I want to standardize a bivariate normal CDF. I tried with Cholesky decomposition. The results are always different. I don’t know why.

sigma=[1,0.5;0.5,1];
X = [1,1];
z=mvncdf(X,[0,0],sigma);
X1=X*sqrtm(inv(sigma));
z1=mvncdf(X1,[0,0],[1,0;0,1]);
disp([z,z1])I want to standardize a bivariate normal CDF. I tried with Cholesky decomposition. The results are always different. I don’t know why.

sigma=[1,0.5;0.5,1];
X = [1,1];
z=mvncdf(X,[0,0],sigma);
X1=X*sqrtm(inv(sigma));
z1=mvncdf(X1,[0,0],[1,0;0,1]);
disp([z,z1]) I want to standardize a bivariate normal CDF. I tried with Cholesky decomposition. The results are always different. I don’t know why.

sigma=[1,0.5;0.5,1];
X = [1,1];
z=mvncdf(X,[0,0],sigma);
X1=X*sqrtm(inv(sigma));
z1=mvncdf(X1,[0,0],[1,0;0,1]);
disp([z,z1]) mvncdf, chol MATLAB Answers — New Questions

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I have been at it for awhile but cannot figure it out, and I have also gotten lost in documentation for a few days now. I am attemping to train a Faster R-CNN model with a pretrained ResNet backbone. So far I have found documentation stating not to use lgraph and to use dlnetwork instead, so I attemtped it that way also and got the same error. More documentation stated not to use net=resnet50 either, and to use [net,classNames] = imagePretrainedNetwork instead. The issue is that I cannot figure out how to fit all of these pieces together. The dataset can be found here and downlaoded for free: https://www.flir.com/oem/adas/adas-dataset-form/
When the model attempts to run, it appears that it detects only 3 classes. I also used analyzeNetwork and network designer to look at the layers and it appears that the boxdeltas and R-CNN classification layers have the correct number of outputs. Any help is greatly appreciated!!

Here is the code so far (some parts generated by chatgpt and others taken from official documentation), but I have several versions of this with slight variations:
%% Define the custom read function
function imgOut = ensureRGB(imgIn)
[~, ~, numChannels] = size(imgIn);
if numChannels == 1
imgOut = repmat(imgIn, [1 1 3]);
else
imgOut = imgIn;
end
end
%% Define the paths
imageFolder = "C:UsersUserDesktopFLIR_Thermal_DatasetFLIR_ADAS_v2images_thermal_train";
annotationFolder = "C:UsersUserDocumentsMATLABtrainingData.mat";
matFile = "C:UsersUserDocumentsMATLABtrainingData.mat"; % MATLAB format annotations(there is a function to convert the original data into this .mat file if anyone needs it)
%% Load the training data from the MAT-file
load(matFile, ‘trainingData’);
% Shuffle the training data
rng(0);
shuffledIdx = randperm(height(trainingData));
trainingData = trainingData(shuffledIdx,:);
%% Create image datastore with custom read function and specify file extensions
imds = imageDatastore(trainingData.imageFilename, …
‘ReadFcn’, @(filename) ensureRGB(imread(filename)), …
‘FileExtensions’, {‘.jpg’, ‘.jpeg’, ‘.png’, ‘.bmp’});
%% Create box label datastore
blds = boxLabelDatastore(trainingData(:, {‘bbox’, ‘label’}));
%% Combine the datastores
ds = combine(imds, blds);
%% Verify with a sample image
sampleImg = readimage(imds, 1);
[height, width, numChannels] = size(sampleImg);
disp([‘Sample Image Number of Channels: ‘, num2str(numChannels)]);
%% Define number of classes
numClasses = 16;
%% Define input image size and anchor boxes
inputImageSize = [512 640 3];
anchorBoxes = [32 32; 64 64; 128 128];
%% Load the ResNet-50 network
lgraph = layerGraph(resnet50);
% Specify the feature extraction layer
featureLayer = ‘activation_40_relu’;
% Create Faster R-CNN layers
dlnetwork = fasterRCNNLayers(inputImageSize, numClasses, anchorBoxes, lgraph, featureLayer);
%% Analyze the network to ensure all layers are correct
analyzeNetwork(dlnetwork);
%% Define training options
options = trainingOptions(‘sgdm’, …
‘MiniBatchSize’, 16, …
‘InitialLearnRate’, 1e-4, …
‘MaxEpochs’, 10, …
‘Verbose’, true, …
‘Shuffle’, ‘every-epoch’, …
‘Plots’, ‘training-progress’);
% Train the network
detector = trainFasterRCNNObjectDetector(ds, dlnetwork, options);

ERROR:
Training a Faster R-CNN Object Detector for the following object classes:
* car
* light
* person
Error using trainFasterRCNNObjectDetector (line 33)
Invalid network.

Error in
untitled (line 74)
detector = trainFasterRCNNObjectDetector(ds, dlnetwork, options);

Caused by:
Layer ‘boxDeltas’: The input size must be 1×1×12. This R-CNN box regression layer expects the third input dimension to be 4 times the number of object classes
the network should detect (3 classes). See the
documentation for more details about creating Fast or Faster R-CNN networks.
Layer ‘rcnnClassification’: The input size must be 1×1×4. The classification layer expects the third input dimension to be the number of object classes the
network should detect (3 classes) plus 1. The additional class is required for the "background" class. See the
documentation for more details about creating
Fast or Faster R-CNN networks.

So far I have tried:
1) using dlnetwork instead of lgraph
2) using [net,classNames] = imagePretrainedNetwork instead of net=resnet50
3) manually changing the layers in the designer
4) changing the channels from 1 to 3. (when loaded into my python environment the images had three channels, in MATLAB they showed 1)
5) resizing the imagesI have been at it for awhile but cannot figure it out, and I have also gotten lost in documentation for a few days now. I am attemping to train a Faster R-CNN model with a pretrained ResNet backbone. So far I have found documentation stating not to use lgraph and to use dlnetwork instead, so I attemtped it that way also and got the same error. More documentation stated not to use net=resnet50 either, and to use [net,classNames] = imagePretrainedNetwork instead. The issue is that I cannot figure out how to fit all of these pieces together. The dataset can be found here and downlaoded for free: https://www.flir.com/oem/adas/adas-dataset-form/
When the model attempts to run, it appears that it detects only 3 classes. I also used analyzeNetwork and network designer to look at the layers and it appears that the boxdeltas and R-CNN classification layers have the correct number of outputs. Any help is greatly appreciated!!

Here is the code so far (some parts generated by chatgpt and others taken from official documentation), but I have several versions of this with slight variations:
%% Define the custom read function
function imgOut = ensureRGB(imgIn)
[~, ~, numChannels] = size(imgIn);
if numChannels == 1
imgOut = repmat(imgIn, [1 1 3]);
else
imgOut = imgIn;
end
end
%% Define the paths
imageFolder = "C:UsersUserDesktopFLIR_Thermal_DatasetFLIR_ADAS_v2images_thermal_train";
annotationFolder = "C:UsersUserDocumentsMATLABtrainingData.mat";
matFile = "C:UsersUserDocumentsMATLABtrainingData.mat"; % MATLAB format annotations(there is a function to convert the original data into this .mat file if anyone needs it)
%% Load the training data from the MAT-file
load(matFile, ‘trainingData’);
% Shuffle the training data
rng(0);
shuffledIdx = randperm(height(trainingData));
trainingData = trainingData(shuffledIdx,:);
%% Create image datastore with custom read function and specify file extensions
imds = imageDatastore(trainingData.imageFilename, …
‘ReadFcn’, @(filename) ensureRGB(imread(filename)), …
‘FileExtensions’, {‘.jpg’, ‘.jpeg’, ‘.png’, ‘.bmp’});
%% Create box label datastore
blds = boxLabelDatastore(trainingData(:, {‘bbox’, ‘label’}));
%% Combine the datastores
ds = combine(imds, blds);
%% Verify with a sample image
sampleImg = readimage(imds, 1);
[height, width, numChannels] = size(sampleImg);
disp([‘Sample Image Number of Channels: ‘, num2str(numChannels)]);
%% Define number of classes
numClasses = 16;
%% Define input image size and anchor boxes
inputImageSize = [512 640 3];
anchorBoxes = [32 32; 64 64; 128 128];
%% Load the ResNet-50 network
lgraph = layerGraph(resnet50);
% Specify the feature extraction layer
featureLayer = ‘activation_40_relu’;
% Create Faster R-CNN layers
dlnetwork = fasterRCNNLayers(inputImageSize, numClasses, anchorBoxes, lgraph, featureLayer);
%% Analyze the network to ensure all layers are correct
analyzeNetwork(dlnetwork);
%% Define training options
options = trainingOptions(‘sgdm’, …
‘MiniBatchSize’, 16, …
‘InitialLearnRate’, 1e-4, …
‘MaxEpochs’, 10, …
‘Verbose’, true, …
‘Shuffle’, ‘every-epoch’, …
‘Plots’, ‘training-progress’);
% Train the network
detector = trainFasterRCNNObjectDetector(ds, dlnetwork, options);

ERROR:
Training a Faster R-CNN Object Detector for the following object classes:
* car
* light
* person
Error using trainFasterRCNNObjectDetector (line 33)
Invalid network.

Error in
untitled (line 74)
detector = trainFasterRCNNObjectDetector(ds, dlnetwork, options);

Caused by:
Layer ‘boxDeltas’: The input size must be 1×1×12. This R-CNN box regression layer expects the third input dimension to be 4 times the number of object classes
the network should detect (3 classes). See the
documentation for more details about creating Fast or Faster R-CNN networks.
Layer ‘rcnnClassification’: The input size must be 1×1×4. The classification layer expects the third input dimension to be the number of object classes the
network should detect (3 classes) plus 1. The additional class is required for the "background" class. See the
documentation for more details about creating
Fast or Faster R-CNN networks.

So far I have tried:
1) using dlnetwork instead of lgraph
2) using [net,classNames] = imagePretrainedNetwork instead of net=resnet50
3) manually changing the layers in the designer
4) changing the channels from 1 to 3. (when loaded into my python environment the images had three channels, in MATLAB they showed 1)
5) resizing the images I have been at it for awhile but cannot figure it out, and I have also gotten lost in documentation for a few days now. I am attemping to train a Faster R-CNN model with a pretrained ResNet backbone. So far I have found documentation stating not to use lgraph and to use dlnetwork instead, so I attemtped it that way also and got the same error. More documentation stated not to use net=resnet50 either, and to use [net,classNames] = imagePretrainedNetwork instead. The issue is that I cannot figure out how to fit all of these pieces together. The dataset can be found here and downlaoded for free: https://www.flir.com/oem/adas/adas-dataset-form/
When the model attempts to run, it appears that it detects only 3 classes. I also used analyzeNetwork and network designer to look at the layers and it appears that the boxdeltas and R-CNN classification layers have the correct number of outputs. Any help is greatly appreciated!!

Here is the code so far (some parts generated by chatgpt and others taken from official documentation), but I have several versions of this with slight variations:
%% Define the custom read function
function imgOut = ensureRGB(imgIn)
[~, ~, numChannels] = size(imgIn);
if numChannels == 1
imgOut = repmat(imgIn, [1 1 3]);
else
imgOut = imgIn;
end
end
%% Define the paths
imageFolder = "C:UsersUserDesktopFLIR_Thermal_DatasetFLIR_ADAS_v2images_thermal_train";
annotationFolder = "C:UsersUserDocumentsMATLABtrainingData.mat";
matFile = "C:UsersUserDocumentsMATLABtrainingData.mat"; % MATLAB format annotations(there is a function to convert the original data into this .mat file if anyone needs it)
%% Load the training data from the MAT-file
load(matFile, ‘trainingData’);
% Shuffle the training data
rng(0);
shuffledIdx = randperm(height(trainingData));
trainingData = trainingData(shuffledIdx,:);
%% Create image datastore with custom read function and specify file extensions
imds = imageDatastore(trainingData.imageFilename, …
‘ReadFcn’, @(filename) ensureRGB(imread(filename)), …
‘FileExtensions’, {‘.jpg’, ‘.jpeg’, ‘.png’, ‘.bmp’});
%% Create box label datastore
blds = boxLabelDatastore(trainingData(:, {‘bbox’, ‘label’}));
%% Combine the datastores
ds = combine(imds, blds);
%% Verify with a sample image
sampleImg = readimage(imds, 1);
[height, width, numChannels] = size(sampleImg);
disp([‘Sample Image Number of Channels: ‘, num2str(numChannels)]);
%% Define number of classes
numClasses = 16;
%% Define input image size and anchor boxes
inputImageSize = [512 640 3];
anchorBoxes = [32 32; 64 64; 128 128];
%% Load the ResNet-50 network
lgraph = layerGraph(resnet50);
% Specify the feature extraction layer
featureLayer = ‘activation_40_relu’;
% Create Faster R-CNN layers
dlnetwork = fasterRCNNLayers(inputImageSize, numClasses, anchorBoxes, lgraph, featureLayer);
%% Analyze the network to ensure all layers are correct
analyzeNetwork(dlnetwork);
%% Define training options
options = trainingOptions(‘sgdm’, …
‘MiniBatchSize’, 16, …
‘InitialLearnRate’, 1e-4, …
‘MaxEpochs’, 10, …
‘Verbose’, true, …
‘Shuffle’, ‘every-epoch’, …
‘Plots’, ‘training-progress’);
% Train the network
detector = trainFasterRCNNObjectDetector(ds, dlnetwork, options);

ERROR:
Training a Faster R-CNN Object Detector for the following object classes:
* car
* light
* person
Error using trainFasterRCNNObjectDetector (line 33)
Invalid network.

Error in
untitled (line 74)
detector = trainFasterRCNNObjectDetector(ds, dlnetwork, options);

Caused by:
Layer ‘boxDeltas’: The input size must be 1×1×12. This R-CNN box regression layer expects the third input dimension to be 4 times the number of object classes
the network should detect (3 classes). See the
documentation for more details about creating Fast or Faster R-CNN networks.
Layer ‘rcnnClassification’: The input size must be 1×1×4. The classification layer expects the third input dimension to be the number of object classes the
network should detect (3 classes) plus 1. The additional class is required for the "background" class. See the
documentation for more details about creating
Fast or Faster R-CNN networks.

So far I have tried:
1) using dlnetwork instead of lgraph
2) using [net,classNames] = imagePretrainedNetwork instead of net=resnet50
3) manually changing the layers in the designer
4) changing the channels from 1 to 3. (when loaded into my python environment the images had three channels, in MATLAB they showed 1)
5) resizing the images faster r-cnn, deep learning, flir adas, resnet50 MATLAB Answers — New Questions

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Is there a way to extract the breast density of a mammography through Matlab code?Is there a way to extract the breast density of a mammography through Matlab code? Is there a way to extract the breast density of a mammography through Matlab code? density, breast, mammography MATLAB Answers — New Questions

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My mathlab online unable to open, everytime login it will automatically return to the login pageMy mathlab online unable to open, everytime login it will automatically return to the login page My mathlab online unable to open, everytime login it will automatically return to the login page #errorinmathlabonline MATLAB Answers — New Questions

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I’m dealing with a structure full of data, one of the data is called BEST and there are 50 of them so that is data(i).best in which i is from 1 to 50, each data(i).best contains a 10×10 table of numbers. I need to calculate mean of each row and column corresponding to each data(i).best, for example I need to sum up all of the data that are in data(i).best(5,7) (i is from 1 to 50) and devide it by 50. how am I supposed to do so? I tried using three layers of "for" and it didn’t seem to help.I’m dealing with a structure full of data, one of the data is called BEST and there are 50 of them so that is data(i).best in which i is from 1 to 50, each data(i).best contains a 10×10 table of numbers. I need to calculate mean of each row and column corresponding to each data(i).best, for example I need to sum up all of the data that are in data(i).best(5,7) (i is from 1 to 50) and devide it by 50. how am I supposed to do so? I tried using three layers of "for" and it didn’t seem to help. I’m dealing with a structure full of data, one of the data is called BEST and there are 50 of them so that is data(i).best in which i is from 1 to 50, each data(i).best contains a 10×10 table of numbers. I need to calculate mean of each row and column corresponding to each data(i).best, for example I need to sum up all of the data that are in data(i).best(5,7) (i is from 1 to 50) and devide it by 50. how am I supposed to do so? I tried using three layers of "for" and it didn’t seem to help. structures, mean, data MATLAB Answers — New Questions

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It’s been nearly ten years since MATLAB began to transition from Java to Javascript graphics. Is there any roadmap for when this transition will be complete? Long ago I was told that the final product would be seemless, i.e. traditional graphic commands would invoke Javascript yet otherwise function like the legacy Java version. Despite many improvements in the new system, the two are still quite different. The difference is most obvious on Mac systems, where uifigures are clearly handled separately from figures. I have stayed with traditional figures for most of this period, waiting for the Javascript alternative to mature, but this is no longer viable due to frequent crashes (as described here).
Recent experence suggests that uifigures are almost ready for general use, but there are a number of missing features. For me, the big ones are limited export options (e.g., improper size scaling for PDF) and the lack of an Extent property; the latter helps size items based on the text held inside them. If I had those features, I could justify the time investment in switching over to uifigures full time. There are also a lot of undocumented things, such as the fact that classic uicontrols can go inside Javascript uifigures, even though the formal documentation says otherwise. I wish Mathworks would be more forthright about this substantial change, focusing less on Live Editor and App Designer updates in each release.It’s been nearly ten years since MATLAB began to transition from Java to Javascript graphics. Is there any roadmap for when this transition will be complete? Long ago I was told that the final product would be seemless, i.e. traditional graphic commands would invoke Javascript yet otherwise function like the legacy Java version. Despite many improvements in the new system, the two are still quite different. The difference is most obvious on Mac systems, where uifigures are clearly handled separately from figures. I have stayed with traditional figures for most of this period, waiting for the Javascript alternative to mature, but this is no longer viable due to frequent crashes (as described here).
Recent experence suggests that uifigures are almost ready for general use, but there are a number of missing features. For me, the big ones are limited export options (e.g., improper size scaling for PDF) and the lack of an Extent property; the latter helps size items based on the text held inside them. If I had those features, I could justify the time investment in switching over to uifigures full time. There are also a lot of undocumented things, such as the fact that classic uicontrols can go inside Javascript uifigures, even though the formal documentation says otherwise. I wish Mathworks would be more forthright about this substantial change, focusing less on Live Editor and App Designer updates in each release. It’s been nearly ten years since MATLAB began to transition from Java to Javascript graphics. Is there any roadmap for when this transition will be complete? Long ago I was told that the final product would be seemless, i.e. traditional graphic commands would invoke Javascript yet otherwise function like the legacy Java version. Despite many improvements in the new system, the two are still quite different. The difference is most obvious on Mac systems, where uifigures are clearly handled separately from figures. I have stayed with traditional figures for most of this period, waiting for the Javascript alternative to mature, but this is no longer viable due to frequent crashes (as described here).
Recent experence suggests that uifigures are almost ready for general use, but there are a number of missing features. For me, the big ones are limited export options (e.g., improper size scaling for PDF) and the lack of an Extent property; the latter helps size items based on the text held inside them. If I had those features, I could justify the time investment in switching over to uifigures full time. There are also a lot of undocumented things, such as the fact that classic uicontrols can go inside Javascript uifigures, even though the formal documentation says otherwise. I wish Mathworks would be more forthright about this substantial change, focusing less on Live Editor and App Designer updates in each release. graphics, java, javascript MATLAB Answers — New Questions

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