本文整理汇总了C++中math::Matrix::allocate方法的典型用法代码示例。如果您正苦于以下问题：C++ Matrix::allocate方法的具体用法？C++ Matrix::allocate怎么用？C++ Matrix::allocate使用的例子？那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类math::Matrix的用法示例。
在下文中一共展示了Matrix::allocate方法的2个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的C++代码示例。
示例1: load_data
bool load_data (const Image::Iterator& pos) {
Image::voxel_assign (dwi, pos);
Image::voxel_assign (dt, pos);
size_t nvox = dwi.dim (row_axis);
if (mask) {
size_t N = 0;
Image::voxel_assign (*mask, pos);
for ((*mask)[row_axis] = 0; (*mask)[row_axis] < mask->dim(row_axis); ++(*mask)[row_axis])
if (mask->value())
++N;
nvox = N;
}
if (!nvox)
return false;
signals.allocate (nvox, dwi.dim (sig_axis));
logsignals.allocate (nvox, dwi.dim (sig_axis));
tensors.allocate (nvox, 7);
size_t N = 0;
for (dwi[row_axis] = 0; dwi[row_axis] < dwi.dim(row_axis); ++dwi[row_axis]) {
if (mask) {
(*mask)[row_axis] = dwi[row_axis];
if (!mask->value()) continue;
}
for (dwi[sig_axis] = 0; dwi[sig_axis] < dwi.dim(sig_axis); ++dwi[sig_axis]) {
cost_value_type val = std::max (cost_value_type (dwi.value()), cost_value_type (1.0));
signals(N, dwi[sig_axis]) = val;
logsignals(N, dwi[sig_axis]) = -Math::log (val);
}
++N;
}
return true;
}示例2: run
void run ()
{
Image::Buffer<value_type> SH_data (argument[0]);
Math::SH::check (SH_data);
Options opt = get_options ("mask");
std::unique_ptr<Image::Buffer<bool> > mask_data;
if (opt.size())
mask_data.reset (new Image::Buffer<bool> (opt[0][0]));
opt = get_options ("seeds");
Math::Matrix<value_type> dirs;
if (opt.size())
dirs.load (opt[0][0]);
else {
dirs.allocate (60,2);
dirs = Math::Matrix<value_type> (default_directions, 60, 2);
}
if (dirs.columns() != 2)
throw Exception ("expecting 2 columns for search directions matrix");
opt = get_options ("num");
int npeaks = opt.size() ? opt[0][0] : 3;
opt = get_options ("direction");
std::vector<Direction> true_peaks;
for (size_t n = 0; n < opt.size(); ++n) {
Direction p (Math::pi*to<float> (opt[n][0]) /180.0, Math::pi*float (opt[n][1]) /180.0);
true_peaks.push_back (p);
}
if (true_peaks.size())
npeaks = true_peaks.size();
opt = get_options ("threshold");
value_type threshold = -INFINITY;
if (opt.size())
threshold = opt[0][0];
Image::Header header (SH_data);
header.datatype() = DataType::Float32;
opt = get_options ("peaks");
std::unique_ptr<Image::Buffer<value_type> > ipeaks_data;
if (opt.size()) {
if (true_peaks.size())
throw Exception ("you can't specify both a peaks file and orientations to be estimated at the same time");
if (opt.size())
ipeaks_data.reset (new Image::Buffer<value_type> (opt[0][0]));
Image::check_dimensions (header, *ipeaks_data, 0, 3);
npeaks = ipeaks_data->dim (3) / 3;
}
header.dim(3) = 3 * npeaks;
Image::Buffer<value_type> peaks_data (argument[1], header);
DataLoader loader (SH_data, mask_data.get());
Processor processor (peaks_data, dirs, Math::SH::LforN (SH_data.dim (3)),
npeaks, true_peaks, threshold, ipeaks_data.get());
Thread::run_queue (loader, Item(), Thread::multi (processor));
}