search for: fvals

Using Ulrik?s example data (and assuming I understand what is wanted), here is what I would do: ex.dat <- c("FName: fname1", "Fval: Fval1.name1", "Fval: ", "FName: fname2", "Fval: Fval2.name2", "FName: fname3") tst <- data.frame(x = ex.dat, stringsAsFactors=FALSE) sp <- strsplit(tst$x, ':', fixed=TRUE) chk <-

@Don your solution does not solve Vijayan's scenario 2. I used spread and gather for that. An alternative solution to insert mising Fval - picking up with Don's newtst - is newtst <- c("FName: fname1", "Fval: Fval1.name1", "FName: fname2", "Fval: Fval2.name2", "FName: fname3", "FName: fname4", "Fval: fval4.fname4")

...at yields a character vector. I would like to extract the actual numeric values. Here is an example of what I am trying to do. fr <- function(x) { ## Rosenbrock Banana function on.exit(print(f)) x1 <- x[1] x2 <- x[2] f <- 100 * (x2 - x1 * x1)^2 + (1 - x1)^2 f } fvals <- capture.output(ans <- optim(c(-1.2,1), fr)) Now, `fvals' contains character elements, but I would like to obtain the actual numerical values. How can I do this? Thanks very much for any suggestions. Best, Ravi. ------------------------------------------------------- Ravi Varadhan,...

Hi r-users,   I have the following code to solve 4 simultaneous eqns with 4 unknowns using newton iteration method.  But I got the error message:   pars <- c(1.15, 40, 50, 0.78) newton.input2 <- function(pars) {  ## parameters to estimate      alp <- pars[1]    b1  <- pars[2]     b2  <- pars[3]    rho <- pars[4]   f1 <- pars[1]*pars[2] f2 <-

Hi, I am trying to find a simple way to numerically solve a system of two equations equal to zero with two unknowns (x_loc and y_loc). Here is a mock data set and below it, the equations I need to solve. theta<-c(180,135,90)/(2*pi) x<-c(0,0,15) y<-c(20,0,0) 0 = -sum((y_loc-y)*(sin(theta)*(x_loc-x)-cos(theta)*(y_loc-y))/(((x_loc-x)^2+(y_loc-y)^2)^0.5)^3) 0 =

...s needs some discussion. Presently, the FeatureVector class is defined as follows, with a fixed number of feature count (19): class FeatureVector::Internal : public Xapian::Internal::intrusive_base{ friend class FeatureVector; double label; double score; std::map<int,double> fvals; int fcount; Xapian::docid did; The two approaches that were discussed were: 1. Using enums as IDs for features in fvals. 2. Making fvals into a configurable vector of feature values. The issues were that the first way would still assume an order in which the features occur, and the secon...

Hi Vijayan, one way going about it *could* be this: library(dplyr) library(tidyr) library(purrr) ex_dat <- c("FName: fname1", "Fval: Fval1.name1", "Fval: ", "FName: fname2", "Fval: Fval2.name2", "FName: fname3") data.frame(x = ex_dat) %>% separate(x, c("F1", "F2"), sep = ": ") %>% filter(F2

Dear R-help Group Scenario 1: I have a text file running to 1000 of lines...that is like as follows: [922] "FieldName: Wk3PackSubMonth" [923] "FieldValue: Apr" [924] "FieldName: Wk3PackSubYear" [925] "FieldValue: 2017" [926] "FieldName: Wk3Code1" [927] "FieldValue: " [928] "FieldValue: K4" [929] "FieldName:

...ependently of RankList. Therefore, eliminating the need of maintaing a global qrel storage (map<string, map<string, int> > qrel; in FeatureManager::Internal) and thus eliminating the need of load_relevance(*) and getlabel(*) functions. The score in FeatureVector is simply the label, and fvals will be returned by FeatureManager (by using feature values obtained from each of the Feature sub-class). While ranking, FeatureVector fvals will be computed similarly by FeatureManager, while the score gets assigned later at the time of ranking. > * when configuring the letor system either...

I am attempting to lower the selectCC instruction to the instruction set of the backend I'm working on and I cannot seem to find a way to correctly implement this instruction. I know how this instruction should get implemented; I just have yet to find a way to do it. I want the select_cc instruction to be lowered into a comparison followed by a conditional move. I've attempted to use a

> Thanks all point about security, I''ll do as follows. > I thought that the point was the following two. > > > 1. Storage place of encrypted password > Should I store it in /etc/xen/passwd ? > Or, should I wait for DB of Xen that will be released in > the future? The xend life cycle management patches were posted by Alistair a couple of months back.

Hi all, I am trying to bootstrap a small data set into 1000 "pseudodatasets" and then run an ANOVA on each one. Can anyone provide guidance on how I could do this? Thank you. -Dan Janes ************************************************ Dan Janes, Ph.D. Harvard University/OEB 26 Oxford St. Cambridge, MA 02138 Office: 617-496-2375 Fax: 617-495-5667 Email: djanes at oeb.harvard.edu

I think SANN method in optim() is failing to report that it has not converged. Here is an example genrose.f<- function(x, gs=NULL){ # objective function ## One generalization of the Rosenbrock banana valley function (n parameters) n <- length(x) if(is.null(gs)) { gs=100.0 } fval<-1.0 + sum (gs*(x[1:(n-1)]^2 - x[2:n])^2 + (x[2:n] - 1)^2) return(fval) }

Hi everyone, i?m a new user of R and i?m trying to translate an linear optimization problem from Matlab into r. The matlab code is as follow: options = optimset('Diagnostics','on'); [x fval exitflag] = linprog(f,A,b,Aeq,beq,lb,ub,[],options); exitflag fval x=round(x); Where: f = Linear objective function vector (vector of 45,rows) A = Matrix for linear inequality

Hi R-list, I am new to optimization in R and would appreciate help on the following question. I would like to minimize the following function using two constraints: ###### fn <- function(par,H,F){ fval <- 0.5 * t(par) %*% H %*% par + F%*% par fval } # matrix H is (n by k) # matrix F is (n by 1) # par is a (n by 1) set of weights # I need two constraints: # 1.

Hi R-users, Does R has a topic on newton's method? Thank you for the info.

...eived code that bootstrapped my dataset and ran an ANOVA on each pseudodataset, producing a histogram of F-values. Here is the code I received that produces this F-histogram. X <- data.frame(b=rnorm(2162),c =factor(rep(1:47,each=46))) ## linear fit with true values lm0 <- lm(b~c,data=X) Fvals <- numeric(1000) for (i in 1:1000) { X.boot <- X[sample(1:nrow(X),replace=TRUE),] Fvals[i] <- anova(lm(b~c,data=X.boot))$"F value"[1] } hist(Fvals) I am grateful for the assistance but the code does not provide what I need. I need the 1000 pseudodatasets produced by th...

Hi, I'm trying to figure out how anova works in R by translating the examples in Sokal And Rohlf's (1995 3rd edition) Biometry. I've hit a snag with planned comparisons, their box 9.4 and section 9.6. It's a basic anova design: treatment <- factor(rep(c("control", "glucose", "fructose", "gluc+fruct",

On Thu, Sep 11, 2008 at 6:14 PM, Villmow, Micah <Micah.Villmow at amd.com> wrote: > I am attempting to lower the selectCC instruction to the instruction set of > the backend I'm working on and I cannot seem to find a way to correctly > implement this instruction. I know how this instruction should get > implemented; I just have yet to find a way to do it. I want the select_cc

Eli, Thanks for the tips. I've been able to get something working using a custom instruction inserter, however, I'm still having the problem of linking together the setcc and the select_cc commands. I want to turn the setcc into a comparison and use the results in the select_cc register. However, the comparison information is in the select_cc instruction and the result of the comparison