// Copyright (C) 2007 Davis E. King (davis@dlib.net)
// License: Boost Software License See LICENSE.txt for the full license.
#undef DLIB_SVm_FUNCTION_ABSTRACT_
#ifdef DLIB_SVm_FUNCTION_ABSTRACT_
#include <cmath>
#include <limits>
#include <sstream>
#include "../matrix/matrix_abstract.h"
#include "../algs.h"
#include "../serialize.h"
#include "../statistics/statistics_abstract.h"
namespace dlib
{
// ----------------------------------------------------------------------------------------
template <
typename K
>
struct decision_function
{
/*!
REQUIREMENTS ON K
K must be a kernel function object type as defined at the
top of dlib/svm/kernel_abstract.h
WHAT THIS OBJECT REPRESENTS
This object represents a classification or regression function that was
learned by a kernel based learning algorithm. Therefore, it is a function
object that takes a sample object and returns a scalar value.
THREAD SAFETY
It is always safe to use distinct instances of this object in different
threads. However, when a single instance is shared between threads then
the following rules apply:
It is safe to call operator() on this object from multiple threads so
long as the kernel, K, is also threadsafe. This is because operator()
is a read-only operation. However, any operation that modifies a
decision_function is not threadsafe.
!*/
typedef K kernel_type;
typedef typename K::scalar_type scalar_type;
typedef typename K::scalar_type result_type;
typedef typename K::sample_type sample_type;
typedef typename K::mem_manager_type mem_manager_type;
typedef matrix<scalar_type,0,1,mem_manager_type> scalar_vector_type;
typedef matrix<sample_type,0,1,mem_manager_type> sample_vector_type;
scalar_vector_type alpha;
scalar_type b;
K kernel_function;
sample_vector_type basis_vectors;
decision_function (
);
/*!
ensures
- #b == 0
- #alpha.nr() == 0
- #basis_vectors.nr() == 0
!*/
decision_function (
const decision_function& f
);
/*!
ensures
- #*this is a copy of f
!*/
decision_function (
const scalar_vector_type& alpha_,
const scalar_type& b_,
const K& kernel_function_,
const sample_vector_type& basis_vectors_
) : alpha(alpha_), b(b_), kernel_function(kernel_function_), basis_vectors(basis_vectors_) {}
/*!
ensures
- populates the decision function with the given basis vectors, weights(i.e. alphas),
b term, and kernel function.
!*/
result_type operator() (
const sample_type& x
) const
/*!
ensures
- evaluates this sample according to the decision
function contained in this object.
!*/
{
result_type temp = 0;
for (long i = 0; i < alpha.nr(); ++i)
temp += alpha(i) * kernel_function(x,basis_vectors(i));
return temp - b;
}
};
template <
typename K
>
void serialize (
const decision_function<K>& item,
std::ostream& out
);
/*!
provides serialization support for decision_function
!*/
template <
typename K
>
void deserialize (
decision_function<K>& item,
std::istream& in
);
/*!
provides serialization support for decision_function
!*/
// ----------------------------------------------------------------------------------------
template <
typename function_type
>
struct probabilistic_function
{
/*!
REQUIREMENTS ON function_type
- function_type must be a function object with an overloaded
operator() similar to the other function objects defined in
this file. The operator() should return a scalar type such as
double or float.
WHAT THIS OBJECT REPRESENTS
This object represents a binary decision function that returns an
estimate of the probability that a given sample is in the +1 class.
THREAD SAFETY
It is always safe to use distinct instances of this object in different
threads. However, when a single instance is shared between threads then
the following rules apply:
It is safe to call operator() on this object from multiple threads so
long as decision_funct is also threadsafe. This is because operator()
is a read-only operation. However, any operation that modifies a
probabilistic_function is not threadsafe.
!*/
typedef typename function_type::scalar_type scalar_type;
typedef typename function_type::result_type result_type;
typedef typename function_type::sample_type sample_type;
typedef typename function_type::mem_manager_type mem_manager_type;
scalar_type alpha;
scalar_type beta;
function_type decision_funct;
probabilistic_function (
);
/*!
ensures
- #alpha == 0
- #beta == 0
- #decision_funct has its initial value
!*/
probabilistic_function (
const probabilistic_function& f
);
/*!
ensures
- #*this is a copy of f
!*/
probabilistic_function (
const scalar_type a,
const scalar_type b,
const function_type& decision_funct_
) : alpha(a), beta(b), decision_funct(decision_funct_) {}
/*!
ensures
- populates the probabilistic decision function with the given alpha, beta,
and decision function.
!*/
result_type operator() (
const sample_type& x
) const
/*!
ensures
- returns a number P such that:
- 0 <= P <= 1
- P represents the probability that sample x is from
the class +1
!*/
{
// Evaluate the normal decision function
result_type f = decision_funct(x);
// Now basically normalize the output so that it is a properly
// conditioned probability of x being in the +1 class given
// the output of the decision function.
return 1/(1 + std::exp(alpha*f + beta));
}
};
template <
typename function_type
>
void serialize (
const probabilistic_function<function_type>& item,
std::ostream& out
);
/*!
provides serialization support for probabilistic_function
!*/
template <
typename function_type
>
void deserialize (
probabilistic_function<function_type>& item,
std::istream& in
);
/*!
provides serialization support for probabilistic_function
!*/
// ----------------------------------------------------------------------------------------
template <
typename K
>
struct probabilistic_decision_function
{
/*!
REQUIREMENTS ON K
K must be a kernel function object type as defined at the
top of dlib/svm/kernel_abstract.h
WHAT THIS OBJECT REPRESENTS
This object represents a binary decision function that returns an
estimate of the probability that a given sample is in the +1 class.
Note that this object is essentially just a copy of
probabilistic_function but with the template argument
changed from being a function type to a kernel type. Therefore, this
type is just a convenient version of probabilistic_function
for the case where the decision function is a dlib::decision_function<K>.
THREAD SAFETY
It is always safe to use distinct instances of this object in different
threads. However, when a single instance is shared between threads then
the following rules apply:
It is safe to call operator() on this object from multiple threads so
long as the kernel, K, is also threadsafe. This is because operator()
is a read-only operation. However, any operation that modifies a
probabilistic_decision_function is not threadsafe.
!*/
typedef K kernel_type;
typedef typename K::scalar_type scalar_type;
typedef typename K::scalar_type result_type;
typedef typename K::sample_type sample_type;
typedef typename K::mem_manager_type mem_manager_type;
scalar_type alpha;
scalar_type beta;
decision_function<K> decision_funct;
probabilistic_decision_function (
);
/*!
ensures
- #alpha == 0
- #beta == 0
- #decision_funct has its initial value
!*/
probabilistic_decision_function (
const probabilistic_decision_function& f
);
/*!
ensures
- #*this is a copy of f
!*/
probabilistic_decision_function (
const probabilistic_function<decision_function<K> >& d
);
/*!
ensures
- #*this is a copy of f
!*/
probabilistic_decision_function (
const scalar_type a,
const scalar_type b,
const decision_function<K>& decision_funct_
) : alpha(a), beta(b), decision_funct(decision_funct_) {}
/*!
ensures
- populates the probabilistic decision function with the given alpha, beta,
and decision_function.
!*/
result_type operator() (
const sample_type& x
) const
/*!
ensures
- returns a number P such that:
- 0 <= P <= 1
- P represents the probability that sample x is from
the class +1
!*/
{
// Evaluate the normal decision function
result_type f = decision_funct(x);
// Now basically normalize the output so that it is a properly
// conditioned probability of x being in the +1 class given
// the output of the decision function.
return 1/(1 + std::exp(alpha*f + beta));
}
};
template <
typename K
>
void serialize (
const probabilistic_decision_function<K>& item,
std::ostream& out
);
/*!
provides serialization support for probabilistic_decision_function
!*/
template <
typename K
>
void deserialize (
probabilistic_decision_function<K>& item,
std::istream& in
);
/*!
provides serialization support for probabilistic_decision_function
!*/
// ----------------------------------------------------------------------------------------
template <
typename K
>
class distance_function
{
/*!
REQUIREMENTS ON K
K must be a kernel function object type as defined at the
top of dlib/svm/kernel_abstract.h
WHAT THIS OBJECT REPRESENTS
This object represents a point in kernel induced feature space.
You may use this object to find the distance from the point it
represents to points in input space as well as other points
represented by distance_functions.
Specifically, if O() is the feature mapping associated with
the kernel used by this object. Then this object represents
the point:
sum alpha(i)*O(basis_vectors(i))
I.e. It represents a linear combination of the basis vectors where
the weights of the linear combination are stored in the alpha vector.
THREAD SAFETY
It is always safe to use distinct instances of this object in different
threads. However, when a single instance is shared between threads then
the following rules apply:
It is safe to call the const members of this object from multiple
threads so long as the kernel, K, is also threadsafe. This is because
the const members are purely read-only operations. However, any
operation that modifies a distance_function is not threadsafe.
!*/
public:
typedef K kernel_type;
typedef typename K::scalar_type scalar_type;
typedef typename K::scalar_type result_type;
typedef typename K::sample_type sample_type;
typedef typename K::mem_manager_type mem_manager_type;
typedef matrix<scalar_type,0,1,mem_manager_type> scalar_vector_type;
typedef matrix<sample_type,0,1,mem_manager_type> sample_vector_type;
distance_function (
);
/*!
ensures
- #get_squared_norm() == 0
- #get_alpha().size() == 0
- #get_basis_vectors().size() == 0
- #get_kernel() == K() (i.e. the default value of the kernel)
!*/
explicit distance_function (
const kernel_type& kern
);
/*!
ensures
- #get_squared_norm() == 0
- #get_alpha().size() == 0
- #get_basis_vectors().size() == 0
- #get_kernel() == kern
!*/
distance_function (
const kernel_type& kern,
const sample_type& samp
);
/*!
ensures
- This object represents the point in kernel feature space which
corresponds directly to the given sample. In particular this means
that:
- #get_kernel() == kern
- #get_alpha() == a vector of length 1 which contains the value 1
- #get_basis_vectors() == a vector of length 1 which contains samp
!*/
distance_function (
const decision_function<K>& f
);
/*!
ensures
- Every decision_function represents a point in kernel feature space along
with a bias value. This constructor discards the bias value and creates
a distance_function which represents the point associated with the given
decision_function f. In particular, this means:
- #get_alpha() == f.alpha
- #get_kernel() == f.kernel_function
- #get_basis_vectors() == f.basis_vectors
!*/
distance_function (
const distance_function& f
);
/*!
requires
- f is a valid distance_function. In particular, this means that
f.alpha.size() == f.basis_vectors.size()
ensures
- #*this is a copy of f
!*/
distance_function (
const scalar_vector_type& alpha,
const scalar_type& squared_norm,
const K& kernel_function,
const sample_vector_type& basis_vectors
);
/*!
requires
- alpha.size() == basis_vectors.size()
- squared_norm == trans(alpha)*kernel_matrix(kernel_function,basis_vectors)*alpha
(Basically, squared_norm needs to be set properly for this object to make sense.
You should prefer to use the following constructor which computes squared_norm for
you. This version is provided just in case you already know squared_norm and
don't want to spend CPU cycles to recompute it.)
ensures
- populates the distance function with the given basis vectors, weights(i.e. alphas),
squared_norm value, and kernel function. I.e.
- #get_alpha() == alpha
- #get_squared_norm() == squared_norm
- #get_kernel() == kernel_function
- #get_basis_vectors() == basis_vectors
!*/
distance_function (
const scalar_vector_type& alpha,
const K& kernel_function,
const sample_vector_type& basis_vectors
);
/*!
requires
- alpha.size() == basis_vectors.size()
ensures
- populates the distance function with the given basis vectors, weights(i.e. alphas),
and kernel function. The correct b value is computed automatically. I.e.
- #get_alpha() == alpha
- #get_squared_norm() == trans(alpha)*kernel_matrix(kernel_function,basis_vectors)*alpha
(i.e. get_squared_norm() will be automatically set to the correct value)
- #get_kernel() == kernel_function
- #get_basis_vectors() == basis_vectors
!*/
const scalar_vector_type& get_alpha (
) const;
/*!
ensures
- returns the set of weights on each basis vector in this object
!*/
const scalar_type& get_squared_norm (
) const;
/*!
ensures
- returns the squared norm of the point represented by this object. This value is
equal to the following expression:
trans(get_alpha()) * kernel_matrix(get_kernel(),get_basis_vectors()) * get_alpha()
!*/
const K& get_kernel(
) const;
/*!
ensures
- returns the kernel used by this object.
!*/
const sample_vector_type& get_basis_vectors (
) const;
/*!
ensures
- returns the set of basis vectors contained in this object
!*/
result_type operator() (
const sample_type& x
) const;
/*!
ensures
- Let O(x) represent the point x projected into kernel induced feature space.
- let c == sum_over_i get_alpha()(i)*O(get_basis_vectors()(i)) == the point in kernel space that
this object represents. That is, c is the weighted sum of basis vectors.
- Then this object returns the distance between the point O(x) and c in kernel
space.
!*/
result_type operator() (
const distance_function& x
) const;
/*!
requires
- kernel_function == x.kernel_function
ensures
- returns the distance between the points in kernel space represented by *this and x.
!*/
distance_function operator* (
const scalar_type& val
) const;
/*!
ensures
- multiplies the point represented by *this by val and returns the result. In
particular, this function returns a decision_function DF such that:
- DF.get_basis_vectors() == get_basis_vectors()
- DF.get_kernel() == get_kernel()
- DF.get_alpha() == get_alpha() * val
!*/
distance_function operator/ (
const scalar_type& val
) const;
/*!
ensures
- divides the point represented by *this by val and returns the result. In
particular, this function returns a decision_function DF such that:
- DF.get_basis_vectors() == get_basis_vectors()
- DF.get_kernel() == get_kernel()
- DF.get_alpha() == get_alpha() / val
!*/
distance_function operator+ (
const distance_function& rhs
) const;
/*!
requires
- get_kernel() == rhs.get_kernel()
ensures
- returns a distance function DF such that:
- DF represents the sum of the point represented by *this and rhs
- DF.get_basis_vectors().size() == get_basis_vectors().size() + rhs.get_basis_vectors().size()
- DF.get_basis_vectors() contains all the basis vectors in both *this and rhs.
- DF.get_kernel() == get_kernel()
- DF.alpha == join_cols(get_alpha(), rhs.get_alpha())
!*/
distance_function operator- (
const distance_function& rhs
) const;
/*!
requires
- get_kernel() == rhs.get_kernel()
ensures
- returns a distance function DF such that:
- DF represents the difference of the point represented by *this and rhs (i.e. *this - rhs)
- DF.get_basis_vectors().size() == get_basis_vectors().size() + rhs.get_basis_vectors().size()
- DF.get_basis_vectors() contains all the basis vectors in both *this and rhs.
- DF.get_kernel() == get_kernel()
- DF.alpha == join_cols(get_alpha(), -1 * rhs.get_alpha())
!*/
};
template <
typename K
>
distance_function<K> operator* (
const typename K::scalar_type& val,
const distance_function<K>& df
) { return df*val; }
/*!
ensures
- multiplies the point represented by *this by val and returns the result. This
function just allows multiplication syntax of the form val*df.
!*/
template <
typename K
>
void serialize (
const distance_function<K>& item,
std::ostream& out
);
/*!
provides serialization support for distance_function
!*/
template <
typename K
>
void deserialize (
distance_function<K>& item,
std::istream& in
);
/*!
provides serialization support for distance_function
!*/
// ----------------------------------------------------------------------------------------
template <
typename function_type,
typename normalizer_type = vector_normalizer<typename function_type::sample_type>
>
struct normalized_function
{
/*!
REQUIREMENTS ON function_type
- function_type must be a function object with an overloaded
operator() similar to the other function objects defined in
this file.
REQUIREMENTS ON normalizer_type
- normalizer_type must be a function object with an overloaded
operator() that takes a sample_type and returns a sample_type.
WHAT THIS OBJECT REPRESENTS
This object represents a container for another function
object and an instance of a normalizer function.
It automatically normalizes all inputs before passing them
off to the contained function object.
!*/
typedef typename function_type::result_type result_type;
typedef typename function_type::sample_type sample_type;
typedef typename function_type::mem_manager_type mem_manager_type;
normalizer_type normalizer;
function_type function;
normalized_function (
);
/*!
ensures
- the members of this object have their default values
!*/
normalized_function (
const normalized_function& f
);
/*!
ensures
- #*this is a copy of f
!*/
normalized_function (
const vector_normalizer<sample_type>& normalizer_,
const function_type& funct
) : normalizer(normalizer_), function(funct) {}
/*!
ensures
- populates this object with the vector_normalizer and function object
!*/
const std::vector<result_type> get_labels(
) const;
/*!
ensures
- returns function.get_labels()
!*/
unsigned long number_of_classes (
) const;
/*!
ensures
- returns function.number_of_classes()
!*/
result_type operator() (
const sample_type& x
) const
/*!
ensures
- returns function(normalizer(x))
!*/
};
template <
typename function_type,
typename normalizer_type
>
void serialize (
const normalized_function<function_type, normalizer_type>& item,
std::ostream& out
);
/*!
provides serialization support for normalized_function
!*/
template <
typename function_type,
typename normalizer_type
>
void deserialize (
normalized_function<function_type, normalizer_type>& item,
std::istream& in
);
/*!
provides serialization support for normalized_function
!*/
// ----------------------------------------------------------------------------------------
template <
typename K
>
struct projection_function
{
/*!
REQUIREMENTS ON K
K must be a kernel function object type as defined at the
top of dlib/svm/kernel_abstract.h
WHAT THIS OBJECT REPRESENTS
This object represents a function that takes a data sample and projects
it into kernel feature space. The result is a real valued column vector that
represents a point in a kernel feature space.
THREAD SAFETY
It is always safe to use distinct instances of this object in different
threads. However, when a single instance is shared between threads then
the following rules apply:
Instances of this object have a mutable cache which is used by const
member functions. Therefore, it is not safe to use one instance of
this object from multiple threads (unless protected by a mutex).
!*/
typedef K kernel_type;
typedef typename K::scalar_type scalar_type;
typedef typename K::sample_type sample_type;
typedef typename K::mem_manager_type mem_manager_type;
typedef matrix<scalar_type,0,1,mem_manager_type> scalar_vector_type;
typedef matrix<scalar_type,0,0,mem_manager_type> scalar_matrix_type;
typedef matrix<sample_type,0,1,mem_manager_type> sample_vector_type;
typedef scalar_vector_type result_type;
scalar_matrix_type weights;
K kernel_function;
sample_vector_type basis_vectors;
projection_function (
);
/*!
ensures
- #weights.size() == 0
- #basis_vectors.size() == 0
!*/
projection_function (
const projection_function& f
);
/*!
ensures
- #*this is a copy of f
!*/
projection_function (
const scalar_matrix_type& weights_,
const K& kernel_function_,
const sample_vector_type& basis_vectors_
) : weights(weights_), kernel_function(kernel_function_), basis_vectors(basis_vectors_) {}
/*!
ensures
- populates the projection function with the given basis vectors, weights,
and kernel function.
!*/
long out_vector_size (
) const;
/*!
ensures
- returns weights.nr()
(i.e. returns the dimensionality of the vectors output by this projection_function.)
!*/
const result_type& operator() (
const sample_type& x
) const
/*!
requires
- weights.nc() == basis_vectors.size()
- out_vector_size() > 0
ensures
- Takes the given x sample and projects it onto part of the kernel feature
space spanned by the basis_vectors. The exact projection arithmetic is
defined below.
!*/
{
// Run the x sample through all the basis functions we have and then
// multiply it by the weights matrix and return the result. Note that
// the temp vectors are here to avoid reallocating their memory every
// time this function is called.
temp1 = kernel_matrix(kernel_function, basis_vectors, x);
temp2 = weights*temp1;
return temp2;
}
private:
mutable result_type temp1, temp2;
};
template <
typename K
>
void serialize (
const projection_function<K>& item,
std::ostream& out
);
/*!
provides serialization support for projection_function
!*/
template <
typename K
>
void deserialize (
projection_function<K>& item,
std::istream& in
);
/*!
provides serialization support for projection_function
!*/
// ----------------------------------------------------------------------------------------
template <
typename K,
typename result_type_ = typename K::scalar_type
>
struct multiclass_linear_decision_function
{
/*!
REQUIREMENTS ON K
K must be either linear_kernel or sparse_linear_kernel.
WHAT THIS OBJECT REPRESENTS
This object represents a multiclass classifier built out of a set of
binary classifiers. Each binary classifier is used to vote for the
correct multiclass label using a one vs. all strategy. Therefore,
if you have N classes then there will be N binary classifiers inside
this object. Additionally, this object is linear in the sense that
each of these binary classifiers is a simple linear plane.
THREAD SAFETY
It is always safe to use distinct instances of this object in different
threads. However, when a single instance is shared between threads then
the following rules apply:
It is safe to call the const member functions of this object from
multiple threads. This is because the const members are purely
read-only operations. However, any operation that modifies a
multiclass_linear_decision_function is not threadsafe.
!*/
typedef result_type_ result_type;
typedef K kernel_type;
typedef typename K::scalar_type scalar_type;
typedef typename K::sample_type sample_type;
typedef typename K::mem_manager_type mem_manager_type;
typedef matrix<scalar_type,0,1,mem_manager_type> scalar_vector_type;
typedef matrix<scalar_type,0,0,mem_manager_type> scalar_matrix_type;
scalar_matrix_type weights;
scalar_vector_type b;
std::vector<result_type> labels;
const std::vector<result_type>& get_labels(
) const { return labels; }
/*!
ensures
- returns a vector containing all the labels which can be
predicted by this object.
!*/
unsigned long number_of_classes (
) const;
/*!
ensures
- returns get_labels().size()
(i.e. returns the number of different labels/classes predicted by
this object)
!*/
std::pair<result_type, scalar_type> predict (
const sample_type& x
) const;
/*!
requires
- weights.size() > 0
- weights.nr() == number_of_classes() == b.size()
- if (x is a dense vector, i.e. a dlib::matrix) then
- is_vector(x) == true
- x.size() == weights.nc()
(i.e. it must be legal to multiply weights with x)
ensures
- Returns the predicted label for the x sample and also it's score.
In particular, it returns the following:
std::make_pair(labels[index_of_max(weights*x-b)], max(weights*x-b))
!*/
result_type operator() (
const sample_type& x
) const;
/*!
requires
- weights.size() > 0
- weights.nr() == number_of_classes() == b.size()
- if (x is a dense vector, i.e. a dlib::matrix) then
- is_vector(x) == true
- x.size() == weights.nc()
(i.e. it must be legal to multiply weights with x)
ensures
- Returns the predicted label for the x sample. In particular, it returns
the following:
labels[index_of_max(weights*x-b)]
Or in other words, this function returns predict(x).first
!*/
};
template <
typename K,
typename result_type_
>
void serialize (
const multiclass_linear_decision_function<K,result_type_>& item,
std::ostream& out
);
/*!
provides serialization support for multiclass_linear_decision_function
!*/
template <
typename K,
typename result_type_
>
void deserialize (
multiclass_linear_decision_function<K,result_type_>& item,
std::istream& in
);
/*!
provides serialization support for multiclass_linear_decision_function
!*/
// ----------------------------------------------------------------------------------------
}
#endif // DLIB_SVm_FUNCTION_ABSTRACT_