search for: thresholds

Rers: I have some ecological data (stream velocity vs. % cover of submerged weeds) that shows strong evidence of a thresholding step-function, e.g. below some velocity, % cover ranges from 0% to 100% (with no apparent relationship to velocity within this range of velocities), but above a certain "threshold" velocity, the % cover does not appear to exceed, say, 10%. There are good

Hi ! I am connecting to Adit 600 thru a T100P card I have configured 1-16 FXS channels and 17-24 FXO. Everything looks fine on Asterisk side I get a tone on all FXS channels, but when I try to dialout thru one of the FXO channels 17-24 it doesn't connect to the POTS line and echoes back my voice. I use fxsls and fxols for the T1 channels and ls on Adit side. Whats wrong here ? here is my Adit

input: > values [1] 0.854400 1.648465 1.829830 1.874704 7.670915 7.673585 7.722619 > thresholds [1] 1 3 5 7 9 expected output: [1] 1 4 4 4 7 That is, need a vector of indexes of the maximum value below the threshold. e.g. First element is "1", because value[1] is the largest below threshold "1". Second element is "4", because value[4] is the largest belo...

For the analysis of financial data wih a large variance, what is the best way to select an outlier threshold? Listed below, is there a best method to select an outlier threshold and how does R calculate it? In R, how do you find the outlier threshold through an interquartile range? In R, how do you find the outlier threshold using the hist command? In R, how do you find the outlier threshold

Are there any easy to use snmp(mainly temperature monitoring) tools around for Centos -4? This is on a Dell sc420 server. -- Computer House Calls, Networks, Security, Web Design: http://www.emmanuelcomputerconsulting.com What businesses are in Brunswick, Maryland? Check Brunswick First! http://www.checkbrunswickfirst.com My "Foundation" verse: Isa 54:17 No weapon that is formed

Dear List, I have a list of length ~1000 filled with numerics. I need to replace the elements of this list that are above a certain numerical threshold with the value of the threshold. e.g example=list(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1) threshold=5 <magic code goes here> example=(1, 2, 3, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 4, 3, 2, 1). I have written a crude

Dear List, I have encountered an odd problem that I cannot understand. It stems from the calculation of true and false positives based on two input vectors x and y based on different thresholds of x, extracted using the quantile function. I am in certain cases getting different values of true positives for the same threshold value when the threshold was found under different quantiles (e.g. the threshold value Z was found with quantile(x, probs = 0.045) and quantile(x, probs = 0.05). The...

My UPS failed and my server took an 'unscheduled outage' a few weeks ago. The only casualty appears to be a volume I used for backup. I usually maintain data on multiple hard disks, but in this case I errantly had some data (of marginal value) on this file system. At this point, the data is not worth enough for me to send the drive out for data recovery, but it's worth enough to

Hello, I would like to test some data to see whether it has the shape of a step function (i.e. y1 up until x_th and then y2 where x_th is the threshold). The threshold x_th is unknown and the x values can only take discrete values (0,1,2,3,4). An example would be: data<- data.frame(x=1:20,y=c(rnorm(10),rnorm(10,10))) I was thinking along the lines of fitting some sort of piiecewise linear

...makes sense to >> me to differentiate the threshold for full unroll and the dynamic/partial >> case. >> > > There is one issue that makes these not orthogonal. > > If even *static* profile hints will reduce some of the code size increase > caused by higher unrolling thresholds for non-cold code, we should factor > that into the tradeoff in picking where the threshold goes. > > However, getting PGO into the full unroller is currently challenging > outside of the new pass manager. We already have some unfortunate hacks > around this in LoopUnswitch that are...

...is (to some extent) orthogonal, and it makes sense to me > to differentiate the threshold for full unroll and the dynamic/partial case. > There is one issue that makes these not orthogonal. If even *static* profile hints will reduce some of the code size increase caused by higher unrolling thresholds for non-cold code, we should factor that into the tradeoff in picking where the threshold goes. However, getting PGO into the full unroller is currently challenging outside of the new pass manager. We already have some unfortunate hacks around this in LoopUnswitch that are making the port of it to...

...hours > threshold[[1]] <- 47 > threshold Time difference of 47 hours In that way I can use my original statement: > my.dt - my.dt[1] < threshold [1] TRUE TRUE TRUE TRUE TRUE In summary, my question is: Is there a more elegant way than my hack from above to specify time difference thresholds of for example 47 hours, or of 246 seconds, or of 33 days and 5 hours? This could be using as.difftime or using other means. Thanks in advance, Lukas P.S. > sessionInfo() R version 2.4.1 (2006-12-18) i386-pc-mingw32 locale: LC_COLLATE=English_United States.1252;LC_CTYPE=English_United State...

hi, In "LoopUnrollPass.cpp", when trying to reduce unroll count to meet the unroll threshold requirement in line 200 and line 206, variable "CurrentThreshold" is used in the computation, instead of the variable "Threshold", which is defined by: // Determine the current unrolling threshold. While this is normally set // from UnrollThreshold, it is overridden to

# I have a dataframe in the following form: track <- c(rep('A', 3), rep('B', 4), rep('C', 4)) value <- c(0.15, 0.25, 0.35, 0.05, 0.99, 0.32, 0.13, 0.80, 0.75, 0.60, 0.44) df <- data.frame(track=factor(track), value=value) #> print(df) #track value #1 A 0.15 #2 A 0.25 #3 A 0.35 #4 B 0.05 #5 B 0.99 #6 B 0.32 #7 B 0.13

...thogonal, and it makes sense to me > to differentiate the threshold for full unroll and the dynamic/partial case. > > > There is one issue that makes these not orthogonal. > > If even *static* profile hints will reduce some of the code size increase > caused by higher unrolling thresholds for non-cold code, we should factor > that into the tradeoff in picking where the threshold goes. > > However, getting PGO into the full unroller is currently challenging > outside of the new pass manager. We already have some unfortunate hacks > around this in LoopUnswitch that are...

...me extent) orthogonal, and it makes sense to me to differentiate the threshold for full unroll and the dynamic/partial case. > > There is one issue that makes these not orthogonal. > > If even *static* profile hints will reduce some of the code size increase caused by higher unrolling thresholds for non-cold code, we should factor that into the tradeoff in picking where the threshold goes. > > However, getting PGO into the full unroller is currently challenging outside of the new pass manager. We already have some unfortunate hacks around this in LoopUnswitch that are making the por...

Dear All, Could?someone please suggest me the way to calculate the optimal threshold in POT method via?any available ?packages in R? Thanks, Fir

> On Jan 30, 2017, at 4:56 PM, Dehao Chen <dehao at google.com> wrote: > > > > On Mon, Jan 30, 2017 at 3:56 PM, Chandler Carruth <chandlerc at google.com <mailto:chandlerc at google.com>> wrote: > On Mon, Jan 30, 2017 at 3:51 PM Mehdi Amini via llvm-dev <llvm-dev at lists.llvm.org <mailto:llvm-dev at lists.llvm.org>> wrote: >> On Jan 30,

Hi All, I am working on some ordinal logistic regresssions using LRM in the Design package. My response variable has three categories (1,2,3) and after using the creating my model and using a call to predict some values and I wanted to use a simple .5 cut-off to classify my probabilities into the categories. I had two questions: a) first, I am having trouble directly accessing the

For a financial data set with large variance, I'm trying to find the outlier threshold of one variable "x" over a two year period. I qqplot(x2001, x2002) and found a normal distribution. The latter part of the normal distribution did not look linear though. Is there a suitable method in R to find the outlier threshold of this variable from 2001 and 2002 in R?