Shuffle | Reusable Card Manager for iOS | User Interface library

 by   jaunesarmiento Swift Version: Current License: MIT

kandi X-RAY | Shuffle Summary

kandi X-RAY | Shuffle Summary

Shuffle is a Swift library typically used in User Interface applications. Shuffle has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. You can download it from GitHub.

Shuffle is a UI framework for your card swiping needs.
Support
    Quality
      Security
        License
          Reuse

            kandi-support Support

              Shuffle has a low active ecosystem.
              It has 6 star(s) with 0 fork(s). There are 1 watchers for this library.
              OutlinedDot
              It had no major release in the last 6 months.
              Shuffle has no issues reported. There are no pull requests.
              It has a neutral sentiment in the developer community.
              The latest version of Shuffle is current.

            kandi-Quality Quality

              Shuffle has no bugs reported.

            kandi-Security Security

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

            kandi-License License

              Shuffle 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

              Shuffle releases are not available. You will need to build from source code and install.
              Installation instructions, examples and code snippets are available.

            Top functions reviewed by kandi - BETA

            kandi's functional review helps you automatically verify the functionalities of the libraries and avoid rework.
            Currently covering the most popular Java, JavaScript and Python libraries. See a Sample of Shuffle
            Get all kandi verified functions for this library.

            Shuffle Key Features

            No Key Features are available at this moment for Shuffle.

            Shuffle Examples and Code Snippets

            Shuffle a batch of tensors .
            pythondot img1Lines of Code : 97dot img1License : Non-SPDX (Apache License 2.0)
            copy iconCopy
            def shuffle_batch(tensors, batch_size, capacity, min_after_dequeue,
                              num_threads=1, seed=None, enqueue_many=False, shapes=None,
                              allow_smaller_final_batch=False, shared_name=None, name=None):
              """Creates batches by   
            Shuffle a batch .
            pythondot img2Lines of Code : 91dot img2License : Non-SPDX (Apache License 2.0)
            copy iconCopy
            def shuffle_batch_join(tensors_list, batch_size, capacity,
                                   min_after_dequeue, seed=None, enqueue_many=False,
                                   shapes=None, allow_smaller_final_batch=False,
                                   shared_name=None, name=Non  
            Shuffle a random index .
            pythondot img3Lines of Code : 75dot img3License : Non-SPDX (Apache License 2.0)
            copy iconCopy
            def index_shuffle(index, seed, max_index):
              """Outputs the position of `index` in a permutation of [0, ..., max_index].
            
              For each possible `seed` and `max_index` there is one pseudorandom permutation
              of the sequence S=[0, ..., max_index]. Instea  

            Community Discussions

            QUESTION

            Columns not properly moving in QTableView (QAbstractTableModel) using beginMoveColumns?
            Asked 2021-Jun-15 at 20:13

            I am trying to use beginMoveColumns to move a single column over in a QTableView, but it doesn't work properly in my example below. The cell selections get shuffled and column widths don't move. Moving rows using the same logic seems to work correctly. What am I doing wrong?

            Video: https://www.screencast.com/t/5UJ0iByZCEE

            ...

            ANSWER

            Answered 2021-Jun-15 at 20:13

            Turns out it was a bug, I made a bug report here: https://bugreports.qt.io/browse/QTBUG-94503

            As a workaround I just clear cell selection on column move, and use this snippet to move column widths

            Source https://stackoverflow.com/questions/67941424

            QUESTION

            Youtube IFrame API Cannot Cue Specific Playlists; no Error?
            Asked 2021-Jun-15 at 13:19

            I've been using the YouTube IFrame API to shuffle multiple of my playlists together. I've got a very bare-bones HTML page with a 'next' and 'previous' button, and a bunch of javascript that loads up and plays videos and handles the button events.

            The general order of events when the script loads is

            ...

            ANSWER

            Answered 2021-Jun-15 at 13:19

            This issue appears to have resolved itself. I suspect it was a bug in the iframe api or maybe the youtube backend which has been fixed by the youtube engineers. So iframe team, if you see this, thanks!

            Source https://stackoverflow.com/questions/67888984

            QUESTION

            Why does Spark perform an unnecessary shuffle during a joinWith on a pre-partitioned dataframe?
            Asked 2021-Jun-15 at 12:49

            This example has been tested with Spark 2.4.x. Let's consider 2 simple dataframes:

            ...

            ANSWER

            Answered 2021-Jun-15 at 12:49

            This seems like a bug introduced by a bug fix in this ticket. The result was wrong for outer joins. Hence the need to add a Project node (packing of the struct) before the Join node.

            However, we end up with this kind of query plan:

            Source https://stackoverflow.com/questions/67400097

            QUESTION

            Move 2nd level sub-array to the top of the 1st level multidimentional array based on value of the sub-array
            Asked 2021-Jun-14 at 21:35

            I'm looping through a multidimensional array and am left with some values.

            This is the complete PHP code.

            ...

            ANSWER

            Answered 2021-Jun-09 at 18:41

            This will reorder $array2 (into a new variable $final). First it assembles a simple array $people of the names, then it reassembles $array2 by prioritizing based on the $people array. Was this what you wanted to accomplish?

            Source https://stackoverflow.com/questions/67909656

            QUESTION

            How to pass Spark configuration parameters to DBT?
            Asked 2021-Jun-14 at 12:34

            I am using DBT to connect to AWS/EMR. I am able to run Spark/SQL queries but where do I set parameters like for example spark.sql.shuffle.partitions, that in normal code you will pass with:

            ...

            ANSWER

            Answered 2021-Jun-14 at 12:34

            As I do not get any answer here, I write what I think is the way at the moment (but repeat, not sure if it is the right way to go):

            Source https://stackoverflow.com/questions/67933530

            QUESTION

            How to calculate the f1-score?
            Asked 2021-Jun-14 at 07:07

            I have a pyTorch-code to train a model that should be able to detect placeholder-images among product-images. I didn't write the code by myself as I am very unexperienced with CNNs and Machine Learning.

            My boss told me to calculate the f1-score for that model and i found out that the formula for that is ((precision * recall)/(precision + recall)) but I don't know how I get precision and recall. Is someone able to tell me how I can get those two parameters from that following code? (Sorry for the long piece of code, but I didn't really know what is necessary and what isn't)

            ...

            ANSWER

            Answered 2021-Jun-13 at 15:17

            You can use sklearn to calculate f1_score

            Source https://stackoverflow.com/questions/67959327

            QUESTION

            What does Collections.shuffle function do
            Asked 2021-Jun-14 at 07:07

            code

            ...

            ANSWER

            Answered 2021-Mar-29 at 09:18

            Collections.shuffle() Randomly permutes the specified list using a default source of randomness. All permutations occur with approximately equal likelihood.

            So if tasks ar shuffled, sometimes task2 run first and then output is different.

            Source https://stackoverflow.com/questions/66851547

            QUESTION

            How to get indices of instances during cross-validation
            Asked 2021-Jun-13 at 17:04

            I am doing a binary classification. May I know how to extract the real indexes of the misclassified or classified instances of the training data frame while doing K fold cross-validation? I found no answer to this question here.

            I got the values in folds as described here:

            ...

            ANSWER

            Answered 2021-Jun-13 at 17:04

            From cross_val_predict you already have the predictions. It's a matter of subsetting your data frame where the predictions are not the same as your true label, for example:

            Source https://stackoverflow.com/questions/67956643

            QUESTION

            From train test split to cross validation in sklearn using pipeline
            Asked 2021-Jun-13 at 15:49

            I have the following piece of code:

            ...

            ANSWER

            Answered 2021-Jun-13 at 15:49

            Pipeline is used to assemble several steps such as preprocessing, transformations, and modeling. StratifiedKFold is used to split your dataset to assess the performance of your model. It is not meant to be used as a part of the Pipeline as you do not want to perform it on new data.

            Therefore it is normal to perform it out of the pipeline's structure.

            Source https://stackoverflow.com/questions/67956414

            QUESTION

            Spark executors and shuffle in local mode
            Asked 2021-Jun-12 at 16:13

            I am running a TPC-DS benchmark for Spark 3.0.1 in local mode and using sparkMeasure to get workload statistics. I have 16 total cores and SparkContext is available as

            Spark context available as 'sc' (master = local[*], app id = local-1623251009819)

            Q1. For local[*], driver and executors are created in a single JVM with 16 threads. Considering Spark's configuration which of the following will be true?

            • 1 worker instance, 1 executor having 16 cores/threads
            • 1 worker instance, 16 executors each having 1 core

            For a particular query, sparkMeasure reports shuffle data as follows

            shuffleRecordsRead => 183364403
            shuffleTotalBlocksFetched => 52582
            shuffleTotalBlocksFetched => 52582
            shuffleLocalBlocksFetched => 52582
            shuffleRemoteBlocksFetched => 0
            shuffleTotalBytesRead => 1570948723 (1498.0 MB)
            shuffleLocalBytesRead => 1570948723 (1498.0 MB)
            shuffleRemoteBytesRead => 0 (0 Bytes)
            shuffleRemoteBytesReadToDisk => 0 (0 Bytes)
            shuffleBytesWritten => 1570948723 (1498.0 MB)
            shuffleRecordsWritten => 183364480

            Q2. Regardless of the query specifics, why is there data shuffling when everything is inside a single JVM?

            ...

            ANSWER

            Answered 2021-Jun-11 at 05:56
            • executor is a jvm process when you use local[*] you run Spark locally with as many worker threads as logical cores on your machine so : 1 executor and as many worker threads as logical cores. when you configure SPARK_WORKER_INSTANCES=5 in spark-env.sh and execute these commands start-master.sh and start-slave.sh spark://local:7077 to bring up a standalone spark cluster in your local machine you have one master and 5 workers, if you want to send your application to this cluster you must configure application like SparkSession.builder().appName("app").master("spark://localhost:7077") in this case you can't specify [*] or [2] for example. but when you specify master to be local[*] a jvm process is created and master and all workers will be in that jvm process and after your application finished that jvm instance will be destroyed. local[*] and spark://localhost:7077 are two separate things.
            • workers do their job using tasks and each task actually is a thread i.e. task = thread. workers have memory and they assign a memory partition to each task in order to they do their job such as reading a part of a dataset into its own memory partition or do a transformation on read data. when a task such as join needs other partitions, shuffle occurs regardless weather the job is ran in cluster or local. if you were in cluster there is a possibility that two tasks were in different machines so Network transmission will be added to other stuffs such as writing the result and then reading by another task. in local if task B needs the data in the partition of the task A, task A should write it down and then task B will read it to do its job

            Source https://stackoverflow.com/questions/67923596

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

            Vulnerabilities

            No vulnerabilities reported

            Install Shuffle

            Embedded frameworks require a minimum deployment target of iOS 8.

            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 .
            Find more information at:

            Find, review, and download reusable Libraries, Code Snippets, Cloud APIs from over 650 million Knowledge Items

            Find more libraries
            CLONE
          • HTTPS

            https://github.com/jaunesarmiento/Shuffle.git

          • CLI

            gh repo clone jaunesarmiento/Shuffle

          • sshUrl

            git@github.com:jaunesarmiento/Shuffle.git

          • Stay Updated

            Subscribe to our newsletter for trending solutions and developer bootcamps

            Agree to Sign up and Terms & Conditions

            Share this Page

            share link

            Explore Related Topics

            Consider Popular User Interface Libraries

            Try Top Libraries by jaunesarmiento

            fries

            by jaunesarmientoCSS

            HelloFries

            by jaunesarmientoJavaScript

            prevue.js

            by jaunesarmientoJavaScript

            favis-ci

            by jaunesarmientoJavaScript

            pt-client

            by jaunesarmientoJavaScript