⛳️ optional - miniconda / environment helpers #1
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you can add a command line target in xcode proj to run this code. instructions above to help. going to keep diggin into sklearn - http://scikit-learn.org/stable/_downloads/scikit-learn-docs.pdf 5.1 sklearn.base: Base classes and utility functions . . . . . . . . . . . . . . . . . . . . . . . . . . 1183 5.2 sklearn.calibration: Probability Calibration . . . . . . . . . . . . . . . . . . . . . . . . . . 1189 5.3 sklearn.cluster: Clustering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1193 5.4 sklearn.cluster.bicluster: Biclustering . . . . . . . . . . . . . . . . . . . . . . . . . . . 1231 5.5 sklearn.covariance: Covariance Estimators . . . . . . . . . . . . . . . . . . . . . . . . . . . 1237 5.6 sklearn.cross_decomposition: Cross decomposition . . . . . . . . . . . . . . . . . . . . 1267 5.7 sklearn.datasets: Datasets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1281 5.8 sklearn.decomposition: Matrix Decomposition . . . . . . . . . . . . . . . . . . . . . . . . 1330 5.9 sklearn.discriminant_analysis: Discriminant Analysis . . . . . . . . . . . . . . . . . . 1383 5.10 sklearn.dummy: Dummy estimators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1391 5.11 sklearn.ensemble: Ensemble Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1396 5.12 sklearn.exceptions: Exceptions and warnings . . . . . . . . . . . . . . . . . . . . . . . . . 1426 5.13 sklearn.feature_extraction: Feature Extraction . . . . . . . . . . . . . . . . . . . . . . . 1431 5.14 sklearn.feature_selection: Feature Selection . . . . . . . . . . . . . . . . . . . . . . . . 1457 5.15 sklearn.gaussian_process: Gaussian Processes . . . . . . . . . . . . . . . . . . . . . . . . 1489 5.16 sklearn.isotonic: Isotonic regression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1527 5.17 sklearn.kernel_approximation Kernel Approximation . . . . . . . . . . . . . . . . . . . 1531 5.18 sklearn.kernel_ridge Kernel Ridge Regression . . . . . . . . . . . . . . . . . . . . . . . . 1540 5.19 sklearn.linear_model: Generalized Linear Models . . . . . . . . . . . . . . . . . . . . . . . 1543 5.20 sklearn.manifold: Manifold Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1642 5.21 sklearn.metrics: Metrics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1661 5.22 sklearn.mixture: Gaussian Mixture Models . . . . . . . . . . . . . . . . . . . . . . . . . . . 1727 5.23 sklearn.model_selection: Model Selection . . . . . . . . . . . . . . . . . . . . . . . . . . 1739 5.24 sklearn.multiclass: Multiclass and multilabel classification . . . . . . . . . . . . . . . . . . 1794 5.25 sklearn.multioutput: Multioutput regression and classification . . . . . . . . . . . . . . . . 1802 5.26 sklearn.naive_bayes: Naive Bayes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1810 5.27 sklearn.neighbors: Nearest Neighbors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1820 5.28 sklearn.neural_network: Neural network models . . . . . . . . . . . . . . . . . . . . . . . 1870 5.29 sklearn.pipeline: Pipeline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1883 5.30 sklearn.preprocessing: Preprocessing and Normalization . . . . . . . . . . . . . . . . . . . 1891 5.31 sklearn.random_projection: Random projection . . . . . . . . . . . . . . . . . . . . . . . 1936 5.32 sklearn.semi_supervised Semi-Supervised Learning . . . . . . . . . . . . . . . . . . . . . 1942 5.33 sklearn.svm: Support Vector Machines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1948 5.34 sklearn.tree: Decision Trees . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1981 5.35 sklearn.utils: Utilities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2005 5.36 Recently deprecated . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2019
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In case it's not so obvious - the point of above PR is to be able to use drop in python code in ide. |
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you can add a command line target in xcode proj to run this code.
instructions above to help.