laurent oget wrote:
> is there something i can do to prevent the following script from leaking
> memory?
There seems to be something happening.
(more below)
> Laurent
>
> from rpy2.robjects import r,RVector,RDataFrame
> from rpy2.rlike.container import TaggedList
> from math import sin
> import array
>
> size=100
> xx=[]
> for x in range(size):
> xx.append(RVector(array.array('f',[sin(0.1*(y+x))for y in range(1000)])))
>
(side note: the line above could write easier
from rpy2.robjects import FloatVector
for x in range(size):
xx.append(FloatVector([sin(0.1 * (x+y) for y in range(1000)]))
)
>
> formula='x%s~%s'%(0,'+'.join(['x%d'%(i+1) for i in range(size-1)]))
>
> t=['x%d'%i for i in range(size)]
> df=RDataFrame(TaggedList(xx,tags=t))
>
> done=False
> i=0
> while not done:
> print i
> i=i+1
> model=r.lm(formula,df)
I thought that the following would solve the problem... but it does not.
The increase in running speed suggests that this is because there is
less copying (therefore the problem about copying and protecting R
objects and never unprotecting them). The growth of the process is
slower, but still there...
from rpy2.robjects import globalEnv
globalEnv["dataf"] = df
done=False
i=0
while not done:
print i
i=i+1
model=r('lm("%s", data=dataf)' %formula)
The problem will likely require a closer look.
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