search for: 150,25

.... (In reality, I'd like to do this for twice - once for a low group and once for a high group - and then I'd like to plot a set of data points for a 'normal' group together with these 2 grey areas.) Here's a simple example of the 2 lines: age=1:10 y.low=rnorm(length(age),150,25)+10*age y.high=rnorm(length(age),250,25)+10*age plot(age,y.high,type='n',ylim=c(100,400),ylab='Y Range',xlab='Age (years)') lines(age,y.low,col='grey') lines(age,y.high,col='grey') Is it possible to fill the area between the 2 lines filled with, for examp...

Hello. I have a problem with set focus on a TextCtrl in wxRuby. I must do something like Login TextCtrl and set focus on a next TextCtrl by clicking Tab key. -- Posted via http://www.ruby-forum.com/.

...$nnet, crs$dataset[, c(1:22)], type="raw") roundedpredictions=round(rawpredictions[,1],digits = 0) trueAndPredicted=cbind(roundedpredictions, targets) howManyEqual=trueAndPredicted[,1]==trueAndPredicted[,2] sum(howManyEqual) samp <- c(sample(1:50,25), sample(51:100,25), sample(101:150,25)) samp <- c(sample(1:250,125), sample(251:500,125), sample(500:920,300)) crs$nnet <- nnet(Value ~ ., data=crs$dataset[samp,c(1:22,922)], size=10, linout=TRUE, skip=TRUE, trace=FALSE, maxit=1000) -- View this message in context: http://www.nabble.com/accuracy-of-a-neural-net-tp23692699p2...

...w is a quick example of the problem I'm experiencing. Any ideas on how to get around it or will it simply not work with nnet()? > library(MASS) > library(nnet) > library(ipred) > data(iris3) > set.seed(191) > > samp <- c(sample(1:50,25), sample(51:100,25), sample(101:150,25)) > ird <- data.frame(rbind(iris3[,,1], iris3[,,2], iris3[,,3]), + species = factor(c(rep("s",50), rep("c", 50), rep("v", 50)))) > errorest(species ~., data = ird, subset = samp, model = nnet, > size = 2, rang =0.1, decay = 5e-4, maxit = 200) #...

...mple(500:920,300)) Here is the example from nnet: data(iris3) # use half the iris data ir <- rbind(iris3[,,1],iris3[,,2],iris3[,,3]) targets <- class.ind( c(rep("s", 50), rep("c", 50), rep("v", 50)) ) samp <- c(sample(1:50,25), sample(51:100,25), sample(101:150,25)) ir1 <- nnet(ir[samp,], targets[samp,], size = 2, rang = 0.1, decay = 5e-4, maxit = 200) test.cl <- function(true, pred) { true <- max.col(true) cres <- max.col(pred) table(true, cres) } test.cl(targets[-samp,], predict(ir1, ir[-samp,])) -- View this message...

...records in the outcomes (y) are the three digits, 0, 0.4677313, and 0.5111955. The script is as below. Please help to understand this behavior. library('AMORE') ir <- rbind(iris3[,,1], iris3[,,2], iris3[,,3]) targets <- matrix(c(rep(c(1,0,0),50), rep(c(0,1,0),50), rep(c(0,0,1),50)), 150, 3, byrow=TRUE) samp <- c(sample(1:50,25), sample(51:100,25), sample(101:150,25)) net <- newff(n.neurons=c(4, 2, 3), # number of units per layer learning.rate.global=1e-2, # learning rate at which every neuron is trained momentum.global=5e-4,...

...l); - control &= ~PCI_MSI_FLAGS_ENABLE; - if (enable) - control |= PCI_MSI_FLAGS_ENABLE; - pci_write_config_word(dev, dev->msi_cap + PCI_MSI_FLAGS, control); + return default_restore_msi_irqs(msi); } static void msix_clear_and_set_ctrl(struct pci_dev *dev, u16 clear, u16 set) @@ -159,6 +150,25 @@ static void msix_clear_and_set_ctrl(struct pci_dev *dev, u16 clear, u16 set) pci_write_config_word(dev, dev->msix_cap + PCI_MSIX_FLAGS, ctrl); } +static void msi_set_enable(struct msi_irqs *msi, int enable, int type) +{ + u16 control; + struct pci_dev *dev = msi->data; + + if (type...

...BLE; > - if (enable) > - control |= PCI_MSI_FLAGS_ENABLE; > - pci_write_config_word(dev, dev->msi_cap + PCI_MSI_FLAGS, control); > + return default_restore_msi_irqs(msi); > } > > static void msix_clear_and_set_ctrl(struct pci_dev *dev, u16 clear, u16 set) > @@ -159,6 +150,25 @@ static void msix_clear_and_set_ctrl(struct pci_dev *dev, > u16 clear, u16 set) > pci_write_config_word(dev, dev->msix_cap + PCI_MSIX_FLAGS, ctrl); > } > > +static void msi_set_enable(struct msi_irqs *msi, int enable, int type) > +{ > + u16 control; > + struct pc...

...BLE; > - if (enable) > - control |= PCI_MSI_FLAGS_ENABLE; > - pci_write_config_word(dev, dev->msi_cap + PCI_MSI_FLAGS, control); > + return default_restore_msi_irqs(msi); > } > > static void msix_clear_and_set_ctrl(struct pci_dev *dev, u16 clear, u16 set) > @@ -159,6 +150,25 @@ static void msix_clear_and_set_ctrl(struct pci_dev *dev, > u16 clear, u16 set) > pci_write_config_word(dev, dev->msix_cap + PCI_MSIX_FLAGS, ctrl); > } > > +static void msi_set_enable(struct msi_irqs *msi, int enable, int type) > +{ > + u16 control; > + struct pc...

...te: Mon Nov 12 23:24:39 2007 +0200 Fix an assert in swfdec_as_function_do_call when called on non-function diff --git a/libswfdec/swfdec_as_function.c b/libswfdec/swfdec_as_function.c index 6fb3429..2679b56 100644 --- a/libswfdec/swfdec_as_function.c +++ b/libswfdec/swfdec_as_function.c @@ -150,25 +150,21 @@ swfdec_as_function_call (SwfdecAsFunction *function, SwfdecAsObject *thisp, guin SWFDEC_AS_NATIVE (101, 10, swfdec_as_function_do_call) void -swfdec_as_function_do_call (SwfdecAsContext *context, SwfdecAsObject *fun, +swfdec_as_function_do_call (SwfdecAsContext *cx, SwfdecAsObject...

Hi all, The series is a draft of generic MSI driver that supports PCI and Non-PCI device which have MSI capability. If you're not interested it, sorry for the noise. The series is based on Linux-3.16-rc1. MSI was introduced in PCI Spec 2.2. Currently, kernel MSI driver codes are bonding with PCI device. Because MSI has a lot advantages in design. More and more non-PCI devices want to use

Hi all, The series is a draft of generic MSI driver that supports PCI and Non-PCI device which have MSI capability. If you're not interested it, sorry for the noise. The series is based on Linux-3.16-rc1. MSI was introduced in PCI Spec 2.2. Currently, kernel MSI driver codes are bonding with PCI device. Because MSI has a lot advantages in design. More and more non-PCI devices want to use

...ulated. When the nnet() command is run, it ouputs, on the console, the number of weights, and values of 'value'. But how do you calculate the number of weights in nnet, say, if you are feeding it an MxN inputs dataframe (i.e. M observations, each having N inputs, like the iris dataset has M=150 and N=4), and getting, say, x number of outputs for each observation? Thank you, Hafsa The code I used is from the Package nnet pdf:(nneds nnet package loaded) # use half the iris data ir <- rbind(iris3[,,1],iris3[,,2],iris3[,,3]) targets <- class.ind( c(rep("s", 50), rep("...