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        "author": "Zhiang Liu",
        "author_email": "me262r@gmail.com",
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        "description": "# Dimension Reduction Function Research (drfr) \nThis package provides a Reduction Model and Regression Model, which respectively \ncontains several choices for reduction and regression of data.\n\n# Discription of Each Model\n## Reduction Model\ncontains \"NPPE\", \"UMAP\", \"LLE\", \"Hessian\", \"Spectral\", \"TSNE\", \"Isomap\", used \nas keyword argument ```tag``` in function ```get_reduction()```.\nTo make tag \"UMAP\" work properly, an install according to https://github.com/lmcinnes/umap\nis needed.\n\n## Regression Model\ncontains \"lasso\", \"ridge\", \"MARS\",  used \nas keyword argument ```tag``` in function ```cal_regression()```. As basis generator \neither those in BasisGenerator or self made function can be used, where data X \nshould be the only positional argument.\n\n## Basis Generator\ncontains several functions as basis generators, with form\n```python\ngenerate_basis_name(X, p=basis_degree)\n```\n\n# Usage\n```python\nfrom drfr import ReductionModel, BasisGenerator, RegressionModel\nfrom sklearn import datasets\nimport matplotlib.pyplot as plt\n\nN = 5000\nk = 24\nX, color = datasets.samples_generator.make_swiss_roll(n_samples=N, noise=0.00001)\nbasis_generator = None\npoly_degree = 4\ntag_red = \"NPPE\"\ntag_reg = \"MARS\"\n\n# preprocessing\nX, color = ReductionModel.pre_process(X, color)\n\n# compute embedded result\nred_model = ReductionModel.ReductionModel()\ny_nppe = red_model.get_reduction(X, tag=tag_red)\n\n# compute regression weights w given X and y, and compute basis(X)*y\nreg_model = RegressionModel.RegressionModel()\ny_reg = reg_model.cal_regression(X, y_nppe, tag=tag_reg, basis_generator=BasisGenerator.generate_fourier, p=poly_degree)\n\n# draw results\nax = fig.add_subplot(311, projection='3d')\nax.scatter(X[:, 1], X[:, 0], X[:, 2], c=color, cmap=plt.cm.Spectral)\n\nax.set_title(\"Original data\")\nax = fig.add_subplot(312)\nax.scatter(y_nppe[:, 1], y_nppe[:, 0], c=color, cmap=plt.cm.Spectral)\nplt.axis('tight')\nplt.xticks([]), plt.yticks([])\nplt.title('Projected data with method' + tag_red)\nax = fig.add_subplot(313)\nax.scatter(y_reg[:, 1], y_reg[:, 0], c=color, cmap=plt.cm.Spectral)\nplt.axis('tight')\nplt.xticks([]), plt.yticks([])\nplt.title(\"NPPE embedded data regressed by \" + tag_reg + \" Model\\n\" + \"with basis degree\" + poly_degree.__str__())\nplt.show()\n```\n\n",
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