search for: new_data

...g mention in the docs/ man page under the --chmod entry. First, I point out one situation where I had it work, then I go on to describe the failing case. Finally, I show a combination which makes it work. (rsync Kung fu) *Sticky bit ON, on source dir drwxr-sr-t. 2 redleader rebels 32 Dec 30 18:32 new_data Use --perms, include trailing slash on source dir rsync -r -p new_data/ destination Result: works, sticky bit ON, on destination dir drwxr-sr-t. 3 redleader rebels 47 Dec 30 19:15 destination *Sticky bit OFF, on source dir drwxr-sr-x. 2 redleader rebels 32 Dec 30 18:32 new_data Attempt to chmo...

...l-time from requests arriving at an HTTP server. The following code demonstrates the sort of R calls that I have in mind: > cases <- 2000000 > data <- data.frame(x1=runif(cases),x2=runif(cases),y=sample(0:1,cases,replace=TRUE)) > lr1 <- glm(y~x1*x2,family=binomial,data=data) > new_data <- data.frame(x1=0,x2=0) > out <- predict(lr1,type="response",newdata=new_data) The first thing I am noticing is that the models that I am storing are very large because I am using large data-sets, and the models seem to store residuals, fitted values and so on, by default. &gt...

...al_status"] == "Private (Op"){ data1[i,"legal_status"]<-data1[-i,"legal_status"] } if (data1[i,"legal_status"] == "Unknown"){ data1[i,"legal_status"]<-data1[-i,"legal_status"] } } return(data1) } new_data<-cleanse(data) Any ideas? -- View this message in context: http://old.nabble.com/cleanse-columns-and-unwanted-rows-tp26342169p26342169.html Sent from the R help mailing list archive at Nabble.com.

...l-time from requests arriving at an HTTP server. The following code demonstrates the sort of R calls that I have in mind: > cases <- 2000000 > data <- data.frame(x1=runif(cases),x2=runif(cases),y=sample(0:1,cases,replace=TRUE)) > lr1 <- glm(y~x1*x2,family=binomial,data=data) > new_data <- data.frame(x1=0,x2=0) > out <- predict(lr1,type="response",newdata=new_data) The first thing I am noticing is that the models that I am storing are very large because I am using large data-sets, and the models seem to store residuals, fitted values and so on, by default. &gt...

....strings = "na",header = FALSE) boxplot(my_data*100, names = data_headings, outline = FALSE, range = 0.3, border = c(2,4)) The result is a boxplot, but it does not show the date along the bottom (the "names = data_headings" bit achieves nothing). I can alternatively try this: new_data<- read.table(testdata, skip = 0, sep = ",", na.strings = "na",header = TRUE) boxplot(new_data,outline = FALSE, range = 0.3,border = c(2,4)) This takes all the data and plots it, but I then lose the ability to multiply by 100 (I'm trying to show percentages: e.g. 10% as &...

...<stdlib.h> // A linked list node struct Node { int data; struct Node* next; struct Node* prev; }; struct Node* head; /* Given a reference (pointer to pointer) to the head of a list and an int, inserts a new node on the front of the list. */ void push(struct Node** head_ref, int new_data) { struct Node* new_node = (struct Node*)malloc(sizeof(struct Node)); new_node->data = new_data; new_node->next = (*head_ref); new_node->prev = NULL; if ((*head_ref) != NULL) (*head_ref)->prev = new_node; (*head_ref) = new_node; } // This function p...

...= data , x=T, y=T) Using bootstrapping, I would like to have a measure of uncertainty around the estimates of beta coefficients. I am using the following code but I don't know how to consider strata and cluster arguments. library(boot) boot.clogit <- function(data, indices){ new_data <- data[indices,] mod <- clogit(event ~ forest + log_area + forest:log_time + cluster(ID_individual) + strata(ID_strata), method = "efron", data = new_data, x=T, y=T) coefficients(mod) } boot_data <- boot(data=data, statistic=boot.clogi...

...onoscopy.csv", header=T) E <- read.csv("CLStest.csv", header=T) dataclstraining <- subset(D,select=c(....)) classesclstraining <- subset(D, select=Class) dataclstest <- subset(E,select=c(.......)) classesclstest <- subset(E, select=Class) bootsvm <- function(data, new_data, newdata_classes, indices) { d <-data[indices,] ** model2 <- best.svm(Class~.,data=d, gamma = 10^(-6:-1), cost = 10^(-1:1), tunecontrol=tune.control(sampling="bootstrap", nboot=1000, boot.size=8/8)) pred.b <- predict(model2, newdata=new_data, decision.values=FALSE, probability=...

...;> }; >> >> >> >> *struct Node* head;* >> >> >> >> /* Given a reference (pointer to pointer) to the head of a list >> >> and an int, inserts a new node on the front of the list. */ >> >> void push(struct Node** head_ref, int new_data) >> >> { >> >> struct Node* new_node = (struct Node*)malloc(sizeof(struct Node)); >> >> >> >> new_node->data = new_data; >> >> >> >> new_node->next = (*head_ref); >> >> new_node->prev = NULL...

Hi, I run R 2.10.1 under ubuntu 10.04 LTS (Lucid Lynx) and klaR version 0.6-4. I compute a model over a 2 classes dataset (composed of 700 examples). To that aim, I use the function NaiveBayes provided in the package klaR. When I then use the prediction function : predict(my_model, new_data). I get the following warning : "In FUN(1:747[[747L]], ...) : Numerical 0 probability with observation 458" As I did not find any documentation or any discussion concerning this warning message, I looked in the klaR source code and found the following line in predict.NaiveBayes.R : &qu...

...<stdlib.h> // A linked list node struct Node { int data; struct Node* next; struct Node* prev; }; struct Node* head; /* Given a reference (pointer to pointer) to the head of a list and an int, inserts a new node on the front of the list. */ void push(struct Node** head_ref, int new_data) { struct Node* new_node = (struct Node*)malloc(sizeof(struct Node)); new_node->data = new_data; new_node->next = (*head_ref); new_node->prev = NULL; if ((*head_ref) != NULL) (*head_ref)->prev = new_node; (*head_ref) = new_node; } // This function p...

...<stdlib.h> // A linked list node struct Node { int data; struct Node* next; struct Node* prev; }; struct Node* head; /* Given a reference (pointer to pointer) to the head of a list and an int, inserts a new node on the front of the list. */ void push(struct Node** head_ref, int new_data) { struct Node* new_node = (struct Node*)malloc(sizeof(struct Node)); new_node->data = new_data; new_node->next = (*head_ref); new_node->prev = NULL; if ((*head_ref) != NULL) (*head_ref)->prev = new_node; (*head_ref) = new_node; } // This function p...

...code the pack_data array will be at most @numeric_length entries because of the format string passed to the unpack call. This results in the code dropping a chunk of data and then hitting the exception in a subsequent Marshal.load call. elsif @parser_state == 1 pack_data,remaining = new_data.unpack("a#{@numeric_length}a*") if pack_data.length < @numeric_length @data << pack_data @numeric_length = @numeric_length - pack_data.length elsif pack_data.length == @numeric_length <======== this should be "elsif remaining.length...

Hi, I'm not very experienced with R and struggle with data selection from a long matrix with two columns. I want to cut out the data between x > 0 and min(y). > x <- c(-10, -5, 0, 5, 10, 15, 20) > y <- c(10, 10, 10, -5, -6, -7, 10) > data <- as.matrix( cbind(x, y) ) > data x y [1,] -10 10 [2,] -5 10 [3,] 0 10 _ [4,] 5 -5 |-- data interval [5,] 10 -6

...;> }; >> >> >> >> *struct Node* head;* >> >> >> >> /* Given a reference (pointer to pointer) to the head of a list >> >> and an int, inserts a new node on the front of the list. */ >> >> void push(struct Node** head_ref, int new_data) >> >> { >> >> struct Node* new_node = (struct Node*)malloc(sizeof(struct Node)); >> >> >> >> new_node->data = new_data; >> >> >> >> new_node->next = (*head_ref); >> >> new_node->prev = NULL...

...,3,4,5) x2 <- c(100,200,300,400,500) test_data <- data.frame(x1,x2,date_tm) test_data start_dt <- as.Date('2011-01-01',format="%Y-%m-%d") #Passing as parameter end_dt <- as.Date('2011-02-31',format="%Y-%m-%d") #Passing as parameter library(sqldf) new_data <- sqldf("select * from test_data where date_tm = start_dt") It shows similar error, when date is passed by parameter start_dt (error in statement: no such column: start_dt) [[elided Yahoo spam]] ~Ajit -- View this message in context: http://r.789695.n4.nabble.com/Passing-date-as...

On 04/25/2013 07:03 AM, syslinux-bot for Thomas Schmitt wrote: > Commit-ID: 5de463f724da515fd6c5ea49ded6dde178362181 > Gitweb: http://www.syslinux.org/commit/5de463f724da515fd6c5ea49ded6dde178362181 > Author: Thomas Schmitt <scdbackup at gmx.net> > AuthorDate: Thu, 4 Apr 2013 20:02:37 +0200 > Committer: Matt Fleming <matt.fleming at intel.com> > CommitDate:

Dear all, Finally, I made it, my RPostgreSQL works. After working through some tutorials, i was able to plot, get and replace several timeseries. Still I miss the opportunity (syntax) to add data. Most tutorials are about: "Download some S&P data from Yahoo and play around a little". What I want to do is, add data to an existing timeseries. Is there a possibility to add data

This series of patches makes Xen pass a (somewhat) valid device tree to dom0. The device tree for dom0 is the same as the one supplied to Xen except the memory and chosen nodes are adjusted appropriately. We don''t yet make use of the device tree to map MMIO regions or setup interrupts for the guest and we still include the UART used for Xen''s console. Note that loading Linux

I''ve addressed all (I think/hope) of the review comments. The main change is to expose the guest virtual platform (e.g. memory layout and interrupt usage etc) to the toolstack via the public interface. This is then used during FDT generation. I have just codified the current defacto standard layout, it''s probably not the best layout but any change can be a separate patch/series.