From 8afecf944f76e52fd0668eb106a717fe64e1341c Mon Sep 17 00:00:00 2001 From: "jan.reininghaus" Date: Fri, 3 May 2013 09:30:12 +0000 Subject: ignore list git-svn-id: https://phat.googlecode.com/svn/trunk@56 8e3bb3c2-eed4-f18f-5264-0b6c94e6926d --- README | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) (limited to 'README') diff --git a/README b/README index 82201e4..8a0ca31 100644 --- a/README +++ b/README @@ -38,9 +38,9 @@ algorithm. We provide the following choices of representation classes: * {{{vector_vector}}}: Each column is represented as a sorted {{{std::vector}}} of integers, containing the indices of the non-zero entries of the column. The matrix itself is a {{{std::vector}}} of such columns. * {{{vector_set}}}: Each column is a {{{std::set}}} of integers, with the same meaning as above. The matrix is stored as a {{{std::vector}}} of such columns. * {{{vector_list}}}: Each column is a sorted {{{std::list}}} of integers, with the same meaning as above. The matrix is stored as a {{{std::vector}}} of such columns. - * {{{sparse_pivot_column}}}: The matrix is stored as in the vector_vector representation. However, when a column is manipulated, it is first converted into a {{{std::set}}}, using an extra data field called the "pivot column". When another column is manipulated later, the pivot column is converted back to the {{{std::vector}}} representation. This can lead to speed improvements when many columns are added to a given pivot column consecutively. In a multicore setup, there is one pivot column per core. + * {{{sparse_pivot_column}}}: The matrix is stored as in the vector_vector representation. However, when a column is manipulated, it is first converted into a {{{std::set}}}, using an extra data field called the "pivot column". When another column is manipulated later, the pivot column is converted back to the {{{std::vector}}} representation. This can lead to significant speed improvements when many columns are added to a given pivot column consecutively. In a multicore setup, there is one pivot column per thread. * {{{full_pivot_column}}}: The same idea as in the sparse version. However, instead of a {{{std::set}}}, the pivot column is expanded into a bit vector of size n (the dimension of the matrix). To avoid costly initializations, the class remembers which entries have been manipulated for a pivot column and updates only those entries when another column becomes the pivot. - * {{{bit_tree_pivot_column}}} (default representation): Similar to the {{{full_pivot_column}}} but the implementation is more efficient. Internally it is a bit-set with fast iteration over present elements, and fast access to the maximum element. The structure is initialized before the reduction algorithm is started and reused. + * {{{bit_tree_pivot_column}}} (default representation): Similar to the {{{full_pivot_column}}} but the implementation is more efficient. Internally it is a bit-set with fast iteration over nonzero elements, and fast access to the maximal element. There are two ways to interface with the library: @@ -56,7 +56,7 @@ There are two ways to interface with the library: # include all headers found in {{{src/phat.cpp}}} # define a boundary matrix object, e.g. {{{ -phat::boundary_matrix< full_pivot_column > boundary_matrix; +phat::boundary_matrix< bit_tree_pivot_column > boundary_matrix; }}} # set the number of columns: {{{ @@ -73,7 +73,7 @@ phat::persistence_pairs pairs; }}} # run an algorithm like this: {{{ -phat::compute_persistence_pairs< phat::chunk_reduction >( pairs, boundary_matrix ); +phat::compute_persistence_pairs< phat::twist_reduction >( pairs, boundary_matrix ); }}} # examine the result: {{{ -- cgit v1.2.3