eqn =
結果:
Too small
22%
Just right
38%
Too large
40%
2648 票
In one of my MATLAB projects, I want to add a button to an existing axes toolbar. The function for doing this is axtoolbarbtn:
axtoolbarbtn(tb,style,Name=Value)
However, I have found that the existing interfaces and behavior make it quite awkward to accomplish this task.
Here are my observations.
Adding a Button to the Default Axes Toolbar Is Unsupported
plot(1:10)
ax = gca;
tb = ax.Toolbar
Calling axtoolbarbtn on ax results in an error:
>> axtoolbarbtn(tb,"state")
Error using axtoolbarbtn (line 77)
Modifying the default axes toolbar is not supported.
Default Axes Toolbar Can't Be Distinguished from an Empty Toolbar
The Children property of the default axes toolbar is empty. Thus, it appears programmatically to have no buttons, just like an empty toolbar created by axtoolbar.
cla
plot(1:10)
ax = gca;
tb = ax.Toolbar;
tb.Children
ans = 0x0 empty GraphicsPlaceholder array.
tb2 = axtoolbar(ax);
tb2.Children
ans = 0x0 empty GraphicsPlaceholder array.
A Workaround
An empty axes toolbar seems to have no use except to initalize a toolbar before immediately adding buttons to it. Therefore, it seems reasonable to assume that an axes toolbar that appears to be empty is really the default toolbar. While we can't add buttons to the default axes toolbar, we can create a new toolbar that has all the same buttons as the default one, using axtoolbar("default"). And then we can add buttons to the new toolbar.
That observation leads to this workaround:
tb = ax.Toolbar;
if isempty(tb.Children)
% Assume tb is the default axes toolbar. Recreate
% it with the default buttons so that we can add a new
% button.
tb = axtoolbar(ax,"default");
end
btn = axtoolbarbtn(tb);
% Then set up the button as desired (icon, callback,
% etc.) by setting its properties.
As workarounds go, it's not horrible. It just seems a shame to have to delete and then recreate a toolbar just to be able to add a button to it.
The worst part about the workaround is that it is so not obvious. It took me a long time of experimentation to figure it out, including briefly giving it up as seemingly impossible.
The documentation for axtoolbarbtn avoids the issue. The most obvious example to write for axtoolbarbtn would be the first thing every user of it will try: add a toolbar button to the toolbar that gets created automatically in every call to plot. The doc page doesn't include that example, of course, because it wouldn't work.
My Request
I like the axes toolbar concept and the axes interactivity that it promotes, and I think the programming interface design is mostly effective. My request to MathWorks is to modify this interface to smooth out the behavior discontinuity of the default axes toolbar, with an eye towards satisfying (and documenting) the general use case that I've described here.
One possible function design solution is to make the default axes toolbar look and behave like the toolbar created by axtoolbar("default"), so that it has Children and so it is modifiable.
I am curious as to how my goal can be accomplished in Matlab.
The present APP called "Matching Network Designer" works quite well, but it is limited to a single section of a "PI", a "TEE", or an "L" topology circuit.
This limits the bandwidth capability of the APP when the intended use is to create an amplifier design intended for wider bandwidth projects.
I am requesting that a "Broadband Matching Network Designer" APP be developed by you, the MathWorks support team.
One suggestion from me is to be able to cascade a second section (or "pole") to the first.
Then the resulting topology would be capable of achieving that wider bandwidth of the microwave amplifier project where it would be later used with the transistor output and input matching networks.
Instead of limiting the APP to a single frequency, the entire s parameter file would be used as an input.
The APP would convert the polar s parameters to rectangular scaler complex impedances that you already use.
At that point, having started out with the first initial center frequency, the other frequencies both greater than and less than the center would come into use by an optimization of the circuit elements.
I'm hoping that you will be able to take on this project.
I can include an attachment of such a Matching Network Designer APP that you presently have if you like.
That network is centered at 10 GHz.
Kimberly Renee Alvarez.
310-367-5768
私の場合、前の会社が音楽認識アプリの会社で、アルゴリズム開発でFFTが使われていたことがきっかけでした。でも、MATLABのすごさが分かったのは、機械学習のオンライン講座で、Andrew Ngが、線型代数を使うと、数式と非常に近い構文のコードで問題が処理できることを学んだ時でした。
Dears,
I am running a MS-DSGE model using RISE toolbox. I want to add a fiscal shock and examine its effect on output, price...
%fiscal shock
shock_type = {'eps_G'};
%here is my variable list of a cell array of character variables and not a struct.
var_list={'log_y','C','pi_ann','B_nominal','B','sp','i_ann','r_real_ann','P'};
% EXOGENOUS SWITCHING
myirfs1=irf(m1,'irf_periods',24,'irf_shock_sign',1);
myirfs1 = struct()
myirfs1.eps_CP = struct();
myirfs1.eps_G = struct();
myirfs1.eps_T = struct();
myirfs1.eps_a = struct();
myirfs1.eps_nu = struct();
myirfs1.eps_z = struct();
var_aux = {'log_y','C','pi_ann','B_nominal','B','sp','i_ann','r_real_ann','P'};
var_aux3 = {'eps_G_log_y','eps_G_C','eps_G_pi_ann','eps_G_B_nominal','eps_G_B','eps_G_sp','eps_G_i_ann','eps_G_r_real_ann','eps_G_P'};
fieldnames(myirfs1)
myirfs1.eps_G.var = var_aux3 % assign the data array to the struct variable
irf_fisc = struct();
for i = 1:numel(var_aux)
irf_fisc.var_aux{i} = [0,myirfs1.eps_G.var{i}]';
end
irf_fisc.var_aux(1)
irf_fisc
% what is the write syntax to assign value (simulated data) to the struct?
myirfs1.eps_G.logy = data(:,1)/10; %Is the suggested code. but where is the data variable located? should I create it data = randn(TMax, N); or it is already simulated?
Dears,
I need your help. hocan I access the subfields within eps_G, where eps_G is a structure.
whos myirfs1
Name Size Bytes Class Attributes
myirfs1 1x1 374094 struct
%% disp(fieldnames(myirfs1))
>> disp(fieldnames(myirfs1))
{'eps_CP'}
{'eps_G' }
{'eps_T' }
{'eps_a' }
{'eps_nu'}
{'eps_z' }
% choose 1 or 2 below
shock_type = {'eps_G','eps_nu'};
var_aux = {'log_y','C','pi_ann','B_nominal','B','sp','i_ann','r_real_ann','P'};
var_aux2 = {'log_y_eps_nu','C_eps_nu','pi_ann_eps_nu','B_nominal_eps_nu','B_eps_nu','sp_eps_nu','i_ann_eps_nu','r_real_ann_eps_nu','P_eps_nu'};
var_aux3 = {'eps_G_log_y','eps_G_C','eps_G_pi_ann','eps_G_B_nominal','eps_G_B','eps_G_sp','eps_G_i_ann','eps_G_r_real_ann','eps_G_P'};
%Irfs of monetary and fiscal policy
irf_mon = struct();
irf_fisc = struct();
%% disp(fieldnames(myirfs1))
>> disp(fieldnames(myirfs1))
{'eps_CP'}
{'eps_G' }
{'eps_T' }
{'eps_a' }
{'eps_nu'}
{'eps_z' }
% when i run the following code it is unrecognized. can you suggest me what to do?
for i = 1:numel(var_aux)
irf_mon.(var_aux{i}) = [0,myirfs1(1).(var_aux3{i})]';
irf_fisc.(var_aux{i}) = [0,myirfs1(1).(var_aux3{i})]';
end
Unrecognized field name "log_y_eps_G".
I have a skeleton and i want to find the start and end point of that skeleton. My skeleton is like an oval/donut where there is little gap between the start and end
Hello,
Now that the "Copilot+PC" (Windows ARM) laptops are rapidly increasing in market share (Microsoft Surface Laptop, Dell XPS 13, HP OmniBook X 14, and more), are there any plans to provide builds for Matlab on Windows arm64?
Since there are already Windows builds of Matlab, it shouldn't be too hard to compile for Windows arm64, as far as I know. But I am not famaliar with Matlab's codebase.
Please try to publish Windows arm64 builds soon so that Matlab can be much more usable on Windows on ARM as it will run natively instead of in emulation.
Thank you very much.
can i add TMS320F28P55SJ launch pad to matlab simulik
Toolbox 全部入りの MATLAB ライセンス
67%
まだ持っていない Toolbox (下記にコメントください）
0%
MATLAB T シャツ
17%
MATLAB ルービックキューブ
0%
MATLAB 靴下
6%
MathWorks オフィス訪問チケット
11%
18 票
この場は MATLAB や Simulink を使っている皆さんが、気軽に質問や情報交換ができる場所として作られました。日本語でも気軽に投稿ができるように今回日本語チャネルを解説します。
ユーザーの皆様とのやり取りを通じて、みんなで知識や経験を共有し、一緒にスキルアップしていきましょう。 どうぞお気軽にご参加ください。
そして日本語チャネル開設にあたってコメントくださった皆様、ありがとうございます！
I have a problem with the movement of a pawn by two fields in its first move does anyone have a suggestion for a solution
function chess_game()
% Funkcja główna inicjalizująca grę w szachy
% Inicjalizacja stanu gry
gameState = struct();
gameState.board = initialize_board();
gameState.currentPlayer = 'white';
gameState.selectedPiece = [];
% Utworzenie GUI
fig = figure('Name', 'Gra w Szachy', 'NumberTitle', 'off', 'MenuBar', 'none', 'UserData', gameState);
ax = axes('Parent', fig, 'Position', [0 0 1 1], 'XTick', [], 'YTick', []);
axis(ax, [0 8 0 8]);
hold on;
% Wyświetlenie planszy
draw_board(ax, gameState.board);
% Obsługa kliknięcia myszy
set(fig, 'WindowButtonDownFcn', @(src, event)on_click(ax, src));
end
function board = initialize_board()
% Inicjalizuje planszę z ustawieniem początkowym figur
board = {
'R', 'N', 'B', 'Q', 'K', 'B', 'N', 'R';
'P', 'P', 'P', 'P', 'P', 'P', 'P', 'P';
'', '', '', '', '', '', '', '';
'', '', '', '', '', '', '', '';
'', '', '', '', '', '', '', '';
'', '', '', '', '', '', '', '';
'p', 'p', 'p', 'p', 'p', 'p', 'p', 'p';
'r', 'n', 'b', 'q', 'k', 'b', 'n', 'r';
};
end
function draw_board(~, board)
% Rysuje szachownicę i figury
colors = [1 1 1; 0.8 0.8 0.8];
for row = 1:8
for col = 1:8
% Rysowanie pól
rectColor = colors(mod(row + col, 2) + 1, :);
rectangle('Position', [col-1, 8-row, 1, 1], 'FaceColor', rectColor, 'EdgeColor', 'k');
% Rysowanie figur
piece = board{row, col};
if ~isempty(piece)
text(col-0.5, 8-row+0.5, piece, 'HorizontalAlignment', 'center', ...
'FontSize', 20, 'FontWeight', 'bold');
end
end
end
end
function on_click(ax, fig)
% Funkcja obsługująca kliknięcia myszy
pos = get(ax, 'CurrentPoint');
x = floor(pos(1,1)) + 1; % Zaokrąglij współrzędne w poziomie i dopasuj do indeksów
y = 8 - floor(pos(1,2)); % Dopasuj współrzędne w pionie (odwrócenie osi Y)
% Pobranie stanu gry z figury
gameState = get(fig, 'UserData');
if x >= 1 && x <= 8 && y >= 1 && y <= 8
disp(['Kliknięto na pole: (', num2str(x), ', ', num2str(y), ')']);
if isempty(gameState.selectedPiece)
% Wybór figury
piece = gameState.board{y, x};
if ~isempty(piece)
if (strcmp(gameState.currentPlayer, 'white') && any(ismember(piece, 'RNBQKP'))) || ...
(strcmp(gameState.currentPlayer, 'black') && any(ismember(piece, 'rnbqkp')))
gameState.selectedPiece = [y, x];
disp(['Wybrano figurę: ', piece, ' na pozycji (', num2str(x), ', ', num2str(y), ')']);
else
disp('Nie możesz wybrać tej figury.');
end
else
disp('Nie wybrano figury.');
end
else
% Sprawdzenie, czy kliknięto ponownie na wybraną figurę
if isequal(gameState.selectedPiece, [y, x])
disp('Anulowano wybór figury.');
gameState.selectedPiece = [];
else
% Ruch figury
[sy, sx] = deal(gameState.selectedPiece(1), gameState.selectedPiece(2));
piece = gameState.board{sy, sx};
if is_valid_move(gameState.board, piece, [sy, sx], [y, x], gameState.currentPlayer)
% Wykonanie ruchu
gameState.board{sy, sx} = ''; % Usuwamy figurę z poprzedniego pola
gameState.board{y, x} = piece; % Umieszczamy figurę na nowym polu
gameState.selectedPiece = [];
% Przełącz gracza
gameState.currentPlayer = switch_player(gameState.currentPlayer);
% Odśwież planszę
cla(ax);
draw_board(ax, gameState.board);
else
disp('Ruch niezgodny z zasadami.');
end
end
end
% Zaktualizowanie stanu gry w figurze
set(fig, 'UserData', gameState);
end
end
function valid = is_valid_move(board, piece, from, to, currentPlayer)
% Funkcja sprawdzająca, czy ruch jest poprawny
[sy, sx] = deal(from(1), from(2));
[dy, dx] = deal(to(1), to(2));
dy_diff = dy - sy;
dx_diff = abs(dx - sx);
targetPiece = board{dy, dx};
% Sprawdzenie, czy ruch jest w granicach planszy
if dx < 1 || dx > 8 || dy < 1 || dy > 8
valid = false;
return;
end
% Nie można zbijać swoich figur
if ~isempty(targetPiece) && ...
((strcmp(currentPlayer, 'white') && ismember(targetPiece, 'RNBQKP')) || ...
(strcmp(currentPlayer, 'black') && ismember(targetPiece, 'rnbqkp')))
valid = false;
return;
end
% Zasady ruchu dla każdej figury
switch lower(piece)
case 'p' % Pion
direction = strcmp(currentPlayer, 'white') * 2 - 1; % 1 dla białych, -1 dla czarnych
startRow = strcmp(currentPlayer, 'white') * 2 + 1; % Rząd startowy dla białych i czarnych
if isempty(targetPiece)
% Ruch o jedno pole do przodu
if dy_diff == direction && dx_diff == 0
valid = true;
% Ruch o dwa pola do przodu z pozycji startowej
elseif dy_diff == 2 * direction && dx_diff == 0 && sy == startRow
if isempty(board{sy + direction, sx}) && isempty(board{dy, dx})
valid = true;
else
valid = false;
end
else
valid = false;
end
else
% Zbijanie na ukos
valid = (dx_diff == 1) && (dy_diff == direction);
end
case 'r' % Wieża
valid = (dx_diff == 0 || dy_diff == 0) && path_is_clear(board, from, to);
case 'n' % Skoczek
valid = (dx_diff == 2 && abs(dy_diff) == 1) || (dx_diff == 1 && abs(dy_diff) == 2);
case 'b' % Goniec
valid = (dx_diff == abs(dy_diff)) && path_is_clear(board, from, to);
case 'q' % Hetman
valid = ((dx_diff == 0 || dy_diff == 0) || (dx_diff == abs(dy_diff))) && path_is_clear(board, from, to);
case 'k' % Król
valid = max(abs(dx_diff), abs(dy_diff)) == 1;
otherwise
valid = false;
end
end
function clear = path_is_clear(board, from, to)
% Sprawdza, czy ścieżka między polami jest wolna od innych figur
[sy, sx] = deal(from(1), from(2));
[dy, dx] = deal(to(1), to(2));
stepY = sign(dy - sy);
stepX = sign(dx - sx);
y = sy + stepY;
x = sx + stepX;
while y ~= dy || x ~= dx
if ~isempty(board{y, x})
clear = false;
return;
end
y = y + stepY;
x = x + stepX;
end
clear = true;
end
function nextPlayer = switch_player(currentPlayer)
% Przełącza aktywnego gracza
if strcmp(currentPlayer, 'white')
nextPlayer = 'black';
else
nextPlayer = 'white';
end
end
Speaking as someone with 31+ years of experience developing and using imshow, I want to advocate for retiring and replacing it.
The function imshow has behaviors and defaults that were appropriate for the MATLAB and computer monitors of the 1990s, but which are not the best choice for most image display situations in today's MATLAB. Also, the 31 years have not been kind to the imshow code base. It is a glitchy, hard-to-maintain monster.
My new File Exchange function, imview, illustrates the kind of changes that I think should be made. The function imview is a much better MATLAB graphics citizen and produces higher quality image display by default, and it dispenses with the whole fraught business of trying to resize the containing figure. Although this is an initial release that does not yet support all the useful options that imshow does, it does enough that I am prepared to stop using imshow in my own work.
The Image Processing Toolbox team has just introduced in R2024b a new image viewer called imageshow, but that image viewer is created in a special-purpose window. It does not satisfy the need for an image display function that works well with the axes and figure objects of the traditional MATLAB graphics system.
I have published a blog post today that describes all this in more detail. I'd be interested to hear what other people think.
Note: Yes, I know there is an Image Processing Toolbox function called imview. That one is a stub for an old toolbox capability that was removed something like 15+ years ago. The only thing the toolbox imview function does now is call error. I have just submitted a support request to MathWorks to remove this old stub.
The int function in the Symbolic Toolbox has a hold/release functionality wherein the expression can be held to delay evaluation
syms x I
eqn = I == int(x,x,'Hold',true)
which allows one to show the integral, and then use release to show the result
release(eqn)
Maybe it would be nice to be able to hold/release any symbolic expression to delay the engine from doing evaluations/simplifications that it typically does. For example:
x*(x+1)/x, sin(sym(pi)/3)
If I'm trying to show a sequence of steps to develop a result, maybe I want to explicitly keep the x/x in the first case and then say "now the x in the numerator and denominator cancel and the result is ..." followed by the release command to get the final result.
Perhaps held expressions could even be nested to show a sequence of results upon subsequent releases.
Held expressions might be subject to other limitations, like maybe they can't be fplotted.
Seems like such a capability might not be useful for problem solving, but might be useful for exposition, instruction, etc.
Is it possible to differenciate the input, output and in-between wires by colors?
I was curious to startup your new AI Chat playground.
The first screen that popped up made the statement:
"Please keep in mind that AI sometimes writes code and text that seems accurate, but isnt"
Can someone elaborate on what exactly this means with respect to your AI Chat playground integration with the Matlab tools?
Are there any accuracy metrics for this integration?
It would be nice to have a function to shade between two curves. This is a common question asked on Answers and there are some File Exchange entries on it but it's such a common thing to want to do I think there should be a built in function for it. I'm thinking of something like
plotsWithShading(x1, y1, 'r-', x2, y2, 'b-', 'ShadingColor', [.7, .5, .3], 'Opacity', 0.5);
So we can specify the coordinates of the two curves, and the shading color to be used, and its opacity, and it would shade the region between the two curves where the x ranges overlap. Other options should also be accepted, like line with, line style, markers or not, etc. Perhaps all those options could be put into a structure as fields, like
plotsWithShading(x1, y1, options1, x2, y2, options2, 'ShadingColor', [.7, .5, .3], 'Opacity', 0.5);
the shading options could also (optionally) be a structure. I know it can be done with a series of other functions like patch or fill, but it's kind of tricky and not obvious as we can see from the number of questions about how to do it.
Does anyone else think this would be a convenient function to add?
In the past two years, large language models have brought us significant changes, leading to the emergence of programming tools such as GitHub Copilot, Tabnine, Kite, CodeGPT, Replit, Cursor, and many others. Most of these tools support code writing by providing auto-completion, prompts, and suggestions, and they can be easily integrated with various IDEs.
As far as I know, aside from the MATLAB-VSCode/MatGPT plugin, MATLAB lacks such AI assistant plugins for its native MATLAB-Desktop, although it can leverage other third-party plugins for intelligent programming assistance. There is hope for a native tool of this kind to be built-in.
clc
clear
close all
% Load the dataset from a CSV file with headers
data = readtable('C:\Users\PMLS\Downloads\output_file.csv');
% Convert the table to a numeric array
dataArray = table2array(data);
% Normalize inputs and outputs using mapminmax
X = mapminmax(dataArray(:, 1:4)', 0, 1); % Normalize input features to [0, 1]
Y = mapminmax(dataArray(:, 5)', 0, 1); % Normalize target variable to [0, 1]
% Set display format to long for better precision
format long;
%rng(0); % Set random seed for reproducibility
% Create a simpler network with one hidden layer of 20 neurons
%net = feedforwardnet([40,70]);
net = feedforwardnet([70,40]);
% Change activation functions
net.layers{1}.transferFcn = 'poslin'; % ReLU
net.layers{2}.transferFcn = 'logsig'; % logsig
% Set the training function to Resilient Backpropagation
net.trainFcn = 'trainrp';
% Enable data division for validation and testing
net.divideFcn = 'divideblock';
net.divideParam.trainRatio = 0.8; % 70% training data
%net.divideParam.valRatio = 0.15; % 15% validation data
net.divideParam.testRatio = 0.2; % 15% testing data
% Custom training parameters
net.trainParam.epochs = 8000;
net.trainParam.goal = 0.0001;
net.trainParam.min_grad = 1e-6;
net.trainParam.max_fail = 2000;
net.trainParam.lr = 0.001; % Learning rate
net.trainParam.momentum = 0.9; % Momentum
%net.trainParam.batchSize = 10; % Example batch size
% Add regularization
net.performParam.regularization = 0.01; % Example L2 regularization
%Train the neural network
[net, tr] = train(net, X, Y);
% Predict and save results
predicted_ranges = net(X);
outputTable = array2table([X' Y' predicted_ranges'], ...
'VariableNames', {'w1', 'w2', 'w3', 'wpl', 'Actual_Range', 'Predicted_Range'});
writetable(outputTable, 'predicted_ranges_with_trainrp.xlsx');
% Calculate performance metrics
Y = Y'; % Transpose to match predicted output dimensions
predicted_ranges = predicted_ranges';
MAE = mean(abs(Y - predicted_ranges)); % Mean Absolute Error
MSE = mean((Y - predicted_ranges).^2); % Mean Squared Error
RMSE = sqrt(MSE); % Root Mean Square Error
R_squared = 1 - sum((Y - predicted_ranges).^2) / sum((Y - mean(Y)).^2); % R-squared
% Display the results in the command window
disp('Performance Metrics:');
disp(['Mean Absolute Error (MAE): ', num2str(MAE)]);
disp(['Mean Squared Error (MSE): ', num2str(MSE)]);
disp(['Root Mean Square Error (RMSE): ', num2str(RMSE)]);
disp(['R-squared (R^2): ', num2str(R_squared)]);
% Plot performance
plotperform(tr);
view(net);
% Plot the actual and predicted values as lines
figure;
plot(Y, 'b-', 'LineWidth', 1.5); % Plot actual values as a blue line
hold on;
plot(predicted_ranges, 'r--', 'LineWidth', 1.5); % Plot predicted values as a red dashed line
xlabel('Sample');
ylabel('Normalized Range');
legend('Actual Range', 'Predicted Range');
title('Comparison of Actual and Predicted Ranges');
grid on;
