purdue-fastr | FastR implements the R Language | Machine Learning library

 by   allr Java Version: Current License: Non-SPDX

kandi X-RAY | purdue-fastr Summary

kandi X-RAY | purdue-fastr Summary

purdue-fastr is a Java library typically used in Artificial Intelligence, Machine Learning, Deep Learning applications. purdue-fastr has no bugs, it has no vulnerabilities, it has build file available and it has low support. However purdue-fastr has a Non-SPDX License. You can download it from GitHub.

FastR implements the R Language. Currently, FastR can run the R implementation of the Language Shootout Benchmarks and the Benchmark 25 suite.

            kandi-support Support

              purdue-fastr has a low active ecosystem.
              It has 270 star(s) with 39 fork(s). There are 34 watchers for this library.
              It had no major release in the last 6 months.
              There are 2 open issues and 2 have been closed. On average issues are closed in 3 days. There are no pull requests.
              It has a neutral sentiment in the developer community.
              The latest version of purdue-fastr is current.

            kandi-Quality Quality

              purdue-fastr has 0 bugs and 0 code smells.

            kandi-Security Security

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

            kandi-License License

              purdue-fastr has a Non-SPDX License.
              Non-SPDX licenses can be open source with a non SPDX compliant license, or non open source licenses, and you need to review them closely before use.

            kandi-Reuse Reuse

              purdue-fastr releases are not available. You will need to build from source code and install.
              Build file is available. You can build the component from source.
              Installation instructions are available. Examples and code snippets are not available.

            Top functions reviewed by kandi - BETA

            kandi has reviewed purdue-fastr and discovered the below as its top functions. This is intended to give you an instant insight into purdue-fastr implemented functionality, and help decide if they suit your requirements.
            • Overrides the visitor to look for equality
            • Handles an update vector
            • Handles an assignment
            • Visits a Function call
            • Generate an IF statement for the given IF node
            • Visit a For for loop
            • Transforms a sequence into a tree
            • Processes a colon
            • Processes a function
            • The loop
            • For update field update
            • Create a built - in AST
            • Returns all classes in the specified package
            • Create a new instance of the builtin
            • Create a new instance of the given arguments
            • Returns the top wrapper loader
            • Create a new instance of a built - in AST
            • Getter for tokens
            • Create a new instance of a given node
            • Create a new instance of the builtin nodes
            • Create an outer product matrix
            • Create an instance of a dimensionality matrix
            • Create a new instance of this class
            • Create a new instance of the given node
            • Initialize primitive values
            • Create an instance of a built - in AST
            • Create a new instance of the built - in method call
            • Create a new RAny object
            • Create a new instance
            • Assigns arguments to the given frame
            Get all kandi verified functions for this library.

            purdue-fastr Key Features

            No Key Features are available at this moment for purdue-fastr.

            purdue-fastr Examples and Code Snippets

            No Code Snippets are available at this moment for purdue-fastr.

            Community Discussions


            Using RNN Trained Model without pytorch installed
            Asked 2022-Feb-28 at 20:17

            I have trained an RNN model with pytorch. I need to use the model for prediction in an environment where I'm unable to install pytorch because of some strange dependency issue with glibc. However, I can install numpy and scipy and other libraries. So, I want to use the trained model, with the network definition, without pytorch.

            I have the weights of the model as I save the model with its state dict and weights in the standard way, but I can also save it using just json/pickle files or similar.

            I also have the network definition, which depends on pytorch in a number of ways. This is my RNN network definition.



            Answered 2022-Feb-17 at 10:47

            You should try to export the model using torch.onnx. The page gives you an example that you can start with.

            An alternative is to use TorchScript, but that requires torch libraries.

            Both of these can be run without python. You can load torchscript in a C++ application https://pytorch.org/tutorials/advanced/cpp_export.html

            ONNX is much more portable and you can use in languages such as C#, Java, or Javascript https://onnxruntime.ai/ (even on the browser)

            A running example

            Just modifying a little your example to go over the errors I found

            Notice that via tracing any if/elif/else, for, while will be unrolled

            Source https://stackoverflow.com/questions/71146140


            Flux.jl : Customizing optimizer
            Asked 2022-Jan-25 at 07:58

            I'm trying to implement a gradient-free optimizer function to train convolutional neural networks with Julia using Flux.jl. The reference paper is this: https://arxiv.org/abs/2005.05955. This paper proposes RSO, a gradient-free optimization algorithm updates single weight at a time on a sampling bases. The pseudocode of this algorithm is depicted in the picture below.


            I'm using MNIST dataset.



            Answered 2022-Jan-14 at 23:47

            Based on the paper you shared, it looks like you need to change the weight arrays per each output neuron per each layer. Unfortunately, this means that the implementation of your optimization routine is going to depend on the layer type, since an "output neuron" for a convolution layer is quite different than a fully-connected layer. In other words, just looping over Flux.params(model) is not going to be sufficient, since this is just a set of all the weight arrays in the model and each weight array is treated differently depending on which layer it comes from.

            Fortunately, Julia's multiple dispatch does make this easier to write if you use separate functions instead of a giant loop. I'll summarize the algorithm using the pseudo-code below:

            Source https://stackoverflow.com/questions/70641453


            How can I check a confusion_matrix after fine-tuning with custom datasets?
            Asked 2021-Nov-24 at 13:26

            This question is the same with How can I check a confusion_matrix after fine-tuning with custom datasets?, on Data Science Stack Exchange.


            I would like to check a confusion_matrix, including precision, recall, and f1-score like below after fine-tuning with custom datasets.

            Fine tuning process and the task are Sequence Classification with IMDb Reviews on the Fine-tuning with custom datasets tutorial on Hugging face.

            After finishing the fine-tune with Trainer, how can I check a confusion_matrix in this case?

            An image of confusion_matrix, including precision, recall, and f1-score original site: just for example output image



            Answered 2021-Nov-24 at 13:26

            What you could do in this situation is to iterate on the validation set(or on the test set for that matter) and manually create a list of y_true and y_pred.

            Source https://stackoverflow.com/questions/68691450


            CUDA OOM - But the numbers don't add upp?
            Asked 2021-Nov-23 at 06:13

            I am trying to train a model using PyTorch. When beginning model training I get the following error message:

            RuntimeError: CUDA out of memory. Tried to allocate 5.37 GiB (GPU 0; 7.79 GiB total capacity; 742.54 MiB already allocated; 5.13 GiB free; 792.00 MiB reserved in total by PyTorch)

            I am wondering why this error is occurring. From the way I see it, I have 7.79 GiB total capacity. The numbers it is stating (742 MiB + 5.13 GiB + 792 MiB) do not add up to be greater than 7.79 GiB. When I check nvidia-smi I see these processes running



            Answered 2021-Nov-23 at 06:13

            This is more of a comment, but worth pointing out.

            The reason in general is indeed what talonmies commented, but you are summing up the numbers incorrectly. Let's see what happens when tensors are moved to GPU (I tried this on my PC with RTX2060 with 5.8G usable GPU memory in total):

            Let's run the following python commands interactively:

            Source https://stackoverflow.com/questions/70074789


            How to compare baseline and GridSearchCV results fair?
            Asked 2021-Nov-04 at 21:17

            I am a bit confusing with comparing best GridSearchCV model and baseline.
            For example, we have classification problem.
            As a baseline, we'll fit a model with default settings (let it be logistic regression):



            Answered 2021-Nov-04 at 21:17

            No, they aren't comparable.

            Your baseline model used X_train to fit the model. Then you're using the fitted model to score the X_train sample. This is like cheating because the model is going to already perform the best since you're evaluating it based on data that it has already seen.

            The grid searched model is at a disadvantage because:

            1. It's working with less data since you have split the X_train sample.
            2. Compound that with the fact that it's getting trained with even less data due to the 5 folds (it's training with only 4/5 of X_val per fold).

            So your score for the grid search is going to be worse than your baseline.

            Now you might ask, "so what's the point of best_model.best_score_? Well, that score is used to compare all the models used when searching for the optimal hyperparameters in your search space, but in no way should be used to compare against a model that was trained outside of the grid search context.

            So how should one go about conducting a fair comparison?

            1. Split your training data for both models.

            Source https://stackoverflow.com/questions/69844028


            Getting Error 524 while running jupyter lab in google cloud platform
            Asked 2021-Oct-15 at 02:14

            I am not able to access jupyter lab created on google cloud

            I created one notebook using Google AI platform. I was able to start it and work but suddenly it stopped and I am not able to start it now. I tried building and restarting the jupyterlab, but of no use. I have checked my disk usages as well, which is only 12%.

            I tried the diagnostic tool, which gave the following result:

            but didn't fix it.

            Thanks in advance.



            Answered 2021-Aug-20 at 14:00


            TypeError: brain.NeuralNetwork is not a constructor
            Asked 2021-Sep-29 at 22:47

            I am new to Machine Learning.

            Having followed the steps in this simple Maching Learning using the Brain.js library, it beats my understanding why I keep getting the error message below:

            I have double-checked my code multiple times. This is particularly frustrating as this is the very first exercise!

            Kindly point out what I am missing here!

            Find below my code:



            Answered 2021-Sep-29 at 22:47

            Turns out its just documented incorrectly.

            In reality the export from brain.js is this:

            Source https://stackoverflow.com/questions/69348213


            Ordinal Encoding or One-Hot-Encoding
            Asked 2021-Sep-04 at 06:43

            IF we are not sure about the nature of categorical features like whether they are nominal or ordinal, which encoding should we use? Ordinal-Encoding or One-Hot-Encoding? Is there a clearly defined rule on this topic?

            I see a lot of people using Ordinal-Encoding on Categorical Data that doesn't have a Direction. Suppose a frequency table:



            Answered 2021-Sep-04 at 06:43

            You're right. Just one thing to consider for choosing OrdinalEncoder or OneHotEncoder is that does the order of data matter?

            Most ML algorithms will assume that two nearby values are more similar than two distant values. This may be fine in some cases e.g., for ordered categories such as:

            • quality = ["bad", "average", "good", "excellent"] or
            • shirt_size = ["large", "medium", "small"]

            but it is obviously not the case for the:

            • color = ["white","orange","black","green"]

            column (except for the cases you need to consider a spectrum, say from white to black. Note that in this case, white category should be encoded as 0 and black should be encoded as the highest number in your categories), or if you have some cases for example, say, categories 0 and 4 may be more similar than categories 0 and 1. To fix this issue, a common solution is to create one binary attribute per category (One-Hot encoding)

            Source https://stackoverflow.com/questions/69052776


            How to increase dimension-vector size of BERT sentence-transformers embedding
            Asked 2021-Aug-15 at 13:35

            I am using sentence-transformers for semantic search but sometimes it does not understand the contextual meaning and returns wrong result eg. BERT problem with context/semantic search in italian language

            by default the vector side of embedding of the sentence is 78 columns, so how do I increase that dimension so that it can understand the contextual meaning in deep.




            Answered 2021-Aug-10 at 07:39

            Increasing the dimension of a trained model is not possible (without many difficulties and re-training the model). The model you are using was pre-trained with dimension 768, i.e., all weight matrices of the model have a corresponding number of trained parameters. Increasing the dimensionality would mean adding parameters which however need to be learned.

            Also, the dimension of the model does not reflect the amount of semantic or context information in the sentence representation. The choice of the model dimension reflects more a trade-off between model capacity, the amount of training data, and reasonable inference speed.

            If the model that you are using does not provide representation that is semantically rich enough, you might want to search for better models, such as RoBERTa or T5.

            Source https://stackoverflow.com/questions/68686272


            How to identify what features affect predictions result?
            Asked 2021-Aug-11 at 15:55

            I have a table with features that were used to build some model to predict whether user will buy a new insurance or not. In the same table I have probability of belonging to the class 1 (will buy) and class 0 (will not buy) predicted by this model. I don't know what kind of algorithm was used to build this model. I only have its predicted probabilities.

            Question: how to identify what features affect these prediction results? Do I need to build correlation matrix or conduct any tests?

            Table example:



            Answered 2021-Aug-11 at 15:55

            You could build a model like this.

            x = features you have. y = true_lable

            from that you can extract features importance. also, if you want to go the extra mile,you can do Bootstrapping, so that the features importance would be more stable (statistical).

            Source https://stackoverflow.com/questions/68744565

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


            No vulnerabilities reported

            Install purdue-fastr

            download the latest code: wget https://github.com/allr/fastr/archive/master.zip
            unzip it: unzip master.zip
            build: cd fastr-master ; ant
            run the console: ./r.sh
            run the binarytrees benchmark for size 5: ./r.sh --args 5 -f test/r/shootout/binarytrees/binarytrees.r
            To run the benchmarks from the Benchmark 25 suite, and for best performance of all benchmarks, build native glue code which links FastR to the GNU-R Math Library, system Math library, and openBLAS. The build scripts are tested on Ubuntu 13.10. Any platform supported by GNU-R and Java could be supported by FastR. To ensure that the openBLAS library is used, run the matcal-4 benchmark with the system profiler: perf record ./nr.sh -f test/r/benchmark25/perfres/b25-matcal-4.r. Check with perf report that DGEMM from openBLAS is used, e.g. dgemm_kernel_SANDYBRIDGE from libopenblas.so.0. Also expect to see the random number generator, e.g. qnorm5 from libRmath.so.1.0.0.
            install Oracle JDK8 (for best performance); if you must use JDK7, customize native/netlib-java/build.sh
            set JAVA_HOME and PATH accordingly
            follow the steps in Quick Start
            install Ubuntu packages r-base, r-mathlib, libopenblas-base
            build glue code for system libraries and GNU-R: cd native ; ./build.sh
            build glue code for native BLAS and LAPACK: cd netlib-java ; ./build.sh
            check the glue code can be loaded: cd ../.. ; ./nr.sh should give output Using LAPACK: org.netlib.lapack.NativeLAPACK Using BLAS: org.netlib.blas.NativeBLAS Using GNUR: yes Using System libraries (C/M): yes Using MKL: not available
            run the matfunc-1 benchmark: ./nr.sh -f test/r/benchmark25/perfres/b25-matfunc-1.r


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