Hey all,
I'm doing some work with machine learning on R (I'm a fairly new user of R),
and I have a question about generating new tables from existing tables. I'm
currently using a table of measurements I read in from a CSV file to
generate training and validation data set tables for future use in a machine
learning algorithm using the code:
#generate probabilities to divide up training / validation data sets
randomly
device_Prob_Vector <- runif(num_Devices)
#NULL-initialize training and validation sets. This seems like a bit of a
hack...
training_Set <- measurements[0]
validation_Set <- measurements[0]
#divide up the training and validation data sets from measurements.
for ( i in 1:num_Devices)
{
if ( device_Prob_Vector[i] > 0.5 )
{
training_Set <- rbind(training_Set, measurements[i,])
}
else
{
validation_Set <- rbind(validation_Set, measurements[i,])
}
}
This code works correctly, but takes quite a long time to execute. I
suspect this is because rbind() is dynamically resizing the tables as it
adds new rows to each table of data. Is there a way to pre-allocate memory
for each of the two tables, and then shrink them after the loop has
completed? Thanks for the help.
~Nate
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