classifier | Bayesian classifier with Redis backend

 by   harthur JavaScript Version: Current License: MIT

kandi X-RAY | classifier Summary

kandi X-RAY | classifier Summary

classifier is a JavaScript library. classifier has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. You can download it from GitHub.

classifier is a JavaScript naive Bayesian classifier with backends for Redis and localStorage:. The first argument to train() can be a string of text or an array of words, the second argument can be any category name you want.
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            kandi-support Support

              classifier has a low active ecosystem.
              It has 619 star(s) with 74 fork(s). There are 32 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 255 days. There are 1 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of classifier is current.

            kandi-Quality Quality

              classifier has 0 bugs and 0 code smells.

            kandi-Security Security

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

            kandi-License License

              classifier 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

              classifier releases are not available. You will need to build from source code and install.
              Installation instructions are not available. Examples and code snippets are available.

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            classifier Key Features

            No Key Features are available at this moment for classifier.

            classifier Examples and Code Snippets

            Use Decision Tree classifier .
            pythondot img1Lines of Code : 51dot img1no licencesLicense : No License
            copy iconCopy
            def main():
                Xtrain, Ytrain, Xtest, Ytest, word2idx = get_data()
            
                # convert to numpy arrays
                Xtrain = np.array(Xtrain)
                Ytrain = np.array(Ytrain)
            
                # convert Xtrain to indicator matrix
                N = len(Xtrain)
                V = len(word2idx) + 1
                 
            Classify the classifier .
            pythondot img2Lines of Code : 28dot img2License : Permissive (MIT License)
            copy iconCopy
            def classifier(train_data, train_target, classes, point, k=5):
                """
                Classifies the point using the KNN algorithm
                k closest points are found (ranked in ascending order of euclidean distance)
                Params:
                :train_data: Set of points that a  
            Fit Decision Tree classifier
            pythondot img3Lines of Code : 21dot img3no licencesLicense : No License
            copy iconCopy
            def fit(self, X, Y, M=None):
                N, D = X.shape
                if M is None:
                  M = int(np.sqrt(D))
            
                self.models = []
                self.features = []
                for b in range(self.B):
                  tree = DecisionTreeClassifier()
            
                  # sample features
                  features = np.ra  

            Community Discussions

            QUESTION

            Compute class weight function issue in 'sklearn' library when used in 'Keras' classification (Python 3.8, only in VS code)
            Asked 2022-Mar-27 at 23:14

            The classifier script I wrote is working fine and recently added weight balancing to the fitting. Since I added the weight estimate function using 'sklearn' library I get the following error :

            ...

            ANSWER

            Answered 2022-Mar-27 at 23:14

            After spending a lot of time, this is how I fixed it. I still don't know why but when the code is modified as follows, it works fine. I got the idea after seeing this solution for a similar but slightly different issue.

            Source https://stackoverflow.com/questions/69783897

            QUESTION

            Keras AttributeError: 'Sequential' object has no attribute 'predict_classes'
            Asked 2022-Mar-23 at 04:30

            Im attempting to find model performance metrics (F1 score, accuracy, recall) following this guide https://machinelearningmastery.com/how-to-calculate-precision-recall-f1-and-more-for-deep-learning-models/

            This exact code was working a few months ago but now returning all sorts of errors, very confusing since i havent changed one character of this code. Maybe a package update has changed things?

            I fit the sequential model with model.fit, then used model.evaluate to find test accuracy. Now i am attempting to use model.predict_classes to make class predictions (model is a multi-class classifier). Code shown below:

            ...

            ANSWER

            Answered 2021-Aug-19 at 03:49

            This function were removed in TensorFlow version 2.6. According to the keras in rstudio reference

            update to

            Source https://stackoverflow.com/questions/68836551

            QUESTION

            How to calculate maximum gradient for each layer given a mini-batch
            Asked 2022-Mar-14 at 07:58

            I try to implement a fully-connected model for classification using the MNIST dataset. A part of the code is the following:

            ...

            ANSWER

            Answered 2022-Mar-10 at 08:19

            You could start off with a custom training loop using tf.GradientTape:

            Source https://stackoverflow.com/questions/71420132

            QUESTION

            What issue could I have in Gradle managed device setup?
            Asked 2022-Mar-07 at 23:47

            There was introduced a new feature Gradle managed devices (see for example here: https://developer.android.com/studio/preview/features?hl=fr)

            The setup seems to be pretty straightforward, just copy a few lines to the module level build.gradle file and everything should work.

            Sadly it is not the case for me and I strive for some advice, please. The code is red and the script doesn't succeed. See my build.gradle.kts file:

            The underlined ManagedVirtualDevice shows the following error:

            My Android studio version is Android Studio Bumblebee | 2021.1.1 Canary 11 Build #AI-211.7628.21.2111.7676841, built on August 26, 2021.

            Syncing Gradle shows this:

            ...

            ANSWER

            Answered 2021-Oct-15 at 11:43

            Just ran into the same issue - you need to instantiate a ManagedVirtualDevice object and configure it, before adding it to your devices list:

            Source https://stackoverflow.com/questions/69159985

            QUESTION

            Unpickle instance from Jupyter Notebook in Flask App
            Asked 2022-Feb-28 at 18:03

            I have created a class for word2vec vectorisation which is working fine. But when I create a model pickle file and use that pickle file in a Flask App, I am getting an error like:

            AttributeError: module '__main__' has no attribute 'GensimWord2VecVectorizer'

            I am creating the model on Google Colab.

            Code in Jupyter Notebook:

            ...

            ANSWER

            Answered 2022-Feb-24 at 11:48

            Import GensimWord2VecVectorizer in your Flask Web app python file.

            Source https://stackoverflow.com/questions/71231611

            QUESTION

            nexus-staging-maven-plugin: maven deploy failed: An API incompatibility was encountered while executing
            Asked 2022-Feb-11 at 22:39

            This worked fine for me be building under Java 8. Now under Java 17.01 I get this when I do mvn deploy.

            mvn install works fine. I tried 3.6.3 and 3.8.4 and updated (I think) all my plugins to the newest versions.

            Any ideas?

            ...

            ANSWER

            Answered 2022-Feb-11 at 22:39

            Update: Version 1.6.9 has been released and should fix this issue! 🎉

            This is actually a known bug, which is now open for quite a while: OSSRH-66257. There are two known workarounds:

            1. Open Modules

            As a workaround, use --add-opens to give the library causing the problem access to the required classes:

            Source https://stackoverflow.com/questions/70153962

            QUESTION

            Getting optimal vocab size and embedding dimensionality using GridSearchCV
            Asked 2022-Feb-06 at 09:13

            I'm trying to use GridSearchCV to find the best hyperparameters for an LSTM model, including the best parameters for vocab size and the word embeddings dimension. First, I prepared my testing and training data.

            ...

            ANSWER

            Answered 2022-Feb-02 at 08:53

            I tried with scikeras but I got errors because it doesn't accept not-numerical inputs (in our case the input is in str format). So I came back to the standard keras wrapper.

            The focal point here is that the model is not built correctly. The TextVectorization must be put inside the Sequential model like shown in the official documentation.

            So the build_model function becomes:

            Source https://stackoverflow.com/questions/70884608

            QUESTION

            InternalError when using TPU for training Keras model
            Asked 2021-Dec-31 at 08:18

            I am attempting to fine-tune a BERT model on Google Colab from the Tensorflow Hub using this link.

            However, I run into the following error:

            ...

            ANSWER

            Answered 2021-Dec-31 at 08:18

            As I don't exactly know what changes you have made in the code... I don't have idea about your dataset. But I can see that you are trying to train the whole datset with one epoch and passing the steps per epoch directly. I would recommend to write it like this

            set some batch_size 2^n power (for example 16 or 32 or etc) if you don't want to batch the dataset just set batch_size to 1

            Source https://stackoverflow.com/questions/70479279

            QUESTION

            How to map function directly over list of lists?
            Asked 2021-Dec-26 at 15:38

            I have built a pixel classifier for images, and for each pixel in the image, I want to define to which pre-defined color cluster it belongs. It works, but at some 5 minutes per image, I think I am doing something unpythonic that can for sure be optimized.

            How can we map the function directly over the list of lists?

            ...

            ANSWER

            Answered 2021-Jul-23 at 07:41

            Just quick speedups:

            1. You can omit math.sqrt()
            2. Create dictionary of colors instead of a list (that way you don't have to search for the index each iteration)
            3. use min() instead of sorted()

            Source https://stackoverflow.com/questions/68495481

            QUESTION

            Sklearn: Calibrate a multi-label classification with CalibratedClassifierCV
            Asked 2021-Dec-18 at 17:38

            I have built a number of sklearn classifier models to perform multi-label classification and I would like to calibrate their predict_proba outputs so that I can obtain confidence scores. I would also like to use metrics such as sklearn.metrics.recall_score to evaluate them.

            I have 4 labels to predict and the true labels are multi-hot encoded (e.g. [0, 1, 1, 1]). As a result, CalibratedClassifierCV does not directly accept my data:

            ...

            ANSWER

            Answered 2021-Dec-17 at 15:33

            In your example, you're using a DecisionTreeClassifier which by default support targets of dimension (n, m) where m > 1.

            However if you want to have as result the marginal probability of each class then use the OneVsRestClassifier.

            Notice that CalibratedClassifierCV expects target to be 1d so the "trick" is to extend it to support Multilabel Classification with MultiOutputClassifier.

            Full Example

            Source https://stackoverflow.com/questions/70388422

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

            Vulnerabilities

            No vulnerabilities reported

            Install classifier

            You can download it from GitHub.

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