Hi folks,

I’m trying to use the readtable function to interpret a spreadsheet "Alberta.csv". I’ve tried using detectImportOpts, sadly the variables are arranged in rows and readtable seems to want to detect them as columns.
The next attempt was as follows:
seriesAL = table2dataset(rows2vars(readtable("Alberta.csv", ReadRowNames=1, MissingRule="omitrow")));
or
seriesAL = table2array(rows2vars(readtable("Alberta.csv", ReadRowNames=1, MissingRule="omitrow")));

then if I try to complete an operation:
rgdp_AL = log(0.001 .* seriesAL{:,40})
Matlab tells me I’m using too many input arguments… If I enter:
seriesAL(:,40), I’m failing to get numbers, yet seriesAL{} doesn’t want to provide the full column of values!

I’m getting very confused by the way Matlab uses tables, cells, structs, etc… Can anyone help me get numbers?Hi folks,

I’m trying to use the readtable function to interpret a spreadsheet "Alberta.csv". I’ve tried using detectImportOpts, sadly the variables are arranged in rows and readtable seems to want to detect them as columns.
The next attempt was as follows:
seriesAL = table2dataset(rows2vars(readtable("Alberta.csv", ReadRowNames=1, MissingRule="omitrow")));
or
seriesAL = table2array(rows2vars(readtable("Alberta.csv", ReadRowNames=1, MissingRule="omitrow")));

then if I try to complete an operation:
rgdp_AL = log(0.001 .* seriesAL{:,40})
Matlab tells me I’m using too many input arguments… If I enter:
seriesAL(:,40), I’m failing to get numbers, yet seriesAL{} doesn’t want to provide the full column of values!

I’m getting very confused by the way Matlab uses tables, cells, structs, etc… Can anyone help me get numbers? Hi folks,

I’m trying to use the readtable function to interpret a spreadsheet "Alberta.csv". I’ve tried using detectImportOpts, sadly the variables are arranged in rows and readtable seems to want to detect them as columns.
The next attempt was as follows:
seriesAL = table2dataset(rows2vars(readtable("Alberta.csv", ReadRowNames=1, MissingRule="omitrow")));
or
seriesAL = table2array(rows2vars(readtable("Alberta.csv", ReadRowNames=1, MissingRule="omitrow")));

then if I try to complete an operation:
rgdp_AL = log(0.001 .* seriesAL{:,40})
Matlab tells me I’m using too many input arguments… If I enter:
seriesAL(:,40), I’m failing to get numbers, yet seriesAL{} doesn’t want to provide the full column of values!

I’m getting very confused by the way Matlab uses tables, cells, structs, etc… Can anyone help me get numbers? readtable, struct MATLAB Answers — New Questions

​

I haven’t used Matlab’s ML or deep learning toolbox since maybe 2022. In the last year, it seems like MathWorks intentionally made the ML portion of Matlab unusable. I’m hoping someone here can help me out because rolling back my matlab two years will make a lot of my recent work unstable. I’ve done my best to document my process here. Error messages are given in italics for ease of reading.
I have some data that is represented by four doubles that are normalzied to between 0 and 1. I would like to use these four doubles to predict two doubles that are also between 0 and 1. I would like to make a multilayer perceptron that will take in the four values and spit out the two values. However, createMLPNetwork doesn’t appear to support multiple outputs – at least, the documentation for it doesn’t explain how to do so. So I have tried to make a MLP from scratch using the following code:
%% Prep NN architecture
layers = [
inputLayer([4, 1], "CU", name="in")
flattenLayer
fullyConnectedLayer(20, name="fc1")
reluLayer
fullyConnectedLayerB(16, name="fc2")
reluLayer
fullyConnectedLayer(12, name="fc3")
reluLayer
fullyConnectedLayer(numel(data{1,1}), name="output")
softmaxLayer
];

net = dlnetwork(layers);

[trainedNet, info] = trainnet(train, layers, "mse", opts);

Attempting to run this gives me the following results:
Error using trainnet (line 46)
Error forming mini-batch for network input "in". Data interpreted with format "CU". To specify a different format, use the InputDataFormats option.

Error in trainRutileModel (line 107)
[trainedNet, info] = trainnet(train, layers, "mse", opts);

Caused by:
The data format must have a batch dimension.

I tried to get around using the generic inputLayer function by using the more specific functions like featureInputLayer but replacing the inputLayer with a featureInputLayer throws the following:
Error using trainnet (line 46)
Number of observations in predictors (4) and targets (2) must match. Check that the data and network are consistent.

Error in trainRutileModel (line 107)
[trainedNet, info] = trainnet(train, layers, "mse", opts);

So that won’t work because I only have two output data points. The same happened when I tried imageInputLayer. Then I tried replacing "CU" with other values – the following error is for "BU" but the other errors are the same – but got the following:
Error using dlnetwork/initialize (line 558)
Invalid network.

Error in dlnetwork (line 167)
net = initialize(net, dlX{:});

Error in trainRutileModel (line 104)
net = dlnetwork(layers);

Caused by:
Layer ‘fc1’: Invalid input data for fully connected layer. The input data must have exactly one channel dimension.

So then I tried flattening the output and got the following error:
Error using dlnetwork/initialize (line 558)
Invalid network.

Error in dlnetwork (line 167)
net = initialize(net, dlX{:});

Error in trainRutileModel (line 105)
net = dlnetwork(layers);

Caused by:
Layer ‘flatten’: Invalid input data. Layer expects data with a channel dimension, but received input data with format "BU".

I’m really not sure what to do here. I have no idea why MathWorks would make this so much more difficult to use and give so much less, and more opaque, documentation. If anyone has any ideas on how to make this work, I’d be happy to hear them. In the meantime, I’m going to take another crack at the createMLPNetwork function and hope that when MathWorks says "a black-box continuous-time or discrete-time neural state-space model with identifiable (estimable) network weights and bias," by "states" they mean "outputs."I haven’t used Matlab’s ML or deep learning toolbox since maybe 2022. In the last year, it seems like MathWorks intentionally made the ML portion of Matlab unusable. I’m hoping someone here can help me out because rolling back my matlab two years will make a lot of my recent work unstable. I’ve done my best to document my process here. Error messages are given in italics for ease of reading.
I have some data that is represented by four doubles that are normalzied to between 0 and 1. I would like to use these four doubles to predict two doubles that are also between 0 and 1. I would like to make a multilayer perceptron that will take in the four values and spit out the two values. However, createMLPNetwork doesn’t appear to support multiple outputs – at least, the documentation for it doesn’t explain how to do so. So I have tried to make a MLP from scratch using the following code:
%% Prep NN architecture
layers = [
inputLayer([4, 1], "CU", name="in")
flattenLayer
fullyConnectedLayer(20, name="fc1")
reluLayer
fullyConnectedLayerB(16, name="fc2")
reluLayer
fullyConnectedLayer(12, name="fc3")
reluLayer
fullyConnectedLayer(numel(data{1,1}), name="output")
softmaxLayer
];

net = dlnetwork(layers);

[trainedNet, info] = trainnet(train, layers, "mse", opts);

Attempting to run this gives me the following results:
Error using trainnet (line 46)
Error forming mini-batch for network input "in". Data interpreted with format "CU". To specify a different format, use the InputDataFormats option.

Error in trainRutileModel (line 107)
[trainedNet, info] = trainnet(train, layers, "mse", opts);

Caused by:
The data format must have a batch dimension.

I tried to get around using the generic inputLayer function by using the more specific functions like featureInputLayer but replacing the inputLayer with a featureInputLayer throws the following:
Error using trainnet (line 46)
Number of observations in predictors (4) and targets (2) must match. Check that the data and network are consistent.

Error in trainRutileModel (line 107)
[trainedNet, info] = trainnet(train, layers, "mse", opts);

So that won’t work because I only have two output data points. The same happened when I tried imageInputLayer. Then I tried replacing "CU" with other values – the following error is for "BU" but the other errors are the same – but got the following:
Error using dlnetwork/initialize (line 558)
Invalid network.

Error in dlnetwork (line 167)
net = initialize(net, dlX{:});

Error in trainRutileModel (line 104)
net = dlnetwork(layers);

Caused by:
Layer ‘fc1’: Invalid input data for fully connected layer. The input data must have exactly one channel dimension.

So then I tried flattening the output and got the following error:
Error using dlnetwork/initialize (line 558)
Invalid network.

Error in dlnetwork (line 167)
net = initialize(net, dlX{:});

Error in trainRutileModel (line 105)
net = dlnetwork(layers);

Caused by:
Layer ‘flatten’: Invalid input data. Layer expects data with a channel dimension, but received input data with format "BU".

I’m really not sure what to do here. I have no idea why MathWorks would make this so much more difficult to use and give so much less, and more opaque, documentation. If anyone has any ideas on how to make this work, I’d be happy to hear them. In the meantime, I’m going to take another crack at the createMLPNetwork function and hope that when MathWorks says "a black-box continuous-time or discrete-time neural state-space model with identifiable (estimable) network weights and bias," by "states" they mean "outputs." I haven’t used Matlab’s ML or deep learning toolbox since maybe 2022. In the last year, it seems like MathWorks intentionally made the ML portion of Matlab unusable. I’m hoping someone here can help me out because rolling back my matlab two years will make a lot of my recent work unstable. I’ve done my best to document my process here. Error messages are given in italics for ease of reading.
I have some data that is represented by four doubles that are normalzied to between 0 and 1. I would like to use these four doubles to predict two doubles that are also between 0 and 1. I would like to make a multilayer perceptron that will take in the four values and spit out the two values. However, createMLPNetwork doesn’t appear to support multiple outputs – at least, the documentation for it doesn’t explain how to do so. So I have tried to make a MLP from scratch using the following code:
%% Prep NN architecture
layers = [
inputLayer([4, 1], "CU", name="in")
flattenLayer
fullyConnectedLayer(20, name="fc1")
reluLayer
fullyConnectedLayerB(16, name="fc2")
reluLayer
fullyConnectedLayer(12, name="fc3")
reluLayer
fullyConnectedLayer(numel(data{1,1}), name="output")
softmaxLayer
];

net = dlnetwork(layers);

[trainedNet, info] = trainnet(train, layers, "mse", opts);

Attempting to run this gives me the following results:
Error using trainnet (line 46)
Error forming mini-batch for network input "in". Data interpreted with format "CU". To specify a different format, use the InputDataFormats option.

Error in trainRutileModel (line 107)
[trainedNet, info] = trainnet(train, layers, "mse", opts);

Caused by:
The data format must have a batch dimension.

I tried to get around using the generic inputLayer function by using the more specific functions like featureInputLayer but replacing the inputLayer with a featureInputLayer throws the following:
Error using trainnet (line 46)
Number of observations in predictors (4) and targets (2) must match. Check that the data and network are consistent.

Error in trainRutileModel (line 107)
[trainedNet, info] = trainnet(train, layers, "mse", opts);

So that won’t work because I only have two output data points. The same happened when I tried imageInputLayer. Then I tried replacing "CU" with other values – the following error is for "BU" but the other errors are the same – but got the following:
Error using dlnetwork/initialize (line 558)
Invalid network.

Error in dlnetwork (line 167)
net = initialize(net, dlX{:});

Error in trainRutileModel (line 104)
net = dlnetwork(layers);

Caused by:
Layer ‘fc1’: Invalid input data for fully connected layer. The input data must have exactly one channel dimension.

So then I tried flattening the output and got the following error:
Error using dlnetwork/initialize (line 558)
Invalid network.

Error in dlnetwork (line 167)
net = initialize(net, dlX{:});

Error in trainRutileModel (line 105)
net = dlnetwork(layers);

Caused by:
Layer ‘flatten’: Invalid input data. Layer expects data with a channel dimension, but received input data with format "BU".

I’m really not sure what to do here. I have no idea why MathWorks would make this so much more difficult to use and give so much less, and more opaque, documentation. If anyone has any ideas on how to make this work, I’d be happy to hear them. In the meantime, I’m going to take another crack at the createMLPNetwork function and hope that when MathWorks says "a black-box continuous-time or discrete-time neural state-space model with identifiable (estimable) network weights and bias," by "states" they mean "outputs." multilayer perceptron, mlp, neural network, inputlayer MATLAB Answers — New Questions

​

An H-section copper conductor carries an overload current of 54000 A. Under steady state conditions, the surface temperature is 60°C.

The dimensions of the bar are given in Figure 1 overleaf, and you are asked to use a numerical method to produce a mesh plot of the temperature distribution and a graph of the temperature distribution along the line y = 0.

The electrical resistivity, ρ, of copper is 2 × 10-8 Ω m, and the thermal conductivity, κ, is 0.311 kW m-1 K-1.

The governing differential equation is ∂2T + ∂2T + g = 0 (1)
∂x2 ∂y2 κ where g is the (constant) generation rate of heat per unit volume:

g = i2ρ (2) and i is the current density.

Using an appropriate Numerical Technique:

Determine the temperature distribution to an accuracy of 4 significant figures

You are expected to hand in the following by the submission date:

Hints

The Gauss-Seidel technique used in Tutorial 9 can be adapted to the present problem by including the additional term g/κ in the finite difference equation. You can generate a mask array as in Tutorial 9 to specify the region of the array covered by the H cross- section.

Try calculating the solution over a square mesh for a range of step lengths h = 5 mm, 2.5 mm, 1.25 mm…
You can if you wish make use of the symmetry of the problem and model just one quarter of the conductor. However, the boundary conditions then become more difficult to apply.

The H section is of width 40mm, depth 30mm and cut outs on top and bottom of depth 10x width 20 mm

Any help would be most appreciated

Cheers

SamAn H-section copper conductor carries an overload current of 54000 A. Under steady state conditions, the surface temperature is 60°C.

The dimensions of the bar are given in Figure 1 overleaf, and you are asked to use a numerical method to produce a mesh plot of the temperature distribution and a graph of the temperature distribution along the line y = 0.

The electrical resistivity, ρ, of copper is 2 × 10-8 Ω m, and the thermal conductivity, κ, is 0.311 kW m-1 K-1.

The governing differential equation is ∂2T + ∂2T + g = 0 (1)
∂x2 ∂y2 κ where g is the (constant) generation rate of heat per unit volume:

g = i2ρ (2) and i is the current density.

Using an appropriate Numerical Technique:

Determine the temperature distribution to an accuracy of 4 significant figures

You are expected to hand in the following by the submission date:

Hints

The Gauss-Seidel technique used in Tutorial 9 can be adapted to the present problem by including the additional term g/κ in the finite difference equation. You can generate a mask array as in Tutorial 9 to specify the region of the array covered by the H cross- section.

Try calculating the solution over a square mesh for a range of step lengths h = 5 mm, 2.5 mm, 1.25 mm…
You can if you wish make use of the symmetry of the problem and model just one quarter of the conductor. However, the boundary conditions then become more difficult to apply.

The H section is of width 40mm, depth 30mm and cut outs on top and bottom of depth 10x width 20 mm

Any help would be most appreciated

Cheers

Sam An H-section copper conductor carries an overload current of 54000 A. Under steady state conditions, the surface temperature is 60°C.

The dimensions of the bar are given in Figure 1 overleaf, and you are asked to use a numerical method to produce a mesh plot of the temperature distribution and a graph of the temperature distribution along the line y = 0.

The electrical resistivity, ρ, of copper is 2 × 10-8 Ω m, and the thermal conductivity, κ, is 0.311 kW m-1 K-1.

The governing differential equation is ∂2T + ∂2T + g = 0 (1)
∂x2 ∂y2 κ where g is the (constant) generation rate of heat per unit volume:

g = i2ρ (2) and i is the current density.

Using an appropriate Numerical Technique:

Determine the temperature distribution to an accuracy of 4 significant figures

You are expected to hand in the following by the submission date:

Hints

The Gauss-Seidel technique used in Tutorial 9 can be adapted to the present problem by including the additional term g/κ in the finite difference equation. You can generate a mask array as in Tutorial 9 to specify the region of the array covered by the H cross- section.

Try calculating the solution over a square mesh for a range of step lengths h = 5 mm, 2.5 mm, 1.25 mm…
You can if you wish make use of the symmetry of the problem and model just one quarter of the conductor. However, the boundary conditions then become more difficult to apply.

The H section is of width 40mm, depth 30mm and cut outs on top and bottom of depth 10x width 20 mm

Any help would be most appreciated

Cheers

Sam differential equations, conductivity, homework, doit4me MATLAB Answers — New Questions

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