[R] R-neuralnet code

arun smartpink111 at yahoo.com
Tue Jul 31 07:14:40 CEST 2012



Hello Rahul,
I can see comments for each line of the code which is not very hard to understand.

From your earlier post, I think you were having some problems with the compute method.  Check this link (http://stackoverflow.com/questions/4891899/how-to-predict-new-cases-using-the-neuralnet-package).  Also, read the neuralnet package.

Other links: 
1.http://rgm2.lab.nig.ac.jp/RGM2/func.php?rd_id=neuralnet:neuralnet-package.
2. http://stackoverflow.com/questions/10939591/predicting-class-for-new-data-using-neuralnet
3. http://www.google.com/url?sa=t&rct=j&q=package%20neuralnet&source=web&cd=3&ved=0CFMQFjAC&url=http%3A%2F%2Fjournal.r-project.org%2Farchive%2F2010-1%2FRJournal_2010-1_Guenther%2BFritsch.pdf&ei=s2cXUIjpNsWvqAG1gIFw&usg=AFQjCNG0d5pvGlgTuHG9kmiQbgzywItDyw&cad=rja

Hope it helps.
A.K.


________________________________

From: Rahul Bhalla <rahulbhalla_3521 at yahoo.com>
To: arun <smartpink111 at yahoo.com> 
Sent: Tuesday, July 31, 2012 12:51 AM
Subject: Re: R-neuralnet code


Thank you for the link but it is not completely convey the concept of neural networks what i need is a simpler code with the interpretation about the GW plots and also if you can give me the interpretation of this code

#install.packages('neuralnet') 
library("neuralnet") 
#Going to create a neural network to perform sqare rooting 
#Type ?neuralnet for more information on the neuralnet library 
#Generate 50 random numbers uniformly distributed between 0 and 100 
#And store them as a dataframe 
traininginput <-  as.data.frame(runif(50, min=0, max=100)) 
trainingoutput <- sqrt(traininginput) 

#Column bind the data into one variable 
trainingdata <- cbind(traininginput,trainingoutput) 
colnames(trainingdata) <-
c("Input","Output") 

#Train the neural network 
#Going to have 10 hidden layers 
#Threshold is a numeric value specifying the threshold for the partial 
#derivatives of the error function as stopping criteria. 
net.sqrt <- neuralnet(Output~Input,trainingdata, hidden=3, threshold=0.01) 
print(net.sqrt) 

#Plot the neural network 
plot(net.sqrt) 

#Test the neural network on some training data 
testdata <- as.data.frame((1:10)^2) #Generate some squared numbers 
net.results <- compute(net.sqrt, testdata) #Run them through the neural network



________________________________
From: arun <smartpink111 at yahoo.com>
To: Rahul Bhalla <rahulbhalla_3521 at yahoo.com> 
Cc: R help <r-help at r-project.org> 
Sent: Tuesday, July 31, 2012 1:37 AM
Subject: Re: R-neuralnet code

Hello,

You can check this link (http://econometricsense.blogspot.com/2010/12/r-code-example-for-neural-networks.html).  


A.K.





________________________________
From: Rahul Bhalla <rahulbhalla_3521 at yahoo.com>
To: arun <smartpink111 at yahoo.com> 
Sent: Monday, July 30, 2012 3:51 PM
Subject: R-neuralnet code


Can you please give me a simple R-neuralnet code and its interpretation.Also the concept of the compute function.


________________________________
From: arun <smartpink111 at yahoo.com>
To: Alaios <alaios at yahoo.com> 
Cc: R
help <r-help at r-project.org> 
Sent: Monday, July 30, 2012 5:34 PM
Subject: Re: [R] Convert variable to STring

Hello,

Not sure whether this is what you need.

colnames(mymatrix)<-c("function1","function2","function3")
> toString(colnames(mymatrix))
#[1] "function1, function2, function3"

A.K.



----- Original Message -----
From: Alaios <alaios at yahoo.com>
To: R help <R-help at r-project.org>
Cc: 
Sent: Monday, July 30, 2012 6:47 AM
Subject: [R] Convert variable to STring

Dear all,
I have a variable that I would like also to use it as a string. The reasons is that I want to collect results from different function to one table.. So
when I use the
 colnames(mymatrix) <-c(function1.function2,function3)
the function1, function2, function3 to be "converted" to simple strings so as

 colnames(mymatrix)
<-c("function1","function2","function3")

Could you please help me understand how I can do that in R?

Regards

    [[alternative HTML version deleted]]


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and provide commented, minimal, self-contained, reproducible code.    



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