VectorMatrixCU32Accessor::VectorMatrixCU32Accessor(VectorMatrix &mat) : mat(mat)
{
mat.writeLock(1); // 1 = CU32Device!
data_x = static_cast<CU32Array*>(mat.getArray(1, 0))->ptr();
data_y = static_cast<CU32Array*>(mat.getArray(1, 1))->ptr();
data_z = static_cast<CU32Array*>(mat.getArray(1, 2))->ptr();
}
VectorMatrixCU64Accessor::VectorMatrixCU64Accessor(VectorMatrix &mat) : mat(mat)
{
mat.writeLock(2); // 2 = CU64Device!
data_x = static_cast<CU64Array*>(mat.getArray(2, 0))->ptr();
data_y = static_cast<CU64Array*>(mat.getArray(2, 1))->ptr();
data_z = static_cast<CU64Array*>(mat.getArray(2, 2))->ptr();
}
void gradient_cpu(double delta_x, double delta_y, double delta_z, const double *phi, VectorMatrix &field)
{
const int dim_x = field.dimX();
const int dim_y = field.dimY();
const int dim_z = field.dimZ();
const int phi_sx = 1;
const int phi_sy = (dim_x+1);
const int phi_sz = (dim_x+1)*(dim_y+1);
VectorMatrix::accessor field_acc(field);
for (int z=0; z<dim_z; ++z)
for (int y=0; y<dim_y; ++y)
for (int x=0; x<dim_x; ++x) {
const int i = phi_sx*x + phi_sy*y + phi_sz*z;
const double dx = (+ phi[i+1*phi_sx+0*phi_sy+0*phi_sz] - phi[i+0*phi_sx+0*phi_sy+0*phi_sz]
+ phi[i+1*phi_sx+1*phi_sy+0*phi_sz] - phi[i+0*phi_sx+1*phi_sy+0*phi_sz]
+ phi[i+1*phi_sx+0*phi_sy+1*phi_sz] - phi[i+0*phi_sx+0*phi_sy+1*phi_sz]
+ phi[i+1*phi_sx+1*phi_sy+1*phi_sz] - phi[i+0*phi_sx+1*phi_sy+1*phi_sz]) / (4.0 * delta_x);
const double dy = (+ phi[i+0*phi_sx+1*phi_sy+0*phi_sz] - phi[i+0*phi_sx+0*phi_sy+0*phi_sz]
+ phi[i+1*phi_sx+1*phi_sy+0*phi_sz] - phi[i+1*phi_sx+0*phi_sy+0*phi_sz]
+ phi[i+0*phi_sx+1*phi_sy+1*phi_sz] - phi[i+0*phi_sx+0*phi_sy+1*phi_sz]
+ phi[i+1*phi_sx+1*phi_sy+1*phi_sz] - phi[i+1*phi_sx+0*phi_sy+1*phi_sz]) / (4.0 * delta_y);
const double dz = (+ phi[i+0*phi_sx+0*phi_sy+1*phi_sz] - phi[i+0*phi_sx+0*phi_sy+0*phi_sz]
+ phi[i+1*phi_sx+0*phi_sy+1*phi_sz] - phi[i+1*phi_sx+0*phi_sy+0*phi_sz]
+ phi[i+0*phi_sx+1*phi_sy+1*phi_sz] - phi[i+0*phi_sx+1*phi_sy+0*phi_sz]
+ phi[i+1*phi_sx+1*phi_sy+1*phi_sz] - phi[i+1*phi_sx+1*phi_sy+0*phi_sz]) / (4.0 * delta_z);
field_acc.set(x, y, z, Vector3d(dx, dy, dz));
}
}
ConstVectorMatrixCU64Accessor::ConstVectorMatrixCU64Accessor(const VectorMatrix &mat) : mat(mat)
{
mat.readLock(2);
data_x = static_cast<CU64Array*>(mat.getArray(2, 0))->ptr();
data_y = static_cast<CU64Array*>(mat.getArray(2, 1))->ptr();
data_z = static_cast<CU64Array*>(mat.getArray(2, 2))->ptr();
}
VectorVectorConvolution_Simple::VectorVectorConvolution_Simple(const VectorMatrix &lhs, int dim_x, int dim_y, int dim_z)
: lhs(lhs), dim_x(dim_x), dim_y(dim_y), dim_z(dim_z)
{
assert(lhs.getShape().getDim(0) == 3);
exp_x = lhs.getShape().getDim(1);
exp_y = lhs.getShape().getDim(2);
exp_z = lhs.getShape().getDim(3);
}
// must pass in a lvalue
void copy_to( VectorMatrix<T>& vector_matrix_obj ) {
vector_matrix_obj.resize( this->nrow_, this->ncol_ );
const size_t length = this->nrow_ * this->ncol_;
std :: vector<T> temp_store;
temp_store.resize(length);
std :: copy_n( this->store_.begin(), length, temp_store.begin() );
vector_matrix_obj.set_store() = std :: move( temp_store );
}
double rk_scaled_error_norm_cpu(double h, double eps_abs, double eps_rel, const VectorMatrix &y, const VectorMatrix &y_error)
{
double norm;
if (false) { // Maximum norm
double max = 0.0;
VectorMatrix::const_accessor y_acc(y), y_error_acc(y_error);
const size_t s = y.size() * 3;
for (size_t i=0; i<s; ++i) {
const Vector3d value = y_acc.get(i);
const Vector3d error = y_error_acc.get(i);
{
const double D0 = eps_rel * std::fabs(value.x) + eps_abs;
const double r = std::fabs(error.x) / std::fabs(D0); // scaled error at equation i
if (r > max) max = r;
} {
const double D0 = eps_rel * std::fabs(value.y) + eps_abs;
const double r = std::fabs(error.y) / std::fabs(D0); // scaled error at equation i
if (r > max) max = r;
} {
const double D0 = eps_rel * std::fabs(value.z) + eps_abs;
const double r = std::fabs(error.z) / std::fabs(D0); // scaled error at equation i
if (r > max) max = r;
}
}
norm = max;
} else { // Euclidian norm
double sum = 0.0;
VectorMatrix::const_accessor y_acc(y), y_error_acc(y_error);
const size_t s = y.size();
for (size_t i=0; i<s; ++i) {
const Vector3d value = y_acc.get(i);
const Vector3d error = y_error_acc.get(i);
{
const double D0 = eps_rel * std::fabs(value.x) + eps_abs;
const double r = std::fabs(error.x) / std::fabs(D0); // scaled error at equation i
sum += r*r;
} {
const double D0 = eps_rel * std::fabs(value.y) + eps_abs;
const double r = std::fabs(error.y) / std::fabs(D0); // scaled error at equation i
sum += r*r;
} {
const double D0 = eps_rel * std::fabs(value.z) + eps_abs;
const double r = std::fabs(error.z) / std::fabs(D0); // scaled error at equation i
sum += r*r;
}
}
norm = std::sqrt(sum / (s*3));
}
return norm;
}
Vector3d findExtremum_cpu(VectorMatrix &M, int z_slice, int component)
{
if (M.getShape().getRank() != 3) {
throw std::runtime_error("findExtremum: Fixme: Need matrix of rank 3");
}
if (component < 0 || component > 2) {
throw std::runtime_error("findExtremum: Invalid 'component' value, must be 0, 1 or 2.");
}
const int dim_x = M.getShape().getDim(0);
const int dim_y = M.getShape().getDim(1);
VectorMatrix::const_accessor M_acc(M);
// Find cell with maximum absolute value
double max_val = -1.0;
int max_x = -1, max_y = -1;
for (int y=1; y<dim_y-1; ++y)
for (int x=1; x<dim_x-1; ++x) {
const int val = std::fabs(M_acc.get(x, y, z_slice)[component]);
if (val > max_val) {
max_val = val;
max_x = x;
max_y = y;
}
}
assert(max_x > 0);
assert(max_y > 0);
// Refine maximum by fitting to sub-cell precision
const double xdir_vals[3] = {
M_acc.get(max_x-1, max_y+0, z_slice)[component],
M_acc.get(max_x+0, max_y+0, z_slice)[component],
M_acc.get(max_x+1, max_y+0, z_slice)[component]
};
const double ydir_vals[3] = {
M_acc.get(max_x+0, max_y-1, z_slice)[component],
M_acc.get(max_x+0, max_y+0, z_slice)[component],
M_acc.get(max_x+0, max_y+1, z_slice)[component]
};
return Vector3d(
fit(max_x-1, max_x+0, max_x+1, xdir_vals[0], xdir_vals[1], xdir_vals[2]),
fit(max_y-1, max_y+0, max_y+1, ydir_vals[0], ydir_vals[1], ydir_vals[2]),
static_cast<double>(z_slice)
);
}
void VectorMatrix::add(const VectorMatrix &op, double factor)
{
if (this == &op) {
scale(1.0 + factor);
} else if (isUniform() && op.isUniform()) {
fill(getUniformValue() + op.getUniformValue() * factor);
} else {
const int dev = computeStrategy2(op);
writeLock(dev); op.readLock(dev);
for (int c=0; c<num_arrays; ++c) {
matty::getDevice(dev)->add(getArray(dev, c), op.getArray(dev, c), factor);
}
writeUnlock(dev); op.readUnlock(dev);
}
}
void VectorMatrix::assign(const VectorMatrix &op)
{
if (this == &op) {
return;
} else if (op.isUniform()) {
fill(op.getUniformValue());
} else {
const int dev = computeStrategy2(op);
writeLock(dev); op.readLock(dev);
for (int c=0; c<num_arrays; ++c) {
matty::getDevice(dev)->assign(getArray(dev, c), op.getArray(dev, c));
}
writeUnlock(dev); op.readUnlock(dev);
}
}
void rk_combine_result_cpu(
double h, ButcherTableau &tab,
const VectorMatrix &k0, const VectorMatrix &k1, const VectorMatrix &k2, const VectorMatrix &k3,
VectorMatrix &y, VectorMatrix &y_error)
{
const size_t s = y.size();
VectorMatrix::accessor y_acc(y), y_error_acc(y_error);
// tab
const double c0 = tab. c[0], c1 = tab. c[1], c2 = tab. c[2], c3 = tab. c[3];
const double ec0 = tab.ec[0], ec1 = tab.ec[1], ec2 = tab.ec[2], ec3 = tab.ec[3];
VectorMatrix::const_accessor k0_acc(k0), k1_acc(k1), k2_acc(k2), k3_acc(k3);
for (size_t i=0; i<s; ++i) {
const Vector3d y_i = y_acc.get(i);
y_acc.set(i,
y_i + h * ( c0*k0_acc.get(i)
+ c1*k1_acc.get(i)
+ c2*k2_acc.get(i)
+ c3*k3_acc.get(i))
);
y_error_acc.set(i,
h * ( ec0*k0_acc.get(i)
+ ec1*k1_acc.get(i)
+ ec2*k2_acc.get(i)
+ ec3*k3_acc.get(i))
);
}
}
void copy_from( const VectorMatrix<T>& vector_matrix_obj ) {
try {
const size_t obj_size = vector_matrix_obj.size();
if( ( obj_size < STACK_DOUBLE_LIMIT ) == false ) {
throw obj_size;
}
this->nrow_ = vector_matrix_obj.nrow();
this->ncol_ = vector_matrix_obj.ncol();
std :: vector<T> temp_store = vector_matrix_obj.store();
std :: copy_n( temp_store.begin(), obj_size, this->store_.begin() );
}
catch( size_t n ) {
std :: cout << " array load exception: " << n << " >= " << STACK_DOUBLE_LIMIT << std :: endl;
abort();
}
}
/*
@method DistanceList
@discussion Allocate storage for an n-unit unitList.
@throws KII_bad_alloc
*/
DistanceList::DistanceList(const FLOAT xlocs[], const FLOAT ylocs[],
const VectorMatrix& radii)
: unitList(NULL), numUnits(radii.Size()), numOverlap(0)
{
// Since this is a symmetric array, there's no need to store
// information for unit n (all that information is already in the
// lists for units 1..n-1
if ((unitList = new ListItem[numUnits-1]) == NULL)
throw KII_bad_alloc("Failed allocating memory for DistanceList.");
// Fill the lists. For unit i, we need only store information for
// connections to units i+1..n, as earlier units already have the
// information for their connections with i.
FLOAT tempDist, tempDist2, tempDelta, deltaX, deltaY;
for (int u1=0; u1<numUnits-1; u1++) {
unitList[u1].radius = radii[u1];
for (int u2=u1+1; u2<numUnits; u2++) {
deltaX = xlocs[u1]-xlocs[u2];
deltaY = ylocs[u1]-ylocs[u2];
tempDist2 = deltaX*deltaX + deltaY*deltaY;
tempDist = sqrt(tempDist2);
tempDelta = tempDist - (radii[u1]+radii[u2]);
if (tempDelta < 0.0) {
unitList[u1].overlapping.push_back(SublistItem(u2, radii[u2], tempDist,
tempDist2, tempDelta));
numOverlap++;
} else {
unitList[u1].nonOverlapping.push_back(SublistItem(u2, radii[u2],
tempDist, tempDist2, tempDelta));
}
}
}
}
void rk_combine_result(
double h, ButcherTableau &tab,
const VectorMatrix &k0, const VectorMatrix &k1, const VectorMatrix &k2,
const VectorMatrix &k3, const VectorMatrix &k4, const VectorMatrix &k5,
VectorMatrix &y, VectorMatrix &y_error)
{
const int s = y.size();
if ( s != y_error.size()
|| s != k1.size()
|| s != k2.size()
|| s != k3.size()
|| s != k4.size()
|| s != k5.size()) throw std::runtime_error("rk_combine_result: Input matrix size mismatch.");
if (!tab.num_steps == 6) throw std::runtime_error("Need num_steps == 6 in rk_combine_result");
if (isCudaEnabled()) {
#ifdef HAVE_CUDA
rk_combine_result_cuda(h, tab, k0, k1, k2, k3, k4, k5, y, y_error, isCuda64Enabled());
#else
assert(0);
#endif
} else {
rk_combine_result_cpu(h, tab, k0, k1, k2, k3, k4, k5, y, y_error);
}
}
void rk_combine_result_cpu(
const double h, ButcherTableau &tab,
const VectorMatrix &k0, const VectorMatrix &k1, const VectorMatrix &k2,
const VectorMatrix &k3, const VectorMatrix &k4, const VectorMatrix &k5,
VectorMatrix &y, VectorMatrix &y_error)
{
const size_t s = y.size();
VectorMatrix::accessor y_acc(y), y_error_acc(y_error);
// tab
const double c0 = tab. c[0], c1 = tab. c[1], c2 = tab. c[2], c3 = tab. c[3], c4 = tab. c[4], c5 = tab. c[5];
const double ec0 = tab.ec[0], ec1 = tab.ec[1], ec2 = tab.ec[2], ec3 = tab.ec[3], ec4 = tab.ec[4], ec5 = tab.ec[5];
// Special case for c1==ec1==0 (as in RK45 and CC45 Butcher tableaus)
if (c1 == 0 && ec1 == 0) {
VectorMatrix::const_accessor k0_acc(k0), k2_acc(k2), k3_acc(k3), k4_acc(k4), k5_acc(k5);
for (size_t i=0; i<s; ++i) {
const Vector3d y_i = y_acc.get(i);
y_acc.set(i,
y_i + h * ( c0*k0_acc.get(i)
+ c2*k2_acc.get(i)
+ c3*k3_acc.get(i)
+ c4*k4_acc.get(i)
+ c5*k5_acc.get(i))
);
y_error_acc.set(i,
h * ( ec0*k0_acc.get(i)
+ ec2*k2_acc.get(i)
+ ec3*k3_acc.get(i)
+ ec4*k4_acc.get(i)
+ ec5*k5_acc.get(i))
);
}
} else { // General case
VectorMatrix::const_accessor k0_acc(k0), k1_acc(k1), k2_acc(k2), k3_acc(k3), k4_acc(k4), k5_acc(k5);
for (size_t i=0; i<s; ++i) {
const Vector3d y_i = y_acc.get(i);
y_acc.set(i,
y_i + h * ( c0*k0_acc.get(i)
+ c1*k1_acc.get(i)
+ c2*k2_acc.get(i)
+ c3*k3_acc.get(i)
+ c4*k4_acc.get(i)
+ c5*k5_acc.get(i))
);
y_error_acc.set(i,
h * ( ec0*k0_acc.get(i)
+ ec1*k1_acc.get(i)
+ ec2*k2_acc.get(i)
+ ec3*k3_acc.get(i)
+ ec4*k4_acc.get(i)
+ ec5*k5_acc.get(i))
);
}
}
}
VectorMatrix linearInterpolate(const VectorMatrix &src, Shape dest_dim)
{
Shape src_dim = src.getShape();
if (src_dim.getRank() != dest_dim.getRank()) {
throw std::runtime_error("linearInterpolate: Source and destination matrices need to have the same rank.");
}
if (src_dim.getRank() != 3) {
throw std::runtime_error("linearInterpolate: Fixme: Need to have matrix of rank 3");
}
VectorMatrix dest(dest_dim);
VectorMatrix:: accessor dest_acc(dest);
VectorMatrix::const_accessor src_acc(src);
const bool sing_x = (src_dim.getDim(0) == 1);
const bool sing_y = (src_dim.getDim(1) == 1);
const bool sing_z = (src_dim.getDim(2) == 1);
Vector3d scale(1.0, 1.0, 1.0);
if (!sing_x) scale.x = double(dest_dim.getDim(0)-1) / double(src_dim.getDim(0)-1);
if (!sing_y) scale.y = double(dest_dim.getDim(1)-1) / double(src_dim.getDim(1)-1);
if (!sing_z) scale.z = double(dest_dim.getDim(2)-1) / double(src_dim.getDim(2)-1);
for (int k=0; k<dest_dim.getDim(2); ++k)
for (int j=0; j<dest_dim.getDim(1); ++j)
for (int i=0; i<dest_dim.getDim(0); ++i) {
// (x,y,z): coordinates of point with indices (i,j,k) in dst matrix
const double x = i / scale.x;
const double y = j / scale.y;
const double z = k / scale.z;
const double u = x - std::floor(x);
const double v = y - std::floor(y);
const double w = z - std::floor(z);
const int I = std::floor(x);
const int J = std::floor(y);
const int K = std::floor(z);
Vector3d tmp(0.0, 0.0, 0.0);
if (true ) tmp = tmp + (1.0-u) * (1.0-v) * (1.0-w) * src_acc.get(I , J , K );
if ( !sing_z) tmp = tmp + (1.0-u) * (1.0-v) * w * src_acc.get(I , J , K+1);
if ( !sing_y ) tmp = tmp + (1.0-u) * v * (1.0-w) * src_acc.get(I , J+1, K );
if ( !sing_y && !sing_z) tmp = tmp + (1.0-u) * v * w * src_acc.get(I , J+1, K+1);
if (!sing_x ) tmp = tmp + u * (1.0-v) * (1.0-w) * src_acc.get(I+1, J , K );
if (!sing_x && !sing_z) tmp = tmp + u * (1.0-v) * w * src_acc.get(I+1, J , K+1);
if (!sing_x && !sing_y ) tmp = tmp + u * v * (1.0-w) * src_acc.get(I+1, J+1, K );
if (!sing_x && !sing_y && !sing_z) tmp = tmp + u * v * w * src_acc.get(I+1, J+1, K+1);
dest_acc.set(i, j, k, tmp);
}
return dest;
}
void Transposer_CUDA::copy_pad(const VectorMatrix &M, float *out_x, float *out_y, float *out_z)
{
// Ifdef HAVE_CUDA_64, we directly support input matrices that
// are stored with 64 bit precision on the GPU.
#ifdef HAVE_CUDA_64
const bool M_is_cuda64_bit = M.isCached(2); // 0 = CPU device, 2 = CUDA_64 device
if (M_is_cuda64_bit) {
VectorMatrix::const_cu64_accessor M_acc(M);
// xyz, M -> s1
cuda_copy_pad_r2r(dim_x, dim_y, dim_z, exp_x, M_acc.ptr_x(), M_acc.ptr_y(), M_acc.ptr_z(), out_x, out_y, out_z);
}
else
#endif
{
VectorMatrix::const_cu32_accessor M_acc(M);
// xyz, M -> s1
cuda_copy_pad_r2r(dim_x, dim_y, dim_z, exp_x, M_acc.ptr_x(), M_acc.ptr_y(), M_acc.ptr_z(), out_x, out_y, out_z);
}
}
ConstVectorMatrixAccessor::ConstVectorMatrixAccessor(const VectorMatrix &mat) : mat(mat)
{
mat.readLock(0);
data_x = static_cast<CPUArray*>(mat.getArray(0, 0))->ptr();
data_y = static_cast<CPUArray*>(mat.getArray(0, 1))->ptr();
data_z = static_cast<CPUArray*>(mat.getArray(0, 2))->ptr();
// Precalculate strides
const int rank = mat.getShape().getRank();
strides[0] = 1;
strides[1] = strides[0] * (rank > 0 ? mat.getShape().getDim(0) : 1);
strides[2] = strides[1] * (rank > 1 ? mat.getShape().getDim(1) : 1);
strides[3] = strides[2] * (rank > 2 ? mat.getShape().getDim(2) : 1);
}
double VectorMatrix::dotSum(const VectorMatrix &other) const
{
if (isUniform() && other.isUniform()) {
const double x = uval[0], y = uval[1], z = uval[2];
const double dot = x*x + y*y + z*z;
return size() * dot;
} else {
const int dev = computeStrategy2(other);
readLock(dev); if (this != &other) other.readLock(dev);
const double sum = matty::getDevice(dev)->sumdot3(
this->getArray(dev, 0), this->getArray(dev, 1), this->getArray(dev, 2),
other.getArray(dev, 0), other.getArray(dev, 1), other.getArray(dev, 2)
);
if (this != &other) other.readUnlock(dev); readUnlock(dev);
return sum;
}
}
void rk_prepare_step_cpu(
int step,
double h,
ButcherTableau &tab,
const VectorMatrix &k0,
const VectorMatrix &k1,
const VectorMatrix &k2,
const VectorMatrix &k3,
const VectorMatrix &k4,
const VectorMatrix &k5,
const VectorMatrix &y,
VectorMatrix &ytmp)
{
const size_t s = y.size();
VectorMatrix::const_accessor y_acc(y);
VectorMatrix::const_accessor k0_acc(k0), k1_acc(k1), k2_acc(k2), k3_acc(k3), k4_acc(k4), k5_acc(k5);
VectorMatrix::accessor ytmp_acc(ytmp);
const double b10 = tab.b[1][0];
const double b20 = tab.b[2][0], b21 = tab.b[2][1];
const double b30 = tab.b[3][0], b31 = tab.b[3][1], b32 = tab.b[3][2];
const double b40 = tab.b[4][0], b41 = tab.b[4][1], b42 = tab.b[4][2], b43 = tab.b[4][3];
const double b50 = tab.b[5][0], b51 = tab.b[5][1], b52 = tab.b[5][2], b53 = tab.b[5][3], b54 = tab.b[5][4];
switch (step) {
case 1:
for (size_t i=0; i<s; ++i) {
ytmp_acc.set(i, y_acc.get(i) + h * (b10*k0_acc.get(i)));
}
break;
case 2:
for (size_t i=0; i<s; ++i) {
ytmp_acc.set(i,
y_acc.get(i) + h * ( b20*k0_acc.get(i)
+ b21*k1_acc.get(i))
);
}
break;
case 3:
for (size_t i=0; i<s; ++i) {
ytmp_acc.set(i,
y_acc.get(i) + h * ( b30*k0_acc.get(i)
+ b31*k1_acc.get(i)
+ b32*k2_acc.get(i))
);
}
break;
case 4:
for (size_t i=0; i<s; ++i) {
ytmp_acc.set(i,
y_acc.get(i) + h * ( b40*k0_acc.get(i)
+ b41*k1_acc.get(i)
+ b42*k2_acc.get(i)
+ b43*k3_acc.get(i))
);
}
break;
case 5:
for (size_t i=0; i<s; ++i) {
ytmp_acc.set(i,
y_acc.get(i) + h * ( b50*k0_acc.get(i)
+ b51*k1_acc.get(i)
+ b52*k2_acc.get(i)
+ b53*k3_acc.get(i)
+ b54*k4_acc.get(i))
);
}
break;
default:
throw std::runtime_error("Cant handle runge-kutta methods with more than 6 steps (not implemented)");
}
}
/*
* Writes simulation results to an output destination.
*
* @param neurons the Neuron list to search from.
**/
void XmlRecorder::saveSimData(vector<Cluster *> &vtClr, vector<ClusterInfo *> &vtClrInfo)
{
// create Neuron Types matrix
VectorMatrix neuronTypes(MATRIX_TYPE, MATRIX_INIT, 1, m_sim_info->totalNeurons, EXC);
for (int i = 0; i < m_sim_info->totalNeurons; i++) {
neuronTypes[i] = m_model->getLayout()->neuron_type_map[i];
}
// create neuron threshold matrix
VectorMatrix neuronThresh(MATRIX_TYPE, MATRIX_INIT, 1, m_sim_info->totalNeurons, 0);
for (CLUSTER_INDEX_TYPE iCluster = 0; iCluster < vtClr.size(); iCluster++) {
AllIFNeurons *neurons = dynamic_cast<AllIFNeurons*>(vtClr[iCluster]->m_neurons);
int neuronLayoutIndex = vtClrInfo[iCluster]->clusterNeuronsBegin;
int totalClusterNeurons = vtClrInfo[iCluster]->totalClusterNeurons;
for (int iNeurons = 0; iNeurons < totalClusterNeurons; iNeurons++, neuronLayoutIndex++) {
neuronThresh[neuronLayoutIndex] = neurons->Vthresh[iNeurons];
}
}
// Write XML header information:
stateOut << "<?xml version=\"1.0\" standalone=\"no\"?>\n" << "<!-- State output file for the DCT growth modeling-->\n";
//stateOut << version; TODO: version
// Write the core state information:
VectorMatrix* xloc = new VectorMatrix(MATRIX_TYPE, MATRIX_INIT, 1, m_sim_info->totalNeurons);
VectorMatrix* yloc = new VectorMatrix(MATRIX_TYPE, MATRIX_INIT, 1, m_sim_info->totalNeurons);
for (int i = 0; i < m_sim_info->totalNeurons; i++) {
(*xloc)[i] = m_model->getLayout()->xloc[i];
(*yloc)[i] = m_model->getLayout()->yloc[i];
}
stateOut << "<SimState>\n";
stateOut << " " << burstinessHist.toXML("burstinessHist") << endl;
stateOut << " " << spikesHistory.toXML("spikesHistory") << endl;
stateOut << " " << xloc->toXML("xloc") << endl;
stateOut << " " << yloc->toXML("yloc") << endl;
stateOut << " " << neuronTypes.toXML("neuronTypes") << endl;
delete xloc;
delete yloc;
// create starter nuerons matrix
int num_starter_neurons = static_cast<int>(m_model->getLayout()->num_endogenously_active_neurons);
if (num_starter_neurons > 0)
{
VectorMatrix starterNeurons(MATRIX_TYPE, MATRIX_INIT, 1, num_starter_neurons);
getStarterNeuronMatrix(starterNeurons, m_model->getLayout()->starter_map, m_sim_info);
stateOut << " " << starterNeurons.toXML("starterNeurons") << endl;
}
// Write neuron thresold
stateOut << " " << neuronThresh.toXML("neuronThresh") << endl;
// write time between growth cycles
stateOut << " <Matrix name=\"Tsim\" type=\"complete\" rows=\"1\" columns=\"1\" multiplier=\"1.0\">" << endl;
stateOut << " " << m_sim_info->epochDuration << endl;
stateOut << "</Matrix>" << endl;
// write simulation end time
stateOut << " <Matrix name=\"simulationEndTime\" type=\"complete\" rows=\"1\" columns=\"1\" multiplier=\"1.0\">" << endl;
stateOut << " " << g_simulationStep * m_sim_info->deltaT << endl;
stateOut << "</Matrix>" << endl;
stateOut << "</SimState>" << endl;
}
/*
@method Update
@discussion Update the DistanceList information based on the given
new radii.
@param radii unit connectivity radii must have same number of
elements as numUnits
@throws KII_invalid_argument
*/
void DistanceList::Update(const VectorMatrix& radii)
{
if (radii.Size() != numUnits)
throw KII_invalid_argument("Wrong number of elements in radii for distance update.");
#ifdef DEBUG2
cerr << "Updating DistanceList with radii " << radii << endl;
#endif
FLOAT tempDelta;
SublistItem tempItem;
list<SublistItem>::iterator u2iter;
// Iterate through all units
for (int u1=0; u1<numUnits-1; u1++) {
#ifdef DEBUG2
cerr << u1 << ", ";
#endif
// Update unit connection radius
unitList[u1].radius = radii[u1];
// Iterate over all overlapping and non-overlapping other units
// First, overlapping:
u2iter = unitList[u1].overlapping.begin();
while (u2iter != unitList[u1].overlapping.end()) {
// Compute and update other unit's information
u2iter->radius = radii[u2iter->otherUnit];
tempDelta = u2iter->dist - (unitList[u1].radius+u2iter->radius);
u2iter->Delta = tempDelta;
// Move it if non-overlapping, if necessary (Delta>=0). If the
// move occurs, the iterator will point to the next item. If
// not, then the iterator must be advanced explicitly.
if (tempDelta >= 0) {
tempItem = *u2iter;
u2iter = unitList[u1].overlapping.erase(u2iter);
unitList[u1].nonOverlapping.push_back(tempItem);
numOverlap--;
} else
u2iter++;
}
// Then, non-overlapping:
u2iter = unitList[u1].nonOverlapping.begin();
while (u2iter != unitList[u1].nonOverlapping.end()) {
// Compute and update other unit's information
u2iter->radius = radii[u2iter->otherUnit];
tempDelta = u2iter->dist - (unitList[u1].radius+u2iter->radius);
u2iter->Delta = tempDelta;
// Move it if overlapping, if necessary (Delta<0). If the
// move occurs, the iterator will point to the next item. If
// not, then the iterator must be advanced explicitly.
if (tempDelta < 0) {
tempItem = *u2iter;
u2iter = unitList[u1].nonOverlapping.erase(u2iter);
unitList[u1].overlapping.push_back(tempItem);
numOverlap++;
#ifdef DLDEBUG
cerr << "\tDL::Update(): overlap of " << u1 << " and " << tempItem.otherUnit
<< " with radii " << unitList[u1].radius << " and " << tempItem.radius
<< ", respectively" << endl;
#endif
} else
u2iter++;
}
} // end for (int u1-0; ...)
#ifdef DEBUG2
cerr << "done." << endl;;
#endif
}