Man page - mlpack_linear_regression(1)
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apt-get install mlpack-bin
Manual
mlpack_linear_regression
NAMESYNOPSIS
DESCRIPTION
OPTIONAL INPUT OPTIONS
OPTIONAL OUTPUT OPTIONS
ADDITIONAL INFORMATION
NAME
mlpack_linear_regression - simple linear regression and prediction
SYNOPSIS
mlpack_linear_regression [ -m unknown ] [ -l double ] [ -T unknown ] [ -t unknown ] [ -r unknown ] [ -V bool ] [ -M unknown ] [ -o unknown ] [ -h -v ]
DESCRIPTION
An implementation of simple linear regression and simple ridge regression using ordinary least squares. This solves the problem
y = X * b + e
where X (specified by ’ --training_file ( -t )’) and y (specified either as the last column of the input matrix ’ --training_file ( -t )’ or via the ’ --training_responses_file ( -r )’ parameter) are known and b is the desired variable. If the covariance matrix (X’X) is not invertible, or if the solution is overdetermined, then specify a Tikhonov regularization constant (with ’ --lambda ( -l )’) greater than 0, which will regularize the covariance matrix to make it invertible. The calculated b may be saved with the ’ --output_predictions_file ( -o )’ output parameter.
Optionally, the calculated value of b is used to predict the responses for another matrix X’ (specified by the ’ --test_file ( -T )’ parameter):
y’ = X’ * b
and the predicted responses y’ may be saved with the ’ --output_predictions_file ( -o )’ output parameter. This type of regression is related to least-angle regression, which mlpack implements as the ’lars’ program.
For example, to run a linear regression on the dataset ’X.csv’ with responses ’y.csv’, saving the trained model to ’lr_model.bin’, the following command could be used:
$ mlpack_linear_regression --training_file X.csv --training_responses_file y.csv --output_model_file lr_model.bin
Then, to use ’lr_model.bin’ to predict responses for a test set ’X_test.csv’, saving the predictions to ’X_test_responses.csv’, the following command could be used:
$ mlpack_linear_regression --input_model_file lr_model.bin --test_file X_test.csv --output_predictions_file X_test_responses.csv
OPTIONAL INPUT OPTIONS
--help (-h) [ bool ]
Default help info.
--info [string]
Print help on a specific option. Default value ’’.
--input_model_file (-m) [ unknown ]
Existing LinearRegression model to use.
--lambda (-l) [ double ]
Tikhonov regularization for ridge regression. If 0, the method reduces to linear regression. Default value 0.
--test_file (-T) [ unknown ]
Matrix containing X’ (test regressors).
--training_file (-t) [ unknown ]
Matrix containing training set X (regressors).
--training_responses_file (-r) [ unknown ]
Optional vector containing y (responses). If not given, the responses are assumed to be the last row of the input file.
--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_model_file (-M) [ unknown ]
Output LinearRegression model.
--output_predictions_file (-o) [ unknown ]
If --test_file is specified, this matrix is where the predicted responses will be saved.
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.