Man page - mlpack_preprocess_binarize(1)
Packages contains this manual
- mlpack_fastmks(1)
- mlpack_mean_shift(1)
- mlpack_hmm_generate(1)
- mlpack_local_coordinate_coding(1)
- mlpack_sparse_coding(1)
- mlpack_preprocess_scale(1)
- mlpack_kmeans(1)
- mlpack_linear_svm(1)
- mlpack_preprocess_split(1)
- mlpack_softmax_regression(1)
- mlpack_hmm_train(1)
- mlpack_nca(1)
- mlpack_range_search(1)
- mlpack_radical(1)
- mlpack_gmm_generate(1)
- mlpack_cf(1)
- mlpack_random_forest(1)
- mlpack_lmnn(1)
- mlpack_gmm_probability(1)
- mlpack_emst(1)
- mlpack_dbscan(1)
- mlpack_nbc(1)
- mlpack_preprocess_one_hot_encoding(1)
- mlpack_lsh(1)
- mlpack_knn(1)
- mlpack_kde(1)
- mlpack_hoeffding_tree(1)
- mlpack_adaboost(1)
- mlpack_hmm_loglik(1)
- mlpack_nmf(1)
- mlpack_pca(1)
- mlpack_bayesian_linear_regression(1)
- mlpack_hmm_viterbi(1)
- mlpack_preprocess_describe(1)
- mlpack_decision_tree(1)
- mlpack_krann(1)
- mlpack_det(1)
- mlpack_lars(1)
- mlpack_preprocess_binarize(1)
- mlpack_logistic_regression(1)
- mlpack_gmm_train(1)
- mlpack_perceptron(1)
- mlpack_preprocess_imputer(1)
- mlpack_kernel_pca(1)
- mlpack_kfn(1)
- mlpack_linear_regression(1)
- mlpack_approx_kfn(1)
apt-get install mlpack-bin
Manual
mlpack_preprocess_binarize
NAMESYNOPSIS
DESCRIPTION
REQUIRED INPUT OPTIONS
OPTIONAL INPUT OPTIONS
OPTIONAL OUTPUT OPTIONS
ADDITIONAL INFORMATION
NAME
mlpack_preprocess_binarize - binarize data
SYNOPSIS
mlpack_preprocess_binarize -i unknown [ -d int ] [ -t double ] [ -V bool ] [ -o unknown ] [ -h -v ]
DESCRIPTION
This utility takes a dataset and binarizes the variables into either 0 or 1 given threshold. User can apply binarization on a dimension or the whole dataset. The dimension to apply binarization to can be specified using the ’ --dimension ( -d )’ parameter; if left unspecified, every dimension will be binarized. The threshold for binarization can also be specified with the ’ --threshold ( -t )’ parameter; the default threshold is 0.0.
The binarized matrix may be saved with the ’ --output_file ( -o )’ output parameter.
For example, if we want to set all variables greater than 5 in the dataset ’X.csv’ to 1 and variables less than or equal to 5.0 to 0, and save the result to ’Y.csv’, we could run
$ mlpack_preprocess_binarize --input_file X.csv --threshold 5 --output_file Y.csv
But if we want to apply this to only the first (0th) dimension of ’X.csv’, we could instead run
$ mlpack_preprocess_binarize --input_file X.csv --threshold 5 --dimension 0 --output_file Y.csv
REQUIRED INPUT OPTIONS
--input_file (-i) [ unknown ]
Input data matrix.
OPTIONAL INPUT OPTIONS
--dimension (-d) [ int ]
Dimension to apply the binarization. If not set, the program will binarize every dimension by default. Default value 0.
--help (-h) [ bool ]
Default help info.
--info [string]
Print help on a specific option. Default value ’’.
--threshold (-t) [ double ]
Threshold to be applied for binarization. If not set, the threshold defaults to 0.0. Default value 0.
--verbose (-v) [ bool ]
Display informational messages and the full list of parameters and timers at the end of execution.
--version (-V) [ bool ]
Display the version of mlpack.
OPTIONAL OUTPUT OPTIONS
--output_file ( -o ) [ unknown ] Matrix in which to save the output.
ADDITIONAL INFORMATION
For further information, including relevant papers, citations, and theory, consult the documentation found at http://www.mlpack.org or included with your distribution of mlpack.