InvGammaMapResult

Namespace: INVLIB  ·  Library: Inverse Library

#include <inv/inv_gamma_map.h>

struct INVLIB::InvGammaMapResult

Result structure for the Gamma-MAP solver.

src/examples/ex_inv_api/main.cpp

// Gamma-MAP: sparse Bayesian learning with unit (whitened) noise, MacKay updates as in mne.inverse_sparse.gamma_map.
const InvGammaMapResult gammaMap = InvGammaMap::compute(gain, measured, MatrixXd::Identity(gain.rows(), gain.rows()), 1000, 1e-8);

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Example

• Full source: src/examples/ex_inv_api/main.cpp
• Build target: ex_inv_api (runs under CTest)
• Data: mne-cpp-test-data

Used in examples

ex_inv_api

Inverse library: minimum-norm estimates, resolution matrices, label time courses, sparse solvers.

src/examples/ex_inv_api/main.cpp, line 250

// Gamma-MAP: sparse Bayesian learning with unit (whitened) noise, MacKay updates as in mne.inverse_sparse.gamma_map.
const InvGammaMapResult gammaMap = InvGammaMap::compute(gain, measured, MatrixXd::Identity(gain.rows(), gain.rows()), 1000, 1e-8);
ok &= expect(!gammaMap.activeVertices.isEmpty() && gammaMap.activeVertices.size() < 70 && gammaMap.vecGamma.size() == 70,
             QString("InvGammaMap: %1 of 70 V1 sources keep a nonzero variance").arg(gammaMap.activeVertices.size()));

Authors of this file

• Christoph Dinh <christoph.dinh@mne-cpp.org>