random-sampling | Java 8 for the problem of random | Machine Learning library

 by   gstamatelat Java Version: 0.28 License: MIT

kandi X-RAY | random-sampling Summary

kandi X-RAY | random-sampling Summary

random-sampling is a Java library typically used in Artificial Intelligence, Machine Learning, Example Codes applications. random-sampling has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can download it from GitHub, Maven.

A collection of algorithms in Java 8 for the problem of random sampling with a reservoir. Reservoir sampling is a family of randomized algorithms for randomly choosing a sample of k items from a list S containing n items, where n is either a very large or unknown number. Typically n is large enough that the list doesn't fit into main memory. [1] In this context, the sample of k items will be referred to as sample and the list S as stream. This package distinguishes these algorithms into two main categories: the ones that assign a weight in each item of the source stream and the ones that don't. These will be referred to as weighted and unweighted random sampling algorithms respectively. In unweighted algorithms, each item in the stream has probability k/n in appearing in the sample. In weighted algorithms this probability depends on the extra parameter weight. Each algorithm may interpret this parameter in a different way, for example in [2] two possible interpretations are mentioned.

            kandi-support Support

              random-sampling has a low active ecosystem.
              It has 31 star(s) with 6 fork(s). There are 2 watchers for this library.
              It had no major release in the last 12 months.
              There are 8 open issues and 39 have been closed. On average issues are closed in 242 days. There are no pull requests.
              It has a neutral sentiment in the developer community.
              The latest version of random-sampling is 0.28

            kandi-Quality Quality

              random-sampling has no bugs reported.

            kandi-Security Security

              random-sampling has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

            kandi-License License

              random-sampling is licensed under the MIT License. This license is Permissive.
              Permissive licenses have the least restrictions, and you can use them in most projects.

            kandi-Reuse Reuse

              random-sampling releases are available to install and integrate.
              Deployable package is available in Maven.
              Build file is available. You can build the component from source.
              Installation instructions are not available. Examples and code snippets are available.

            Top functions reviewed by kandi - BETA

            kandi has reviewed random-sampling and discovered the below as its top functions. This is intended to give you an instant insight into random-sampling implemented functionality, and help decide if they suit your requirements.
            • Feeds an item into the stream
            • Get the sample size
            • Overrides the superclass method
            • Get the sample size
            • Feeds an item
            • Feed the elements of an iterable
            • Calls the superclass method
            • Feed the elements of an iterable
            • Synchronized
            • Feed the elements of an iterable
            • The main implementation of this algorithm
            • Get the sample size
            • Performs the final transformation
            • Returns true if two Iterators are equal
            • Returns the number of items in the algorithm
            • Returns a set of all the characteristics of this Collector
            • Returns the hashCode value for the object
            • Returns a string representation of this object
            • Folds a function to fold a value into one
            • Run VitterZ sampling
            • Run a test sample
            • Command - line test
            • Main method
            • Generates a key for the given weight
            • Generate a key for the given weight
            • Checks if two collections contain the same elements
            • Runs a sample
            • Main method for testing
            • Returns an unweighted selection of the population
            • Returns a set of all the characteristics of this collector
            Get all kandi verified functions for this library.

            random-sampling Key Features

            No Key Features are available at this moment for random-sampling.

            random-sampling Examples and Code Snippets

            No Code Snippets are available at this moment for random-sampling.

            Community Discussions


            Rowwise, how to specify which column a certain value is from?
            Asked 2020-Nov-10 at 13:34

            I have a dataframe with several columns, and I create a new column which randomly samples a single value from either of the other columns. How can I trace back to tell which column the value came from?

            I've seen the exact same question and solution here, but it's in python, and couldn't find an R equivalent.

            Data 1 :: each row has different values across columns ...


            Answered 2020-Nov-10 at 11:18


            Stratified random sampling from data frame_follow up
            Asked 2020-Jun-24 at 14:36

            I am trying to randomly sample 50% of the data for each of the group following Stratified random sampling from data frame. A reproducible example using mtcars dataset in R looks like below. What I dont understand is, the sample index clearly shows a group of gear labeled as '5', but when the index is applied to the mtcars dataset, the sampled data mtcars2 does not contain any record from gear='5'. What went wrong? Thank you very much.



            Answered 2020-Jun-24 at 14:36

            I think the approach you've done creates a number 1:length(mtcars$gear) for each gear group so you will have repeat row numbers for each group. Then, when you subset it isn't working, see in your output above you have row number 7 in both gear group 3 and 4.

            Base R

            I would use split first to split by gear:

            Source https://stackoverflow.com/questions/62555491


            Stratified random sample to match a different table in BigQuery
            Asked 2020-Feb-10 at 22:55

            This should be a simple extension of this question, but my result is not correct and I can't figure it out. I'd like the proportions in the table I'm drawing from to match the proportions of another table. I'd also like to have it stratified by two categories. I think it should be something like:



            Answered 2020-Feb-10 at 22:55

            I think your rand() comparison is off:

            Source https://stackoverflow.com/questions/60158984


            Pandas Replace NaN values based on random sample of values conditional on another column
            Asked 2020-Jan-30 at 18:51

            Say I have a dataframe like so:



            Answered 2020-Jan-30 at 18:51
            transform with choice

            I forgo efficiency for readability. Note that I generate a random choice for each row but only pick the number I need to fill in the nulls. Theoretically, I can make it such that I only pick random numbers for those missing values.

            Source https://stackoverflow.com/questions/59992059


            Elasticsearch random selection based on weighting out of 100
            Asked 2019-Jul-18 at 16:36

            I have been running a Rails site for a couple of years and some articles are being pulled from the DB based on a weight field. The data structure is:



            Answered 2019-Jul-18 at 16:36

            Your elasticsearch query is correct and you don't need scripts to perform what you want. It is just a problem with probabilities. For a short answer, replace the multiplier (i.e., field_value_factor) for the weight of 50 by 40 and the multiplier for the weight of 25 by 30 and you will get the expected result.

            Basically, the problem is that multiplying a random value by a weight is not producing a weighted distribution where the weight is the multiplier. The multiplier can be derived from the weight, but there are not the same.

            I can give you an example with your case. For the weight 50, if the random value is above 0.5, it will necessarily have the highest score (0.5 * 50 >= 1 * 25). Since a value of 0.5 as a probability of 50%, you now for sure that the item with weight 50 will be returned at least half of the time.

            But even if the random value for weight 50 is below 0.5, it can still be selected. In fact its probability to be selected in this case is 1/3.

            I'm just a bit surprised by your result because its probability should be more like 66% (i.e., 50% + 50%/3) and the other probabilities should be around 16.5%. Maybe try to increase the number of runs to be sure.

            Solution for any weight using script_score

            You do not need to compute the multiplier with this solution but you must provide a range, e.g., min_value and max_value for each document. max_value is the sum of min_value and the document wight and min_value is the cumulative sum of the weight of the previous documents.

            If you have for example 4 documents with weights 5, 15, 30, 50, then the ranges could be :

            • Documents with weight 5 : min_value = 0, max_value = 5
            • Documents with weight 15 : min_value = 5, max_value = 5+15 = 20
            • Documents with weight 30 : min_value = 20, max_value = 20+30 = 50
            • Documents with weight 30 : min_value = 50, max_value = 50+50 = 100

            The corresponding elasticsearch query is

            Source https://stackoverflow.com/questions/57043856


            Haskell package for sampling from standard probability distributions
            Asked 2019-May-21 at 03:36

            I would like to do some Monte Carlo analysis in Haskell. I would like to be able to write code like this:



            Answered 2019-May-10 at 21:41

            Well, if you want to be able to write code like this:

            Source https://stackoverflow.com/questions/56084779


            Create new column by sampling bits of other columns
            Asked 2019-Apr-11 at 17:13

            Consider the dataframe containing N columns as shown below. Each entry is an 8-bit integer.



            Answered 2019-Apr-11 at 17:13

            Same logic like before when you do the sample , but here I convert between the binary and decimal twice, with unnesting , then join back the result

            Source https://stackoverflow.com/questions/55637638


            R (Stratified) Random Sampling for Defined Cases
            Asked 2018-Mar-04 at 19:22

            I have a data frame:



            Answered 2018-Mar-04 at 19:04

            You can subset the data first by case ID == 1. To ensure occurrence of 1s and 0s, we use rep function and set replace to False in sample function.
            Here's a solution.

            Source https://stackoverflow.com/questions/49098669


            Efficient sampling of a fixed number of rows in BigQuery
            Asked 2018-Feb-23 at 15:03

            I have a large dataset of size N, and want to get a (uniformly) random sample of size n. This question offers two possible solutions:



            Answered 2018-Feb-23 at 15:03

            I compared the two queries execution times using BigQuery standard SQL with the natality sample dataset (137,826,763 rows) and getting a sample for source_year column of size n. The queries are executed without using cached results.


            Source https://stackoverflow.com/questions/48915647


            How to exactly unselect xy % of grid cells?
            Asked 2017-May-31 at 09:33

            From a 10*10 raster I want to unselect for example 90 percent, that is, 10 percent remain visible. To do this I adapted this code, see below. But there is some variation in the resulting pixels (more then 10 or less then 10 pixels remain). Is there a possibility to set precision of random selection?



            Answered 2017-May-31 at 09:33

            Instead of your runif line, use

            Source https://stackoverflow.com/questions/44280770

            Community Discussions, Code Snippets contain sources that include Stack Exchange Network


            No vulnerabilities reported

            Install random-sampling

            You can download it from GitHub, Maven.
            You can use random-sampling like any standard Java library. Please include the the jar files in your classpath. You can also use any IDE and you can run and debug the random-sampling component as you would do with any other Java program. Best practice is to use a build tool that supports dependency management such as Maven or Gradle. For Maven installation, please refer maven.apache.org. For Gradle installation, please refer gradle.org .


            For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .
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