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cleaned up lars_main and added command line options

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This commit is contained in:
tekhnofiend
2011-12-10 17:53:34 +00:00
parent 5e657c0b78
commit 54e84c3870
1 changed files with 42 additions and 65 deletions
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src/mlpack/methods/lars/lars_main.cpp
+42 -65
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@@ -1,13 +1,12 @@
/** @file new_main.cc
/**
* @file lars_main.cpp
* @author Nishant Mehta
*
* Driver file for testing LARS
*
* @author Nishant Mehta (niche)
* Executable for LARS
*/
//#include <fastlib/fastlib.h>
#include <mlpack/core.hpp>
#include <armadillo>
#include "lars.hpp"
using namespace arma;
@@ -15,70 +14,48 @@ using namespace std;
using namespace mlpack;
using namespace mlpack::lars;
PROGRAM_INFO("LARS", "An implementation of LARS: Least Angle Regression (Stagewise/laSso)");
PARAM_STRING_REQ("X", "Covariates filename (observations of input random "
"variables)", "");
PARAM_STRING_REQ("y", "Targets filename (observations of output random "
"variable", "");
PARAM_STRING_REQ("beta", "Solution filename (linear estimator)", "");
PARAM_DOUBLE("lambda1", "Regularization parameter for l1-norm penalty", "", 0);
PARAM_DOUBLE("lambda2", "Regularization parameter for l2-norm penalty", "", 0);
PARAM_FLAG("use_cholesky", "Use Cholesky decomposition during computation "
"rather than explicitly computing full Gram matrix", "");
int main(int argc, char* argv[])
{
//bool use_cholesky = false;
double lambda_1 = 1;
double lambda_2 = 0.5;
u32 n = 100;
u32 p = 10;
mat X = randu<mat>(n,p);
/*
mat X_reg = zeros(n + p, p);
X_reg(span(0, n - 1), span::all) = X;
for(u32 i = 0; i < p; i++) {
X_reg(n + i, i) = sqrt(lambda_2);
}
//X_reg.print("X_reg");
*/
mat beta_true = zeros(p,1);
beta_true(0) = 1;
beta_true(1) = -1;
beta_true(9) = 1;
vec y = X * beta_true + 0.1 * randu<vec>(n);
//vec y = randu(n);
//y.load("y.dat", raw_ascii);
//y.load("x.dat", raw_ascii);
vec y_reg = zeros(n + p);
y_reg.subvec(0, n - 1) = y;
//y_reg.print("y_reg");
mat Gram = trans(X) * X;
LARS lars(X, y, true, lambda_1, lambda_2);
//lars.Init(X, y, true);
//lars.Init(X, y, false);
//lars.SetGram(Gram.memptr(), X.n_cols);
//lars.Init(X_reg, y_reg, false, lambda_1);
//lars.Init(X_reg, y_reg, use_cholesky);
// Handle parameters
CLI::ParseCommandLine(argc, argv);
double lambda1 = CLI::GetParam<double>("lambda1");
double lambda2 = CLI::GetParam<double>("lambda2");
bool useCholesky = CLI::GetParam<bool>("use_cholesky");
// load covariates
const std::string matXFilename = CLI::GetParam<std::string>("X");
mat matX;
matX.load(matXFilename, raw_ascii);
// load targets
const std::string yFilename = CLI::GetParam<std::string>("y");
vec y;
y.load(yFilename, raw_ascii);
// do LARS
LARS lars(matX, y, useCholesky, lambda1, lambda2);
lars.DoLARS();
u32 path_length = lars.beta_path().size();
mat beta_matrix = mat(p, path_length);
for(u32 i = 0; i < path_length; i++)
{
beta_matrix.col(i) = lars.beta_path()[i];
}
//beta_matrix.print("beta matrix");
vec lambda_path_vec = conv_to<colvec>::from(lars.lambda_path());
//lambda_path_vec.print("lambda path");
X.save("X.dat", raw_ascii);
y.save("y.dat", raw_ascii);
////beta_matrix.save("beta.dat", raw_ascii);
////lambda_path_vec.save("lambda.dat", raw_ascii);
// get and save solution
vec beta;
lars.Solution(beta);
beta.print("final beta");
const std::string betaFilename = CLI::GetParam<std::string>("beta");
beta.save(betaFilename, raw_ascii);
}