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    "info": {
        "author": "Christian Bock",
        "author_email": "christian.bock@bsse.ethz.ch",
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        "classifiers": [
            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python :: 3"
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        "description": "# Wasserstein Time Series Kernel\n\n### Installation\nThe easiest way is to install WTK from the Python Package Index (PyPI) via\n\n```\n$ pip install wtk\n```\n\n### Usage\nThe package provides functions to transform a set of `n` training time series and `o` test time series into an `n x n` distance matrix for training and an `o x n` distance matrix for testing.\nAdditionally, we provide a way to run a grid search for a krein space SVM. `krein_svm_grid_search` runs a `5`-fold\ncross-validation on the training set to determine the best hyperparameters. Then, its classification accuracy is\ncomputed on the test set.\n\n```\nfrom wtk import transform_to_dist_matrix\nfrom wtk.utilities import get_ucr_dataset, krein_svm_grid_search\n\n# Read UCR data\nX_train, y_train, X_test, y_test = get_ucr_dataset('../data/UCR/raw_data/', 'DistalPhalanxTW')\n\n# Compute wasserstein distance matrices with subsequent length k=10\nD_train, D_test = transform_to_dist_matrix(X_train, X_test, 10)\n\n# Run the grid search\nsvm_clf = krein_svm_grid_search(D_train, D_test, y_train, y_test)\n```\n\nAlternatively, you can get the kernel matrices computed from the distance matrices and train your own classifier.\n```\nfrom sklearn.svm import SVC\nfrom wtk import get_kernel_matrix\nfrom sklearn.metrics import accuracy_score\n\n# Get the kernel matrices\nK_train = get_kernel_matrix(D_train, psd=True, gamma=0.2)\nK_test = get_kernel_matrix(D_test, psd=False, gamma=0.2)\n\n# Train your classifier\nclf = SVC(C=5, kernel='precomputed')\nclf.fit(K_train, y_train)\n\ny_pred = clf.predict(K_test)\nprint(accuracy_score(y_test, y_pred))\n```\n\n# Help\n\nIf you have questions concerning WTK or you encounter problems when\ntrying to build the tool under your own system, please open an issue in\n[the issue tracker](https://github.com/BorgwardtLab/WTK/issues). Try to\ndescribe the issue in sufficient detail in order to make it possible for\nus to help you.\n\n# Contributors\n\nWTK is developed and maintained by members of the [Machine Learning and\nComputational Biology Lab](https://www.bsse.ethz.ch/mlcb) of [Prof. Dr.\nKarsten Borgwardt](https://www.bsse.ethz.ch/mlcb/karsten.html):\n\n- Christian Bock ([GitHub](https://github.com/chrisby))\n- Matteo Togninalli ([GitHub](https://github.com/mtog))\n- Bastian Rieck ([GitHub](https://github.com/Submanifold))\n",
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