similar to: Optimizing a model toward desired outputs once trained?

I'm trying to find the variance of various outputs in a matrix: for(l in 2:vl){ for(o in 1:(l-1)){ # Make sure the inputs are for the matrix "m" input3=rownames(v)[o] input4=colnames(v)[l] r=t[(t$Rec1==input3 & t$Rec2==input4),output] if(length(r)==0){ r=t[(t$Rec1==input4 & t$Rec2==input3),output] } v[l,o]=var(q,na.rm=TRUE)

On Sat, May 29, 2010 at 1:18 AM, Eli Friedman <eli.friedman at gmail.com> wrote: > On Sat, May 29, 2010 at 12:42 AM, Stéphane Letz <letz at grame.fr> wrote: >> >> Le 29 mai 2010 à 01:08, Bill Wendling a écrit : >> >>> Hi Stéphane, >>> >>> The SSE support is the LLVM backend is fine. What is the code that's generated? Do you have some

On Sat, May 29, 2010 at 12:42 AM, Stéphane Letz <letz at grame.fr> wrote: > > Le 29 mai 2010 à 01:08, Bill Wendling a écrit : > >> Hi Stéphane, >> >> The SSE support is the LLVM backend is fine. What is the code that's generated? Do you have some short examples of where LLVM doesn't do as well as the equivalent scalar code? >> >> -bw >>

Le 29 mai 2010 à 01:08, Bill Wendling a écrit : > Hi Stéphane, > > The SSE support is the LLVM backend is fine. What is the code that's generated? Do you have some short examples of where LLVM doesn't do as well as the equivalent scalar code? > > -bw > > On May 28, 2010, at 12:13 PM, Stéphane Letz wrote: We are actually testing LLVM for the Faust language

Ian Campbell <ijc at hellion.org.uk> writes: #I think at a minimum to make any progress we would need to see some #logs. Probably the dom0 dmesg (perhaps compared to the native boot #dmesg) and the set of modules which are loaded would be a good start. #Probably xen's dmesg would be useful too. Lastly, it would be good to #mention what sort of keyboard this is (USB, PS2?) # #Also the

Hi, I am having difficulty in training a neural network using the package "neuralnet". My neural network has 2 input neurons (covariates), 1 hidden layer with 2 hidden neurons and 2 output neurons (responses). I am training my neural network with a dataset that has been transformed so that each column is of type "numeric". The difficulty I am facing is that the responses of

How can I join two lists? I have q1 and q2 and I want to merge them. I have tried to use the comand merge, but not work. Any solutions? Thanks! > q1 $Input1 7.84615384615385 0.5 $Input2 8.92307692307692 -3.2 $Input3 4.53846153846154 -5 > q2 $Input1 7.84615384615385 2 $Input2 8.92307692307692 -0.3125 $Input3

Dear R helpers   I have some variables say ABC, DEF, PQR, LMN and XYZ. I am choosing any three varaibles at random at a time for my analysis and name these files as input1.csv, input2.csv and input3.csv. So if I choose variables say ABC, DEF and PQR, I am passing the specifications of these variables to input1.csv, input2.csv and input3.csv respectively.   This means in another case even if I

Dear all, the exclude and constant.weights options are used as follows: exclude: A matrix with n rows and 3 columns will exclude n weights. The the first column refers to the layer, the second column to the input neuron and the third column to the output neuron of the weight. constant.weights: A vector specifying the values of the weights that are excluded from the training process and treated

Dear Frauke, Thank you very much for taking the time to respond. You explanation was very helpful, and I now have that part figured out! Best Wishes, Dan Frauke Message: 3 Date: Mon, 12 Oct 2020 08:33:44 +0200 (CEST) From: =?UTF-8?Q?Frauke_G=C3=BCnther?= <guenther at leibniz-bips.de> To: "r-help at r-project.org" <r-help at r-project.org> Cc: William Michels <wjm1

Hi, I am having a hard time rationalizing why virt-resize would be the cause of this issue, however it appears to definitely be the trigger, hence trying this list. My environment comprises Debian 10 hosts and guests, although this issue has also been witnessed on Debian 9 hosts running Debian9/10 guests. In all cases, stable versions of libvirt, qemu-kvm, libguestfs- tools, etc running on AMD64

https://bugs.freedesktop.org/show_bug.cgi?id=64645 Priority: medium Bug ID: 64645 Assignee: nouveau at lists.freedesktop.org Summary: Massive corruption QA Contact: xorg-team at lists.x.org Severity: major Classification: Unclassified OS: Linux (All) Reporter: roucaries.bastien+bugs at gmail.com

I''m wondering if anyone might have any suggestions... We''ve installed CentOS5.2 on 3 servers and have an FC switch, dual-port cards, and FC RAID device. We configured the RAID as two separate logical RAIDs. One is RAID-10 for speed and it hosts Xen VM images as LVs. The rest of the drive is RAID-5 to maximize space and houses a lot of lighter access data on a GFS2 filesystem.

Dear members, I have started to work with R recently and there is one thing which I could not solve so far. I don't know how to define macros in R. The problem at hand is the following: I want R to go through a list of 1:54 and create the matrices input1, input2, input3 up to input54. I have tried the following: for ( i in 1:54) { input[i] = matrix(nrow = 1, ncol = 107)

good night Again I ask for help to the community, as I am new at this, I have some basic questions. I am looking for packages on neural networks and so you can search found these two that I think are the most used, neuralnet, nnet. So you can test, and correct me if I'm wrong the neuralnet only accepts as input values ??nomer, did a little test data (iris) library (neuralnet)

good night Again I ask for help to the community, as I am new at this, I have some basic questions. I am looking for packages on neural networks and so you can search found these two that I think are the most used, neuralnet, nnet. So you can test, and correct me if I'm wrong the neuralnet only accepts as input values ??nomer, did a little test data (iris) library (neuralnet)

Hi R, Here's a question/problem on the 'neuralnet' package for neural networks. I have more than 50 factors in each of my independent variables. When I apply the command 'neuralnet', I get the below warning message, > net.sum <- neuralnet( Sum~Var1+Var2+Var3, b, hidden=0,linear.output=TRUE) Warning message: 'predictions' will not be calculated, as at

Hi Dr. Snow, & R-helpers, Thank you for your reply! I hadn't heard of the {microbenchmark} package & was excited to try it! Thank you for the suggestion! I did check the reference source for which() beforehand, which included the statement to remove NAa, and I didn't have any missing values or NAs: sum(is.na(dat$gender2)) sum(is.na(dat$gender)) sum(is.na(dat$y)) [1] 0 [1] 0 [1]

Sorry for alI these individual emails, but I think is more readable this way, by having each independent information in a separate email. I also have these additional cards that I can try: Gefoce GT 1030 and Geforce GTX 1050 Ti. I have tried Geforce GT 1030 and it is working nicely but without hardware acceleration. Without the hardware acceleration I am able to get into X (Ubuntu Mate 17.10)

Hola a todos, en primer lugar quería agradecer la ayuda recibida desde el foro con respecto a la creación de una red neuronal. Estoy utilizando el paquete Neuralnet, que me parece que es bastante bueno, pero tengo el problema que soy incapaz de hacer las predicciones del modelo. Sé que se hace con el comando "compute", pero no encuentro ningún ejemplo de cómo hacerlo. ¿Alguien me puede