PLearn 0.1
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#include <plearn/base/Object.h>
#include <plearn/base/ProgressBar.h>
#include "VMat_linalg.h"
#include <plearn/math/TMat_maths.h>
#include "VMat.h"
#include "ExtendedVMatrix.h"
#include <plearn/math/plapack.h>
Go to the source code of this file.
Namespaces | |
namespace | PLearn |
< for swap | |
Functions | |
Mat | PLearn::transposeProduct (VMat m) |
computes M'.M | |
Mat | PLearn::transposeProduct (VMat m1, VMat m2) |
computes M1'.M2 | |
Vec | PLearn::transposeProduct (VMat m1, Vec v2) |
computes M1'.V2 | |
Mat | PLearn::productTranspose (VMat m1, VMat m2) |
computes M1.M2' | |
Mat | PLearn::product (Mat m1, VMat m2) |
computes M1.M2 | |
VMat | PLearn::transpose (VMat m1) |
returns M1' | |
real | PLearn::linearRegression (VMat inputs, VMat outputs, real weight_decay, Mat theta_t, bool use_precomputed_XtX_XtY, Mat XtX, Mat XtY, real &sum_squared_Y, Vec &outputwise_sum_squared_Y, bool return_squared_loss, int verbose_every, bool cholesky, int apply_decay_from) |
Mat | PLearn::linearRegression (VMat inputs, VMat outputs, real weight_decay, bool include_bias=false) |
Version that does all the memory allocations of XtX, XtY and theta_t. | |
real | PLearn::weightedLinearRegression (VMat inputs, VMat outputs, VMat gammas, real weight_decay, Mat theta_t, bool use_precomputed_XtX_XtY, Mat XtX, Mat XtY, real &sum_squared_Y, Vec &outputwise_sum_squared_Y, real &sum_gammas, bool return_squared_loss=false, int verbose_computation_every=0, bool cholesky=true, int apply_decay_from=1) |
Linear regression where each input point is given a different importance weight (the gammas); returns weighted average of squared loss This regression is made with no added bias. | |
Mat | PLearn::weightedLinearRegression (VMat inputs, VMat outputs, VMat gammas, real weight_decay, bool include_bias) |
Version that does all the memory allocations of XtX, XtY and theta_t. |
Definition in file VMat_linalg.cc.