h = stackedplot(rand(6,3)); I want to set x-axis ticks according to my own defined set i.e., instead of 1:6, I want to replace x-axisticks [1, 2,3 ,4,5,6] to ['A', 'S','T', 'AAA', 'BBB', 'ZZZ'] , by rotating it to 90 degree that is vertically insted of horizontally? NOTE:- Matlabsolutions.com provide latest MatLab Homework Help, MatLab Assignment Help , Finance Assignment Help for students, engineers and researchers in Multiple Branches like ECE, EEE, CSE, Mechanical, Civil with 100% output.Matlab Code for B.E, B.Tech,M.E,M.Tech, Ph.D. Scholars with 100% privacy guaranteed. Get MATLAB projects with source code for your learning and research. There does not seem to be an easy way to set the XTick or XTickLabel of a StackedLineChart object (such as what's created by stackedplot ): data = rand(6,3); h = stackedplot(1:6,data); % try a couple of things, neither of which work try set(h,'XTick',1:6,'XTic
I am using nueral network to prdict the output of four inputs ( x1,...x4)
I need to call the netowrk from another matlab file currently i am using save and load the net but this method takes time to load the net do you know any alternative method to call the net please.
data=readmatrix( 'input.txt')
x=data(:,1:4)
y=data(:,5)
m=length(y);
Visulaisation of the data
histogram(y,10)
Normalise the features and transform the output
y2=log(1+y)
histogram(y2,10)
plot(x(:,2),y2,'o')
Normalise the input variables
for i=1:4
x2(:,i)=(x(:,i)-min(x(:,i)))/(max(x(:,i))-min(x(:,i)))
end
Train an artificial neural network (ANN)
rng default % For reproducibility
xt=x2'
yt=y2'
hiddenLayerSize=16;
net=fitnet(hiddenLayerSize)
net.divideParam.trainratio=70/100;
net.divideParam.valratio=30/100;
net.divideParam.testratio=0/100;
[net,tr]=train(net,xt,yt)
performance of N.N
yTrain=exp(net(xt(:,tr.trainInd)))-1
yTrainTrue=exp(yt(:,tr.trainInd))-1
sqrt(mean((yTrain-yTrainTrue).^2))
yVal=exp(net(xt(:,tr.valInd)))-1
yValTrue=exp(yt(:,tr.valInd))-1
sqrt(mean((yVal-yValTrue).^2))
gregnet1 = net;
save gregnet1
ANSWER
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You can use the sim function: The sim function is usually called implicitly by calling the neural network as a function. For instance, these two expressions return the same result: y = sim(net,x)
y = net(x)
I think for your case, you need something like this: % Read data
data = readmatrix("new_data.txt")
x=data(:,1:4)
y=data(:,5)
% Load saved network
load gregnet
net = gregnet1;
% Evaluate network on data
xt = x.';
yhat = exp(net(xt)-1).';
SEE COMPLETE ANSWER CLICK THE LINK
https://www.matlabsolutions.com/resources/how-can-i-call-network-.php
Matlabsolutions.com provide latest MatLab Homework Help,MatLab Assignment Help for students, engineers and researchers in Multiple Branches like ECE, EEE, CSE, Mechanical, Civil with 100% output.Matlab Code for B.E, B.Tech,M.E,M.Tech, Ph.D. Scholars with 100% privacy guaranteed. Get MATLAB projects with source code for your learning and research.
You can use the sim function:
The sim function is usually called implicitly by calling the neural network as a function. For instance, these two expressions return the same result:
y = sim(net,x) y = net(x)
I think for your case, you need something like this:
% Read data
data = readmatrix("new_data.txt")
x=data(:,1:4)
y=data(:,5)
% Load saved network
load gregnet
net = gregnet1;
% Evaluate network on data
xt = x.';
yhat = exp(net(xt)-1).';SEE COMPLETE ANSWER CLICK THE LINK
https://www.matlabsolutions.com/resources/how-can-i-call-network-.php
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