// Copyright (C) 2012 Davis E. King (davis@dlib.net)
// License: Boost Software License See LICENSE.txt for the full license.
#ifndef DLIB_SVm_C_LINEAR_DCD_TRAINER_Hh_
#define DLIB_SVm_C_LINEAR_DCD_TRAINER_Hh_
#include "svm_c_linear_dcd_trainer_abstract.h"
#include <cmath>
#include <limits>
#include "../matrix.h"
#include "../algs.h"
#include "../rand.h"
#include "function.h"
#include "kernel.h"
namespace dlib
{
// ----------------------------------------------------------------------------------------
template <
typename K
>
class svm_c_linear_dcd_trainer
{
public:
typedef K kernel_type;
typedef typename kernel_type::scalar_type scalar_type;
typedef typename kernel_type::sample_type sample_type;
typedef typename kernel_type::mem_manager_type mem_manager_type;
typedef decision_function<kernel_type> trained_function_type;
typedef typename decision_function<K>::sample_vector_type sample_vector_type;
typedef typename decision_function<K>::scalar_vector_type scalar_vector_type;
// You are getting a compiler error on this line because you supplied a non-linear
// kernel to the svm_c_linear_dcd_trainer object. You have to use one of the
// linear kernels with this trainer.
COMPILE_TIME_ASSERT((is_same_type<K, linear_kernel<sample_type> >::value ||
is_same_type<K, sparse_linear_kernel<sample_type> >::value ));
svm_c_linear_dcd_trainer (
) :
Cpos(1),
Cneg(1),
eps(0.1),
max_iterations(10000),
verbose(false),
have_bias(true),
last_weight_1(false),
do_shrinking(true),
do_svm_l2(false)
{
}
explicit svm_c_linear_dcd_trainer (
const scalar_type& C_
) :
Cpos(C_),
Cneg(C_),
eps(0.1),
max_iterations(10000),
verbose(false),
have_bias(true),
last_weight_1(false),
do_shrinking(true),
do_svm_l2(false)
{
// make sure requires clause is not broken
DLIB_ASSERT(0 < C_,
"\tsvm_c_trainer::svm_c_linear_dcd_trainer(kernel,C)"
<< "\n\t invalid inputs were given to this function"
<< "\n\t C_: " << C_
);
}
bool includes_bias (
) const
{
return have_bias;
}
void include_bias (
bool should_have_bias
)
{
have_bias = should_have_bias;
}
bool forces_last_weight_to_1 (
) const
{
return last_weight_1;
}
void force_last_weight_to_1 (
bool should_last_weight_be_1
)
{
last_weight_1 = should_last_weight_be_1;
}
bool shrinking_enabled (
) const { return do_shrinking; }
void enable_shrinking (
bool enabled
) { do_shrinking = enabled; }
bool solving_svm_l2_problem (
) const { return do_svm_l2; }
void solve_svm_l2_problem (
bool enabled
) { do_svm_l2 = enabled; }
void be_verbose (
)
{
verbose = true;
}
void be_quiet (
)
{
verbose = false;
}
void set_epsilon (
scalar_type eps_
)
{
// make sure requires clause is not broken
DLIB_ASSERT(eps_ > 0,
"\tvoid svm_c_linear_dcd_trainer::set_epsilon(eps_)"
<< "\n\t invalid inputs were given to this function"
<< "\n\t eps_: " << eps_
);
eps = eps_;
}
const scalar_type get_epsilon (
) const
{
return eps;
}
const kernel_type& get_kernel (
) const
{
return kernel_type();
}
unsigned long get_max_iterations (
) const { return max_iterations; }
void set_max_iterations (
unsigned long max_iter
)
{
max_iterations = max_iter;
}
void set_c (
scalar_type C
)
{
// make sure requires clause is not broken
DLIB_ASSERT(C > 0,
"\t void svm_c_linear_dcd_trainer::set_c()"
<< "\n\t C must be greater than 0"
<< "\n\t C: " << C
<< "\n\t this: " << this
);
Cpos = C;
Cneg = C;
}
const scalar_type get_c_class1 (
) const
{
return Cpos;
}
const scalar_type get_c_class2 (
) const
{
return Cneg;
}
void set_c_class1 (
scalar_type C
)
{
// make sure requires clause is not broken
DLIB_ASSERT(C > 0,
"\t void svm_c_linear_dcd_trainer::set_c_class1()"
<< "\n\t C must be greater than 0"
<< "\n\t C: " << C
<< "\n\t this: " << this
);
Cpos = C;
}
void set_c_class2 (
scalar_type C
)
{
// make sure requires clause is not broken
DLIB_ASSERT(C > 0,
"\t void svm_c_linear_dcd_trainer::set_c_class2()"
<< "\n\t C must be greater than 0"
<< "\n\t C: " << C
<< "\n\t this: " << this
);
Cneg = C;
}
class optimizer_state
{
friend class svm_c_linear_dcd_trainer;
public:
optimizer_state() : did_init(false) {}
private:
template <
typename in_sample_vector_type,
typename in_scalar_vector_type
>
void init(
const in_sample_vector_type& x,
const in_scalar_vector_type& y,
bool have_bias_,
bool last_weight_1_,
bool do_svm_l2_,
scalar_type Cpos,
scalar_type Cneg
)
{
const long new_dims = max_index_plus_one(x);
long new_idx = 0;
if (did_init)
{
DLIB_CASSERT(have_bias_ == have_bias &&
last_weight_1_ == last_weight_1,
"\t decision_function svm_c_linear_dcd_trainer::train(x,y,state)"
<< "\n\t The given state object is invalid because the previous trainer was configured differently."
<< "\n\t have_bias_: " << have_bias_
<< "\n\t have_bias: " << have_bias
<< "\n\t last_weight_1_: " << last_weight_1_
<< "\n\t last_weight_1: " << last_weight_1
);
DLIB_CASSERT( new_dims >= dims,
"\t decision_function svm_c_linear_dcd_trainer::train(x,y,state)"
<< "\n\t The given state object is invalid because the training data dimensions have shrunk."
<< "\n\t new_dims: " << new_dims
<< "\n\t dims: " << dims
);
DLIB_CASSERT( x.size() >= static_cast<long>(alpha.size()),
"\t decision_function svm_c_linear_dcd_trainer::train(x,y,state)"
<< "\n\t The given state object is invalid because the training data has fewer samples than previously."
<< "\n\t x.size(): " << x.size()
<< "\n\t alpha.size(): " << alpha.size()
);
// make sure we amortize the cost of growing the alpha vector.
if (alpha.capacity() < static_cast<unsigned long>(x.size()))
alpha.reserve(x.size()*2);
new_idx = alpha.size();
// Make sure alpha has the same length as x. So pad with extra zeros if
// necessary to make this happen.
alpha.resize(x.size(),0);
if (new_dims != dims)
{
// The only valid way the dimensions can be different here is if
// you are using a sparse vector type. This is because we might
// have had training samples which just happened to not include all
// the features previously. Therefore, max_index_plus_one() would
// have given too low of a result. But for dense vectors it is
// definitely a user error if the dimensions don't match.
DLIB_CASSERT(is_matrix<sample_type>::value == false,
"\t decision_function svm_c_linear_dcd_trainer::train(x,y,state)"
<< "\n\t The given state object is invalid because the training data dimensions have changed."
<< "\n\t new_dims: " << new_dims
<< "\n\t dims: " << dims
);
// extend w by the right number of elements
if (have_bias && !last_weight_1)
{
// Splice some zeros into the w vector so it will have the
// right length. Here we are being careful to move the bias
// weight to the end of the resulting vector.
w = join_cols(join_cols(
colm(w,0,dims),
zeros_matrix<scalar_type>(new_dims-dims,1)),
uniform_matrix<scalar_type>(1,1,w(dims))
);
}
else
{
// Just concatenate the right number of zeros.
w = join_cols(w, zeros_matrix<scalar_type>(new_dims-dims,1));
}
dims = new_dims;
}
}
else
{
did_init = true;
have_bias = have_bias_;
last_weight_1 = last_weight_1_;
dims = new_dims;
alpha.resize(x.size());
index.reserve(x.size());
Q.reserve(x.size());
if (have_bias && !last_weight_1)
w.set_size(dims+1);
else
w.set_size(dims);
w = 0;
}
for (long i = new_idx; i < x.size(); ++i)
{
Q.push_back(length_squared(x(i)));
if (have_bias && !last_weight_1)
{
index.push_back(i);
Q.back() += 1;
}
else if (Q.back() != 0)
{
index.push_back(i);
}
if (do_svm_l2_)
{
if (y(i) > 0)
Q.back() += 1/(2*Cpos);
else
Q.back() += 1/(2*Cneg);
}
}
if (last_weight_1)
w(dims-1) = 1;
}
template <typename T>
typename enable_if<is_matrix<T>,scalar_type>::type length_squared (const T& x) const
{
if (!last_weight_1)
{
return dlib::dot(x,x);
}
else
{
// skip the last dimension
return dlib::dot(colm(x,0,x.size()-1),
colm(x,0,x.size()-1));
}
}
template <typename T>
typename disable_if<is_matrix<T>,scalar_type>::type length_squared (const T& x) const
{
if (!last_weight_1)
{
return dlib::dot(x,x);
}
else
{
scalar_type temp = 0;
typename T::const_iterator i;
for (i = x.begin(); i != x.end(); ++i)
{
// skip the last dimension
if (static_cast<long>(i->first) < dims-1)
temp += i->second*i->second;
}
return temp;
}
}
bool did_init;
bool have_bias;
bool last_weight_1;
std::vector<scalar_type> alpha;
scalar_vector_type w;
std::vector<scalar_type> Q;
std::vector<long> index;
long dims;
dlib::rand rnd;
public:
const std::vector<scalar_type>& get_alpha () const { return alpha; }
friend void serialize(const optimizer_state& item, std::ostream& out)
{
const int version = 1;
dlib::serialize(version, out);
dlib::serialize(item.did_init, out);
dlib::serialize(item.have_bias, out);
dlib::serialize(item.last_weight_1, out);
dlib::serialize(item.alpha, out);
dlib::serialize(item.w, out);
dlib::serialize(item.Q, out);
dlib::serialize(item.index, out);
dlib::serialize(item.dims, out);
dlib::serialize(item.rnd, out);
}
friend void deserialize(optimizer_state& item, std::istream& in)
{
int version = 0;
dlib::deserialize(version, in);
if (version != 1)
{
throw dlib::serialization_error(
"Error while deserializing dlib::svm_c_linear_dcd_trainer::optimizer_state, unexpected version."
);
}
dlib::deserialize(item.did_init, in);
dlib::deserialize(item.have_bias, in);
dlib::deserialize(item.last_weight_1, in);
dlib::deserialize(item.alpha, in);
dlib::deserialize(item.w, in);
dlib::deserialize(item.Q, in);
dlib::deserialize(item.index, in);
dlib::deserialize(item.dims, in);
dlib::deserialize(item.rnd, in);
}
};
template <
typename in_sample_vector_type,
typename in_scalar_vector_type
>
const decision_function<kernel_type> train (
const in_sample_vector_type& x,
const in_scalar_vector_type& y
) const
{
optimizer_state state;
return do_train(mat(x), mat(y), state);
}
template <
typename in_sample_vector_type,
typename in_scalar_vector_type
>
const decision_function<kernel_type> train (
const in_sample_vector_type& x,
const in_scalar_vector_type& y,
optimizer_state& state
) const
{
return do_train(mat(x), mat(y), state);
}
private:
// ------------------------------------------------------------------------------------
template <
typename in_sample_vector_type,
typename in_scalar_vector_type
>
const decision_function<kernel_type> do_train (
const in_sample_vector_type& x,
const in_scalar_vector_type& y,
optimizer_state& state
) const
{
// make sure requires clause is not broken
DLIB_ASSERT(is_learning_problem(x,y) == true,
"\t decision_function svm_c_linear_dcd_trainer::train(x,y)"
<< "\n\t invalid inputs were given to this function"
<< "\n\t x.size(): " << x.size()
<< "\n\t y.size(): " << y.size()
<< "\n\t is_learning_problem(x,y): " << is_learning_problem(x,y)
);
#ifdef ENABLE_ASSERTS
for (long i = 0; i < x.size(); ++i)
{
DLIB_ASSERT(y(i) == +1 || y(i) == -1,
"\t decision_function svm_c_linear_dcd_trainer::train(x,y)"
<< "\n\t invalid inputs were given to this function"
<< "\n\t y("<<i<<"): " << y(i)
);
}
#endif
state.init(x,y,have_bias,last_weight_1,do_svm_l2,Cpos,Cneg);
std::vector<scalar_type>& alpha = state.alpha;
scalar_vector_type& w = state.w;
std::vector<long>& index = state.index;
const long dims = state.dims;
unsigned long active_size = index.size();
scalar_type PG_max_prev = std::numeric_limits<scalar_type>::infinity();
scalar_type PG_min_prev = -std::numeric_limits<scalar_type>::infinity();
const scalar_type Dii_pos = 1/(2*Cpos);
const scalar_type Dii_neg = 1/(2*Cneg);
// main loop
for (unsigned long iter = 0; iter < max_iterations; ++iter)
{
scalar_type PG_max = -std::numeric_limits<scalar_type>::infinity();
scalar_type PG_min = std::numeric_limits<scalar_type>::infinity();
// randomly shuffle the indices
for (unsigned long i = 0; i < active_size; ++i)
{
// pick a random index >= i
const long j = i + state.rnd.get_random_32bit_number()%(active_size-i);
std::swap(index[i], index[j]);
}
// for all the active training samples
for (unsigned long ii = 0; ii < active_size; ++ii)
{
const long i = index[ii];
scalar_type G = y(i)*dot(w, x(i)) - 1;
if (do_svm_l2)
{
if (y(i) > 0)
G += Dii_pos*alpha[i];
else
G += Dii_neg*alpha[i];
}
const scalar_type C = (y(i) > 0) ? Cpos : Cneg;
const scalar_type U = do_svm_l2 ? std::numeric_limits<scalar_type>::infinity() : C;
scalar_type PG = 0;
if (alpha[i] == 0)
{
if (G > PG_max_prev)
{
// shrink the active set of training examples
--active_size;
std::swap(index[ii], index[active_size]);
--ii;
continue;
}
if (G < 0)
PG = G;
}
else if (alpha[i] == U)
{
if (G < PG_min_prev)
{
// shrink the active set of training examples
--active_size;
std::swap(index[ii], index[active_size]);
--ii;
continue;
}
if (G > 0)
PG = G;
}
else
{
PG = G;
}
if (PG > PG_max)
PG_max = PG;
if (PG < PG_min)
PG_min = PG;
// if PG != 0
if (std::abs(PG) > 1e-12)
{
const scalar_type alpha_old = alpha[i];
alpha[i] = std::min(std::max(alpha[i] - G/state.Q[i], (scalar_type)0.0), U);
const scalar_type delta = (alpha[i]-alpha_old)*y(i);
add_to(w, x(i), delta);
if (have_bias && !last_weight_1)
w(w.size()-1) -= delta;
if (last_weight_1)
w(dims-1) = 1;
}
}
if (verbose)
{
using namespace std;
cout << "gap: " << PG_max - PG_min << endl;
cout << "active_size: " << active_size << endl;
cout << "iter: " << iter << endl;
cout << endl;
}
if (PG_max - PG_min <= eps)
{
// stop if we are within eps tolerance and the last iteration
// was over all the samples
if (active_size == index.size())
break;
// Turn off shrinking on the next iteration. We will stop if the
// tolerance is still <= eps when shrinking is off.
active_size = index.size();
PG_max_prev = std::numeric_limits<scalar_type>::infinity();
PG_min_prev = -std::numeric_limits<scalar_type>::infinity();
}
else if (do_shrinking)
{
PG_max_prev = PG_max;
PG_min_prev = PG_min;
if (PG_max_prev <= 0)
PG_max_prev = std::numeric_limits<scalar_type>::infinity();
if (PG_min_prev >= 0)
PG_min_prev = -std::numeric_limits<scalar_type>::infinity();
}
} // end of main optimization loop
// put the solution into a decision function and then return it
decision_function<kernel_type> df;
if (have_bias && !last_weight_1)
df.b = w(w.size()-1);
else
df.b = 0;
df.basis_vectors.set_size(1);
// Copy the plane normal into the output basis vector. The output vector might
// be a sparse vector container so we need to use this special kind of copy to
// handle that case.
assign(df.basis_vectors(0), colm(w, 0, dims));
df.alpha.set_size(1);
df.alpha(0) = 1;
return df;
}
scalar_type dot (
const scalar_vector_type& w,
const sample_type& sample
) const
{
if (have_bias && !last_weight_1)
{
const long w_size_m1 = w.size()-1;
return dlib::dot(colm(w,0,w_size_m1), sample) - w(w_size_m1);
}
else
{
return dlib::dot(w, sample);
}
}
// ------------------------------------------------------------------------------------
scalar_type Cpos;
scalar_type Cneg;
scalar_type eps;
unsigned long max_iterations;
bool verbose;
bool have_bias; // having a bias means we pretend all x vectors have an extra element which is always -1.
bool last_weight_1;
bool do_shrinking;
bool do_svm_l2;
}; // end of class svm_c_linear_dcd_trainer
// ----------------------------------------------------------------------------------------
}
#endif // DLIB_SVm_C_LINEAR_DCD_TRAINER_Hh_