.\"Text automatically generated by txt2man
.TH lars  "1" "" ""
.SH NAME
\fBlars \fP- lars
.SH SYNOPSIS
.nf
.fam C
 \fBlars\fP [\fB-h\fP] [\fB-v\fP] \fB-i\fP \fIstring\fP \fB-r\fP \fIstring\fP [\fB-l\fP \fIdouble\fP] [\fB-L\fP \fIdouble\fP] [\fB-o\fP \fIstring\fP] [\fB-c\fP] \fB-V\fP 
.fam T
.fi
.fam T
.fi
.SH DESCRIPTION


An implementation of LARS: Least Angle Regression (Stagewise/laSso). This is
a stage-wise homotopy-based algorithm for L1-regularized linear regression
(LASSO) and L1+L2-regularized linear regression (Elastic Net).
.PP
Let X be a matrix where each row is a point and each column is a dimension,
and let y be a vector of targets.
.PP
The Elastic Net problem is to solve
.PP
.nf
.fam C
  min_beta 0.5 || X * beta - y ||_2^2 + lambda_1 ||beta||_1 +
    0.5 lambda_2 ||beta||_2^2

.fam T
.fi
If lambda_1 > 0 and lambda_2 = 0, the problem is the LASSO.
If lambda_1 > 0 and lambda_2 > 0, the problem is the Elastic Net.
If lambda_1 = 0 and lambda_2 > 0, the problem is ridge regression.
If lambda_1 = 0 and lambda_2 = 0, the problem is unregularized linear
regression.
.PP
For efficiency reasons, it is not recommended to use this algorithm with
lambda_1 = 0. In that case, use the 'linear_regression' program, which
implements both unregularized linear regression and ridge regression.
.RE
.PP

.SH REQUIRED OPTIONS 

.TP
.B
\fB--input_file\fP (\fB-i\fP) [\fIstring\fP]
File containing covariates (X). 
.TP
.B
\fB--responses_file\fP (\fB-r\fP) [\fIstring\fP]
File containing y (responses/observations).  
.SH OPTIONS 

.TP
.B
\fB--help\fP (\fB-h\fP)
Default help info. 
.TP
.B
\fB--info\fP [\fIstring\fP]
Get help on a specific module or option.  Default value ''. 
.TP
.B
\fB--lambda1\fP (\fB-l\fP) [\fIdouble\fP]
Regularization parameter for l1-norm penalty.  Default value 0. 
.TP
.B
\fB--lambda2\fP (\fB-L\fP) [\fIdouble\fP]
Regularization parameter for l2-norm penalty.  Default value 0. 
.TP
.B
\fB--output_file\fP (\fB-o\fP) [\fIstring\fP]
File to save beta (linear estimator) to.  Default value 'output.csv'. 
.TP
.B
\fB--use_cholesky\fP (\fB-c\fP)
Use Cholesky decomposition during computation rather than explicitly computing the full Gram matrix. 
.TP
.B
\fB--verbose\fP (\fB-v\fP)
Display informational messages and the full list of parameters and timers at the end of execution. 
.TP
.B
\fB--version\fP (\fB-V\fP)
Display the version of mlpack.
.SH 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.