svm | : rocket : : purple_heart : SVM - Spacemesh Virtual Machine | Binary Executable Format library

 by   spacemeshos Rust Version: v0.0.34 License: MIT

kandi X-RAY | svm Summary

kandi X-RAY | svm Summary

svm is a Rust library typically used in Programming Style, Binary Executable Format applications. svm has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. You can download it from GitHub.

:rocket: :purple_heart: SVM - Spacemesh Virtual Machine
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              svm has a low active ecosystem.
              It has 71 star(s) with 12 fork(s). There are 16 watchers for this library.
              OutlinedDot
              It had no major release in the last 12 months.
              There are 80 open issues and 105 have been closed. On average issues are closed in 61 days. There are 2 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of svm is v0.0.34

            kandi-Quality Quality

              svm has no bugs reported.

            kandi-Security Security

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

            kandi-License License

              svm 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

              svm releases are available to install and integrate.
              Installation instructions, examples and code snippets are available.

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            svm Key Features

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            svm Examples and Code Snippets

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            Community Discussions

            QUESTION

            Remove levels and set index 0 as columns names
            Asked 2021-Jun-14 at 08:42

            How to remove first line with names level_0 and all and convert index 0 as columns.

            My df ...

            ANSWER

            Answered 2021-Jun-14 at 08:42

            I suggest first create MultiIndex in columns by header=[0,1] by convert first 2 headers rows:

            Source https://stackoverflow.com/questions/67967100

            QUESTION

            Why did I get AttributeError?
            Asked 2021-Jun-11 at 02:42

            I tried to change a few lines from the original code however when I tried to run , I got error that say 'AttributeError: module 'PngImageFile' has no attribute 'shape'. However, I had no problem when running the original code. What should I do to remove this error in my modified code?

            Here is the original code :

            ...

            ANSWER

            Answered 2021-Jun-11 at 02:11

            I saw anna_phog on other portal.

            Problem is because this function needs numpy array but you read image with pillow Image.open() and you have to convert img to numpy array

            Source https://stackoverflow.com/questions/67930163

            QUESTION

            Remove all columns or rows with only zeros out of a data frame
            Asked 2021-Jun-08 at 21:34

            I have a question to NLP in R. My data is very big and so I need to reduce my data for further analysis to apply a SVM on it.

            I have a Document-Term-Matrix like this:

            ...

            ANSWER

            Answered 2021-Jun-06 at 17:25

            Here is how I would do it:

            Source https://stackoverflow.com/questions/67861799

            QUESTION

            Why are all my classification accuracy scores the same?
            Asked 2021-Jun-08 at 00:47

            I'm running several machine learning models to find the one which the highest accuracy score, however, all the accuracy scores are the exact same. I performed NLP on social media text and I'm training my models to tag sentiment based on sentiment determined from NLTK.

            I'm using the same training and test sets, but I've done this method before in the past and received different scores on different models. Why are all of mine the same? Am I overfitting perhaps?

            Here is my code where I'm splitting and training:

            ...

            ANSWER

            Answered 2021-Jun-08 at 00:47

            I'm not sure what is the cause of the problem, but since the output of you SVM model and DecisionTreeClassfier always output 1, I suggest you try a more complex model like RandomForestClassifier and see what it comes out.

            I've similar experience before, no matter how I tuned the training hyperparameters, the model always give the same performance metric -- this may cause by 2 probabilities:

            1. Our data is not suitable for the model, for example all values in the vector is zero: [0, 0, 0, 0, 0, 0, 0]
            2. Our model is too simple, which could only perform linear modeling, so that it could not learn too complex mapping function.

            Since your SVM is built with linear kernel, could you try an more complex model and see what it comes out? And could you examine that if your X_train_vectors is all zero's in the matrix?

            Source https://stackoverflow.com/questions/67878862

            QUESTION

            Sklearn custom kernel gives wrong decision function
            Asked 2021-Jun-07 at 07:14

            I have successfully implemented my own custom linear kernel which works totally fine using the clf.predict. However, when I want to use the clf.decision_function it gives constant values for all points.

            This is the code for the custom kernel:

            ...

            ANSWER

            Answered 2021-Jun-07 at 00:48

            The actual problem is really silly, but since it took quite some time to track down, I'll share an outline of my debugging.

            First, rather than plotting, print the actual values of the decision_function: you'll find that the first one comes out unique, but that after that everything is constant. Running the same on various slices of the dataset, this pattern persists. So I thought perhaps some values were being overwritten, and I dug into the SVC code a bit. That lead to some useful internal functions/attributes, like ._BaseLibSVM__Xfit containing the training data, _decision_function and _dense_decision_function, and _compute_kernel. But none of the code indicated a problem, and running them just showed the same problem. Running _compute_kernel gave results that were all-zeros past the first row, and then coming back to your code, running linear_kernel does that already. So, finally, it comes back to your linear_kernel function.

            You return inside the outer for loop, so you only ever use the first row of X, never computing the rest of the matrix. (This brings up a surprise: why did the predictions look good? That seems to have been a fluke. Changing the definition for f_lin, to change the classes, the model still learns the slope-1 line.)

            Source https://stackoverflow.com/questions/67858166

            QUESTION

            How to extract the hidden vector (the output of the ReLU after the third encoder layer) as the image representation
            Asked 2021-Jun-02 at 17:17

            I am implementing an autoencoder using the Fashion Mnsit dataset. The code for the encoder-

            ...

            ANSWER

            Answered 2021-Jun-02 at 17:17

            I recommend to use Functional API in order to define multiple outputs of your model because of a more clear code. However, you can do this with Sequential model by getting the output of any layer you want and add to your model's output.

            Print your model.summary() and check your layers to find which layer you want to branch. You can access each layer's output by it's index with model.layers[index].output .

            Then you can create a multi-output model of the layers you want, like this:

            Source https://stackoverflow.com/questions/67770595

            QUESTION

            Create a list that represents all the possible paths an individual can take
            Asked 2021-Jun-01 at 12:41

            The below dataframe represents presumed residence of a set of individuals:

            ...

            ANSWER

            Answered 2021-Jun-01 at 12:41

            Use more_itertools.powerset which will result in list of tuples so convert it into list of list using map()

            Source https://stackoverflow.com/questions/67788862

            QUESTION

            ValueError: Found input variables with inconsistent numbers of samples: [6, 80]
            Asked 2021-May-31 at 10:26

            I'm failing to process the below code in the pipeline (it's imblearn pipepline)

            ...

            ANSWER

            Answered 2021-May-31 at 10:26

            This is happening because you have a text transformer object in your pipeline. The problem with this approach is that the pipeline will pass the whole dataframe to the TfidfVectorizer. However, the text transformers of scikit-learn expect a 1d input.

            Passing a 2d dataframe to TfidfVectorizer causes some weird processing where it mistakes the column names as documents. You can check with this simple example:

            Source https://stackoverflow.com/questions/67768470

            QUESTION

            ValueError: x and y must be the same size when try to draw the SVM
            Asked 2021-May-30 at 12:40

            I am new at machine learning , I found python code to visulastion the result of SVM modle from sklearn in python the code is

            ...

            ANSWER

            Answered 2021-May-30 at 10:49

            .scatter() funciton takes float or array-like of x and y coordinates. As these are coordinates they are supposed to be in pairs and therefore x and y should be of equal length. In your code x is clf.support_vectors_[:0] and y is clf.support_vectors_[:1]. The syntax iterator[:k] says that we should pick every element in the iterator until the k-th element which we should not pick. So when we combine these things together we can spot that x and y have different lengths and therefore throw an error.

            Source https://stackoverflow.com/questions/67759888

            QUESTION

            different score when using train_test_split before vs after SMOTETomek
            Asked 2021-May-29 at 19:35

            I'm trying to classify a text to a 6 different classes. Since I'm having an imbalanced dataset, I'm also using SMOTETomek method that should synthetically balance the dataset with additional artificial samples.

            I've noticed a huge score difference when applying it via pipeline vs 'Step by step" where the only difference is (I believe) the place I'm using train_test_split

            Here are my features and labels:

            ...

            ANSWER

            Answered 2021-May-29 at 13:28

            There is nothing wrong with your code by itself. But your step-by-step approach is using bad practice in Machine Learning theory:

            Do not resample your testing data

            In your step-by-step approach, you resample all of the data first and then split them into train and test sets. This will lead to an overestimation of model performance because you have altered the original distribution of classes in your test set and it is not representative of the original problem anymore.

            What you should do instead is to leave the testing data in its original distribution in order to get a valid approximation of how your model will perform on the original data, which is representing the situation in production. Therefore, your approach with the pipeline is the way to go.

            As a side note: you could think about shifting the whole data preparation (vectorization and resampling) out of your fitting and testing loop as you probably want to compare the model performance against the same data anyway. Then you would only have to run these steps once and your code executes faster.

            Source https://stackoverflow.com/questions/67750208

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

            Vulnerabilities

            No vulnerabilities reported

            Install svm

            The SVM project is organized into a couple of crates. The root crate is called SVM and it's a workspace crate.

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