I'm not familiar with MatLab textscan(). Any help you could provide would be much appreciated.
The text file that I want to read looks as follows:
test.0.xml
myFunc(x) = 0.0294118
execution time: 5.87ms
test.1.xml
myFunc(x) = 0.0625
execution time: 1.618ms
test.10.xml
test.11.xml
test.12.xml
myFunc(x) = 0.0416667
execution time: 0.38ms
test.13.xml
myFunc(x) = 0.0625
execution time: 7.076ms
test.14.xml
myFunc(x) = 0.0384615
execution time: 10.51ms
...
Preferably the result would be formatted similar to this:
test = [0, 1, 10, 11, 12, 13, ...];
myFunc = [0.0294118, 0.0625, NaN, NaN, 0.0416667, 0.0625, ...];
executionTime = [5.87, 1.618, NaN, NaN, 0.38, 7.076, ...];
Thank you in advance.
fileID = fopen('file.txt');
missing = false;
test = [];
myFunc = [];
eTime = [];
while ~feof(fileID)
thisLine = fgetl(fileID);
if size(thisLine) >= 1
if thisLine(1) == 't'
if missing == true
myFunc(end+1) = NaN;
eTime(end+1) = NaN;
end
test(end+1) = sscanf(thisLine,'test.%d');
missing = true;
elseif thisLine(1) == 'm'
myFunc(end+1) = sscanf(thisLine,'myFunc(x) = %f');
missing = false;
else
eTime(end+1) = sscanf(thisLine,'eTime: %f');
missing = false;
end
end
% disp(thisLine)
end
fclose(fileID);
Related
Basically I have a code where it produces a plot of all possible permutations between Cost and Reliability. There's a total of 864 data points split up between 8 rows. Five of the rows have 2 options and three of them 3 options.
Given here is a copy of my code. I'm trying to have the permutations of 'Other Cameras' and 'Depth & Structure Testing' have a different color with the other six possibilities. I tried using the 'gscatter' command but didn't have much luck with it.
I believe I need to have the scatter command in the if/else statements themselves, although I'm not too sure what to plot in the 'X' and 'Y' for the 'scatter' command. Currently my code is set up for plotting all the data in one color. I deleted my code with the 'gscatter' because I got many errors and when I tried to fix them the plot ultimately didn't work as planned.
% Pareto_Eval
baseline_cost = 45;
nrows = 8;
%Initialize Variables
for aa = 1:nrows
cost_delta(aa) = 0;
reliability(aa) = 1;
end
icount = 1;
%Propulsion
for row1 = 1:2
if row1 == 1
cost_delta(1)= -7;
reliability(1) = 0.995;
elseif row1==2
cost_delta(1)=0;
reliability(1)=.99;
end
%Entry Mode
for row2 = 1:2
if row2 == 1
cost_delta(2) = -3;
reliability(2) = .99;
else
cost_delta(2) = 0;
reliability(2) = .98;
end
%Landing Method
for row3 = 1:3
if row3 == 1 %if needs declaration
cost_delta(3)= 0;
reliability(3) = .99;
elseif row3 == 2 %elseif needs declaration
cost_delta(3) = 4;
reliability(3) = .995;
else %else does not need declaration
cost_delta(3) = -2;
reliability(3) = .95;
end
%Lander Type
for row4 = 1:3
if row4 == 1
cost_delta(4)= 10;
reliability(4) = .99;
elseif row4 == 2
cost_delta(4) = 0;
reliability(4) = .99;
else
cost_delta(4) = 15;
reliability(4) = .95;
end
%Rover Type
for row5 = 1:2
if row5 == 1
cost_delta(5)= -2;
reliability(5) = .98;
else
cost_delta(5) = 0;
reliability(5) = .975;
end
%Power Source
for row6 = 1:2
if row6 == 1
cost_delta(6) = -3;
reliability(6) = .95;
else
cost_delta(6) = 0;
reliability(6) = .995;
end
%Depth & Structure Testing
for row7 = 1:2
if row7 == 1
cost_delta(7) = 0;
reliability(7) = .99;
else
cost_delta(7) = 2;
reliability(7) = .85;
end
%Other Cameras
for row8 = 1:3
if row8 == 1
cost_delta(8)= -1;
reliability(8) = .99;
elseif row8 == 2
cost_delta(8) = -1;
reliability(8) = .99;
else
cost_delta(8) = 0;
reliability(8) = .9801;
end
cost_delta_total = 0;
reliability_product = 1;
for bb=1:nrows
cost_delta_total = cost_delta_total + cost_delta(bb);
reliability_product = reliability_product*reliability(bb);
end
total_cost(icount) = baseline_cost + cost_delta_total;
total_reliability(icount) = reliability_product;
icount = icount + 1;
end; end; end; %Rows 1,2,3
end; end; end; %Rows 4,5,6
end; end; %Rows 7,8
%Plot the Pareto Evaluation
fignum=1;
figure(fignum)
sz = 5;
scatter(total_reliability, total_cost, sz, 'blue')
xlabel('Reliability')
ylabel('Cost')
title('Pareto Plot')
Any help is appreciated. I don't have a lot of experience with Matlab and I've tried looking around for help but nothing really worked.
Here is a sample code to make questions easier I created:
% Pareto_Eval
baseline_cost = 55;
nrows = 3;
%Initialize Variables
for aa = 1:nrows
cost_delta(aa) = 0;
reliability(aa) = 1;
end
icount = 1;
%Group 1
for row1 = 1:2
if row1 == 1
cost_delta(1)= 5;
reliability(1) = 0.999;
elseif row1==2
cost_delta(1) = 0;
reliability(1) = .995;
end
%Group 2
for row2 = 1:2
if row2 == 1
cost_delta(2) = 0;
reliability(2) = .98;
else
cost_delta(2) = -2;
reliability(2) = .95;
end
%Group 3
for row3 = 1:2
if row3 == 1
cost_delta(3) = 3;
reliability(3) = .997;
else
cost_delta(3) = 0;
reliability(3) = .96;
end
%initializing each row
cost_delta_total = 0;
reliability_product = 1;
for bb = 1:nrows
cost_delta_total = cost_delta_total + cost_delta(bb);
reliability_product = reliability_product*reliability(bb);
end
total_cost(icount) = baseline_cost + cost_delta_total;
total_reliability(icount) = reliability_product;
icount = icount + 1;
end
end
end
fignum=1;
figure(fignum)
sz = 25;
scatter(total_reliability, total_cost, sz)
xlabel('Reliability')
ylabel('Cost')
title('Pareto Plot')
Basically I need to make a plot in each if-loop, but I'm not sure how to do it and have them all on the same plot
sounds like an interesting project! Not sure if I understood your intended plots correctly, but hopefully the code below gets you a bit closer to what you are looking for.
I've started off with a rather deep mess of nested for loops (as you did) but kept it more concise bybuilding a permutations matrix.
counter = 0;
for propulsion_options = 1:2
for entry_mode = 1:2
for landing_method = 1:3
for lander_type = 1:3
for rover_type = 1:2
for power_source = 1:2
for depth_testing = 1:2
for other_cameras = 1:3
counter = counter +1
permutations(counter,:) = [...
propulsion_options,...
entry_mode,...
landing_method,...
lander_type,...
rover_type,...
power_source,...
depth_testing,...
other_cameras];
end
end
end
end
end
end
end
end
This way I kept the actual scoring out of the loops, and perhaps easier to tweak the values. I initialised the cost and reliabiltiy arrays to be the same size as the permutations array:
cost_delta = zeros(size(permutations));
reliability = zeros(size(permutations));
Then for each metric, I searched the permutations array for all occurances of each possible value and assigned the appropriate score:
%propulsion
propertyNo = 1;
cost_delta(find(permutations(:,propertyNo)==1),propertyNo) = -7;
cost_delta(find(permutations(:,propertyNo)==2),propertyNo) = 0;
reliability(find(permutations(:,propertyNo)==1),propertyNo) = 0.995;
reliability(find(permutations(:,propertyNo)==2),propertyNo) = 0.99;
%entry_mode (2)
propertyNo = 2;
cost_delta(find(permutations(:,propertyNo)==1),propertyNo) = -3;
cost_delta(find(permutations(:,propertyNo)==2),propertyNo) = 0;
reliability(find(permutations(:,propertyNo)==1),propertyNo) = 0.99;
reliability(find(permutations(:,propertyNo)==2),propertyNo) = 0.98;
%landing_method (3)
propertyNo = 3;
cost_delta(find(permutations(:,propertyNo)==1),propertyNo) = 0;
cost_delta(find(permutations(:,propertyNo)==2),propertyNo) = 4;
cost_delta(find(permutations(:,propertyNo)==3),propertyNo) = -2;
reliability(find(permutations(:,propertyNo)==1),propertyNo) = 0.99;
reliability(find(permutations(:,propertyNo)==2),propertyNo) = 0.995;
reliability(find(permutations(:,propertyNo)==3),propertyNo) = 0.95;
%lander_type (3)
propertyNo = 4;
cost_delta(find(permutations(:,propertyNo)==1),propertyNo) = 10;
cost_delta(find(permutations(:,propertyNo)==2),propertyNo) = 0;
cost_delta(find(permutations(:,propertyNo)==3),propertyNo) = 15;
reliability(find(permutations(:,propertyNo)==1),propertyNo) = 0.99;
reliability(find(permutations(:,propertyNo)==2),propertyNo) = 0.99;
reliability(find(permutations(:,propertyNo)==3),propertyNo) = 0.95;
%rover_type (2)
propertyNo = 5;
cost_delta(find(permutations(:,propertyNo)==1),propertyNo) = -2;
cost_delta(find(permutations(:,propertyNo)==2),propertyNo) = 0;
reliability(find(permutations(:,propertyNo)==1),propertyNo) = 0.98;
reliability(find(permutations(:,propertyNo)==2),propertyNo) = 0.975;
%power_source (2)
propertyNo = 6;
cost_delta(find(permutations(:,propertyNo)==1),propertyNo) = -3;
cost_delta(find(permutations(:,propertyNo)==2),propertyNo) = 0;
reliability(find(permutations(:,propertyNo)==1),propertyNo) = 0.95;
reliability(find(permutations(:,propertyNo)==2),propertyNo) = 0.995;
%depth_testing (2)
propertyNo = 7;
cost_delta(find(permutations(:,propertyNo)==1),propertyNo) = 0;
cost_delta(find(permutations(:,propertyNo)==2),propertyNo) = 2;
reliability(find(permutations(:,propertyNo)==1),propertyNo) = 0.99;
reliability(find(permutations(:,propertyNo)==2),propertyNo) = 0.85;
%other_cameras (3)
propertyNo = 8;
cost_delta(find(permutations(:,propertyNo)==1),propertyNo) = -1;
cost_delta(find(permutations(:,propertyNo)==2),propertyNo) = -1;
cost_delta(find(permutations(:,propertyNo)==3),propertyNo) = 0;
reliability(find(permutations(:,propertyNo)==1),propertyNo) = 0.99;
reliability(find(permutations(:,propertyNo)==2),propertyNo) = 0.99;
reliability(find(permutations(:,propertyNo)==3),propertyNo) = 0.9801;
Then each permutation can have a total cost / reliabiltiy score by summing and takign the product along the second dimension:
cost_delta_total = sum(cost_delta,2);
reliability_product = prod(reliability,2);
Finally, you can plot all points (as per your original):
%Plot the Pareto Evaluation
fignum=1;
figure(fignum)
sz = 5;
scatter(reliability_product, cost_delta_total, sz, 'b')
xlabel('Reliability')
ylabel('Cost')
title('Pareto Plot')
or you can create an index into the permutations by searching for specific property values and plot these different colours (actually this bit answers your most specific question of how to plot two things on the same axes - you just need the hold on; command):
propertyNo = 7;
indexDepth1 = find(permutations(:,propertyNo)==1);
indexDepth2 = find(permutations(:,propertyNo)==2);
fignum=2;
figure(fignum)
sz = 5;
scatter(reliability_product(indexDepth1), cost_delta_total(indexDepth1), sz, 'k');
hold on;
scatter(reliability_product(indexDepth2), cost_delta_total(indexDepth2), sz, 'b');
xlabel('Reliability')
ylabel('Cost')
title('Pareto Plot')
legend('Depth & Structure Test 1','Depth & Structure Test 2')
propertyNo = 8;
indexCam1 = find(permutations(:,propertyNo)==1);
indexCam2 = find(permutations(:,propertyNo)==2);
indexCam3 = find(permutations(:,propertyNo)==3);
fignum=3;
figure(fignum)
sz = 5;
scatter(reliability_product(indexCam1), cost_delta_total(indexCam1), sz, 'k');
hold on;
scatter(reliability_product(indexCam2), cost_delta_total(indexCam2), sz, 'b');
scatter(reliability_product(indexCam3), cost_delta_total(indexCam3), sz, 'g');
xlabel('Reliability')
ylabel('Cost')
title('Pareto Plot')
legend('Other Camera 1','Other Camera 2','Other Camera 3')
Good luck with the mission! When is launch day?
function Test()
a = 2;
b = 1;
c = 0.5;
q = 0.001;
r = 10;
function F = Useful(x) %calculates existing values for x with size 11
eq1 = (1*(0.903*x(2))^(-1))-(0.903*x(1));
eq2 = (1*(0.665*x(3))*(0.903*x(2))^(-1))-0.903*x(4);
eq3 = (1*(0.399*x(5))*(0.903*x(2)))-0.665*x(6);
eq4 = (1*(0.399*x(5))*(0.903*x(2))^2)-0.903*x(7);
eq5 = (1*(0.399*x(5))*(0.903*x(2))^3)-1*x(8);
eq6 = (1*(0.665*x(3))*(0.399*x(5))*(0.903*x(2)))-1*x(9);
eq7 = (1*(0.665*x(3))*(0.399*x(5))*(0.903*x(2))^2)-0.903*x(10);
eq8 = (1*(0.665*x(3))*(0.399*x(5)))-0.903*x(11);
eq9 = x(3)+x(4)+x(9)+x(10)+x(11)-a;
eq10 = x(5)+x(6)+x(7)+x(8)+x(9)+x(10)+x(11)-b;
eq11 = x(2)+x(6)+2*x(7)+3*x(8)+x(9)+2*x(10)-x(1)-x(4)-c;
F = [eq1;eq2;eq3;eq4;eq5;eq6;eq7;eq8; eq9; eq10; eq11];
end
Value(1,1) = 0;
for d = 2:100
x = fsolve(#Useful,x0,options); %Produces the x(1) to x(11) values
Value(1,d) = (x(3)+x(5))*d+Value(1,d-1); %Gives a new value after each iteration
a = a-x(3);
b = b-x(5);
c = c-x(2);
end
function Zdot = rhs(t,z) %z = (e1,e2,e3,e4,e5)
Zdot=zeros(5,1);
Zdot(1) = -1*z(1);
Zdot(2) = 1*z(1);
Zdot(3) = 1*z(1) - 1*z(2)*z(3);
Zdot(4) = 1*1*z(1) - Value(1,100)*H(z(3))*z(4)*z(4);
Zdot(5) = Value(1,100)*H(z(3))*(z(4));
end
function hill = H(x)
hill = q/(q+x^r);
end
[T,Y] = ode15s(#rhs, [0, 120], [1, 0, 1, 0, 0]); %Solve second function with values giving z(1) to z(5)
plot(T,Y(:,5))
end
I'm wondering, is it possible to pass on each Value obtained (Value (1), Value (2)... so on), into "function Zdot" or is only the final value possible to pass on? Essentially is this possible to implement:
function Zdot = rhs(t,z) %z = (e1,e2,e3,e4,e5)
Zdot=zeros(5,1);
Zdot(1) = -1*z(1);
Zdot(2) = 1*z(1);
Zdot(3) = 1*z(1) - 1*z(2)*z(3);
Zdot(4) = 1*1*z(1) - Value(1,d)*H(z(3))*z(4)*z(4);
Zdot(5) = Value(1,d)*H(z(3))*(z(4));
end
Any insights would be much appreciated and I would be extremely grateful. Thank you in advance!
I tried to set same figure size for several images using for loop in matlab and save in png
But some (usually one) of them has different size.
In below code, I tried to save image in (48,64).
Why some figure sizes are not set properly as I commanded?
nMarker = 5;
mark = ['o', 's', 'd', '^', 'p'];
nSize = 3;
mSize = [9, 18, 27];
nRow = 48;
nCol = 64;
BG = zeros(nRow, nCol);
idxStage = 2;
numAction = 1;
numPositionX = 4;
numPositionY = 4;
xtrain = [1,2,3,4];
ytrain = [1,2,3,4];
xpos = [20, 30, 40, 50];
ypos = [8, 18, 28, 38];
nStepS = 10;
nStepB = 10;
nStep = nStepS + nStepB;
for a = 1
for x = 1:numPositionX
for y = 1:numPositionY
for obj = 1:nMarker
for s = 1:nSize
obj_command = x*1000 + y*100 + obj*10 + s;
fig1 = figure(1);
imagesc(BG)
hold on
scatter(xpos(x), ypos(y), mSize(s), mark(obj), 'k', 'filled')
axis off
set(fig1, 'Position', [500, 500, 64, 48]);
set(gca,'position',[0 0 1 1],'units','normalized')
F = getframe(gcf);
pause(0.05)
[X, Map] = frame2im(F);%
tmp_frame = rgb2gray(X);
tmp_im_fn = sprintf('tmp/image_seq%04d.png',obj_command);
imwrite(tmp_frame, tmp_im_fn)
clf
end
end
end
end
end
I found some trick to solve the problem for now.
I put,
fig1 = figure(1);
drawnow
in front of the for loop and it seems all sizes are equal now.
But still waiting for better solution...
function [den, p] = findUpperBound(data)
newData = data;
ranData = data;
pValue = Inf;
density = 0.99;
decr = 0.01;
x = [];
y = [];
while(pValue>0.05)
for i=1:numel(data)
newData(i).matrix = findNetworkAtDensity(density, 0.01, data(i).matrix);
newData(i).smallWorldness = (sum(clustering_coef_wu(newData(i).matrix))/90)/avgPathLen(newData(i).matrix);
x(i,1) = newData(i).matrix;
ranData(i).matrix = randmio_und_connected(newData(i).matrix, 5);
ranData(i).smallWorldness = (sum(clustering_coef_wu(ranData(i).matrix))/90)/avgPathLen(ranData(i).matrix);
y(i,1) = ranData(i).matrix;
end
[h, pValue] = ttest2(x,y);
density = density - decr;
end
den = density;
p = pValue;
end
I need to assign some values into newData and ranData, and there is an error:
"Improper index matrix reference. Error in findUpperBound (line 11)newData(i).matrix = findNetworkAtDensity(density, 0.01, data(i).matrix);"
It is weird that if I use the command window to do this, there will be no error:
>> a = data;
>> a(1).matrix = data(1).matrix;
>>
I've search for this on the Matlab site, but still can't find the answer.
Could anyone tell me how to solve this problem? Thanks a lot!
my code is
pathname=uigetdir;
filename=uigetfile('*.txt','choose a file name.');
data=importdata(filename);
element= (data.data(:,10));
in_array=element; pattern= [1 3];
locations = cell(1, numel(pattern));
for p = 1:(numel(pattern))
locations{p} = find(in_array == pattern(p));
end
idx2 = [];
for p = 1:numel(locations{1})
start_value = locations{1}(p);
for q = 2:numel(locations)
found = true;
if (~any((start_value + q - 1) == locations{q}))
found = false;
break;
end
end
if (found)
idx2(end + 1) = locations{1}(p);
end
end
[m2,n2]=size(idx2)
res_name= {'one' 'two'};
res=[n n2];
In this code I finding a pattern in one of the column of my data file and counting how many times it's repeated.
I have like 200 files that I want to do the same with them but unfotunatlly I'm stuck.
this is what I have added so far
pathname=uigetdir;
files=dir('*.txt');
for k=1:length(files)
filename=files(k).name;
data(k)=importdata(files(k).name);
element{k}=data(1,k).data(:,20);
in_array=element;pattern= [1 3];
locations = cell(1, numel(pattern));
for p = 1:(numel(pattern))
locations{p} = find(in_array{k}== pattern(p));
end
idx2{k} = [];
how can I continue this code..??
OK, first define this function:
function [inds, indsy] = findPattern(M, pat, dim)
indices = [];
if nargin == 2
dim = 1;
if size(M,1) == 1
dim = 2; end
end
if dim == 1
if numel(pat) > size(M,1)
return; end
for ii = 1:size(M,2)
inds = findPatternCol(M(:,ii), pat);
indices = [indices; repmat(ii,numel(inds),1) inds]%#ok
end
elseif dim == 2
if numel(pat) > size(M,2)
return; end
for ii = 1:size(M,1)
inds = findPatternCol(M(ii,:).', pat);
indices = [indices; inds repmat(ii,numel(inds),1)]%#ok
end
else
end
inds = indices;
if nargout > 1
inds = indices(:,1);
indsy = indices(:,2);
end
end
function indices = findPatternCol(col, pat)
inds = find(col == pat(1));
ii = 1;
prevInds = [];
while ~isempty(inds) && ii<numel(pat) && numel(prevInds)~=numel(inds)
prevInds = inds;
inds = inds(inds+ii<=numel(col) & col(inds+ii)==pat(ii+1));
ii = ii + 1;
end
indices = inds(:);
end
which is decent but probably not the most efficient. If performance becomes a problem, start here with optimizations.
Now loop through each file like so:
pathname = uigetdir;
files = dir('*.txt');
indices = cell(length(files), 1);
for k = 1:length(files)
filename = files(k).name;
data(k) = importdata(files(k).name);
array = data(1,k).data(:,20);
pattern = [1 3];
indices{k} = findPattern(array, pattern);
end
The number of occurrences of the pattern can be found like so:
counts = cellfun(#(x)size(x,1), indices);