Thank you for the prompt answer Eugene
On Dec 11, 2017 9:05 PM, "Ryan Curtin" <[email protected]> wrote: > On Mon, Dec 11, 2017 at 06:37:10PM +0000, Evgeny Freyman wrote: > > Hi > > > > Cannot find any example of using MLPack from Python. Are they exists? > > We haven't generated these yet---hang tight. Before the 3.0 release > there needs to be better documentation. > > In the mean time you could do the following: > > >>> import mlpack > >>> dir(mlpack) > ['__builtins__', '__doc__', '__file__', '__name__', '__package__', > '__path__', 'adaboost', 'approx_kfn', 'arma_numpy', 'cf', > 'decision_stump', 'decision_tree', 'det', 'emst', 'fastmks', > 'gmm_generate', 'gmm_probability', 'gmm_train', 'hmm_generate', > 'hmm_loglik', 'hmm_train', 'hmm_viterbi', 'hoeffding_tree', > 'kernel_pca', 'kfn', 'kmeans', 'knn', 'krann', 'lars', > 'linear_regression', 'local_coordinate_coding', 'logistic_regression', > 'lsh', 'matrix_utils', 'mean_shift', 'nbc', 'nca', 'nmf', 'pca', > 'perceptron', 'preprocess_binarize', 'preprocess_describe', > 'preprocess_split', 'radical', 'softmax_regression', 'sparse_coding', > 'test_python_binding'] > > That's a list of all the bindings the mlpack package has (plus some > extras like test_python_binding and arma_numpy). Then you can use > help() to get documentation for each. (This help is generated from the > same documentation as the --help command for the command-line programs.) > > >>> help(mlpack.logistic_regression) > > That should give you all you need to actually use the bindings. For > matrix data you can pass in your usual numpy or pandas matrices. > > These are still somewhat new so speak up if you find any problems! But > I think they are bug-free at this point and ready for production use. > > -- > Ryan Curtin | "I know... but I really liked those ones." > [email protected] | - Vincent >
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