knn | Advanced Data Management assignment | Machine Learning library

 by   allenlsy Java Version: Current License: No License

kandi X-RAY | knn Summary

kandi X-RAY | knn Summary

knn is a Java library typically used in Artificial Intelligence, Machine Learning applications. knn has no bugs, it has no vulnerabilities and it has low support. However knn build file is not available. You can download it from GitHub.

Download the latest source code from:

            kandi-support Support

              knn has a low active ecosystem.
              It has 4 star(s) with 7 fork(s). There are 2 watchers for this library.
              It had no major release in the last 6 months.
              knn has no issues reported. There are no pull requests.
              It has a neutral sentiment in the developer community.
              The latest version of knn is current.

            kandi-Quality Quality

              knn has no bugs reported.

            kandi-Security Security

              knn has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

            kandi-License License

              knn does not have a standard license declared.
              Check the repository for any license declaration and review the terms closely.
              Without a license, all rights are reserved, and you cannot use the library in your applications.

            kandi-Reuse Reuse

              knn releases are not available. You will need to build from source code and install.
              knn has no build file. You will be need to create the build yourself to 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 knn and discovered the below as its top functions. This is intended to give you an instant insight into knn implemented functionality, and help decide if they suit your requirements.
            • The main classifier
            • Display usage message
            • Test the test dataset
            • Initialize the LSH Record
            • Create a training dataset
            • Prints usage
            • Classify a record
            • Hashing function
            • Initialize the LSH dataset
            • Display usage
            • Train the training algorithm
            • Hashes a single lsh record
            • Returns a hash value for the given hash
            • Returns the number of trainDS
            • Performs training on the model
            • Returns the size of trainDS
            • Computes the hamming function
            • Returns a string representation of this dataset
            • Compute the euclidean distance
            • Main training algorithm
            • The cosine function
            • Compares this object to the specified value
            Get all kandi verified functions for this library.

            knn Key Features

            No Key Features are available at this moment for knn.

            knn Examples and Code Snippets

            No Code Snippets are available at this moment for knn.

            Community Discussions


            Does deleting a variable before assigning it to another value solves any memory issues?
            Asked 2021-Jun-14 at 17:26

            I was going through a college assignment on KNN given in python and in that assignment there was one block of code where they delete X_train,Y_train,X_test and Y_test variables before assigning those variables to other data. And in the comments they added that it prevents memory issues.



            Answered 2021-Jun-14 at 17:23

            Both examples accomplish the same thing - they decrease the reference count of the value "any_dataset" by one. Using del does this explicitly, overwriting a variable does this implicitly. When a value has zero references to it, it will be garbage-collected at some point in the future.

            This being the case, I can't see any "memory issues" being prevented by doing it one way or the other.

            Further reading material:



            How can I optimize the code to make one df
            Asked 2021-Jun-13 at 16:33

            I have some CSV files. These files consist of some rows and columns. First, I filtered the file (after reading based on 2 conditions) and then calculate the correlation using df.corr().



            Answered 2021-Jun-13 at 16:33


            semantic content recommendation system with Amazon SageMaker, storing in S3
            Asked 2021-Jun-07 at 04:41

            I am fairly new to AWS and Sagemaker and have decided to follow some of the tutorials Amazon has to familiarize myself with it. I've been following this one (tutorial) and I've realized that it's an older tutorial using Sagemaker v1. I've been able to look up and change whatever is needed for the tutorial to work in v2 but I became stuck at this part for storing the training data in a S3 bucket to deploy the model.



            Answered 2021-Jun-07 at 02:39

            It looks like they've left some of the code out, or changed the terminology and left in predictions by accident. predictions is an object that is defined on this page

            You'll have to work out what predictions is in your case.



            Error "Unknown label type: 'continuous'" when I use IterativeImputer with KNeighborsClassifier
            Asked 2021-Jun-05 at 18:31

            I want to do a multiple imputation with IterativeImputer.

            Here is the dataset (the original is from :


            The variables to impute are "educ" and "ses". As they are categorical I've choose to use a classifier (KNeighborsClassifier from sklearn). Predictors are continuous (except "sex").

            This is the code :



            Answered 2021-Jun-05 at 18:31

            I just understood why it does not works. It's because IterativeImputer works only for continuous variables. So, apparently you can't apply multiple imputation for continuous variables with IterativeImputer. There is discussion about this here.

            I saw it's possible to do simple imputation with categorical variables in python. However, it does not seem possible to do multiple imputation with this type of variables (anyway, I did not find).



            AttributeError: 'dict' object has no attribute 'data'
            Asked 2021-Jun-05 at 17:06

            An error occurred while executing the KNN algorithm. I don't know where the error occurred. Can anyone help me? Please. There is a code below. I don't know why, but the code was cut.



            Answered 2021-Jun-05 at 17:06


            Speed up and scheduling with OpenMP
            Asked 2021-Jun-01 at 15:53

            i'm using OpenMP for a kNN project. The two parallelized for loops are:



            Answered 2021-Jun-01 at 10:36

            Why the 16 Threads case differs so much from the others? I'm running the algorithm on a Google VM machine with 24 Threads and 96 GB of ram.

            As you have mentioned on the comments:

            It's a Intel Xeon CPU @2.30 GHz, 12 physical core

            That is the reason that when you moved to 16 thread you stop (almost) linearly scaling, because you are no longer just using physical cores but also logic cores (i.e., hyper-threading).

            I expected that static would be the best since the iterations takes approximately the same time, while the dynamic would introduce too much overhead.

            Most of the overhead of the dynamic distribution comes from the locking step performed by the threads to acquire the new iteration to work with. It just looks to me that there is not much thread locking contention going on, and even if it is, it is being compensated by better loading balancing achieved with the dynamic scheduler. I have seen this exact pattern before there is not wrong with it.

            Aside note you can transform your code into:



            Heroku "Missing required flag -a --app" error after succesfully running heroku container:push web and heroku container:release web
            Asked 2021-May-31 at 00:47

            I have a Docker container which I'm trying to deploy as a Heroku application. My application is called



            Answered 2021-May-31 at 00:47

            Since you do not have a detailed log file, it is difficult to troubleshoot here. You can try doing this first to pinpoint the exact issue:



            Find nearest point using PostGIS
            Asked 2021-May-27 at 16:34

            On PostgreSQL 12 with PostGIS extension, I have two tables defined as follows:



            Answered 2021-May-19 at 19:37

            Processing records 1 by 1, in a loop, induces a lot of network traffic to the DB.
            Instead, try to update all entries at once, in a single statement (which you can send from the pyton script if you wish).



            GridSearchCV, Data Leaks & Production Process Clarity
            Asked 2021-May-27 at 06:18

            I've read a bit about integrating scaling with cross-fold validation and hyperparameter tuning without risking data leaks. The most sensical solution I've found (according to my knowledge) involves creating a pipeline that includes the scalar and GridSeachCV, for when you want to grid search and cross-fold validate. I've also read that, even when using cross-fold validation, it is useful to, at the very beginning, create a hold-out test set for an additional, final evaluation of your model after hyperparameter tuning. Putting that all together looks like this:



            Answered 2021-May-27 at 06:18

            GridSearchCV will help you find the best set of hyperparameter according to your pipeline and dataset. In order to do that it will use cross validation (split the your train dataset into 5 equal subset in you case). This means that your best_estimator will be trained on 80% of the train set.

            As you know the more data a model see, the better its result is. Therefore once you have the optimal hyperparameters, it is wise to retrain the best estimator on all your training set and assess its performance with the test set.

            You can retrain the best estimator using the whole train set by specifying the parameter refit=True of the Gridsearch and then score your model on the best_estimator as follows:



            Submit button onClick function only if all requested inputs are filled
            Asked 2021-May-27 at 06:04

            I have a form with mandatory inputs and added a onClick event listener on the submit button to display a loading git when the program is charging. The problem is that the onClick function is triggered every time the button is clicked and I want it to be only if the form is complete and sent.

            How can I put a condition in my jQuery function for that ?

            Here is the HTML and JS:



            Answered 2021-May-27 at 05:58

            You can use checkValidity() this will return true/false depending on this you can show your loading div.

            Demo Code :


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


            No vulnerabilities reported

            Install knn

            You can download it from GitHub.
            You can use knn 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 knn 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 For Gradle installation, please refer .


            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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