int main() {
LinearRegression lr;
vector<DataGroup> train_set;
DataGroup train1, train2, train3, train4, train5;
//y = 3 * x1 - 5 * x2 + 3;
DataGroup test1;
//1 1 1
train1.in.push_back(1); train1.in.push_back(1); train1.out.push_back(1);
//1 -1 1
train2.in.push_back(2); train2.in.push_back(1); train2.out.push_back(4);
//-1 1 1
train3.in.push_back(-1); train3.in.push_back(3); train3.out.push_back(-15);
//0 0 1
train4.in.push_back(9); train4.in.push_back(1); train4.out.push_back(25);
//-1 -1 0
train5.in.push_back(0); train5.in.push_back(1); train5.out.push_back(-2);
train_set.push_back(train1);
train_set.push_back(train2);
train_set.push_back(train3);
train_set.push_back(train4);
train_set.push_back(train5);
lr.train(train_set, 1e-5);
test1.in.push_back(1); test1.in.push_back(1); test1.out.push_back(0);
lr.predict(test1);
cout << "result:" << test1.out[0] << endl;
return 0;
}
TEUCHOS_UNIT_TEST( Rythmos_ConvergenceTestHelpers, zeroData ) {
LinearRegression<double> lr;
Array<double> x,y;
x.push_back(0.0);
x.push_back(0.0);
y.push_back(1.0);
y.push_back(2.0);
TEST_THROW(lr.setData(x,y), std::logic_error);
}
TEUCHOS_UNIT_TEST( Rythmos_ConvergenceTestHelpers, trivialData ) {
LinearRegression<double> lr;
Array<double> x,y;
x.push_back(0.0);
x.push_back(1.0);
y.push_back(0.0);
y.push_back(1.0);
lr.setData(x,y);
TEST_EQUALITY_CONST( lr.getSlope(), 1.0 );
TEST_EQUALITY_CONST( lr.getYIntercept(), 0.0 );
}
int main(int argc, char* argv[]) {
LinearRegression lr;
LinearRegressionScoreTest lrst;
LinearRegressionPermutationTest lrpt;
Vector y;
Matrix x;
LoadVector("input.y", y);
LoadMatrix("input.x", x);
if (lr.FitLinearModel(x, y) == false) {
fprintf(stderr, "Fitting failed!\n");
return -1;
}
Vector& beta = lr.GetCovEst();
Matrix& v = lr.GetCovB();
Vector& pWald = lr.GetAsyPvalue();
fprintf(stdout, "wald_beta\t");
Print(beta);
fputc('\n', stdout);
fprintf(stdout, "wald_vcov\t");
Print(v);
fputc('\n', stdout);
fprintf(stdout, "wald_p\t");
Print(pWald[1]);
fputc('\n', stdout);
if (lrpt.FitLinearModel(x, 1, y, 200, 0.05) == false) {
fprintf(stderr, "Fitting failed!\n");
return -1;
}
fprintf(stdout, "permutation_p\t");
double permu_p = lrpt.getPvalue();
Print(permu_p);
fputc('\n', stdout);
if (lrst.FitLinearModel(x, y, 1) == false) {
fprintf(stderr, "Fitting failed!\n");
return -1;
}
fprintf(stdout, "score_p\t");
double score_p = lrst.GetPvalue();
Print(score_p);
fputc('\n', stdout);
return 0;
};
int main(int argc, char** argv) {
QApplication app(argc, argv);
std::vector<Eigen::Matrix<double, 2, 1> > data;
LinearRegression<2> dist;
for (double x = -10; x < 10; x += 0.01) {
dist.setBasis((Eigen::Matrix<double, 1, 1>() << x).finished());
data.push_back(dist.getSample());
}
ScatterPlot<2> plot("LinearRegressionRndScatterPlot1v", data);
plot.show();
return app.exec();
}
int main(int argc, char** argv) {
QApplication app(argc, argv);
std::vector<Eigen::Matrix<double, 3, 1> > data;
LinearRegression<3> dist;
for (double x = -10; x < 10; x += 0.1)
for (double y = -10; y < 10; y += 0.1) {
dist.setBasis(Eigen::Matrix<double, 2, 1>(x, y));
data.push_back(dist.getSample());
}
ScatterPlot<3> plot("LinearRegressionRndScatterPlot2v", data);
plot.show();
return app.exec();
}
TEUCHOS_UNIT_TEST( Rythmos_ConvergenceTestHelpers, nonUniqueXData ) {
LinearRegression<double> lr;
Array<double> x,y;
x.push_back(0.0);
x.push_back(1.0);
x.push_back(1.0);
y.push_back(0.0);
y.push_back(0.5);
y.push_back(1.5);
lr.setData(x,y);
double tol = 1.0e-10;
TEST_FLOATING_EQUALITY( lr.getSlope(), 1.0, tol );
TEST_FLOATING_EQUALITY( lr.getYIntercept(), 0.0, tol );
}
TEUCHOS_UNIT_TEST( Rythmos_ConvergenceTestHelpers, CoSineData ) {
LinearRegression<double> lr;
Array<double> x,y;
int N = 11;
double dt = 0.1;
for (int i=0 ; i<N ; ++i) {
double xval = dt*i;
x.push_back( xval );
y.push_back( cos(xval) );
}
lr.setData(x,y);
// 1.0e-14 works on rancilio but not on gabriel, exetazo, or s858352
double tol = 1.0e-13;
TEST_FLOATING_EQUALITY( lr.getSlope(), -4.653508042678562e-01, tol ); // These came from matlab
TEST_FLOATING_EQUALITY( lr.getYIntercept(), 1.067025181571952, tol );
}
TEUCHOS_UNIT_TEST( Rythmos_ConvergenceTestHelpers, SineData ) {
LinearRegression<double> lr;
Array<double> x,y;
int N = 11;
double dt = 0.1;
for (int i=0 ; i<N ; ++i) {
double xval = dt*i;
x.push_back( xval );
y.push_back( sin(xval) );
}
lr.setData(x,y);
// 1.0e-14 works on rancilio but not on gabriel, exetazo, or s858352
double tol = 1.0e-13;
TEST_FLOATING_EQUALITY( lr.getSlope(), 8.518189335013251e-01, tol ); // These came from matlab
TEST_FLOATING_EQUALITY( lr.getYIntercept(), 2.989789515694744e-02, tol );
}
void LDA_predict(const std::string &year) {
string type = "simulation";
//string modelFile = "LDA_result/" + year + "LDA_model_whole.csv";
string modelFile = "LDA_result/" + year + "LDA_model.csv";
string testFile = "training_set/" + type + "/" + year + "/" + year + "training_set_normalise" + ".csv";
string label_file = "training_set/" + type + "/" + year + "/" + year + "label_set" + ".csv";
// string label_file = "wholeTrainingset/" + year + "label_set_whole.csv";
LinearRegression lr;
lr = LinearRegression(modelFile);
arma::mat points;
data::Load(testFile, points, true);
// Load the test file data.
// arma::mat points;
// data::Load(training_file, points, true);
// Perform the predictions using our model.
arma::vec predictions;
lr.Predict(points, predictions);
// Save predictions.
//predictions = arma::trans(predictions);
string predictFile = "LDA_result/" + year + "LDA_prediction.csv";
data::Save(predictFile, predictions, true);
std::vector<int> labels = read_line(label_file);
std::vector<int> p = read_line (predictFile);
int num = 0;
if (p.size() == labels.size()) {
for (unsigned i = 0; i < p.size(); i++) {
if (p[i] > 0) {
p[i] = 1;
} else {
p[i] = -1;
}
if (p[i] == labels[i]) {
num++;
}
}
}
double rate = (double)num/(double)p.size();
std::cout<<num<<"/"<<p.size()<<" "<<rate<<endl;
}
void ClothoidPath::OptimiseLine( const CarModel& cm, int idx, int step, double hLimit, PathPt* l3, const PathPt* l2, const PathPt* l4 )
{
LinearRegression l;
const int NSEG = m_pTrack->GetSize();
int i = (idx + NSEG - step) % NSEG;
while( m_pPath[i].h > hLimit )
{
l.Sample( m_pPath[i].pt.GetXY() );
i = (i + NSEG - step) % NSEG;
}
l.Sample( m_pPath[i].pt.GetXY() );
i = idx;
while( m_pPath[i].h > hLimit )
{
l.Sample( m_pPath[i].pt.GetXY() );
i = (i + step) % NSEG;
}
l.Sample( m_pPath[i].pt.GetXY() );
GfOut( "%4d ", idx );
Vec2d p, v;
l.CalcLine( p, v );
double t;
Utils::LineCrossesLine( l3->Pt().GetXY(), l3->Norm().GetXY(), p, v, t );
SetOffset( cm, 0, t, l3, l2, l4 );
}
Scalar computeLinearRegressionSlope(Array<Scalar>& x, Array<Scalar>& y)
{
LinearRegression<Scalar> lr;
lr.setData(x,y);
return(lr.getSlope());
}
/*
Computes the linear regression for a series of measurements, the
x-axis intercept of the regression line and its confidence interval, and
writes a couple of files from which a nice plot of all this can be
generated using the gnuplot program.
*/
bool computeRegressionAndWriteGnuplotFiles_(vector<double>::const_iterator const conc_vec_begin,
vector<double>::const_iterator const conc_vec_end,
vector<double>::const_iterator const area_vec_begin,
double const confidence_p,
String const filename_prefix,
String const output_filename,
String const format = "",
bool const write_gnuplot = true
)
{
try
{
LinearRegression linreg;
linreg.computeRegression(confidence_p, conc_vec_begin, conc_vec_end, area_vec_begin);
if (write_gnuplot)
{
// the peak data goes here
String datafilename(filename_prefix);
datafilename += String(".dat");
ofstream dataout(datafilename.c_str());
// the gnuplot commands go here
String commandfilename(filename_prefix);
commandfilename += String(".cmd");
ofstream cmdout(commandfilename.c_str());
// the error bar for the x-axis intercept goes here
String errorbarfilename(filename_prefix);
errorbarfilename += String(".err");
ofstream errout(errorbarfilename.c_str());
// writing the commands
cmdout <<
"set ylabel \"ion count\"\n"
"set xlabel \"concentration\"\n"
"set key left Left reverse\n";
if (!format.empty())
{
if (format == "png")
{
cmdout <<
"set terminal png \n"
"set output \"" << filename_prefix << ".png\"\n";
}
else if (format == "eps")
{
cmdout <<
"set terminal postscript eps \n"
"set output \"" << filename_prefix << ".eps\"\n";
}
}
cmdout <<
"plot \"" << datafilename << "\" w points ps 2 pt 1 lt 8 title \"data\" " // want data on first line of key
", " << linreg.getIntercept() << "+" << linreg.getSlope() << "*x lt 2 lw 3 title \"linear regression: "
<< linreg.getIntercept() << " + " << linreg.getSlope() << " * x\" "
", \"" << datafilename << "\" w points ps 2 pt 1 lt 8 notitle " // draw data a second time, on top of reg. line
", \"" << errorbarfilename << "\" using ($1):(0) w points pt 13 ps 2 lt 1 title \"x-intercept: " << linreg.getXIntercept() << "\" "
", \"" << errorbarfilename << "\" w xerrorbars lw 3 lt 1 title \"95% interval: [ " << linreg.getLower() << ", " << linreg.getUpper() << " ]\"\n";
cmdout.close();
// writing the x-axis intercept error bar
errout << linreg.getXIntercept() << " 0 " << linreg.getLower() << " " << linreg.getUpper() << endl;
errout.close();
// writing the peak data points
vector<double>::const_iterator cit = conc_vec_begin;
vector<double>::const_iterator ait = area_vec_begin;
dataout.precision(writtenDigits<double>(0.0));
for (; cit != conc_vec_end; ++cit, ++ait)
{
dataout << *cit << ' ' << *ait << '\n';
}
dataout.close();
} // end if (write_gnuplot)
// write results to XML file
ofstream results;
results.open(output_filename.c_str());
results << "<?xml version=\"1.0\" encoding=\"ISO-8859-1\"?>" << endl;
results << "<results_additiveseries>" << endl;
results << "\t<slope>" << linreg.getSlope() << "</slope>" << endl;
results << "\t<intercept>" << linreg.getIntercept() << "</intercept>" << endl;
results << "\t<x_intercept>" << linreg.getXIntercept() << "</x_intercept>" << endl;
results << "\t<confidence_lowerlimit>" << linreg.getLower() << "</confidence_lowerlimit>" << endl;
results << "\t<confidence_upperlimit>" << linreg.getUpper() << "</confidence_upperlimit>" << endl;
results << "\t<pearson_squared>" << linreg.getRSquared() << "</pearson_squared>" << endl;
results << "\t<std_residuals>" << linreg.getStandDevRes() << "</std_residuals>" << endl;
results << "\t<t_statistic>" << linreg.getTValue() << "</t_statistic>" << endl;
results << "</results_additiveseries>" << endl;
results.close();
}
catch (string & s)
{
cout << s << endl;
return 1;
}
return 0;
}
void test_linearity() {
Radix r;
SimpleLSystemWithBranching sls;
int b_index = 3;
int grid_size = 10;
//int grid_size = 5;
int N = 10000;
cv::Mat_<double> X(N, (grid_size - b_index - 2) * 2 + b_index);
//cv::Mat_<double> X(N, (grid_size - 3) * 2 + 1);
cv::Mat_<double> Y(N, grid_size * grid_size);
for (int i = 0; i < N; ++i) {
cv::Mat_<double> param(1, X.cols);
for (int c = 0; c < X.cols; ++c) {
param(0, c) = rand() % 3 - 1;
}
param.copyTo(X.row(i));
}
cv::Mat_<double> X2(X.rows, X.cols);
int count = 0;
for (int i = 0; i < X.rows; ++i) {
try {
//cv::Mat_<double> density = sls.computeDensity(grid_size, X.row(i), true, true);
cv::Mat_<double> density = sls.computeDensity(grid_size, X.row(i), true, false);
density.copyTo(Y.row(count));
X.row(i).copyTo(X2.row(count));
count++;
} catch (char* ex) {
//cout << "conflict" << endl;
}
}
ml::saveDataset("dataX.txt", X2(cv::Rect(0, 0, X2.cols, count)));
ml::saveDataset("dataY.txt", Y(cv::Rect(0, 0, Y.cols, count)));
//cv::Mat_<double> X, Y;
ml::loadDataset("dataX.txt", X);
ml::loadDataset("dataY.txt", Y);
cv::Mat_<double> trainX, trainY;
cv::Mat_<double> testX, testY;
ml::splitDataset(X, 0.8, trainX, testX);
ml::splitDataset(Y, 0.8, trainY, testY);
// Forward
{
LinearRegression lr;
lr.train(trainX, trainY);
cv::Mat_<double> Y_hat = lr.predict(testX);
cv::Mat_<double> Y_avg;
cv::reduce(trainY, Y_avg, 0, CV_REDUCE_AVG);
Y_avg = cv::repeat(Y_avg, testY.rows, 1);
cout << "-----------------------" << endl;
cout << "Forward:" << endl;
cout << "RMSE: " << ml::rmse(testY, Y_hat, true) << endl;
cout << "Baselime: " << ml::rmse(testY, Y_avg, true) << endl;
}
// Inverse
{
LinearRegression lr;
lr.train(trainY, trainX);
cv::Mat_<double> X_hat = lr.predict(testY);
// Xの各値を-1,0,1にdiscretizeする
{
for (int r = 0; r < X_hat.rows; ++r) {
for (int c = 0; c < X_hat.cols; ++c) {
if (X_hat(r, c) < -0.5) {
X_hat(r, c) = -1;
} else if (X_hat(r,c ) > 0.5) {
X_hat(r, c) = 1;
} else {
X_hat(r, c) = 0;
}
}
}
}
for (int i = 0; i < testX.cols; ++i) {
cout << ml::rmse(testX.col(i), X_hat.col(i), true) << endl;
}
cv::Mat_<double> Y_hat(testY.rows, testY.cols);
for (int i = 0; i < testX.rows; ++i) {
cv::Mat_<double> density_hat = sls.computeDensity(grid_size, X_hat.row(i), true, false);
density_hat.copyTo(Y_hat.row(i));
}
cv::Mat X_avg;
cv::reduce(trainX, X_avg, 0, CV_REDUCE_AVG);
double baselineX = ml::rmse(testX, cv::repeat(X_avg, testX.rows, 1), true);
cv::Mat Y_avg = sls.computeDensity(grid_size, X_avg, true, false);
//cv::reduce(trainY, Y_avg, 0, CV_REDUCE_AVG);
double baselineY = ml::rmse(testY, cv::repeat(Y_avg, testY.rows, 1), true);
cout << "-----------------------" << endl;
cout << "Inverse:" << endl;
cout << "RMSE in Parameter: " << ml::rmse(testX, X_hat, true) << endl;
cout << "Baselime: " << baselineX << endl;
cout << "RMSE in Indicator: " << ml::rmse(testY, Y_hat, true) << endl;
cout << "Baseline: " << baselineY << endl;
}
}
int main(int argc, char** argv) {
LinearRegression<2> linearRegression;
std::cout << "Default parameters: " << std::endl << linearRegression
<< std::endl;
std::cout << "l.getLinearBasisFunction(): " << std::endl <<
linearRegression.getLinearBasisFunction() << std::endl;
std::cout << "l.getVariance(): " << linearRegression.getVariance()
<< std::endl;
std::cout << "l.setLinearBasisFunction(2.0, 2.0)" << std::endl;
std::cout << "l.setVariance(2.0)" << std::endl;
std::cout << "l.setBasis(2.0)" << std::endl;
linearRegression.setLinearBasisFunction(LinearBasisFunction<double, 2>(
Eigen::Matrix<double, 2, 1>(2.0, 2.0)));
linearRegression.setVariance(2.0);
linearRegression.setBasis((Eigen::Matrix<double, 1, 1>() << 2.0).finished());
if (linearRegression.getLinearBasisFunction().getCoefficients() !=
Eigen::Matrix<double, 2, 1>(2.0, 2.0))
return 1;
if (linearRegression.getVariance() != 2.0)
return 1;
if (linearRegression.getBasis() !=
(Eigen::Matrix<double, 1, 1>() << 2.0).finished())
return 1;
std::cout << "New parameters: " << std::endl << linearRegression
<< std::endl;
std::cout << "l.getValue(2): " << linearRegression(
Eigen::Matrix<double, 2, 1>(2, 6)) << std::endl;
std::cout << "l.getSample(): " << std::endl << linearRegression.getSample()
<< std::endl;
std::cout << "l.getMean(): " << linearRegression.getMean() << std::endl;
LinearRegression<2> linCopy(linearRegression);
std::cout << "Copy constructor: " << std::endl << linCopy << std::endl
<< std::endl;
if (linCopy.getLinearBasisFunction().getCoefficients() !=
linearRegression.getLinearBasisFunction().getCoefficients())
return 1;
if (linCopy.getVariance() != linearRegression.getVariance())
return 1;
LinearRegression<2> linAssign = linearRegression;
std::cout << "Assignment operator: " << std::endl << linAssign << std::endl;
if (linAssign.getLinearBasisFunction().getCoefficients() !=
linearRegression.getLinearBasisFunction().getCoefficients())
return 1;
if (linAssign.getVariance() != linearRegression.getVariance())
return 1;
return 0;
}
int DataLoader::useResidualAsPhenotype() {
if (binaryPhenotype) {
logger->warn(
"WARNING: Skip transforming binary phenotype, although you want to "
"use residual as phenotype!");
return 0;
}
LinearRegression lr;
Vector pheno;
Matrix covAndInt;
const int numCovariate = covariate.ncol();
copyPhenotype(phenotype, &pheno);
copyCovariateAndIntercept(pheno.Length(), covariate, &covAndInt);
if (!lr.FitLinearModel(covAndInt, pheno)) {
if (numCovariate > 0) {
logger->error(
"Cannot fit model: [ phenotype ~ 1 + covariates ], now use the "
"original phenotype");
} else {
logger->error(
"Cannot fit model: [ phenotype ~ 1 ], now use the "
"original phenotype");
}
} else { // linear model fitted successfully
copyVectorToMatrixColumn(lr.GetResiduals(), &phenotype, 0);
// const int n = lr.GetResiduals().Length();
// for (int i = 0; i < n; ++i) {
// // phenotypeInOrder[i] = lr.GetResiduals()[i];
// phenotype[i][0] = lr.GetResiduals()[i];
// }
covariate.clear();
if (numCovariate > 0) {
logger->info(
"DONE: Fit model [ phenotype ~ 1 + covariates ] and model "
"residuals will be used as responses");
} else {
logger->info("DONE: Use residual as phenotype by centerng it");
}
// store fitting results
Vector& beta = lr.GetCovEst();
Matrix& betaSd = lr.GetCovB();
const int n = beta.Length();
for (int i = 0; i < n; ++i) {
addFittedParameter(covAndInt.GetColumnLabel(i), beta[i], betaSd[i][i]);
}
addFittedParameter("Sigma2", lr.GetSigma2(), NAN);
}
#if 0
if (covariate.ncol() > 0) {
LinearRegression lr;
Vector pheno;
Matrix covAndInt;
copyPhenotype(phenotype, &pheno);
copyCovariateAndIntercept(covariate.nrow(), covariate, &covAndInt);
if (!lr.FitLinearModel(covAndInt, pheno)) {
logger->error(
"Cannot fit model: [ phenotype ~ 1 + covariates ], now use the "
"original phenotype");
} else {
const int n = lr.GetResiduals().Length();
for (int i = 0; i < n; ++i) {
// phenotypeInOrder[i] = lr.GetResiduals()[i];
phenotype[i][0] = lr.GetResiduals()[i];
}
covariate.clear();
logger->info(
"DONE: Fit model [ phenotype ~ 1 + covariates ] and model "
"residuals will be used as responses");
}
storeFittedModel(lr);
} else { // no covaraites
// centerVector(&phenotypeInOrder);
std::vector<double> v;
phenotype.extractCol(0, &v);
centerVector(&v);
phenotype.setCol(0, v);
logger->info("DONE: Use residual as phenotype by centerng it");
}
#endif
return 0;
}
static void mlpackMain()
{
const double lambda = CLI::GetParam<double>("lambda");
RequireOnlyOnePassed({ "training", "input_model" }, true);
ReportIgnoredParam({{ "test", true }}, "output_predictions");
mat regressors;
rowvec responses;
LinearRegression lr;
const bool computeModel = !CLI::HasParam("input_model");
const bool computePrediction = CLI::HasParam("test");
// If they specified a model file, we also need a test file or we
// have nothing to do.
if (!computeModel)
{
RequireAtLeastOnePassed({ "test" }, true, "test points must be specified "
"when an input model is given");
}
ReportIgnoredParam({{ "input_model", true }}, "lambda");
RequireAtLeastOnePassed({ "output_model", "output_predictions" }, false,
"no output will be saved");
// An input file was given and we need to generate the model.
if (computeModel)
{
Timer::Start("load_regressors");
regressors = std::move(CLI::GetParam<mat>("training"));
Timer::Stop("load_regressors");
// Are the responses in a separate file?
if (!CLI::HasParam("training_responses"))
{
// The initial predictors for y, Nx1.
if (regressors.n_rows < 2)
{
Log::Fatal << "Can't get responses from training data "
"since it has less than 2 rows." << endl;
}
responses = regressors.row(regressors.n_rows - 1);
regressors.shed_row(regressors.n_rows - 1);
}
else
{
// The initial predictors for y, Nx1.
Timer::Start("load_responses");
responses = CLI::GetParam<rowvec>("training_responses");
Timer::Stop("load_responses");
if (responses.n_cols != regressors.n_cols)
{
Log::Fatal << "The responses must have the same number of columns "
"as the training set." << endl;
}
}
Timer::Start("regression");
lr = LinearRegression(regressors, responses, lambda);
Timer::Stop("regression");
}
else
{
// A model file was passed in, so load it.
Timer::Start("load_model");
lr = std::move(CLI::GetParam<LinearRegression>("input_model"));
Timer::Stop("load_model");
}
// Did we want to predict, too?
if (computePrediction)
{
// Load the test file data.
Timer::Start("load_test_points");
mat points = std::move(CLI::GetParam<mat>("test"));
Timer::Stop("load_test_points");
// Ensure that test file data has the right number of features.
if ((lr.Parameters().n_elem - 1) != points.n_rows)
{
Log::Fatal << "The model was trained on " << lr.Parameters().n_elem - 1
<< "-dimensional data, but the test points in '"
<< CLI::GetPrintableParam<mat>("test") << "' are " << points.n_rows
<< "-dimensional!" << endl;
}
// Perform the predictions using our model.
rowvec predictions;
Timer::Start("prediction");
lr.Predict(points, predictions);
Timer::Stop("prediction");
// Save predictions.
if (CLI::HasParam("output_predictions"))
CLI::GetParam<rowvec>("output_predictions") = std::move(predictions);
}
// Save the model if needed.
if (CLI::HasParam("output_model"))
CLI::GetParam<LinearRegression>("output_model") = std::move(lr);
}
int main(int argc, char* argv[])
{
// Handle parameters.
CLI::ParseCommandLine(argc, argv);
const string inputModelFile = CLI::GetParam<string>("input_model_file");
const string outputModelFile = CLI::GetParam<string>("output_model_file");
const string outputPredictionsFile =
CLI::GetParam<string>("output_predictions");
const string trainingResponsesFile =
CLI::GetParam<string>("training_responses");
const string testFile = CLI::GetParam<string>("test_file");
const string trainFile = CLI::GetParam<string>("training_file");
const double lambda = CLI::GetParam<double>("lambda");
mat regressors;
mat responses;
LinearRegression lr;
lr.Lambda() = lambda;
bool computeModel = false;
// We want to determine if an input file XOR model file were given.
if (!CLI::HasParam("training_file"))
{
if (!CLI::HasParam("input_model_file"))
Log::Fatal << "You must specify either --input_file or --model_file."
<< endl;
else // The model file was specified, no problems.
computeModel = false;
}
// The user specified an input file but no model file, no problems.
else if (!CLI::HasParam("input_model_file"))
computeModel = true;
// The user specified both an input file and model file.
// This is ambiguous -- which model should we use? A generated one or given
// one? Report error and exit.
else
{
Log::Fatal << "You must specify either --input_file or --model_file, not "
<< "both." << endl;
}
if (CLI::HasParam("test_file") && !CLI::HasParam("output_predictions"))
Log::Warn << "--test_file (-t) specified, but --output_predictions "
<< "(-o) is not; no results will be saved." << endl;
// If they specified a model file, we also need a test file or we
// have nothing to do.
if (!computeModel && !CLI::HasParam("test_file"))
{
Log::Fatal << "When specifying --model_file, you must also specify "
<< "--test_file." << endl;
}
if (!computeModel && CLI::HasParam("lambda"))
{
Log::Warn << "--lambda ignored because no model is being trained." << endl;
}
// An input file was given and we need to generate the model.
if (computeModel)
{
Timer::Start("load_regressors");
data::Load(trainFile, regressors, true);
Timer::Stop("load_regressors");
// Are the responses in a separate file?
if (CLI::HasParam("training_responses"))
{
// The initial predictors for y, Nx1.
responses = trans(regressors.row(regressors.n_rows - 1));
regressors.shed_row(regressors.n_rows - 1);
}
else
{
// The initial predictors for y, Nx1.
Timer::Start("load_responses");
data::Load(trainingResponsesFile, responses, true);
Timer::Stop("load_responses");
if (responses.n_rows == 1)
responses = trans(responses); // Probably loaded backwards.
if (responses.n_cols > 1)
Log::Fatal << "The responses must have one column.\n";
if (responses.n_rows != regressors.n_cols)
Log::Fatal << "The responses must have the same number of rows as the "
"training file.\n";
}
Timer::Start("regression");
lr = LinearRegression(regressors, responses.unsafe_col(0));
Timer::Stop("regression");
// Save the parameters.
if (CLI::HasParam("output_model_file"))
data::Save(outputModelFile, "linearRegressionModel", lr);
}
// Did we want to predict, too?
if (CLI::HasParam("test_file"))
{
// A model file was passed in, so load it.
if (!computeModel)
{
Timer::Start("load_model");
data::Load(inputModelFile, "linearRegressionModel", lr, true);
Timer::Stop("load_model");
}
// Load the test file data.
arma::mat points;
Timer::Start("load_test_points");
data::Load(testFile, points, true);
Timer::Stop("load_test_points");
// Ensure that test file data has the right number of features.
if ((lr.Parameters().n_elem - 1) != points.n_rows)
{
Log::Fatal << "The model was trained on " << lr.Parameters().n_elem - 1
<< "-dimensional data, but the test points in '" << testFile
<< "' are " << points.n_rows << "-dimensional!" << endl;
}
// Perform the predictions using our model.
arma::vec predictions;
Timer::Start("prediction");
lr.Predict(points, predictions);
Timer::Stop("prediction");
// Save predictions.
if (CLI::HasParam("output_predictions"))
data::Save(outputPredictionsFile, predictions, true, false);
}
}
TEUCHOS_UNIT_TEST( Rythmos_ConvergenceTestHelpers, create ) {
LinearRegression<double> lr;
TEST_THROW(lr.getSlope(), std::logic_error);
TEST_THROW(lr.getYIntercept(), std::logic_error);
}
bool train( CommandLineParser &parser ){
infoLog << "Training regression model..." << endl;
string trainDatasetFilename = "";
string modelFilename = "";
float learningRate = 0;
float minChange = 0;
unsigned int maxEpoch = 0;
unsigned int batchSize = 0;
//Get the filename
if( !parser.get("filename",trainDatasetFilename) ){
errorLog << "Failed to parse filename from command line! You can set the filename using the -f." << endl;
printHelp();
return false;
}
//Get the parameters from the parser
parser.get("model-filename",modelFilename);
parser.get( "learning-rate", learningRate );
parser.get( "min-change", minChange );
parser.get( "max-epoch", maxEpoch );
parser.get( "batch-size", batchSize );
infoLog << "settings: learning-rate: " << learningRate << " min-change: " << minChange << " max-epoch: " << maxEpoch << " batch-size: " << batchSize << endl;
//Load the training data to train the model
RegressionData trainingData;
//Try and parse the input and target dimensions
unsigned int numInputDimensions = 0;
unsigned int numTargetDimensions = 0;
if( parser.get("num-inputs",numInputDimensions) && parser.get("num-targets",numTargetDimensions) ){
infoLog << "num input dimensions: " << numInputDimensions << " num target dimensions: " << numTargetDimensions << endl;
trainingData.setInputAndTargetDimensions( numInputDimensions, numTargetDimensions );
}
if( (numInputDimensions == 0 || numTargetDimensions == 0) && Util::stringEndsWith( trainDatasetFilename, ".csv" ) ){
errorLog << "Failed to parse num input dimensions and num target dimensions from input arguments. You must supply the input and target dimensions if the data format is CSV!" << endl;
printHelp();
return false;
}
infoLog << "- Loading Training Data..." << endl;
if( !trainingData.load( trainDatasetFilename ) ){
errorLog << "Failed to load training data!\n";
return false;
}
const unsigned int N = trainingData.getNumInputDimensions();
const unsigned int T = trainingData.getNumTargetDimensions();
infoLog << "- Num training samples: " << trainingData.getNumSamples() << endl;
infoLog << "- Num input dimensions: " << N << endl;
infoLog << "- Num target dimensions: " << T << endl;
//Create a new regression instance
LinearRegression regression;
regression.setMaxNumEpochs( maxEpoch );
regression.setMinChange( minChange );
regression.setUseValidationSet( true );
regression.setValidationSetSize( 20 );
regression.setRandomiseTrainingOrder( true );
regression.enableScaling( true );
//Create a new pipeline that will hold the regression algorithm
GestureRecognitionPipeline pipeline;
//Add a multidimensional regression instance and set the regression algorithm to Linear Regression
pipeline.setRegressifier( MultidimensionalRegression( regression, true ) );
infoLog << "- Training model...\n";
//Train the classifier
if( !pipeline.train( trainingData ) ){
errorLog << "Failed to train model!" << endl;
return false;
}
infoLog << "- Model trained!" << endl;
infoLog << "- Saving model to: " << modelFilename << endl;
//Save the pipeline
if( pipeline.save( modelFilename ) ){
infoLog << "- Model saved." << endl;
}else warningLog << "Failed to save model to file: " << modelFilename << endl;
infoLog << "- TrainingTime: " << pipeline.getTrainingTime() << endl;
return true;
}
