Hi all,
I have a problem with the predict command. Here is an example:
from rpy import *
r.library("nnet")
model = r("Fxy~x+y")
df = r.data_frame(x = r.c(0,2,5,10,15)
,y = r.c(0,2,5,8,10)
,Fxy = r.c(0,2,5,8,10))
NNModel = r.nnet(model, data = df
, size =10, decay =1e-3
, lineout=True, skip=True
, maxit=1000, Hess =True)
XG = r.expand_grid(x = r.seq(0,7,1), y = r.seq(0,7,1))
x = r.seq(0,7,1)
y = r.seq(0,7,1)
fit = r.predict(NNModel,XG)
Running that skript delivers an error:
Traceback (most recent call last):
File "F:\test\Python\test.py", line 11, in <module>
fit = r.predict(NNModel,XG)
rpy.RPy_RException: Fehler in UseMethod("predict") : keine anwendbare
Methode für "predict"
But the same skript in R shows no problems. It seems that I got not the
right way to speech to the predict function, but I can't see why.
Here's the code in R:
library(nnet)
model <- Fxy~x+y
df <- data.frame(x=c(0,2,5,10,15)
,y=c(0,2,5,8,10)
,Fxy=c(0,2,5,8,10))
NNModel <- nnet(model, data =df, size =10
, decay =1e-3, lineout=TRUE
, skip=TRUE, maxit=1000, Hess =TRUE)
XG <- expand.grid(x=seq(0,7,1),y=seq(0,7,1))
x <- seq(0,7,1)
y <- seq(0,7,1)
fit <- predict(NNModel,XG)
which shows no problems. Where is the difference? I want to use the nnet
Library to interpolate several 3D Datas. The python code loops over
Data files and sorts them.. Maybe I have to put the whole code to R?
I have to say that I don't complete understand the predict function- But
in R does it what I want. I looked in the thread of Thomas few days
ago, but it does not really help me.
Thanks for any hints!
Marco
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