similar to: Neural Network resource

Hi all, Just migrated users to a samba 4 server on built on Solaris 11 samba pkg. Some users are mapping all required drives (G:, H:, and I:) but some are not able to map them. The affected users user log files indicate that the %g variable is being translated to '-1' instead of the users Unix group. I would welcome any feedback or suggestions on how to resolve this issue. >From my

Hi all, Samba 2.2.8a is currently running on Solaris 8 as a member of a domain. For testing purposes I have installed Samba 3.0.10 on a test server (Solaris 9) a a member of a Workgroup. In a couple of weeks we will be upgrading our production server to Solaris 9 and are planning on doing a fresh install of Samba 3.0.10 at the same time. I will be saving the following files to be restored after

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When I import a simple dataset, run LDA, and then try to use the model to forecast out of sample data, I get a forecast for the training set not the out of sample set. Others have posted this question, but I do not see the answers to their posts. Here is some sample data: Date Names v1 v2 v3 c1 1/31/2009 Name1 0.714472361 0.902552278 0.783353694 a 1/31/2009 Name2 0.512158919 0.770451596

As a follow-up to my email below: The input data frame to nnet() has dimensions: > dim(coreaff.trn.nn) [1] 5088 8 And the predictions from the neural network (35 records are dropped - see email below for more details) has dimensions: > pred <- predict(coreaff.nn1) > dim(pred) [1] 5053 1 So, the following line of R code does not work as the dimensions are

Sorry for these multiple postings. I solved the problem using na.omit() to drop records with missing values for the time being. I will worry about imputation, etc. later. I calculated the sum of squared errors for 3 models, linear regression, neural networks, and support vector machines. This is the first run. Without doing any parameter tuning on the SVM or playing around with the number of

I am exploring neural networks (adding non-linearities) to see if I can get more predictive power than a linear regression model I built. I am using the function nnet and following the example of Venables and Ripley, in Modern Applied Statistics with S, on pages 246 to 249. I have standardized variables (z-scores) such as assets, age and tenure. I have other variables that are binary (0 or 1). In

I am in the process of planning for a samba upgrade. The current version of Samba (2.2.8a) runs on a Sunfire 280R with Solaris 8. The intended new platform is a sunfire V240 with Solaris 9 as the OS. The new version of Samba will be Samba-3.0.10. Please note that the Samba server will be a member of a domain. I've read Samba-3 by Example, Chapter 8 which describes to the process of updating

Could you just use lines(newX, myPred, col=2) -----Original Message----- From: r-help-bounces at stat.math.ethz.ch [mailto:r-help-bounces at stat.math.ethz.ch]On Behalf Of Paul Johnson Sent: Thursday, September 23, 2004 10:3 AM To: r help Subject: followup: Re: [R] Issue with predict() for glm models I have a follow up question that fits with this thread. Can you force an overlaid plot

Hi I would like to know how I can change the name of a model for each trainning cycle of a model. I work with the RSNNS package and to build a neural network, I used : for (i in 5:30) .... model_ANN <- mlp(X, Y, size=n,....) # where size is the number of neurons in the hidden layer but I need to save each time that the model that is build (the end of each cycle), e.g., when i = 5, I need to

Good Morning, I am using the neuralnet package in R, and am able to produce some basic neural nets, and use the output. I would like to exclude some of the weights and biases from the iteration process and fix their values. However I do not seem to be able to correctly define the exclude and constant.weights vectors. Question: Can someone point me to an example where exclude and

Hello, I'm a neurophysiologist, and actually quite new to R. In fact, to analyse the responses of my neurons, I would like to have R doing an non-orthogonal ANOVA with a type III sum of squares. Could anybody help me? Thank you a lot in advance. Francois -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read

Dear List Members, I'm presently carrying out morphological analysis of a data set of neuronal structures. These are essentially 3D binary trees. In due course I will be trying to use discriminant analysis or other methods to classify these neurons based on morphological variables such as total tree length, segment number etc. I would like to calculate a 3D convex hull for a set of X,Y,Z

Hi all, I'm trying to learn R after years of Matlab's experience. Here is an issue I couldn't solve today. Consider the following piece of code (written by memory): for(i in 1:n){ submat <- data[1:i,] C <- colSums(submat) } The problem is that at the first iteration, data[1:1,] reduces to a vector and colSums returns an error. This sounds really strange to me

Hello everybody, I am afraid this might be a stupid question, but after scanning a little bit through the help archive and the internet, I didn't come up with an answer myself. I have done a principle component analysis on a data set, which yields the mean response of 14 olfactory neurons to 16 different odors. When plotting the result in a biplot, it nicely shows me the odors in

I require some help in debugging this code  library(neuralnet) ir<-read.table(file="iris_data.txt",header=TRUE,row.names=NULL) ir1 <- data.frame(ir[1:100,2:6]) ir2 <- data.frame(ifelse(ir1$Species=="setosa",1,ifelse(ir1$Species=="versicolor",0,""))) colnames(ir2)<-("Output") ir3 <- data.frame(rbind(ir1[1:4],ir2))

Hi, I am trying to build a VERY basic neural network as a practice before hopefully increasing my scope. To do so, I have been using package "neural" and the MLP related functions (mlp and mlptrain) within that package. So far, I have created a basic network, but I have been unable to change the default activation function. If someone has a suggestion, please advise. The goal of the

On 29/04/15 20:37, Sketch wrote: > On Wed, 29 Apr 2015, Andrey Repin wrote: > >> Greetings, Sketch! >> >>>> workgroup = INTERNAL >>>> realm = EXAMPLE.COM >>>> netbios name = SAMBA >> >>> Looks that way to me. Your realm should include the workgroup name: >>> INTERNAL.EXAMPLE.COM. >> >> Nothing is

Dear R users, I have been playing with the nnet and predict.nnet functions and have two questions. 1) Is it possible to specify a validation set as well as a training set in the nnet function before using predict.nnet to test the nnet object against new data? 2) Is it possible to specify more than one layer of neurons? Thanks in advance Matt Oliver

Hi guys, my goal is to create a reliable virtualization environment using CentOS 6.4 and KVM, I've three nodes and a clustered GFS2. The enviroment is up and working, but I'm worry for the reliability, if I turn the network interface down on one node to simulate a crash (for example on the node "node6.blade"): 1) GFS2 hangs (processes go in D state) until node6.blade get