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update interface files with latest develop
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,81 @@ | ||
| #include <gtsam/linear/LossFunctions.h> | ||
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| #include <Eigen/unsupported/Eigen/CX11/Tensor> | ||
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| namespace gtsam { | ||
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| Vector FastCaratheodory(Matrix& P, Vector& weights, size_t coreset_size) { | ||
| size_t n = P.rows(), d = P.cols(); | ||
| size_t m = 2 * d + 2; | ||
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| if (n < d + 1) { | ||
| return weights; | ||
| } | ||
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| Vector weights = weights / weights.sum(); | ||
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| size_t chunk_size = ceil(n / m); | ||
| size_t current_m = ceil(n / chunk_size); | ||
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| size_t add_z = chunk_size - size_t(n % chunk_size); | ||
| Matrix u(weights.size(), 1); | ||
| u.col(0) = weights; | ||
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| if (add_z != chunk_size) { | ||
| Matrix zeros = Matrix::Zero(add_z, d); | ||
| Matrix P_new = Matrix(P.rows() + zeros.rows(), P.cols() + zeros.cols()); | ||
| P_new << P, zeros; | ||
| zeros = Matrix::Zero(add_z, u.cols()); | ||
| Matrix u_new(u.rows() + zeros.rows(), u.cols() + zeros.cols()); | ||
| u_new << u, zeros; | ||
| } | ||
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| Vector idxarray = Vector::LinSpaced(n, 0, n - 1); | ||
| Eigen::Tensor p_groups; | ||
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| // p_groups = P.reshape(current_m, chunk_size, P.shape[1]) | ||
| // u_groups = u.reshape(current_m, chunk_size) | ||
| // idx_group = idxarray.reshape(current_m, chunk_size) | ||
| // u_nonzero = np.count_nonzero(u) | ||
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| // if not coreset_size: | ||
| // coreset_size = d+1 | ||
| // while u_nonzero > coreset_size: | ||
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| // groups_means = np.einsum('ijk,ij->ik',p_groups, u_groups) | ||
| // group_weigts = np.ones(groups_means.shape[0], dtype = | ||
| // dtype)*1/current_m | ||
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| // Cara_u_idx = Caratheodory(groups_means , group_weigts,dtype = dtype ) | ||
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| // IDX = np.nonzero(Cara_u_idx) | ||
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| // new_P = p_groups[IDX].reshape(-1,d) | ||
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| // subset_u = (current_m * u_groups[IDX] * Cara_u_idx[IDX][:, | ||
| // np.newaxis]).reshape(-1, 1) new_idx_array = | ||
| // idx_group[IDX].reshape(-1,1) | ||
| // ##############################################################################3 | ||
| // u_nonzero = np.count_nonzero(subset_u) | ||
| // chunk_size = math.ceil(new_P.shape[0]/ m) | ||
| // current_m = math.ceil(new_P.shape[0]/ chunk_size) | ||
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| // add_z = chunk_size - int(new_P.shape[0] % chunk_size) | ||
| // if add_z != chunk_size: | ||
| // new_P = np.concatenate((new_P, np.zeros((add_z, new_P.shape[1]), | ||
| // dtype = dtype))) subset_u = np.concatenate((subset_u, | ||
| // np.zeros((add_z, subset_u.shape[1]),dtype = dtype))) | ||
| // new_idx_array = np.concatenate((new_idx_array, np.zeros((add_z, | ||
| // new_idx_array.shape[1]),dtype = dtype))) | ||
| // p_groups = new_P.reshape(current_m, chunk_size, new_P.shape[1]) | ||
| // u_groups = subset_u.reshape(current_m, chunk_size) | ||
| // idx_group = new_idx_array.reshape(current_m , chunk_size) | ||
| // ########################################################### | ||
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| // new_u = np.zeros(n) | ||
| // subset_u = subset_u[(new_idx_array < n)] | ||
| // new_idx_array = new_idx_array[(new_idx_array < | ||
| // n)].reshape(-1).astype(int) new_u[new_idx_array] = subset_u return u_sum | ||
| // * new_u | ||
| } | ||
| } // namespace gtsam |
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