I am trying to run the example at:
http://nl.mathworks.com/help/stats/group-comparisons-using-categorical-arrays.html
using Matlab R2013b.
clear
load('carsmall')
cars = table(MPG,Weight,Model_Year);
cars.Model_Year = nominal(cars.Model_Year);
fit = fitlm(cars,'MPG~Weight*Model_Year')
Unfortunatelly I get the error:
Error using classreg.regr.FitObject/assignData (line 257)
Predictor and response variables must have the same length.
Error in classreg.regr.TermsRegression/assignData (line 349)
model = assignData#classreg.regr.ParametricRegression(model,X,y,w,asCat,varNames,excl);
Error in LinearModel.fit (line 852)
model = assignData(model,X,y,weights,asCatVar,dummyCoding,model.Formula.VariableNames,exclude);
Error in fitlm (line 111)
model = LinearModel.fit(X,varargin{:});
Any clue why?
Related
I have multiple test grey-scale images with a reference image. I want to measure the similarity between test and reference images in matlab R2016a. As per the website,
ssimval=ssim(A, ref)
[ssimval, ssimmap]=ssim(A, ref)
I am using these commands but they didn't work. I get following error everytime.
Undefined function or variable 'iptgetpref'.
Error in useIPPLibrary (line 15)
prefFlag = iptgetpref('UseIPPL');
Error in imfilter>useIPPL (line 304) prefFlag = useIPPLibrary();
Error in imfilter>filterPartOrWhole (line 355) ippFlag =
useIPPL(a,outSize,h,nonzero_h);
Error in imfilter (line 169) b_tmp = filterPartOrWhole(a,
out_size_row, hrow, start, sameSize, convMode);
Error in ssim (line 133) mux2 = imfilter(A,
gaussFilt,'conv','replicate');
Please do let me know how to resolve these issues.
I am trying to learn relevant features in a 300*299 training matrix by taking a random row from it as my test data and applying sequentialfs on it. I have used the following code:
>> Md1=fitcdiscr(xtrain,ytrain);
>> func = #(xtrain, ytrain, xtest, ytest) sum(ytest ~= predict(Md1,xtest));
>> learnt = sequentialfs(func,xtrain,ytrain)
xtrain and ytrain are 299*299 and 299*1 respectively. Predict will give me the predicted label for xtest(which is some random row from original xtrain).
However, when I run my code, I get the following error:
Error using crossval>evalFun (line 480)
The function '#(xtrain,ytrain,xtest,ytest)sum(ytest~=predict(Md1,xtest))' generated the following error:
X must have 299 columns.
Error in crossval>getFuncVal (line 497)
funResult = evalFun(funorStr,arg(:));
Error in crossval (line 343)
funResult = getFuncVal(1, nData, cvp, data, funorStr, []);
Error in sequentialfs>callfun (line 485)
funResult = crossval(fun,x,other_data{:},...
Error in sequentialfs (line 353)
crit(k) = callfun(fun,x,other_data,cv,mcreps,ParOptions);
Error in new (line 13)
learnt = sequentialfs(func,xtrain,ytrain)
Where did I go wrong?
You should build your classifier inside func, not before.
sequentialfs calls the function each time on different sets, and a classifier must be built specifically for each set, using only the features sequentialfs selected for that iteration.
I'm not sure I managed to be clear, in practice you should move the first line of your code inside the body of func
Source: MathWorks
I have a 10^5 square sparse matrix called pbAttack. Each element represents if there is connection between node i and node j. If there is connection, pbAttack(i,j) = 1. Otherwise, pbAttack (i,j) = 0. Then I want to use it following this tutorial: Matlab Autoencoders. I use the same code as in the linked Matlab tutorial. I only change it to use my data.
However, I got following errors:
Error using bsxfun
Out of memory. Type HELP MEMORY for your options.
Error in mapminmax.apply (line 8)
y = bsxfun(#plus,y,settings.ymin);
Error in mapminmax.create (line 44)
y = mapminmax.apply(x,settings);
Error in mapminmax (line 51)
[y,settings] = mapminmax.create(x,param);
Error in nnet.internal.configure.input (line 31)
[x,config] = feval(processFcns{j},x,processParams{j});
Error in network/configure (line 234)
net = nnet.internal.configure.input(net,i,X{i});
Error in nntraining.config (line 116)
net = configure(network(net),X,T);
Error in nntraining.setup>setupPerWorker (line 68)
[net,X,Xi,Ai,T,EW,Q,TS,err] = nntraining.config(net,X,Xi,Ai,T,EW,configNetEnable);
Error in nntraining.setup (line 43)
[net,data,tr,err] = setupPerWorker(net,trainFcn,X,Xi,Ai,T,EW,enableConfigure);
Error in network/train (line 335)
[net,data,tr,err] = nntraining.setup(net,net.trainFcn,X,Xi,Ai,T,EW,enableConfigure,isComposite);
Error in Autoencoder.train (line 490)
net = train(net,X,X,'useGPU',iYesOrNo(useGPU));
Error in trainAutoencoder (line 105)
autoenc = Autoencoder.train(X, autonet, paramsStruct.UseGPU);
Error in workflow_autoencoder (line 8)
autoenc1 = trainAutoencoder(pbAttack,hiddenSize,...
Are all these errors caused by the huge size of the matrix? Is there a work around so that it does not need so much memory? Thank you so much.
It just means that you don't have enough memory for this (since your matrix is not so sparse). You can try to repeat your code by reducing the size of the matrix.
I have variable (type table) in matlab:
and I'm using fitcnb for training naive bayes. I execute code:
Mdl = fitcnb(data,'class')
But I get an error :
Error using classreg.learning.FullClassificationRegressionModel.prepareDataCR (line 129) X must be a numeric matrix.
Error in classreg.learning.classif.FullClassificationModel.prepareData (line 455) [X,Y,W,dataSummary] = ...
Error in classreg.learning.FitTemplate/fit (line 205 [X,Y,dataPrepOut{1:this.NDataPrepOut}] = ...
Error in ClassificationNaiveBayes.fit (line 127) this = fit(temp,X,Y);
Error in fitcnb (line 219) this = ClassificationNaiveBayes.fit(X,Y,varargin{:});
fitcnb call's syntax is correct
Mdl = fitcnb(tbl,ResponseVarName)
But why I get an error ?
I'm trying to use TreeBagger to build a classifier based on the UCI Diabetes 130-US database, http://archive.ics.uci.edu/ml/datasets/Diabetes+130-US+hospitals+for+years+1999-2008.
I have imported the data as a table (letting Matlab decide on the data types), and have done some cleaning on the data. I'm calling the classifier as from an example, using my own data:
num_trees = 50;
B = TreeBagger(num_trees, train, train.readmitted,...
'OOBPrediction','On',...
'Method','classification');
oobErrorBaggedEnsemble = oobError(B);
plot(oobErrorBaggedEnsemble)
xlabel 'Number of grown trees';
ylabel 'Out-of-bag classification error';
I get the following error:
Error using classreg.learning.internal.table2PredictMatrix>makeXMatrix (line 100) Table variable is not a valid predictor.
Error in classreg.learning.internal.table2PredictMatrix (line 57)
Xout = makeXMatrix(X,CategoricalPredictors,vrange,pnames);
Error in classreg.learning.classif.CompactClassificationTree/predict (line 639)
X = classreg.learning.internal.table2PredictMatrix(X,[],[],...
Error in CompactTreeBagger/treeEval (line 1083)
[labels,~,nodes] = predict(tree,x);
Error in CompactTreeBagger/predictAccum (line 1414)
thisR = treeEval(bagger,it,thisX,doclassregtree);
Error in CompactTreeBagger/error (line 470)
predictAccum(bagger,X,'useifort',useIforT,...
Error in TreeBagger/oobError (line 1479)
err = error(bagger.Compact,bagger.X,bagger.Y,...
train is a table, and table.readmitted is a cell retrieved from the table. Most of the rows are cells, as most of the data in this dataset is categorical.
I'm wondering is there are certain datatypes that the classifier can't handle.
Thanks for any help!
The use of tables for the Machine Learning toolbox was introduced in R2016a. For previous versions, the data could only be passed to the fit* functions or TreeBagger as arrays.
The behavior changed from R2015(a,b) to R2016a.