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Fixed conflicts after merging with branch eigenize

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This commit is contained in:
Enrico Turri 2018-08-23 15:37:38 +02:00
commit 66ce638439
211 changed files with 4309 additions and 4920 deletions
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12
lib/Slic3r/ExPolygon.pm
View File

@ -7,18 +7,6 @@ use warnings;
use List::Util qw(first);
use Slic3r::Geometry::Clipper qw(union_ex diff_pl);
sub wkt {
my $self = shift;
return sprintf "POLYGON(%s)",
join ',', map "($_)", map { join ',', map "$_->[0] $_->[1]", @$_ } @$self;
}
sub dump_perl {
my $self = shift;
return sprintf "[%s]",
join ',', map "[$_]", map { join ',', map "[$_->[0],$_->[1]]", @$_ } @$self;
}
sub offset {
my $self = shift;
return Slic3r::Geometry::Clipper::offset(\@$self, @_);

1
lib/Slic3r/GUI/2DBed.pm
View File

@ -1,4 +1,5 @@
# Bed shape dialog
# still used by the Slic3r::GUI::Controller::ManualControlDialog Perl module.
package Slic3r::GUI::2DBed;
use strict;

5
lib/Slic3r/Point.pm
View File

@ -7,11 +7,6 @@ sub new_scale {
return $class->new(map Slic3r::Geometry::scale($_), @_);
}
sub dump_perl {
my $self = shift;
return sprintf "[%s,%s]", @$self;
}
package Slic3r::Pointf;
use strict;
use warnings;

5
lib/Slic3r/Polyline.pm
View File

@ -10,9 +10,4 @@ sub new_scale {
return $class->new(map [ Slic3r::Geometry::scale($_->[X]), Slic3r::Geometry::scale($_->[Y]) ], @points);
}
sub dump_perl {
my $self = shift;
return sprintf "[%s]", join ',', map "[$_->[0],$_->[1]]", @$self;
}
1;

10
xs/lib/Slic3r/XS.pm
View File

@ -33,16 +33,6 @@ use overload
'@{}' => sub { $_[0]->arrayref },
'fallback' => 1;
package Slic3r::Point3;
use overload
'@{}' => sub { [ $_[0]->x, $_[0]->y, $_[0]->z ] }, #,
'fallback' => 1;
sub pp {
my ($self) = @_;
return [ @$self ];
}
package Slic3r::Pointf;
use overload
'@{}' => sub { $_[0]->arrayref },

333
xs/src/admesh/connect.cpp
View File

@ -25,11 +25,11 @@
#include <string.h>
#include <math.h>
#include <boost/detail/endian.hpp>
#include "stl.h"
static void stl_match_neighbors_exact(stl_file *stl,
stl_hash_edge *edge_a, stl_hash_edge *edge_b);
static void stl_match_neighbors_nearby(stl_file *stl,
stl_hash_edge *edge_a, stl_hash_edge *edge_b);
static void stl_record_neighbors(stl_file *stl,
@ -43,7 +43,6 @@ static int stl_load_edge_nearby(stl_file *stl, stl_hash_edge *edge,
static void insert_hash_edge(stl_file *stl, stl_hash_edge edge,
void (*match_neighbors)(stl_file *stl,
stl_hash_edge *edge_a, stl_hash_edge *edge_b));
static int stl_get_hash_for_edge(int M, stl_hash_edge *edge);
static int stl_compare_function(stl_hash_edge *edge_a, stl_hash_edge *edge_b);
static void stl_free_edges(stl_file *stl);
static void stl_remove_facet(stl_file *stl, int facet_number);
@ -82,37 +81,20 @@ stl_check_facets_exact(stl_file *stl) {
for(i = 0; i < stl->stats.number_of_facets; i++) {
facet = stl->facet_start[i];
// Positive and negative zeros are possible in the floats, which are considered equal by the FP unit.
// When using a memcmp on raw floats, those numbers report to be different.
// Unify all +0 and -0 to +0 to make the floats equal under memcmp.
{
uint32_t *f = (uint32_t*)&facet;
for (int j = 0; j < 12; ++ j, ++ f) // 3x vertex + normal: 4x3 = 12 floats
if (*f == 0x80000000)
// Negative zero, switch to positive zero.
*f = 0;
}
/* If any two of the three vertices are found to be exactally the same, call them degenerate and remove the facet. */
if( !memcmp(&facet.vertex[0], &facet.vertex[1],
sizeof(stl_vertex))
|| !memcmp(&facet.vertex[1], &facet.vertex[2],
sizeof(stl_vertex))
|| !memcmp(&facet.vertex[0], &facet.vertex[2],
sizeof(stl_vertex))) {
// If any two of the three vertices are found to be exactally the same, call them degenerate and remove the facet.
if (facet.vertex[0] == facet.vertex[1] ||
facet.vertex[1] == facet.vertex[2] ||
facet.vertex[0] == facet.vertex[2]) {
stl->stats.degenerate_facets += 1;
stl_remove_facet(stl, i);
i--;
-- i;
continue;
}
for(j = 0; j < 3; j++) {
edge.facet_number = i;
edge.which_edge = j;
stl_load_edge_exact(stl, &edge, &facet.vertex[j],
&facet.vertex[(j + 1) % 3]);
insert_hash_edge(stl, edge, stl_match_neighbors_exact);
stl_load_edge_exact(stl, &edge, &facet.vertex[j], &facet.vertex[(j + 1) % 3]);
insert_hash_edge(stl, edge, stl_record_neighbors);
}
}
stl_free_edges(stl);
@ -131,28 +113,33 @@ stl_load_edge_exact(stl_file *stl, stl_hash_edge *edge,
if (stl->error) return;
{
float diff_x = ABS(a->x - b->x);
float diff_y = ABS(a->y - b->y);
float diff_z = ABS(a->z - b->z);
float max_diff = STL_MAX(diff_x, diff_y);
max_diff = STL_MAX(diff_z, max_diff);
stl->stats.shortest_edge = STL_MIN(max_diff, stl->stats.shortest_edge);
stl_vertex diff = (*a - *b).cwiseAbs();
float max_diff = std::max(diff(0), std::max(diff(1), diff(2)));
stl->stats.shortest_edge = std::min(max_diff, stl->stats.shortest_edge);
}
// Ensure identical vertex ordering of equal edges.
// This method is numerically robust.
if ((a->x != b->x) ?
(a->x < b->x) :
((a->y != b->y) ?
(a->y < b->y) :
(a->z < b->z))) {
memcpy(&edge->key[0], a, sizeof(stl_vertex));
memcpy(&edge->key[3], b, sizeof(stl_vertex));
if (stl_vertex_lower(*a, *b)) {
} else {
memcpy(&edge->key[0], b, sizeof(stl_vertex));
memcpy(&edge->key[3], a, sizeof(stl_vertex));
std::swap(a, b);
edge->which_edge += 3; /* this edge is loaded backwards */
}
memcpy(&edge->key[0], a->data(), sizeof(stl_vertex));
memcpy(&edge->key[sizeof(stl_vertex)], b->data(), sizeof(stl_vertex));
// Switch negative zeros to positive zeros, so memcmp will consider them to be equal.
for (size_t i = 0; i < 6; ++ i) {
unsigned char *p = edge->key + i * 4;
#ifdef BOOST_LITTLE_ENDIAN
if (p[0] == 0 && p[1] == 0 && p[2] == 0 && p[3] == 0x80)
// Negative zero, switch to positive zero.
p[3] = 0;
#else /* BOOST_LITTLE_ENDIAN */
if (p[0] == 0x80 && p[1] == 0 && p[2] == 0 && p[3] == 0)
// Negative zero, switch to positive zero.
p[0] = 0;
#endif /* BOOST_LITTLE_ENDIAN */
}
}
static void
@ -188,21 +175,17 @@ stl_initialize_facet_check_exact(stl_file *stl) {
}
}
static void
insert_hash_edge(stl_file *stl, stl_hash_edge edge,
static void insert_hash_edge(stl_file *stl, stl_hash_edge edge,
void (*match_neighbors)(stl_file *stl,
stl_hash_edge *edge_a, stl_hash_edge *edge_b)) {
stl_hash_edge *link;
stl_hash_edge *new_edge;
stl_hash_edge *temp;
int chain_number;
stl_hash_edge *edge_a, stl_hash_edge *edge_b))
{
if (stl->error) return;
chain_number = stl_get_hash_for_edge(stl->M, &edge);
link = stl->heads[chain_number];
int chain_number = edge.hash(stl->M);
stl_hash_edge *link = stl->heads[chain_number];
stl_hash_edge *new_edge;
stl_hash_edge *temp;
if(link == stl->tail) {
/* This list doesn't have any edges currently in it. Add this one. */
new_edge = (stl_hash_edge*)malloc(sizeof(stl_hash_edge));
@ -252,30 +235,17 @@ insert_hash_edge(stl_file *stl, stl_hash_edge edge,
}
}
static int
stl_get_hash_for_edge(int M, stl_hash_edge *edge) {
return ((edge->key[0] / 23 + edge->key[1] / 19 + edge->key[2] / 17
+ edge->key[3] /13 + edge->key[4] / 11 + edge->key[5] / 7 ) % M);
// Return 1 if the edges are not matched.
static inline int stl_compare_function(stl_hash_edge *edge_a, stl_hash_edge *edge_b)
{
// Don't match edges of the same facet
return (edge_a->facet_number == edge_b->facet_number) || (*edge_a != *edge_b);
}
static int
stl_compare_function(stl_hash_edge *edge_a, stl_hash_edge *edge_b) {
if(edge_a->facet_number == edge_b->facet_number) {
return 1; /* Don't match edges of the same facet */
} else {
return memcmp(edge_a, edge_b, SIZEOF_EDGE_SORT);
}
}
void
stl_check_facets_nearby(stl_file *stl, float tolerance) {
stl_hash_edge edge[3];
stl_facet facet;
int i;
int j;
if (stl->error) return;
void stl_check_facets_nearby(stl_file *stl, float tolerance)
{
if (stl->error)
return;
if( (stl->stats.connected_facets_1_edge == stl->stats.number_of_facets)
&& (stl->stats.connected_facets_2_edge == stl->stats.number_of_facets)
@ -286,27 +256,19 @@ stl_check_facets_nearby(stl_file *stl, float tolerance) {
stl_initialize_facet_check_nearby(stl);
for(i = 0; i < stl->stats.number_of_facets; i++) {
facet = stl->facet_start[i];
// Positive and negative zeros are possible in the floats, which are considered equal by the FP unit.
// When using a memcmp on raw floats, those numbers report to be different.
// Unify all +0 and -0 to +0 to make the floats equal under memcmp.
{
uint32_t *f = (uint32_t*)&facet;
for (int j = 0; j < 12; ++ j, ++ f) // 3x vertex + normal: 4x3 = 12 floats
if (*f == 0x80000000)
// Negative zero, switch to positive zero.
*f = 0;
}
for(j = 0; j < 3; j++) {
for (int i = 0; i < stl->stats.number_of_facets; ++ i) {
//FIXME is the copy necessary?
stl_facet facet = stl->facet_start[i];
for (int j = 0; j < 3; j++) {
if(stl->neighbors_start[i].neighbor[j] == -1) {
edge[j].facet_number = i;
edge[j].which_edge = j;
if(stl_load_edge_nearby(stl, &edge[j], &facet.vertex[j],
stl_hash_edge edge;
edge.facet_number = i;
edge.which_edge = j;
if(stl_load_edge_nearby(stl, &edge, &facet.vertex[j],
&facet.vertex[(j + 1) % 3],
tolerance)) {
/* only insert edges that have different keys */
insert_hash_edge(stl, edge[j], stl_match_neighbors_nearby);
insert_hash_edge(stl, edge, stl_match_neighbors_nearby);
}
}
}
@ -315,27 +277,17 @@ stl_check_facets_nearby(stl_file *stl, float tolerance) {
stl_free_edges(stl);
}
static int
stl_load_edge_nearby(stl_file *stl, stl_hash_edge *edge,
stl_vertex *a, stl_vertex *b, float tolerance) {
static int stl_load_edge_nearby(stl_file *stl, stl_hash_edge *edge, stl_vertex *a, stl_vertex *b, float tolerance)
{
// Index of a grid cell spaced by tolerance.
uint32_t vertex1[3] = {
(uint32_t)((a->x - stl->stats.min.x) / tolerance),
(uint32_t)((a->y - stl->stats.min.y) / tolerance),
(uint32_t)((a->z - stl->stats.min.z) / tolerance)
};
uint32_t vertex2[3] = {
(uint32_t)((b->x - stl->stats.min.x) / tolerance),
(uint32_t)((b->y - stl->stats.min.y) / tolerance),
(uint32_t)((b->z - stl->stats.min.z) / tolerance)
};
typedef Eigen::Matrix<int32_t, 3, 1, Eigen::DontAlign> Vec3i;
Vec3i vertex1 = (*a / tolerance).cast<int32_t>();
Vec3i vertex2 = (*b / tolerance).cast<int32_t>();
static_assert(sizeof(Vec3i) == 12, "size of Vec3i incorrect");
if( (vertex1[0] == vertex2[0])
&& (vertex1[1] == vertex2[1])
&& (vertex1[2] == vertex2[2])) {
/* Both vertices hash to the same value */
if (vertex1 == vertex2)
// Both vertices hash to the same value
return 0;
}
// Ensure identical vertex ordering of edges, which vertices land into equal grid cells.
// This method is numerically robust.
@ -344,30 +296,27 @@ stl_load_edge_nearby(stl_file *stl, stl_hash_edge *edge,
((vertex1[1] != vertex2[1]) ?
(vertex1[1] < vertex2[1]) :
(vertex1[2] < vertex2[2]))) {
memcpy(&edge->key[0], vertex1, sizeof(stl_vertex));
memcpy(&edge->key[3], vertex2, sizeof(stl_vertex));
memcpy(&edge->key[0], vertex1.data(), sizeof(stl_vertex));
memcpy(&edge->key[sizeof(stl_vertex)], vertex2.data(), sizeof(stl_vertex));
} else {
memcpy(&edge->key[0], vertex2, sizeof(stl_vertex));
memcpy(&edge->key[3], vertex1, sizeof(stl_vertex));
memcpy(&edge->key[0], vertex2.data(), sizeof(stl_vertex));
memcpy(&edge->key[sizeof(stl_vertex)], vertex1.data(), sizeof(stl_vertex));
edge->which_edge += 3; /* this edge is loaded backwards */
}
return 1;
}
static void
stl_free_edges(stl_file *stl) {
int i;
stl_hash_edge *temp;
if (stl->error) return;
static void stl_free_edges(stl_file *stl)
{
if (stl->error)
return;
if(stl->stats.malloced != stl->stats.freed) {
for(i = 0; i < stl->M; i++) {
for(temp = stl->heads[i]; stl->heads[i] != stl->tail;
temp = stl->heads[i]) {
for (int i = 0; i < stl->M; i++) {
for (stl_hash_edge *temp = stl->heads[i]; stl->heads[i] != stl->tail; temp = stl->heads[i]) {
stl->heads[i] = stl->heads[i]->next;
free(temp);
stl->stats.freed++;
++ stl->stats.freed;
}
}
}
@ -375,8 +324,8 @@ stl_free_edges(stl_file *stl) {
free(stl->tail);
}
static void
stl_initialize_facet_check_nearby(stl_file *stl) {
static void stl_initialize_facet_check_nearby(stl_file *stl)
{
int i;
if (stl->error) return;
@ -467,16 +416,8 @@ stl_record_neighbors(stl_file *stl,
}
}
static void
stl_match_neighbors_exact(stl_file *stl,
stl_hash_edge *edge_a, stl_hash_edge *edge_b) {
if (stl->error) return;
stl_record_neighbors(stl, edge_a, edge_b);
}
static void
stl_match_neighbors_nearby(stl_file *stl,
stl_hash_edge *edge_a, stl_hash_edge *edge_b) {
static void stl_match_neighbors_nearby(stl_file *stl, stl_hash_edge *edge_a, stl_hash_edge *edge_b)
{
int facet1;
int facet2;
int vertex1;
@ -517,9 +458,7 @@ stl_match_neighbors_nearby(stl_file *stl,
}
static void
stl_change_vertices(stl_file *stl, int facet_num, int vnot,
stl_vertex new_vertex) {
static void stl_change_vertices(stl_file *stl, int facet_num, int vnot, stl_vertex new_vertex) {
int first_facet;
int direction;
int next_edge;
@ -551,30 +490,30 @@ stl_change_vertices(stl_file *stl, int facet_num, int vnot,
}
}
#if 0
if (stl->facet_start[facet_num].vertex[pivot_vertex].x == new_vertex.x &&
stl->facet_start[facet_num].vertex[pivot_vertex].y == new_vertex.y &&
stl->facet_start[facet_num].vertex[pivot_vertex].z == new_vertex.z)
if (stl->facet_start[facet_num].vertex[pivot_vertex](0) == new_vertex(0) &&
stl->facet_start[facet_num].vertex[pivot_vertex](1) == new_vertex(1) &&
stl->facet_start[facet_num].vertex[pivot_vertex](2) == new_vertex(2))
printf("Changing vertex %f,%f,%f: Same !!!\r\n",
new_vertex.x, new_vertex.y, new_vertex.z);
new_vertex(0), new_vertex(1), new_vertex(2));
else {
if (stl->facet_start[facet_num].vertex[pivot_vertex].x != new_vertex.x)
if (stl->facet_start[facet_num].vertex[pivot_vertex](0) != new_vertex(0))
printf("Changing coordinate x, vertex %e (0x%08x) to %e(0x%08x)\r\n",
stl->facet_start[facet_num].vertex[pivot_vertex].x,
*reinterpret_cast<const int*>(&stl->facet_start[facet_num].vertex[pivot_vertex].x),
new_vertex.x,
*reinterpret_cast<const int*>(&new_vertex.x));
if (stl->facet_start[facet_num].vertex[pivot_vertex].y != new_vertex.y)
stl->facet_start[facet_num].vertex[pivot_vertex](0),
*reinterpret_cast<const int*>(&stl->facet_start[facet_num].vertex[pivot_vertex](0)),
new_vertex(0),
*reinterpret_cast<const int*>(&new_vertex(0)));
if (stl->facet_start[facet_num].vertex[pivot_vertex](1) != new_vertex(1))
printf("Changing coordinate x, vertex %e (0x%08x) to %e(0x%08x)\r\n",
stl->facet_start[facet_num].vertex[pivot_vertex].y,
*reinterpret_cast<const int*>(&stl->facet_start[facet_num].vertex[pivot_vertex].y),
new_vertex.y,
*reinterpret_cast<const int*>(&new_vertex.y));
if (stl->facet_start[facet_num].vertex[pivot_vertex].z != new_vertex.z)
stl->facet_start[facet_num].vertex[pivot_vertex](1),
*reinterpret_cast<const int*>(&stl->facet_start[facet_num].vertex[pivot_vertex](1)),
new_vertex(1),
*reinterpret_cast<const int*>(&new_vertex(1)));
if (stl->facet_start[facet_num].vertex[pivot_vertex](2) != new_vertex(2))
printf("Changing coordinate x, vertex %e (0x%08x) to %e(0x%08x)\r\n",
stl->facet_start[facet_num].vertex[pivot_vertex].z,
*reinterpret_cast<const int*>(&stl->facet_start[facet_num].vertex[pivot_vertex].z),
new_vertex.z,
*reinterpret_cast<const int*>(&new_vertex.z));
stl->facet_start[facet_num].vertex[pivot_vertex](2),
*reinterpret_cast<const int*>(&stl->facet_start[facet_num].vertex[pivot_vertex](2)),
new_vertex(2),
*reinterpret_cast<const int*>(&new_vertex(2)));
}
#endif
stl->facet_start[facet_num].vertex[pivot_vertex] = new_vertex;
@ -595,7 +534,6 @@ Try using a smaller tolerance or don't do a nearby check\n");
}
}
static void
stl_which_vertices_to_change(stl_file *stl, stl_hash_edge *edge_a,
stl_hash_edge *edge_b, int *facet1, int *vertex1,
@ -622,11 +560,10 @@ stl_which_vertices_to_change(stl_file *stl, stl_hash_edge *edge_a,
v1b = (edge_b->which_edge + 1) % 3;
}
/* Of the first pair, which vertex, if any, should be changed */
if(!memcmp(&stl->facet_start[edge_a->facet_number].vertex[v1a],
&stl->facet_start[edge_b->facet_number].vertex[v1b],
sizeof(stl_vertex))) {
/* These facets are already equal. No need to change. */
// Of the first pair, which vertex, if any, should be changed
if(stl->facet_start[edge_a->facet_number].vertex[v1a] ==
stl->facet_start[edge_b->facet_number].vertex[v1b]) {
// These facets are already equal. No need to change.
*facet1 = -1;
} else {
if( (stl->neighbors_start[edge_a->facet_number].neighbor[v1a] == -1)
@ -644,10 +581,9 @@ stl_which_vertices_to_change(stl_file *stl, stl_hash_edge *edge_a,
}
/* Of the second pair, which vertex, if any, should be changed */
if(!memcmp(&stl->facet_start[edge_a->facet_number].vertex[v2a],
&stl->facet_start[edge_b->facet_number].vertex[v2b],
sizeof(stl_vertex))) {
/* These facets are already equal. No need to change. */
if(stl->facet_start[edge_a->facet_number].vertex[v2a] ==
stl->facet_start[edge_b->facet_number].vertex[v2b]) {
// These facets are already equal. No need to change.
*facet2 = -1;
} else {
if( (stl->neighbors_start[edge_a->facet_number].neighbor[v2a] == -1)
@ -718,40 +654,35 @@ in stl_remove_facet: neighbor = %d numfacets = %d this is wrong\n",
}
}
void
stl_remove_unconnected_facets(stl_file *stl) {
void stl_remove_unconnected_facets(stl_file *stl)
{
/* A couple of things need to be done here. One is to remove any */
/* completely unconnected facets (0 edges connected) since these are */
/* useless and could be completely wrong. The second thing that needs to */
/* be done is to remove any degenerate facets that were created during */
/* stl_check_facets_nearby(). */
if (stl->error)
return;
int i;
if (stl->error) return;
/* remove degenerate facets */
for(i = 0; i < stl->stats.number_of_facets; i++) {
if( !memcmp(&stl->facet_start[i].vertex[0],
&stl->facet_start[i].vertex[1], sizeof(stl_vertex))
|| !memcmp(&stl->facet_start[i].vertex[1],
&stl->facet_start[i].vertex[2], sizeof(stl_vertex))
|| !memcmp(&stl->facet_start[i].vertex[0],
&stl->facet_start[i].vertex[2], sizeof(stl_vertex))) {
// remove degenerate facets
for (int i = 0; i < stl->stats.number_of_facets; ++ i) {
if(stl->facet_start[i].vertex[0] == stl->facet_start[i].vertex[1] ||
stl->facet_start[i].vertex[0] == stl->facet_start[i].vertex[2] ||
stl->facet_start[i].vertex[1] == stl->facet_start[i].vertex[2]) {
stl_remove_degenerate(stl, i);
i--;
}
}
if(stl->stats.connected_facets_1_edge < stl->stats.number_of_facets) {
/* remove completely unconnected facets */
for(i = 0; i < stl->stats.number_of_facets; i++) {
if( (stl->neighbors_start[i].neighbor[0] == -1)
&& (stl->neighbors_start[i].neighbor[1] == -1)
&& (stl->neighbors_start[i].neighbor[2] == -1)) {
/* This facet is completely unconnected. Remove it. */
// remove completely unconnected facets
for (int i = 0; i < stl->stats.number_of_facets; i++) {
if (stl->neighbors_start[i].neighbor[0] == -1 &&
stl->neighbors_start[i].neighbor[1] == -1 &&
stl->neighbors_start[i].neighbor[2] == -1) {
// This facet is completely unconnected. Remove it.
stl_remove_facet(stl, i);
i--;
-- i;
}
}
}
@ -771,30 +702,24 @@ stl_remove_degenerate(stl_file *stl, int facet) {
if (stl->error) return;
if( !memcmp(&stl->facet_start[facet].vertex[0],
&stl->facet_start[facet].vertex[1], sizeof(stl_vertex))
&& !memcmp(&stl->facet_start[facet].vertex[1],
&stl->facet_start[facet].vertex[2], sizeof(stl_vertex))) {
if (stl->facet_start[facet].vertex[0] == stl->facet_start[facet].vertex[1] &&
stl->facet_start[facet].vertex[1] == stl->facet_start[facet].vertex[2]) {
/* all 3 vertices are equal. Just remove the facet. I don't think*/
/* this is really possible, but just in case... */
printf("removing a facet in stl_remove_degenerate\n");
stl_remove_facet(stl, facet);
return;
}
if(!memcmp(&stl->facet_start[facet].vertex[0],
&stl->facet_start[facet].vertex[1], sizeof(stl_vertex))) {
if (stl->facet_start[facet].vertex[0] == stl->facet_start[facet].vertex[1]) {
edge1 = 1;
edge2 = 2;
edge3 = 0;
} else if(!memcmp(&stl->facet_start[facet].vertex[1],
&stl->facet_start[facet].vertex[2], sizeof(stl_vertex))) {
} else if (stl->facet_start[facet].vertex[1] == stl->facet_start[facet].vertex[2]) {
edge1 = 0;
edge2 = 2;
edge3 = 1;
} else if(!memcmp(&stl->facet_start[facet].vertex[2],
&stl->facet_start[facet].vertex[0], sizeof(stl_vertex))) {
} else if (stl->facet_start[facet].vertex[2] == stl->facet_start[facet].vertex[0]) {
edge1 = 0;
edge2 = 1;
edge3 = 2;
@ -883,7 +808,7 @@ stl_fill_holes(stl_file *stl) {
stl_load_edge_exact(stl, &edge, &facet.vertex[j],
&facet.vertex[(j + 1) % 3]);
insert_hash_edge(stl, edge, stl_match_neighbors_exact);
insert_hash_edge(stl, edge, stl_record_neighbors);
}
}
@ -939,7 +864,7 @@ stl_fill_holes(stl_file *stl) {
stl_load_edge_exact(stl, &edge, &new_facet.vertex[k],
&new_facet.vertex[(k + 1) % 3]);
insert_hash_edge(stl, edge, stl_match_neighbors_exact);
insert_hash_edge(stl, edge, stl_record_neighbors);
}
break;
} else {
@ -977,9 +902,7 @@ stl_add_facet(stl_file *stl, stl_facet *new_facet) {
stl->facet_start[stl->stats.number_of_facets] = *new_facet;
/* note that the normal vector is not set here, just initialized to 0 */
stl->facet_start[stl->stats.number_of_facets].normal.x = 0.0;
stl->facet_start[stl->stats.number_of_facets].normal.y = 0.0;
stl->facet_start[stl->stats.number_of_facets].normal.z = 0.0;
stl->facet_start[stl->stats.number_of_facets].normal = stl_normal::Zero();
stl->neighbors_start[stl->stats.number_of_facets].neighbor[0] = -1;
stl->neighbors_start[stl->stats.number_of_facets].neighbor[1] = -1;

106
xs/src/admesh/normals.cpp
View File

@ -27,12 +27,6 @@
#include "stl.h"
static void stl_reverse_vector(float v[]) {
v[0] *= -1;
v[1] *= -1;
v[2] *= -1;
}
static int stl_check_normal_vector(stl_file *stl, int facet_num, int normal_fix_flag);
static void
@ -228,102 +222,52 @@ static int stl_check_normal_vector(stl_file *stl, int facet_num, int normal_fix_
/* Returns 2 if the normal is not within tolerance and backwards */
/* Returns 4 if the status is unknown. */
float normal[3];
float test_norm[3];
stl_facet *facet;
facet = &stl->facet_start[facet_num];
stl_normal normal;
stl_calculate_normal(normal, facet);
stl_normalize_vector(normal);
stl_normal normal_dif = (normal - facet->normal).cwiseAbs();
if( (ABS(normal[0] - facet->normal.x) < 0.001)
&& (ABS(normal[1] - facet->normal.y) < 0.001)
&& (ABS(normal[2] - facet->normal.z) < 0.001)) {
const float eps = 0.001f;
if (normal_dif(0) < eps && normal_dif(1) < eps && normal_dif(2) < eps) {
/* It is not really necessary to change the values here */
/* but just for consistency, I will. */
facet->normal.x = normal[0];
facet->normal.y = normal[1];
facet->normal.z = normal[2];
facet->normal = normal;
return 0;
}
test_norm[0] = facet->normal.x;
test_norm[1] = facet->normal.y;
test_norm[2] = facet->normal.z;
stl_normal test_norm = facet->normal;
stl_normalize_vector(test_norm);
if( (ABS(normal[0] - test_norm[0]) < 0.001)
&& (ABS(normal[1] - test_norm[1]) < 0.001)
&& (ABS(normal[2] - test_norm[2]) < 0.001)) {
normal_dif = (normal - test_norm).cwiseAbs();
if (normal_dif(0) < eps && normal_dif(1) < eps && normal_dif(2) < eps) {
if(normal_fix_flag) {
facet->normal.x = normal[0];
facet->normal.y = normal[1];
facet->normal.z = normal[2];
facet->normal = normal;
stl->stats.normals_fixed += 1;
}
return 1;
}
stl_reverse_vector(test_norm);
if( (ABS(normal[0] - test_norm[0]) < 0.001)
&& (ABS(normal[1] - test_norm[1]) < 0.001)
&& (ABS(normal[2] - test_norm[2]) < 0.001)) {
/* Facet is backwards. */
test_norm *= -1.f;
normal_dif = (normal - test_norm).cwiseAbs();
if (normal_dif(0) < eps && normal_dif(1) < eps && normal_dif(2) < eps) {
// Facet is backwards.
if(normal_fix_flag) {
facet->normal.x = normal[0];
facet->normal.y = normal[1];
facet->normal.z = normal[2];
facet->normal = normal;
stl->stats.normals_fixed += 1;
}
return 2;
}
if(normal_fix_flag) {
facet->normal.x = normal[0];
facet->normal.y = normal[1];
facet->normal.z = normal[2];
facet->normal = normal;
stl->stats.normals_fixed += 1;
}
return 4;
}
void stl_calculate_normal(float normal[], stl_facet *facet) {
float v1[3] = {
facet->vertex[1].x - facet->vertex[0].x,
facet->vertex[1].y - facet->vertex[0].y,
facet->vertex[1].z - facet->vertex[0].z
};
float v2[3] = {
facet->vertex[2].x - facet->vertex[0].x,
facet->vertex[2].y - facet->vertex[0].y,
facet->vertex[2].z - facet->vertex[0].z
};
normal[0] = (float)((double)v1[1] * (double)v2[2]) - ((double)v1[2] * (double)v2[1]);
normal[1] = (float)((double)v1[2] * (double)v2[0]) - ((double)v1[0] * (double)v2[2]);
normal[2] = (float)((double)v1[0] * (double)v2[1]) - ((double)v1[1] * (double)v2[0]);
}
void stl_normalize_vector(float v[]) {
double length;
double factor;
float min_normal_length;
length = sqrt((double)v[0] * (double)v[0] + (double)v[1] * (double)v[1] + (double)v[2] * (double)v[2]);
min_normal_length = 0.000000000001;
if(length < min_normal_length) {
v[0] = 0.0;
v[1] = 0.0;
v[2] = 0.0;
return;
}
factor = 1.0 / length;
v[0] *= factor;
v[1] *= factor;
v[2] *= factor;
}
void
stl_fix_normal_values(stl_file *stl) {
void stl_fix_normal_values(stl_file *stl) {
int i;
if (stl->error) return;
@ -333,20 +277,16 @@ stl_fix_normal_values(stl_file *stl) {
}
}
void
stl_reverse_all_facets(stl_file *stl) {
int i;
float normal[3];
void stl_reverse_all_facets(stl_file *stl)
{
if (stl->error)
return;
if (stl->error) return;
for(i = 0; i < stl->stats.number_of_facets; i++) {
stl_normal normal;
for(int i = 0; i < stl->stats.number_of_facets; i++) {
stl_reverse_facet(stl, i);
stl_calculate_normal(normal, &stl->facet_start[i]);
stl_normalize_vector(normal);
stl->facet_start[i].normal.x = normal[0];
stl->facet_start[i].normal.y = normal[1];
stl->facet_start[i].normal.z = normal[2];
stl->facet_start[i].normal = normal;
}
}

8
xs/src/admesh/shared.cpp
View File

@ -169,7 +169,7 @@ stl_write_off(stl_file *stl, char *file) {
for(i = 0; i < stl->stats.shared_vertices; i++) {
fprintf(fp, "\t%f %f %f\n",
stl->v_shared[i].x, stl->v_shared[i].y, stl->v_shared[i].z);
stl->v_shared[i](0), stl->v_shared[i](1), stl->v_shared[i](2));
}
for(i = 0; i < stl->stats.number_of_facets; i++) {
fprintf(fp, "\t3 %d %d %d\n", stl->v_indices[i].vertex[0],
@ -216,10 +216,10 @@ stl_write_vrml(stl_file *stl, char *file) {
for(i = 0; i < (stl->stats.shared_vertices - 1); i++) {
fprintf(fp, "\t\t\t\t%f %f %f,\n",
stl->v_shared[i].x, stl->v_shared[i].y, stl->v_shared[i].z);
stl->v_shared[i](0), stl->v_shared[i](1), stl->v_shared[i](2));
}
fprintf(fp, "\t\t\t\t%f %f %f]\n",
stl->v_shared[i].x, stl->v_shared[i].y, stl->v_shared[i].z);
stl->v_shared[i](0), stl->v_shared[i](1), stl->v_shared[i](2));
fprintf(fp, "\t\t}\n");
fprintf(fp, "\t\tDEF STLTriangles IndexedFaceSet {\n");
fprintf(fp, "\t\t\tcoordIndex [\n");
@ -254,7 +254,7 @@ void stl_write_obj (stl_file *stl, char *file) {
}
for (i = 0; i < stl->stats.shared_vertices; i++) {
fprintf(fp, "v %f %f %f\n", stl->v_shared[i].x, stl->v_shared[i].y, stl->v_shared[i].z);
fprintf(fp, "v %f %f %f\n", stl->v_shared[i](0), stl->v_shared[i](1), stl->v_shared[i](2));
}
for (i = 0; i < stl->stats.number_of_facets; i++) {
fprintf(fp, "f %d %d %d\n", stl->v_indices[i].vertex[0]+1, stl->v_indices[i].vertex[1]+1, stl->v_indices[i].vertex[2]+1);

59
xs/src/admesh/stl.h
View File

@ -27,9 +27,7 @@
#include <stdint.h>
#include <stddef.h>
#define STL_MAX(A,B) ((A)>(B)? (A):(B))
#define STL_MIN(A,B) ((A)<(B)? (A):(B))
#define ABS(X) ((X) < 0 ? -(X) : (X))
#include <Eigen/Geometry>
// Size of the binary STL header, free form.
#define LABEL_SIZE 80
@ -39,31 +37,16 @@
#define HEADER_SIZE 84
#define STL_MIN_FILE_SIZE 284
#define ASCII_LINES_PER_FACET 7
// Comparing an edge by memcmp, 2x3x4 bytes = 24
#define SIZEOF_EDGE_SORT 24
typedef struct {
float x;
float y;
float z;
} stl_vertex;
typedef Eigen::Matrix<float, 3, 1, Eigen::DontAlign> stl_vertex;
typedef Eigen::Matrix<float, 3, 1, Eigen::DontAlign> stl_normal;
static_assert(sizeof(stl_vertex) == 12, "size of stl_vertex incorrect");
typedef struct {
float x;
float y;
float z;
} stl_normal;
static_assert(sizeof(stl_normal) == 12, "size of stl_normal incorrect");
typedef char stl_extra[2];
typedef struct {
stl_normal normal;
stl_vertex vertex[3];
stl_extra extra;
char extra[2];
} stl_facet;
#define SIZEOF_STL_FACET 50
@ -81,8 +64,12 @@ typedef struct {
} stl_edge;
typedef struct stl_hash_edge {
// Key of a hash edge: 2x binary copy of a floating point vertex.
uint32_t key[6];
// Key of a hash edge: sorted vertices of the edge.
unsigned char key[2 * sizeof(stl_vertex)];
// Compare two keys.
bool operator==(const stl_hash_edge &rhs) { return memcmp(key, rhs.key, sizeof(key)) == 0; }
bool operator!=(const stl_hash_edge &rhs) { return ! (*this == rhs); }
int hash(int M) const { return ((key[0] / 23 + key[1] / 19 + key[2] / 17 + key[3] /13 + key[4] / 11 + key[5] / 7 ) % M); }
// Index of a facet owning this edge.
int facet_number;
// Index of this edge inside the facet with an index of facet_number.
@ -91,8 +78,6 @@ typedef struct stl_hash_edge {
struct stl_hash_edge *next;
} stl_hash_edge;
static_assert(offsetof(stl_hash_edge, facet_number) == SIZEOF_EDGE_SORT, "size of stl_hash_edge.key incorrect");
typedef struct {
// Index of a neighbor facet.
int neighbor[3];
@ -179,8 +164,8 @@ extern void stl_fix_normal_values(stl_file *stl);
extern void stl_reverse_all_facets(stl_file *stl);
extern void stl_translate(stl_file *stl, float x, float y, float z);
extern void stl_translate_relative(stl_file *stl, float x, float y, float z);
extern void stl_scale_versor(stl_file *stl, float versor[3]);
extern void stl_scale(stl_file *stl, float factor);
extern void stl_scale_versor(stl_file *stl, const stl_vertex &versor);
inline void stl_scale(stl_file *stl, float factor) { stl_scale_versor(stl, stl_vertex(factor, factor, factor)); }
extern void stl_rotate_x(stl_file *stl, float angle);
extern void stl_rotate_y(stl_file *stl, float angle);
extern void stl_rotate_z(stl_file *stl, float angle);
@ -195,8 +180,20 @@ extern void stl_write_obj(stl_file *stl, char *file);
extern void stl_write_off(stl_file *stl, char *file);
extern void stl_write_dxf(stl_file *stl, char *file, char *label);
extern void stl_write_vrml(stl_file *stl, char *file);
extern void stl_calculate_normal(float normal[], stl_facet *facet);
extern void stl_normalize_vector(float v[]);
inline void stl_calculate_normal(stl_normal &normal, stl_facet *facet) {
normal = (facet->vertex[1] - facet->vertex[0]).cross(facet->vertex[2] - facet->vertex[0]);
}
inline void stl_normalize_vector(stl_normal &normal) {
double length = normal.cast<double>().norm();
if (length < 0.000000000001)
normal = stl_normal::Zero();
else
normal *= (1.0 / length);
}
inline bool stl_vertex_lower(const stl_vertex &a, const stl_vertex &b) {
return (a(0) != b(0)) ? (a(0) < b(0)) :
((a(1) != b(1)) ? (a(1) < b(1)) : (a(2) < b(2)));
}
extern void stl_calculate_volume(stl_file *stl);
extern void stl_repair(stl_file *stl, int fixall_flag, int exact_flag, int tolerance_flag, float tolerance, int increment_flag, float increment, int nearby_flag, int iterations, int remove_unconnected_flag, int fill_holes_flag, int normal_directions_flag, int normal_values_flag, int reverse_all_flag, int verbose_flag);
@ -204,8 +201,8 @@ extern void stl_repair(stl_file *stl, int fixall_flag, int exact_flag, int toler
extern void stl_initialize(stl_file *stl);
extern void stl_count_facets(stl_file *stl, const char *file);
extern void stl_allocate(stl_file *stl);
extern void stl_read(stl_file *stl, int first_facet, int first);
extern void stl_facet_stats(stl_file *stl, stl_facet facet, int first);
extern void stl_read(stl_file *stl, int first_facet, bool first);
extern void stl_facet_stats(stl_file *stl, stl_facet facet, bool &first);
extern void stl_reallocate(stl_file *stl);
extern void stl_add_facet(stl_file *stl, stl_facet *new_facet);
extern void stl_get_size(stl_file *stl);

95
xs/src/admesh/stl_io.cpp
View File

@ -44,9 +44,9 @@ stl_print_edges(stl_file *stl, FILE *file) {
for(i = 0; i < edges_allocated; i++) {
fprintf(file, "%d, %f, %f, %f, %f, %f, %f\n",
stl->edge_start[i].facet_number,
stl->edge_start[i].p1.x, stl->edge_start[i].p1.y,
stl->edge_start[i].p1.z, stl->edge_start[i].p2.x,
stl->edge_start[i].p2.y, stl->edge_start[i].p2.z);
stl->edge_start[i].p1(0), stl->edge_start[i].p1(1),
stl->edge_start[i].p1(2), stl->edge_start[i].p2(0),
stl->edge_start[i].p2(1), stl->edge_start[i].p2(2));
}
}
@ -75,11 +75,11 @@ File type : ASCII STL file\n");
Header : %s\n", stl->stats.header);
fprintf(file, "============== Size ==============\n");
fprintf(file, "Min X = % f, Max X = % f\n",
stl->stats.min.x, stl->stats.max.x);
stl->stats.min(0), stl->stats.max(0));
fprintf(file, "Min Y = % f, Max Y = % f\n",
stl->stats.min.y, stl->stats.max.y);
stl->stats.min(1), stl->stats.max(1));
fprintf(file, "Min Z = % f, Max Z = % f\n",
stl->stats.min.z, stl->stats.max.z);
stl->stats.min(2), stl->stats.max(2));
fprintf(file, "\
========= Facet Status ========== Original ============ Final ====\n");
@ -149,18 +149,18 @@ stl_write_ascii(stl_file *stl, const char *file, const char *label) {
for(i = 0; i < stl->stats.number_of_facets; i++) {
fprintf(fp, " facet normal % .8E % .8E % .8E\n",
stl->facet_start[i].normal.x, stl->facet_start[i].normal.y,
stl->facet_start[i].normal.z);
stl->facet_start[i].normal(0), stl->facet_start[i].normal(1),
stl->facet_start[i].normal(2));
fprintf(fp, " outer loop\n");
fprintf(fp, " vertex % .8E % .8E % .8E\n",
stl->facet_start[i].vertex[0].x, stl->facet_start[i].vertex[0].y,
stl->facet_start[i].vertex[0].z);
stl->facet_start[i].vertex[0](0), stl->facet_start[i].vertex[0](1),
stl->facet_start[i].vertex[0](2));
fprintf(fp, " vertex % .8E % .8E % .8E\n",
stl->facet_start[i].vertex[1].x, stl->facet_start[i].vertex[1].y,
stl->facet_start[i].vertex[1].z);
stl->facet_start[i].vertex[1](0), stl->facet_start[i].vertex[1](1),
stl->facet_start[i].vertex[1](2));
fprintf(fp, " vertex % .8E % .8E % .8E\n",
stl->facet_start[i].vertex[2].x, stl->facet_start[i].vertex[2].y,
stl->facet_start[i].vertex[2].z);
stl->facet_start[i].vertex[2](0), stl->facet_start[i].vertex[2](1),
stl->facet_start[i].vertex[2](2));
fprintf(fp, " endloop\n");
fprintf(fp, " endfacet\n");
}
@ -264,9 +264,9 @@ void
stl_write_vertex(stl_file *stl, int facet, int vertex) {
if (stl->error) return;
printf(" vertex %d/%d % .8E % .8E % .8E\n", vertex, facet,
stl->facet_start[facet].vertex[vertex].x,
stl->facet_start[facet].vertex[vertex].y,
stl->facet_start[facet].vertex[vertex].z);
stl->facet_start[facet].vertex[vertex](0),
stl->facet_start[facet].vertex[vertex](1),
stl->facet_start[facet].vertex[vertex](2));
}
void
@ -309,10 +309,10 @@ stl_write_quad_object(stl_file *stl, char *file) {
int i;
int j;
char *error_msg;
stl_vertex connect_color;
stl_vertex uncon_1_color;
stl_vertex uncon_2_color;
stl_vertex uncon_3_color;
stl_vertex connect_color = stl_vertex::Zero();
stl_vertex uncon_1_color = stl_vertex::Zero();
stl_vertex uncon_2_color = stl_vertex::Zero();
stl_vertex uncon_3_color = stl_vertex::Zero();
stl_vertex color;
if (stl->error) return;
@ -330,19 +330,6 @@ stl_write_quad_object(stl_file *stl, char *file) {
return;
}
connect_color.x = 0.0;
connect_color.y = 0.0;
connect_color.z = 1.0;
uncon_1_color.x = 0.0;
uncon_1_color.y = 1.0;
uncon_1_color.z = 0.0;
uncon_2_color.x = 1.0;
uncon_2_color.y = 1.0;
uncon_2_color.z = 1.0;
uncon_3_color.x = 1.0;
uncon_3_color.y = 0.0;
uncon_3_color.z = 0.0;
fprintf(fp, "CQUAD\n");
for(i = 0; i < stl->stats.number_of_facets; i++) {
j = ((stl->neighbors_start[i].neighbor[0] == -1) +
@ -358,21 +345,21 @@ stl_write_quad_object(stl_file *stl, char *file) {
color = uncon_3_color;
}
fprintf(fp, "%f %f %f %1.1f %1.1f %1.1f 1\n",
stl->facet_start[i].vertex[0].x,
stl->facet_start[i].vertex[0].y,
stl->facet_start[i].vertex[0].z, color.x, color.y, color.z);
stl->facet_start[i].vertex[0](0),
stl->facet_start[i].vertex[0](1),
stl->facet_start[i].vertex[0](2), color(0), color(1), color(2));
fprintf(fp, "%f %f %f %1.1f %1.1f %1.1f 1\n",
stl->facet_start[i].vertex[1].x,
stl->facet_start[i].vertex[1].y,
stl->facet_start[i].vertex[1].z, color.x, color.y, color.z);
stl->facet_start[i].vertex[1](0),
stl->facet_start[i].vertex[1](1),
stl->facet_start[i].vertex[1](2), color(0), color(1), color(2));
fprintf(fp, "%f %f %f %1.1f %1.1f %1.1f 1\n",
stl->facet_start[i].vertex[2].x,
stl->facet_start[i].vertex[2].y,
stl->facet_start[i].vertex[2].z, color.x, color.y, color.z);
stl->facet_start[i].vertex[2](0),
stl->facet_start[i].vertex[2](1),
stl->facet_start[i].vertex[2](2), color(0), color(1), color(2));
fprintf(fp, "%f %f %f %1.1f %1.1f %1.1f 1\n",
stl->facet_start[i].vertex[2].x,
stl->facet_start[i].vertex[2].y,
stl->facet_start[i].vertex[2].z, color.x, color.y, color.z);
stl->facet_start[i].vertex[2](0),
stl->facet_start[i].vertex[2](1),
stl->facet_start[i].vertex[2](2), color(0), color(1), color(2));
}
fclose(fp);
}
@ -409,17 +396,17 @@ stl_write_dxf(stl_file *stl, char *file, char *label) {
for(i = 0; i < stl->stats.number_of_facets; i++) {
fprintf(fp, "0\n3DFACE\n8\n0\n");
fprintf(fp, "10\n%f\n20\n%f\n30\n%f\n",
stl->facet_start[i].vertex[0].x, stl->facet_start[i].vertex[0].y,
stl->facet_start[i].vertex[0].z);
stl->facet_start[i].vertex[0](0), stl->facet_start[i].vertex[0](1),
stl->facet_start[i].vertex[0](2));
fprintf(fp, "11\n%f\n21\n%f\n31\n%f\n",
stl->facet_start[i].vertex[1].x, stl->facet_start[i].vertex[1].y,
stl->facet_start[i].vertex[1].z);
stl->facet_start[i].vertex[1](0), stl->facet_start[i].vertex[1](1),
stl->facet_start[i].vertex[1](2));
fprintf(fp, "12\n%f\n22\n%f\n32\n%f\n",
stl->facet_start[i].vertex[2].x, stl->facet_start[i].vertex[2].y,
stl->facet_start[i].vertex[2].z);
stl->facet_start[i].vertex[2](0), stl->facet_start[i].vertex[2](1),
stl->facet_start[i].vertex[2](2));
fprintf(fp, "13\n%f\n23\n%f\n33\n%f\n",
stl->facet_start[i].vertex[2].x, stl->facet_start[i].vertex[2].y,
stl->facet_start[i].vertex[2].z);
stl->facet_start[i].vertex[2](0), stl->facet_start[i].vertex[2](1),
stl->facet_start[i].vertex[2](2));
}
fprintf(fp, "0\nENDSEC\n0\nEOF\n");

130
xs/src/admesh/stlinit.cpp
View File

@ -40,7 +40,7 @@ stl_open(stl_file *stl, const char *file) {
stl_initialize(stl);
stl_count_facets(stl, file);
stl_allocate(stl);
stl_read(stl, 0, 1);
stl_read(stl, 0, true);
if (!stl->error) fclose(stl->fp);
}
@ -227,7 +227,7 @@ stl_open_merge(stl_file *stl, char *file_to_merge) {
Start at num_facets_so_far, the index to the first unused facet. Also say
that this isn't our first time so we should augment stats like min and max
instead of erasing them. */
stl_read(stl, num_facets_so_far, 0);
stl_read(stl, num_facets_so_far, false);
/* Restore the stl information we overwrote (for stl_read) so that it still accurately
reflects the subject part: */
@ -255,8 +255,7 @@ stl_reallocate(stl_file *stl) {
/* Reads the contents of the file pointed to by stl->fp into the stl structure,
starting at facet first_facet. The second argument says if it's our first
time running this for the stl and therefore we should reset our max and min stats. */
void
stl_read(stl_file *stl, int first_facet, int first) {
void stl_read(stl_file *stl, int first_facet, bool first) {
stl_facet facet;
int i;
@ -294,11 +293,11 @@ stl_read(stl_file *stl, int first_facet, int first) {
assert(res_normal == 3);
int res_outer_loop = fscanf(stl->fp, " outer loop");
assert(res_outer_loop == 0);
int res_vertex1 = fscanf(stl->fp, " vertex %f %f %f", &facet.vertex[0].x, &facet.vertex[0].y, &facet.vertex[0].z);
int res_vertex1 = fscanf(stl->fp, " vertex %f %f %f", &facet.vertex[0](0), &facet.vertex[0](1), &facet.vertex[0](2));
assert(res_vertex1 == 3);
int res_vertex2 = fscanf(stl->fp, " vertex %f %f %f", &facet.vertex[1].x, &facet.vertex[1].y, &facet.vertex[1].z);
int res_vertex2 = fscanf(stl->fp, " vertex %f %f %f", &facet.vertex[1](0), &facet.vertex[1](1), &facet.vertex[1](2));
assert(res_vertex2 == 3);
int res_vertex3 = fscanf(stl->fp, " vertex %f %f %f", &facet.vertex[2].x, &facet.vertex[2].y, &facet.vertex[2].z);
int res_vertex3 = fscanf(stl->fp, " vertex %f %f %f", &facet.vertex[2](0), &facet.vertex[2](1), &facet.vertex[2](2));
assert(res_vertex3 == 3);
int res_endloop = fscanf(stl->fp, " endloop");
assert(res_endloop == 0);
@ -311,9 +310,9 @@ stl_read(stl_file *stl, int first_facet, int first) {
}
// The facet normal has been parsed as a single string as to workaround for not a numbers in the normal definition.
if (sscanf(normal_buf[0], "%f", &facet.normal.x) != 1 ||
sscanf(normal_buf[1], "%f", &facet.normal.y) != 1 ||
sscanf(normal_buf[2], "%f", &facet.normal.z) != 1) {
if (sscanf(normal_buf[0], "%f", &facet.normal(0)) != 1 ||
sscanf(normal_buf[1], "%f", &facet.normal(1)) != 1 ||
sscanf(normal_buf[2], "%f", &facet.normal(2)) != 1) {
// Normal was mangled. Maybe denormals or "not a number" were stored?
// Just reset the normal and silently ignore it.
memset(&facet.normal, 0, sizeof(facet.normal));
@ -326,104 +325,45 @@ stl_read(stl_file *stl, int first_facet, int first) {
// It may be worth to round these numbers to zero during loading to reduce the number of errors reported
// during the STL import.
for (size_t j = 0; j < 3; ++ j) {
if (facet.vertex[j].x > -1e-12f && facet.vertex[j].x < 1e-12f)
printf("stl_read: facet %d.x = %e\r\n", j, facet.vertex[j].x);
if (facet.vertex[j].y > -1e-12f && facet.vertex[j].y < 1e-12f)
printf("stl_read: facet %d.y = %e\r\n", j, facet.vertex[j].y);
if (facet.vertex[j].z > -1e-12f && facet.vertex[j].z < 1e-12f)
printf("stl_read: facet %d.z = %e\r\n", j, facet.vertex[j].z);
if (facet.vertex[j](0) > -1e-12f && facet.vertex[j](0) < 1e-12f)
printf("stl_read: facet %d(0) = %e\r\n", j, facet.vertex[j](0));
if (facet.vertex[j](1) > -1e-12f && facet.vertex[j](1) < 1e-12f)
printf("stl_read: facet %d(1) = %e\r\n", j, facet.vertex[j](1));
if (facet.vertex[j](2) > -1e-12f && facet.vertex[j](2) < 1e-12f)
printf("stl_read: facet %d(2) = %e\r\n", j, facet.vertex[j](2));
}
#endif
#if 1
{
// Positive and negative zeros are possible in the floats, which are considered equal by the FP unit.
// When using a memcmp on raw floats, those numbers report to be different.
// Unify all +0 and -0 to +0 to make the floats equal under memcmp.
uint32_t *f = (uint32_t*)&facet;
for (int j = 0; j < 12; ++ j, ++ f) // 3x vertex + normal: 4x3 = 12 floats
if (*f == 0x80000000)
// Negative zero, switch to positive zero.
*f = 0;
}
#else
{
// Due to the nature of the floating point numbers, close to zero values may be represented with singificantly higher precision
// than the rest of the vertices. Round them to zero.
float *f = (float*)&facet;
for (int j = 0; j < 12; ++ j, ++ f) // 3x vertex + normal: 4x3 = 12 floats
if (*f > -1e-12f && *f < 1e-12f)
// Negative zero, switch to positive zero.
*f = 0;
}
#endif
/* Write the facet into memory. */
memcpy(stl->facet_start+i, &facet, SIZEOF_STL_FACET);
stl->facet_start[i] = facet;
stl_facet_stats(stl, facet, first);
first = 0;
}
stl->stats.size.x = stl->stats.max.x - stl->stats.min.x;
stl->stats.size.y = stl->stats.max.y - stl->stats.min.y;
stl->stats.size.z = stl->stats.max.z - stl->stats.min.z;
stl->stats.bounding_diameter = sqrt(
stl->stats.size.x * stl->stats.size.x +
stl->stats.size.y * stl->stats.size.y +
stl->stats.size.z * stl->stats.size.z
);
stl->stats.size = stl->stats.max - stl->stats.min;
stl->stats.bounding_diameter = stl->stats.size.norm();
}
void
stl_facet_stats(stl_file *stl, stl_facet facet, int first) {
float diff_x;
float diff_y;
float diff_z;
float max_diff;
void stl_facet_stats(stl_file *stl, stl_facet facet, bool &first)
{
if (stl->error)
return;
if (stl->error) return;
// While we are going through all of the facets, let's find the
// maximum and minimum values for x, y, and z
/* while we are going through all of the facets, let's find the */
/* maximum and minimum values for x, y, and z */
/* Initialize the max and min values the first time through*/
if (first) {
stl->stats.max.x = facet.vertex[0].x;
stl->stats.min.x = facet.vertex[0].x;
stl->stats.max.y = facet.vertex[0].y;
stl->stats.min.y = facet.vertex[0].y;
stl->stats.max.z = facet.vertex[0].z;
stl->stats.min.z = facet.vertex[0].z;
diff_x = ABS(facet.vertex[0].x - facet.vertex[1].x);
diff_y = ABS(facet.vertex[0].y - facet.vertex[1].y);
diff_z = ABS(facet.vertex[0].z - facet.vertex[1].z);
max_diff = STL_MAX(diff_x, diff_y);
max_diff = STL_MAX(diff_z, max_diff);
stl->stats.shortest_edge = max_diff;
first = 0;
// Initialize the max and min values the first time through
stl->stats.min = facet.vertex[0];
stl->stats.max = facet.vertex[0];
stl_vertex diff = (facet.vertex[1] - facet.vertex[0]).cwiseAbs();
stl->stats.shortest_edge = std::max(diff(0), std::max(diff(1), diff(2)));
first = false;
}
/* now find the max and min values */
stl->stats.max.x = STL_MAX(stl->stats.max.x, facet.vertex[0].x);
stl->stats.min.x = STL_MIN(stl->stats.min.x, facet.vertex[0].x);
stl->stats.max.y = STL_MAX(stl->stats.max.y, facet.vertex[0].y);
stl->stats.min.y = STL_MIN(stl->stats.min.y, facet.vertex[0].y);
stl->stats.max.z = STL_MAX(stl->stats.max.z, facet.vertex[0].z);
stl->stats.min.z = STL_MIN(stl->stats.min.z, facet.vertex[0].z);
stl->stats.max.x = STL_MAX(stl->stats.max.x, facet.vertex[1].x);
stl->stats.min.x = STL_MIN(stl->stats.min.x, facet.vertex[1].x);
stl->stats.max.y = STL_MAX(stl->stats.max.y, facet.vertex[1].y);
stl->stats.min.y = STL_MIN(stl->stats.min.y, facet.vertex[1].y);
stl->stats.max.z = STL_MAX(stl->stats.max.z, facet.vertex[1].z);
stl->stats.min.z = STL_MIN(stl->stats.min.z, facet.vertex[1].z);
stl->stats.max.x = STL_MAX(stl->stats.max.x, facet.vertex[2].x);
stl->stats.min.x = STL_MIN(stl->stats.min.x, facet.vertex[2].x);
stl->stats.max.y = STL_MAX(stl->stats.max.y, facet.vertex[2].y);
stl->stats.min.y = STL_MIN(stl->stats.min.y, facet.vertex[2].y);
stl->stats.max.z = STL_MAX(stl->stats.max.z, facet.vertex[2].z);
stl->stats.min.z = STL_MIN(stl->stats.min.z, facet.vertex[2].z);
// Now find the max and min values.
for (size_t i = 0; i < 3; ++ i) {
stl->stats.min = stl->stats.min.cwiseMin(facet.vertex[i]);
stl->stats.max = stl->stats.max.cwiseMax(facet.vertex[i]);
}
}
void

358
xs/src/admesh/util.cpp
View File

@ -62,7 +62,7 @@ stl_verify_neighbors(stl_file *stl) {
edge_b.p1 = stl->facet_start[neighbor].vertex[(vnot + 1) % 3];
edge_b.p2 = stl->facet_start[neighbor].vertex[(vnot + 2) % 3];
}
if(memcmp(&edge_a, &edge_b, SIZEOF_EDGE_SORT) != 0) {
if (edge_a.p1 != edge_b.p1 || edge_a.p2 != edge_b.p2) {
/* These edges should match but they don't. Print results. */
printf("edge %d of facet %d doesn't match edge %d of facet %d\n",
j, i, vnot + 1, neighbor);
@ -73,114 +73,67 @@ stl_verify_neighbors(stl_file *stl) {
}
}
void
stl_translate(stl_file *stl, float x, float y, float z) {
int i;
int j;
if (stl->error) return;
for(i = 0; i < stl->stats.number_of_facets; i++) {
for(j = 0; j < 3; j++) {
stl->facet_start[i].vertex[j].x -= (stl->stats.min.x - x);
stl->facet_start[i].vertex[j].y -= (stl->stats.min.y - y);
stl->facet_start[i].vertex[j].z -= (stl->stats.min.z - z);
}
}
stl->stats.max.x -= (stl->stats.min.x - x);
stl->stats.max.y -= (stl->stats.min.y - y);
stl->stats.max.z -= (stl->stats.min.z - z);
stl->stats.min.x = x;
stl->stats.min.y = y;
stl->stats.min.z = z;
void stl_translate(stl_file *stl, float x, float y, float z)
{
if (stl->error)
return;
stl_vertex new_min(x, y, z);
stl_vertex shift = new_min - stl->stats.min;
for (int i = 0; i < stl->stats.number_of_facets; ++ i)
for (int j = 0; j < 3; ++ j)
stl->facet_start[i].vertex[j] += shift;
stl->stats.min = new_min;
stl->stats.max += shift;
stl_invalidate_shared_vertices(stl);
}
/* Translates the stl by x,y,z, relatively from wherever it is currently */
void
stl_translate_relative(stl_file *stl, float x, float y, float z) {
int i;
int j;
if (stl->error) return;
for(i = 0; i < stl->stats.number_of_facets; i++) {
for(j = 0; j < 3; j++) {
stl->facet_start[i].vertex[j].x += x;
stl->facet_start[i].vertex[j].y += y;
stl->facet_start[i].vertex[j].z += z;
}
}
stl->stats.min.x += x;
stl->stats.min.y += y;
stl->stats.min.z += z;
stl->stats.max.x += x;
stl->stats.max.y += y;
stl->stats.max.z += z;
void stl_translate_relative(stl_file *stl, float x, float y, float z)
{
if (stl->error)
return;
stl_vertex shift(x, y, z);
for (int i = 0; i < stl->stats.number_of_facets; ++ i)
for (int j = 0; j < 3; ++ j)
stl->facet_start[i].vertex[j] += shift;
stl->stats.min += shift;
stl->stats.max += shift;
stl_invalidate_shared_vertices(stl);
}
void
stl_scale_versor(stl_file *stl, float versor[3]) {
int i;
int j;
if (stl->error) return;
/* scale extents */
stl->stats.min.x *= versor[0];
stl->stats.min.y *= versor[1];
stl->stats.min.z *= versor[2];
stl->stats.max.x *= versor[0];
stl->stats.max.y *= versor[1];
stl->stats.max.z *= versor[2];
/* scale size */
stl->stats.size.x *= versor[0];
stl->stats.size.y *= versor[1];
stl->stats.size.z *= versor[2];
/* scale volume */
if (stl->stats.volume > 0.0) {
stl->stats.volume *= (versor[0] * versor[1] * versor[2]);
}
for(i = 0; i < stl->stats.number_of_facets; i++) {
for(j = 0; j < 3; j++) {
stl->facet_start[i].vertex[j].x *= versor[0];
stl->facet_start[i].vertex[j].y *= versor[1];
stl->facet_start[i].vertex[j].z *= versor[2];
}
}
void stl_scale_versor(stl_file *stl, const stl_vertex &versor)
{
if (stl->error)
return;
// Scale extents.
auto s = versor.array();
stl->stats.min.array() *= s;
stl->stats.max.array() *= s;
// Scale size.
stl->stats.size.array() *= s;
// Scale volume.
if (stl->stats.volume > 0.0)
stl->stats.volume *= versor(0) * versor(1) * versor(2);
// Scale the mesh.
for (int i = 0; i < stl->stats.number_of_facets; ++ i)
for (int j = 0; j < 3; ++ j)
stl->facet_start[i].vertex[j].array() *= s;
stl_invalidate_shared_vertices(stl);
}
void
stl_scale(stl_file *stl, float factor) {
float versor[3];
if (stl->error) return;
versor[0] = factor;
versor[1] = factor;
versor[2] = factor;
stl_scale_versor(stl, versor);
}
static void calculate_normals(stl_file *stl) {
float normal[3];
if (stl->error) return;
static void calculate_normals(stl_file *stl)
{
if (stl->error)
return;
stl_normal normal;
for(uint32_t i = 0; i < stl->stats.number_of_facets; i++) {
stl_calculate_normal(normal, &stl->facet_start[i]);
stl_normalize_vector(normal);
stl->facet_start[i].normal.x = normal[0];
stl->facet_start[i].normal.y = normal[1];
stl->facet_start[i].normal.z = normal[2];
stl->facet_start[i].normal = normal;
}
}
@ -193,9 +146,9 @@ void stl_transform(stl_file *stl, float *trafo3x4) {
for (i_vertex = 0; i_vertex < 3; ++ i_vertex) {
stl_vertex &v_dst = vertices[i_vertex];
stl_vertex v_src = v_dst;
v_dst.x = trafo3x4[0] * v_src.x + trafo3x4[1] * v_src.y + trafo3x4[2] * v_src.z + trafo3x4[3];
v_dst.y = trafo3x4[4] * v_src.x + trafo3x4[5] * v_src.y + trafo3x4[6] * v_src.z + trafo3x4[7];
v_dst.z = trafo3x4[8] * v_src.x + trafo3x4[9] * v_src.y + trafo3x4[10] * v_src.z + trafo3x4[11];
v_dst(0) = trafo3x4[0] * v_src(0) + trafo3x4[1] * v_src(1) + trafo3x4[2] * v_src(2) + trafo3x4[3];
v_dst(1) = trafo3x4[4] * v_src(0) + trafo3x4[5] * v_src(1) + trafo3x4[6] * v_src(2) + trafo3x4[7];
v_dst(2) = trafo3x4[8] * v_src(0) + trafo3x4[9] * v_src(1) + trafo3x4[10] * v_src(2) + trafo3x4[11];
}
}
stl_get_size(stl);
@ -214,8 +167,8 @@ stl_rotate_x(stl_file *stl, float angle) {
for(i = 0; i < stl->stats.number_of_facets; i++) {
for(j = 0; j < 3; j++) {
stl_rotate(&stl->facet_start[i].vertex[j].y,
&stl->facet_start[i].vertex[j].z, c, s);
stl_rotate(&stl->facet_start[i].vertex[j](1),
&stl->facet_start[i].vertex[j](2), c, s);
}
}
stl_get_size(stl);
@ -234,8 +187,8 @@ stl_rotate_y(stl_file *stl, float angle) {
for(i = 0; i < stl->stats.number_of_facets; i++) {
for(j = 0; j < 3; j++) {
stl_rotate(&stl->facet_start[i].vertex[j].z,
&stl->facet_start[i].vertex[j].x, c, s);
stl_rotate(&stl->facet_start[i].vertex[j](2),
&stl->facet_start[i].vertex[j](0), c, s);
}
}
stl_get_size(stl);
@ -254,8 +207,8 @@ stl_rotate_z(stl_file *stl, float angle) {
for(i = 0; i < stl->stats.number_of_facets; i++) {
for(j = 0; j < 3; j++) {
stl_rotate(&stl->facet_start[i].vertex[j].x,
&stl->facet_start[i].vertex[j].y, c, s);
stl_rotate(&stl->facet_start[i].vertex[j](0),
&stl->facet_start[i].vertex[j](1), c, s);
}
}
stl_get_size(stl);
@ -272,142 +225,98 @@ stl_rotate(float *x, float *y, const double c, const double s) {
*y = float(s * xold + c * yold);
}
extern void
stl_get_size(stl_file *stl) {
int i;
int j;
if (stl->error) return;
if (stl->stats.number_of_facets == 0) return;
stl->stats.min.x = stl->facet_start[0].vertex[0].x;
stl->stats.min.y = stl->facet_start[0].vertex[0].y;
stl->stats.min.z = stl->facet_start[0].vertex[0].z;
stl->stats.max.x = stl->facet_start[0].vertex[0].x;
stl->stats.max.y = stl->facet_start[0].vertex[0].y;
stl->stats.max.z = stl->facet_start[0].vertex[0].z;
for(i = 0; i < stl->stats.number_of_facets; i++) {
for(j = 0; j < 3; j++) {
stl->stats.min.x = STL_MIN(stl->stats.min.x,
stl->facet_start[i].vertex[j].x);
stl->stats.min.y = STL_MIN(stl->stats.min.y,
stl->facet_start[i].vertex[j].y);
stl->stats.min.z = STL_MIN(stl->stats.min.z,
stl->facet_start[i].vertex[j].z);
stl->stats.max.x = STL_MAX(stl->stats.max.x,
stl->facet_start[i].vertex[j].x);
stl->stats.max.y = STL_MAX(stl->stats.max.y,
stl->facet_start[i].vertex[j].y);
stl->stats.max.z = STL_MAX(stl->stats.max.z,
stl->facet_start[i].vertex[j].z);
void stl_get_size(stl_file *stl)
{
if (stl->error || stl->stats.number_of_facets == 0)
return;
stl->stats.min = stl->facet_start[0].vertex[0];
stl->stats.max = stl->stats.min;
for (int i = 0; i < stl->stats.number_of_facets; ++ i) {
const stl_facet &face = stl->facet_start[i];
for (int j = 0; j < 3; ++ j) {
stl->stats.min = stl->stats.min.cwiseMin(face.vertex[j]);
stl->stats.max = stl->stats.max.cwiseMax(face.vertex[j]);
}
}
stl->stats.size.x = stl->stats.max.x - stl->stats.min.x;
stl->stats.size.y = stl->stats.max.y - stl->stats.min.y;
stl->stats.size.z = stl->stats.max.z - stl->stats.min.z;
stl->stats.bounding_diameter = sqrt(
stl->stats.size.x * stl->stats.size.x +
stl->stats.size.y * stl->stats.size.y +
stl->stats.size.z * stl->stats.size.z
);
stl->stats.size = stl->stats.max - stl->stats.min;
stl->stats.bounding_diameter = stl->stats.size.norm();
}
void
stl_mirror_xy(stl_file *stl) {
int i;
int j;
float temp_size;
void stl_mirror_xy(stl_file *stl)
{
if (stl->error)
return;
if (stl->error) return;
for(i = 0; i < stl->stats.number_of_facets; i++) {
for(j = 0; j < 3; j++) {
stl->facet_start[i].vertex[j].z *= -1.0;
for(int i = 0; i < stl->stats.number_of_facets; i++) {
for(int j = 0; j < 3; j++) {
stl->facet_start[i].vertex[j](2) *= -1.0;
}
}
temp_size = stl->stats.min.z;
stl->stats.min.z = stl->stats.max.z;
stl->stats.max.z = temp_size;
stl->stats.min.z *= -1.0;
stl->stats.max.z *= -1.0;
float temp_size = stl->stats.min(2);
stl->stats.min(2) = stl->stats.max(2);
stl->stats.max(2) = temp_size;
stl->stats.min(2) *= -1.0;
stl->stats.max(2) *= -1.0;
stl_reverse_all_facets(stl);
stl->stats.facets_reversed -= stl->stats.number_of_facets; /* for not altering stats */
}
void
stl_mirror_yz(stl_file *stl) {
int i;
int j;
float temp_size;
void stl_mirror_yz(stl_file *stl)
{
if (stl->error) return;
for(i = 0; i < stl->stats.number_of_facets; i++) {
for(j = 0; j < 3; j++) {
stl->facet_start[i].vertex[j].x *= -1.0;
for (int i = 0; i < stl->stats.number_of_facets; i++) {
for (int j = 0; j < 3; j++) {
stl->facet_start[i].vertex[j](0) *= -1.0;
}
}
temp_size = stl->stats.min.x;
stl->stats.min.x = stl->stats.max.x;
stl->stats.max.x = temp_size;
stl->stats.min.x *= -1.0;
stl->stats.max.x *= -1.0;
float temp_size = stl->stats.min(0);
stl->stats.min(0) = stl->stats.max(0);
stl->stats.max(0) = temp_size;
stl->stats.min(0) *= -1.0;
stl->stats.max(0) *= -1.0;
stl_reverse_all_facets(stl);
stl->stats.facets_reversed -= stl->stats.number_of_facets; /* for not altering stats */
}
void
stl_mirror_xz(stl_file *stl) {
int i;
int j;
float temp_size;
void stl_mirror_xz(stl_file *stl)
{
if (stl->error)
return;
if (stl->error) return;
for(i = 0; i < stl->stats.number_of_facets; i++) {
for(j = 0; j < 3; j++) {
stl->facet_start[i].vertex[j].y *= -1.0;
for (int i = 0; i < stl->stats.number_of_facets; i++) {
for (int j = 0; j < 3; j++) {
stl->facet_start[i].vertex[j](1) *= -1.0;
}
}
temp_size = stl->stats.min.y;
stl->stats.min.y = stl->stats.max.y;
stl->stats.max.y = temp_size;
stl->stats.min.y *= -1.0;
stl->stats.max.y *= -1.0;
float temp_size = stl->stats.min(1);
stl->stats.min(1) = stl->stats.max(1);
stl->stats.max(1) = temp_size;
stl->stats.min(1) *= -1.0;
stl->stats.max(1) *= -1.0;
stl_reverse_all_facets(stl);
stl->stats.facets_reversed -= stl->stats.number_of_facets; /* for not altering stats */
}
static float get_volume(stl_file *stl) {
stl_vertex p0;
stl_vertex p;
stl_normal n;
float height;
float area;
float volume = 0.0;
static float get_volume(stl_file *stl)
{
if (stl->error)
return 0;
if (stl->error) return 0;
/* Choose a point, any point as the reference */
p0.x = stl->facet_start[0].vertex[0].x;
p0.y = stl->facet_start[0].vertex[0].y;
p0.z = stl->facet_start[0].vertex[0].z;
for(uint32_t i = 0; i < stl->stats.number_of_facets; i++) {
p.x = stl->facet_start[i].vertex[0].x - p0.x;
p.y = stl->facet_start[i].vertex[0].y - p0.y;
p.z = stl->facet_start[i].vertex[0].z - p0.z;
/* Do dot product to get distance from point to plane */
n = stl->facet_start[i].normal;
height = (n.x * p.x) + (n.y * p.y) + (n.z * p.z);
area = get_area(&stl->facet_start[i]);
// Choose a point, any point as the reference.
stl_vertex p0 = stl->facet_start[0].vertex[0];
float volume = 0.f;
for(uint32_t i = 0; i < stl->stats.number_of_facets; ++ i) {
// Do dot product to get distance from point to plane.
float height = stl->facet_start[i].normal.dot(stl->facet_start[i].vertex[0] - p0);
float area = get_area(&stl->facet_start[i]);
volume += (area * height) / 3.0f;
}
return volume;
}
void stl_calculate_volume(stl_file *stl) {
void stl_calculate_volume(stl_file *stl)
{
if (stl->error) return;
stl->stats.volume = get_volume(stl);
if(stl->stats.volume < 0.0) {
@ -416,35 +325,32 @@ void stl_calculate_volume(stl_file *stl) {
}
}
static float get_area(stl_facet *facet) {
double cross[3][3];
float sum[3];
float n[3];
float area;
int i;
static float get_area(stl_facet *facet)
{
/* cast to double before calculating cross product because large coordinates
can result in overflowing product
(bad area is responsible for bad volume and bad facets reversal) */
for(i = 0; i < 3; i++) {
cross[i][0]=(((double)facet->vertex[i].y * (double)facet->vertex[(i + 1) % 3].z) -
((double)facet->vertex[i].z * (double)facet->vertex[(i + 1) % 3].y));
cross[i][1]=(((double)facet->vertex[i].z * (double)facet->vertex[(i + 1) % 3].x) -
((double)facet->vertex[i].x * (double)facet->vertex[(i + 1) % 3].z));
cross[i][2]=(((double)facet->vertex[i].x * (double)facet->vertex[(i + 1) % 3].y) -
((double)facet->vertex[i].y * (double)facet->vertex[(i + 1) % 3].x));
double cross[3][3];
for (int i = 0; i < 3; i++) {
cross[i][0]=(((double)facet->vertex[i](1) * (double)facet->vertex[(i + 1) % 3](2)) -
((double)facet->vertex[i](2) * (double)facet->vertex[(i + 1) % 3](1)));
cross[i][1]=(((double)facet->vertex[i](2) * (double)facet->vertex[(i + 1) % 3](0)) -
((double)facet->vertex[i](0) * (double)facet->vertex[(i + 1) % 3](2)));
cross[i][2]=(((double)facet->vertex[i](0) * (double)facet->vertex[(i + 1) % 3](1)) -
((double)facet->vertex[i](1) * (double)facet->vertex[(i + 1) % 3](0)));
}
sum[0] = cross[0][0] + cross[1][0] + cross[2][0];
sum[1] = cross[0][1] + cross[1][1] + cross[2][1];
sum[2] = cross[0][2] + cross[1][2] + cross[2][2];
stl_normal sum;
sum(0) = cross[0][0] + cross[1][0] + cross[2][0];
sum(1) = cross[0][1] + cross[1][1] + cross[2][1];
sum(2) = cross[0][2] + cross[1][2] + cross[2][2];
/* This should already be done. But just in case, let's do it again */
// This should already be done. But just in case, let's do it again.
//FIXME this is questionable. the "sum" normal should be accurate, while the normal "n" may be calculated with a low accuracy.
stl_normal n;
stl_calculate_normal(n, facet);
stl_normalize_vector(n);
area = 0.5 * (n[0] * sum[0] + n[1] * sum[1] + n[2] * sum[2]);
return area;
return 0.5f * n.dot(sum);
}
void stl_repair(stl_file *stl,

5
xs/src/eigen/Eigen/Cholesky
View File

@ -9,6 +9,7 @@
#define EIGEN_CHOLESKY_MODULE_H
#include "Core"
#include "Jacobi"
#include "src/Core/util/DisableStupidWarnings.h"
@ -31,7 +32,11 @@
#include "src/Cholesky/LLT.h"
#include "src/Cholesky/LDLT.h"
#ifdef EIGEN_USE_LAPACKE
#ifdef EIGEN_USE_MKL
#include "mkl_lapacke.h"
#else
#include "src/misc/lapacke.h"
#endif
#include "src/Cholesky/LLT_LAPACKE.h"
#endif

23
xs/src/eigen/Eigen/Core
View File

@ -14,6 +14,22 @@
// first thing Eigen does: stop the compiler from committing suicide
#include "src/Core/util/DisableStupidWarnings.h"
#if defined(__CUDACC__) && !defined(EIGEN_NO_CUDA)
#define EIGEN_CUDACC __CUDACC__
#endif
#if defined(__CUDA_ARCH__) && !defined(EIGEN_NO_CUDA)
#define EIGEN_CUDA_ARCH __CUDA_ARCH__
#endif
#if defined(__CUDACC_VER_MAJOR__) && (__CUDACC_VER_MAJOR__ >= 9)
#define EIGEN_CUDACC_VER ((__CUDACC_VER_MAJOR__ * 10000) + (__CUDACC_VER_MINOR__ * 100))
#elif defined(__CUDACC_VER__)
#define EIGEN_CUDACC_VER __CUDACC_VER__
#else
#define EIGEN_CUDACC_VER 0
#endif
// Handle NVCC/CUDA/SYCL
#if defined(__CUDACC__) || defined(__SYCL_DEVICE_ONLY__)
// Do not try asserts on CUDA and SYCL!
@ -155,6 +171,9 @@
#ifdef __AVX512DQ__
#define EIGEN_VECTORIZE_AVX512DQ
#endif
#ifdef __AVX512ER__
#define EIGEN_VECTORIZE_AVX512ER
#endif
#endif
// include files
@ -229,7 +248,7 @@
#if defined __CUDACC__
#define EIGEN_VECTORIZE_CUDA
#include <vector_types.h>
#if defined __CUDACC_VER__ && __CUDACC_VER__ >= 70500
#if EIGEN_CUDACC_VER >= 70500
#define EIGEN_HAS_CUDA_FP16
#endif
#endif
@ -352,6 +371,7 @@ using std::ptrdiff_t;
#include "src/Core/MathFunctions.h"
#include "src/Core/GenericPacketMath.h"
#include "src/Core/MathFunctionsImpl.h"
#include "src/Core/arch/Default/ConjHelper.h"
#if defined EIGEN_VECTORIZE_AVX512
#include "src/Core/arch/SSE/PacketMath.h"
@ -367,6 +387,7 @@ using std::ptrdiff_t;
#include "src/Core/arch/AVX/MathFunctions.h"
#include "src/Core/arch/AVX/Complex.h"
#include "src/Core/arch/AVX/TypeCasting.h"
#include "src/Core/arch/SSE/TypeCasting.h"
#elif defined EIGEN_VECTORIZE_SSE
#include "src/Core/arch/SSE/PacketMath.h"
#include "src/Core/arch/SSE/MathFunctions.h"

4
xs/src/eigen/Eigen/Eigenvalues
View File

@ -45,7 +45,11 @@
#include "src/Eigenvalues/GeneralizedEigenSolver.h"
#include "src/Eigenvalues/MatrixBaseEigenvalues.h"
#ifdef EIGEN_USE_LAPACKE
#ifdef EIGEN_USE_MKL
#include "mkl_lapacke.h"
#else
#include "src/misc/lapacke.h"
#endif
#include "src/Eigenvalues/RealSchur_LAPACKE.h"
#include "src/Eigenvalues/ComplexSchur_LAPACKE.h"
#include "src/Eigenvalues/SelfAdjointEigenSolver_LAPACKE.h"

4
xs/src/eigen/Eigen/LU
View File

@ -28,7 +28,11 @@
#include "src/LU/FullPivLU.h"
#include "src/LU/PartialPivLU.h"
#ifdef EIGEN_USE_LAPACKE
#ifdef EIGEN_USE_MKL
#include "mkl_lapacke.h"
#else
#include "src/misc/lapacke.h"
#endif
#include "src/LU/PartialPivLU_LAPACKE.h"
#endif
#include "src/LU/Determinant.h"

4
xs/src/eigen/Eigen/QR
View File

@ -36,7 +36,11 @@
#include "src/QR/ColPivHouseholderQR.h"
#include "src/QR/CompleteOrthogonalDecomposition.h"
#ifdef EIGEN_USE_LAPACKE
#ifdef EIGEN_USE_MKL
#include "mkl_lapacke.h"
#else
#include "src/misc/lapacke.h"
#endif
#include "src/QR/HouseholderQR_LAPACKE.h"
#include "src/QR/ColPivHouseholderQR_LAPACKE.h"
#endif

2
xs/src/eigen/Eigen/QtAlignedMalloc
View File

@ -27,7 +27,7 @@ void qFree(void *ptr)
void *qRealloc(void *ptr, std::size_t size)
{
void* newPtr = Eigen::internal::aligned_malloc(size);
memcpy(newPtr, ptr, size);
std::memcpy(newPtr, ptr, size);
Eigen::internal::aligned_free(ptr);
return newPtr;
}

4
xs/src/eigen/Eigen/SVD
View File

@ -37,7 +37,11 @@
#include "src/SVD/JacobiSVD.h"
#include "src/SVD/BDCSVD.h"
#if defined(EIGEN_USE_LAPACKE) && !defined(EIGEN_USE_LAPACKE_STRICT)
#ifdef EIGEN_USE_MKL
#include "mkl_lapacke.h"
#else
#include "src/misc/lapacke.h"
#endif
#include "src/SVD/JacobiSVD_LAPACKE.h"
#endif

11
xs/src/eigen/Eigen/src/Cholesky/LDLT.h
View File

@ -248,7 +248,7 @@ template<typename _MatrixType, int _UpLo> class LDLT
/** \brief Reports whether previous computation was successful.
*
* \returns \c Success if computation was succesful,
* \c NumericalIssue if the matrix.appears to be negative.
* \c NumericalIssue if the factorization failed because of a zero pivot.
*/
ComputationInfo info() const
{
@ -376,6 +376,8 @@ template<> struct ldlt_inplace<Lower>
if((rs>0) && pivot_is_valid)
A21 /= realAkk;
else if(rs>0)
ret = ret && (A21.array()==Scalar(0)).all();
if(found_zero_pivot && pivot_is_valid) ret = false; // factorization failed
else if(!pivot_is_valid) found_zero_pivot = true;
@ -568,13 +570,14 @@ void LDLT<_MatrixType,_UpLo>::_solve_impl(const RhsType &rhs, DstType &dst) cons
// more precisely, use pseudo-inverse of D (see bug 241)
using std::abs;
const typename Diagonal<const MatrixType>::RealReturnType vecD(vectorD());
// In some previous versions, tolerance was set to the max of 1/highest and the maximal diagonal entry * epsilon
// as motivated by LAPACK's xGELSS:
// In some previous versions, tolerance was set to the max of 1/highest (or rather numeric_limits::min())
// and the maximal diagonal entry * epsilon as motivated by LAPACK's xGELSS:
// RealScalar tolerance = numext::maxi(vecD.array().abs().maxCoeff() * NumTraits<RealScalar>::epsilon(),RealScalar(1) / NumTraits<RealScalar>::highest());
// However, LDLT is not rank revealing, and so adjusting the tolerance wrt to the highest
// diagonal element is not well justified and leads to numerical issues in some cases.
// Moreover, Lapack's xSYTRS routines use 0 for the tolerance.
RealScalar tolerance = RealScalar(1) / NumTraits<RealScalar>::highest();
// Using numeric_limits::min() gives us more robustness to denormals.
RealScalar tolerance = (std::numeric_limits<RealScalar>::min)();
for (Index i = 0; i < vecD.size(); ++i)
{

22
xs/src/eigen/Eigen/src/Cholesky/LLT.h
View File

@ -41,14 +41,18 @@ template<typename MatrixType, int UpLo> struct LLT_Traits;
* Example: \include LLT_example.cpp
* Output: \verbinclude LLT_example.out
*
* \b Performance: for best performance, it is recommended to use a column-major storage format
* with the Lower triangular part (the default), or, equivalently, a row-major storage format
* with the Upper triangular part. Otherwise, you might get a 20% slowdown for the full factorization
* step, and rank-updates can be up to 3 times slower.
*
* This class supports the \link InplaceDecomposition inplace decomposition \endlink mechanism.
*
* Note that during the decomposition, only the lower (or upper, as defined by _UpLo) triangular part of A is considered.
* Therefore, the strict lower part does not have to store correct values.
*
* \sa MatrixBase::llt(), SelfAdjointView::llt(), class LDLT
*/
/* HEY THIS DOX IS DISABLED BECAUSE THERE's A BUG EITHER HERE OR IN LDLT ABOUT THAT (OR BOTH)
* Note that during the decomposition, only the upper triangular part of A is considered. Therefore,
* the strict lower part does not have to store correct values.
*/
template<typename _MatrixType, int _UpLo> class LLT
{
public:
@ -146,7 +150,7 @@ template<typename _MatrixType, int _UpLo> class LLT
}
template<typename Derived>
void solveInPlace(MatrixBase<Derived> &bAndX) const;
void solveInPlace(const MatrixBase<Derived> &bAndX) const;
template<typename InputType>
LLT& compute(const EigenBase<InputType>& matrix);
@ -177,7 +181,7 @@ template<typename _MatrixType, int _UpLo> class LLT
/** \brief Reports whether previous computation was successful.
*
* \returns \c Success if computation was succesful,
* \c NumericalIssue if the matrix.appears to be negative.
* \c NumericalIssue if the matrix.appears not to be positive definite.
*/
ComputationInfo info() const
{
@ -425,6 +429,7 @@ LLT<MatrixType,_UpLo>& LLT<MatrixType,_UpLo>::compute(const EigenBase<InputType>
eigen_assert(a.rows()==a.cols());
const Index size = a.rows();
m_matrix.resize(size, size);
if (!internal::is_same_dense(m_matrix, a.derived()))
m_matrix = a.derived();
// Compute matrix L1 norm = max abs column sum.
@ -485,11 +490,14 @@ void LLT<_MatrixType,_UpLo>::_solve_impl(const RhsType &rhs, DstType &dst) const
*
* This version avoids a copy when the right hand side matrix b is not needed anymore.
*
* \warning The parameter is only marked 'const' to make the C++ compiler accept a temporary expression here.
* This function will const_cast it, so constness isn't honored here.
*
* \sa LLT::solve(), MatrixBase::llt()
*/
template<typename MatrixType, int _UpLo>
template<typename Derived>
void LLT<MatrixType,_UpLo>::solveInPlace(MatrixBase<Derived> &bAndX) const
void LLT<MatrixType,_UpLo>::solveInPlace(const MatrixBase<Derived> &bAndX) const
{
eigen_assert(m_isInitialized && "LLT is not initialized.");
eigen_assert(m_matrix.rows()==bAndX.rows());

8
xs/src/eigen/Eigen/src/Core/Array.h
View File

@ -231,10 +231,16 @@ class Array
: Base(other)
{ }
private:
struct PrivateType {};
public:
/** \sa MatrixBase::operator=(const EigenBase<OtherDerived>&) */
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Array(const EigenBase<OtherDerived> &other)
EIGEN_STRONG_INLINE Array(const EigenBase<OtherDerived> &other,
typename internal::enable_if<internal::is_convertible<typename OtherDerived::Scalar,Scalar>::value,
PrivateType>::type = PrivateType())
: Base(other.derived())
{ }

8
xs/src/eigen/Eigen/src/Core/ArrayBase.h
View File

@ -175,7 +175,7 @@ template<typename Derived> class ArrayBase
*/
template<typename Derived>
template<typename OtherDerived>
EIGEN_STRONG_INLINE Derived &
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived &
ArrayBase<Derived>::operator-=(const ArrayBase<OtherDerived> &other)
{
call_assignment(derived(), other.derived(), internal::sub_assign_op<Scalar,typename OtherDerived::Scalar>());
@ -188,7 +188,7 @@ ArrayBase<Derived>::operator-=(const ArrayBase<OtherDerived> &other)
*/
template<typename Derived>
template<typename OtherDerived>
EIGEN_STRONG_INLINE Derived &
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived &
ArrayBase<Derived>::operator+=(const ArrayBase<OtherDerived>& other)
{
call_assignment(derived(), other.derived(), internal::add_assign_op<Scalar,typename OtherDerived::Scalar>());
@ -201,7 +201,7 @@ ArrayBase<Derived>::operator+=(const ArrayBase<OtherDerived>& other)
*/
template<typename Derived>
template<typename OtherDerived>
EIGEN_STRONG_INLINE Derived &
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived &
ArrayBase<Derived>::operator*=(const ArrayBase<OtherDerived>& other)
{
call_assignment(derived(), other.derived(), internal::mul_assign_op<Scalar,typename OtherDerived::Scalar>());
@ -214,7 +214,7 @@ ArrayBase<Derived>::operator*=(const ArrayBase<OtherDerived>& other)
*/
template<typename Derived>
template<typename OtherDerived>
EIGEN_STRONG_INLINE Derived &
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived &
ArrayBase<Derived>::operator/=(const ArrayBase<OtherDerived>& other)
{
call_assignment(derived(), other.derived(), internal::div_assign_op<Scalar,typename OtherDerived::Scalar>());

6
xs/src/eigen/Eigen/src/Core/ArrayWrapper.h
View File

@ -32,7 +32,8 @@ struct traits<ArrayWrapper<ExpressionType> >
// Let's remove NestByRefBit
enum {
Flags0 = traits<typename remove_all<typename ExpressionType::Nested>::type >::Flags,
Flags = Flags0 & ~NestByRefBit
LvalueBitFlag = is_lvalue<ExpressionType>::value ? LvalueBit : 0,
Flags = (Flags0 & ~(NestByRefBit | LvalueBit)) | LvalueBitFlag
};
};
}
@ -129,7 +130,8 @@ struct traits<MatrixWrapper<ExpressionType> >
// Let's remove NestByRefBit
enum {
Flags0 = traits<typename remove_all<typename ExpressionType::Nested>::type >::Flags,
Flags = Flags0 & ~NestByRefBit
LvalueBitFlag = is_lvalue<ExpressionType>::value ? LvalueBit : 0,
Flags = (Flags0 & ~(NestByRefBit | LvalueBit)) | LvalueBitFlag
};
};
}

4
xs/src/eigen/Eigen/src/Core/AssignEvaluator.h
View File

@ -39,7 +39,7 @@ public:
enum {
DstAlignment = DstEvaluator::Alignment,
SrcAlignment = SrcEvaluator::Alignment,
DstHasDirectAccess = DstFlags & DirectAccessBit,
DstHasDirectAccess = (DstFlags & DirectAccessBit) == DirectAccessBit,
JointAlignment = EIGEN_PLAIN_ENUM_MIN(DstAlignment,SrcAlignment)
};
@ -83,7 +83,7 @@ private:
&& int(OuterStride)!=Dynamic && int(OuterStride)%int(InnerPacketSize)==0
&& (EIGEN_UNALIGNED_VECTORIZE || int(JointAlignment)>=int(InnerRequiredAlignment)),
MayLinearize = bool(StorageOrdersAgree) && (int(DstFlags) & int(SrcFlags) & LinearAccessBit),
MayLinearVectorize = bool(MightVectorize) && MayLinearize && DstHasDirectAccess
MayLinearVectorize = bool(MightVectorize) && bool(MayLinearize) && bool(DstHasDirectAccess)
&& (EIGEN_UNALIGNED_VECTORIZE || (int(DstAlignment)>=int(LinearRequiredAlignment)) || MaxSizeAtCompileTime == Dynamic),
/* If the destination isn't aligned, we have to do runtime checks and we don't unroll,
so it's only good for large enough sizes. */

6
xs/src/eigen/Eigen/src/Core/Assign_MKL.h
View File

@ -84,7 +84,8 @@ class vml_assign_traits
struct Assignment<DstXprType, CwiseUnaryOp<scalar_##EIGENOP##_op<EIGENTYPE>, SrcXprNested>, assign_op<EIGENTYPE,EIGENTYPE>, \
Dense2Dense, typename enable_if<vml_assign_traits<DstXprType,SrcXprNested>::EnableVml>::type> { \
typedef CwiseUnaryOp<scalar_##EIGENOP##_op<EIGENTYPE>, SrcXprNested> SrcXprType; \
static void run(DstXprType &dst, const SrcXprType &src, const assign_op<EIGENTYPE,EIGENTYPE> &/*func*/) { \
static void run(DstXprType &dst, const SrcXprType &src, const assign_op<EIGENTYPE,EIGENTYPE> &func) { \
resize_if_allowed(dst, src, func); \
eigen_assert(dst.rows() == src.rows() && dst.cols() == src.cols()); \
if(vml_assign_traits<DstXprType,SrcXprNested>::Traversal==LinearTraversal) { \
VMLOP(dst.size(), (const VMLTYPE*)src.nestedExpression().data(), \
@ -144,7 +145,8 @@ EIGEN_MKL_VML_DECLARE_UNARY_CALLS_REAL(ceil, Ceil, _)
Dense2Dense, typename enable_if<vml_assign_traits<DstXprType,SrcXprNested>::EnableVml>::type> { \
typedef CwiseBinaryOp<scalar_##EIGENOP##_op<EIGENTYPE,EIGENTYPE>, SrcXprNested, \
const CwiseNullaryOp<internal::scalar_constant_op<EIGENTYPE>,Plain> > SrcXprType; \
static void run(DstXprType &dst, const SrcXprType &src, const assign_op<EIGENTYPE,EIGENTYPE> &/*func*/) { \
static void run(DstXprType &dst, const SrcXprType &src, const assign_op<EIGENTYPE,EIGENTYPE> &func) { \
resize_if_allowed(dst, src, func); \
eigen_assert(dst.rows() == src.rows() && dst.cols() == src.cols()); \
VMLTYPE exponent = reinterpret_cast<const VMLTYPE&>(src.rhs().functor().m_other); \
if(vml_assign_traits<DstXprType,SrcXprNested>::Traversal==LinearTraversal) \

25
xs/src/eigen/Eigen/src/Core/CoreEvaluators.h
View File

@ -977,7 +977,7 @@ struct evaluator<Block<ArgType, BlockRows, BlockCols, InnerPanel> >
OuterStrideAtCompileTime = HasSameStorageOrderAsArgType
? int(outer_stride_at_compile_time<ArgType>::ret)
: int(inner_stride_at_compile_time<ArgType>::ret),
MaskPacketAccessBit = (InnerStrideAtCompileTime == 1) ? PacketAccessBit : 0,
MaskPacketAccessBit = (InnerStrideAtCompileTime == 1 || HasSameStorageOrderAsArgType) ? PacketAccessBit : 0,
FlagsLinearAccessBit = (RowsAtCompileTime == 1 || ColsAtCompileTime == 1 || (InnerPanel && (evaluator<ArgType>::Flags&LinearAccessBit))) ? LinearAccessBit : 0,
FlagsRowMajorBit = XprType::Flags&RowMajorBit,
@ -987,7 +987,9 @@ struct evaluator<Block<ArgType, BlockRows, BlockCols, InnerPanel> >
Flags = Flags0 | FlagsLinearAccessBit | FlagsRowMajorBit,
PacketAlignment = unpacket_traits<PacketScalar>::alignment,
Alignment0 = (InnerPanel && (OuterStrideAtCompileTime!=Dynamic) && (((OuterStrideAtCompileTime * int(sizeof(Scalar))) % int(PacketAlignment)) == 0)) ? int(PacketAlignment) : 0,
Alignment0 = (InnerPanel && (OuterStrideAtCompileTime!=Dynamic)
&& (OuterStrideAtCompileTime!=0)
&& (((OuterStrideAtCompileTime * int(sizeof(Scalar))) % int(PacketAlignment)) == 0)) ? int(PacketAlignment) : 0,
Alignment = EIGEN_PLAIN_ENUM_MIN(evaluator<ArgType>::Alignment, Alignment0)
};
typedef block_evaluator<ArgType, BlockRows, BlockCols, InnerPanel> block_evaluator_type;
@ -1018,14 +1020,16 @@ struct unary_evaluator<Block<ArgType, BlockRows, BlockCols, InnerPanel>, IndexBa
EIGEN_DEVICE_FUNC explicit unary_evaluator(const XprType& block)
: m_argImpl(block.nestedExpression()),
m_startRow(block.startRow()),
m_startCol(block.startCol())
m_startCol(block.startCol()),
m_linear_offset(InnerPanel?(XprType::IsRowMajor ? block.startRow()*block.cols() : block.startCol()*block.rows()):0)
{ }
typedef typename XprType::Scalar Scalar;
typedef typename XprType::CoeffReturnType CoeffReturnType;
enum {
RowsAtCompileTime = XprType::RowsAtCompileTime
RowsAtCompileTime = XprType::RowsAtCompileTime,
ForwardLinearAccess = InnerPanel && bool(evaluator<ArgType>::Flags&LinearAccessBit)
};
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
@ -1037,6 +1041,9 @@ struct unary_evaluator<Block<ArgType, BlockRows, BlockCols, InnerPanel>, IndexBa
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
CoeffReturnType coeff(Index index) const
{
if (ForwardLinearAccess)
return m_argImpl.coeff(m_linear_offset.value() + index);
else
return coeff(RowsAtCompileTime == 1 ? 0 : index, RowsAtCompileTime == 1 ? index : 0);
}
@ -1049,6 +1056,9 @@ struct unary_evaluator<Block<ArgType, BlockRows, BlockCols, InnerPanel>, IndexBa
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
Scalar& coeffRef(Index index)
{
if (ForwardLinearAccess)
return m_argImpl.coeffRef(m_linear_offset.value() + index);
else
return coeffRef(RowsAtCompileTime == 1 ? 0 : index, RowsAtCompileTime == 1 ? index : 0);
}
@ -1063,6 +1073,9 @@ struct unary_evaluator<Block<ArgType, BlockRows, BlockCols, InnerPanel>, IndexBa
EIGEN_STRONG_INLINE
PacketType packet(Index index) const
{
if (ForwardLinearAccess)
return m_argImpl.template packet<LoadMode,PacketType>(m_linear_offset.value() + index);
else
return packet<LoadMode,PacketType>(RowsAtCompileTime == 1 ? 0 : index,
RowsAtCompileTime == 1 ? index : 0);
}
@ -1078,6 +1091,9 @@ struct unary_evaluator<Block<ArgType, BlockRows, BlockCols, InnerPanel>, IndexBa
EIGEN_STRONG_INLINE
void writePacket(Index index, const PacketType& x)
{
if (ForwardLinearAccess)
return m_argImpl.template writePacket<StoreMode,PacketType>(m_linear_offset.value() + index, x);
else
return writePacket<StoreMode,PacketType>(RowsAtCompileTime == 1 ? 0 : index,
RowsAtCompileTime == 1 ? index : 0,
x);
@ -1087,6 +1103,7 @@ protected:
evaluator<ArgType> m_argImpl;
const variable_if_dynamic<Index, (ArgType::RowsAtCompileTime == 1 && BlockRows==1) ? 0 : Dynamic> m_startRow;
const variable_if_dynamic<Index, (ArgType::ColsAtCompileTime == 1 && BlockCols==1) ? 0 : Dynamic> m_startCol;
const variable_if_dynamic<Index, InnerPanel ? Dynamic : 0> m_linear_offset;
};
// TODO: This evaluator does not actually use the child evaluator;

80
xs/src/eigen/Eigen/src/Core/CwiseNullaryOp.h
View File

@ -105,7 +105,7 @@ class CwiseNullaryOp : public internal::dense_xpr_base< CwiseNullaryOp<NullaryOp
*/
template<typename Derived>
template<typename CustomNullaryOp>
EIGEN_STRONG_INLINE const CwiseNullaryOp<CustomNullaryOp, typename DenseBase<Derived>::PlainObject>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const CwiseNullaryOp<CustomNullaryOp, typename DenseBase<Derived>::PlainObject>
DenseBase<Derived>::NullaryExpr(Index rows, Index cols, const CustomNullaryOp& func)
{
return CwiseNullaryOp<CustomNullaryOp, PlainObject>(rows, cols, func);
@ -150,7 +150,7 @@ DenseBase<Derived>::NullaryExpr(Index size, const CustomNullaryOp& func)
*/
template<typename Derived>
template<typename CustomNullaryOp>
EIGEN_STRONG_INLINE const CwiseNullaryOp<CustomNullaryOp, typename DenseBase<Derived>::PlainObject>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const CwiseNullaryOp<CustomNullaryOp, typename DenseBase<Derived>::PlainObject>
DenseBase<Derived>::NullaryExpr(const CustomNullaryOp& func)
{
return CwiseNullaryOp<CustomNullaryOp, PlainObject>(RowsAtCompileTime, ColsAtCompileTime, func);
@ -192,7 +192,7 @@ DenseBase<Derived>::Constant(Index rows, Index cols, const Scalar& value)
* \sa class CwiseNullaryOp
*/
template<typename Derived>
EIGEN_STRONG_INLINE const typename DenseBase<Derived>::ConstantReturnType
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename DenseBase<Derived>::ConstantReturnType
DenseBase<Derived>::Constant(Index size, const Scalar& value)
{
return DenseBase<Derived>::NullaryExpr(size, internal::scalar_constant_op<Scalar>(value));
@ -208,7 +208,7 @@ DenseBase<Derived>::Constant(Index size, const Scalar& value)
* \sa class CwiseNullaryOp
*/
template<typename Derived>
EIGEN_STRONG_INLINE const typename DenseBase<Derived>::ConstantReturnType
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename DenseBase<Derived>::ConstantReturnType
DenseBase<Derived>::Constant(const Scalar& value)
{
EIGEN_STATIC_ASSERT_FIXED_SIZE(Derived)
@ -220,7 +220,7 @@ DenseBase<Derived>::Constant(const Scalar& value)
* \sa LinSpaced(Index,Scalar,Scalar), setLinSpaced(Index,const Scalar&,const Scalar&)
*/
template<typename Derived>
EIGEN_STRONG_INLINE const typename DenseBase<Derived>::RandomAccessLinSpacedReturnType
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename DenseBase<Derived>::RandomAccessLinSpacedReturnType
DenseBase<Derived>::LinSpaced(Sequential_t, Index size, const Scalar& low, const Scalar& high)
{
EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
@ -232,7 +232,7 @@ DenseBase<Derived>::LinSpaced(Sequential_t, Index size, const Scalar& low, const
* \sa LinSpaced(Scalar,Scalar)
*/
template<typename Derived>
EIGEN_STRONG_INLINE const typename DenseBase<Derived>::RandomAccessLinSpacedReturnType
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename DenseBase<Derived>::RandomAccessLinSpacedReturnType
DenseBase<Derived>::LinSpaced(Sequential_t, const Scalar& low, const Scalar& high)
{
EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
@ -264,7 +264,7 @@ DenseBase<Derived>::LinSpaced(Sequential_t, const Scalar& low, const Scalar& hig
* \sa setLinSpaced(Index,const Scalar&,const Scalar&), CwiseNullaryOp
*/
template<typename Derived>
EIGEN_STRONG_INLINE const typename DenseBase<Derived>::RandomAccessLinSpacedReturnType
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename DenseBase<Derived>::RandomAccessLinSpacedReturnType
DenseBase<Derived>::LinSpaced(Index size, const Scalar& low, const Scalar& high)
{
EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
@ -276,7 +276,7 @@ DenseBase<Derived>::LinSpaced(Index size, const Scalar& low, const Scalar& high)
* Special version for fixed size types which does not require the size parameter.
*/
template<typename Derived>
EIGEN_STRONG_INLINE const typename DenseBase<Derived>::RandomAccessLinSpacedReturnType
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename DenseBase<Derived>::RandomAccessLinSpacedReturnType
DenseBase<Derived>::LinSpaced(const Scalar& low, const Scalar& high)
{
EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
@ -286,7 +286,7 @@ DenseBase<Derived>::LinSpaced(const Scalar& low, const Scalar& high)
/** \returns true if all coefficients in this matrix are approximately equal to \a val, to within precision \a prec */
template<typename Derived>
bool DenseBase<Derived>::isApproxToConstant
EIGEN_DEVICE_FUNC bool DenseBase<Derived>::isApproxToConstant
(const Scalar& val, const RealScalar& prec) const
{
typename internal::nested_eval<Derived,1>::type self(derived());
@ -301,7 +301,7 @@ bool DenseBase<Derived>::isApproxToConstant
*
* \returns true if all coefficients in this matrix are approximately equal to \a value, to within precision \a prec */
template<typename Derived>
bool DenseBase<Derived>::isConstant
EIGEN_DEVICE_FUNC bool DenseBase<Derived>::isConstant
(const Scalar& val, const RealScalar& prec) const
{
return isApproxToConstant(val, prec);
@ -312,7 +312,7 @@ bool DenseBase<Derived>::isConstant
* \sa setConstant(), Constant(), class CwiseNullaryOp
*/
template<typename Derived>
EIGEN_STRONG_INLINE void DenseBase<Derived>::fill(const Scalar& val)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void DenseBase<Derived>::fill(const Scalar& val)
{
setConstant(val);
}
@ -322,7 +322,7 @@ EIGEN_STRONG_INLINE void DenseBase<Derived>::fill(const Scalar& val)
* \sa fill(), setConstant(Index,const Scalar&), setConstant(Index,Index,const Scalar&), setZero(), setOnes(), Constant(), class CwiseNullaryOp, setZero(), setOnes()
*/
template<typename Derived>
EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::setConstant(const Scalar& val)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::setConstant(const Scalar& val)
{
return derived() = Constant(rows(), cols(), val);
}
@ -337,7 +337,7 @@ EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::setConstant(const Scalar& val)
* \sa MatrixBase::setConstant(const Scalar&), setConstant(Index,Index,const Scalar&), class CwiseNullaryOp, MatrixBase::Constant(const Scalar&)
*/
template<typename Derived>
EIGEN_STRONG_INLINE Derived&
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived&
PlainObjectBase<Derived>::setConstant(Index size, const Scalar& val)
{
resize(size);
@ -356,7 +356,7 @@ PlainObjectBase<Derived>::setConstant(Index size, const Scalar& val)
* \sa MatrixBase::setConstant(const Scalar&), setConstant(Index,const Scalar&), class CwiseNullaryOp, MatrixBase::Constant(const Scalar&)
*/
template<typename Derived>
EIGEN_STRONG_INLINE Derived&
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived&
PlainObjectBase<Derived>::setConstant(Index rows, Index cols, const Scalar& val)
{
resize(rows, cols);
@ -380,7 +380,7 @@ PlainObjectBase<Derived>::setConstant(Index rows, Index cols, const Scalar& val)
* \sa LinSpaced(Index,const Scalar&,const Scalar&), CwiseNullaryOp
*/
template<typename Derived>
EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::setLinSpaced(Index newSize, const Scalar& low, const Scalar& high)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::setLinSpaced(Index newSize, const Scalar& low, const Scalar& high)
{
EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
return derived() = Derived::NullaryExpr(newSize, internal::linspaced_op<Scalar,PacketScalar>(low,high,newSize));
@ -400,7 +400,7 @@ EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::setLinSpaced(Index newSize, con
* \sa LinSpaced(Index,const Scalar&,const Scalar&), setLinSpaced(Index, const Scalar&, const Scalar&), CwiseNullaryOp
*/
template<typename Derived>
EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::setLinSpaced(const Scalar& low, const Scalar& high)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::setLinSpaced(const Scalar& low, const Scalar& high)
{
EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
return setLinSpaced(size(), low, high);
@ -423,7 +423,7 @@ EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::setLinSpaced(const Scalar& low,
* \sa Zero(), Zero(Index)
*/
template<typename Derived>
EIGEN_STRONG_INLINE const typename DenseBase<Derived>::ConstantReturnType
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename DenseBase<Derived>::ConstantReturnType
DenseBase<Derived>::Zero(Index rows, Index cols)
{
return Constant(rows, cols, Scalar(0));
@ -446,7 +446,7 @@ DenseBase<Derived>::Zero(Index rows, Index cols)
* \sa Zero(), Zero(Index,Index)
*/
template<typename Derived>
EIGEN_STRONG_INLINE const typename DenseBase<Derived>::ConstantReturnType
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename DenseBase<Derived>::ConstantReturnType
DenseBase<Derived>::Zero(Index size)
{
return Constant(size, Scalar(0));
@ -463,7 +463,7 @@ DenseBase<Derived>::Zero(Index size)
* \sa Zero(Index), Zero(Index,Index)
*/
template<typename Derived>
EIGEN_STRONG_INLINE const typename DenseBase<Derived>::ConstantReturnType
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename DenseBase<Derived>::ConstantReturnType
DenseBase<Derived>::Zero()
{
return Constant(Scalar(0));
@ -478,7 +478,7 @@ DenseBase<Derived>::Zero()
* \sa class CwiseNullaryOp, Zero()
*/
template<typename Derived>
bool DenseBase<Derived>::isZero(const RealScalar& prec) const
EIGEN_DEVICE_FUNC bool DenseBase<Derived>::isZero(const RealScalar& prec) const
{
typename internal::nested_eval<Derived,1>::type self(derived());
for(Index j = 0; j < cols(); ++j)
@ -496,7 +496,7 @@ bool DenseBase<Derived>::isZero(const RealScalar& prec) const
* \sa class CwiseNullaryOp, Zero()
*/
template<typename Derived>
EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::setZero()
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::setZero()
{
return setConstant(Scalar(0));
}
@ -511,7 +511,7 @@ EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::setZero()
* \sa DenseBase::setZero(), setZero(Index,Index), class CwiseNullaryOp, DenseBase::Zero()
*/
template<typename Derived>
EIGEN_STRONG_INLINE Derived&
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived&
PlainObjectBase<Derived>::setZero(Index newSize)
{
resize(newSize);
@ -529,7 +529,7 @@ PlainObjectBase<Derived>::setZero(Index newSize)
* \sa DenseBase::setZero(), setZero(Index), class CwiseNullaryOp, DenseBase::Zero()
*/
template<typename Derived>
EIGEN_STRONG_INLINE Derived&
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived&
PlainObjectBase<Derived>::setZero(Index rows, Index cols)
{
resize(rows, cols);
@ -553,7 +553,7 @@ PlainObjectBase<Derived>::setZero(Index rows, Index cols)
* \sa Ones(), Ones(Index), isOnes(), class Ones
*/
template<typename Derived>
EIGEN_STRONG_INLINE const typename DenseBase<Derived>::ConstantReturnType
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename DenseBase<Derived>::ConstantReturnType
DenseBase<Derived>::Ones(Index rows, Index cols)
{
return Constant(rows, cols, Scalar(1));
@ -576,7 +576,7 @@ DenseBase<Derived>::Ones(Index rows, Index cols)
* \sa Ones(), Ones(Index,Index), isOnes(), class Ones
*/
template<typename Derived>
EIGEN_STRONG_INLINE const typename DenseBase<Derived>::ConstantReturnType
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename DenseBase<Derived>::ConstantReturnType
DenseBase<Derived>::Ones(Index newSize)
{
return Constant(newSize, Scalar(1));
@ -593,7 +593,7 @@ DenseBase<Derived>::Ones(Index newSize)
* \sa Ones(Index), Ones(Index,Index), isOnes(), class Ones
*/
template<typename Derived>
EIGEN_STRONG_INLINE const typename DenseBase<Derived>::ConstantReturnType
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename DenseBase<Derived>::ConstantReturnType
DenseBase<Derived>::Ones()
{
return Constant(Scalar(1));
@ -608,7 +608,7 @@ DenseBase<Derived>::Ones()
* \sa class CwiseNullaryOp, Ones()
*/
template<typename Derived>
bool DenseBase<Derived>::isOnes
EIGEN_DEVICE_FUNC bool DenseBase<Derived>::isOnes
(const RealScalar& prec) const
{
return isApproxToConstant(Scalar(1), prec);
@ -622,7 +622,7 @@ bool DenseBase<Derived>::isOnes
* \sa class CwiseNullaryOp, Ones()
*/
template<typename Derived>
EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::setOnes()
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::setOnes()
{
return setConstant(Scalar(1));
}
@ -637,7 +637,7 @@ EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::setOnes()
* \sa MatrixBase::setOnes(), setOnes(Index,Index), class CwiseNullaryOp, MatrixBase::Ones()
*/
template<typename Derived>
EIGEN_STRONG_INLINE Derived&
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived&
PlainObjectBase<Derived>::setOnes(Index newSize)
{
resize(newSize);
@ -655,7 +655,7 @@ PlainObjectBase<Derived>::setOnes(Index newSize)
* \sa MatrixBase::setOnes(), setOnes(Index), class CwiseNullaryOp, MatrixBase::Ones()
*/
template<typename Derived>
EIGEN_STRONG_INLINE Derived&
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived&
PlainObjectBase<Derived>::setOnes(Index rows, Index cols)
{
resize(rows, cols);
@ -679,7 +679,7 @@ PlainObjectBase<Derived>::setOnes(Index rows, Index cols)
* \sa Identity(), setIdentity(), isIdentity()
*/
template<typename Derived>
EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::IdentityReturnType
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::IdentityReturnType
MatrixBase<Derived>::Identity(Index rows, Index cols)
{
return DenseBase<Derived>::NullaryExpr(rows, cols, internal::scalar_identity_op<Scalar>());
@ -696,7 +696,7 @@ MatrixBase<Derived>::Identity(Index rows, Index cols)
* \sa Identity(Index,Index), setIdentity(), isIdentity()
*/
template<typename Derived>
EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::IdentityReturnType
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::IdentityReturnType
MatrixBase<Derived>::Identity()
{
EIGEN_STATIC_ASSERT_FIXED_SIZE(Derived)
@ -771,7 +771,7 @@ struct setIdentity_impl<Derived, true>
* \sa class CwiseNullaryOp, Identity(), Identity(Index,Index), isIdentity()
*/
template<typename Derived>
EIGEN_STRONG_INLINE Derived& MatrixBase<Derived>::setIdentity()
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& MatrixBase<Derived>::setIdentity()
{
return internal::setIdentity_impl<Derived>::run(derived());
}
@ -787,7 +787,7 @@ EIGEN_STRONG_INLINE Derived& MatrixBase<Derived>::setIdentity()
* \sa MatrixBase::setIdentity(), class CwiseNullaryOp, MatrixBase::Identity()
*/
template<typename Derived>
EIGEN_STRONG_INLINE Derived& MatrixBase<Derived>::setIdentity(Index rows, Index cols)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& MatrixBase<Derived>::setIdentity(Index rows, Index cols)
{
derived().resize(rows, cols);
return setIdentity();
@ -800,7 +800,7 @@ EIGEN_STRONG_INLINE Derived& MatrixBase<Derived>::setIdentity(Index rows, Index
* \sa MatrixBase::Unit(Index), MatrixBase::UnitX(), MatrixBase::UnitY(), MatrixBase::UnitZ(), MatrixBase::UnitW()
*/
template<typename Derived>
EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::BasisReturnType MatrixBase<Derived>::Unit(Index newSize, Index i)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::BasisReturnType MatrixBase<Derived>::Unit(Index newSize, Index i)
{
EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
return BasisReturnType(SquareMatrixType::Identity(newSize,newSize), i);
@ -815,7 +815,7 @@ EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::BasisReturnType MatrixBa
* \sa MatrixBase::Unit(Index,Index), MatrixBase::UnitX(), MatrixBase::UnitY(), MatrixBase::UnitZ(), MatrixBase::UnitW()
*/
template<typename Derived>
EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::BasisReturnType MatrixBase<Derived>::Unit(Index i)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::BasisReturnType MatrixBase<Derived>::Unit(Index i)
{
EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
return BasisReturnType(SquareMatrixType::Identity(),i);
@ -828,7 +828,7 @@ EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::BasisReturnType MatrixBa
* \sa MatrixBase::Unit(Index,Index), MatrixBase::Unit(Index), MatrixBase::UnitY(), MatrixBase::UnitZ(), MatrixBase::UnitW()
*/
template<typename Derived>
EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::BasisReturnType MatrixBase<Derived>::UnitX()
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::BasisReturnType MatrixBase<Derived>::UnitX()
{ return Derived::Unit(0); }
/** \returns an expression of the Y axis unit vector (0,1{,0}^*)
@ -838,7 +838,7 @@ EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::BasisReturnType MatrixBa
* \sa MatrixBase::Unit(Index,Index), MatrixBase::Unit(Index), MatrixBase::UnitY(), MatrixBase::UnitZ(), MatrixBase::UnitW()
*/
template<typename Derived>
EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::BasisReturnType MatrixBase<Derived>::UnitY()
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::BasisReturnType MatrixBase<Derived>::UnitY()
{ return Derived::Unit(1); }
/** \returns an expression of the Z axis unit vector (0,0,1{,0}^*)
@ -848,7 +848,7 @@ EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::BasisReturnType MatrixBa
* \sa MatrixBase::Unit(Index,Index), MatrixBase::Unit(Index), MatrixBase::UnitY(), MatrixBase::UnitZ(), MatrixBase::UnitW()
*/
template<typename Derived>
EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::BasisReturnType MatrixBase<Derived>::UnitZ()
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::BasisReturnType MatrixBase<Derived>::UnitZ()
{ return Derived::Unit(2); }
/** \returns an expression of the W axis unit vector (0,0,0,1)
@ -858,7 +858,7 @@ EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::BasisReturnType MatrixBa
* \sa MatrixBase::Unit(Index,Index), MatrixBase::Unit(Index), MatrixBase::UnitY(), MatrixBase::UnitZ(), MatrixBase::UnitW()
*/
template<typename Derived>
EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::BasisReturnType MatrixBase<Derived>::UnitW()
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::BasisReturnType MatrixBase<Derived>::UnitW()
{ return Derived::Unit(3); }
} // end namespace Eigen

8
xs/src/eigen/Eigen/src/Core/DenseBase.h
View File

@ -296,7 +296,7 @@ template<typename Derived> class DenseBase
EIGEN_DEVICE_FUNC
Derived& operator=(const ReturnByValue<OtherDerived>& func);
/** \ínternal
/** \internal
* Copies \a other into *this without evaluating other. \returns a reference to *this.
* \deprecated */
template<typename OtherDerived>
@ -484,9 +484,9 @@ template<typename Derived> class DenseBase
return derived().coeff(0,0);
}
bool all() const;
bool any() const;
Index count() const;
EIGEN_DEVICE_FUNC bool all() const;
EIGEN_DEVICE_FUNC bool any() const;
EIGEN_DEVICE_FUNC Index count() const;
typedef VectorwiseOp<Derived, Horizontal> RowwiseReturnType;
typedef const VectorwiseOp<const Derived, Horizontal> ConstRowwiseReturnType;

5
xs/src/eigen/Eigen/src/Core/Diagonal.h
View File

@ -70,7 +70,10 @@ template<typename MatrixType, int _DiagIndex> class Diagonal
EIGEN_DENSE_PUBLIC_INTERFACE(Diagonal)
EIGEN_DEVICE_FUNC
explicit inline Diagonal(MatrixType& matrix, Index a_index = DiagIndex) : m_matrix(matrix), m_index(a_index) {}
explicit inline Diagonal(MatrixType& matrix, Index a_index = DiagIndex) : m_matrix(matrix), m_index(a_index)
{
eigen_assert( a_index <= m_matrix.cols() && -a_index <= m_matrix.rows() );
}
EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Diagonal)

17
xs/src/eigen/Eigen/src/Core/Dot.h
View File

@ -31,7 +31,8 @@ struct dot_nocheck
typedef scalar_conj_product_op<typename traits<T>::Scalar,typename traits<U>::Scalar> conj_prod;
typedef typename conj_prod::result_type ResScalar;
EIGEN_DEVICE_FUNC
static inline ResScalar run(const MatrixBase<T>& a, const MatrixBase<U>& b)
EIGEN_STRONG_INLINE
static ResScalar run(const MatrixBase<T>& a, const MatrixBase<U>& b)
{
return a.template binaryExpr<conj_prod>(b).sum();
}
@ -43,7 +44,8 @@ struct dot_nocheck<T, U, true>
typedef scalar_conj_product_op<typename traits<T>::Scalar,typename traits<U>::Scalar> conj_prod;
typedef typename conj_prod::result_type ResScalar;
EIGEN_DEVICE_FUNC
static inline ResScalar run(const MatrixBase<T>& a, const MatrixBase<U>& b)
EIGEN_STRONG_INLINE
static ResScalar run(const MatrixBase<T>& a, const MatrixBase<U>& b)
{
return a.transpose().template binaryExpr<conj_prod>(b).sum();
}
@ -65,6 +67,7 @@ struct dot_nocheck<T, U, true>
template<typename Derived>
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE
typename ScalarBinaryOpTraits<typename internal::traits<Derived>::Scalar,typename internal::traits<OtherDerived>::Scalar>::ReturnType
MatrixBase<Derived>::dot(const MatrixBase<OtherDerived>& other) const
{
@ -102,7 +105,7 @@ EIGEN_STRONG_INLINE typename NumTraits<typename internal::traits<Derived>::Scala
* \sa lpNorm(), dot(), squaredNorm()
*/
template<typename Derived>
inline typename NumTraits<typename internal::traits<Derived>::Scalar>::Real MatrixBase<Derived>::norm() const
EIGEN_STRONG_INLINE typename NumTraits<typename internal::traits<Derived>::Scalar>::Real MatrixBase<Derived>::norm() const
{
return numext::sqrt(squaredNorm());
}
@ -117,7 +120,7 @@ inline typename NumTraits<typename internal::traits<Derived>::Scalar>::Real Matr
* \sa norm(), normalize()
*/
template<typename Derived>
inline const typename MatrixBase<Derived>::PlainObject
EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::PlainObject
MatrixBase<Derived>::normalized() const
{
typedef typename internal::nested_eval<Derived,2>::type _Nested;
@ -139,7 +142,7 @@ MatrixBase<Derived>::normalized() const
* \sa norm(), normalized()
*/
template<typename Derived>
inline void MatrixBase<Derived>::normalize()
EIGEN_STRONG_INLINE void MatrixBase<Derived>::normalize()
{
RealScalar z = squaredNorm();
// NOTE: after extensive benchmarking, this conditional does not impact performance, at least on recent x86 CPU
@ -160,7 +163,7 @@ inline void MatrixBase<Derived>::normalize()
* \sa stableNorm(), stableNormalize(), normalized()
*/
template<typename Derived>
inline const typename MatrixBase<Derived>::PlainObject
EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::PlainObject
MatrixBase<Derived>::stableNormalized() const
{
typedef typename internal::nested_eval<Derived,3>::type _Nested;
@ -185,7 +188,7 @@ MatrixBase<Derived>::stableNormalized() const
* \sa stableNorm(), stableNormalized(), normalize()
*/
template<typename Derived>
inline void MatrixBase<Derived>::stableNormalize()
EIGEN_STRONG_INLINE void MatrixBase<Derived>::stableNormalize()
{
RealScalar w = cwiseAbs().maxCoeff();
RealScalar z = (derived()/w).squaredNorm();

4
xs/src/eigen/Eigen/src/Core/EigenBase.h
View File

@ -14,6 +14,7 @@
namespace Eigen {
/** \class EigenBase
* \ingroup Core_Module
*
* Common base class for all classes T such that MatrixBase has an operator=(T) and a constructor MatrixBase(T).
*
@ -128,6 +129,7 @@ template<typename Derived> struct EigenBase
*/
template<typename Derived>
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
Derived& DenseBase<Derived>::operator=(const EigenBase<OtherDerived> &other)
{
call_assignment(derived(), other.derived());
@ -136,6 +138,7 @@ Derived& DenseBase<Derived>::operator=(const EigenBase<OtherDerived> &other)
template<typename Derived>
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
Derived& DenseBase<Derived>::operator+=(const EigenBase<OtherDerived> &other)
{
call_assignment(derived(), other.derived(), internal::add_assign_op<Scalar,typename OtherDerived::Scalar>());
@ -144,6 +147,7 @@ Derived& DenseBase<Derived>::operator+=(const EigenBase<OtherDerived> &other)
template<typename Derived>
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
Derived& DenseBase<Derived>::operator-=(const EigenBase<OtherDerived> &other)
{
call_assignment(derived(), other.derived(), internal::sub_assign_op<Scalar,typename OtherDerived::Scalar>());

11
xs/src/eigen/Eigen/src/Core/GeneralProduct.h
View File

@ -24,9 +24,14 @@ template<int Rows, int Cols, int Depth> struct product_type_selector;
template<int Size, int MaxSize> struct product_size_category
{
enum { is_large = MaxSize == Dynamic ||
enum {
#ifndef EIGEN_CUDA_ARCH
is_large = MaxSize == Dynamic ||
Size >= EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD ||
(Size==Dynamic && MaxSize>=EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD),
#else
is_large = 0,
#endif
value = is_large ? Large
: Size == 1 ? 1
: Small
@ -379,8 +384,6 @@ template<> struct gemv_dense_selector<OnTheRight,RowMajor,false>
*
* \sa lazyProduct(), operator*=(const MatrixBase&), Cwise::operator*()
*/
#ifndef __CUDACC__
template<typename Derived>
template<typename OtherDerived>
inline const Product<Derived, OtherDerived>
@ -412,8 +415,6 @@ MatrixBase<Derived>::operator*(const MatrixBase<OtherDerived> &other) const
return Product<Derived, OtherDerived>(derived(), other.derived());
}
#endif // __CUDACC__
/** \returns an expression of the matrix product of \c *this and \a other without implicit evaluation.
*
* The returned product will behave like any other expressions: the coefficients of the product will be

6
xs/src/eigen/Eigen/src/Core/GenericPacketMath.h
View File

@ -230,7 +230,7 @@ pload1(const typename unpacket_traits<Packet>::type *a) { return pset1<Packet>(
* duplicated to form: {from[0],from[0],from[1],from[1],from[2],from[2],from[3],from[3]}
* Currently, this function is only used for scalar * complex products.
*/
template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
template<typename Packet> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet
ploaddup(const typename unpacket_traits<Packet>::type* from) { return *from; }
/** \internal \returns a packet with elements of \a *from quadrupled.
@ -278,7 +278,7 @@ inline void pbroadcast2(const typename unpacket_traits<Packet>::type *a,
}
/** \internal \brief Returns a packet with coefficients (a,a+1,...,a+packet_size-1). */
template<typename Packet> inline Packet
template<typename Packet> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet
plset(const typename unpacket_traits<Packet>::type& a) { return a; }
/** \internal copy the packet \a from to \a *to, \a to must be 16 bytes aligned */
@ -482,7 +482,7 @@ EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void pstoret(Scalar* to, const Packet& fro
* by the current computation.
*/
template<typename Packet, int LoadMode>
inline Packet ploadt_ro(const typename unpacket_traits<Packet>::type* from)
EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Packet ploadt_ro(const typename unpacket_traits<Packet>::type* from)
{
return ploadt<Packet, LoadMode>(from);
}

17
xs/src/eigen/Eigen/src/Core/Map.h
View File

@ -20,11 +20,17 @@ struct traits<Map<PlainObjectType, MapOptions, StrideType> >
{
typedef traits<PlainObjectType> TraitsBase;
enum {
PlainObjectTypeInnerSize = ((traits<PlainObjectType>::Flags&RowMajorBit)==RowMajorBit)
? PlainObjectType::ColsAtCompileTime
: PlainObjectType::RowsAtCompileTime,
InnerStrideAtCompileTime = StrideType::InnerStrideAtCompileTime == 0
? int(PlainObjectType::InnerStrideAtCompileTime)
: int(StrideType::InnerStrideAtCompileTime),
OuterStrideAtCompileTime = StrideType::OuterStrideAtCompileTime == 0
? int(PlainObjectType::OuterStrideAtCompileTime)
? (InnerStrideAtCompileTime==Dynamic || PlainObjectTypeInnerSize==Dynamic
? Dynamic
: int(InnerStrideAtCompileTime) * int(PlainObjectTypeInnerSize))
: int(StrideType::OuterStrideAtCompileTime),
Alignment = int(MapOptions)&int(AlignedMask),
Flags0 = TraitsBase::Flags & (~NestByRefBit),
@ -107,10 +113,11 @@ template<typename PlainObjectType, int MapOptions, typename StrideType> class Ma
EIGEN_DEVICE_FUNC
inline Index outerStride() const
{
return StrideType::OuterStrideAtCompileTime != 0 ? m_stride.outer()
: IsVectorAtCompileTime ? this->size()
: int(Flags)&RowMajorBit ? this->cols()
: this->rows();
return int(StrideType::OuterStrideAtCompileTime) != 0 ? m_stride.outer()
: int(internal::traits<Map>::OuterStrideAtCompileTime) != Dynamic ? Index(internal::traits<Map>::OuterStrideAtCompileTime)
: IsVectorAtCompileTime ? (this->size() * innerStride())
: (int(Flags)&RowMajorBit) ? (this->cols() * innerStride())
: (this->rows() * innerStride());
}
/** Constructor in the fixed-size case.

43
xs/src/eigen/Eigen/src/Core/MathFunctions.h
View File

@ -348,31 +348,7 @@ struct norm1_retval
* Implementation of hypot *
****************************************************************************/
template<typename Scalar>
struct hypot_impl
{
typedef typename NumTraits<Scalar>::Real RealScalar;
static inline RealScalar run(const Scalar& x, const Scalar& y)
{
EIGEN_USING_STD_MATH(abs);
EIGEN_USING_STD_MATH(sqrt);
RealScalar _x = abs(x);
RealScalar _y = abs(y);
Scalar p, qp;
if(_x>_y)
{
p = _x;
qp = _y / p;
}
else
{
p = _y;
qp = _x / p;
}
if(p==RealScalar(0)) return RealScalar(0);
return p * sqrt(RealScalar(1) + qp*qp);
}
};
template<typename Scalar> struct hypot_impl;
template<typename Scalar>
struct hypot_retval
@ -495,7 +471,7 @@ namespace std_fallback {
typedef typename NumTraits<Scalar>::Real RealScalar;
EIGEN_USING_STD_MATH(log);
Scalar x1p = RealScalar(1) + x;
return ( x1p == Scalar(1) ) ? x : x * ( log(x1p) / (x1p - RealScalar(1)) );
return numext::equal_strict(x1p, Scalar(1)) ? x : x * ( log(x1p) / (x1p - RealScalar(1)) );
}
}
@ -1061,11 +1037,24 @@ double log(const double &x) { return ::log(x); }
template<typename T>
EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
typename NumTraits<T>::Real abs(const T &x) {
typename internal::enable_if<NumTraits<T>::IsSigned || NumTraits<T>::IsComplex,typename NumTraits<T>::Real>::type
abs(const T &x) {
EIGEN_USING_STD_MATH(abs);
return abs(x);
}
template<typename T>
EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
typename internal::enable_if<!(NumTraits<T>::IsSigned || NumTraits<T>::IsComplex),typename NumTraits<T>::Real>::type
abs(const T &x) {
return x;
}
#if defined(__SYCL_DEVICE_ONLY__)
EIGEN_ALWAYS_INLINE float abs(float x) { return cl::sycl::fabs(x); }
EIGEN_ALWAYS_INLINE double abs(double x) { return cl::sycl::fabs(x); }
#endif // defined(__SYCL_DEVICE_ONLY__)
#ifdef __CUDACC__
template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
float abs(const float &x) { return ::fabsf(x); }

23
xs/src/eigen/Eigen/src/Core/MathFunctionsImpl.h
View File

@ -71,6 +71,29 @@ T generic_fast_tanh_float(const T& a_x)
return pdiv(p, q);
}
template<typename RealScalar>
EIGEN_STRONG_INLINE
RealScalar positive_real_hypot(const RealScalar& x, const RealScalar& y)
{
EIGEN_USING_STD_MATH(sqrt);
RealScalar p, qp;
p = numext::maxi(x,y);
if(p==RealScalar(0)) return RealScalar(0);
qp = numext::mini(y,x) / p;
return p * sqrt(RealScalar(1) + qp*qp);
}
template<typename Scalar>
struct hypot_impl
{
typedef typename NumTraits<Scalar>::Real RealScalar;
static inline RealScalar run(const Scalar& x, const Scalar& y)
{
EIGEN_USING_STD_MATH(abs);
return positive_real_hypot<RealScalar>(abs(x), abs(y));
}
};
} // end namespace internal
} // end namespace Eigen

13
xs/src/eigen/Eigen/src/Core/MatrixBase.h
View File

@ -160,20 +160,11 @@ template<typename Derived> class MatrixBase
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
Derived& operator-=(const MatrixBase<OtherDerived>& other);
#ifdef __CUDACC__
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
const Product<Derived,OtherDerived,LazyProduct>
operator*(const MatrixBase<OtherDerived> &other) const
{ return this->lazyProduct(other); }
#else
template<typename OtherDerived>
const Product<Derived,OtherDerived>
operator*(const MatrixBase<OtherDerived> &other) const;
#endif
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
const Product<Derived,OtherDerived,LazyProduct>
@ -294,7 +285,7 @@ template<typename Derived> class MatrixBase
* fuzzy comparison such as isApprox()
* \sa isApprox(), operator!= */
template<typename OtherDerived>
inline bool operator==(const MatrixBase<OtherDerived>& other) const
EIGEN_DEVICE_FUNC inline bool operator==(const MatrixBase<OtherDerived>& other) const
{ return cwiseEqual(other).all(); }
/** \returns true if at least one pair of coefficients of \c *this and \a other are not exactly equal to each other.
@ -302,7 +293,7 @@ template<typename Derived> class MatrixBase
* fuzzy comparison such as isApprox()
* \sa isApprox(), operator== */
template<typename OtherDerived>
inline bool operator!=(const MatrixBase<OtherDerived>& other) const
EIGEN_DEVICE_FUNC inline bool operator!=(const MatrixBase<OtherDerived>& other) const
{ return cwiseNotEqual(other).any(); }
NoAlias<Derived,Eigen::MatrixBase > noalias();

2
xs/src/eigen/Eigen/src/Core/NumTraits.h
View File

@ -215,6 +215,8 @@ struct NumTraits<Array<Scalar, Rows, Cols, Options, MaxRows, MaxCols> >
static inline RealScalar epsilon() { return NumTraits<RealScalar>::epsilon(); }
EIGEN_DEVICE_FUNC
static inline RealScalar dummy_precision() { return NumTraits<RealScalar>::dummy_precision(); }
static inline int digits10() { return NumTraits<Scalar>::digits10(); }
};
template<> struct NumTraits<std::string>

4
xs/src/eigen/Eigen/src/Core/PlainObjectBase.h
View File

@ -577,6 +577,10 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
* while the AlignedMap() functions return aligned Map objects and thus should be called only with 16-byte-aligned
* \a data pointers.
*
* Here is an example using strides:
* \include Matrix_Map_stride.cpp
* Output: \verbinclude Matrix_Map_stride.out
*
* \see class Map
*/
//@{

10
xs/src/eigen/Eigen/src/Core/Product.h
View File

@ -97,8 +97,8 @@ class Product : public ProductImpl<_Lhs,_Rhs,Option,
&& "if you wanted a coeff-wise or a dot product use the respective explicit functions");
}
EIGEN_DEVICE_FUNC inline Index rows() const { return m_lhs.rows(); }
EIGEN_DEVICE_FUNC inline Index cols() const { return m_rhs.cols(); }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index rows() const { return m_lhs.rows(); }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index cols() const { return m_rhs.cols(); }
EIGEN_DEVICE_FUNC const LhsNestedCleaned& lhs() const { return m_lhs; }
EIGEN_DEVICE_FUNC const RhsNestedCleaned& rhs() const { return m_rhs; }
@ -127,7 +127,7 @@ public:
using Base::derived;
typedef typename Base::Scalar Scalar;
operator const Scalar() const
EIGEN_STRONG_INLINE operator const Scalar() const
{
return internal::evaluator<ProductXpr>(derived()).coeff(0,0);
}
@ -162,7 +162,7 @@ class ProductImpl<Lhs,Rhs,Option,Dense>
public:
EIGEN_DEVICE_FUNC Scalar coeff(Index row, Index col) const
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar coeff(Index row, Index col) const
{
EIGEN_STATIC_ASSERT(EnableCoeff, THIS_METHOD_IS_ONLY_FOR_INNER_OR_LAZY_PRODUCTS);
eigen_assert( (Option==LazyProduct) || (this->rows() == 1 && this->cols() == 1) );
@ -170,7 +170,7 @@ class ProductImpl<Lhs,Rhs,Option,Dense>
return internal::evaluator<Derived>(derived()).coeff(row,col);
}
EIGEN_DEVICE_FUNC Scalar coeff(Index i) const
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar coeff(Index i) const
{
EIGEN_STATIC_ASSERT(EnableCoeff, THIS_METHOD_IS_ONLY_FOR_INNER_OR_LAZY_PRODUCTS);
eigen_assert( (Option==LazyProduct) || (this->rows() == 1 && this->cols() == 1) );

35
xs/src/eigen/Eigen/src/Core/ProductEvaluators.h
View File

@ -32,7 +32,7 @@ struct evaluator<Product<Lhs, Rhs, Options> >
typedef Product<Lhs, Rhs, Options> XprType;
typedef product_evaluator<XprType> Base;
EIGEN_DEVICE_FUNC explicit evaluator(const XprType& xpr) : Base(xpr) {}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE explicit evaluator(const XprType& xpr) : Base(xpr) {}
};
// Catch "scalar * ( A * B )" and transform it to "(A*scalar) * B"
@ -55,7 +55,7 @@ struct evaluator<CwiseBinaryOp<internal::scalar_product_op<Scalar1,Scalar2>,
const Product<Lhs, Rhs, DefaultProduct> > XprType;
typedef evaluator<Product<EIGEN_SCALAR_BINARYOP_EXPR_RETURN_TYPE(Scalar1,Lhs,product), Rhs, DefaultProduct> > Base;
EIGEN_DEVICE_FUNC explicit evaluator(const XprType& xpr)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE explicit evaluator(const XprType& xpr)
: Base(xpr.lhs().functor().m_other * xpr.rhs().lhs() * xpr.rhs().rhs())
{}
};
@ -68,7 +68,7 @@ struct evaluator<Diagonal<const Product<Lhs, Rhs, DefaultProduct>, DiagIndex> >
typedef Diagonal<const Product<Lhs, Rhs, DefaultProduct>, DiagIndex> XprType;
typedef evaluator<Diagonal<const Product<Lhs, Rhs, LazyProduct>, DiagIndex> > Base;
EIGEN_DEVICE_FUNC explicit evaluator(const XprType& xpr)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE explicit evaluator(const XprType& xpr)
: Base(Diagonal<const Product<Lhs, Rhs, LazyProduct>, DiagIndex>(
Product<Lhs, Rhs, LazyProduct>(xpr.nestedExpression().lhs(), xpr.nestedExpression().rhs()),
xpr.index() ))
@ -207,6 +207,12 @@ struct evaluator_assume_aliasing<CwiseBinaryOp<internal::scalar_sum_op<typename
static const bool value = true;
};
template<typename OtherXpr, typename Lhs, typename Rhs>
struct evaluator_assume_aliasing<CwiseBinaryOp<internal::scalar_difference_op<typename OtherXpr::Scalar,typename Product<Lhs,Rhs,DefaultProduct>::Scalar>, const OtherXpr,
const Product<Lhs,Rhs,DefaultProduct> >, DenseShape > {
static const bool value = true;
};
template<typename DstXprType, typename OtherXpr, typename ProductType, typename Func1, typename Func2>
struct assignment_from_xpr_op_product
{
@ -240,19 +246,19 @@ template<typename Lhs, typename Rhs>
struct generic_product_impl<Lhs,Rhs,DenseShape,DenseShape,InnerProduct>
{
template<typename Dst>
static inline void evalTo(Dst& dst, const Lhs& lhs, const Rhs& rhs)
static EIGEN_STRONG_INLINE void evalTo(Dst& dst, const Lhs& lhs, const Rhs& rhs)
{
dst.coeffRef(0,0) = (lhs.transpose().cwiseProduct(rhs)).sum();
}
template<typename Dst>
static inline void addTo(Dst& dst, const Lhs& lhs, const Rhs& rhs)
static EIGEN_STRONG_INLINE void addTo(Dst& dst, const Lhs& lhs, const Rhs& rhs)
{
dst.coeffRef(0,0) += (lhs.transpose().cwiseProduct(rhs)).sum();
}
template<typename Dst>
static void subTo(Dst& dst, const Lhs& lhs, const Rhs& rhs)
static EIGEN_STRONG_INLINE void subTo(Dst& dst, const Lhs& lhs, const Rhs& rhs)
{ dst.coeffRef(0,0) -= (lhs.transpose().cwiseProduct(rhs)).sum(); }
};
@ -306,25 +312,25 @@ struct generic_product_impl<Lhs,Rhs,DenseShape,DenseShape,OuterProduct>
};
template<typename Dst>
static inline void evalTo(Dst& dst, const Lhs& lhs, const Rhs& rhs)
static EIGEN_STRONG_INLINE void evalTo(Dst& dst, const Lhs& lhs, const Rhs& rhs)
{
internal::outer_product_selector_run(dst, lhs, rhs, set(), is_row_major<Dst>());
}
template<typename Dst>
static inline void addTo(Dst& dst, const Lhs& lhs, const Rhs& rhs)
static EIGEN_STRONG_INLINE void addTo(Dst& dst, const Lhs& lhs, const Rhs& rhs)
{
internal::outer_product_selector_run(dst, lhs, rhs, add(), is_row_major<Dst>());
}
template<typename Dst>
static inline void subTo(Dst& dst, const Lhs& lhs, const Rhs& rhs)
static EIGEN_STRONG_INLINE void subTo(Dst& dst, const Lhs& lhs, const Rhs& rhs)
{
internal::outer_product_selector_run(dst, lhs, rhs, sub(), is_row_major<Dst>());
}
template<typename Dst>
static inline void scaleAndAddTo(Dst& dst, const Lhs& lhs, const Rhs& rhs, const Scalar& alpha)
static EIGEN_STRONG_INLINE void scaleAndAddTo(Dst& dst, const Lhs& lhs, const Rhs& rhs, const Scalar& alpha)
{
internal::outer_product_selector_run(dst, lhs, rhs, adds(alpha), is_row_major<Dst>());
}
@ -779,7 +785,11 @@ public:
_Vectorizable = bool(int(MatrixFlags)&PacketAccessBit) && _SameTypes && (_ScalarAccessOnDiag || (bool(int(DiagFlags)&PacketAccessBit))),
_LinearAccessMask = (MatrixType::RowsAtCompileTime==1 || MatrixType::ColsAtCompileTime==1) ? LinearAccessBit : 0,
Flags = ((HereditaryBits|_LinearAccessMask) & (unsigned int)(MatrixFlags)) | (_Vectorizable ? PacketAccessBit : 0),
Alignment = evaluator<MatrixType>::Alignment
Alignment = evaluator<MatrixType>::Alignment,
AsScalarProduct = (DiagonalType::SizeAtCompileTime==1)
|| (DiagonalType::SizeAtCompileTime==Dynamic && MatrixType::RowsAtCompileTime==1 && ProductOrder==OnTheLeft)
|| (DiagonalType::SizeAtCompileTime==Dynamic && MatrixType::ColsAtCompileTime==1 && ProductOrder==OnTheRight)
};
diagonal_product_evaluator_base(const MatrixType &mat, const DiagonalType &diag)
@ -791,6 +801,9 @@ public:
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar coeff(Index idx) const
{
if(AsScalarProduct)
return m_diagImpl.coeff(0) * m_matImpl.coeff(idx);
else
return m_diagImpl.coeff(idx) * m_matImpl.coeff(idx);
}

2
xs/src/eigen/Eigen/src/Core/Redux.h
View File

@ -407,7 +407,7 @@ protected:
*/
template<typename Derived>
template<typename Func>
typename internal::traits<Derived>::Scalar
EIGEN_STRONG_INLINE typename internal::traits<Derived>::Scalar
DenseBase<Derived>::redux(const Func& func) const
{
eigen_assert(this->rows()>0 && this->cols()>0 && "you are using an empty matrix");

2
xs/src/eigen/Eigen/src/Core/Ref.h
View File

@ -95,6 +95,8 @@ protected:
template<typename Expression>
EIGEN_DEVICE_FUNC void construct(Expression& expr)
{
EIGEN_STATIC_ASSERT_SAME_MATRIX_SIZE(PlainObjectType,Expression);
if(PlainObjectType::RowsAtCompileTime==1)
{
eigen_assert(expr.rows()==1 || expr.cols()==1);

6
xs/src/eigen/Eigen/src/Core/SelfAdjointView.h
View File

@ -71,7 +71,9 @@ template<typename _MatrixType, unsigned int UpLo> class SelfAdjointView
EIGEN_DEVICE_FUNC
explicit inline SelfAdjointView(MatrixType& matrix) : m_matrix(matrix)
{}
{
EIGEN_STATIC_ASSERT(UpLo==Lower || UpLo==Upper,SELFADJOINTVIEW_ACCEPTS_UPPER_AND_LOWER_MODE_ONLY);
}
EIGEN_DEVICE_FUNC
inline Index rows() const { return m_matrix.rows(); }
@ -189,7 +191,7 @@ template<typename _MatrixType, unsigned int UpLo> class SelfAdjointView
TriangularView<typename MatrixType::AdjointReturnType,TriMode> >::type(tmp2);
}
typedef SelfAdjointView<const MatrixConjugateReturnType,Mode> ConjugateReturnType;
typedef SelfAdjointView<const MatrixConjugateReturnType,UpLo> ConjugateReturnType;
/** \sa MatrixBase::conjugate() const */
EIGEN_DEVICE_FUNC
inline const ConjugateReturnType conjugate() const

12
xs/src/eigen/Eigen/src/Core/SelfCwiseBinaryOp.h
View File

@ -15,33 +15,29 @@ namespace Eigen {
// TODO generalize the scalar type of 'other'
template<typename Derived>
EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::operator*=(const Scalar& other)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::operator*=(const Scalar& other)
{
typedef typename Derived::PlainObject PlainObject;
internal::call_assignment(this->derived(), PlainObject::Constant(rows(),cols(),other), internal::mul_assign_op<Scalar,Scalar>());
return derived();
}
template<typename Derived>
EIGEN_STRONG_INLINE Derived& ArrayBase<Derived>::operator+=(const Scalar& other)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& ArrayBase<Derived>::operator+=(const Scalar& other)
{
typedef typename Derived::PlainObject PlainObject;
internal::call_assignment(this->derived(), PlainObject::Constant(rows(),cols(),other), internal::add_assign_op<Scalar,Scalar>());
return derived();
}
template<typename Derived>
EIGEN_STRONG_INLINE Derived& ArrayBase<Derived>::operator-=(const Scalar& other)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& ArrayBase<Derived>::operator-=(const Scalar& other)
{
typedef typename Derived::PlainObject PlainObject;
internal::call_assignment(this->derived(), PlainObject::Constant(rows(),cols(),other), internal::sub_assign_op<Scalar,Scalar>());
return derived();
}
template<typename Derived>
EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::operator/=(const Scalar& other)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::operator/=(const Scalar& other)
{
typedef typename Derived::PlainObject PlainObject;
internal::call_assignment(this->derived(), PlainObject::Constant(rows(),cols(),other), internal::div_assign_op<Scalar,Scalar>());
return derived();
}

4
xs/src/eigen/Eigen/src/Core/Solve.h
View File

@ -34,12 +34,12 @@ template<typename Decomposition, typename RhsType,typename StorageKind> struct s
template<typename Decomposition, typename RhsType>
struct solve_traits<Decomposition,RhsType,Dense>
{
typedef Matrix<typename RhsType::Scalar,
typedef typename make_proper_matrix_type<typename RhsType::Scalar,
Decomposition::ColsAtCompileTime,
RhsType::ColsAtCompileTime,
RhsType::PlainObject::Options,
Decomposition::MaxColsAtCompileTime,
RhsType::MaxColsAtCompileTime> PlainObject;
RhsType::MaxColsAtCompileTime>::type PlainObject;
};
template<typename Decomposition, typename RhsType>

5
xs/src/eigen/Eigen/src/Core/StableNorm.h
View File

@ -165,12 +165,13 @@ MatrixBase<Derived>::stableNorm() const
typedef typename internal::nested_eval<Derived,2>::type DerivedCopy;
typedef typename internal::remove_all<DerivedCopy>::type DerivedCopyClean;
DerivedCopy copy(derived());
const DerivedCopy copy(derived());
enum {
CanAlign = ( (int(DerivedCopyClean::Flags)&DirectAccessBit)
|| (int(internal::evaluator<DerivedCopyClean>::Alignment)>0) // FIXME Alignment)>0 might not be enough
) && (blockSize*sizeof(Scalar)*2<EIGEN_STACK_ALLOCATION_LIMIT) // ifwe cannot allocate on the stack, then let's not bother about this optimization
) && (blockSize*sizeof(Scalar)*2<EIGEN_STACK_ALLOCATION_LIMIT)
&& (EIGEN_MAX_STATIC_ALIGN_BYTES>0) // if we cannot allocate on the stack, then let's not bother about this optimization
};
typedef typename internal::conditional<CanAlign, Ref<const Matrix<Scalar,Dynamic,1,0,blockSize,1>, internal::evaluator<DerivedCopyClean>::Alignment>,
typename DerivedCopyClean::ConstSegmentReturnType>::type SegmentWrapper;

2
xs/src/eigen/Eigen/src/Core/Transpositions.h
View File

@ -384,7 +384,7 @@ class Transpose<TranspositionsBase<TranspositionsDerived> >
const Product<OtherDerived, Transpose, AliasFreeProduct>
operator*(const MatrixBase<OtherDerived>& matrix, const Transpose& trt)
{
return Product<OtherDerived, Transpose, AliasFreeProduct>(matrix.derived(), trt.derived());
return Product<OtherDerived, Transpose, AliasFreeProduct>(matrix.derived(), trt);
}
/** \returns the \a matrix with the inverse transpositions applied to the rows.

36
xs/src/eigen/Eigen/src/Core/arch/AVX/Complex.h
View File

@ -204,23 +204,7 @@ template<> struct conj_helper<Packet4cf, Packet4cf, true,true>
}
};
template<> struct conj_helper<Packet8f, Packet4cf, false,false>
{
EIGEN_STRONG_INLINE Packet4cf pmadd(const Packet8f& x, const Packet4cf& y, const Packet4cf& c) const
{ return padd(c, pmul(x,y)); }
EIGEN_STRONG_INLINE Packet4cf pmul(const Packet8f& x, const Packet4cf& y) const
{ return Packet4cf(Eigen::internal::pmul(x, y.v)); }
};
template<> struct conj_helper<Packet4cf, Packet8f, false,false>
{
EIGEN_STRONG_INLINE Packet4cf pmadd(const Packet4cf& x, const Packet8f& y, const Packet4cf& c) const
{ return padd(c, pmul(x,y)); }
EIGEN_STRONG_INLINE Packet4cf pmul(const Packet4cf& x, const Packet8f& y) const
{ return Packet4cf(Eigen::internal::pmul(x.v, y)); }
};
EIGEN_MAKE_CONJ_HELPER_CPLX_REAL(Packet4cf,Packet8f)
template<> EIGEN_STRONG_INLINE Packet4cf pdiv<Packet4cf>(const Packet4cf& a, const Packet4cf& b)
{
@ -400,23 +384,7 @@ template<> struct conj_helper<Packet2cd, Packet2cd, true,true>
}
};
template<> struct conj_helper<Packet4d, Packet2cd, false,false>
{
EIGEN_STRONG_INLINE Packet2cd pmadd(const Packet4d& x, const Packet2cd& y, const Packet2cd& c) const
{ return padd(c, pmul(x,y)); }
EIGEN_STRONG_INLINE Packet2cd pmul(const Packet4d& x, const Packet2cd& y) const
{ return Packet2cd(Eigen::internal::pmul(x, y.v)); }
};
template<> struct conj_helper<Packet2cd, Packet4d, false,false>
{
EIGEN_STRONG_INLINE Packet2cd pmadd(const Packet2cd& x, const Packet4d& y, const Packet2cd& c) const
{ return padd(c, pmul(x,y)); }
EIGEN_STRONG_INLINE Packet2cd pmul(const Packet2cd& x, const Packet4d& y) const
{ return Packet2cd(Eigen::internal::pmul(x.v, y)); }
};
EIGEN_MAKE_CONJ_HELPER_CPLX_REAL(Packet2cd,Packet4d)
template<> EIGEN_STRONG_INLINE Packet2cd pdiv<Packet2cd>(const Packet2cd& a, const Packet2cd& b)
{

11
xs/src/eigen/Eigen/src/Core/arch/AVX/PacketMath.h
View File

@ -308,9 +308,9 @@ template<> EIGEN_STRONG_INLINE void pstore1<Packet8i>(int* to, const int& a)
}
#ifndef EIGEN_VECTORIZE_AVX512
template<> EIGEN_STRONG_INLINE void prefetch<float>(const float* addr) { _mm_prefetch((const char*)(addr), _MM_HINT_T0); }
template<> EIGEN_STRONG_INLINE void prefetch<double>(const double* addr) { _mm_prefetch((const char*)(addr), _MM_HINT_T0); }
template<> EIGEN_STRONG_INLINE void prefetch<int>(const int* addr) { _mm_prefetch((const char*)(addr), _MM_HINT_T0); }
template<> EIGEN_STRONG_INLINE void prefetch<float>(const float* addr) { _mm_prefetch((SsePrefetchPtrType)(addr), _MM_HINT_T0); }
template<> EIGEN_STRONG_INLINE void prefetch<double>(const double* addr) { _mm_prefetch((SsePrefetchPtrType)(addr), _MM_HINT_T0); }
template<> EIGEN_STRONG_INLINE void prefetch<int>(const int* addr) { _mm_prefetch((SsePrefetchPtrType)(addr), _MM_HINT_T0); }
#endif
template<> EIGEN_STRONG_INLINE float pfirst<Packet8f>(const Packet8f& a) {
@ -333,9 +333,12 @@ template<> EIGEN_STRONG_INLINE Packet4d preverse(const Packet4d& a)
{
__m256d tmp = _mm256_shuffle_pd(a,a,5);
return _mm256_permute2f128_pd(tmp, tmp, 1);
#if 0
// This version is unlikely to be faster as _mm256_shuffle_ps and _mm256_permute_pd
// exhibit the same latency/throughput, but it is here for future reference/benchmarking...
__m256d swap_halves = _mm256_permute2f128_pd(a,a,1);
return _mm256_permute_pd(swap_halves,5);
#endif
}
// pabs should be ok

33
xs/src/eigen/Eigen/src/Core/arch/AVX512/MathFunctions.h
View File

@ -88,9 +88,9 @@ plog<Packet16f>(const Packet16f& _x) {
// x = x + x - 1.0;
// } else { x = x - 1.0; }
__mmask16 mask = _mm512_cmp_ps_mask(x, p16f_cephes_SQRTHF, _CMP_LT_OQ);
Packet16f tmp = _mm512_mask_blend_ps(mask, x, _mm512_setzero_ps());
Packet16f tmp = _mm512_mask_blend_ps(mask, _mm512_setzero_ps(), x);
x = psub(x, p16f_1);
e = psub(e, _mm512_mask_blend_ps(mask, p16f_1, _mm512_setzero_ps()));
e = psub(e, _mm512_mask_blend_ps(mask, _mm512_setzero_ps(), p16f_1));
x = padd(x, tmp);
Packet16f x2 = pmul(x, x);
@ -119,8 +119,9 @@ plog<Packet16f>(const Packet16f& _x) {
x = padd(x, y2);
// Filter out invalid inputs, i.e. negative arg will be NAN, 0 will be -INF.
return _mm512_mask_blend_ps(iszero_mask, p16f_minus_inf,
_mm512_mask_blend_ps(invalid_mask, p16f_nan, x));
return _mm512_mask_blend_ps(iszero_mask,
_mm512_mask_blend_ps(invalid_mask, x, p16f_nan),
p16f_minus_inf);
}
#endif
@ -266,8 +267,7 @@ psqrt<Packet16f>(const Packet16f& _x) {
// select only the inverse sqrt of positive normal inputs (denormals are
// flushed to zero and cause infs as well).
__mmask16 non_zero_mask = _mm512_cmp_ps_mask(_x, p16f_flt_min, _CMP_GE_OQ);
Packet16f x = _mm512_mask_blend_ps(non_zero_mask, _mm512_rsqrt14_ps(_x),
_mm512_setzero_ps());
Packet16f x = _mm512_mask_blend_ps(non_zero_mask, _mm512_setzero_ps(), _mm512_rsqrt14_ps(_x));
// Do a single step of Newton's iteration.
x = pmul(x, pmadd(neg_half, pmul(x, x), p16f_one_point_five));
@ -289,8 +289,7 @@ psqrt<Packet8d>(const Packet8d& _x) {
// select only the inverse sqrt of positive normal inputs (denormals are
// flushed to zero and cause infs as well).
__mmask8 non_zero_mask = _mm512_cmp_pd_mask(_x, p8d_dbl_min, _CMP_GE_OQ);
Packet8d x = _mm512_mask_blend_pd(non_zero_mask, _mm512_rsqrt14_pd(_x),
_mm512_setzero_pd());
Packet8d x = _mm512_mask_blend_pd(non_zero_mask, _mm512_setzero_pd(), _mm512_rsqrt14_pd(_x));
// Do a first step of Newton's iteration.
x = pmul(x, pmadd(neg_half, pmul(x, x), p8d_one_point_five));
@ -333,20 +332,18 @@ prsqrt<Packet16f>(const Packet16f& _x) {
// select only the inverse sqrt of positive normal inputs (denormals are
// flushed to zero and cause infs as well).
__mmask16 le_zero_mask = _mm512_cmp_ps_mask(_x, p16f_flt_min, _CMP_LT_OQ);
Packet16f x = _mm512_mask_blend_ps(le_zero_mask, _mm512_setzero_ps(),
_mm512_rsqrt14_ps(_x));
Packet16f x = _mm512_mask_blend_ps(le_zero_mask, _mm512_rsqrt14_ps(_x), _mm512_setzero_ps());
// Fill in NaNs and Infs for the negative/zero entries.
__mmask16 neg_mask = _mm512_cmp_ps_mask(_x, _mm512_setzero_ps(), _CMP_LT_OQ);
Packet16f infs_and_nans = _mm512_mask_blend_ps(
neg_mask, p16f_nan,
_mm512_mask_blend_ps(le_zero_mask, p16f_inf, _mm512_setzero_ps()));
neg_mask, _mm512_mask_blend_ps(le_zero_mask, _mm512_setzero_ps(), p16f_inf), p16f_nan);
// Do a single step of Newton's iteration.
x = pmul(x, pmadd(neg_half, pmul(x, x), p16f_one_point_five));
// Insert NaNs and Infs in all the right places.
return _mm512_mask_blend_ps(le_zero_mask, infs_and_nans, x);
return _mm512_mask_blend_ps(le_zero_mask, x, infs_and_nans);
}
template <>
@ -363,14 +360,12 @@ prsqrt<Packet8d>(const Packet8d& _x) {
// select only the inverse sqrt of positive normal inputs (denormals are
// flushed to zero and cause infs as well).
__mmask8 le_zero_mask = _mm512_cmp_pd_mask(_x, p8d_dbl_min, _CMP_LT_OQ);
Packet8d x = _mm512_mask_blend_pd(le_zero_mask, _mm512_setzero_pd(),
_mm512_rsqrt14_pd(_x));
Packet8d x = _mm512_mask_blend_pd(le_zero_mask, _mm512_rsqrt14_pd(_x), _mm512_setzero_pd());
// Fill in NaNs and Infs for the negative/zero entries.
__mmask8 neg_mask = _mm512_cmp_pd_mask(_x, _mm512_setzero_pd(), _CMP_LT_OQ);
Packet8d infs_and_nans = _mm512_mask_blend_pd(
neg_mask, p8d_nan,
_mm512_mask_blend_pd(le_zero_mask, p8d_inf, _mm512_setzero_pd()));
neg_mask, _mm512_mask_blend_pd(le_zero_mask, _mm512_setzero_pd(), p8d_inf), p8d_nan);
// Do a first step of Newton's iteration.
x = pmul(x, pmadd(neg_half, pmul(x, x), p8d_one_point_five));
@ -379,9 +374,9 @@ prsqrt<Packet8d>(const Packet8d& _x) {
x = pmul(x, pmadd(neg_half, pmul(x, x), p8d_one_point_five));
// Insert NaNs and Infs in all the right places.
return _mm512_mask_blend_pd(le_zero_mask, infs_and_nans, x);
return _mm512_mask_blend_pd(le_zero_mask, x, infs_and_nans);
}
#else
#elif defined(EIGEN_VECTORIZE_AVX512ER)
template <>
EIGEN_STRONG_INLINE Packet16f prsqrt<Packet16f>(const Packet16f& x) {
return _mm512_rsqrt28_ps(x);

6
xs/src/eigen/Eigen/src/Core/arch/AVX512/PacketMath.h
View File

@ -618,9 +618,9 @@ EIGEN_STRONG_INLINE void pstore1<Packet16i>(int* to, const int& a) {
pstore(to, pa);
}
template<> EIGEN_STRONG_INLINE void prefetch<float>(const float* addr) { _mm_prefetch((const char*)(addr), _MM_HINT_T0); }
template<> EIGEN_STRONG_INLINE void prefetch<double>(const double* addr) { _mm_prefetch((const char*)(addr), _MM_HINT_T0); }
template<> EIGEN_STRONG_INLINE void prefetch<int>(const int* addr) { _mm_prefetch((const char*)(addr), _MM_HINT_T0); }
template<> EIGEN_STRONG_INLINE void prefetch<float>(const float* addr) { _mm_prefetch((SsePrefetchPtrType)(addr), _MM_HINT_T0); }
template<> EIGEN_STRONG_INLINE void prefetch<double>(const double* addr) { _mm_prefetch((SsePrefetchPtrType)(addr), _MM_HINT_T0); }
template<> EIGEN_STRONG_INLINE void prefetch<int>(const int* addr) { _mm_prefetch((SsePrefetchPtrType)(addr), _MM_HINT_T0); }
template <>
EIGEN_STRONG_INLINE float pfirst<Packet16f>(const Packet16f& a) {

35
xs/src/eigen/Eigen/src/Core/arch/AltiVec/Complex.h
View File

@ -224,23 +224,7 @@ template<> struct conj_helper<Packet2cf, Packet2cf, true,true>
}
};
template<> struct conj_helper<Packet4f, Packet2cf, false,false>
{
EIGEN_STRONG_INLINE Packet2cf pmadd(const Packet4f& x, const Packet2cf& y, const Packet2cf& c) const
{ return padd(c, pmul(x,y)); }
EIGEN_STRONG_INLINE Packet2cf pmul(const Packet4f& x, const Packet2cf& y) const
{ return Packet2cf(internal::pmul<Packet4f>(x, y.v)); }
};
template<> struct conj_helper<Packet2cf, Packet4f, false,false>
{
EIGEN_STRONG_INLINE Packet2cf pmadd(const Packet2cf& x, const Packet4f& y, const Packet2cf& c) const
{ return padd(c, pmul(x,y)); }
EIGEN_STRONG_INLINE Packet2cf pmul(const Packet2cf& x, const Packet4f& y) const
{ return Packet2cf(internal::pmul<Packet4f>(x.v, y)); }
};
EIGEN_MAKE_CONJ_HELPER_CPLX_REAL(Packet2cf,Packet4f)
template<> EIGEN_STRONG_INLINE Packet2cf pdiv<Packet2cf>(const Packet2cf& a, const Packet2cf& b)
{
@ -416,23 +400,8 @@ template<> struct conj_helper<Packet1cd, Packet1cd, true,true>
return pconj(internal::pmul(a, b));
}
};
template<> struct conj_helper<Packet2d, Packet1cd, false,false>
{
EIGEN_STRONG_INLINE Packet1cd pmadd(const Packet2d& x, const Packet1cd& y, const Packet1cd& c) const
{ return padd(c, pmul(x,y)); }
EIGEN_STRONG_INLINE Packet1cd pmul(const Packet2d& x, const Packet1cd& y) const
{ return Packet1cd(internal::pmul<Packet2d>(x, y.v)); }
};
template<> struct conj_helper<Packet1cd, Packet2d, false,false>
{
EIGEN_STRONG_INLINE Packet1cd pmadd(const Packet1cd& x, const Packet2d& y, const Packet1cd& c) const
{ return padd(c, pmul(x,y)); }
EIGEN_STRONG_INLINE Packet1cd pmul(const Packet1cd& x, const Packet2d& y) const
{ return Packet1cd(internal::pmul<Packet2d>(x.v, y)); }
};
EIGEN_MAKE_CONJ_HELPER_CPLX_REAL(Packet1cd,Packet2d)
template<> EIGEN_STRONG_INLINE Packet1cd pdiv<Packet1cd>(const Packet1cd& a, const Packet1cd& b)
{

38
xs/src/eigen/Eigen/src/Core/arch/AltiVec/PacketMath.h
View File

@ -388,10 +388,28 @@ template<> EIGEN_STRONG_INLINE Packet4i pdiv<Packet4i>(const Packet4i& /*a*/, co
template<> EIGEN_STRONG_INLINE Packet4f pmadd(const Packet4f& a, const Packet4f& b, const Packet4f& c) { return vec_madd(a,b,c); }
template<> EIGEN_STRONG_INLINE Packet4i pmadd(const Packet4i& a, const Packet4i& b, const Packet4i& c) { return a*b + c; }
template<> EIGEN_STRONG_INLINE Packet4f pmin<Packet4f>(const Packet4f& a, const Packet4f& b) { return vec_min(a, b); }
template<> EIGEN_STRONG_INLINE Packet4f pmin<Packet4f>(const Packet4f& a, const Packet4f& b)
{
#ifdef __VSX__
Packet4f ret;
__asm__ ("xvcmpgesp %x0,%x1,%x2\n\txxsel %x0,%x1,%x2,%x0" : "=&wa" (ret) : "wa" (a), "wa" (b));
return ret;
#else
return vec_min(a, b);
#endif
}
template<> EIGEN_STRONG_INLINE Packet4i pmin<Packet4i>(const Packet4i& a, const Packet4i& b) { return vec_min(a, b); }
template<> EIGEN_STRONG_INLINE Packet4f pmax<Packet4f>(const Packet4f& a, const Packet4f& b) { return vec_max(a, b); }
template<> EIGEN_STRONG_INLINE Packet4f pmax<Packet4f>(const Packet4f& a, const Packet4f& b)
{
#ifdef __VSX__
Packet4f ret;
__asm__ ("xvcmpgtsp %x0,%x2,%x1\n\txxsel %x0,%x1,%x2,%x0" : "=&wa" (ret) : "wa" (a), "wa" (b));
return ret;
#else
return vec_max(a, b);
#endif
}
template<> EIGEN_STRONG_INLINE Packet4i pmax<Packet4i>(const Packet4i& a, const Packet4i& b) { return vec_max(a, b); }
template<> EIGEN_STRONG_INLINE Packet4f pand<Packet4f>(const Packet4f& a, const Packet4f& b) { return vec_and(a, b); }
@ -910,9 +928,19 @@ template<> EIGEN_STRONG_INLINE Packet2d pdiv<Packet2d>(const Packet2d& a, const
// for some weird raisons, it has to be overloaded for packet of integers
template<> EIGEN_STRONG_INLINE Packet2d pmadd(const Packet2d& a, const Packet2d& b, const Packet2d& c) { return vec_madd(a, b, c); }
template<> EIGEN_STRONG_INLINE Packet2d pmin<Packet2d>(const Packet2d& a, const Packet2d& b) { return vec_min(a, b); }
template<> EIGEN_STRONG_INLINE Packet2d pmin<Packet2d>(const Packet2d& a, const Packet2d& b)
{
Packet2d ret;
__asm__ ("xvcmpgedp %x0,%x1,%x2\n\txxsel %x0,%x1,%x2,%x0" : "=&wa" (ret) : "wa" (a), "wa" (b));
return ret;
}
template<> EIGEN_STRONG_INLINE Packet2d pmax<Packet2d>(const Packet2d& a, const Packet2d& b) { return vec_max(a, b); }
template<> EIGEN_STRONG_INLINE Packet2d pmax<Packet2d>(const Packet2d& a, const Packet2d& b)
{
Packet2d ret;
__asm__ ("xvcmpgtdp %x0,%x2,%x1\n\txxsel %x0,%x1,%x2,%x0" : "=&wa" (ret) : "wa" (a), "wa" (b));
return ret;
}
template<> EIGEN_STRONG_INLINE Packet2d pand<Packet2d>(const Packet2d& a, const Packet2d& b) { return vec_and(a, b); }
@ -1022,7 +1050,7 @@ ptranspose(PacketBlock<Packet2d,2>& kernel) {
template<> EIGEN_STRONG_INLINE Packet2d pblend(const Selector<2>& ifPacket, const Packet2d& thenPacket, const Packet2d& elsePacket) {
Packet2l select = { ifPacket.select[0], ifPacket.select[1] };
Packet2bl mask = vec_cmpeq(reinterpret_cast<Packet2d>(select), reinterpret_cast<Packet2d>(p2l_ONE));
Packet2bl mask = reinterpret_cast<Packet2bl>( vec_cmpeq(reinterpret_cast<Packet2d>(select), reinterpret_cast<Packet2d>(p2l_ONE)) );
return vec_sel(elsePacket, thenPacket, mask);
}
#endif // __VSX__

108
xs/src/eigen/Eigen/src/Core/arch/CUDA/Half.h
View File

@ -13,7 +13,7 @@
// Redistribution and use in source and binary forms, with or without
// modification, are permitted.
// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
// “AS IS” AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
// "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
// LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
// A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
// HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
@ -147,55 +147,55 @@ namespace half_impl {
// versions to get the ALU speed increased), but you do save the
// conversion steps back and forth.
__device__ half operator + (const half& a, const half& b) {
EIGEN_STRONG_INLINE __device__ half operator + (const half& a, const half& b) {
return __hadd(a, b);
}
__device__ half operator * (const half& a, const half& b) {
EIGEN_STRONG_INLINE __device__ half operator * (const half& a, const half& b) {
return __hmul(a, b);
}
__device__ half operator - (const half& a, const half& b) {
EIGEN_STRONG_INLINE __device__ half operator - (const half& a, const half& b) {
return __hsub(a, b);
}
__device__ half operator / (const half& a, const half& b) {
EIGEN_STRONG_INLINE __device__ half operator / (const half& a, const half& b) {
float num = __half2float(a);
float denom = __half2float(b);
return __float2half(num / denom);
}
__device__ half operator - (const half& a) {
EIGEN_STRONG_INLINE __device__ half operator - (const half& a) {
return __hneg(a);
}
__device__ half& operator += (half& a, const half& b) {
EIGEN_STRONG_INLINE __device__ half& operator += (half& a, const half& b) {
a = a + b;
return a;
}
__device__ half& operator *= (half& a, const half& b) {
EIGEN_STRONG_INLINE __device__ half& operator *= (half& a, const half& b) {
a = a * b;
return a;
}
__device__ half& operator -= (half& a, const half& b) {
EIGEN_STRONG_INLINE __device__ half& operator -= (half& a, const half& b) {
a = a - b;
return a;
}
__device__ half& operator /= (half& a, const half& b) {
EIGEN_STRONG_INLINE __device__ half& operator /= (half& a, const half& b) {
a = a / b;
return a;
}
__device__ bool operator == (const half& a, const half& b) {
EIGEN_STRONG_INLINE __device__ bool operator == (const half& a, const half& b) {
return __heq(a, b);
}
__device__ bool operator != (const half& a, const half& b) {
EIGEN_STRONG_INLINE __device__ bool operator != (const half& a, const half& b) {
return __hne(a, b);
}
__device__ bool operator < (const half& a, const half& b) {
EIGEN_STRONG_INLINE __device__ bool operator < (const half& a, const half& b) {
return __hlt(a, b);
}
__device__ bool operator <= (const half& a, const half& b) {
EIGEN_STRONG_INLINE __device__ bool operator <= (const half& a, const half& b) {
return __hle(a, b);
}
__device__ bool operator > (const half& a, const half& b) {
EIGEN_STRONG_INLINE __device__ bool operator > (const half& a, const half& b) {
return __hgt(a, b);
}
__device__ bool operator >= (const half& a, const half& b) {
EIGEN_STRONG_INLINE __device__ bool operator >= (const half& a, const half& b) {
return __hge(a, b);
}
@ -238,10 +238,10 @@ EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half& operator /= (half& a, const half& b)
return a;
}
EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool operator == (const half& a, const half& b) {
return float(a) == float(b);
return numext::equal_strict(float(a),float(b));
}
EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool operator != (const half& a, const half& b) {
return float(a) != float(b);
return numext::not_equal_strict(float(a), float(b));
}
EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool operator < (const half& a, const half& b) {
return float(a) < float(b);
@ -386,11 +386,15 @@ EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half abs(const half& a) {
return result;
}
EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half exp(const half& a) {
#if EIGEN_CUDACC_VER >= 80000 && defined EIGEN_CUDA_ARCH && EIGEN_CUDA_ARCH >= 530
return half(hexp(a));
#else
return half(::expf(float(a)));
#endif
}
EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half log(const half& a) {
#if defined(EIGEN_HAS_CUDA_FP16) && defined __CUDACC_VER__ && __CUDACC_VER__ >= 80000 && defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 530
return Eigen::half(::hlog(a));
#if defined(EIGEN_HAS_CUDA_FP16) && EIGEN_CUDACC_VER >= 80000 && defined(EIGEN_CUDA_ARCH) && EIGEN_CUDA_ARCH >= 530
return half(::hlog(a));
#else
return half(::logf(float(a)));
#endif
@ -402,7 +406,11 @@ EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half log10(const half& a) {
return half(::log10f(float(a)));
}
EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half sqrt(const half& a) {
#if EIGEN_CUDACC_VER >= 80000 && defined EIGEN_CUDA_ARCH && EIGEN_CUDA_ARCH >= 530
return half(hsqrt(a));
#else
return half(::sqrtf(float(a)));
#endif
}
EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half pow(const half& a, const half& b) {
return half(::powf(float(a), float(b)));
@ -420,10 +428,18 @@ EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half tanh(const half& a) {
return half(::tanhf(float(a)));
}
EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half floor(const half& a) {
#if EIGEN_CUDACC_VER >= 80000 && defined EIGEN_CUDA_ARCH && EIGEN_CUDA_ARCH >= 300
return half(hfloor(a));
#else
return half(::floorf(float(a)));
#endif
}
EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half ceil(const half& a) {
#if EIGEN_CUDACC_VER >= 80000 && defined EIGEN_CUDA_ARCH && EIGEN_CUDA_ARCH >= 300
return half(hceil(a));
#else
return half(::ceilf(float(a)));
#endif
}
EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half (min)(const half& a, const half& b) {
@ -474,9 +490,59 @@ template<> struct is_arithmetic<half> { enum { value = true }; };
} // end namespace internal
} // end namespace Eigen
namespace std {
template<>
struct numeric_limits<Eigen::half> {
static const bool is_specialized = true;
static const bool is_signed = true;
static const bool is_integer = false;
static const bool is_exact = false;
static const bool has_infinity = true;
static const bool has_quiet_NaN = true;
static const bool has_signaling_NaN = true;
static const float_denorm_style has_denorm = denorm_present;
static const bool has_denorm_loss = false;
static const std::float_round_style round_style = std::round_to_nearest;
static const bool is_iec559 = false;
static const bool is_bounded = false;
static const bool is_modulo = false;
static const int digits = 11;
static const int digits10 = 3; // according to http://half.sourceforge.net/structstd_1_1numeric__limits_3_01half__float_1_1half_01_4.html
static const int max_digits10 = 5; // according to http://half.sourceforge.net/structstd_1_1numeric__limits_3_01half__float_1_1half_01_4.html
static const int radix = 2;
static const int min_exponent = -13;
static const int min_exponent10 = -4;
static const int max_exponent = 16;
static const int max_exponent10 = 4;
static const bool traps = true;
static const bool tinyness_before = false;
static Eigen::half (min)() { return Eigen::half_impl::raw_uint16_to_half(0x400); }
static Eigen::half lowest() { return Eigen::half_impl::raw_uint16_to_half(0xfbff); }
static Eigen::half (max)() { return Eigen::half_impl::raw_uint16_to_half(0x7bff); }
static Eigen::half epsilon() { return Eigen::half_impl::raw_uint16_to_half(0x0800); }
static Eigen::half round_error() { return Eigen::half(0.5); }
static Eigen::half infinity() { return Eigen::half_impl::raw_uint16_to_half(0x7c00); }
static Eigen::half quiet_NaN() { return Eigen::half_impl::raw_uint16_to_half(0x7e00); }
static Eigen::half signaling_NaN() { return Eigen::half_impl::raw_uint16_to_half(0x7e00); }
static Eigen::half denorm_min() { return Eigen::half_impl::raw_uint16_to_half(0x1); }
};
}
namespace Eigen {
template<> struct NumTraits<Eigen::half>
: GenericNumTraits<Eigen::half>
{
enum {
IsSigned = true,
IsInteger = false,
IsComplex = false,
RequireInitialization = false
};
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE Eigen::half epsilon() {
return half_impl::raw_uint16_to_half(0x0800);
}
@ -507,7 +573,7 @@ EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::half exph(const Eigen::half& a) {
return Eigen::half(::expf(float(a)));
}
EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::half logh(const Eigen::half& a) {
#if defined __CUDACC_VER__ && __CUDACC_VER__ >= 80000 && defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 530
#if EIGEN_CUDACC_VER >= 80000 && defined(EIGEN_CUDA_ARCH) && EIGEN_CUDA_ARCH >= 530
return Eigen::half(::hlog(a));
#else
return Eigen::half(::logf(float(a)));

2
xs/src/eigen/Eigen/src/Core/arch/CUDA/PacketMath.h
View File

@ -291,7 +291,7 @@ template<> EIGEN_DEVICE_FUNC inline double2 pabs<double2>(const double2& a) {
EIGEN_DEVICE_FUNC inline void
ptranspose(PacketBlock<float4,4>& kernel) {
double tmp = kernel.packet[0].y;
float tmp = kernel.packet[0].y;
kernel.packet[0].y = kernel.packet[1].x;
kernel.packet[1].x = tmp;

2
xs/src/eigen/Eigen/src/Core/arch/CUDA/PacketMathHalf.h
View File

@ -275,7 +275,7 @@ template<> __device__ EIGEN_STRONG_INLINE half2 plog1p<half2>(const half2& a) {
return __floats2half2_rn(r1, r2);
}
#if defined __CUDACC_VER__ && __CUDACC_VER__ >= 80000 && defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 530
#if EIGEN_CUDACC_VER >= 80000 && defined EIGEN_CUDA_ARCH && EIGEN_CUDA_ARCH >= 530
template<> __device__ EIGEN_STRONG_INLINE
half2 plog<half2>(const half2& a) {

29
xs/src/eigen/Eigen/src/Core/arch/Default/ConjHelper.h Normal file
View File

@ -0,0 +1,29 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2017 Gael Guennebaud <gael.guennebaud@inria.fr>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_ARCH_CONJ_HELPER_H
#define EIGEN_ARCH_CONJ_HELPER_H
#define EIGEN_MAKE_CONJ_HELPER_CPLX_REAL(PACKET_CPLX, PACKET_REAL) \
template<> struct conj_helper<PACKET_REAL, PACKET_CPLX, false,false> { \
EIGEN_STRONG_INLINE PACKET_CPLX pmadd(const PACKET_REAL& x, const PACKET_CPLX& y, const PACKET_CPLX& c) const \
{ return padd(c, pmul(x,y)); } \
EIGEN_STRONG_INLINE PACKET_CPLX pmul(const PACKET_REAL& x, const PACKET_CPLX& y) const \
{ return PACKET_CPLX(Eigen::internal::pmul<PACKET_REAL>(x, y.v)); } \
}; \
\
template<> struct conj_helper<PACKET_CPLX, PACKET_REAL, false,false> { \
EIGEN_STRONG_INLINE PACKET_CPLX pmadd(const PACKET_CPLX& x, const PACKET_REAL& y, const PACKET_CPLX& c) const \
{ return padd(c, pmul(x,y)); } \
EIGEN_STRONG_INLINE PACKET_CPLX pmul(const PACKET_CPLX& x, const PACKET_REAL& y) const \
{ return PACKET_CPLX(Eigen::internal::pmul<PACKET_REAL>(x.v, y)); } \
};
#endif // EIGEN_ARCH_CONJ_HELPER_H

12
xs/src/eigen/Eigen/src/Core/arch/NEON/Complex.h
View File

@ -67,7 +67,7 @@ template<> struct unpacket_traits<Packet2cf> { typedef std::complex<float> type;
template<> EIGEN_STRONG_INLINE Packet2cf pset1<Packet2cf>(const std::complex<float>& from)
{
float32x2_t r64;
r64 = vld1_f32((float *)&from);
r64 = vld1_f32((const float *)&from);
return Packet2cf(vcombine_f32(r64, r64));
}
@ -142,7 +142,7 @@ template<> EIGEN_DEVICE_FUNC inline void pscatter<std::complex<float>, Packet2cf
to[stride*1] = std::complex<float>(vgetq_lane_f32(from.v, 2), vgetq_lane_f32(from.v, 3));
}
template<> EIGEN_STRONG_INLINE void prefetch<std::complex<float> >(const std::complex<float> * addr) { EIGEN_ARM_PREFETCH((float *)addr); }
template<> EIGEN_STRONG_INLINE void prefetch<std::complex<float> >(const std::complex<float> * addr) { EIGEN_ARM_PREFETCH((const float *)addr); }
template<> EIGEN_STRONG_INLINE std::complex<float> pfirst<Packet2cf>(const Packet2cf& a)
{
@ -265,6 +265,8 @@ template<> struct conj_helper<Packet2cf, Packet2cf, true,true>
}
};
EIGEN_MAKE_CONJ_HELPER_CPLX_REAL(Packet2cf,Packet4f)
template<> EIGEN_STRONG_INLINE Packet2cf pdiv<Packet2cf>(const Packet2cf& a, const Packet2cf& b)
{
// TODO optimize it for NEON
@ -275,7 +277,7 @@ template<> EIGEN_STRONG_INLINE Packet2cf pdiv<Packet2cf>(const Packet2cf& a, con
s = vmulq_f32(b.v, b.v);
rev_s = vrev64q_f32(s);
return Packet2cf(pdiv(res.v, vaddq_f32(s,rev_s)));
return Packet2cf(pdiv<Packet4f>(res.v, vaddq_f32(s,rev_s)));
}
EIGEN_DEVICE_FUNC inline void
@ -381,7 +383,7 @@ template<> EIGEN_STRONG_INLINE Packet1cd ploaddup<Packet1cd>(const std::complex<
template<> EIGEN_STRONG_INLINE void pstore <std::complex<double> >(std::complex<double> * to, const Packet1cd& from) { EIGEN_DEBUG_ALIGNED_STORE pstore((double*)to, from.v); }
template<> EIGEN_STRONG_INLINE void pstoreu<std::complex<double> >(std::complex<double> * to, const Packet1cd& from) { EIGEN_DEBUG_UNALIGNED_STORE pstoreu((double*)to, from.v); }
template<> EIGEN_STRONG_INLINE void prefetch<std::complex<double> >(const std::complex<double> * addr) { EIGEN_ARM_PREFETCH((double *)addr); }
template<> EIGEN_STRONG_INLINE void prefetch<std::complex<double> >(const std::complex<double> * addr) { EIGEN_ARM_PREFETCH((const double *)addr); }
template<> EIGEN_DEVICE_FUNC inline Packet1cd pgather<std::complex<double>, Packet1cd>(const std::complex<double>* from, Index stride)
{
@ -456,6 +458,8 @@ template<> struct conj_helper<Packet1cd, Packet1cd, true,true>
}
};
EIGEN_MAKE_CONJ_HELPER_CPLX_REAL(Packet1cd,Packet2d)
template<> EIGEN_STRONG_INLINE Packet1cd pdiv<Packet1cd>(const Packet1cd& a, const Packet1cd& b)
{
// TODO optimize it for NEON

46
xs/src/eigen/Eigen/src/Core/arch/NEON/PacketMath.h
View File

@ -36,12 +36,43 @@ namespace internal {
#endif
#endif
#if EIGEN_COMP_MSVC
// In MSVC's arm_neon.h header file, all NEON vector types
// are aliases to the same underlying type __n128.
// We thus have to wrap them to make them different C++ types.
// (See also bug 1428)
template<typename T,int unique_id>
struct eigen_packet_wrapper
{
operator T&() { return m_val; }
operator const T&() const { return m_val; }
eigen_packet_wrapper() {}
eigen_packet_wrapper(const T &v) : m_val(v) {}
eigen_packet_wrapper& operator=(const T &v) {
m_val = v;
return *this;
}
T m_val;
};
typedef eigen_packet_wrapper<float32x2_t,0> Packet2f;
typedef eigen_packet_wrapper<float32x4_t,1> Packet4f;
typedef eigen_packet_wrapper<int32x4_t ,2> Packet4i;
typedef eigen_packet_wrapper<int32x2_t ,3> Packet2i;
typedef eigen_packet_wrapper<uint32x4_t ,4> Packet4ui;
#else
typedef float32x2_t Packet2f;
typedef float32x4_t Packet4f;
typedef int32x4_t Packet4i;
typedef int32x2_t Packet2i;
typedef uint32x4_t Packet4ui;
#endif // EIGEN_COMP_MSVC
#define _EIGEN_DECLARE_CONST_Packet4f(NAME,X) \
const Packet4f p4f_##NAME = pset1<Packet4f>(X)
@ -51,14 +82,17 @@ typedef uint32x4_t Packet4ui;
#define _EIGEN_DECLARE_CONST_Packet4i(NAME,X) \
const Packet4i p4i_##NAME = pset1<Packet4i>(X)
// arm64 does have the pld instruction. If available, let's trust the __builtin_prefetch built-in function
// which available on LLVM and GCC (at least)
#if EIGEN_HAS_BUILTIN(__builtin_prefetch) || EIGEN_COMP_GNUC
#if EIGEN_ARCH_ARM64
// __builtin_prefetch tends to do nothing on ARM64 compilers because the
// prefetch instructions there are too detailed for __builtin_prefetch to map
// meaningfully to them.
#define EIGEN_ARM_PREFETCH(ADDR) __asm__ __volatile__("prfm pldl1keep, [%[addr]]\n" ::[addr] "r"(ADDR) : );
#elif EIGEN_HAS_BUILTIN(__builtin_prefetch) || EIGEN_COMP_GNUC
#define EIGEN_ARM_PREFETCH(ADDR) __builtin_prefetch(ADDR);
#elif defined __pld
#define EIGEN_ARM_PREFETCH(ADDR) __pld(ADDR)
#elif !EIGEN_ARCH_ARM64
#define EIGEN_ARM_PREFETCH(ADDR) __asm__ __volatile__ ( " pld [%[addr]]\n" :: [addr] "r" (ADDR) : "cc" );
#elif EIGEN_ARCH_ARM32
#define EIGEN_ARM_PREFETCH(ADDR) __asm__ __volatile__ ("pld [%[addr]]\n" :: [addr] "r" (ADDR) : );
#else
// by default no explicit prefetching
#define EIGEN_ARM_PREFETCH(ADDR)
@ -113,7 +147,7 @@ template<> EIGEN_STRONG_INLINE Packet4i pset1<Packet4i>(const int32_t& from)
template<> EIGEN_STRONG_INLINE Packet4f plset<Packet4f>(const float& a)
{
const float32_t f[] = {0, 1, 2, 3};
const float f[] = {0, 1, 2, 3};
Packet4f countdown = vld1q_f32(f);
return vaddq_f32(pset1<Packet4f>(a), countdown);
}

40
xs/src/eigen/Eigen/src/Core/arch/SSE/Complex.h
View File

@ -128,7 +128,7 @@ template<> EIGEN_DEVICE_FUNC inline void pscatter<std::complex<float>, Packet2cf
_mm_cvtss_f32(_mm_shuffle_ps(from.v, from.v, 3)));
}
template<> EIGEN_STRONG_INLINE void prefetch<std::complex<float> >(const std::complex<float> * addr) { _mm_prefetch((const char*)(addr), _MM_HINT_T0); }
template<> EIGEN_STRONG_INLINE void prefetch<std::complex<float> >(const std::complex<float> * addr) { _mm_prefetch((SsePrefetchPtrType)(addr), _MM_HINT_T0); }
template<> EIGEN_STRONG_INLINE std::complex<float> pfirst<Packet2cf>(const Packet2cf& a)
{
@ -229,23 +229,7 @@ template<> struct conj_helper<Packet2cf, Packet2cf, true,true>
}
};
template<> struct conj_helper<Packet4f, Packet2cf, false,false>
{
EIGEN_STRONG_INLINE Packet2cf pmadd(const Packet4f& x, const Packet2cf& y, const Packet2cf& c) const
{ return padd(c, pmul(x,y)); }
EIGEN_STRONG_INLINE Packet2cf pmul(const Packet4f& x, const Packet2cf& y) const
{ return Packet2cf(Eigen::internal::pmul<Packet4f>(x, y.v)); }
};
template<> struct conj_helper<Packet2cf, Packet4f, false,false>
{
EIGEN_STRONG_INLINE Packet2cf pmadd(const Packet2cf& x, const Packet4f& y, const Packet2cf& c) const
{ return padd(c, pmul(x,y)); }
EIGEN_STRONG_INLINE Packet2cf pmul(const Packet2cf& x, const Packet4f& y) const
{ return Packet2cf(Eigen::internal::pmul<Packet4f>(x.v, y)); }
};
EIGEN_MAKE_CONJ_HELPER_CPLX_REAL(Packet2cf,Packet4f)
template<> EIGEN_STRONG_INLINE Packet2cf pdiv<Packet2cf>(const Packet2cf& a, const Packet2cf& b)
{
@ -340,7 +324,7 @@ template<> EIGEN_STRONG_INLINE Packet1cd ploaddup<Packet1cd>(const std::complex<
template<> EIGEN_STRONG_INLINE void pstore <std::complex<double> >(std::complex<double> * to, const Packet1cd& from) { EIGEN_DEBUG_ALIGNED_STORE pstore((double*)to, Packet2d(from.v)); }
template<> EIGEN_STRONG_INLINE void pstoreu<std::complex<double> >(std::complex<double> * to, const Packet1cd& from) { EIGEN_DEBUG_UNALIGNED_STORE pstoreu((double*)to, Packet2d(from.v)); }
template<> EIGEN_STRONG_INLINE void prefetch<std::complex<double> >(const std::complex<double> * addr) { _mm_prefetch((const char*)(addr), _MM_HINT_T0); }
template<> EIGEN_STRONG_INLINE void prefetch<std::complex<double> >(const std::complex<double> * addr) { _mm_prefetch((SsePrefetchPtrType)(addr), _MM_HINT_T0); }
template<> EIGEN_STRONG_INLINE std::complex<double> pfirst<Packet1cd>(const Packet1cd& a)
{
@ -430,23 +414,7 @@ template<> struct conj_helper<Packet1cd, Packet1cd, true,true>
}
};
template<> struct conj_helper<Packet2d, Packet1cd, false,false>
{
EIGEN_STRONG_INLINE Packet1cd pmadd(const Packet2d& x, const Packet1cd& y, const Packet1cd& c) const
{ return padd(c, pmul(x,y)); }
EIGEN_STRONG_INLINE Packet1cd pmul(const Packet2d& x, const Packet1cd& y) const
{ return Packet1cd(Eigen::internal::pmul<Packet2d>(x, y.v)); }
};
template<> struct conj_helper<Packet1cd, Packet2d, false,false>
{
EIGEN_STRONG_INLINE Packet1cd pmadd(const Packet1cd& x, const Packet2d& y, const Packet1cd& c) const
{ return padd(c, pmul(x,y)); }
EIGEN_STRONG_INLINE Packet1cd pmul(const Packet1cd& x, const Packet2d& y) const
{ return Packet1cd(Eigen::internal::pmul<Packet2d>(x.v, y)); }
};
EIGEN_MAKE_CONJ_HELPER_CPLX_REAL(Packet1cd,Packet2d)
template<> EIGEN_STRONG_INLINE Packet1cd pdiv<Packet1cd>(const Packet1cd& a, const Packet1cd& b)
{

22
xs/src/eigen/Eigen/src/Core/arch/SSE/PacketMath.h
View File

@ -409,10 +409,16 @@ template<> EIGEN_STRONG_INLINE void pstore1<Packet2d>(double* to, const double&
pstore(to, Packet2d(vec2d_swizzle1(pa,0,0)));
}
#if EIGEN_COMP_PGI
typedef const void * SsePrefetchPtrType;
#else
typedef const char * SsePrefetchPtrType;
#endif
#ifndef EIGEN_VECTORIZE_AVX
template<> EIGEN_STRONG_INLINE void prefetch<float>(const float* addr) { _mm_prefetch((const char*)(addr), _MM_HINT_T0); }
template<> EIGEN_STRONG_INLINE void prefetch<double>(const double* addr) { _mm_prefetch((const char*)(addr), _MM_HINT_T0); }
template<> EIGEN_STRONG_INLINE void prefetch<int>(const int* addr) { _mm_prefetch((const char*)(addr), _MM_HINT_T0); }
template<> EIGEN_STRONG_INLINE void prefetch<float>(const float* addr) { _mm_prefetch((SsePrefetchPtrType)(addr), _MM_HINT_T0); }
template<> EIGEN_STRONG_INLINE void prefetch<double>(const double* addr) { _mm_prefetch((SsePrefetchPtrType)(addr), _MM_HINT_T0); }
template<> EIGEN_STRONG_INLINE void prefetch<int>(const int* addr) { _mm_prefetch((SsePrefetchPtrType)(addr), _MM_HINT_T0); }
#endif
#if EIGEN_COMP_MSVC_STRICT && EIGEN_OS_WIN64
@ -876,4 +882,14 @@ template<> EIGEN_STRONG_INLINE double pmadd(const double& a, const double& b, co
} // end namespace Eigen
#if EIGEN_COMP_PGI
// PGI++ does not define the following intrinsics in C++ mode.
static inline __m128 _mm_castpd_ps (__m128d x) { return reinterpret_cast<__m128&>(x); }
static inline __m128i _mm_castpd_si128(__m128d x) { return reinterpret_cast<__m128i&>(x); }
static inline __m128d _mm_castps_pd (__m128 x) { return reinterpret_cast<__m128d&>(x); }
static inline __m128i _mm_castps_si128(__m128 x) { return reinterpret_cast<__m128i&>(x); }
static inline __m128 _mm_castsi128_ps(__m128i x) { return reinterpret_cast<__m128&>(x); }
static inline __m128d _mm_castsi128_pd(__m128i x) { return reinterpret_cast<__m128d&>(x); }
#endif
#endif // EIGEN_PACKET_MATH_SSE_H

28
xs/src/eigen/Eigen/src/Core/arch/SSE/TypeCasting.h
View File

@ -14,6 +14,7 @@ namespace Eigen {
namespace internal {
#ifndef EIGEN_VECTORIZE_AVX
template <>
struct type_casting_traits<float, int> {
enum {
@ -23,11 +24,6 @@ struct type_casting_traits<float, int> {
};
};
template<> EIGEN_STRONG_INLINE Packet4i pcast<Packet4f, Packet4i>(const Packet4f& a) {
return _mm_cvttps_epi32(a);
}
template <>
struct type_casting_traits<int, float> {
enum {
@ -37,11 +33,6 @@ struct type_casting_traits<int, float> {
};
};
template<> EIGEN_STRONG_INLINE Packet4f pcast<Packet4i, Packet4f>(const Packet4i& a) {
return _mm_cvtepi32_ps(a);
}
template <>
struct type_casting_traits<double, float> {
enum {
@ -51,10 +42,6 @@ struct type_casting_traits<double, float> {
};
};
template<> EIGEN_STRONG_INLINE Packet4f pcast<Packet2d, Packet4f>(const Packet2d& a, const Packet2d& b) {
return _mm_shuffle_ps(_mm_cvtpd_ps(a), _mm_cvtpd_ps(b), (1 << 2) | (1 << 6));
}
template <>
struct type_casting_traits<float, double> {
enum {
@ -63,6 +50,19 @@ struct type_casting_traits<float, double> {
TgtCoeffRatio = 2
};
};
#endif
template<> EIGEN_STRONG_INLINE Packet4i pcast<Packet4f, Packet4i>(const Packet4f& a) {
return _mm_cvttps_epi32(a);
}
template<> EIGEN_STRONG_INLINE Packet4f pcast<Packet4i, Packet4f>(const Packet4i& a) {
return _mm_cvtepi32_ps(a);
}
template<> EIGEN_STRONG_INLINE Packet4f pcast<Packet2d, Packet4f>(const Packet2d& a, const Packet2d& b) {
return _mm_shuffle_ps(_mm_cvtpd_ps(a), _mm_cvtpd_ps(b), (1 << 2) | (1 << 6));
}
template<> EIGEN_STRONG_INLINE Packet2d pcast<Packet4f, Packet2d>(const Packet4f& a) {
// Simply discard the second half of the input

3
xs/src/eigen/Eigen/src/Core/arch/ZVector/Complex.h
View File

@ -336,6 +336,9 @@ template<> struct conj_helper<Packet2cf, Packet2cf, true,true>
}
};
EIGEN_MAKE_CONJ_HELPER_CPLX_REAL(Packet2cf,Packet4f)
EIGEN_MAKE_CONJ_HELPER_CPLX_REAL(Packet1cd,Packet2d)
template<> EIGEN_STRONG_INLINE Packet1cd pdiv<Packet1cd>(const Packet1cd& a, const Packet1cd& b)
{
// TODO optimize it for AltiVec

25
xs/src/eigen/Eigen/src/Core/functors/BinaryFunctors.h
View File

@ -255,7 +255,7 @@ struct scalar_cmp_op<LhsScalar,RhsScalar, cmp_NEQ> : binary_op_base<LhsScalar,Rh
/** \internal
* \brief Template functor to compute the hypot of two scalars
* \brief Template functor to compute the hypot of two \b positive \b and \b real scalars
*
* \sa MatrixBase::stableNorm(), class Redux
*/
@ -263,22 +263,15 @@ template<typename Scalar>
struct scalar_hypot_op<Scalar,Scalar> : binary_op_base<Scalar,Scalar>
{
EIGEN_EMPTY_STRUCT_CTOR(scalar_hypot_op)
// typedef typename NumTraits<Scalar>::Real result_type;
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator() (const Scalar& _x, const Scalar& _y) const
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator() (const Scalar &x, const Scalar &y) const
{
EIGEN_USING_STD_MATH(sqrt)
Scalar p, qp;
if(_x>_y)
{
p = _x;
qp = _y / p;
}
else
{
p = _y;
qp = _x / p;
}
return p * sqrt(Scalar(1) + qp*qp);
// This functor is used by hypotNorm only for which it is faster to first apply abs
// on all coefficients prior to reduction through hypot.
// This way we avoid calling abs on positive and real entries, and this also permits
// to seamlessly handle complexes. Otherwise we would have to handle both real and complexes
// through the same functor...
return internal::positive_real_hypot(x,y);
}
};
template<typename Scalar>

11
xs/src/eigen/Eigen/src/Core/functors/NullaryFunctors.h
View File

@ -44,16 +44,16 @@ struct linspaced_op_impl<Scalar,Packet,/*IsInteger*/false>
{
linspaced_op_impl(const Scalar& low, const Scalar& high, Index num_steps) :
m_low(low), m_high(high), m_size1(num_steps==1 ? 1 : num_steps-1), m_step(num_steps==1 ? Scalar() : (high-low)/Scalar(num_steps-1)),
m_interPacket(plset<Packet>(0)),
m_flip(numext::abs(high)<numext::abs(low))
{}
template<typename IndexType>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator() (IndexType i) const {
typedef typename NumTraits<Scalar>::Real RealScalar;
if(m_flip)
return (i==0)? m_low : (m_high - (m_size1-i)*m_step);
return (i==0)? m_low : (m_high - RealScalar(m_size1-i)*m_step);
else
return (i==m_size1)? m_high : (m_low + i*m_step);
return (i==m_size1)? m_high : (m_low + RealScalar(i)*m_step);
}
template<typename IndexType>
@ -63,7 +63,7 @@ struct linspaced_op_impl<Scalar,Packet,/*IsInteger*/false>
// [low, ..., low] + ( [step, ..., step] * ( [i, ..., i] + [0, ..., size] ) )
if(m_flip)
{
Packet pi = padd(pset1<Packet>(Scalar(i-m_size1)),m_interPacket);
Packet pi = plset<Packet>(Scalar(i-m_size1));
Packet res = padd(pset1<Packet>(m_high), pmul(pset1<Packet>(m_step), pi));
if(i==0)
res = pinsertfirst(res, m_low);
@ -71,7 +71,7 @@ struct linspaced_op_impl<Scalar,Packet,/*IsInteger*/false>
}
else
{
Packet pi = padd(pset1<Packet>(Scalar(i)),m_interPacket);
Packet pi = plset<Packet>(Scalar(i));
Packet res = padd(pset1<Packet>(m_low), pmul(pset1<Packet>(m_step), pi));
if(i==m_size1-unpacket_traits<Packet>::size+1)
res = pinsertlast(res, m_high);
@ -83,7 +83,6 @@ struct linspaced_op_impl<Scalar,Packet,/*IsInteger*/false>
const Scalar m_high;
const Index m_size1;
const Scalar m_step;
const Packet m_interPacket;
const bool m_flip;
};

4
xs/src/eigen/Eigen/src/Core/functors/StlFunctors.h
View File

@ -83,13 +83,17 @@ struct functor_traits<std::binder1st<T> >
{ enum { Cost = functor_traits<T>::Cost, PacketAccess = false }; };
#endif
#if (__cplusplus < 201703L) && (EIGEN_COMP_MSVC < 1910)
// std::unary_negate is deprecated since c++17 and will be removed in c++20
template<typename T>
struct functor_traits<std::unary_negate<T> >
{ enum { Cost = 1 + functor_traits<T>::Cost, PacketAccess = false }; };
// std::binary_negate is deprecated since c++17 and will be removed in c++20
template<typename T>
struct functor_traits<std::binary_negate<T> >
{ enum { Cost = 1 + functor_traits<T>::Cost, PacketAccess = false }; };
#endif
#ifdef EIGEN_STDEXT_SUPPORT

13
xs/src/eigen/Eigen/src/Core/products/GeneralMatrixMatrixTriangular.h
View File

@ -269,10 +269,13 @@ struct general_product_to_triangular_selector<MatrixType,ProductType,UpLo,false>
enum {
IsRowMajor = (internal::traits<MatrixType>::Flags&RowMajorBit) ? 1 : 0,
LhsIsRowMajor = _ActualLhs::Flags&RowMajorBit ? 1 : 0,
RhsIsRowMajor = _ActualRhs::Flags&RowMajorBit ? 1 : 0
RhsIsRowMajor = _ActualRhs::Flags&RowMajorBit ? 1 : 0,
SkipDiag = (UpLo&(UnitDiag|ZeroDiag))!=0
};
Index size = mat.cols();
if(SkipDiag)
size--;
Index depth = actualLhs.cols();
typedef internal::gemm_blocking_space<IsRowMajor ? RowMajor : ColMajor,typename Lhs::Scalar,typename Rhs::Scalar,
@ -283,10 +286,11 @@ struct general_product_to_triangular_selector<MatrixType,ProductType,UpLo,false>
internal::general_matrix_matrix_triangular_product<Index,
typename Lhs::Scalar, LhsIsRowMajor ? RowMajor : ColMajor, LhsBlasTraits::NeedToConjugate,
typename Rhs::Scalar, RhsIsRowMajor ? RowMajor : ColMajor, RhsBlasTraits::NeedToConjugate,
IsRowMajor ? RowMajor : ColMajor, UpLo>
IsRowMajor ? RowMajor : ColMajor, UpLo&(Lower|Upper)>
::run(size, depth,
&actualLhs.coeffRef(0,0), actualLhs.outerStride(), &actualRhs.coeffRef(0,0), actualRhs.outerStride(),
mat.data(), mat.outerStride(), actualAlpha, blocking);
&actualLhs.coeffRef(SkipDiag&&(UpLo&Lower)==Lower ? 1 : 0,0), actualLhs.outerStride(),
&actualRhs.coeffRef(0,SkipDiag&&(UpLo&Upper)==Upper ? 1 : 0), actualRhs.outerStride(),
mat.data() + (SkipDiag ? (bool(IsRowMajor) != ((UpLo&Lower)==Lower) ? 1 : mat.outerStride() ) : 0), mat.outerStride(), actualAlpha, blocking);
}
};
@ -294,6 +298,7 @@ template<typename MatrixType, unsigned int UpLo>
template<typename ProductType>
TriangularView<MatrixType,UpLo>& TriangularViewImpl<MatrixType,UpLo,Dense>::_assignProduct(const ProductType& prod, const Scalar& alpha, bool beta)
{
EIGEN_STATIC_ASSERT((UpLo&UnitDiag)==0, WRITING_TO_TRIANGULAR_PART_WITH_UNIT_DIAGONAL_IS_NOT_SUPPORTED);
eigen_assert(derived().nestedExpression().rows() == prod.rows() && derived().cols() == prod.cols());
general_product_to_triangular_selector<MatrixType, ProductType, UpLo, internal::traits<ProductType>::InnerSize==1>::run(derived().nestedExpression().const_cast_derived(), prod, alpha, beta);

10
xs/src/eigen/Eigen/src/Core/products/GeneralMatrixMatrixTriangular_BLAS.h
View File

@ -52,7 +52,7 @@ struct general_matrix_matrix_triangular_product<Index,Scalar,LhsStorageOrder,Con
static EIGEN_STRONG_INLINE void run(Index size, Index depth,const Scalar* lhs, Index lhsStride, \
const Scalar* rhs, Index rhsStride, Scalar* res, Index resStride, Scalar alpha, level3_blocking<Scalar, Scalar>& blocking) \
{ \
if (lhs==rhs) { \
if ( lhs==rhs && ((UpLo&(Lower|Upper)==UpLo)) ) { \
general_matrix_matrix_rankupdate<Index,Scalar,LhsStorageOrder,ConjugateLhs,ColMajor,UpLo> \
::run(size,depth,lhs,lhsStride,rhs,rhsStride,res,resStride,alpha,blocking); \
} else { \
@ -88,7 +88,7 @@ struct general_matrix_matrix_rankupdate<Index,EIGTYPE,AStorageOrder,ConjugateA,C
BlasIndex lda=convert_index<BlasIndex>(lhsStride), ldc=convert_index<BlasIndex>(resStride), n=convert_index<BlasIndex>(size), k=convert_index<BlasIndex>(depth); \
char uplo=((IsLower) ? 'L' : 'U'), trans=((AStorageOrder==RowMajor) ? 'T':'N'); \
EIGTYPE beta(1); \
BLASFUNC(&uplo, &trans, &n, &k, &numext::real_ref(alpha), lhs, &lda, &numext::real_ref(beta), res, &ldc); \
BLASFUNC(&uplo, &trans, &n, &k, (const BLASTYPE*)&numext::real_ref(alpha), lhs, &lda, (const BLASTYPE*)&numext::real_ref(beta), res, &ldc); \
} \
};
@ -125,9 +125,13 @@ struct general_matrix_matrix_rankupdate<Index,EIGTYPE,AStorageOrder,ConjugateA,C
} \
};
#ifdef EIGEN_USE_MKL
EIGEN_BLAS_RANKUPDATE_R(double, double, dsyrk)
EIGEN_BLAS_RANKUPDATE_R(float, float, ssyrk)
#else
EIGEN_BLAS_RANKUPDATE_R(double, double, dsyrk_)
EIGEN_BLAS_RANKUPDATE_R(float, float, ssyrk_)
#endif
// TODO hanlde complex cases
// EIGEN_BLAS_RANKUPDATE_C(dcomplex, double, double, zherk_)

19
xs/src/eigen/Eigen/src/Core/products/GeneralMatrixMatrix_BLAS.h
View File

@ -46,7 +46,7 @@ namespace internal {
// gemm specialization
#define GEMM_SPECIALIZATION(EIGTYPE, EIGPREFIX, BLASTYPE, BLASPREFIX) \
#define GEMM_SPECIALIZATION(EIGTYPE, EIGPREFIX, BLASTYPE, BLASFUNC) \
template< \
typename Index, \
int LhsStorageOrder, bool ConjugateLhs, \
@ -100,13 +100,20 @@ static void run(Index rows, Index cols, Index depth, \
ldb = convert_index<BlasIndex>(b_tmp.outerStride()); \
} else b = _rhs; \
\
BLASPREFIX##gemm_(&transa, &transb, &m, &n, &k, &numext::real_ref(alpha), (const BLASTYPE*)a, &lda, (const BLASTYPE*)b, &ldb, &numext::real_ref(beta), (BLASTYPE*)res, &ldc); \
BLASFUNC(&transa, &transb, &m, &n, &k, (const BLASTYPE*)&numext::real_ref(alpha), (const BLASTYPE*)a, &lda, (const BLASTYPE*)b, &ldb, (const BLASTYPE*)&numext::real_ref(beta), (BLASTYPE*)res, &ldc); \
}};
GEMM_SPECIALIZATION(double, d, double, d)
GEMM_SPECIALIZATION(float, f, float, s)
GEMM_SPECIALIZATION(dcomplex, cd, double, z)
GEMM_SPECIALIZATION(scomplex, cf, float, c)
#ifdef EIGEN_USE_MKL
GEMM_SPECIALIZATION(double, d, double, dgemm)
GEMM_SPECIALIZATION(float, f, float, sgemm)
GEMM_SPECIALIZATION(dcomplex, cd, MKL_Complex16, zgemm)
GEMM_SPECIALIZATION(scomplex, cf, MKL_Complex8, cgemm)
#else
GEMM_SPECIALIZATION(double, d, double, dgemm_)
GEMM_SPECIALIZATION(float, f, float, sgemm_)
GEMM_SPECIALIZATION(dcomplex, cd, double, zgemm_)
GEMM_SPECIALIZATION(scomplex, cf, float, cgemm_)
#endif
} // end namespase internal

8
xs/src/eigen/Eigen/src/Core/products/GeneralMatrixVector.h
View File

@ -183,8 +183,8 @@ EIGEN_DONT_INLINE void general_matrix_vector_product<Index,LhsScalar,LhsMapper,C
alignmentPattern = AllAligned;
}
const Index offset1 = (FirstAligned && alignmentStep==1)?3:1;
const Index offset3 = (FirstAligned && alignmentStep==1)?1:3;
const Index offset1 = (alignmentPattern==FirstAligned && alignmentStep==1)?3:1;
const Index offset3 = (alignmentPattern==FirstAligned && alignmentStep==1)?1:3;
Index columnBound = ((cols-skipColumns)/columnsAtOnce)*columnsAtOnce + skipColumns;
for (Index i=skipColumns; i<columnBound; i+=columnsAtOnce)
@ -457,8 +457,8 @@ EIGEN_DONT_INLINE void general_matrix_vector_product<Index,LhsScalar,LhsMapper,R
alignmentPattern = AllAligned;
}
const Index offset1 = (FirstAligned && alignmentStep==1)?3:1;
const Index offset3 = (FirstAligned && alignmentStep==1)?1:3;
const Index offset1 = (alignmentPattern==FirstAligned && alignmentStep==1)?3:1;
const Index offset3 = (alignmentPattern==FirstAligned && alignmentStep==1)?1:3;
Index rowBound = ((rows-skipRows)/rowsAtOnce)*rowsAtOnce + skipRows;
for (Index i=skipRows; i<rowBound; i+=rowsAtOnce)

19
xs/src/eigen/Eigen/src/Core/products/GeneralMatrixVector_BLAS.h
View File

@ -85,7 +85,7 @@ EIGEN_BLAS_GEMV_SPECIALIZE(float)
EIGEN_BLAS_GEMV_SPECIALIZE(dcomplex)
EIGEN_BLAS_GEMV_SPECIALIZE(scomplex)
#define EIGEN_BLAS_GEMV_SPECIALIZATION(EIGTYPE,BLASTYPE,BLASPREFIX) \
#define EIGEN_BLAS_GEMV_SPECIALIZATION(EIGTYPE,BLASTYPE,BLASFUNC) \
template<typename Index, int LhsStorageOrder, bool ConjugateLhs, bool ConjugateRhs> \
struct general_matrix_vector_product_gemv<Index,EIGTYPE,LhsStorageOrder,ConjugateLhs,EIGTYPE,ConjugateRhs> \
{ \
@ -113,14 +113,21 @@ static void run( \
x_ptr=x_tmp.data(); \
incx=1; \
} else x_ptr=rhs; \
BLASPREFIX##gemv_(&trans, &m, &n, &numext::real_ref(alpha), (const BLASTYPE*)lhs, &lda, (const BLASTYPE*)x_ptr, &incx, &numext::real_ref(beta), (BLASTYPE*)res, &incy); \
BLASFUNC(&trans, &m, &n, (const BLASTYPE*)&numext::real_ref(alpha), (const BLASTYPE*)lhs, &lda, (const BLASTYPE*)x_ptr, &incx, (const BLASTYPE*)&numext::real_ref(beta), (BLASTYPE*)res, &incy); \
}\
};
EIGEN_BLAS_GEMV_SPECIALIZATION(double, double, d)
EIGEN_BLAS_GEMV_SPECIALIZATION(float, float, s)
EIGEN_BLAS_GEMV_SPECIALIZATION(dcomplex, double, z)
EIGEN_BLAS_GEMV_SPECIALIZATION(scomplex, float, c)
#ifdef EIGEN_USE_MKL
EIGEN_BLAS_GEMV_SPECIALIZATION(double, double, dgemv)
EIGEN_BLAS_GEMV_SPECIALIZATION(float, float, sgemv)
EIGEN_BLAS_GEMV_SPECIALIZATION(dcomplex, MKL_Complex16, zgemv)
EIGEN_BLAS_GEMV_SPECIALIZATION(scomplex, MKL_Complex8 , cgemv)
#else
EIGEN_BLAS_GEMV_SPECIALIZATION(double, double, dgemv_)
EIGEN_BLAS_GEMV_SPECIALIZATION(float, float, sgemv_)
EIGEN_BLAS_GEMV_SPECIALIZATION(dcomplex, double, zgemv_)
EIGEN_BLAS_GEMV_SPECIALIZATION(scomplex, float, cgemv_)
#endif
} // end namespase internal

48
xs/src/eigen/Eigen/src/Core/products/SelfadjointMatrixMatrix_BLAS.h
View File

@ -40,7 +40,7 @@ namespace internal {
/* Optimized selfadjoint matrix * matrix (?SYMM/?HEMM) product */
#define EIGEN_BLAS_SYMM_L(EIGTYPE, BLASTYPE, EIGPREFIX, BLASPREFIX) \
#define EIGEN_BLAS_SYMM_L(EIGTYPE, BLASTYPE, EIGPREFIX, BLASFUNC) \
template <typename Index, \
int LhsStorageOrder, bool ConjugateLhs, \
int RhsStorageOrder, bool ConjugateRhs> \
@ -81,13 +81,13 @@ struct product_selfadjoint_matrix<EIGTYPE,Index,LhsStorageOrder,true,ConjugateLh
ldb = convert_index<BlasIndex>(b_tmp.outerStride()); \
} else b = _rhs; \
\
BLASPREFIX##symm_(&side, &uplo, &m, &n, &numext::real_ref(alpha), (const BLASTYPE*)a, &lda, (const BLASTYPE*)b, &ldb, &numext::real_ref(beta), (BLASTYPE*)res, &ldc); \
BLASFUNC(&side, &uplo, &m, &n, (const BLASTYPE*)&numext::real_ref(alpha), (const BLASTYPE*)a, &lda, (const BLASTYPE*)b, &ldb, (const BLASTYPE*)&numext::real_ref(beta), (BLASTYPE*)res, &ldc); \
\
} \
};
#define EIGEN_BLAS_HEMM_L(EIGTYPE, BLASTYPE, EIGPREFIX, BLASPREFIX) \
#define EIGEN_BLAS_HEMM_L(EIGTYPE, BLASTYPE, EIGPREFIX, BLASFUNC) \
template <typename Index, \
int LhsStorageOrder, bool ConjugateLhs, \
int RhsStorageOrder, bool ConjugateRhs> \
@ -144,20 +144,26 @@ struct product_selfadjoint_matrix<EIGTYPE,Index,LhsStorageOrder,true,ConjugateLh
ldb = convert_index<BlasIndex>(b_tmp.outerStride()); \
} \
\
BLASPREFIX##hemm_(&side, &uplo, &m, &n, &numext::real_ref(alpha), (const BLASTYPE*)a, &lda, (const BLASTYPE*)b, &ldb, &numext::real_ref(beta), (BLASTYPE*)res, &ldc); \
BLASFUNC(&side, &uplo, &m, &n, (const BLASTYPE*)&numext::real_ref(alpha), (const BLASTYPE*)a, &lda, (const BLASTYPE*)b, &ldb, (const BLASTYPE*)&numext::real_ref(beta), (BLASTYPE*)res, &ldc); \
\
} \
};
EIGEN_BLAS_SYMM_L(double, double, d, d)
EIGEN_BLAS_SYMM_L(float, float, f, s)
EIGEN_BLAS_HEMM_L(dcomplex, double, cd, z)
EIGEN_BLAS_HEMM_L(scomplex, float, cf, c)
#ifdef EIGEN_USE_MKL
EIGEN_BLAS_SYMM_L(double, double, d, dsymm)
EIGEN_BLAS_SYMM_L(float, float, f, ssymm)
EIGEN_BLAS_HEMM_L(dcomplex, MKL_Complex16, cd, zhemm)
EIGEN_BLAS_HEMM_L(scomplex, MKL_Complex8, cf, chemm)
#else
EIGEN_BLAS_SYMM_L(double, double, d, dsymm_)
EIGEN_BLAS_SYMM_L(float, float, f, ssymm_)
EIGEN_BLAS_HEMM_L(dcomplex, double, cd, zhemm_)
EIGEN_BLAS_HEMM_L(scomplex, float, cf, chemm_)
#endif
/* Optimized matrix * selfadjoint matrix (?SYMM/?HEMM) product */
#define EIGEN_BLAS_SYMM_R(EIGTYPE, BLASTYPE, EIGPREFIX, BLASPREFIX) \
#define EIGEN_BLAS_SYMM_R(EIGTYPE, BLASTYPE, EIGPREFIX, BLASFUNC) \
template <typename Index, \
int LhsStorageOrder, bool ConjugateLhs, \
int RhsStorageOrder, bool ConjugateRhs> \
@ -197,13 +203,13 @@ struct product_selfadjoint_matrix<EIGTYPE,Index,LhsStorageOrder,false,ConjugateL
ldb = convert_index<BlasIndex>(b_tmp.outerStride()); \
} else b = _lhs; \
\
BLASPREFIX##symm_(&side, &uplo, &m, &n, &numext::real_ref(alpha), (const BLASTYPE*)a, &lda, (const BLASTYPE*)b, &ldb, &numext::real_ref(beta), (BLASTYPE*)res, &ldc); \
BLASFUNC(&side, &uplo, &m, &n, (const BLASTYPE*)&numext::real_ref(alpha), (const BLASTYPE*)a, &lda, (const BLASTYPE*)b, &ldb, (const BLASTYPE*)&numext::real_ref(beta), (BLASTYPE*)res, &ldc); \
\
} \
};
#define EIGEN_BLAS_HEMM_R(EIGTYPE, BLASTYPE, EIGPREFIX, BLASPREFIX) \
#define EIGEN_BLAS_HEMM_R(EIGTYPE, BLASTYPE, EIGPREFIX, BLASFUNC) \
template <typename Index, \
int LhsStorageOrder, bool ConjugateLhs, \
int RhsStorageOrder, bool ConjugateRhs> \
@ -259,15 +265,21 @@ struct product_selfadjoint_matrix<EIGTYPE,Index,LhsStorageOrder,false,ConjugateL
ldb = convert_index<BlasIndex>(b_tmp.outerStride()); \
} \
\
BLASPREFIX##hemm_(&side, &uplo, &m, &n, &numext::real_ref(alpha), (const BLASTYPE*)a, &lda, (const BLASTYPE*)b, &ldb, &numext::real_ref(beta), (BLASTYPE*)res, &ldc); \
BLASFUNC(&side, &uplo, &m, &n, (const BLASTYPE*)&numext::real_ref(alpha), (const BLASTYPE*)a, &lda, (const BLASTYPE*)b, &ldb, (const BLASTYPE*)&numext::real_ref(beta), (BLASTYPE*)res, &ldc); \
} \
};
EIGEN_BLAS_SYMM_R(double, double, d, d)
EIGEN_BLAS_SYMM_R(float, float, f, s)
EIGEN_BLAS_HEMM_R(dcomplex, double, cd, z)
EIGEN_BLAS_HEMM_R(scomplex, float, cf, c)
#ifdef EIGEN_USE_MKL
EIGEN_BLAS_SYMM_R(double, double, d, dsymm)
EIGEN_BLAS_SYMM_R(float, float, f, ssymm)
EIGEN_BLAS_HEMM_R(dcomplex, MKL_Complex16, cd, zhemm)
EIGEN_BLAS_HEMM_R(scomplex, MKL_Complex8, cf, chemm)
#else
EIGEN_BLAS_SYMM_R(double, double, d, dsymm_)
EIGEN_BLAS_SYMM_R(float, float, f, ssymm_)
EIGEN_BLAS_HEMM_R(dcomplex, double, cd, zhemm_)
EIGEN_BLAS_HEMM_R(scomplex, float, cf, chemm_)
#endif
} // end namespace internal
} // end namespace Eigen

9
xs/src/eigen/Eigen/src/Core/products/SelfadjointMatrixVector_BLAS.h
View File

@ -95,14 +95,21 @@ const EIGTYPE* _rhs, EIGTYPE* res, EIGTYPE alpha) \
x_tmp=map_x.conjugate(); \
x_ptr=x_tmp.data(); \
} else x_ptr=_rhs; \
BLASFUNC(&uplo, &n, &numext::real_ref(alpha), (const BLASTYPE*)lhs, &lda, (const BLASTYPE*)x_ptr, &incx, &numext::real_ref(beta), (BLASTYPE*)res, &incy); \
BLASFUNC(&uplo, &n, (const BLASTYPE*)&numext::real_ref(alpha), (const BLASTYPE*)lhs, &lda, (const BLASTYPE*)x_ptr, &incx, (const BLASTYPE*)&numext::real_ref(beta), (BLASTYPE*)res, &incy); \
}\
};
#ifdef EIGEN_USE_MKL
EIGEN_BLAS_SYMV_SPECIALIZATION(double, double, dsymv)
EIGEN_BLAS_SYMV_SPECIALIZATION(float, float, ssymv)
EIGEN_BLAS_SYMV_SPECIALIZATION(dcomplex, MKL_Complex16, zhemv)
EIGEN_BLAS_SYMV_SPECIALIZATION(scomplex, MKL_Complex8, chemv)
#else
EIGEN_BLAS_SYMV_SPECIALIZATION(double, double, dsymv_)
EIGEN_BLAS_SYMV_SPECIALIZATION(float, float, ssymv_)
EIGEN_BLAS_SYMV_SPECIALIZATION(dcomplex, double, zhemv_)
EIGEN_BLAS_SYMV_SPECIALIZATION(scomplex, float, chemv_)
#endif
} // end namespace internal

33
xs/src/eigen/Eigen/src/Core/products/TriangularMatrixMatrix.h
View File

@ -137,7 +137,13 @@ EIGEN_DONT_INLINE void product_triangular_matrix_matrix<Scalar,Index,Mode,true,
ei_declare_aligned_stack_constructed_variable(Scalar, blockA, sizeA, blocking.blockA());
ei_declare_aligned_stack_constructed_variable(Scalar, blockB, sizeB, blocking.blockB());
Matrix<Scalar,SmallPanelWidth,SmallPanelWidth,LhsStorageOrder> triangularBuffer((internal::constructor_without_unaligned_array_assert()));
// To work around an "error: member reference base type 'Matrix<...>
// (Eigen::internal::constructor_without_unaligned_array_assert (*)())' is
// not a structure or union" compilation error in nvcc (tested V8.0.61),
// create a dummy internal::constructor_without_unaligned_array_assert
// object to pass to the Matrix constructor.
internal::constructor_without_unaligned_array_assert a;
Matrix<Scalar,SmallPanelWidth,SmallPanelWidth,LhsStorageOrder> triangularBuffer(a);
triangularBuffer.setZero();
if((Mode&ZeroDiag)==ZeroDiag)
triangularBuffer.diagonal().setZero();
@ -284,7 +290,8 @@ EIGEN_DONT_INLINE void product_triangular_matrix_matrix<Scalar,Index,Mode,false,
ei_declare_aligned_stack_constructed_variable(Scalar, blockA, sizeA, blocking.blockA());
ei_declare_aligned_stack_constructed_variable(Scalar, blockB, sizeB, blocking.blockB());
Matrix<Scalar,SmallPanelWidth,SmallPanelWidth,RhsStorageOrder> triangularBuffer((internal::constructor_without_unaligned_array_assert()));
internal::constructor_without_unaligned_array_assert a;
Matrix<Scalar,SmallPanelWidth,SmallPanelWidth,RhsStorageOrder> triangularBuffer(a);
triangularBuffer.setZero();
if((Mode&ZeroDiag)==ZeroDiag)
triangularBuffer.diagonal().setZero();
@ -393,6 +400,8 @@ struct triangular_product_impl<Mode,LhsIsTriangular,Lhs,false,Rhs,false>
{
template<typename Dest> static void run(Dest& dst, const Lhs &a_lhs, const Rhs &a_rhs, const typename Dest::Scalar& alpha)
{
typedef typename Lhs::Scalar LhsScalar;
typedef typename Rhs::Scalar RhsScalar;
typedef typename Dest::Scalar Scalar;
typedef internal::blas_traits<Lhs> LhsBlasTraits;
@ -405,8 +414,9 @@ struct triangular_product_impl<Mode,LhsIsTriangular,Lhs,false,Rhs,false>
typename internal::add_const_on_value_type<ActualLhsType>::type lhs = LhsBlasTraits::extract(a_lhs);
typename internal::add_const_on_value_type<ActualRhsType>::type rhs = RhsBlasTraits::extract(a_rhs);
Scalar actualAlpha = alpha * LhsBlasTraits::extractScalarFactor(a_lhs)
* RhsBlasTraits::extractScalarFactor(a_rhs);
LhsScalar lhs_alpha = LhsBlasTraits::extractScalarFactor(a_lhs);
RhsScalar rhs_alpha = RhsBlasTraits::extractScalarFactor(a_rhs);
Scalar actualAlpha = alpha * lhs_alpha * rhs_alpha;
typedef internal::gemm_blocking_space<(Dest::Flags&RowMajorBit) ? RowMajor : ColMajor,Scalar,Scalar,
Lhs::MaxRowsAtCompileTime, Rhs::MaxColsAtCompileTime, Lhs::MaxColsAtCompileTime,4> BlockingType;
@ -431,6 +441,21 @@ struct triangular_product_impl<Mode,LhsIsTriangular,Lhs,false,Rhs,false>
&dst.coeffRef(0,0), dst.outerStride(), // result info
actualAlpha, blocking
);
// Apply correction if the diagonal is unit and a scalar factor was nested:
if ((Mode&UnitDiag)==UnitDiag)
{
if (LhsIsTriangular && lhs_alpha!=LhsScalar(1))
{
Index diagSize = (std::min)(lhs.rows(),lhs.cols());
dst.topRows(diagSize) -= ((lhs_alpha-LhsScalar(1))*a_rhs).topRows(diagSize);
}
else if ((!LhsIsTriangular) && rhs_alpha!=RhsScalar(1))
{
Index diagSize = (std::min)(rhs.rows(),rhs.cols());
dst.leftCols(diagSize) -= (rhs_alpha-RhsScalar(1))*a_lhs.leftCols(diagSize);
}
}
}
};

39
xs/src/eigen/Eigen/src/Core/products/TriangularMatrixMatrix_BLAS.h
View File

@ -75,7 +75,7 @@ EIGEN_BLAS_TRMM_SPECIALIZE(scomplex, true)
EIGEN_BLAS_TRMM_SPECIALIZE(scomplex, false)
// implements col-major += alpha * op(triangular) * op(general)
#define EIGEN_BLAS_TRMM_L(EIGTYPE, BLASTYPE, EIGPREFIX, BLASPREFIX) \
#define EIGEN_BLAS_TRMM_L(EIGTYPE, BLASTYPE, EIGPREFIX, BLASFUNC) \
template <typename Index, int Mode, \
int LhsStorageOrder, bool ConjugateLhs, \
int RhsStorageOrder, bool ConjugateRhs> \
@ -172,7 +172,7 @@ struct product_triangular_matrix_matrix_trmm<EIGTYPE,Index,Mode,true, \
} \
/*std::cout << "TRMM_L: A is square! Go to BLAS TRMM implementation! \n";*/ \
/* call ?trmm*/ \
BLASPREFIX##trmm_(&side, &uplo, &transa, &diag, &m, &n, &numext::real_ref(alpha), (const BLASTYPE*)a, &lda, (BLASTYPE*)b, &ldb); \
BLASFUNC(&side, &uplo, &transa, &diag, &m, &n, (const BLASTYPE*)&numext::real_ref(alpha), (const BLASTYPE*)a, &lda, (BLASTYPE*)b, &ldb); \
\
/* Add op(a_triangular)*b into res*/ \
Map<MatrixX##EIGPREFIX, 0, OuterStride<> > res_tmp(res,rows,cols,OuterStride<>(resStride)); \
@ -180,13 +180,20 @@ struct product_triangular_matrix_matrix_trmm<EIGTYPE,Index,Mode,true, \
} \
};
EIGEN_BLAS_TRMM_L(double, double, d, d)
EIGEN_BLAS_TRMM_L(dcomplex, double, cd, z)
EIGEN_BLAS_TRMM_L(float, float, f, s)
EIGEN_BLAS_TRMM_L(scomplex, float, cf, c)
#ifdef EIGEN_USE_MKL
EIGEN_BLAS_TRMM_L(double, double, d, dtrmm)
EIGEN_BLAS_TRMM_L(dcomplex, MKL_Complex16, cd, ztrmm)
EIGEN_BLAS_TRMM_L(float, float, f, strmm)
EIGEN_BLAS_TRMM_L(scomplex, MKL_Complex8, cf, ctrmm)
#else
EIGEN_BLAS_TRMM_L(double, double, d, dtrmm_)
EIGEN_BLAS_TRMM_L(dcomplex, double, cd, ztrmm_)
EIGEN_BLAS_TRMM_L(float, float, f, strmm_)
EIGEN_BLAS_TRMM_L(scomplex, float, cf, ctrmm_)
#endif
// implements col-major += alpha * op(general) * op(triangular)
#define EIGEN_BLAS_TRMM_R(EIGTYPE, BLASTYPE, EIGPREFIX, BLASPREFIX) \
#define EIGEN_BLAS_TRMM_R(EIGTYPE, BLASTYPE, EIGPREFIX, BLASFUNC) \
template <typename Index, int Mode, \
int LhsStorageOrder, bool ConjugateLhs, \
int RhsStorageOrder, bool ConjugateRhs> \
@ -282,7 +289,7 @@ struct product_triangular_matrix_matrix_trmm<EIGTYPE,Index,Mode,false, \
} \
/*std::cout << "TRMM_R: A is square! Go to BLAS TRMM implementation! \n";*/ \
/* call ?trmm*/ \
BLASPREFIX##trmm_(&side, &uplo, &transa, &diag, &m, &n, &numext::real_ref(alpha), (const BLASTYPE*)a, &lda, (BLASTYPE*)b, &ldb); \
BLASFUNC(&side, &uplo, &transa, &diag, &m, &n, (const BLASTYPE*)&numext::real_ref(alpha), (const BLASTYPE*)a, &lda, (BLASTYPE*)b, &ldb); \
\
/* Add op(a_triangular)*b into res*/ \
Map<MatrixX##EIGPREFIX, 0, OuterStride<> > res_tmp(res,rows,cols,OuterStride<>(resStride)); \
@ -290,11 +297,17 @@ struct product_triangular_matrix_matrix_trmm<EIGTYPE,Index,Mode,false, \
} \
};
EIGEN_BLAS_TRMM_R(double, double, d, d)
EIGEN_BLAS_TRMM_R(dcomplex, double, cd, z)
EIGEN_BLAS_TRMM_R(float, float, f, s)
EIGEN_BLAS_TRMM_R(scomplex, float, cf, c)
#ifdef EIGEN_USE_MKL
EIGEN_BLAS_TRMM_R(double, double, d, dtrmm)
EIGEN_BLAS_TRMM_R(dcomplex, MKL_Complex16, cd, ztrmm)
EIGEN_BLAS_TRMM_R(float, float, f, strmm)
EIGEN_BLAS_TRMM_R(scomplex, MKL_Complex8, cf, ctrmm)
#else
EIGEN_BLAS_TRMM_R(double, double, d, dtrmm_)
EIGEN_BLAS_TRMM_R(dcomplex, double, cd, ztrmm_)
EIGEN_BLAS_TRMM_R(float, float, f, strmm_)
EIGEN_BLAS_TRMM_R(scomplex, float, cf, ctrmm_)
#endif
} // end namespace internal
} // end namespace Eigen

22
xs/src/eigen/Eigen/src/Core/products/TriangularMatrixVector.h
View File

@ -221,8 +221,9 @@ template<int Mode> struct trmv_selector<Mode,ColMajor>
typename internal::add_const_on_value_type<ActualLhsType>::type actualLhs = LhsBlasTraits::extract(lhs);
typename internal::add_const_on_value_type<ActualRhsType>::type actualRhs = RhsBlasTraits::extract(rhs);
ResScalar actualAlpha = alpha * LhsBlasTraits::extractScalarFactor(lhs)
* RhsBlasTraits::extractScalarFactor(rhs);
LhsScalar lhs_alpha = LhsBlasTraits::extractScalarFactor(lhs);
RhsScalar rhs_alpha = RhsBlasTraits::extractScalarFactor(rhs);
ResScalar actualAlpha = alpha * lhs_alpha * rhs_alpha;
enum {
// FIXME find a way to allow an inner stride on the result if packet_traits<Scalar>::size==1
@ -274,6 +275,12 @@ template<int Mode> struct trmv_selector<Mode,ColMajor>
else
dest = MappedDest(actualDestPtr, dest.size());
}
if ( ((Mode&UnitDiag)==UnitDiag) && (lhs_alpha!=LhsScalar(1)) )
{
Index diagSize = (std::min)(lhs.rows(),lhs.cols());
dest.head(diagSize) -= (lhs_alpha-LhsScalar(1))*rhs.head(diagSize);
}
}
};
@ -295,8 +302,9 @@ template<int Mode> struct trmv_selector<Mode,RowMajor>
typename add_const<ActualLhsType>::type actualLhs = LhsBlasTraits::extract(lhs);
typename add_const<ActualRhsType>::type actualRhs = RhsBlasTraits::extract(rhs);
ResScalar actualAlpha = alpha * LhsBlasTraits::extractScalarFactor(lhs)
* RhsBlasTraits::extractScalarFactor(rhs);
LhsScalar lhs_alpha = LhsBlasTraits::extractScalarFactor(lhs);
RhsScalar rhs_alpha = RhsBlasTraits::extractScalarFactor(rhs);
ResScalar actualAlpha = alpha * lhs_alpha * rhs_alpha;
enum {
DirectlyUseRhs = ActualRhsTypeCleaned::InnerStrideAtCompileTime==1
@ -326,6 +334,12 @@ template<int Mode> struct trmv_selector<Mode,RowMajor>
actualRhsPtr,1,
dest.data(),dest.innerStride(),
actualAlpha);
if ( ((Mode&UnitDiag)==UnitDiag) && (lhs_alpha!=LhsScalar(1)) )
{
Index diagSize = (std::min)(lhs.rows(),lhs.cols());
dest.head(diagSize) -= (lhs_alpha-LhsScalar(1))*rhs.head(diagSize);
}
}
};

46
xs/src/eigen/Eigen/src/Core/products/TriangularMatrixVector_BLAS.h
View File

@ -71,7 +71,7 @@ EIGEN_BLAS_TRMV_SPECIALIZE(dcomplex)
EIGEN_BLAS_TRMV_SPECIALIZE(scomplex)
// implements col-major: res += alpha * op(triangular) * vector
#define EIGEN_BLAS_TRMV_CM(EIGTYPE, BLASTYPE, EIGPREFIX, BLASPREFIX) \
#define EIGEN_BLAS_TRMV_CM(EIGTYPE, BLASTYPE, EIGPREFIX, BLASPREFIX, BLASPOSTFIX) \
template<typename Index, int Mode, bool ConjLhs, bool ConjRhs> \
struct triangular_matrix_vector_product_trmv<Index,Mode,EIGTYPE,ConjLhs,EIGTYPE,ConjRhs,ColMajor> { \
enum { \
@ -121,10 +121,10 @@ struct triangular_matrix_vector_product_trmv<Index,Mode,EIGTYPE,ConjLhs,EIGTYPE,
diag = IsUnitDiag ? 'U' : 'N'; \
\
/* call ?TRMV*/ \
BLASPREFIX##trmv_(&uplo, &trans, &diag, &n, (const BLASTYPE*)_lhs, &lda, (BLASTYPE*)x, &incx); \
BLASPREFIX##trmv##BLASPOSTFIX(&uplo, &trans, &diag, &n, (const BLASTYPE*)_lhs, &lda, (BLASTYPE*)x, &incx); \
\
/* Add op(a_tr)rhs into res*/ \
BLASPREFIX##axpy_(&n, &numext::real_ref(alpha),(const BLASTYPE*)x, &incx, (BLASTYPE*)_res, &incy); \
BLASPREFIX##axpy##BLASPOSTFIX(&n, (const BLASTYPE*)&numext::real_ref(alpha),(const BLASTYPE*)x, &incx, (BLASTYPE*)_res, &incy); \
/* Non-square case - doesn't fit to BLAS ?TRMV. Fall to default triangular product*/ \
if (size<(std::max)(rows,cols)) { \
if (ConjRhs) x_tmp = rhs.conjugate(); else x_tmp = rhs; \
@ -142,18 +142,25 @@ struct triangular_matrix_vector_product_trmv<Index,Mode,EIGTYPE,ConjLhs,EIGTYPE,
m = convert_index<BlasIndex>(size); \
n = convert_index<BlasIndex>(cols-size); \
} \
BLASPREFIX##gemv_(&trans, &m, &n, &numext::real_ref(alpha), (const BLASTYPE*)a, &lda, (const BLASTYPE*)x, &incx, &numext::real_ref(beta), (BLASTYPE*)y, &incy); \
BLASPREFIX##gemv##BLASPOSTFIX(&trans, &m, &n, (const BLASTYPE*)&numext::real_ref(alpha), (const BLASTYPE*)a, &lda, (const BLASTYPE*)x, &incx, (const BLASTYPE*)&numext::real_ref(beta), (BLASTYPE*)y, &incy); \
} \
} \
};
EIGEN_BLAS_TRMV_CM(double, double, d, d)
EIGEN_BLAS_TRMV_CM(dcomplex, double, cd, z)
EIGEN_BLAS_TRMV_CM(float, float, f, s)
EIGEN_BLAS_TRMV_CM(scomplex, float, cf, c)
#ifdef EIGEN_USE_MKL
EIGEN_BLAS_TRMV_CM(double, double, d, d,)
EIGEN_BLAS_TRMV_CM(dcomplex, MKL_Complex16, cd, z,)
EIGEN_BLAS_TRMV_CM(float, float, f, s,)
EIGEN_BLAS_TRMV_CM(scomplex, MKL_Complex8, cf, c,)
#else
EIGEN_BLAS_TRMV_CM(double, double, d, d, _)
EIGEN_BLAS_TRMV_CM(dcomplex, double, cd, z, _)
EIGEN_BLAS_TRMV_CM(float, float, f, s, _)
EIGEN_BLAS_TRMV_CM(scomplex, float, cf, c, _)
#endif
// implements row-major: res += alpha * op(triangular) * vector
#define EIGEN_BLAS_TRMV_RM(EIGTYPE, BLASTYPE, EIGPREFIX, BLASPREFIX) \
#define EIGEN_BLAS_TRMV_RM(EIGTYPE, BLASTYPE, EIGPREFIX, BLASPREFIX, BLASPOSTFIX) \
template<typename Index, int Mode, bool ConjLhs, bool ConjRhs> \
struct triangular_matrix_vector_product_trmv<Index,Mode,EIGTYPE,ConjLhs,EIGTYPE,ConjRhs,RowMajor> { \
enum { \
@ -203,10 +210,10 @@ struct triangular_matrix_vector_product_trmv<Index,Mode,EIGTYPE,ConjLhs,EIGTYPE,
diag = IsUnitDiag ? 'U' : 'N'; \
\
/* call ?TRMV*/ \
BLASPREFIX##trmv_(&uplo, &trans, &diag, &n, (const BLASTYPE*)_lhs, &lda, (BLASTYPE*)x, &incx); \
BLASPREFIX##trmv##BLASPOSTFIX(&uplo, &trans, &diag, &n, (const BLASTYPE*)_lhs, &lda, (BLASTYPE*)x, &incx); \
\
/* Add op(a_tr)rhs into res*/ \
BLASPREFIX##axpy_(&n, &numext::real_ref(alpha),(const BLASTYPE*)x, &incx, (BLASTYPE*)_res, &incy); \
BLASPREFIX##axpy##BLASPOSTFIX(&n, (const BLASTYPE*)&numext::real_ref(alpha),(const BLASTYPE*)x, &incx, (BLASTYPE*)_res, &incy); \
/* Non-square case - doesn't fit to BLAS ?TRMV. Fall to default triangular product*/ \
if (size<(std::max)(rows,cols)) { \
if (ConjRhs) x_tmp = rhs.conjugate(); else x_tmp = rhs; \
@ -224,15 +231,22 @@ struct triangular_matrix_vector_product_trmv<Index,Mode,EIGTYPE,ConjLhs,EIGTYPE,
m = convert_index<BlasIndex>(size); \
n = convert_index<BlasIndex>(cols-size); \
} \
BLASPREFIX##gemv_(&trans, &n, &m, &numext::real_ref(alpha), (const BLASTYPE*)a, &lda, (const BLASTYPE*)x, &incx, &numext::real_ref(beta), (BLASTYPE*)y, &incy); \
BLASPREFIX##gemv##BLASPOSTFIX(&trans, &n, &m, (const BLASTYPE*)&numext::real_ref(alpha), (const BLASTYPE*)a, &lda, (const BLASTYPE*)x, &incx, (const BLASTYPE*)&numext::real_ref(beta), (BLASTYPE*)y, &incy); \
} \
} \
};
EIGEN_BLAS_TRMV_RM(double, double, d, d)
EIGEN_BLAS_TRMV_RM(dcomplex, double, cd, z)
EIGEN_BLAS_TRMV_RM(float, float, f, s)
EIGEN_BLAS_TRMV_RM(scomplex, float, cf, c)
#ifdef EIGEN_USE_MKL
EIGEN_BLAS_TRMV_RM(double, double, d, d,)
EIGEN_BLAS_TRMV_RM(dcomplex, MKL_Complex16, cd, z,)
EIGEN_BLAS_TRMV_RM(float, float, f, s,)
EIGEN_BLAS_TRMV_RM(scomplex, MKL_Complex8, cf, c,)
#else
EIGEN_BLAS_TRMV_RM(double, double, d, d,_)
EIGEN_BLAS_TRMV_RM(dcomplex, double, cd, z,_)
EIGEN_BLAS_TRMV_RM(float, float, f, s,_)
EIGEN_BLAS_TRMV_RM(scomplex, float, cf, c,_)
#endif
} // end namespase internal

40
xs/src/eigen/Eigen/src/Core/products/TriangularSolverMatrix_BLAS.h
View File

@ -38,7 +38,7 @@ namespace Eigen {
namespace internal {
// implements LeftSide op(triangular)^-1 * general
#define EIGEN_BLAS_TRSM_L(EIGTYPE, BLASTYPE, BLASPREFIX) \
#define EIGEN_BLAS_TRSM_L(EIGTYPE, BLASTYPE, BLASFUNC) \
template <typename Index, int Mode, bool Conjugate, int TriStorageOrder> \
struct triangular_solve_matrix<EIGTYPE,Index,OnTheLeft,Mode,Conjugate,TriStorageOrder,ColMajor> \
{ \
@ -80,18 +80,24 @@ struct triangular_solve_matrix<EIGTYPE,Index,OnTheLeft,Mode,Conjugate,TriStorage
} \
if (IsUnitDiag) diag='U'; \
/* call ?trsm*/ \
BLASPREFIX##trsm_(&side, &uplo, &transa, &diag, &m, &n, &numext::real_ref(alpha), (const BLASTYPE*)a, &lda, (BLASTYPE*)_other, &ldb); \
BLASFUNC(&side, &uplo, &transa, &diag, &m, &n, (const BLASTYPE*)&numext::real_ref(alpha), (const BLASTYPE*)a, &lda, (BLASTYPE*)_other, &ldb); \
} \
};
EIGEN_BLAS_TRSM_L(double, double, d)
EIGEN_BLAS_TRSM_L(dcomplex, double, z)
EIGEN_BLAS_TRSM_L(float, float, s)
EIGEN_BLAS_TRSM_L(scomplex, float, c)
#ifdef EIGEN_USE_MKL
EIGEN_BLAS_TRSM_L(double, double, dtrsm)
EIGEN_BLAS_TRSM_L(dcomplex, MKL_Complex16, ztrsm)
EIGEN_BLAS_TRSM_L(float, float, strsm)
EIGEN_BLAS_TRSM_L(scomplex, MKL_Complex8, ctrsm)
#else
EIGEN_BLAS_TRSM_L(double, double, dtrsm_)
EIGEN_BLAS_TRSM_L(dcomplex, double, ztrsm_)
EIGEN_BLAS_TRSM_L(float, float, strsm_)
EIGEN_BLAS_TRSM_L(scomplex, float, ctrsm_)
#endif
// implements RightSide general * op(triangular)^-1
#define EIGEN_BLAS_TRSM_R(EIGTYPE, BLASTYPE, BLASPREFIX) \
#define EIGEN_BLAS_TRSM_R(EIGTYPE, BLASTYPE, BLASFUNC) \
template <typename Index, int Mode, bool Conjugate, int TriStorageOrder> \
struct triangular_solve_matrix<EIGTYPE,Index,OnTheRight,Mode,Conjugate,TriStorageOrder,ColMajor> \
{ \
@ -133,16 +139,22 @@ struct triangular_solve_matrix<EIGTYPE,Index,OnTheRight,Mode,Conjugate,TriStorag
} \
if (IsUnitDiag) diag='U'; \
/* call ?trsm*/ \
BLASPREFIX##trsm_(&side, &uplo, &transa, &diag, &m, &n, &numext::real_ref(alpha), (const BLASTYPE*)a, &lda, (BLASTYPE*)_other, &ldb); \
BLASFUNC(&side, &uplo, &transa, &diag, &m, &n, (const BLASTYPE*)&numext::real_ref(alpha), (const BLASTYPE*)a, &lda, (BLASTYPE*)_other, &ldb); \
/*std::cout << "TRMS_L specialization!\n";*/ \
} \
};
EIGEN_BLAS_TRSM_R(double, double, d)
EIGEN_BLAS_TRSM_R(dcomplex, double, z)
EIGEN_BLAS_TRSM_R(float, float, s)
EIGEN_BLAS_TRSM_R(scomplex, float, c)
#ifdef EIGEN_USE_MKL
EIGEN_BLAS_TRSM_R(double, double, dtrsm)
EIGEN_BLAS_TRSM_R(dcomplex, MKL_Complex16, ztrsm)
EIGEN_BLAS_TRSM_R(float, float, strsm)
EIGEN_BLAS_TRSM_R(scomplex, MKL_Complex8, ctrsm)
#else
EIGEN_BLAS_TRSM_R(double, double, dtrsm_)
EIGEN_BLAS_TRSM_R(dcomplex, double, ztrsm_)
EIGEN_BLAS_TRSM_R(float, float, strsm_)
EIGEN_BLAS_TRSM_R(scomplex, float, ctrsm_)
#endif
} // end namespace internal

10
xs/src/eigen/Eigen/src/Core/util/MKL_support.h
View File

@ -49,10 +49,11 @@
#define EIGEN_USE_LAPACKE
#endif
#if defined(EIGEN_USE_MKL_VML)
#if defined(EIGEN_USE_MKL_VML) && !defined(EIGEN_USE_MKL)
#define EIGEN_USE_MKL
#endif
#if defined EIGEN_USE_MKL
# include <mkl.h>
/*Check IMKL version for compatibility: < 10.3 is not usable with Eigen*/
@ -108,6 +109,10 @@
#endif
#endif
#if defined(EIGEN_USE_BLAS) && !defined(EIGEN_USE_MKL)
#include "../../misc/blas.h"
#endif
namespace Eigen {
typedef std::complex<double> dcomplex;
@ -121,8 +126,5 @@ typedef int BlasIndex;
} // end namespace Eigen
#if defined(EIGEN_USE_BLAS)
#include "../../misc/blas.h"
#endif
#endif // EIGEN_MKL_SUPPORT_H

17
xs/src/eigen/Eigen/src/Core/util/Macros.h
View File

@ -13,7 +13,7 @@
#define EIGEN_WORLD_VERSION 3
#define EIGEN_MAJOR_VERSION 3
#define EIGEN_MINOR_VERSION 3
#define EIGEN_MINOR_VERSION 5
#define EIGEN_VERSION_AT_LEAST(x,y,z) (EIGEN_WORLD_VERSION>x || (EIGEN_WORLD_VERSION>=x && \
(EIGEN_MAJOR_VERSION>y || (EIGEN_MAJOR_VERSION>=y && \
@ -399,7 +399,7 @@
// Does the compiler support variadic templates?
#ifndef EIGEN_HAS_VARIADIC_TEMPLATES
#if EIGEN_MAX_CPP_VER>=11 && (__cplusplus > 199711L || EIGEN_COMP_MSVC >= 1900) \
&& ( !defined(__NVCC__) || !EIGEN_ARCH_ARM_OR_ARM64 || (defined __CUDACC_VER__ && __CUDACC_VER__ >= 80000) )
&& (!defined(__NVCC__) || !EIGEN_ARCH_ARM_OR_ARM64 || (EIGEN_CUDACC_VER >= 80000) )
// ^^ Disable the use of variadic templates when compiling with versions of nvcc older than 8.0 on ARM devices:
// this prevents nvcc from crashing when compiling Eigen on Tegra X1
#define EIGEN_HAS_VARIADIC_TEMPLATES 1
@ -413,7 +413,7 @@
#ifdef __CUDACC__
// Const expressions are supported provided that c++11 is enabled and we're using either clang or nvcc 7.5 or above
#if EIGEN_MAX_CPP_VER>=14 && (__cplusplus > 199711L && defined(__CUDACC_VER__) && (EIGEN_COMP_CLANG || __CUDACC_VER__ >= 70500))
#if EIGEN_MAX_CPP_VER>=14 && (__cplusplus > 199711L && (EIGEN_COMP_CLANG || EIGEN_CUDACC_VER >= 70500))
#define EIGEN_HAS_CONSTEXPR 1
#endif
#elif EIGEN_MAX_CPP_VER>=14 && (__has_feature(cxx_relaxed_constexpr) || (defined(__cplusplus) && __cplusplus >= 201402L) || \
@ -487,11 +487,13 @@
// EIGEN_STRONG_INLINE is a stronger version of the inline, using __forceinline on MSVC,
// but it still doesn't use GCC's always_inline. This is useful in (common) situations where MSVC needs forceinline
// but GCC is still doing fine with just inline.
#ifndef EIGEN_STRONG_INLINE
#if EIGEN_COMP_MSVC || EIGEN_COMP_ICC
#define EIGEN_STRONG_INLINE __forceinline
#else
#define EIGEN_STRONG_INLINE inline
#endif
#endif
// EIGEN_ALWAYS_INLINE is the stronget, it has the effect of making the function inline and adding every possible
// attribute to maximize inlining. This should only be used when really necessary: in particular,
@ -812,7 +814,8 @@ namespace Eigen {
// just an empty macro !
#define EIGEN_EMPTY
#if EIGEN_COMP_MSVC_STRICT && (EIGEN_COMP_MSVC < 1900 || defined(__CUDACC_VER__)) // for older MSVC versions, as well as 1900 && CUDA 8, using the base operator is sufficient (cf Bugs 1000, 1324)
#if EIGEN_COMP_MSVC_STRICT && (EIGEN_COMP_MSVC < 1900 || EIGEN_CUDACC_VER>0)
// for older MSVC versions, as well as 1900 && CUDA 8, using the base operator is sufficient (cf Bugs 1000, 1324)
#define EIGEN_INHERIT_ASSIGNMENT_EQUAL_OPERATOR(Derived) \
using Base::operator =;
#elif EIGEN_COMP_CLANG // workaround clang bug (see http://forum.kde.org/viewtopic.php?f=74&t=102653)
@ -986,7 +989,13 @@ namespace Eigen {
# define EIGEN_NOEXCEPT
# define EIGEN_NOEXCEPT_IF(x)
# define EIGEN_NO_THROW throw()
# if EIGEN_COMP_MSVC
// MSVC does not support exception specifications (warning C4290),
// and they are deprecated in c++11 anyway.
# define EIGEN_EXCEPTION_SPEC(X) throw()
# else
# define EIGEN_EXCEPTION_SPEC(X) throw(X)
# endif
#endif
#endif // EIGEN_MACROS_H

14
xs/src/eigen/Eigen/src/Core/util/Memory.h
View File

@ -70,7 +70,7 @@ inline void throw_std_bad_alloc()
throw std::bad_alloc();
#else
std::size_t huge = static_cast<std::size_t>(-1);
new int[huge];
::operator new(huge);
#endif
}
@ -493,7 +493,7 @@ template<typename T> struct smart_copy_helper<T,true> {
IntPtr size = IntPtr(end)-IntPtr(start);
if(size==0) return;
eigen_internal_assert(start!=0 && end!=0 && target!=0);
memcpy(target, start, size);
std::memcpy(target, start, size);
}
};
@ -696,7 +696,15 @@ template<typename T> void swap(scoped_array<T> &a,scoped_array<T> &b)
/** \class aligned_allocator
* \ingroup Core_Module
*
* \brief STL compatible allocator to use with with 16 byte aligned types
* \brief STL compatible allocator to use with types requiring a non standrad alignment.
*
* The memory is aligned as for dynamically aligned matrix/array types such as MatrixXd.
* By default, it will thus provide at least 16 bytes alignment and more in following cases:
* - 32 bytes alignment if AVX is enabled.
* - 64 bytes alignment if AVX512 is enabled.
*
* This can be controled using the \c EIGEN_MAX_ALIGN_BYTES macro as documented
* \link TopicPreprocessorDirectivesPerformance there \endlink.
*
* Example:
* \code

20
xs/src/eigen/Eigen/src/Core/util/Meta.h
View File

@ -485,6 +485,26 @@ T div_ceil(const T &a, const T &b)
return (a+b-1) / b;
}
// The aim of the following functions is to bypass -Wfloat-equal warnings
// when we really want a strict equality comparison on floating points.
template<typename X, typename Y> EIGEN_STRONG_INLINE
bool equal_strict(const X& x,const Y& y) { return x == y; }
template<> EIGEN_STRONG_INLINE
bool equal_strict(const float& x,const float& y) { return std::equal_to<float>()(x,y); }
template<> EIGEN_STRONG_INLINE
bool equal_strict(const double& x,const double& y) { return std::equal_to<double>()(x,y); }
template<typename X, typename Y> EIGEN_STRONG_INLINE
bool not_equal_strict(const X& x,const Y& y) { return x != y; }
template<> EIGEN_STRONG_INLINE
bool not_equal_strict(const float& x,const float& y) { return std::not_equal_to<float>()(x,y); }
template<> EIGEN_STRONG_INLINE
bool not_equal_strict(const double& x,const double& y) { return std::not_equal_to<double>()(x,y); }
} // end namespace numext
} // end namespace Eigen

120
xs/src/eigen/Eigen/src/Core/util/StaticAssert.h
View File

@ -24,6 +24,7 @@
*
*/
#ifndef EIGEN_STATIC_ASSERT
#ifndef EIGEN_NO_STATIC_ASSERT
#if EIGEN_MAX_CPP_VER>=11 && (__has_feature(cxx_static_assert) || (defined(__cplusplus) && __cplusplus >= 201103L) || (EIGEN_COMP_MSVC >= 1600))
@ -44,64 +45,65 @@
struct static_assertion<true>
{
enum {
YOU_TRIED_CALLING_A_VECTOR_METHOD_ON_A_MATRIX,
YOU_MIXED_VECTORS_OF_DIFFERENT_SIZES,
YOU_MIXED_MATRICES_OF_DIFFERENT_SIZES,
THIS_METHOD_IS_ONLY_FOR_VECTORS_OF_A_SPECIFIC_SIZE,
THIS_METHOD_IS_ONLY_FOR_MATRICES_OF_A_SPECIFIC_SIZE,
THIS_METHOD_IS_ONLY_FOR_OBJECTS_OF_A_SPECIFIC_SIZE,
OUT_OF_RANGE_ACCESS,
YOU_MADE_A_PROGRAMMING_MISTAKE,
EIGEN_INTERNAL_ERROR_PLEASE_FILE_A_BUG_REPORT,
EIGEN_INTERNAL_COMPILATION_ERROR_OR_YOU_MADE_A_PROGRAMMING_MISTAKE,
YOU_CALLED_A_FIXED_SIZE_METHOD_ON_A_DYNAMIC_SIZE_MATRIX_OR_VECTOR,
YOU_CALLED_A_DYNAMIC_SIZE_METHOD_ON_A_FIXED_SIZE_MATRIX_OR_VECTOR,
UNALIGNED_LOAD_AND_STORE_OPERATIONS_UNIMPLEMENTED_ON_ALTIVEC,
THIS_FUNCTION_IS_NOT_FOR_INTEGER_NUMERIC_TYPES,
FLOATING_POINT_ARGUMENT_PASSED__INTEGER_WAS_EXPECTED,
NUMERIC_TYPE_MUST_BE_REAL,
COEFFICIENT_WRITE_ACCESS_TO_SELFADJOINT_NOT_SUPPORTED,
WRITING_TO_TRIANGULAR_PART_WITH_UNIT_DIAGONAL_IS_NOT_SUPPORTED,
THIS_METHOD_IS_ONLY_FOR_FIXED_SIZE,
INVALID_MATRIX_PRODUCT,
INVALID_VECTOR_VECTOR_PRODUCT__IF_YOU_WANTED_A_DOT_OR_COEFF_WISE_PRODUCT_YOU_MUST_USE_THE_EXPLICIT_FUNCTIONS,
INVALID_MATRIX_PRODUCT__IF_YOU_WANTED_A_COEFF_WISE_PRODUCT_YOU_MUST_USE_THE_EXPLICIT_FUNCTION,
YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY,
THIS_METHOD_IS_ONLY_FOR_COLUMN_MAJOR_MATRICES,
THIS_METHOD_IS_ONLY_FOR_ROW_MAJOR_MATRICES,
INVALID_MATRIX_TEMPLATE_PARAMETERS,
INVALID_MATRIXBASE_TEMPLATE_PARAMETERS,
BOTH_MATRICES_MUST_HAVE_THE_SAME_STORAGE_ORDER,
THIS_METHOD_IS_ONLY_FOR_DIAGONAL_MATRIX,
THE_MATRIX_OR_EXPRESSION_THAT_YOU_PASSED_DOES_NOT_HAVE_THE_EXPECTED_TYPE,
THIS_METHOD_IS_ONLY_FOR_EXPRESSIONS_WITH_DIRECT_MEMORY_ACCESS_SUCH_AS_MAP_OR_PLAIN_MATRICES,
YOU_ALREADY_SPECIFIED_THIS_STRIDE,
INVALID_STORAGE_ORDER_FOR_THIS_VECTOR_EXPRESSION,
THE_BRACKET_OPERATOR_IS_ONLY_FOR_VECTORS__USE_THE_PARENTHESIS_OPERATOR_INSTEAD,
PACKET_ACCESS_REQUIRES_TO_HAVE_INNER_STRIDE_FIXED_TO_1,
THIS_METHOD_IS_ONLY_FOR_SPECIFIC_TRANSFORMATIONS,
YOU_CANNOT_MIX_ARRAYS_AND_MATRICES,
YOU_PERFORMED_AN_INVALID_TRANSFORMATION_CONVERSION,
THIS_EXPRESSION_IS_NOT_A_LVALUE__IT_IS_READ_ONLY,
YOU_ARE_TRYING_TO_USE_AN_INDEX_BASED_ACCESSOR_ON_AN_EXPRESSION_THAT_DOES_NOT_SUPPORT_THAT,
THIS_METHOD_IS_ONLY_FOR_1x1_EXPRESSIONS,
THIS_METHOD_IS_ONLY_FOR_INNER_OR_LAZY_PRODUCTS,
THIS_METHOD_IS_ONLY_FOR_EXPRESSIONS_OF_BOOL,
THIS_METHOD_IS_ONLY_FOR_ARRAYS_NOT_MATRICES,
YOU_PASSED_A_ROW_VECTOR_BUT_A_COLUMN_VECTOR_WAS_EXPECTED,
YOU_PASSED_A_COLUMN_VECTOR_BUT_A_ROW_VECTOR_WAS_EXPECTED,
THE_INDEX_TYPE_MUST_BE_A_SIGNED_TYPE,
THE_STORAGE_ORDER_OF_BOTH_SIDES_MUST_MATCH,
OBJECT_ALLOCATED_ON_STACK_IS_TOO_BIG,
IMPLICIT_CONVERSION_TO_SCALAR_IS_FOR_INNER_PRODUCT_ONLY,
STORAGE_LAYOUT_DOES_NOT_MATCH,
EIGEN_INTERNAL_ERROR_PLEASE_FILE_A_BUG_REPORT__INVALID_COST_VALUE,
THIS_COEFFICIENT_ACCESSOR_TAKING_ONE_ACCESS_IS_ONLY_FOR_EXPRESSIONS_ALLOWING_LINEAR_ACCESS,
MATRIX_FREE_CONJUGATE_GRADIENT_IS_COMPATIBLE_WITH_UPPER_UNION_LOWER_MODE_ONLY,
THIS_TYPE_IS_NOT_SUPPORTED,
STORAGE_KIND_MUST_MATCH,
STORAGE_INDEX_MUST_MATCH,
CHOLMOD_SUPPORTS_DOUBLE_PRECISION_ONLY
YOU_TRIED_CALLING_A_VECTOR_METHOD_ON_A_MATRIX=1,
YOU_MIXED_VECTORS_OF_DIFFERENT_SIZES=1,
YOU_MIXED_MATRICES_OF_DIFFERENT_SIZES=1,
THIS_METHOD_IS_ONLY_FOR_VECTORS_OF_A_SPECIFIC_SIZE=1,
THIS_METHOD_IS_ONLY_FOR_MATRICES_OF_A_SPECIFIC_SIZE=1,
THIS_METHOD_IS_ONLY_FOR_OBJECTS_OF_A_SPECIFIC_SIZE=1,
OUT_OF_RANGE_ACCESS=1,
YOU_MADE_A_PROGRAMMING_MISTAKE=1,
EIGEN_INTERNAL_ERROR_PLEASE_FILE_A_BUG_REPORT=1,
EIGEN_INTERNAL_COMPILATION_ERROR_OR_YOU_MADE_A_PROGRAMMING_MISTAKE=1,
YOU_CALLED_A_FIXED_SIZE_METHOD_ON_A_DYNAMIC_SIZE_MATRIX_OR_VECTOR=1,
YOU_CALLED_A_DYNAMIC_SIZE_METHOD_ON_A_FIXED_SIZE_MATRIX_OR_VECTOR=1,
UNALIGNED_LOAD_AND_STORE_OPERATIONS_UNIMPLEMENTED_ON_ALTIVEC=1,
THIS_FUNCTION_IS_NOT_FOR_INTEGER_NUMERIC_TYPES=1,
FLOATING_POINT_ARGUMENT_PASSED__INTEGER_WAS_EXPECTED=1,
NUMERIC_TYPE_MUST_BE_REAL=1,
COEFFICIENT_WRITE_ACCESS_TO_SELFADJOINT_NOT_SUPPORTED=1,
WRITING_TO_TRIANGULAR_PART_WITH_UNIT_DIAGONAL_IS_NOT_SUPPORTED=1,
THIS_METHOD_IS_ONLY_FOR_FIXED_SIZE=1,
INVALID_MATRIX_PRODUCT=1,
INVALID_VECTOR_VECTOR_PRODUCT__IF_YOU_WANTED_A_DOT_OR_COEFF_WISE_PRODUCT_YOU_MUST_USE_THE_EXPLICIT_FUNCTIONS=1,
INVALID_MATRIX_PRODUCT__IF_YOU_WANTED_A_COEFF_WISE_PRODUCT_YOU_MUST_USE_THE_EXPLICIT_FUNCTION=1,
YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY=1,
THIS_METHOD_IS_ONLY_FOR_COLUMN_MAJOR_MATRICES=1,
THIS_METHOD_IS_ONLY_FOR_ROW_MAJOR_MATRICES=1,
INVALID_MATRIX_TEMPLATE_PARAMETERS=1,
INVALID_MATRIXBASE_TEMPLATE_PARAMETERS=1,
BOTH_MATRICES_MUST_HAVE_THE_SAME_STORAGE_ORDER=1,
THIS_METHOD_IS_ONLY_FOR_DIAGONAL_MATRIX=1,
THE_MATRIX_OR_EXPRESSION_THAT_YOU_PASSED_DOES_NOT_HAVE_THE_EXPECTED_TYPE=1,
THIS_METHOD_IS_ONLY_FOR_EXPRESSIONS_WITH_DIRECT_MEMORY_ACCESS_SUCH_AS_MAP_OR_PLAIN_MATRICES=1,
YOU_ALREADY_SPECIFIED_THIS_STRIDE=1,
INVALID_STORAGE_ORDER_FOR_THIS_VECTOR_EXPRESSION=1,
THE_BRACKET_OPERATOR_IS_ONLY_FOR_VECTORS__USE_THE_PARENTHESIS_OPERATOR_INSTEAD=1,
PACKET_ACCESS_REQUIRES_TO_HAVE_INNER_STRIDE_FIXED_TO_1=1,
THIS_METHOD_IS_ONLY_FOR_SPECIFIC_TRANSFORMATIONS=1,
YOU_CANNOT_MIX_ARRAYS_AND_MATRICES=1,
YOU_PERFORMED_AN_INVALID_TRANSFORMATION_CONVERSION=1,
THIS_EXPRESSION_IS_NOT_A_LVALUE__IT_IS_READ_ONLY=1,
YOU_ARE_TRYING_TO_USE_AN_INDEX_BASED_ACCESSOR_ON_AN_EXPRESSION_THAT_DOES_NOT_SUPPORT_THAT=1,
THIS_METHOD_IS_ONLY_FOR_1x1_EXPRESSIONS=1,
THIS_METHOD_IS_ONLY_FOR_INNER_OR_LAZY_PRODUCTS=1,
THIS_METHOD_IS_ONLY_FOR_EXPRESSIONS_OF_BOOL=1,
THIS_METHOD_IS_ONLY_FOR_ARRAYS_NOT_MATRICES=1,
YOU_PASSED_A_ROW_VECTOR_BUT_A_COLUMN_VECTOR_WAS_EXPECTED=1,
YOU_PASSED_A_COLUMN_VECTOR_BUT_A_ROW_VECTOR_WAS_EXPECTED=1,
THE_INDEX_TYPE_MUST_BE_A_SIGNED_TYPE=1,
THE_STORAGE_ORDER_OF_BOTH_SIDES_MUST_MATCH=1,
OBJECT_ALLOCATED_ON_STACK_IS_TOO_BIG=1,
IMPLICIT_CONVERSION_TO_SCALAR_IS_FOR_INNER_PRODUCT_ONLY=1,
STORAGE_LAYOUT_DOES_NOT_MATCH=1,
EIGEN_INTERNAL_ERROR_PLEASE_FILE_A_BUG_REPORT__INVALID_COST_VALUE=1,
THIS_COEFFICIENT_ACCESSOR_TAKING_ONE_ACCESS_IS_ONLY_FOR_EXPRESSIONS_ALLOWING_LINEAR_ACCESS=1,
MATRIX_FREE_CONJUGATE_GRADIENT_IS_COMPATIBLE_WITH_UPPER_UNION_LOWER_MODE_ONLY=1,
THIS_TYPE_IS_NOT_SUPPORTED=1,
STORAGE_KIND_MUST_MATCH=1,
STORAGE_INDEX_MUST_MATCH=1,
CHOLMOD_SUPPORTS_DOUBLE_PRECISION_ONLY=1,
SELFADJOINTVIEW_ACCEPTS_UPPER_AND_LOWER_MODE_ONLY=1
};
};
@ -131,7 +133,7 @@
#define EIGEN_STATIC_ASSERT(CONDITION,MSG) eigen_assert((CONDITION) && #MSG);
#endif // EIGEN_NO_STATIC_ASSERT
#endif // EIGEN_STATIC_ASSERT
// static assertion failing if the type \a TYPE is not a vector type
#define EIGEN_STATIC_ASSERT_VECTOR_ONLY(TYPE) \

5
xs/src/eigen/Eigen/src/Eigenvalues/GeneralizedEigenSolver.h
View File

@ -311,7 +311,6 @@ GeneralizedEigenSolver<MatrixType>::compute(const MatrixType& A, const MatrixTyp
// Aliases:
Map<VectorType> v(reinterpret_cast<Scalar*>(m_tmp.data()), size);
ComplexVectorType &cv = m_tmp;
const MatrixType &mZ = m_realQZ.matrixZ();
const MatrixType &mS = m_realQZ.matrixS();
const MatrixType &mT = m_realQZ.matrixT();
@ -351,7 +350,7 @@ GeneralizedEigenSolver<MatrixType>::compute(const MatrixType& A, const MatrixTyp
}
}
}
m_eivec.col(i).real().noalias() = mZ.transpose() * v;
m_eivec.col(i).real().noalias() = m_realQZ.matrixZ().transpose() * v;
m_eivec.col(i).real().normalize();
m_eivec.col(i).imag().setConstant(0);
}
@ -400,7 +399,7 @@ GeneralizedEigenSolver<MatrixType>::compute(const MatrixType& A, const MatrixTyp
/ (alpha*mT.coeffRef(j,j) - static_cast<Scalar>(beta*mS.coeffRef(j,j)));
}
}
m_eivec.col(i+1).noalias() = (mZ.transpose() * cv);
m_eivec.col(i+1).noalias() = (m_realQZ.matrixZ().transpose() * cv);
m_eivec.col(i+1).normalize();
m_eivec.col(i) = m_eivec.col(i+1).conjugate();
}

4
xs/src/eigen/Eigen/src/Eigenvalues/RealSchur.h
View File

@ -303,7 +303,7 @@ RealSchur<MatrixType>& RealSchur<MatrixType>::computeFromHessenberg(const HessMa
Scalar exshift(0); // sum of exceptional shifts
Scalar norm = computeNormOfT();
if(norm!=0)
if(norm!=Scalar(0))
{
while (iu >= 0)
{
@ -327,7 +327,7 @@ RealSchur<MatrixType>& RealSchur<MatrixType>::computeFromHessenberg(const HessMa
else // No convergence yet
{
// The firstHouseholderVector vector has to be initialized to something to get rid of a silly GCC warning (-O1 -Wall -DNDEBUG )
Vector3s firstHouseholderVector(0,0,0), shiftInfo;
Vector3s firstHouseholderVector = Vector3s::Zero(), shiftInfo;
computeShift(iu, iter, exshift, shiftInfo);
iter = iter + 1;
totalIter = totalIter + 1;

23
xs/src/eigen/Eigen/src/Eigenvalues/SelfAdjointEigenSolver_LAPACKE.h
View File

@ -37,7 +37,7 @@ namespace Eigen {
/** \internal Specialization for the data types supported by LAPACKe */
#define EIGEN_LAPACKE_EIG_SELFADJ(EIGTYPE, LAPACKE_TYPE, LAPACKE_RTYPE, LAPACKE_NAME, EIGCOLROW, LAPACKE_COLROW ) \
#define EIGEN_LAPACKE_EIG_SELFADJ_2(EIGTYPE, LAPACKE_TYPE, LAPACKE_RTYPE, LAPACKE_NAME, EIGCOLROW ) \
template<> template<typename InputType> inline \
SelfAdjointEigenSolver<Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW> >& \
SelfAdjointEigenSolver<Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW> >::compute(const EigenBase<InputType>& matrix, int options) \
@ -47,7 +47,7 @@ SelfAdjointEigenSolver<Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW> >::compute(c
&& (options&EigVecMask)!=EigVecMask \
&& "invalid option parameter"); \
bool computeEigenvectors = (options&ComputeEigenvectors)==ComputeEigenvectors; \
lapack_int n = internal::convert_index<lapack_int>(matrix.cols()), lda, matrix_order, info; \
lapack_int n = internal::convert_index<lapack_int>(matrix.cols()), lda, info; \
m_eivalues.resize(n,1); \
m_subdiag.resize(n-1); \
m_eivec = matrix; \
@ -63,27 +63,24 @@ SelfAdjointEigenSolver<Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW> >::compute(c
} \
\
lda = internal::convert_index<lapack_int>(m_eivec.outerStride()); \
matrix_order=LAPACKE_COLROW; \
char jobz, uplo='L'/*, range='A'*/; \
jobz = computeEigenvectors ? 'V' : 'N'; \
\
info = LAPACKE_##LAPACKE_NAME( matrix_order, jobz, uplo, n, (LAPACKE_TYPE*)m_eivec.data(), lda, (LAPACKE_RTYPE*)m_eivalues.data() ); \
info = LAPACKE_##LAPACKE_NAME( LAPACK_COL_MAJOR, jobz, uplo, n, (LAPACKE_TYPE*)m_eivec.data(), lda, (LAPACKE_RTYPE*)m_eivalues.data() ); \
m_info = (info==0) ? Success : NoConvergence; \
m_isInitialized = true; \
m_eigenvectorsOk = computeEigenvectors; \
return *this; \
}
#define EIGEN_LAPACKE_EIG_SELFADJ(EIGTYPE, LAPACKE_TYPE, LAPACKE_RTYPE, LAPACKE_NAME ) \
EIGEN_LAPACKE_EIG_SELFADJ_2(EIGTYPE, LAPACKE_TYPE, LAPACKE_RTYPE, LAPACKE_NAME, ColMajor ) \
EIGEN_LAPACKE_EIG_SELFADJ_2(EIGTYPE, LAPACKE_TYPE, LAPACKE_RTYPE, LAPACKE_NAME, RowMajor )
EIGEN_LAPACKE_EIG_SELFADJ(double, double, double, dsyev, ColMajor, LAPACK_COL_MAJOR)
EIGEN_LAPACKE_EIG_SELFADJ(float, float, float, ssyev, ColMajor, LAPACK_COL_MAJOR)
EIGEN_LAPACKE_EIG_SELFADJ(dcomplex, lapack_complex_double, double, zheev, ColMajor, LAPACK_COL_MAJOR)
EIGEN_LAPACKE_EIG_SELFADJ(scomplex, lapack_complex_float, float, cheev, ColMajor, LAPACK_COL_MAJOR)
EIGEN_LAPACKE_EIG_SELFADJ(double, double, double, dsyev, RowMajor, LAPACK_ROW_MAJOR)
EIGEN_LAPACKE_EIG_SELFADJ(float, float, float, ssyev, RowMajor, LAPACK_ROW_MAJOR)
EIGEN_LAPACKE_EIG_SELFADJ(dcomplex, lapack_complex_double, double, zheev, RowMajor, LAPACK_ROW_MAJOR)
EIGEN_LAPACKE_EIG_SELFADJ(scomplex, lapack_complex_float, float, cheev, RowMajor, LAPACK_ROW_MAJOR)
EIGEN_LAPACKE_EIG_SELFADJ(double, double, double, dsyev)
EIGEN_LAPACKE_EIG_SELFADJ(float, float, float, ssyev)
EIGEN_LAPACKE_EIG_SELFADJ(dcomplex, lapack_complex_double, double, zheev)
EIGEN_LAPACKE_EIG_SELFADJ(scomplex, lapack_complex_float, float, cheev)
} // end namespace Eigen

2
xs/src/eigen/Eigen/src/Geometry/AngleAxis.h
View File

@ -178,7 +178,7 @@ EIGEN_DEVICE_FUNC AngleAxis<Scalar>& AngleAxis<Scalar>::operator=(const Quaterni
if (n != Scalar(0))
{
m_angle = Scalar(2)*atan2(n, abs(q.w()));
if(q.w() < 0)
if(q.w() < Scalar(0))
n = -n;
m_axis = q.vec() / n;
}

39
xs/src/eigen/Eigen/src/Geometry/Quaternion.h
View File

@ -43,6 +43,11 @@ class QuaternionBase : public RotationBase<Derived, 3>
typedef typename internal::traits<Derived>::Scalar Scalar;
typedef typename NumTraits<Scalar>::Real RealScalar;
typedef typename internal::traits<Derived>::Coefficients Coefficients;
typedef typename Coefficients::CoeffReturnType CoeffReturnType;
typedef typename internal::conditional<bool(internal::traits<Derived>::Flags&LvalueBit),
Scalar&, CoeffReturnType>::type NonConstCoeffReturnType;
enum {
Flags = Eigen::internal::traits<Derived>::Flags
};
@ -58,22 +63,22 @@ class QuaternionBase : public RotationBase<Derived, 3>
/** \returns the \c x coefficient */
EIGEN_DEVICE_FUNC inline Scalar x() const { return this->derived().coeffs().coeff(0); }
EIGEN_DEVICE_FUNC inline CoeffReturnType x() const { return this->derived().coeffs().coeff(0); }
/** \returns the \c y coefficient */
EIGEN_DEVICE_FUNC inline Scalar y() const { return this->derived().coeffs().coeff(1); }
EIGEN_DEVICE_FUNC inline CoeffReturnType y() const { return this->derived().coeffs().coeff(1); }
/** \returns the \c z coefficient */
EIGEN_DEVICE_FUNC inline Scalar z() const { return this->derived().coeffs().coeff(2); }
EIGEN_DEVICE_FUNC inline CoeffReturnType z() const { return this->derived().coeffs().coeff(2); }
/** \returns the \c w coefficient */
EIGEN_DEVICE_FUNC inline Scalar w() const { return this->derived().coeffs().coeff(3); }
EIGEN_DEVICE_FUNC inline CoeffReturnType w() const { return this->derived().coeffs().coeff(3); }
/** \returns a reference to the \c x coefficient */
EIGEN_DEVICE_FUNC inline Scalar& x() { return this->derived().coeffs().coeffRef(0); }
/** \returns a reference to the \c y coefficient */
EIGEN_DEVICE_FUNC inline Scalar& y() { return this->derived().coeffs().coeffRef(1); }
/** \returns a reference to the \c z coefficient */
EIGEN_DEVICE_FUNC inline Scalar& z() { return this->derived().coeffs().coeffRef(2); }
/** \returns a reference to the \c w coefficient */
EIGEN_DEVICE_FUNC inline Scalar& w() { return this->derived().coeffs().coeffRef(3); }
/** \returns a reference to the \c x coefficient (if Derived is a non-const lvalue) */
EIGEN_DEVICE_FUNC inline NonConstCoeffReturnType x() { return this->derived().coeffs().x(); }
/** \returns a reference to the \c y coefficient (if Derived is a non-const lvalue) */
EIGEN_DEVICE_FUNC inline NonConstCoeffReturnType y() { return this->derived().coeffs().y(); }
/** \returns a reference to the \c z coefficient (if Derived is a non-const lvalue) */
EIGEN_DEVICE_FUNC inline NonConstCoeffReturnType z() { return this->derived().coeffs().z(); }
/** \returns a reference to the \c w coefficient (if Derived is a non-const lvalue) */
EIGEN_DEVICE_FUNC inline NonConstCoeffReturnType w() { return this->derived().coeffs().w(); }
/** \returns a read-only vector expression of the imaginary part (x,y,z) */
EIGEN_DEVICE_FUNC inline const VectorBlock<const Coefficients,3> vec() const { return coeffs().template head<3>(); }
@ -423,7 +428,7 @@ typedef Map<Quaternion<double>, Aligned> QuaternionMapAlignedd;
// Generic Quaternion * Quaternion product
// This product can be specialized for a given architecture via the Arch template argument.
namespace internal {
template<int Arch, class Derived1, class Derived2, typename Scalar, int _Options> struct quat_product
template<int Arch, class Derived1, class Derived2, typename Scalar> struct quat_product
{
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE Quaternion<Scalar> run(const QuaternionBase<Derived1>& a, const QuaternionBase<Derived2>& b){
return Quaternion<Scalar>
@ -446,8 +451,7 @@ QuaternionBase<Derived>::operator* (const QuaternionBase<OtherDerived>& other) c
EIGEN_STATIC_ASSERT((internal::is_same<typename Derived::Scalar, typename OtherDerived::Scalar>::value),
YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY)
return internal::quat_product<Architecture::Target, Derived, OtherDerived,
typename internal::traits<Derived>::Scalar,
EIGEN_PLAIN_ENUM_MIN(internal::traits<Derived>::Alignment, internal::traits<OtherDerived>::Alignment)>::run(*this, other);
typename internal::traits<Derived>::Scalar>::run(*this, other);
}
/** \sa operator*(Quaternion) */
@ -672,7 +676,7 @@ EIGEN_DEVICE_FUNC inline Quaternion<typename internal::traits<Derived>::Scalar>
// Generic conjugate of a Quaternion
namespace internal {
template<int Arch, class Derived, typename Scalar, int _Options> struct quat_conj
template<int Arch, class Derived, typename Scalar> struct quat_conj
{
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE Quaternion<Scalar> run(const QuaternionBase<Derived>& q){
return Quaternion<Scalar>(q.w(),-q.x(),-q.y(),-q.z());
@ -691,8 +695,7 @@ EIGEN_DEVICE_FUNC inline Quaternion<typename internal::traits<Derived>::Scalar>
QuaternionBase<Derived>::conjugate() const
{
return internal::quat_conj<Architecture::Target, Derived,
typename internal::traits<Derived>::Scalar,
internal::traits<Derived>::Alignment>::run(*this);
typename internal::traits<Derived>::Scalar>::run(*this);
}

60
xs/src/eigen/Eigen/src/Geometry/arch/Geometry_SSE.h
View File

@ -16,17 +16,23 @@ namespace Eigen {
namespace internal {
template<class Derived, class OtherDerived>
struct quat_product<Architecture::SSE, Derived, OtherDerived, float, Aligned16>
struct quat_product<Architecture::SSE, Derived, OtherDerived, float>
{
enum {
AAlignment = traits<Derived>::Alignment,
BAlignment = traits<OtherDerived>::Alignment,
ResAlignment = traits<Quaternion<float> >::Alignment
};
static inline Quaternion<float> run(const QuaternionBase<Derived>& _a, const QuaternionBase<OtherDerived>& _b)
{
Quaternion<float> res;
const __m128 mask = _mm_setr_ps(0.f,0.f,0.f,-0.f);
__m128 a = _a.coeffs().template packet<Aligned16>(0);
__m128 b = _b.coeffs().template packet<Aligned16>(0);
__m128 a = _a.coeffs().template packet<AAlignment>(0);
__m128 b = _b.coeffs().template packet<BAlignment>(0);
__m128 s1 = _mm_mul_ps(vec4f_swizzle1(a,1,2,0,2),vec4f_swizzle1(b,2,0,1,2));
__m128 s2 = _mm_mul_ps(vec4f_swizzle1(a,3,3,3,1),vec4f_swizzle1(b,0,1,2,1));
pstore(&res.x(),
pstoret<float,Packet4f,ResAlignment>(
&res.x(),
_mm_add_ps(_mm_sub_ps(_mm_mul_ps(a,vec4f_swizzle1(b,3,3,3,3)),
_mm_mul_ps(vec4f_swizzle1(a,2,0,1,0),
vec4f_swizzle1(b,1,2,0,0))),
@ -36,14 +42,17 @@ struct quat_product<Architecture::SSE, Derived, OtherDerived, float, Aligned16>
}
};
template<class Derived, int Alignment>
struct quat_conj<Architecture::SSE, Derived, float, Alignment>
template<class Derived>
struct quat_conj<Architecture::SSE, Derived, float>
{
enum {
ResAlignment = traits<Quaternion<float> >::Alignment
};
static inline Quaternion<float> run(const QuaternionBase<Derived>& q)
{
Quaternion<float> res;
const __m128 mask = _mm_setr_ps(-0.f,-0.f,-0.f,0.f);
pstore(&res.x(), _mm_xor_ps(mask, q.coeffs().template packet<Alignment>(0)));
pstoret<float,Packet4f,ResAlignment>(&res.x(), _mm_xor_ps(mask, q.coeffs().template packet<traits<Derived>::Alignment>(0)));
return res;
}
};
@ -52,6 +61,9 @@ struct quat_conj<Architecture::SSE, Derived, float, Alignment>
template<typename VectorLhs,typename VectorRhs>
struct cross3_impl<Architecture::SSE,VectorLhs,VectorRhs,float,true>
{
enum {
ResAlignment = traits<typename plain_matrix_type<VectorLhs>::type>::Alignment
};
static inline typename plain_matrix_type<VectorLhs>::type
run(const VectorLhs& lhs, const VectorRhs& rhs)
{
@ -60,7 +72,7 @@ struct cross3_impl<Architecture::SSE,VectorLhs,VectorRhs,float,true>
__m128 mul1=_mm_mul_ps(vec4f_swizzle1(a,1,2,0,3),vec4f_swizzle1(b,2,0,1,3));
__m128 mul2=_mm_mul_ps(vec4f_swizzle1(a,2,0,1,3),vec4f_swizzle1(b,1,2,0,3));
typename plain_matrix_type<VectorLhs>::type res;
pstore(&res.x(),_mm_sub_ps(mul1,mul2));
pstoret<float,Packet4f,ResAlignment>(&res.x(),_mm_sub_ps(mul1,mul2));
return res;
}
};
@ -68,9 +80,14 @@ struct cross3_impl<Architecture::SSE,VectorLhs,VectorRhs,float,true>
template<class Derived, class OtherDerived, int Alignment>
struct quat_product<Architecture::SSE, Derived, OtherDerived, double, Alignment>
template<class Derived, class OtherDerived>
struct quat_product<Architecture::SSE, Derived, OtherDerived, double>
{
enum {
BAlignment = traits<OtherDerived>::Alignment,
ResAlignment = traits<Quaternion<double> >::Alignment
};
static inline Quaternion<double> run(const QuaternionBase<Derived>& _a, const QuaternionBase<OtherDerived>& _b)
{
const Packet2d mask = _mm_castsi128_pd(_mm_set_epi32(0x0,0x0,0x80000000,0x0));
@ -78,8 +95,8 @@ struct quat_product<Architecture::SSE, Derived, OtherDerived, double, Alignment>
Quaternion<double> res;
const double* a = _a.coeffs().data();
Packet2d b_xy = _b.coeffs().template packet<Alignment>(0);
Packet2d b_zw = _b.coeffs().template packet<Alignment>(2);
Packet2d b_xy = _b.coeffs().template packet<BAlignment>(0);
Packet2d b_zw = _b.coeffs().template packet<BAlignment>(2);
Packet2d a_xx = pset1<Packet2d>(a[0]);
Packet2d a_yy = pset1<Packet2d>(a[1]);
Packet2d a_zz = pset1<Packet2d>(a[2]);
@ -97,9 +114,9 @@ struct quat_product<Architecture::SSE, Derived, OtherDerived, double, Alignment>
t2 = psub(pmul(a_zz, b_xy), pmul(a_xx, b_zw));
#ifdef EIGEN_VECTORIZE_SSE3
EIGEN_UNUSED_VARIABLE(mask)
pstore(&res.x(), _mm_addsub_pd(t1, preverse(t2)));
pstoret<double,Packet2d,ResAlignment>(&res.x(), _mm_addsub_pd(t1, preverse(t2)));
#else
pstore(&res.x(), padd(t1, pxor(mask,preverse(t2))));
pstoret<double,Packet2d,ResAlignment>(&res.x(), padd(t1, pxor(mask,preverse(t2))));
#endif
/*
@ -111,25 +128,28 @@ struct quat_product<Architecture::SSE, Derived, OtherDerived, double, Alignment>
t2 = padd(pmul(a_zz, b_zw), pmul(a_xx, b_xy));
#ifdef EIGEN_VECTORIZE_SSE3
EIGEN_UNUSED_VARIABLE(mask)
pstore(&res.z(), preverse(_mm_addsub_pd(preverse(t1), t2)));
pstoret<double,Packet2d,ResAlignment>(&res.z(), preverse(_mm_addsub_pd(preverse(t1), t2)));
#else
pstore(&res.z(), psub(t1, pxor(mask,preverse(t2))));
pstoret<double,Packet2d,ResAlignment>(&res.z(), psub(t1, pxor(mask,preverse(t2))));
#endif
return res;
}
};
template<class Derived, int Alignment>
struct quat_conj<Architecture::SSE, Derived, double, Alignment>
template<class Derived>
struct quat_conj<Architecture::SSE, Derived, double>
{
enum {
ResAlignment = traits<Quaternion<double> >::Alignment
};
static inline Quaternion<double> run(const QuaternionBase<Derived>& q)
{
Quaternion<double> res;
const __m128d mask0 = _mm_setr_pd(-0.,-0.);
const __m128d mask2 = _mm_setr_pd(-0.,0.);
pstore(&res.x(), _mm_xor_pd(mask0, q.coeffs().template packet<Alignment>(0)));
pstore(&res.z(), _mm_xor_pd(mask2, q.coeffs().template packet<Alignment>(2)));
pstoret<double,Packet2d,ResAlignment>(&res.x(), _mm_xor_pd(mask0, q.coeffs().template packet<traits<Derived>::Alignment>(0)));
pstoret<double,Packet2d,ResAlignment>(&res.z(), _mm_xor_pd(mask2, q.coeffs().template packet<traits<Derived>::Alignment>(2)));
return res;
}
};

19
xs/src/eigen/Eigen/src/IterativeLinearSolvers/BasicPreconditioners.h
View File

@ -152,14 +152,29 @@ class LeastSquareDiagonalPreconditioner : public DiagonalPreconditioner<_Scalar>
{
// Compute the inverse squared-norm of each column of mat
m_invdiag.resize(mat.cols());
if(MatType::IsRowMajor)
{
m_invdiag.setZero();
for(Index j=0; j<mat.outerSize(); ++j)
{
RealScalar sum = mat.innerVector(j).squaredNorm();
if(sum>0)
for(typename MatType::InnerIterator it(mat,j); it; ++it)
m_invdiag(it.index()) += numext::abs2(it.value());
}
for(Index j=0; j<mat.cols(); ++j)
if(numext::real(m_invdiag(j))>RealScalar(0))
m_invdiag(j) = RealScalar(1)/numext::real(m_invdiag(j));
}
else
{
for(Index j=0; j<mat.outerSize(); ++j)
{
RealScalar sum = mat.col(j).squaredNorm();
if(sum>RealScalar(0))
m_invdiag(j) = RealScalar(1)/sum;
else
m_invdiag(j) = RealScalar(1);
}
}
Base::m_isInitialized = true;
return *this;
}

126
xs/src/eigen/Eigen/src/Jacobi/Jacobi.h
View File

@ -298,30 +298,40 @@ inline void MatrixBase<Derived>::applyOnTheRight(Index p, Index q, const JacobiR
}
namespace internal {
template<typename VectorX, typename VectorY, typename OtherScalar>
void /*EIGEN_DONT_INLINE*/ apply_rotation_in_the_plane(DenseBase<VectorX>& xpr_x, DenseBase<VectorY>& xpr_y, const JacobiRotation<OtherScalar>& j)
template<typename Scalar, typename OtherScalar,
int SizeAtCompileTime, int MinAlignment, bool Vectorizable>
struct apply_rotation_in_the_plane_selector
{
typedef typename VectorX::Scalar Scalar;
enum { PacketSize = packet_traits<Scalar>::size };
static inline void run(Scalar *x, Index incrx, Scalar *y, Index incry, Index size, OtherScalar c, OtherScalar s)
{
for(Index i=0; i<size; ++i)
{
Scalar xi = *x;
Scalar yi = *y;
*x = c * xi + numext::conj(s) * yi;
*y = -s * xi + numext::conj(c) * yi;
x += incrx;
y += incry;
}
}
};
template<typename Scalar, typename OtherScalar,
int SizeAtCompileTime, int MinAlignment>
struct apply_rotation_in_the_plane_selector<Scalar,OtherScalar,SizeAtCompileTime,MinAlignment,true /* vectorizable */>
{
static inline void run(Scalar *x, Index incrx, Scalar *y, Index incry, Index size, OtherScalar c, OtherScalar s)
{
enum {
PacketSize = packet_traits<Scalar>::size,
OtherPacketSize = packet_traits<OtherScalar>::size
};
typedef typename packet_traits<Scalar>::type Packet;
eigen_assert(xpr_x.size() == xpr_y.size());
Index size = xpr_x.size();
Index incrx = xpr_x.derived().innerStride();
Index incry = xpr_y.derived().innerStride();
Scalar* EIGEN_RESTRICT x = &xpr_x.derived().coeffRef(0);
Scalar* EIGEN_RESTRICT y = &xpr_y.derived().coeffRef(0);
OtherScalar c = j.c();
OtherScalar s = j.s();
if (c==OtherScalar(1) && s==OtherScalar(0))
return;
typedef typename packet_traits<OtherScalar>::type OtherPacket;
/*** dynamic-size vectorized paths ***/
if(VectorX::SizeAtCompileTime == Dynamic &&
(VectorX::Flags & VectorY::Flags & PacketAccessBit) &&
((incrx==1 && incry==1) || PacketSize == 1))
if(SizeAtCompileTime == Dynamic && ((incrx==1 && incry==1) || PacketSize == 1))
{
// both vectors are sequentially stored in memory => vectorization
enum { Peeling = 2 };
@ -329,9 +339,10 @@ void /*EIGEN_DONT_INLINE*/ apply_rotation_in_the_plane(DenseBase<VectorX>& xpr_x
Index alignedStart = internal::first_default_aligned(y, size);
Index alignedEnd = alignedStart + ((size-alignedStart)/PacketSize)*PacketSize;
const Packet pc = pset1<Packet>(c);
const Packet ps = pset1<Packet>(s);
conj_helper<Packet,Packet,NumTraits<Scalar>::IsComplex,false> pcj;
const OtherPacket pc = pset1<OtherPacket>(c);
const OtherPacket ps = pset1<OtherPacket>(s);
conj_helper<OtherPacket,Packet,NumTraits<OtherScalar>::IsComplex,false> pcj;
conj_helper<OtherPacket,Packet,false,false> pm;
for(Index i=0; i<alignedStart; ++i)
{
@ -350,8 +361,8 @@ void /*EIGEN_DONT_INLINE*/ apply_rotation_in_the_plane(DenseBase<VectorX>& xpr_x
{
Packet xi = pload<Packet>(px);
Packet yi = pload<Packet>(py);
pstore(px, padd(pmul(pc,xi),pcj.pmul(ps,yi)));
pstore(py, psub(pcj.pmul(pc,yi),pmul(ps,xi)));
pstore(px, padd(pm.pmul(pc,xi),pcj.pmul(ps,yi)));
pstore(py, psub(pcj.pmul(pc,yi),pm.pmul(ps,xi)));
px += PacketSize;
py += PacketSize;
}
@ -365,10 +376,10 @@ void /*EIGEN_DONT_INLINE*/ apply_rotation_in_the_plane(DenseBase<VectorX>& xpr_x
Packet xi1 = ploadu<Packet>(px+PacketSize);
Packet yi = pload <Packet>(py);
Packet yi1 = pload <Packet>(py+PacketSize);
pstoreu(px, padd(pmul(pc,xi),pcj.pmul(ps,yi)));
pstoreu(px+PacketSize, padd(pmul(pc,xi1),pcj.pmul(ps,yi1)));
pstore (py, psub(pcj.pmul(pc,yi),pmul(ps,xi)));
pstore (py+PacketSize, psub(pcj.pmul(pc,yi1),pmul(ps,xi1)));
pstoreu(px, padd(pm.pmul(pc,xi),pcj.pmul(ps,yi)));
pstoreu(px+PacketSize, padd(pm.pmul(pc,xi1),pcj.pmul(ps,yi1)));
pstore (py, psub(pcj.pmul(pc,yi),pm.pmul(ps,xi)));
pstore (py+PacketSize, psub(pcj.pmul(pc,yi1),pm.pmul(ps,xi1)));
px += Peeling*PacketSize;
py += Peeling*PacketSize;
}
@ -376,8 +387,8 @@ void /*EIGEN_DONT_INLINE*/ apply_rotation_in_the_plane(DenseBase<VectorX>& xpr_x
{
Packet xi = ploadu<Packet>(x+peelingEnd);
Packet yi = pload <Packet>(y+peelingEnd);
pstoreu(x+peelingEnd, padd(pmul(pc,xi),pcj.pmul(ps,yi)));
pstore (y+peelingEnd, psub(pcj.pmul(pc,yi),pmul(ps,xi)));
pstoreu(x+peelingEnd, padd(pm.pmul(pc,xi),pcj.pmul(ps,yi)));
pstore (y+peelingEnd, psub(pcj.pmul(pc,yi),pm.pmul(ps,xi)));
}
}
@ -391,21 +402,20 @@ void /*EIGEN_DONT_INLINE*/ apply_rotation_in_the_plane(DenseBase<VectorX>& xpr_x
}
/*** fixed-size vectorized path ***/
else if(VectorX::SizeAtCompileTime != Dynamic &&
(VectorX::Flags & VectorY::Flags & PacketAccessBit) &&
(EIGEN_PLAIN_ENUM_MIN(evaluator<VectorX>::Alignment, evaluator<VectorY>::Alignment)>0)) // FIXME should be compared to the required alignment
else if(SizeAtCompileTime != Dynamic && MinAlignment>0) // FIXME should be compared to the required alignment
{
const Packet pc = pset1<Packet>(c);
const Packet ps = pset1<Packet>(s);
conj_helper<Packet,Packet,NumTraits<Scalar>::IsComplex,false> pcj;
const OtherPacket pc = pset1<OtherPacket>(c);
const OtherPacket ps = pset1<OtherPacket>(s);
conj_helper<OtherPacket,Packet,NumTraits<OtherPacket>::IsComplex,false> pcj;
conj_helper<OtherPacket,Packet,false,false> pm;
Scalar* EIGEN_RESTRICT px = x;
Scalar* EIGEN_RESTRICT py = y;
for(Index i=0; i<size; i+=PacketSize)
{
Packet xi = pload<Packet>(px);
Packet yi = pload<Packet>(py);
pstore(px, padd(pmul(pc,xi),pcj.pmul(ps,yi)));
pstore(py, psub(pcj.pmul(pc,yi),pmul(ps,xi)));
pstore(px, padd(pm.pmul(pc,xi),pcj.pmul(ps,yi)));
pstore(py, psub(pcj.pmul(pc,yi),pm.pmul(ps,xi)));
px += PacketSize;
py += PacketSize;
}
@ -414,16 +424,36 @@ void /*EIGEN_DONT_INLINE*/ apply_rotation_in_the_plane(DenseBase<VectorX>& xpr_x
/*** non-vectorized path ***/
else
{
for(Index i=0; i<size; ++i)
apply_rotation_in_the_plane_selector<Scalar,OtherScalar,SizeAtCompileTime,MinAlignment,false>::run(x,incrx,y,incry,size,c,s);
}
}
};
template<typename VectorX, typename VectorY, typename OtherScalar>
void /*EIGEN_DONT_INLINE*/ apply_rotation_in_the_plane(DenseBase<VectorX>& xpr_x, DenseBase<VectorY>& xpr_y, const JacobiRotation<OtherScalar>& j)
{
Scalar xi = *x;
Scalar yi = *y;
*x = c * xi + numext::conj(s) * yi;
*y = -s * xi + numext::conj(c) * yi;
x += incrx;
y += incry;
}
}
typedef typename VectorX::Scalar Scalar;
const bool Vectorizable = (VectorX::Flags & VectorY::Flags & PacketAccessBit)
&& (int(packet_traits<Scalar>::size) == int(packet_traits<OtherScalar>::size));
eigen_assert(xpr_x.size() == xpr_y.size());
Index size = xpr_x.size();
Index incrx = xpr_x.derived().innerStride();
Index incry = xpr_y.derived().innerStride();
Scalar* EIGEN_RESTRICT x = &xpr_x.derived().coeffRef(0);
Scalar* EIGEN_RESTRICT y = &xpr_y.derived().coeffRef(0);
OtherScalar c = j.c();
OtherScalar s = j.s();
if (c==OtherScalar(1) && s==OtherScalar(0))
return;
apply_rotation_in_the_plane_selector<
Scalar,OtherScalar,
VectorX::SizeAtCompileTime,
EIGEN_PLAIN_ENUM_MIN(evaluator<VectorX>::Alignment, evaluator<VectorY>::Alignment),
Vectorizable>::run(x,incrx,y,incry,size,c,s);
}
} // end namespace internal

2
xs/src/eigen/Eigen/src/LU/InverseImpl.h
View File

@ -404,7 +404,7 @@ inline void MatrixBase<Derived>::computeInverseWithCheck(
const RealScalar& absDeterminantThreshold
) const
{
RealScalar determinant;
Scalar determinant;
// i'd love to put some static assertions there, but SFINAE means that they have no effect...
eigen_assert(rows() == cols());
computeInverseAndDetWithCheck(inverse,determinant,invertible,absDeterminantThreshold);

2
xs/src/eigen/Eigen/src/OrderingMethods/Eigen_Colamd.h
View File

@ -1004,7 +1004,7 @@ static IndexType find_ordering /* return the number of garbage collections */
COLAMD_ASSERT (head [min_score] >= COLAMD_EMPTY) ;
/* get pivot column from head of minimum degree list */
while (head [min_score] == COLAMD_EMPTY && min_score < n_col)
while (min_score < n_col && head [min_score] == COLAMD_EMPTY)
{
min_score++ ;
}

8
xs/src/eigen/Eigen/src/PaStiXSupport/PaStiXSupport.h
View File

@ -64,28 +64,28 @@ namespace internal
typedef typename _MatrixType::StorageIndex StorageIndex;
};
void eigen_pastix(pastix_data_t **pastix_data, int pastix_comm, int n, int *ptr, int *idx, float *vals, int *perm, int * invp, float *x, int nbrhs, int *iparm, double *dparm)
inline void eigen_pastix(pastix_data_t **pastix_data, int pastix_comm, int n, int *ptr, int *idx, float *vals, int *perm, int * invp, float *x, int nbrhs, int *iparm, double *dparm)
{
if (n == 0) { ptr = NULL; idx = NULL; vals = NULL; }
if (nbrhs == 0) {x = NULL; nbrhs=1;}
s_pastix(pastix_data, pastix_comm, n, ptr, idx, vals, perm, invp, x, nbrhs, iparm, dparm);
}
void eigen_pastix(pastix_data_t **pastix_data, int pastix_comm, int n, int *ptr, int *idx, double *vals, int *perm, int * invp, double *x, int nbrhs, int *iparm, double *dparm)
inline void eigen_pastix(pastix_data_t **pastix_data, int pastix_comm, int n, int *ptr, int *idx, double *vals, int *perm, int * invp, double *x, int nbrhs, int *iparm, double *dparm)
{
if (n == 0) { ptr = NULL; idx = NULL; vals = NULL; }
if (nbrhs == 0) {x = NULL; nbrhs=1;}
d_pastix(pastix_data, pastix_comm, n, ptr, idx, vals, perm, invp, x, nbrhs, iparm, dparm);
}
void eigen_pastix(pastix_data_t **pastix_data, int pastix_comm, int n, int *ptr, int *idx, std::complex<float> *vals, int *perm, int * invp, std::complex<float> *x, int nbrhs, int *iparm, double *dparm)
inline void eigen_pastix(pastix_data_t **pastix_data, int pastix_comm, int n, int *ptr, int *idx, std::complex<float> *vals, int *perm, int * invp, std::complex<float> *x, int nbrhs, int *iparm, double *dparm)
{
if (n == 0) { ptr = NULL; idx = NULL; vals = NULL; }
if (nbrhs == 0) {x = NULL; nbrhs=1;}
c_pastix(pastix_data, pastix_comm, n, ptr, idx, reinterpret_cast<PASTIX_COMPLEX*>(vals), perm, invp, reinterpret_cast<PASTIX_COMPLEX*>(x), nbrhs, iparm, dparm);
}
void eigen_pastix(pastix_data_t **pastix_data, int pastix_comm, int n, int *ptr, int *idx, std::complex<double> *vals, int *perm, int * invp, std::complex<double> *x, int nbrhs, int *iparm, double *dparm)
inline void eigen_pastix(pastix_data_t **pastix_data, int pastix_comm, int n, int *ptr, int *idx, std::complex<double> *vals, int *perm, int * invp, std::complex<double> *x, int nbrhs, int *iparm, double *dparm)
{
if (n == 0) { ptr = NULL; idx = NULL; vals = NULL; }
if (nbrhs == 0) {x = NULL; nbrhs=1;}

10
xs/src/eigen/Eigen/src/QR/ColPivHouseholderQR.h
View File

@ -506,8 +506,8 @@ void ColPivHouseholderQR<MatrixType>::computeInPlace()
m_colNormsUpdated.coeffRef(k) = m_colNormsDirect.coeffRef(k);
}
RealScalar threshold_helper = numext::abs2<Scalar>(m_colNormsUpdated.maxCoeff() * NumTraits<Scalar>::epsilon()) / RealScalar(rows);
RealScalar norm_downdate_threshold = numext::sqrt(NumTraits<Scalar>::epsilon());
RealScalar threshold_helper = numext::abs2<RealScalar>(m_colNormsUpdated.maxCoeff() * NumTraits<RealScalar>::epsilon()) / RealScalar(rows);
RealScalar norm_downdate_threshold = numext::sqrt(NumTraits<RealScalar>::epsilon());
m_nonzero_pivots = size; // the generic case is that in which all pivots are nonzero (invertible case)
m_maxpivot = RealScalar(0);
@ -553,11 +553,11 @@ void ColPivHouseholderQR<MatrixType>::computeInPlace()
// http://www.netlib.org/lapack/lawnspdf/lawn176.pdf
// and used in LAPACK routines xGEQPF and xGEQP3.
// See lines 278-297 in http://www.netlib.org/lapack/explore-html/dc/df4/sgeqpf_8f_source.html
if (m_colNormsUpdated.coeffRef(j) != 0) {
if (m_colNormsUpdated.coeffRef(j) != RealScalar(0)) {
RealScalar temp = abs(m_qr.coeffRef(k, j)) / m_colNormsUpdated.coeffRef(j);
temp = (RealScalar(1) + temp) * (RealScalar(1) - temp);
temp = temp < 0 ? 0 : temp;
RealScalar temp2 = temp * numext::abs2<Scalar>(m_colNormsUpdated.coeffRef(j) /
temp = temp < RealScalar(0) ? RealScalar(0) : temp;
RealScalar temp2 = temp * numext::abs2<RealScalar>(m_colNormsUpdated.coeffRef(j) /
m_colNormsDirect.coeffRef(j));
if (temp2 <= norm_downdate_threshold) {
// The updated norm has become too inaccurate so re-compute the column

110
xs/src/eigen/Eigen/src/SVD/BDCSVD.h
View File

@ -11,7 +11,7 @@
// Copyright (C) 2013 Jean Ceccato <jean.ceccato@ensimag.fr>
// Copyright (C) 2013 Pierre Zoppitelli <pierre.zoppitelli@ensimag.fr>
// Copyright (C) 2013 Jitse Niesen <jitse@maths.leeds.ac.uk>
// Copyright (C) 2014-2016 Gael Guennebaud <gael.guennebaud@inria.fr>
// Copyright (C) 2014-2017 Gael Guennebaud <gael.guennebaud@inria.fr>
//
// Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
@ -77,6 +77,7 @@ public:
typedef _MatrixType MatrixType;
typedef typename MatrixType::Scalar Scalar;
typedef typename NumTraits<typename MatrixType::Scalar>::Real RealScalar;
typedef typename NumTraits<RealScalar>::Literal Literal;
enum {
RowsAtCompileTime = MatrixType::RowsAtCompileTime,
ColsAtCompileTime = MatrixType::ColsAtCompileTime,
@ -259,7 +260,7 @@ BDCSVD<MatrixType>& BDCSVD<MatrixType>::compute(const MatrixType& matrix, unsign
//**** step 0 - Copy the input matrix and apply scaling to reduce over/under-flows
RealScalar scale = matrix.cwiseAbs().maxCoeff();
if(scale==RealScalar(0)) scale = RealScalar(1);
if(scale==Literal(0)) scale = Literal(1);
MatrixX copy;
if (m_isTranspose) copy = matrix.adjoint()/scale;
else copy = matrix/scale;
@ -351,13 +352,13 @@ void BDCSVD<MatrixType>::structured_update(Block<MatrixXr,Dynamic,Dynamic> A, co
Index k1=0, k2=0;
for(Index j=0; j<n; ++j)
{
if( (A.col(j).head(n1).array()!=0).any() )
if( (A.col(j).head(n1).array()!=Literal(0)).any() )
{
A1.col(k1) = A.col(j).head(n1);
B1.row(k1) = B.row(j);
++k1;
}
if( (A.col(j).tail(n2).array()!=0).any() )
if( (A.col(j).tail(n2).array()!=Literal(0)).any() )
{
A2.col(k2) = A.col(j).tail(n2);
B2.row(k2) = B.row(j);
@ -449,11 +450,11 @@ void BDCSVD<MatrixType>::divide (Index firstCol, Index lastCol, Index firstRowW,
l = m_naiveU.row(1).segment(firstCol, k);
f = m_naiveU.row(0).segment(firstCol + k + 1, n - k - 1);
}
if (m_compV) m_naiveV(firstRowW+k, firstColW) = 1;
if (m_compV) m_naiveV(firstRowW+k, firstColW) = Literal(1);
if (r0<considerZero)
{
c0 = 1;
s0 = 0;
c0 = Literal(1);
s0 = Literal(0);
}
else
{
@ -574,7 +575,7 @@ void BDCSVD<MatrixType>::computeSVDofM(Index firstCol, Index n, MatrixXr& U, Vec
ArrayRef col0 = m_computed.col(firstCol).segment(firstCol, n);
m_workspace.head(n) = m_computed.block(firstCol, firstCol, n, n).diagonal();
ArrayRef diag = m_workspace.head(n);
diag(0) = 0;
diag(0) = Literal(0);
// Allocate space for singular values and vectors
singVals.resize(n);
@ -590,7 +591,7 @@ void BDCSVD<MatrixType>::computeSVDofM(Index firstCol, Index n, MatrixXr& U, Vec
// but others are interleaved and we must ignore them at this stage.
// To this end, let's compute a permutation skipping them:
Index actual_n = n;
while(actual_n>1 && diag(actual_n-1)==0) --actual_n;
while(actual_n>1 && diag(actual_n-1)==Literal(0)) --actual_n;
Index m = 0; // size of the deflated problem
for(Index k=0;k<actual_n;++k)
if(abs(col0(k))>considerZero)
@ -691,11 +692,13 @@ template <typename MatrixType>
typename BDCSVD<MatrixType>::RealScalar BDCSVD<MatrixType>::secularEq(RealScalar mu, const ArrayRef& col0, const ArrayRef& diag, const IndicesRef &perm, const ArrayRef& diagShifted, RealScalar shift)
{
Index m = perm.size();
RealScalar res = 1;
RealScalar res = Literal(1);
for(Index i=0; i<m; ++i)
{
Index j = perm(i);
res += numext::abs2(col0(j)) / ((diagShifted(j) - mu) * (diag(j) + shift + mu));
// The following expression could be rewritten to involve only a single division,
// but this would make the expression more sensitive to overflow.
res += (col0(j) / (diagShifted(j) - mu)) * (col0(j) / (diag(j) + shift + mu));
}
return res;
@ -707,19 +710,22 @@ void BDCSVD<MatrixType>::computeSingVals(const ArrayRef& col0, const ArrayRef& d
{
using std::abs;
using std::swap;
using std::sqrt;
Index n = col0.size();
Index actual_n = n;
while(actual_n>1 && col0(actual_n-1)==0) --actual_n;
// Note that here actual_n is computed based on col0(i)==0 instead of diag(i)==0 as above
// because 1) we have diag(i)==0 => col0(i)==0 and 2) if col0(i)==0, then diag(i) is already a singular value.
while(actual_n>1 && col0(actual_n-1)==Literal(0)) --actual_n;
for (Index k = 0; k < n; ++k)
{
if (col0(k) == 0 || actual_n==1)
if (col0(k) == Literal(0) || actual_n==1)
{
// if col0(k) == 0, then entry is deflated, so singular value is on diagonal
// if actual_n==1, then the deflated problem is already diagonalized
singVals(k) = k==0 ? col0(0) : diag(k);
mus(k) = 0;
mus(k) = Literal(0);
shifts(k) = k==0 ? col0(0) : diag(k);
continue;
}
@ -731,15 +737,17 @@ void BDCSVD<MatrixType>::computeSingVals(const ArrayRef& col0, const ArrayRef& d
right = (diag(actual_n-1) + col0.matrix().norm());
else
{
// Skip deflated singular values
// Skip deflated singular values,
// recall that at this stage we assume that z[j]!=0 and all entries for which z[j]==0 have been put aside.
// This should be equivalent to using perm[]
Index l = k+1;
while(col0(l)==0) { ++l; eigen_internal_assert(l<actual_n); }
while(col0(l)==Literal(0)) { ++l; eigen_internal_assert(l<actual_n); }
right = diag(l);
}
// first decide whether it's closer to the left end or the right end
RealScalar mid = left + (right-left) / 2;
RealScalar fMid = secularEq(mid, col0, diag, perm, diag, 0);
RealScalar mid = left + (right-left) / Literal(2);
RealScalar fMid = secularEq(mid, col0, diag, perm, diag, Literal(0));
#ifdef EIGEN_BDCSVD_DEBUG_VERBOSE
std::cout << right-left << "\n";
std::cout << "fMid = " << fMid << " " << secularEq(mid-left, col0, diag, perm, diag-left, left) << " " << secularEq(mid-right, col0, diag, perm, diag-right, right) << "\n";
@ -755,7 +763,7 @@ void BDCSVD<MatrixType>::computeSingVals(const ArrayRef& col0, const ArrayRef& d
<< " " << secularEq(0.8*(left+right), col0, diag, perm, diag, 0)
<< " " << secularEq(0.9*(left+right), col0, diag, perm, diag, 0) << "\n";
#endif
RealScalar shift = (k == actual_n-1 || fMid > 0) ? left : right;
RealScalar shift = (k == actual_n-1 || fMid > Literal(0)) ? left : right;
// measure everything relative to shift
Map<ArrayXr> diagShifted(m_workspace.data()+4*n, n);
@ -785,13 +793,13 @@ void BDCSVD<MatrixType>::computeSingVals(const ArrayRef& col0, const ArrayRef& d
// rational interpolation: fit a function of the form a / mu + b through the two previous
// iterates and use its zero to compute the next iterate
bool useBisection = fPrev*fCur>0;
while (fCur!=0 && abs(muCur - muPrev) > 8 * NumTraits<RealScalar>::epsilon() * numext::maxi<RealScalar>(abs(muCur), abs(muPrev)) && abs(fCur - fPrev)>NumTraits<RealScalar>::epsilon() && !useBisection)
bool useBisection = fPrev*fCur>Literal(0);
while (fCur!=Literal(0) && abs(muCur - muPrev) > Literal(8) * NumTraits<RealScalar>::epsilon() * numext::maxi<RealScalar>(abs(muCur), abs(muPrev)) && abs(fCur - fPrev)>NumTraits<RealScalar>::epsilon() && !useBisection)
{
++m_numIters;
// Find a and b such that the function f(mu) = a / mu + b matches the current and previous samples.
RealScalar a = (fCur - fPrev) / (1/muCur - 1/muPrev);
RealScalar a = (fCur - fPrev) / (Literal(1)/muCur - Literal(1)/muPrev);
RealScalar b = fCur - a / muCur;
// And find mu such that f(mu)==0:
RealScalar muZero = -a/b;
@ -803,8 +811,8 @@ void BDCSVD<MatrixType>::computeSingVals(const ArrayRef& col0, const ArrayRef& d
fCur = fZero;
if (shift == left && (muCur < 0 || muCur > right - left)) useBisection = true;
if (shift == right && (muCur < -(right - left) || muCur > 0)) useBisection = true;
if (shift == left && (muCur < Literal(0) || muCur > right - left)) useBisection = true;
if (shift == right && (muCur < -(right - left) || muCur > Literal(0))) useBisection = true;
if (abs(fCur)>abs(fPrev)) useBisection = true;
}
@ -817,14 +825,22 @@ void BDCSVD<MatrixType>::computeSingVals(const ArrayRef& col0, const ArrayRef& d
RealScalar leftShifted, rightShifted;
if (shift == left)
{
leftShifted = (std::numeric_limits<RealScalar>::min)();
// to avoid overflow, we must have mu > max(real_min, |z(k)|/sqrt(real_max)),
// the factor 2 is to be more conservative
leftShifted = numext::maxi<RealScalar>( (std::numeric_limits<RealScalar>::min)(), Literal(2) * abs(col0(k)) / sqrt((std::numeric_limits<RealScalar>::max)()) );
// check that we did it right:
eigen_internal_assert( (numext::isfinite)( (col0(k)/leftShifted)*(col0(k)/(diag(k)+shift+leftShifted)) ) );
// I don't understand why the case k==0 would be special there:
// if (k == 0) rightShifted = right - left; else
rightShifted = (k==actual_n-1) ? right : ((right - left) * RealScalar(0.6)); // theoretically we can take 0.5, but let's be safe
rightShifted = (k==actual_n-1) ? right : ((right - left) * RealScalar(0.51)); // theoretically we can take 0.5, but let's be safe
}
else
{
leftShifted = -(right - left) * RealScalar(0.6);
leftShifted = -(right - left) * RealScalar(0.51);
if(k+1<n)
rightShifted = -numext::maxi<RealScalar>( (std::numeric_limits<RealScalar>::min)(), abs(col0(k+1)) / sqrt((std::numeric_limits<RealScalar>::max)()) );
else
rightShifted = -(std::numeric_limits<RealScalar>::min)();
}
@ -841,13 +857,13 @@ void BDCSVD<MatrixType>::computeSingVals(const ArrayRef& col0, const ArrayRef& d
std::cout << k << " : " << fLeft << " * " << fRight << " == " << fLeft * fRight << " ; " << left << " - " << right << " -> " << leftShifted << " " << rightShifted << " shift=" << shift << "\n";
}
#endif
eigen_internal_assert(fLeft * fRight < 0);
eigen_internal_assert(fLeft * fRight < Literal(0));
while (rightShifted - leftShifted > 2 * NumTraits<RealScalar>::epsilon() * numext::maxi<RealScalar>(abs(leftShifted), abs(rightShifted)))
while (rightShifted - leftShifted > Literal(2) * NumTraits<RealScalar>::epsilon() * numext::maxi<RealScalar>(abs(leftShifted), abs(rightShifted)))
{
RealScalar midShifted = (leftShifted + rightShifted) / 2;
RealScalar midShifted = (leftShifted + rightShifted) / Literal(2);
fMid = secularEq(midShifted, col0, diag, perm, diagShifted, shift);
if (fLeft * fMid < 0)
if (fLeft * fMid < Literal(0))
{
rightShifted = midShifted;
}
@ -858,7 +874,7 @@ void BDCSVD<MatrixType>::computeSingVals(const ArrayRef& col0, const ArrayRef& d
}
}
muCur = (leftShifted + rightShifted) / 2;
muCur = (leftShifted + rightShifted) / Literal(2);
}
singVals[k] = shift + muCur;
@ -892,8 +908,8 @@ void BDCSVD<MatrixType>::perturbCol0
// The offset permits to skip deflated entries while computing zhat
for (Index k = 0; k < n; ++k)
{
if (col0(k) == 0) // deflated
zhat(k) = 0;
if (col0(k) == Literal(0)) // deflated
zhat(k) = Literal(0);
else
{
// see equation (3.6)
@ -918,7 +934,7 @@ void BDCSVD<MatrixType>::perturbCol0
std::cout << "zhat(" << k << ") = sqrt( " << prod << ") ; " << (singVals(last) + dk) << " * " << mus(last) + shifts(last) << " - " << dk << "\n";
#endif
RealScalar tmp = sqrt(prod);
zhat(k) = col0(k) > 0 ? tmp : -tmp;
zhat(k) = col0(k) > Literal(0) ? tmp : -tmp;
}
}
}
@ -934,7 +950,7 @@ void BDCSVD<MatrixType>::computeSingVecs
for (Index k = 0; k < n; ++k)
{
if (zhat(k) == 0)
if (zhat(k) == Literal(0))
{
U.col(k) = VectorType::Unit(n+1, k);
if (m_compV) V.col(k) = VectorType::Unit(n, k);
@ -947,7 +963,7 @@ void BDCSVD<MatrixType>::computeSingVecs
Index i = perm(l);
U(i,k) = zhat(i)/(((diag(i) - shifts(k)) - mus(k)) )/( (diag(i) + singVals[k]));
}
U(n,k) = 0;
U(n,k) = Literal(0);
U.col(k).normalize();
if (m_compV)
@ -958,7 +974,7 @@ void BDCSVD<MatrixType>::computeSingVecs
Index i = perm(l);
V(i,k) = diag(i) * zhat(i) / (((diag(i) - shifts(k)) - mus(k)) )/( (diag(i) + singVals[k]));
}
V(0,k) = -1;
V(0,k) = Literal(-1);
V.col(k).normalize();
}
}
@ -979,15 +995,15 @@ void BDCSVD<MatrixType>::deflation43(Index firstCol, Index shift, Index i, Index
Index start = firstCol + shift;
RealScalar c = m_computed(start, start);
RealScalar s = m_computed(start+i, start);
RealScalar r = sqrt(numext::abs2(c) + numext::abs2(s));
if (r == 0)
RealScalar r = numext::hypot(c,s);
if (r == Literal(0))
{
m_computed(start+i, start+i) = 0;
m_computed(start+i, start+i) = Literal(0);
return;
}
m_computed(start,start) = r;
m_computed(start+i, start) = 0;
m_computed(start+i, start+i) = 0;
m_computed(start+i, start) = Literal(0);
m_computed(start+i, start+i) = Literal(0);
JacobiRotation<RealScalar> J(c/r,-s/r);
if (m_compU) m_naiveU.middleRows(firstCol, size+1).applyOnTheRight(firstCol, firstCol+i, J);
@ -1020,7 +1036,7 @@ void BDCSVD<MatrixType>::deflation44(Index firstColu , Index firstColm, Index fi
<< m_computed(firstColm + i+1, firstColm+i+1) << " "
<< m_computed(firstColm + i+2, firstColm+i+2) << "\n";
#endif
if (r==0)
if (r==Literal(0))
{
m_computed(firstColm + i, firstColm + i) = m_computed(firstColm + j, firstColm + j);
return;
@ -1029,7 +1045,7 @@ void BDCSVD<MatrixType>::deflation44(Index firstColu , Index firstColm, Index fi
s/=r;
m_computed(firstColm + i, firstColm) = r;
m_computed(firstColm + j, firstColm + j) = m_computed(firstColm + i, firstColm + i);
m_computed(firstColm + j, firstColm) = 0;
m_computed(firstColm + j, firstColm) = Literal(0);
JacobiRotation<RealScalar> J(c,-s);
if (m_compU) m_naiveU.middleRows(firstColu, size+1).applyOnTheRight(firstColu + i, firstColu + j, J);
@ -1053,7 +1069,7 @@ void BDCSVD<MatrixType>::deflation(Index firstCol, Index lastCol, Index k, Index
const RealScalar considerZero = (std::numeric_limits<RealScalar>::min)();
RealScalar maxDiag = diag.tail((std::max)(Index(1),length-1)).cwiseAbs().maxCoeff();
RealScalar epsilon_strict = numext::maxi<RealScalar>(considerZero,NumTraits<RealScalar>::epsilon() * maxDiag);
RealScalar epsilon_coarse = 8 * NumTraits<RealScalar>::epsilon() * numext::maxi<RealScalar>(col0.cwiseAbs().maxCoeff(), maxDiag);
RealScalar epsilon_coarse = Literal(8) * NumTraits<RealScalar>::epsilon() * numext::maxi<RealScalar>(col0.cwiseAbs().maxCoeff(), maxDiag);
#ifdef EIGEN_BDCSVD_SANITY_CHECKS
assert(m_naiveU.allFinite());
@ -1081,7 +1097,7 @@ void BDCSVD<MatrixType>::deflation(Index firstCol, Index lastCol, Index k, Index
#ifdef EIGEN_BDCSVD_DEBUG_VERBOSE
std::cout << "deflation 4.2, set z(" << i << ") to zero because " << abs(col0(i)) << " < " << epsilon_strict << " (diag(" << i << ")=" << diag(i) << ")\n";
#endif
col0(i) = 0;
col0(i) = Literal(0);
}
//condition 4.3

5
xs/src/eigen/Eigen/src/SVD/JacobiSVD_LAPACKE.h
View File

@ -61,9 +61,10 @@ JacobiSVD<Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW, Dynamic, Dynamic>, ColPiv
u = (LAPACKE_TYPE*)m_matrixU.data(); \
} else { ldu=1; u=&dummy; }\
MatrixType localV; \
ldvt = (m_computeFullV) ? internal::convert_index<lapack_int>(m_cols) : (m_computeThinV) ? internal::convert_index<lapack_int>(m_diagSize) : 1; \
lapack_int vt_rows = (m_computeFullV) ? internal::convert_index<lapack_int>(m_cols) : (m_computeThinV) ? internal::convert_index<lapack_int>(m_diagSize) : 1; \
if (computeV()) { \
localV.resize(ldvt, m_cols); \
localV.resize(vt_rows, m_cols); \
ldvt = internal::convert_index<lapack_int>(localV.outerStride()); \
vt = (LAPACKE_TYPE*)localV.data(); \
} else { ldvt=1; vt=&dummy; }\
Matrix<LAPACKE_RTYPE, Dynamic, Dynamic> superb; superb.resize(m_diagSize, 1); \

4
xs/src/eigen/Eigen/src/SVD/UpperBidiagonalization.h
View File

@ -159,6 +159,8 @@ void upperbidiagonalization_blocked_helper(MatrixType& A,
traits<MatrixType>::Flags & RowMajorBit> > Y)
{
typedef typename MatrixType::Scalar Scalar;
typedef typename MatrixType::RealScalar RealScalar;
typedef typename NumTraits<RealScalar>::Literal Literal;
enum { StorageOrder = traits<MatrixType>::Flags & RowMajorBit };
typedef InnerStride<int(StorageOrder) == int(ColMajor) ? 1 : Dynamic> ColInnerStride;
typedef InnerStride<int(StorageOrder) == int(ColMajor) ? Dynamic : 1> RowInnerStride;
@ -263,7 +265,7 @@ void upperbidiagonalization_blocked_helper(MatrixType& A,
SubMatType A10( A.block(bs,0, brows-bs,bs) );
SubMatType A01( A.block(0,bs, bs,bcols-bs) );
Scalar tmp = A01(bs-1,0);
A01(bs-1,0) = 1;
A01(bs-1,0) = Literal(1);
A11.noalias() -= A10 * Y.topLeftCorner(bcols,bs).bottomRows(bcols-bs).adjoint();
A11.noalias() -= X.topLeftCorner(brows,bs).bottomRows(brows-bs) * A01;
A01(bs-1,0) = tmp;

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