quantiles | Approximations to Histograms | Cryptography library
kandi X-RAY | quantiles Summary
kandi X-RAY | quantiles Summary
This crate is intended to be a collection of approxiate quantile algorithms that provide guarantees around space and computation. Recent literature has advanced approximation techniques but none are generally applicable and have fundamental tradeoffs. Initial work was done to support internal Postmates projects but the hope is that the crate can be generally useful.
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quantiles Key Features
quantiles Examples and Code Snippets
Lines of Code : 21License : Permissive (MIT)
Copyuse quantiles::greenwald_khanna::*;
let epsilon = 0.01;
let mut stream = Stream::new(epsilon);
let n = 1001;
for i in 1..n {
stream.insert(i);
}
let in_range = |phi: f64, value: u32| {
let lower = ((phi - epsilon) * (n as f64)) as u32;
let use quantiles::misra_gries::*;
let k: usize = 3;
let numbers: Vec = vec![1,3,2,1,3,4,3,1,2,1];
let counts = misra_gries(numbers.iter(), k);
let bound = numbers.len() / (k+1);
let in_range = |f_expected: usize, f_approx: usize| {
f_approx <= f_e use quantiles::CKMS;
let mut ckms = CKMS::::new(0.001);
for i in 1..1001 {
ckms.insert(i as u16);
}
assert_eq!(ckms.query(0.0), Some((1, 1)));
assert_eq!(ckms.query(0.998), Some((998, 998)));
assert_eq!(ckms.query(0.999), Some((999, 999)));
ass Community Discussions
Trending Discussions on quantiles
QUESTION
I'm a newbie to R programming and I have an assignment to submit so my prob is the following: I wrote the following function :
...ANSWER
Answered 2022-Apr-16 at 21:59You could do the following:
QUESTION
I'm trying to use GEKKO to solve quite a large optimization problem locally (with remote=False).
When running the code, I get the error:
...ANSWER
Answered 2022-Mar-22 at 17:29The Windows binary is 32-bit while the Linux, MacOS, and ARM Linux are 64-bit executables when remote=False with Gekko v1.0.2. With remote=True, it runs on a Linux server that has 64 GB of RAM and uses a 64-bit executable. It is running into a memory limit issue with the Windows binary up to 4 GB RAM because of the 32-bit executable. The 64-bit executables have a 16 billion GB capacity (no limit). The 64-bit Windows local executable is a planned development with a future release. The Linux VM or an APM Linux server (such as host IP 10.0.0.10) are options for those who need to solve large problems on a Local Network with a Windows local gekko client.
QUESTION
I have the dataframe below and I create a kable out of this. How could I add commas between numbers every 3 digits?
...ANSWER
Answered 2022-Mar-21 at 16:36You could use the kable format argument, this avoids mucking around with the data prior to putting into the table.
And if you want to clear up the NAs and NaNs you could add in this line of code: options(knitr.kable.NA = '')
QUESTION
I have the shiny app below in which I try to present a descriptives table but my app breaks down with error:
...ANSWER
Answered 2022-Mar-18 at 16:53In the code, there are places where the OP specified , at the end of a line e.g.
QUESTION
I have a simple ggplot boxplot like in this example
...ANSWER
Answered 2022-Mar-17 at 20:42You could do like this: First, calculate the median, first and third quartile (quart1 and quart3) for ToothGrowth, grouped by supp and dose. Afterwards you can fill in the lower and upper rectangles of the boxplots. Since you wanted four different colors, I added four different rectangles.
QUESTION
I have a calculator that iterates a couple of hundred object and produces Nx1 arrays for each of those objects. N here being 1-10m depending on configurations. Right now I am summing over these by using a generator expression, so memory consumption is low. However, I would like to store the Nx1 arrays to file, so I can do other computations.(Compute quantiles, partial sums etc. pandas style) Preferably I would like to use pa.memory_map on a single file (in order to have dataframes not loaded into memory), but I can not see how I can produce such a file without generating the entire result first. (Monte Carlo results on 200-500*10m floats).
If I understand correctly RecordBatchStreamWriter needs a part of the entire table, and I can not produce only a part of it. The parts the calculator produces is the columns, not parts of all columns. Is there any way of writing "columns" one by one? Either by appending, or create an empty arrow file which can be filled? (schema known).
As I see it, my alternative is to write several files and use "dataset" /tabular data to "join" them together. My "other computations" would then have to filter or pull parts into memory as I can`t see in the docs that "dataset()" work with memory_map.The result set is to big to fit in memory. (At least on the server it is running on)
I`m on day 2 of digging the docs and trying to understand how it all works, so apologies if the "lingo" is not all correct.
On further inspection, it looks like all files used in datasets() must have same schema, so I can not split "columns" in separate files either, can I..
EDIT After wrestling with this library, I now produce single column files which I later combine in a single file. However, in following the suggested solution visible memory consumption (task manager) skyrockets in the step of combining the files. I would expect peaks for every "rowgroup" or combined recordbatch, but instead steadily increase to use all memory. A snip of this step:
...ANSWER
Answered 2022-Feb-25 at 08:06You could do the write in two passes.
- First, write each column to its own file. Make sure to set a row group size small enough that a table consisting of one row group from each file comfortably fits into memory.
- Second, create a streaming reader for each file you created and one writer. Read a single row group from each one. Create a table by combining all of the partial columns and write the table to your writer. Repeat until you've exhausted all your readers.
I'm not sure that memory mapping is going to help you much here.
QUESTION
I'm following the vegan tutorial on procrustes analysis:
https://www.rdocumentation.org/packages/vegan/versions/2.4-2/topics/procrustes
...ANSWER
Answered 2022-Jan-27 at 03:09It's not exactly the same, but I think it's pretty close.
I brought the varespec data into Python for this. Then tried to mimic the actions that the vegan package (and your code) took. Along those lines, I stuck to the object names you used (for the most part).
There has to be an easier way to do this.
QUESTION
When a file is read through fread, the columns may be read as integer64 (correctly so), but when these are multiplied with numeric, they are not upcasted to numeric (as in C++ or integers in R). While this is a documented behavior in bit64 package. But it is not intuitive, when numbers are multiplied etc. integer64 behaves differently compared to integer.
Also, integer64 when divided against integer gives a numeric variable. So the behavior is very bizarre !
Should we then always fread using colClasses = numeric for columns to be used in arithmeric expressions with numeric etc ?
ANSWER
Answered 2022-Feb-25 at 10:12This is the documented behaviour of bit64 package, see Arithmetic precision and coercion in ?bit64:
The fact that we introduce 64 bit long long integers – without introducing 128-bit long doubles – creates some subtle challenges
The multiplication operator * coerces its first argument to integer64 but allows its second argument to be also double: the second argument is internaly coerced to 'long double' and the result of the multiplication is returned as integer64
QUESTION
so I'm a total newbie with R, and in one of our final assignments, we want to do quantiles per country and per column on this data.
We have tried to do it with the apply function, with a loop, but we have not been able to crack it yet:
...ANSWER
Answered 2022-Feb-21 at 16:39We can do a group by operation and then get the quantile on each of those numeric columns by looping across the columns and then return a list object which can be converted to columns with unnest_wider etc.
QUESTION
I have a csv file with timeseries data of hourly PV production for a whole year. I want to get the quantiles (from 0.1 to 0.9) for each hour in order to represent their behavior every 24 h for all days of the year.
It seemed to work ok at first, but then I realized that in my some of the quantiles I have values different than zero (0) at 00:00 which is impossible as my dataset does not have such values in that time of the day (I triple checked it).
Where is the mistake?
My code:
...ANSWER
Answered 2022-Jan-30 at 19:43You calculate the quantiles by testing where the relevant time string is part of the date string in the input data. The date string consists of year, month, day, hour, minutes and seconds. The time string, however, only consists of the hour and minutes. With the input dates have all their seconds set to zero, your input date timestamps are like "01:00:00", "02:00:00" and so. Which is fine, but when comparing the strings (with in), there is a problem for the comparison time string "00:00": it fits all date strings: it just matches hours and minutes with minutes and seconds. E.g., "00:00" is in "02:00:00" evaluates to True. As a result, all(?) input data will be added to the "00:00" timestamp, instead of no input data.
So, an easy solution is to add seconds to your comparison times:
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