mlpack 3.4.2
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A simple linear regression algorithm using ordinary least squares. More...
#include <linear_regression.hpp>
Public Member Functions | |
LinearRegression () | |
Empty constructor. More... | |
LinearRegression (const arma::mat &predictors, const arma::rowvec &responses, const arma::rowvec &weights, const double lambda=0, const bool intercept=true) | |
Creates the model with weighted learning. More... | |
LinearRegression (const arma::mat &predictors, const arma::rowvec &responses, const double lambda=0, const bool intercept=true) | |
Creates the model. More... | |
double | ComputeError (const arma::mat &points, const arma::rowvec &responses) const |
Calculate the L2 squared error on the given predictors and responses using this linear regression model. More... | |
bool | Intercept () const |
Return whether or not an intercept term is used in the model. More... | |
double & | Lambda () |
Modify the Tikhonov regularization parameter for ridge regression. More... | |
double | Lambda () const |
Return the Tikhonov regularization parameter for ridge regression. More... | |
arma::vec & | Parameters () |
Modify the parameters (the b vector). More... | |
const arma::vec & | Parameters () const |
Return the parameters (the b vector). More... | |
void | Predict (const arma::mat &points, arma::rowvec &predictions) const |
Calculate y_i for each data point in points. More... | |
template<typename Archive > | |
void | serialize (Archive &ar, const unsigned int) |
Serialize the model. More... | |
double | Train (const arma::mat &predictors, const arma::rowvec &responses, const arma::rowvec &weights, const bool intercept=true) |
Train the LinearRegression model on the given data and weights. More... | |
double | Train (const arma::mat &predictors, const arma::rowvec &responses, const bool intercept=true) |
Train the LinearRegression model on the given data. More... | |
A simple linear regression algorithm using ordinary least squares.
Optionally, this class can perform ridge regression, if the lambda parameter is set to a number greater than zero.
Definition at line 26 of file linear_regression.hpp.
LinearRegression | ( | const arma::mat & | predictors, |
const arma::rowvec & | responses, | ||
const double | lambda = 0 , |
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const bool | intercept = true |
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Creates the model.
predictors | X, matrix of data points. |
responses | y, the measured data for each point in X. |
lambda | Regularization constant for ridge regression. |
intercept | Whether or not to include an intercept term. |
LinearRegression | ( | const arma::mat & | predictors, |
const arma::rowvec & | responses, | ||
const arma::rowvec & | weights, | ||
const double | lambda = 0 , |
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const bool | intercept = true |
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) |
Creates the model with weighted learning.
predictors | X, matrix of data points. |
responses | y, the measured data for each point in X. |
weights | Observation weights (for boosting). |
lambda | Regularization constant for ridge regression. |
intercept | Whether or not to include an intercept term. |
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Empty constructor.
This gives a non-working model, so make sure Train() is called (or make sure the model parameters are set) before calling Predict()!
Definition at line 62 of file linear_regression.hpp.
double ComputeError | ( | const arma::mat & | points, |
const arma::rowvec & | responses | ||
) | const |
Calculate the L2 squared error on the given predictors and responses using this linear regression model.
This calculation returns
where is the responses vector,
is the matrix of predictors, and
is the parameters of the trained linear regression model.
As this number decreases to 0, the linear regression fit is better.
points | Matrix of predictors (X). |
responses | Transposed vector of responses (y^T). |
Referenced by RegressionDistribution::RegressionDistribution().
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Return whether or not an intercept term is used in the model.
Definition at line 137 of file linear_regression.hpp.
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Modify the Tikhonov regularization parameter for ridge regression.
Definition at line 134 of file linear_regression.hpp.
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Return the Tikhonov regularization parameter for ridge regression.
Definition at line 132 of file linear_regression.hpp.
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Modify the parameters (the b vector).
Definition at line 129 of file linear_regression.hpp.
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Return the parameters (the b vector).
Definition at line 127 of file linear_regression.hpp.
Referenced by RegressionDistribution::Dimensionality(), and RegressionDistribution::Parameters().
void Predict | ( | const arma::mat & | points, |
arma::rowvec & | predictions | ||
) | const |
Calculate y_i for each data point in points.
points | the data points to calculate with. |
predictions | y, will contain calculated values on completion. |
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inline |
Serialize the model.
Definition at line 143 of file linear_regression.hpp.
double Train | ( | const arma::mat & | predictors, |
const arma::rowvec & | responses, | ||
const arma::rowvec & | weights, | ||
const bool | intercept = true |
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) |
Train the LinearRegression model on the given data and weights.
Careful! This will completely ignore and overwrite the existing model. This particular implementation does not have an incremental training algorithm. To set the regularization parameter lambda, call Lambda() or set a different value in the constructor.
predictors | X, the matrix of data points to train the model on. |
responses | y, the responses to the data points. |
weights | Observation weights (for boosting). |
intercept | Whether or not to fit an intercept term. |
double Train | ( | const arma::mat & | predictors, |
const arma::rowvec & | responses, | ||
const bool | intercept = true |
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) |
Train the LinearRegression model on the given data.
Careful! This will completely ignore and overwrite the existing model. This particular implementation does not have an incremental training algorithm. To set the regularization parameter lambda, call Lambda() or set a different value in the constructor.
predictors | X, the matrix of data points to train the model on. |
responses | y, the responses to the data points. |
intercept | Whether or not to fit an intercept term. |
Referenced by RegressionDistribution::RegressionDistribution().