{ "info": { "author": "Julian Blank", "author_email": "blankjul@egr.msu.edu", "bugtrack_url": null, "classifiers": [], "description": "pydacefit\n==================================\n\nThe documentation can be found here:\nhttps://www.egr.msu.edu/coinlab/blankjul/pydacefit/\n\nThe purpose of this clone is to have a python version of the popular dacefit toolbox in MATLAB .\nThe toolbox can be found `here `_.\n\nThis framework is an exact clone of the original code and the correctness has been checked.\nPlease contact me if you should be scenarios where the values are significantly different (10^6).\n\nInstallation\n==================================\n\nThe test problems are uploaded to the PyPi Repository.\n\n.. code:: bash\n\n pip install pydacefit\n\nUsage\n==================================\n\n.. code:: python\n\n\n import numpy as np\n\n from pydacefit.corr import corr_gauss, corr_cubic, corr_exp, corr_expg, corr_spline, corr_spherical\n from pydacefit.dace import DACE, regr_linear, regr_quadratic\n from pydacefit.regr import regr_constant\n\n import matplotlib.pyplot as plt\n\n # -----------------------------------------------\n # Different ways of initialization\n # -----------------------------------------------\n\n # regression can be: regr_constant, regr_linear or regr_quadratic\n regression = regr_constant\n # regression = regr_linear\n # regression = regr_quadratic\n\n\n # then define the correlation (all possible correlations are shown below)\n # please have a look at the MATLAB document for more details\n correlation = corr_gauss\n # correlation = corr_cubic\n # correlation = corr_exp\n # correlation = corr_expg\n # correlation = corr_spline\n # correlation = corr_spherical\n # correlation = corr_cubic\n\n\n # This initializes a DACEFIT objective using the provided regression and correlation\n # because an initial theta is provided and also thetaL and thetaU the hyper parameter\n # optimization is done\n dacefit = DACE(regr=regression, corr=correlation,\n theta=1.0, thetaL=0.00001, thetaU=100)\n\n # if no lower and upper bounds are defined, then no hyperparameter optimization is executed\n dacefit_no_hyperparameter_optimization = DACE(regr=regression, corr=correlation,\n theta=1.0, thetaL=None, thetaU=None)\n\n # to turn on the automatic relevance detection use a vector for theta and define bounds\n dacefit_with_ard = DACE(regr=regression, corr=correlation,\n theta=[1.0, 1.0], thetaL=[0.001, 0.0001], thetaU=[20, 20])\n\n\n # -----------------------------------------------\n # Create some data for the purpose of testing\n # -----------------------------------------------\n\n def fun(X):\n return np.sum(np.sin(X * 2 * np.pi), axis=1)\n\n\n X = np.random.random((20, 1))\n F = fun(X)\n\n # -----------------------------------------------\n # Fit the model with the data and predict\n # -----------------------------------------------\n\n # create the model and fit it\n dacefit.fit(X, F)\n\n # predict values for plotting\n _X = np.linspace(0, 1, 100)[:, None]\n _F = dacefit.predict(_X)\n\n # -----------------------------------------------\n # Plot the results\n # -----------------------------------------------\n\n plt.scatter(X, F, label=\"prediction\")\n plt.plot(_X, _F, label=\"data\")\n plt.legend()\n plt.show()\n\n print(\"MSE: \", np.mean(np.abs(fun(_X)[:, None] - _F)))\n\nContact\n==================================\nFeel free to contact me if you have any question:\n\n| Julian Blank (blankjul [at] egr.msu.edu)\n| Michigan State University\n| Computational Optimization and Innovation Laboratory (COIN)\n| East Lansing, MI 48824, USA\n\n\n", "description_content_type": "", "docs_url": null, "download_url": "", "downloads": { "last_day": -1, "last_month": -1, "last_week": -1 }, "home_page": "https://github.com/msu-coinlab/pydacefit", "keywords": "metamodel,surrogate,response surface", "license": "Apache License 2.0", "maintainer": "", "maintainer_email": "", "name": "pydacefit", "package_url": "https://pypi.org/project/pydacefit/", "platform": "any", "project_url": "https://pypi.org/project/pydacefit/", "project_urls": { "Homepage": "https://github.com/msu-coinlab/pydacefit" }, "release_url": "https://pypi.org/project/pydacefit/1.0.1/", "requires_dist": [ "numpy (>=1.15)" ], "requires_python": "", "summary": "Surrogate Model", "version": "1.0.1" }, "last_serial": 4994313, "releases": { "1.0.0": [ { "comment_text": "", "digests": { "md5": "e1c9ef63fc7fa8bdafbfaba4dd81f323", "sha256": "7910f45d4cbf09821a8be3f2f8796a2d1f9367309e8e42479e8ae76896ec6fd6" }, "downloads": -1, "filename": "pydacefit-1.0.0-py3-none-any.whl", "has_sig": false, "md5_digest": "e1c9ef63fc7fa8bdafbfaba4dd81f323", "packagetype": "bdist_wheel", "python_version": "py3", "requires_python": null, "size": 9811, "upload_time": "2019-03-27T18:34:05", "url": "https://files.pythonhosted.org/packages/46/7e/c169ac1eb070e27fdd88e712756ebf283435e3a380a1b066504c846255bf/pydacefit-1.0.0-py3-none-any.whl" }, { "comment_text": "", "digests": { "md5": "9ba143d2818e598bbf95917c982edf6b", "sha256": "0a6e409315cde2fbba1ada6526302f26d115e426680bf8c846f051180f71dd25" }, "downloads": -1, "filename": "pydacefit-1.0.0.tar.gz", "has_sig": false, "md5_digest": "9ba143d2818e598bbf95917c982edf6b", "packagetype": "sdist", "python_version": "source", "requires_python": null, "size": 8594, "upload_time": "2019-03-27T18:34:07", "url": "https://files.pythonhosted.org/packages/b4/fb/86f6d6acb97ce58adc8644e2d7b0a1c94448b0a06d9228a4d937a0b3c221/pydacefit-1.0.0.tar.gz" } ], "1.0.1": [ { "comment_text": "", "digests": { "md5": "4e452e66e60f24d5e4f88aab2429e63d", "sha256": "210e14ef6dad5372a9ab25128df38c0af9a495508b07f9f6570c09362ca494f4" }, "downloads": -1, "filename": "pydacefit-1.0.1-py3-none-any.whl", "has_sig": false, "md5_digest": "4e452e66e60f24d5e4f88aab2429e63d", "packagetype": "bdist_wheel", "python_version": "py3", "requires_python": null, "size": 9805, "upload_time": "2019-03-27T18:48:00", "url": "https://files.pythonhosted.org/packages/5e/28/73b349b7557251fd5730606c5b5477651518b313aad4b445d1e08c482808/pydacefit-1.0.1-py3-none-any.whl" }, { "comment_text": "", "digests": { "md5": "196be74b76cc34a1401f0ed10c90df6a", "sha256": "13f4bb9b2f9511b298511f71410294790be8c12e216b1704adcfabbc1d073083" }, "downloads": -1, "filename": "pydacefit-1.0.1.tar.gz", "has_sig": false, "md5_digest": "196be74b76cc34a1401f0ed10c90df6a", "packagetype": "sdist", "python_version": "source", "requires_python": null, "size": 8582, "upload_time": "2019-03-27T18:48:02", "url": "https://files.pythonhosted.org/packages/10/f1/2e582500e666625798b2c3e3ddce0bc00fdf8b0e3a3f49e2008051b7739b/pydacefit-1.0.1.tar.gz" } ] }, "urls": [ { "comment_text": "", "digests": { "md5": "4e452e66e60f24d5e4f88aab2429e63d", "sha256": "210e14ef6dad5372a9ab25128df38c0af9a495508b07f9f6570c09362ca494f4" }, "downloads": -1, "filename": "pydacefit-1.0.1-py3-none-any.whl", "has_sig": false, "md5_digest": "4e452e66e60f24d5e4f88aab2429e63d", "packagetype": "bdist_wheel", "python_version": "py3", "requires_python": null, "size": 9805, "upload_time": "2019-03-27T18:48:00", "url": "https://files.pythonhosted.org/packages/5e/28/73b349b7557251fd5730606c5b5477651518b313aad4b445d1e08c482808/pydacefit-1.0.1-py3-none-any.whl" }, { "comment_text": "", "digests": { "md5": "196be74b76cc34a1401f0ed10c90df6a", "sha256": "13f4bb9b2f9511b298511f71410294790be8c12e216b1704adcfabbc1d073083" }, "downloads": -1, "filename": "pydacefit-1.0.1.tar.gz", "has_sig": false, "md5_digest": "196be74b76cc34a1401f0ed10c90df6a", "packagetype": "sdist", "python_version": "source", "requires_python": null, "size": 8582, "upload_time": "2019-03-27T18:48:02", "url": "https://files.pythonhosted.org/packages/10/f1/2e582500e666625798b2c3e3ddce0bc00fdf8b0e3a3f49e2008051b7739b/pydacefit-1.0.1.tar.gz" } ] }