KNN | To know more about this project , please refer

 by   wihoho Java Version: Current License: No License

kandi X-RAY | KNN Summary

kandi X-RAY | KNN Summary

KNN is a Java library. 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.

This is a course assignment, and we are supposed to implement the basic K-Nearest Neighbor algorithm. Below are the basic classes deisgned in the initial phase:.
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            kandi-support Support

              KNN has a low active ecosystem.
              It has 23 star(s) with 24 fork(s). There are 1 watchers for this library.
              OutlinedDot
              It had no major release in the last 6 months.
              There are 0 open issues and 1 have been closed. On average issues are closed in 9 days. 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 0 bugs and 0 code smells.

            kandi-Security Security

              KNN has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              KNN code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              KNN does not have a standard license declared.
              Check the repository for any license declaration and review the terms closely.
              OutlinedDot
              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.
              KNN saves you 114 person hours of effort in developing the same functionality from scratch.
              It has 289 lines of code, 15 functions and 9 files.
              It has low code complexity. Code complexity directly impacts maintainability of the code.

            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.
            • Test program
            • Computes the knn for the given dataset
            • Read test file from file
            • Read train records from a file
            • Get the most likely label from the array of neighbors
            • Outputs the predictions file
            • Finds the nearest neighbors of the training record
            • Extract group name from file path
            • Returns the KNN distance between two records
            • Returns the Euclidean distance between two records
            • Returns the distance between two records
            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

            QUESTION

            Pairing control group uniquely to test group using KNN in Python
            Asked 2022-Apr-04 at 13:56

            I want to find unique pairs for a test group, meaning each individual in the control group should only be chosen once. I have Gender, Age, and Education available to match them. I segmented the groups for Gender and Education since they are binary categories. Afterward, I want to find the best match in Age to a certain test individual - therefore the KNN approach with 1 nearest neighbor. The dummyData I'm using is available here.

            The following part is the initialization and the segmentation:

            ...

            ANSWER

            Answered 2022-Apr-04 at 13:56

            I ended up using only age in the KNN matching (and manually matching on the binary features), doing the following solution:

            Source https://stackoverflow.com/questions/69734503

            QUESTION

            how to save tensorflow model to pickle file
            Asked 2022-Mar-16 at 07:12

            I want to save a Tensorflow model and then later use it for deployment purposes. I dont want to use model.save() to save it because my purpose is to somehow 'pickle' it and use it in a different system where tensorflow is not installed, like:

            ...

            ANSWER

            Answered 2022-Mar-16 at 07:12

            Using joblib seems to work on TF 2.8 and since you have a very simple model, you can train it on Google Colab and then just use the pickled file on your other system:

            Source https://stackoverflow.com/questions/71492778

            QUESTION

            KNN Classifier Python
            Asked 2022-Mar-08 at 11:14

            I am currently using the scikit learn module in order to help with a crime prediction problem. I am having an issue batch coding the entire Dataframe that I have with the knn.predict method.

            How can I batch code the entire two columns of my Dataframe with the knn.predict() method in order to store in another Dataframe the output?

            ...

            ANSWER

            Answered 2022-Mar-08 at 03:29

            Let the new set to be predicted is 'knn_df_predict'. Assuming same column names,try the following lines of code :

            Source https://stackoverflow.com/questions/71389535

            QUESTION

            What is the difference between standardisation and normalisation in Machine Learning?
            Asked 2022-Mar-01 at 07:19

            I would like to train a SVM and KNN model, I was wondering if anyone could advise for training a Machine Learning model which processing is more appropriate?

            ...

            ANSWER

            Answered 2022-Mar-01 at 07:19

            Normalization rescales the values into a range of [0,1], Standardization rescales data to have a mean (μ) of 0 and standard deviation (σ) of 1.

            It's recommended to use Standardization before applying KNN, check this article for some explanation.

            Source https://stackoverflow.com/questions/71301132

            QUESTION

            Creating nested columns in python dataframe
            Asked 2022-Feb-20 at 15:56

            I have 3 columns namely Models(should be taken as index), Accuracy without normalization, Accuracy with normalization (zscore, minmax, maxabs, robust) and these are required to be created as:

            ...

            ANSWER

            Answered 2022-Feb-20 at 13:01

            There's a dirty way to do this, I'll write about it till someone answers with a better idea. Here we go:

            Source https://stackoverflow.com/questions/71193085

            QUESTION

            Best way to use KNNimputer?
            Asked 2022-Feb-20 at 13:57

            I want to impute missing values by KNN, and I use this method to select best K:

            ...

            ANSWER

            Answered 2022-Feb-20 at 13:52

            There is actually one way to check best K where there is no need to split between train & test.

            The method is to study the Density with different K numbers, but it is just for One variable (I will select the one with more imputations needed). The one nearest to original distribution is the best to select.

            Source https://stackoverflow.com/questions/70831736

            QUESTION

            confusionMatrix for knn classification in R
            Asked 2022-Feb-11 at 08:24

            I wanted to use the optimal k value to conduct kNN clasification and predict the dependent variable diabetes in test set using train set and compare the results with the real values.

            I've already got optimal k value and got the accruacy already. After that, I wanted to compare the results with the real value with using confussionMatrix but I got the problem with the different length.

            I've already checked that nrow and length quantities are same(with 74) but it still have same problem.

            Could you help me to overcome this problem?

            My codes are as like below

            ...

            ANSWER

            Answered 2022-Feb-11 at 05:27

            I think you are looking to this:

            Source https://stackoverflow.com/questions/71075383

            QUESTION

            'train' and 'class' have different lengths error in R
            Asked 2022-Feb-10 at 15:00

            I just wanted to conduct a kNN classification with the situation when k is 3. I would like to predict the dependent variable “diabetes” in valid set using train set and calculate the accuracy.

            But I faced to the error message with

            Error in knn(train = TrainXNormDF, test = ValidXNormDF, cl = MLdata2[, : 'train' and 'class' have different lengths

            I can't solve this problem with get approach with

            ...

            ANSWER

            Answered 2022-Feb-10 at 14:48

            Your cl variable is not the same length as your train variable. MLValidY only has 74 observations, while TrainXNormDF has 224.

            cl should provide the true classification for every row in your training set.

            Furthermore, cl is a data.frame instead of a vector.

            Try the following:

            Source https://stackoverflow.com/questions/71066938

            QUESTION

            embedding vector search efficient algorithm
            Asked 2022-Jan-19 at 22:35

            background: I have a machine learning model in which given an object returns an embedding vector with dimension d, the model is trained in a way such that the semantic similarity of two embedding vectors is very close. Now, the verification process is relatively simple, I can take something like the cosine similarity of the two vectors. For recognition, it's a little bit complicated, either I can loop through all the anchor documents and compare the cosine similarity, or use something like kNN (online).

            problem: I have a list of embedding vectors, each vector has a dimension d, with length N. Each vector contains floating-point data.

            What will be an efficient data structure + algorithm that can do the following:

            1. Can add a new vector with a unique ID to the list efficiently (<= logarithmic complexity)
            2. Search with a random vector in the list, and retrieve top k vectors, such that the Manhattan distance / L1 norm is minimum for those vectors efficiently (hopefully, <= logarithmic complexity).

            example:

            ...

            ANSWER

            Answered 2021-Dec-25 at 05:09

            I think Faiss is exactly what you are looking for. The Github page is here, if you are interested in the implementation details (this is pretty technical) see here, and the tutorial is here.

            Source https://stackoverflow.com/questions/69820812

            QUESTION

            Passing a vector from C# to Python
            Asked 2022-Jan-07 at 01:26
            1. I use Python.Net for C# interaction with Python libraries. I solve the problem of text classification. I use FastText to index and get the vector, as well as Sklearn to train the classifier (Knn).During the implementation, I encountered a lot of problems, but all were solved, with the exception of one. After receiving the vectors of the texts on which I train Knn, I save them to a separate text file and then, if necessary, use it.
            ...

            ANSWER

            Answered 2021-Dec-10 at 21:59

            I solved this issue for a couple of days and each time I thought it was worth reading the documentation on python.net . As a result, I found a solution and it turned out to be quite banal, it is necessary to represent X_vec not as a float[] , but as a List

            Source https://stackoverflow.com/questions/70310329

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

            Vulnerabilities

            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 maven.apache.org. For Gradle installation, please refer gradle.org .

            Support

            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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