entropy | An explainer on entropy | Animation library

 by   aatishb JavaScript Version: Current License: No License

kandi X-RAY | entropy Summary

kandi X-RAY | entropy Summary

entropy is a JavaScript library typically used in User Interface, Animation applications. entropy has no bugs, it has no vulnerabilities and it has low support. You can download it from GitHub.

An explainer on entropy
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            kandi-support Support

              entropy has a low active ecosystem.
              It has 56 star(s) with 15 fork(s). There are 6 watchers for this library.
              OutlinedDot
              It had no major release in the last 6 months.
              There are 1 open issues and 2 have been closed. On average issues are closed in 455 days. There are 3 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of entropy is current.

            kandi-Quality Quality

              entropy has 0 bugs and 0 code smells.

            kandi-Security Security

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

            kandi-License License

              entropy 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

              entropy releases are not available. You will need to build from source code and install.

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            entropy Key Features

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            entropy Examples and Code Snippets

            No Code Snippets are available at this moment for entropy.

            Community Discussions

            QUESTION

            PyTorch's CrossEntropyLoss - how to deal with the sequence length dimension with transformers?
            Asked 2021-Jun-10 at 19:24

            I'm training a transformer model for text generation.

            let's assume:

            ...

            ANSWER

            Answered 2021-Jun-10 at 19:24

            Use torch.BCELoss() instead (Binary cross entropy). This expects input and target to be the same size but they can be any size, and should fall within the range [0,1]. It performs cross-entropy loss element-wise.

            Source https://stackoverflow.com/questions/67926524

            QUESTION

            System.InvalidOperationException when using GetAwaiter().GetResult() with ServiceBusReceiver.PeekMessagesAsync
            Asked 2021-Jun-10 at 01:11
            Context

            We are using GetAwaiter().GetResult() because PowerShell's Cmdlet.ProcessRecord() does not support async/await.

            Code Sample ...

            ANSWER

            Answered 2021-Jun-10 at 00:18

            According to the documentation of the ValueTask struct:

            The following operations should never be performed on a ValueTask instance:

            • Awaiting the instance multiple times.
            • Calling AsTask multiple times.
            • Using .Result or .GetAwaiter().GetResult() when the operation hasn't yet completed, or using them multiple times.
            • Using more than one of these techniques to consume the instance.

            If you do any of the above, the results are undefined.

            What you can do is to convert the ValueTask to a Task, by using the AsTask method:

            Source https://stackoverflow.com/questions/67913032

            QUESTION

            ITU T.87 JPEG LS Standard and sample .jls SOS encoded streams have no escape sequence 0xFF 0x00
            Asked 2021-Jun-09 at 23:32

            ITU T.81 states the following:

            B.1.1.2 Markers Markers serve to identify the various structural parts of the compressed data formats. Most markers start marker segments containing a related group of parameters; some markers stand alone. All markers are assigned two-byte codes: an X’FF’ byte followed by a byte which is not equal to 0 or X’FF’ (see Table B.1). Any marker may optionally be preceded by any number of fill bytes, which are bytes assigned code X’FF’. NOTE – Because of this special code-assignment structure, markers make it possible for a decoder to parse the compressed data and locate its various parts without having to decode other segments of image data. "

            B.1.1.5 Entropy-coded data segments An entropy-coded data segment contains the output of an entropy-coding procedure. It consists of an integer number of bytes, whether the entropy-coding procedure used is Huffman or arithmetic.

            NOTES

            (1) Making entropy-coded segments an integer number of bytes is performed as follows: for Huffman coding, 1-bits are used, if necessary, to pad the end of the compressed data to complete the final byte of a segment. For arithmetic coding, byte alignment is performed in the procedure which terminates the entropy-coded segment (see D.1.8).

            (2) In order to ensure that a marker does not occur within an entropy-coded segment, any X’FF’ byte generated by either a Huffman or arithmetic encoder, or an X’FF’ byte that was generated by the padding of 1-bits described in NOTE 1 above, is followed by a “stuffed” zero byte (see D.1.6 and F.1.2.3).

            And in many other places where well known Stuff_0() function is also named.

            Not sure where standard ITU T.87 stands in regard to the encoding escape sequence 0xFF 0x00 specified by standard ITU T.81:

            • Standard ITU T.87 it self that do not specify this but expects it. Where Standard test samples are incorrectly formed, clearly do not have encoding escape sequence 0xFF 0x00 in encoded streams. For example 0xFF 0x7F, 0xFF 0x2F, and other sequences can be found in encoded streams of .jsl test samples : namelly "T8C0E3.JLS". And no one saw it all these years;
            • Or if Standard ITU T.87 actually overrides the ITU T.81 regarding this rule for encoded streams and doesn't allow encoding of escape sequence;

            In decoder we could make logic to detect decoder errors when 0xFF and !0x00 is to actually use that byte and not skip it if component is not fully decoded. But what if jls file do not have escape sequence and we encounter 0xFF 0x00 sequence should we skip 0x00 byte or not?

            Would like some clarification on subject of standard ITU T.87 JPEG-LS encoding, and what is the correct procedure. Should we, or shouldn't we, encode escape sequnce 0xFF 0x00 in encoded streams?

            ...

            ANSWER

            Answered 2021-Jun-09 at 23:32

            The answer : ITU T.87 - ANNEX A - point A1 - pass 3

            Marker segments are inserted in the data stream as specified in Annex D. In order to provide for easy detection of marker segments, a single byte with the value X'FF' in a coded image data segment shall be followed with the insertion of a single bit '0'. This inserted bit shall occupy the most significant bit of the next byte. If the X'FF' byte is followed by a single bit '1', then the decoder shall treat the byte which follows as the second byte of a marker, and process it in accordance with Annex C. If a '0' bit was inserted by the encoder, the decoder shall discard the inserted bit, which does not form part of the data stream to be decoded.

            NOTE 2 – This marker segment detection procedure differs from the one specified in CCITT Rec. T.81 | ISO/IEC 10918-1.

            JPEG-LS T.87 overrides T.81 JPEG Standard for encoded data stream to have byte 0xFF followed by byte with value between 0x00 and 0x7F (inclusive).

            Source https://stackoverflow.com/questions/67834862

            QUESTION

            Newbie : How evaluate model to increase accuracy model in classification
            Asked 2021-Jun-09 at 08:41

            my data

            how do I increase the accuracy of the model, if some of my models when run produce results like the one below `

            ...

            ANSWER

            Answered 2021-Jun-09 at 05:44

            There are several ways to achieve this:

            1. Look at the data. Are they in the best shape for the algorithm? Regarding NaN, Covariance and so on? Are they normalized, are the categorical ones translated well? This is a question too far-reaching for a forum.

            2. Look at the problem and the different algorithm suitable for this problem. Maybe

            • Logistic Regression
            • SVN
            • XGBoost
            • ....
            1. Try hyper parameter tuning with RandomisedsearvCV or GridSearchCV

            This is quite high-level.

            Source https://stackoverflow.com/questions/67897317

            QUESTION

            FirebaseFirestore.getInstance() and app has stoped
            Asked 2021-Jun-09 at 03:39

            I run my Android app (based on Java), and it works. Next, I add to my app code:

            FirebaseFirestore fdb = FirebaseFirestore.getInstance();

            This code I got from the official Android site https://firebase.google.com/docs/firestore/quickstart

            App runs, but next the running device shows the message "app has stopped".

            I use a device simulator available in Android Studio.

            It is my first Android app, and I can't understand what is going.

            ----Trace------ 2021-06-08 20:57:30.186 7155-7155/? D/AndroidRuntime: >>>>>> START com.android.internal.os.RuntimeInit uid 2000 <<<<<< 2021-06-08 20:57:30.188 7155-7155/? D/AndroidRuntime: CheckJNI is ON 2021-06-08 20:57:30.210 7155-7155/? W/art: Unexpected CPU variant for X86 using defaults: x86 2021-06-08 20:57:30.214 7155-7155/? D/ICU: No timezone override file found: /data/misc/zoneinfo/current/icu/icu_tzdata.dat 2021-06-08 20:57:30.229 7155-7155/? E/memtrack: Couldn't load memtrack module (No such file or directory) 2021-06-08 20:57:30.229 7155-7155/? E/android.os.Debug: failed to load memtrack module: -2 2021-06-08 20:57:30.230 7155-7155/? I/Radio-JNI: register_android_hardware_Radio DONE 2021-06-08 20:57:30.239 7155-7155/? D/AndroidRuntime: Calling main entry com.android.commands.am.Am

            ...

            ANSWER

            Answered 2021-Jun-09 at 03:39

            At the end of your log, just before the initial crash. there is a warning:

            Default FirebaseApp failed to initialize because no default options were found. This usually means that com.google.gms:google-services was not applied to your gradle project.

            simply adding com.google.gms:google-services should fix any issues you have, if you have issues, ensure your Gradle cache is cleared or run without the build cache --no-build-cache

            Source https://stackoverflow.com/questions/67889792

            QUESTION

            using random forest as base classifier with adaboost
            Asked 2021-Jun-06 at 12:54

            Can I use AdaBoost with random forest as a base classifier? I searched on the internet and I didn't find anyone who does it.

            Like in the following code; I try to run it but it takes a lot of time:

            ...

            ANSWER

            Answered 2021-Apr-07 at 11:30

            No wonder you have not actually seen anyone doing it - it is an absurd and bad idea.

            You are trying to build an ensemble (Adaboost) which in itself consists of ensemble base classifiers (RFs) - essentially an "ensemble-squared"; so, no wonder about the high computation time.

            But even if it was practical, there are good theoretical reasons not to do it; quoting from my own answer in Execution time of AdaBoost with SVM base classifier:

            Adaboost (and similar ensemble methods) were conceived using decision trees as base classifiers (more specifically, decision stumps, i.e. DTs with a depth of only 1); there is good reason why still today, if you don't specify explicitly the base_classifier argument, it assumes a value of DecisionTreeClassifier(max_depth=1). DTs are suitable for such ensembling because they are essentially unstable classifiers, which is not the case with SVMs, hence the latter are not expected to offer much when used as base classifiers.

            On top of this, SVMs are computationally much more expensive than decision trees (let alone decision stumps), which is the reason for the long processing times you have observed.

            The argument holds for RFs, too - they are not unstable classifiers, hence there is not any reason to actually expect performance improvements when using them as base classifiers for boosting algorithms, like Adaboost.

            Source https://stackoverflow.com/questions/66977025

            QUESTION

            How to put two or more Elements sit next to each other with overflow?
            Asked 2021-Jun-06 at 02:39

            I would like to know how to put two or more Elements sit next to each other with overflow. I can do it if I change the width of the slide-screen for example 1500px or bigger. I need to hide the second image to make a slide with javascript later. Please teach me how to solve this problem or teach me another way to do it if there is...

            HTML

            ...

            ANSWER

            Answered 2021-Jun-06 at 02:38

            you can use the max-width css property, which will hide the image once it reaches a certain width.

            Source https://stackoverflow.com/questions/67855362

            QUESTION

            PyTorch preserving gradient using external libraries
            Asked 2021-Jun-03 at 18:49

            I have a GAN that returns a predicted torch.tensor. To guide this network, I have a loss function which is a summation of binary cross entropy loss (BCELoss) and Wasserstein distance. However, in order to calculate Wasserstein distance, I am using scipy.stats.wasserstein_distance function from SciPy library. As you might know, this function requires two NumPy arrays as input. So, to use this function, I am converting my predicted tensor and ground-truth tensor to NumPy arrays as follows

            ...

            ANSWER

            Answered 2021-Jun-03 at 18:49

            Adding an object that is not a tensor that requires_grad to your loss is essentially adding a constant. The derivative of a constant is zero, so this added term is not doing anything to your network's weights.

            tl;dr: You need to rewrite the loss computation in pytorch (or just find an existing implementation, there's numerous on the internets).

            Source https://stackoverflow.com/questions/67825139

            QUESTION

            How do I make an image full screen on click?
            Asked 2021-Jun-03 at 10:16

            I'm making a gallery and I want each photo to go fullscreen when you click on it like this:

            Currently, I have a click handler on each image that adds a class zoom to the clicked image. The CSS selectors I wrote only blow the image up and don't have it centered on the full page like in the example. Here is my code:

            ...

            ANSWER

            Answered 2021-Jun-03 at 05:38

            I suggest look at intensejs library: https://github.com/tholman/intense-images

            It's fast and easy implementation that will fit your needs.

            Source https://stackoverflow.com/questions/67815853

            QUESTION

            How do I make my image fit the entire container?
            Asked 2021-Jun-02 at 20:37

            I am building a simple gallery using Javascript and CSS but can't seem to get the image to fit full frame. Here is what it looks like right now:

            ...

            ANSWER

            Answered 2021-Jun-02 at 20:26

            Add this code to your css

            Source https://stackoverflow.com/questions/67811833

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

            Vulnerabilities

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            Install entropy

            You can download it from GitHub.

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