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钟尚武
dlib
Commits
e0532b54
Commit
e0532b54
authored
May 17, 2011
by
Davis King
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Added some more comments.
parent
ea2a5184
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svm_multiclass_linear_trainer.h
dlib/svm/svm_multiclass_linear_trainer.h
+14
-1
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dlib/svm/svm_multiclass_linear_trainer.h
View file @
e0532b54
...
...
@@ -28,6 +28,15 @@ namespace dlib
WHAT THIS OBJECT REPRESENTS
This object defines the optimization problem for the multiclass SVM trainer
object at the bottom of this file.
The joint feature vectors used by this object, the PSI(x,y) vectors, are
defined as follows:
PSI(x,0) = [x,0,0,0,0, ...,0]
PSI(x,1) = [0,x,0,0,0, ...,0]
PSI(x,2) = [0,0,x,0,0, ...,0]
That is, if there are N labels then the joint feature vector has a
dimension that is N times the dimension of a single x sample. Also,
note that we append a -1 value onto each x to account for the bias term.
!*/
public
:
...
...
@@ -81,17 +90,21 @@ namespace dlib
scalar_type
best_val
=
-
std
::
numeric_limits
<
scalar_type
>::
infinity
();
unsigned
long
best_idx
=
0
;
// figure out which label is the best
// Figure out which label is the best. That is, what label maximizes
// LOSS(idx,y) + F(x,y). Note that y in this case is given by distinct_labels[i].
for
(
unsigned
long
i
=
0
;
i
<
distinct_labels
.
size
();
++
i
)
{
using
dlib
::
sparse_vector
::
dot
;
using
dlib
::
dot
;
// Compute the F(x,y) part:
// perform: temp == dot(relevant part of current solution, samples[idx]) - current_bias
scalar_type
temp
=
dot
(
rowm
(
current_solution
,
range
(
i
*
dims
,
(
i
+
1
)
*
dims
-
2
)),
samples
[
idx
])
-
current_solution
((
i
+
1
)
*
dims
-
1
);
// Add the LOSS(idx,y) part:
if
(
labels
[
idx
]
!=
distinct_labels
[
i
])
temp
+=
1
;
// Now temp == LOSS(idx,y) + F(x,y). Check if it is the biggest we have seen.
if
(
temp
>
best_val
)
{
best_val
=
temp
;
...
...
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