Greetings, I have recently started using mlpack for a C++ application and I came across a problem that I haven't been able to solve. I am using Linear Regression to learn the parameters of a linear model. My training data is a vector of 1-dimensional points. It consists of a vector of type double (64-bit). I initialize the data points matrix from a std::vector structure (where I have the data) using this constructor: arma::mat(std::vector) Depending on the datasize of the dataset that I use to create the Linear Regression model I get this error:
> error: arma::memory::acquire(): out of memory > terminate called after throwing an instance of 'std::bad_alloc' > what(): std::bad_alloc > I am running the application on a machine which has 250GB of memory. When I use 100k points (less then 1MB of data) I observe that ~28% of the memory is being used to build the model. When I increase this number to 160k points I observe that ~50% of the memory is being used and then the process is killed. When I increase it a bit more the above error is immediately thrown when trying to build the model. I was wondering whether it is normal for the model to consume this much memory for a small amount of data and if this is the case then what can one do to use a larger dataset? Any help would be appreciated. Best regards, Anisa Llaveshi
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