~= 0);
for ii = 1:numel(oddRow)
B(oddRow(ii), J(oddRow(ii))) = abs(B(oddRow(ii), J(oddRow(ii))) - 1);
end
I guess for the last part it is best to use a for loop.
Edit: See the neat trick by EBH to do the last part without a for loop
Just to add to #ammportal good answer, also the last part can be done without a loop with the use of linear indices. For that, sub2ind is useful. So adopting the last part of the previous answer, this can be done:
% Find all rows in B with an odd sum
oddRow = find(mod(sum(B, 2), 2) ~= 0);
% convert the locations to linear indices
ind = sub2ind(size(B),oddRow,J(oddRow));
B(ind) = abs(B(ind)- 1);
I have a question regarding indexing and loops in MATLAB. I have a vector of length n (named data in the code below). I want to examine this vector 4 elements at a time inside of a for loop. How can I do this? My attempt included below does not work because it will exceed the array dimensions at the end of the loop.
for k = 1:length(data)
index = k:k+3;
cur_data = data(index);
pre_q_data1 = cur_data(1);
pre_q_data2 = cur_data(2);
% Interweaving the data
q = [pre_q_data1; pre_q_data2];
qdata = q(:)';
pre_i_data1 = cur_data(3);
pre_i_data2 = cur_data(4);
i = [pre_i_data1; pre_i_data2];
idata = i(:)';
end
You shouldn't have k go all the way to length(data) if you're planning on indexing up to k+3.
I've also taken the liberty of greatly simplifying your code, but feel free to ignore that!
for k = 1:length(data)-3
% maximum k = length(data)-3, so maximum index = length(data)-3+3=length(data)
index = k:k+3;
cur_data = data(k:k+3);
% Interweaving the data
q = cur_data(1:2); % transpose at end of line here if need be
i = cur_data(3:4); % could just use data(k+2:k+3) and not use cur_data
end
I have a column vector (V1) of real numbers like:
123.2100
125.1290
...
954.2190
If I add, let's say, a number 1 to each row in this vector, I will get (V2):
124.2100
126.1290
...
955.2190
I need to find out how many elements from V2 are inside some error-window created from V1. For example the error-window = 0.1 (but in my case every element in V1 has it's own error window):
123.1100 123.3100
125.0290 125.2290
...
954.1190 954.3190
I can create some code like this:
% x - my vector
% ppm - a variable responsible for error-window
window = [(1-(ppm/1000000))*x, (1+(ppm/1000000))*x]; % - error-window
mdiff = 1:0.001:20; % the numbers I will iteratively add to x
% (like the number 1 in the example)
cdiff = zeros(length(mdiff),1); % a vector that will contain counts of elements
% corresponding to different mdiff temp = 0;
for i = 1:length(mdiff)
for j = 1:size(window,1)
xx = x + mdiff(i);
indxx = find( xx => window(j,1) & xx <= window(j,2) );
if any(indxx)
temp = temp + length(indxx); %edited
end
end
cdiff(i) = temp;
temp = 0;
end
So, at the end cdiff will contain all the counts corresponding to mdiff. The only thing, I would like to make the code faster. Or is there a way to avoid using the second loop (with j)? I mean to directly use a multidimensional condition.
EDIT
I decided to simpify the code like this (thanking to the feedback I got here):
% x - my vector
% ppm - a variable responsible for error-window
window = [(1-(ppm/1000000))*x, (1+(ppm/1000000))*x]; % - error-window
mdiff = 1:0.001:20; % the numbers I will iteratively add to x
% (like the number 1 in the example)
cdiff = zeros(length(mdiff),1); % a vector that will contain counts of elements
% corresponding to different mdiff temp = 0;
for i = 1:length(mdiff)
xx = x + mdiff(i);
cdiff(i) = sum(sum(bsxfun(#and,bsxfun(#ge,xx,window(:,1)'),bsxfun(#le,xx,window(:,2)'))));
end
In this case the code works faster and seems properly
add = 1; %// how much to add
error = .1; %// maximum allowed error
V2 = V1 + add; %// build V2
ind = sum(abs(bsxfun(#minus, V1(:).', V2(:)))<error)>1; %'// index of elements
%// of V1 satisfying the maximum error condition. ">1" is used to because each
%// element is at least equal to itself
count = nnz(ind);
Think this might work for you -
%%// Input data
V1 = 52+rand(4,1)
V2 = V1+1;
t= 0.1;
low_bd = any(abs(bsxfun(#minus,V2,[V1-t]'))<t,2); %%//'
up_bd = any(abs(bsxfun(#minus,V2,[V1+t]'))<t,2); %%//'
count = nnz( low_bd | up_bd )
One could also write it as -
diff_map = abs(bsxfun(#minus,[V1-t V1+t],permute(V2,[3 2 1])));
count = nnz(any(any(diff_map<t,2),1))
Edit 1:
low_bd = any(abs(bsxfun(#minus,V2,window(:,1)'))<t,2); %%//'
up_bd = any(abs(bsxfun(#minus,V2,window(:,2)'))<t,2); %%//'
count = nnz( low_bd | up_bd )
Edit 2: Vectorized form for the edited code
t1 = bsxfun(#plus,x,mdiff);
d1 = bsxfun(#ge,t1,permute(window(:,1),[3 2 1]));
d2 = bsxfun(#le,t1,permute(window(:,2),[3 2 1]));
t2 = d1.*d2;
cdiff_vect = max(sum(t2,3),[],1)';