// parallel version with arbitrary dimensions
int gridSearchParallel(DoublePyArrayVector const &controlGridArray, MaximizerCallParams ¶ms, double &rMaxVal, DoubleVector &rArgMaxArray) {
int nGrids = controlGridArray.size();
int i;
IntVector lenArray(nGrids);
for (i=0; i<nGrids; i++) {
// check that all grids are 1-dimensional
assert(controlGridArray[i].ndim() == 1);
lenArray[i] = controlGridArray[i].dims()[0];
}
unsigned int totalGridSize = 1;
double totalGridSize2 = 1.0;
for (i=0; i<nGrids; i++) {
totalGridSize *= lenArray[i];
totalGridSize2 *= double(lenArray[i]);
}
// check that total grid space isn't too large to be indexed
assert(totalGridSize2 < double(UINT_MAX));
MaxIndexFnObj2 fnObj(controlGridArray, params);
parallel_reduce( blocked_range<size_t>(0, totalGridSize), fnObj);
rMaxVal = fnObj.m_value_of_max;
rArgMaxArray.resize(fnObj.m_argmax.size());
copy(fnObj.m_argmax.begin(), fnObj.m_argmax.end(), rArgMaxArray.begin());
return 1;
}
int RateGammaInvar::computePatternRates(DoubleVector &pattern_rates, IntVector &pattern_cat) {
//cout << "Computing Gamma site rates by empirical Bayes..." << endl;
phylo_tree->computePatternLhCat(WSL_RATECAT);
int npattern = phylo_tree->aln->getNPattern();
pattern_rates.resize(npattern);
pattern_cat.resize(npattern);
double *lh_cat = phylo_tree->_pattern_lh_cat;
for (int i = 0; i < npattern; i++) {
double sum_rate = 0.0, sum_lh = phylo_tree->ptn_invar[i];
int best = 0;
double best_lh = phylo_tree->ptn_invar[i];
for (int c = 0; c < ncategory; c++) {
sum_rate += rates[c] * lh_cat[c];
sum_lh += lh_cat[c];
if (lh_cat[c] > best_lh || (lh_cat[c] == best_lh && random_double()<0.5)) { // break tie at random
best = c+1;
best_lh = lh_cat[c];
}
}
pattern_rates[i] = sum_rate / sum_lh;
pattern_cat[i] = best;
lh_cat += ncategory;
}
return ncategory+1;
}
int RateGamma::computePatternRates(DoubleVector &pattern_rates, IntVector &pattern_cat) {
//cout << "Computing Gamma site rates by empirical Bayes..." << endl;
phylo_tree->computePatternLhCat(WSL_RATECAT);
int npattern = phylo_tree->aln->getNPattern();
pattern_rates.resize(npattern);
pattern_cat.resize(npattern);
double *lh_cat = phylo_tree->_pattern_lh_cat;
for (int i = 0; i < npattern; i++) {
double sum_rate = 0.0, sum_lh = 0.0;
int best = 0;
for (int c = 0; c < ncategory; c++) {
sum_rate += rates[c] * lh_cat[c];
sum_lh += lh_cat[c];
if (lh_cat[c] > lh_cat[best] || (lh_cat[c] == lh_cat[best] && random_double()<0.5)) // break tie at random
best = c;
}
pattern_rates[i] = sum_rate / sum_lh;
pattern_cat[i] = best;
lh_cat += ncategory;
}
return ncategory;
// pattern_rates.clear();
// pattern_rates.insert(pattern_rates.begin(), ptn_rates, ptn_rates + npattern);
// pattern_cat.resize(npattern, 0);
// for (int i = 0; i < npattern; i++)
// for (int j = 1; j < ncategory; j++)
// if (fabs(rates[j] - ptn_rates[i]) < fabs(rates[pattern_cat[i]] - ptn_rates[i]))
// pattern_cat[i] = j;
// delete [] ptn_rates;
}
void RRSchedule::init2DEmpty(DoubleVector &_invect, int row, int col)
{
_invect.clear();
_invect.resize(row);
for (int i = 0; i < row; i++)
allocate1D(_invect[i], col);
}
void RateMeyerHaeseler::getRates(DoubleVector &rates) {
rates.clear();
if (empty()) {
rates.resize(phylo_tree->aln->size(), 1.0);
} else {
rates.insert(rates.begin(), begin(), end());
}
}
void StopRule::multiple (DoubleVector &vec1, DoubleMatrix &mat2, DoubleVector &proVec) {
int row_, col_;
proVec.resize(mat2[0].size());
for (col_ = 0; col_ < mat2[0].size(); col_ ++) {
proVec[col_] = 0.0;
for (row_ = 0; row_ < mat2.size(); row_ ++)
proVec[col_] += vec1[row_] * mat2[row_][col_];
}
}
void HeatControl2Delta::initialize()
{
U.clear();
U.resize(N2+1, N1+1);
DoubleVector x = O;
#ifdef POWER_OPTIMIZE
x.resize( 2*L + (M+1)*L );
#else
x.resize( 2*L );
#endif
#ifdef POWER_OPTIMIZE
for (unsigned int k=0; k<=M; k++)
{
x[2*L + 0*(M+1) + k] = g1(k*ht);
x[2*L + 1*(M+1) + k] = g2(k*ht);
x[2*L + 2*(M+1) + k] = g3(k*ht);
}
#endif
px = &x;
IParabolicEquation2D::calculateMVD(U, h1, h2, ht, N1, N2, M, a1, a2);
puts("+------------------------------------------------------------------------------------------------------------------------------------------------------------------+");
IPrinter::printMatrix(U, 10, 10);
printf("eo: [%12.8f, %12.8f] [%12.8f, %12.8f] [%12.8f, %12.8f]\n", x[0], x[1], x[2], x[3], x[4], x[5]);
puts("+------------------------------------------------------------------------------------------------------------------------------------------------------------------+");
write("optimal.txt", U);
FILE* f = fopen("optimal1.txt", "w");
IPrinter::printMatrix(U, N1, N2, NULL, f);
fclose(f);
}
DoubleVector& get_cleaved_masses ( const string& protein, const IntVector& cleavageIndex )
{
static DoubleVector cleavedMassArray ( 36000 );
StringSizeType numAA = protein.length ();
if ( numAA > cleavedMassArray.size () ) cleavedMassArray.resize ( numAA );
for ( StringSizeType i = 0, j = 0 ; i < numAA ; ) {
double mass = 0.0;
StringSizeType index = cleavageIndex [j];
while ( i <= index ) {
mass += amino_acid_wt [protein[i++]];
}
cleavedMassArray [j++] = mass;
}
return cleavedMassArray;
}
void Example1::runge_kutta(OrdDifEquation *ode, double x0, double y0, unsigned int N, DoubleVector &y, double h)
{
double k1 = 0.0;
double k2 = 0.0;
double k3 = 0.0;
double k4 = 0.0;
y.clear();
y.resize(N+1,0.0);
y[0] = y0;
for (unsigned int i=0; i<N; i++)
{
double _x = x0 + i*h;
double _y = y[i];
k1 = ode->fx(_x, _y);
k2 = ode->fx(_x+h/2.0, _y+(h/2.0)*k1);
k3 = ode->fx(_x+h/2.0, _y+(h/2.0)*k2);
k4 = ode->fx(_x+h, _y+h*k3);
y[i+1] = y[i] + (h/6.0) * (k1 + 2.0*k2 + 2.0*k3 + k4);;
}
}
void IFunctional::toVector(const Parameter &prm, DoubleVector &pv) const
{
unsigned int Lc = prm.Lc;
unsigned int Lo = prm.Lo;
//unsigned int length = 0;
//if (optimizeK) length += Lc*Lo;
//if (optimizeZ) length += Lc*Lo;
//if (optimizeC) length += 2*Lc;
//if (optimizeO) length += 2*Lo;
pv.clear();
pv.resize(2*Lc*Lo+2*Lo+2*Lc);
//if (optimizeK)
{
for (unsigned int i=0; i<Lc; i++)
{
for (unsigned int j=0; j<Lo; j++)
{
pv[i*Lo + j] = prm.k[i][j];
}
}
}
//if (optimizeZ)
{
for (unsigned int i=0; i<Lc; i++)
{
for (unsigned int j=0; j<Lo; j++)
{
pv[i*Lo + j + Lc*Lo] = prm.z[i][j];
}
}
}
//if (optimizeO)
{
for (unsigned int j=0; j<Lo; j++)
{
pv[2*j + 0 + 2*Lc*Lo] = prm.xi[j].x;
pv[2*j + 1 + 2*Lc*Lo] = prm.xi[j].y;
}
}
//if (optimizeC)
{
for (unsigned int i=0; i<Lc; i++)
{
pv[2*i + 0 + 2*Lo + 2*Lc*Lo] = prm.eta[i].x;
pv[2*i + 1 + 2*Lo + 2*Lc*Lo] = prm.eta[i].y;
}
}
// for (unsigned int i=0; i<Lc; i++)
// {
// x[2*i + 0 + 2*Lc*Lo] = prm.eta[i].x;
// x[2*i + 1 + 2*Lc*Lo] = prm.eta[i].y;
// for (unsigned int j=0; j<Lo; j++)
// {
// x[i*Lo + j] = prm.k[i][j];
// x[i*Lo + j + Lc*Lo] = prm.z[i][j];
// x[2*j + 0 + 2*Lc + 2*Lc*Lo] = prm.xi[j].x;
// x[2*j + 1 + 2*Lc + 2*Lc*Lo] = prm.xi[j].y;
// }
// }
}
void IFunctional::gradient(const DoubleVector &pv, DoubleVector &g) const
{
IFunctional *ifunc = const_cast<IFunctional*>(this);
unsigned int L = mTimeDimension.sizeN();
double ht = mTimeDimension.step();
ifunc->fromVector(pv, ifunc->mParameter);
forward->setParameter(mParameter);
backward->setParameter(mParameter);
DoubleMatrix u;
DoubleMatrix p;
vector<ExtendedSpaceNode2D> u_info;
forward->calculateMVD(u, u_info, true);
backward->u = &u;
backward->U = &ifunc->U;
backward->info = &u_info;
vector<ExtendedSpaceNode2D> p_info;
backward->calculateMVD(p, p_info, true);
g.clear();
g.resize(pv.length(), 0.0);
unsigned int gi = 0;
// k
if (optimizeK)
{
for (unsigned int i=0; i<mParameter.Lc; i++)
{
ExtendedSpaceNode2D &pi = p_info[i];
for (unsigned int j=0; j<mParameter.Lo; j++)
{
ExtendedSpaceNode2D &uj = u_info[j];
double grad_Kij = 0.0;
grad_Kij += 0.5 * (pi.value(0)+2.0*r*gpi(i,0,u_info)*sgn(g0i(i,0,u_info))) * (uj.value(0) - mParameter.z.at(i,j));
for (unsigned int m=1; m<=L-1; m++)
{
grad_Kij += (pi.value(m)+2.0*r*gpi(i,m,u_info)*sgn(g0i(i,m,u_info))) * (uj.value(m) - mParameter.z.at(i,j));
}
grad_Kij += 0.5 * (pi.value(L)+2.0*r*gpi(i,L,u_info)*sgn(g0i(i,L,u_info))) * (uj.value(L) - mParameter.z.at(i,j));
grad_Kij *= -ht;
g[gi++] = grad_Kij + 2.0*regEpsilon*(mParameter.k.at(i,j) - mParameter0.k.at(i,j));
}
}
}
else
{
for (unsigned int i=0; i<mParameter.Lc; i++)
{
for (unsigned int j=0; j<mParameter.Lo; j++)
{
g[gi++] = 0.0;
}
}
}
// z
if (optimizeZ)
{
for (unsigned int i=0; i<mParameter.Lc; i++)
{
ExtendedSpaceNode2D &pi = p_info[i];
for (unsigned int j=0; j<mParameter.Lo; j++)
{
double grad_Zij = 0.0;
grad_Zij += 0.5 * (pi.value(0)+2.0*r*gpi(i,0,u_info)*sgn(g0i(i,0,u_info))) * mParameter.k.at(i,j);
for (unsigned int m=1; m<=L-1; m++)
{
grad_Zij += (pi.value(m)+2.0*r*gpi(i,m,u_info)*sgn(g0i(i,m,u_info))) * mParameter.k.at(i,j);
}
grad_Zij += 0.5 * (pi.value(L)+2.0*r*gpi(i,L,u_info)*sgn(g0i(i,L,u_info))) * mParameter.k.at(i,j);
grad_Zij *= ht;
g[gi++] = grad_Zij + 2.0*regEpsilon*(mParameter.z[i][j] - mParameter0.z[i][j]);
}
}
}
else
{
for (unsigned int i=0; i<mParameter.Lc; i++)
{
for (unsigned int j=0; j<mParameter.Lo; j++)
{
g[gi++] = 0.0;
}
}
}
// xi
if (optimizeO)
{
for (unsigned int j=0; j<mParameter.Lo; j++)
{
ExtendedSpaceNode2D &uj = u_info[j];
double gradXijX = 0.0;
double gradXijY = 0.0;
double vi = 0.0;
vi = 0.0;
for (unsigned int i=0; i<mParameter.Lc; i++) vi += mParameter.k.at(i,j) * (p_info[i].value(0)+2.0*r*gpi(i,0,u_info)*sgn(g0i(i,0,u_info)));
gradXijX += 0.5 * uj.valueDx(0) * vi;
gradXijY += 0.5 * uj.valueDy(0) * vi;
for (unsigned int m=1; m<=L-1; m++)
{
vi = 0.0;
for (unsigned int i=0; i<mParameter.Lc; i++) vi += mParameter.k.at(i,j)*(p_info[i].value(m)+2.0*r*gpi(i,m,u_info)*sgn(g0i(i,m,u_info)));
gradXijX += uj.valueDx(m) * vi;
gradXijY += uj.valueDy(m) * vi;
}
vi = 0.0;
for (unsigned int i=0; i<mParameter.Lc; i++) vi += mParameter.k.at(i,j)*(p_info[i].value(L)+2.0*r*gpi(i,L,u_info)*sgn(g0i(i,L,u_info)));
gradXijX += 0.5 * uj.valueDx(L) * vi;
gradXijY += 0.5 * uj.valueDy(L) * vi;
gradXijX *= -ht;
gradXijY *= -ht;
g[gi++] = gradXijX + 2.0*regEpsilon*(mParameter.xi[j].x - mParameter0.xi[j].x);
g[gi++] = gradXijY + 2.0*regEpsilon*(mParameter.xi[j].y - mParameter0.xi[j].y);
}
}
else
{
for (unsigned int j=0; j<mParameter.Lo; j++)
{
g[gi++] = 0.0;
g[gi++] = 0.0;
}
}
// eta
if (optimizeC)
{
for (unsigned int i=0; i<mParameter.Lc; i++)
{
ExtendedSpaceNode2D &pi = p_info[i];
double grad_EtaiX = 0.0;
double grad_EtaiY = 0.0;
double vi = 0.0;
vi = 0.0;
for (unsigned int j=0; j<mParameter.Lo; j++) vi += mParameter.k.at(i,j) * (u_info[j].value(0) - mParameter.z.at(i,j));
grad_EtaiX += 0.5 * pi.valueDx(0) * vi;
grad_EtaiY += 0.5 * pi.valueDy(0) * vi;
for (unsigned int m=1; m<=L-1; m++)
{
vi = 0.0;
for (unsigned int j=0; j<mParameter.Lo; j++) vi += mParameter.k.at(i,j) * (u_info[j].value(m) - mParameter.z.at(i,j));
grad_EtaiX += pi.valueDx(m) * vi;
grad_EtaiY += pi.valueDy(m) * vi;
}
vi = 0.0;
for (unsigned int j=0; j<mParameter.Lo; j++) vi += mParameter.k.at(i,j) * (u_info[j].value(L) - mParameter.z.at(i,j));
grad_EtaiX += 0.5 * pi.valueDx(L) * vi;
grad_EtaiY += 0.5 * pi.valueDy(L) * vi;
grad_EtaiX *= -ht;
grad_EtaiY *= -ht;
g[gi++] = grad_EtaiX + 2.0*regEpsilon*(mParameter.eta[i].x - mParameter0.eta[i].x);
g[gi++] = grad_EtaiY + 2.0*regEpsilon*(mParameter.eta[i].y - mParameter0.eta[i].y);
}
}
else
{
for (unsigned int i=0; i<mParameter.Lc; i++)
{
g[gi++] = 0.0;
g[gi++] = 0.0;
}
}
u_info.clear();
p_info.clear();
}
void CplexSolver::rc(DoubleVector & result)const{
result.resize(ncols());
CPXgetdj(_env, _prob, result.data(), 0, ncols() - 1);
}
void CplexSolver::solution(int i, DoubleVector & x) const {
x.resize(ncols());
CPXgetsolnpoolx(_env, _prob, (int) i, x.data(), 0, (int) (x.size() - 1));
}
// single-threaded grid search with an arbitrary number of dimensions
int gridSearch(DoublePyArrayVector const &controlGridArray, MaximizerCallParams ¶ms, double &rMaxVal, DoubleVector &rArgMaxArray) {
int nGrids = controlGridArray.size();
int i;
IntVector lenArray(nGrids), strideArray(nGrids), dataIndexArray(nGrids);
for (i=0; i<nGrids; i++) {
// check that all grids are 1-dimensional
assert(controlGridArray[i].ndim() == 1);
lenArray[i] = controlGridArray[i].dims()[0];
strideArray[i] = controlGridArray[i].strides()[0];
dataIndexArray[i] = 0;
}
DoubleVector argArray(nGrids);
int nIter = 0;
int nMaxMultiplicity = 0;
bool bDone = false;
// check for zero size, if one grid has zero size then there's nothing to do
for (i=0; i<nGrids; i++) {
if (lenArray[i] == 0) {
bDone = true;
break;
}
}
double max = -DBL_MAX;
while (!bDone) {
double result;
// get double array args from char* data
for (i=0; i<nGrids; i++) {
char *pData = (char*) (controlGridArray[i].array().data());
char *pArg = pData + (strideArray[i] * dataIndexArray[i]);
argArray[i] = *(double*) pArg;
}
result = params.objectiveFunction(argArray);
if (nIter == 0) { // first iteration
max = result;
rArgMaxArray.resize(argArray.size());
copy(argArray.begin(), argArray.end(), rArgMaxArray.begin());
nMaxMultiplicity = 1;
} else {
if (result > max) {
max = result;
rArgMaxArray.resize(argArray.size());
copy(argArray.begin(), argArray.end(), rArgMaxArray.begin());
nMaxMultiplicity = 1;
} else if (result == max) {
nMaxMultiplicity++;
}
}
nIter++;
// increment indices for next iteration
bDone = true;
for (i=nGrids-1; i>=0; i--) {
// increment index i
dataIndexArray[i] += 1;
if (dataIndexArray[i] == lenArray[i]) {
// cycle this index
dataIndexArray[i] = 0;
} else {
bDone = false;
break;
}
// if we make it here, all indices have cycled, therefore we are done
}
}
rMaxVal = max;
return nMaxMultiplicity;
}
//////////////////////////////////////////////////////////////////////
// CInterpolationEngine Interface
//////////////////////////////////////////////////////////////////////
void CInterpolationEngine::RecalculateVector() throw(CInterpolationException)
{
// Sort point vector
std::sort( m_pts.begin(), m_pts.end() );
// Spline interpolation is used for more than 2 points
long i, size = m_pts.size();
if( size < MIN_SPLINE_POINTS )
return;
// Calculate points offsets
m_h.resize( size );
for( i = 0; i < size - 1; i++ )
m_h[i] = m_pts[i+1].x - m_pts[i].x;
// Fill auxiliary arrays
DoubleVector lam, mu, d;
lam.resize( size );
mu.resize( size );
d.resize( size );
for( i = 1; i < size - 1; i++ )
{
lam[i] = m_h[i]/( m_h[i-1] + m_h[i] );
mu[i] = 1 - lam[i];
d[i] = 2 * (3 + m_dr) * (lam[i] * (m_pts[i].f - m_pts[i-1].f)/m_h[i-1] +
mu[i] * (m_pts[i+1].f - m_pts[i].f)/m_h[i]);
}
d[0] = (3 + m_dr) * (m_pts[1].f - m_pts[0].f)/m_h[0] -
m_h[0]/m_dR * (m_pts[0].f/m_h[1]/(m_h[1]+m_h[0]) - m_pts[1].f/m_h[1]/m_h[0] + m_pts[2].f/m_h[0]/(m_h[1] + m_h[0]));
d[size-1] = (3 + m_dr) * (m_pts[size-1].f - m_pts[size-2].f)/m_h[size-2] +
m_h[size-2]/m_dR * (m_pts[size-1].f/m_h[size-3]/(m_h[size-2] + m_h[size-3]) - m_pts[size-2].f/m_h[size-2]/m_h[size-3] +
m_pts[size-3].f/m_h[size-2]/(m_h[size-2] + m_h[size-3]));
// Forward
DoubleVector P, Q;
P.resize( size );
Q.resize( size );
P[0] = -1/( 2 + m_dr );
Q[0] = d[0]/( 2 + m_dr );
for( i = 1; i < size - 1; i++ )
{
P[i] = -mu[i]/(lam[i] * P[i-1] + 2 + m_dr);
Q[i] = P[i] * (2 * lam[i] * Q[i-1] - d[i]);
}
// Backward
DoubleVector m;
m.resize( size );
m[size-1] = (d[size-1] - Q[size-2])/(2 + m_dr + P[size-2]);
for( i = size - 2; i >= 0; i-- )
m[i] = P[i] * m[i+1] + Q[i];
// Factor arrays
m_A.resize( size - 1 );
m_B.resize( size - 1 );
m_C.resize( size - 1 );
m_D.resize( size - 1 );
for( i = 0; i < size - 1; i++ )
{
m_C[i] = (-(3 + m_dr) * (m_pts[i+1].f - m_pts[i].f) + m_h[i] * m[i] + (2 + m_dr) * m_h[i] * m[i+1])/(3 + 4 * m_dr + m_dr * m_dr);
m_D[i] = ( (3 + m_dr) * (m_pts[i+1].f - m_pts[i].f) - m_h[i] * m[i+1] - (2 + m_dr) * m_h[i] * m[i])/(3 + 4 * m_dr + m_dr * m_dr);
m_A[i] = m_pts[i+1].f - m_C[i];
m_B[i] = m_pts[i].f - m_D[i];
}
}
void BackwardParabolicIBVP::gridMethod(DoubleVector &p, SweepMethodDirection direction) const
{
typedef void (*t_algorithm)(const double*, const double*, const double*, const double*, double*, unsigned int);
t_algorithm algorithm = &tomasAlgorithm;
if (direction == ForwardSweep) algorithm = &tomasAlgorithmL2R;
if (direction == BackwardSweep) algorithm = &tomasAlgorithmR2L;
Dimension time = mtimeDimension;
Dimension dim1 = mspaceDimension.at(0);
double ht = time.step();
unsigned int minM = time.min();
unsigned int maxM = time.max();
unsigned int M = maxM-minM;
double hx = dim1.step();
unsigned int minN = dim1.min();
unsigned int maxN = dim1.max();
unsigned int N = maxN-minN;
double h = ht/(hx*hx);
p.clear();
p.resize(N+1);
double *ka = (double*) malloc(sizeof(double)*(N-1));
double *kb = (double*) malloc(sizeof(double)*(N-1));
double *kc = (double*) malloc(sizeof(double)*(N-1));
double *kd = (double*) malloc(sizeof(double)*(N-1));
double *rx = (double*) malloc(sizeof(double)*(N-1));
/* initial condition */
SpaceNodePDE isn;
for (unsigned int n=0; n<=N; n++)
{
isn.i = n+minN;
isn.x = isn.i*hx;
p[n] = initial(isn);
}
layerInfo(p, M);
SpaceNodePDE lsn;
lsn.i = minN;
lsn.x = minN*hx;
SpaceNodePDE rsn;
rsn.i = maxN;
rsn.x = maxN*hx;
TimeNodePDE tn;
for (unsigned int m=M-1; m!=UINT32_MAX; m--)
{
tn.i = m+minM;
tn.t = tn.i*ht;
for (unsigned int n=1; n<=N-1; n++)
{
isn.i = n+minN;
isn.x = isn.i*hx;
double alpha = -a(isn,tn)*h;
double betta = 1.0 - 2.0*alpha;
ka[n-1] = alpha;
kb[n-1] = betta;
kc[n-1] = alpha;
kd[n-1] = p[n] - ht * f(isn, tn);
}
ka[0] = 0.0;
kc[N-2] = 0.0;
/* border conditions */
p[0] = boundary(lsn, tn);
p[N] = boundary(rsn, tn);
kd[0] -= a(lsn,tn) * h * p[0];
kd[N-2] -= a(rsn,tn) * h * p[N];
(*algorithm)(ka, kb, kc, kd, rx, N-1);
for (unsigned int n=1; n<=N-1; n++) p[n] = rx[n-1];
layerInfo(p, m);
}
free(ka);
free(kb);
free(kc);
free(kd);
free(rx);
}
