bool Fisher::build (ISamples &samples, ssi_size_t stream_index) {
if (samples.getSize () == 0) {
ssi_wrn ("empty sample list");
return false;
}
if (isBuild ()) {
ssi_wrn ("already trained");
return false;
}
ae_state state;
ae_int_t info;
ae_matrix data;
ae_matrix_init (&data, 0, 0, DT_REAL, &state, ae_true);
// convert the samples to a matrix where the last column holds the class number to which the sample belongs
AlgLibTools::Samples2MatrixWithClass(samples, 0, &data);
_basis = new ae_matrix;
ae_matrix_init (_basis, 0, 0, DT_REAL, &state, ae_true);
fisherldan(&data,data.rows,data.cols-1 , samples.getClassSize(),&info,_basis,&state);
ae_matrix_clear (&data);
_is_build = true;
return true;
}
/*************************************************************************
Multiclass Fisher LDA
Subroutine finds coefficients of linear combination which optimally separates
training set on classes.
INPUT PARAMETERS:
XY - training set, array[0..NPoints-1,0..NVars].
First NVars columns store values of independent
variables, next column stores number of class (from 0
to NClasses-1) which dataset element belongs to. Fractional
values are rounded to nearest integer.
NPoints - training set size, NPoints>=0
NVars - number of independent variables, NVars>=1
NClasses - number of classes, NClasses>=2
OUTPUT PARAMETERS:
Info - return code:
* -4, if internal EVD subroutine hasn't converged
* -2, if there is a point with class number
outside of [0..NClasses-1].
* -1, if incorrect parameters was passed (NPoints<0,
NVars<1, NClasses<2)
* 1, if task has been solved
* 2, if there was a multicollinearity in training set,
but task has been solved.
W - linear combination coefficients, array[0..NVars-1]
-- ALGLIB --
Copyright 31.05.2008 by Bochkanov Sergey
*************************************************************************/
void fisherlda(const ap::real_2d_array& xy,
int npoints,
int nvars,
int nclasses,
int& info,
ap::real_1d_array& w)
{
ap::real_2d_array w2;
fisherldan(xy, npoints, nvars, nclasses, info, w2);
if( info>0 )
{
w.setbounds(0, nvars-1);
ap::vmove(&w(0), 1, &w2(0, 0), w2.getstride(), ap::vlen(0,nvars-1));
}
}
/*************************************************************************
N-dimensional multiclass Fisher LDA
Subroutine finds coefficients of linear combinations which optimally separates
training set on classes. It returns N-dimensional basis whose vector are sorted
by quality of training set separation (in descending order).
INPUT PARAMETERS:
XY - training set, array[0..NPoints-1,0..NVars].
First NVars columns store values of independent
variables, next column stores number of class (from 0
to NClasses-1) which dataset element belongs to. Fractional
values are rounded to nearest integer.
NPoints - training set size, NPoints>=0
NVars - number of independent variables, NVars>=1
NClasses - number of classes, NClasses>=2
OUTPUT PARAMETERS:
Info - return code:
* -4, if internal EVD subroutine hasn't converged
* -2, if there is a point with class number
outside of [0..NClasses-1].
* -1, if incorrect parameters was passed (NPoints<0,
NVars<1, NClasses<2)
* 1, if task has been solved
* 2, if there was a multicollinearity in training set,
but task has been solved.
W - basis, array[0..NVars-1,0..NVars-1]
columns of matrix stores basis vectors, sorted by
quality of training set separation (in descending order)
-- ALGLIB --
Copyright 31.05.2008 by Bochkanov Sergey
*************************************************************************/
void fisherldan(const ap::real_2d_array& xy,
int npoints,
int nvars,
int nclasses,
int& info,
ap::real_2d_array& w)
{
int i;
int j;
int k;
int m;
double v;
ap::integer_1d_array c;
ap::real_1d_array mu;
ap::real_2d_array muc;
ap::integer_1d_array nc;
ap::real_2d_array sw;
ap::real_2d_array st;
ap::real_2d_array z;
ap::real_2d_array z2;
ap::real_2d_array tm;
ap::real_2d_array sbroot;
ap::real_2d_array a;
ap::real_2d_array xyproj;
ap::real_2d_array wproj;
ap::real_1d_array tf;
ap::real_1d_array d;
ap::real_1d_array d2;
ap::real_1d_array work;
//
// Test data
//
if( npoints<0||nvars<1||nclasses<2 )
{
info = -1;
return;
}
for(i = 0; i <= npoints-1; i++)
{
if( ap::round(xy(i,nvars))<0||ap::round(xy(i,nvars))>=nclasses )
{
info = -2;
return;
}
}
info = 1;
//
// Special case: NPoints<=1
// Degenerate task.
//
if( npoints<=1 )
{
info = 2;
w.setbounds(0, nvars-1, 0, nvars-1);
for(i = 0; i <= nvars-1; i++)
{
for(j = 0; j <= nvars-1; j++)
{
if( i==j )
{
w(i,j) = 1;
}
else
{
w(i,j) = 0;
}
}
}
return;
}
//
// Prepare temporaries
//
tf.setbounds(0, nvars-1);
work.setbounds(1, ap::maxint(nvars, npoints));
//
// Convert class labels from reals to integers (just for convenience)
//
c.setbounds(0, npoints-1);
for(i = 0; i <= npoints-1; i++)
{
c(i) = ap::round(xy(i,nvars));
}
//
// Calculate class sizes and means
//
mu.setbounds(0, nvars-1);
muc.setbounds(0, nclasses-1, 0, nvars-1);
nc.setbounds(0, nclasses-1);
for(j = 0; j <= nvars-1; j++)
{
mu(j) = 0;
}
for(i = 0; i <= nclasses-1; i++)
{
nc(i) = 0;
for(j = 0; j <= nvars-1; j++)
{
muc(i,j) = 0;
}
}
for(i = 0; i <= npoints-1; i++)
{
ap::vadd(&mu(0), 1, &xy(i, 0), 1, ap::vlen(0,nvars-1));
ap::vadd(&muc(c(i), 0), 1, &xy(i, 0), 1, ap::vlen(0,nvars-1));
nc(c(i)) = nc(c(i))+1;
}
for(i = 0; i <= nclasses-1; i++)
{
v = double(1)/double(nc(i));
ap::vmul(&muc(i, 0), 1, ap::vlen(0,nvars-1), v);
}
v = double(1)/double(npoints);
ap::vmul(&mu(0), 1, ap::vlen(0,nvars-1), v);
//
// Create ST matrix
//
st.setbounds(0, nvars-1, 0, nvars-1);
for(i = 0; i <= nvars-1; i++)
{
for(j = 0; j <= nvars-1; j++)
{
st(i,j) = 0;
}
}
for(k = 0; k <= npoints-1; k++)
{
ap::vmove(&tf(0), 1, &xy(k, 0), 1, ap::vlen(0,nvars-1));
ap::vsub(&tf(0), 1, &mu(0), 1, ap::vlen(0,nvars-1));
for(i = 0; i <= nvars-1; i++)
{
v = tf(i);
ap::vadd(&st(i, 0), 1, &tf(0), 1, ap::vlen(0,nvars-1), v);
}
}
//
// Create SW matrix
//
sw.setbounds(0, nvars-1, 0, nvars-1);
for(i = 0; i <= nvars-1; i++)
{
for(j = 0; j <= nvars-1; j++)
{
sw(i,j) = 0;
}
}
for(k = 0; k <= npoints-1; k++)
{
ap::vmove(&tf(0), 1, &xy(k, 0), 1, ap::vlen(0,nvars-1));
ap::vsub(&tf(0), 1, &muc(c(k), 0), 1, ap::vlen(0,nvars-1));
for(i = 0; i <= nvars-1; i++)
{
v = tf(i);
ap::vadd(&sw(i, 0), 1, &tf(0), 1, ap::vlen(0,nvars-1), v);
}
}
//
// Maximize ratio J=(w'*ST*w)/(w'*SW*w).
//
// First, make transition from w to v such that w'*ST*w becomes v'*v:
// v = root(ST)*w = R*w
// R = root(D)*Z'
// w = (root(ST)^-1)*v = RI*v
// RI = Z*inv(root(D))
// J = (v'*v)/(v'*(RI'*SW*RI)*v)
// ST = Z*D*Z'
//
// so we have
//
// J = (v'*v) / (v'*(inv(root(D))*Z'*SW*Z*inv(root(D)))*v) =
// = (v'*v) / (v'*A*v)
//
if( !smatrixevd(st, nvars, 1, true, d, z) )
{
info = -4;
return;
}
w.setbounds(0, nvars-1, 0, nvars-1);
if( ap::fp_less_eq(d(nvars-1),0)||ap::fp_less_eq(d(0),1000*ap::machineepsilon*d(nvars-1)) )
{
//
// Special case: D[NVars-1]<=0
// Degenerate task (all variables takes the same value).
//
if( ap::fp_less_eq(d(nvars-1),0) )
{
info = 2;
for(i = 0; i <= nvars-1; i++)
{
for(j = 0; j <= nvars-1; j++)
{
if( i==j )
{
w(i,j) = 1;
}
else
{
w(i,j) = 0;
}
}
}
return;
}
//
// Special case: degenerate ST matrix, multicollinearity found.
// Since we know ST eigenvalues/vectors we can translate task to
// non-degenerate form.
//
// Let WG is orthogonal basis of the non zero variance subspace
// of the ST and let WZ is orthogonal basis of the zero variance
// subspace.
//
// Projection on WG allows us to use LDA on reduced M-dimensional
// subspace, N-M vectors of WZ allows us to update reduced LDA
// factors to full N-dimensional subspace.
//
m = 0;
for(k = 0; k <= nvars-1; k++)
{
if( ap::fp_less_eq(d(k),1000*ap::machineepsilon*d(nvars-1)) )
{
m = k+1;
}
}
ap::ap_error::make_assertion(m!=0, "FisherLDAN: internal error #1");
xyproj.setbounds(0, npoints-1, 0, nvars-m);
matrixmatrixmultiply(xy, 0, npoints-1, 0, nvars-1, false, z, 0, nvars-1, m, nvars-1, false, 1.0, xyproj, 0, npoints-1, 0, nvars-m-1, 0.0, work);
for(i = 0; i <= npoints-1; i++)
{
xyproj(i,nvars-m) = xy(i,nvars);
}
fisherldan(xyproj, npoints, nvars-m, nclasses, info, wproj);
if( info<0 )
{
return;
}
matrixmatrixmultiply(z, 0, nvars-1, m, nvars-1, false, wproj, 0, nvars-m-1, 0, nvars-m-1, false, 1.0, w, 0, nvars-1, 0, nvars-m-1, 0.0, work);
for(k = nvars-m; k <= nvars-1; k++)
{
ap::vmove(&w(0, k), w.getstride(), &z(0, k-(nvars-m)), z.getstride(), ap::vlen(0,nvars-1));
}
info = 2;
}
else
{
//
// General case: no multicollinearity
//
tm.setbounds(0, nvars-1, 0, nvars-1);
a.setbounds(0, nvars-1, 0, nvars-1);
matrixmatrixmultiply(sw, 0, nvars-1, 0, nvars-1, false, z, 0, nvars-1, 0, nvars-1, false, 1.0, tm, 0, nvars-1, 0, nvars-1, 0.0, work);
matrixmatrixmultiply(z, 0, nvars-1, 0, nvars-1, true, tm, 0, nvars-1, 0, nvars-1, false, 1.0, a, 0, nvars-1, 0, nvars-1, 0.0, work);
for(i = 0; i <= nvars-1; i++)
{
for(j = 0; j <= nvars-1; j++)
{
a(i,j) = a(i,j)/sqrt(d(i)*d(j));
}
}
if( !smatrixevd(a, nvars, 1, true, d2, z2) )
{
info = -4;
return;
}
for(k = 0; k <= nvars-1; k++)
{
for(i = 0; i <= nvars-1; i++)
{
tf(i) = z2(i,k)/sqrt(d(i));
}
for(i = 0; i <= nvars-1; i++)
{
v = ap::vdotproduct(&z(i, 0), 1, &tf(0), 1, ap::vlen(0,nvars-1));
w(i,k) = v;
}
}
}
//
// Post-processing:
// * normalization
// * converting to non-negative form, if possible
//
for(k = 0; k <= nvars-1; k++)
{
v = ap::vdotproduct(&w(0, k), w.getstride(), &w(0, k), w.getstride(), ap::vlen(0,nvars-1));
v = 1/sqrt(v);
ap::vmul(&w(0, k), w.getstride(), ap::vlen(0,nvars-1), v);
v = 0;
for(i = 0; i <= nvars-1; i++)
{
v = v+w(i,k);
}
if( ap::fp_less(v,0) )
{
ap::vmul(&w(0, k), w.getstride(), ap::vlen(0,nvars-1), -1);
}
}
}
