PLearn 0.1
Namespaces | Functions
VMat_linalg.cc File Reference
#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>
Include dependency graph for VMat_linalg.cc:

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.

Detailed Description

Definition in file VMat_linalg.cc.

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