HiddenMarkovNeuralNetwork
An hybrid between HMM and Neural networks for sequential training and time series forecasting. In this repository we display some tutorials on how to use HMNN in different scenarios.
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FLAG-example. In "FLAG-example.ipynb" we explains how to run an HMNN for video frame prediction. You require the libraries:
- cv2
- torch (PyTorch)
- random
- sklearn
- matplotlib
- numpy
- pickle: this is use only for saving and loading files
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MNIST_evolvingClassifier-example. In "MNIST_evolving_classifier-example.ipynb" we explains how to use HMNN with an evolving classifier on MNIST. We also show how to build the dataset. You require the following libraries:
- gzip
- torch (PyTorch)
- random
- sklearn
- matplotlib
- numpy
- pickle: this is use only for saving and loading files
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MNIST-variationalDropConnect. In "MNIST_variational_dropconnect-example.ipynb" we explains how to use HMNN with Variational DropConnect in MNIST. You require the following libraries:
- gzip
- torch (PyTorch)
- random
- sklearn
- matplotlib
- numpy
- pickle: this is use only for saving and loading files