Fix non UTF8-encoded thirdparty files
This commit is contained in:
Rémi Verschelde
parent
0acdeb2e12
commit
b7e737639f
14
thirdparty/assimp/code/res/resource.h
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14
thirdparty/assimp/code/res/resource.h
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@ -1,14 +0,0 @@
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//{{NO_DEPENDENCIES}}
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// Microsoft Visual C++ generated include file.
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// Used by assimp.rc
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// Nächste Standardwerte für neue Objekte
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//
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#ifdef APSTUDIO_INVOKED
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#ifndef APSTUDIO_READONLY_SYMBOLS
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#define _APS_NEXT_RESOURCE_VALUE 101
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#define _APS_NEXT_COMMAND_VALUE 40001
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#define _APS_NEXT_CONTROL_VALUE 1001
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#define _APS_NEXT_SYMED_VALUE 101
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#endif
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#endif
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8
thirdparty/misc/clipper.cpp
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8
thirdparty/misc/clipper.cpp
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@ -4329,10 +4329,10 @@ double DistanceFromLineSqrd(
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const IntPoint& pt, const IntPoint& ln1, const IntPoint& ln2)
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{
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//The equation of a line in general form (Ax + By + C = 0)
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//given 2 points (x¹,y¹) & (x²,y²) is ...
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//(y¹ - y²)x + (x² - x¹)y + (y² - y¹)x¹ - (x² - x¹)y¹ = 0
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//A = (y¹ - y²); B = (x² - x¹); C = (y² - y¹)x¹ - (x² - x¹)y¹
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//perpendicular distance of point (x³,y³) = (Ax³ + By³ + C)/Sqrt(A² + B²)
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//given 2 points (x¹,y¹) & (x²,y²) is ...
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//(y¹ - y²)x + (x² - x¹)y + (y² - y¹)x¹ - (x² - x¹)y¹ = 0
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//A = (y¹ - y²); B = (x² - x¹); C = (y² - y¹)x¹ - (x² - x¹)y¹
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//perpendicular distance of point (x³,y³) = (Ax³ + By³ + C)/Sqrt(A² + B²)
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//see http://en.wikipedia.org/wiki/Perpendicular_distance
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double A = double(ln1.Y - ln2.Y);
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double B = double(ln2.X - ln1.X);
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44
thirdparty/xatlas/xatlas.cpp
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44
thirdparty/xatlas/xatlas.cpp
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@ -4388,7 +4388,7 @@ private:
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class Solver
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{
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public:
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// Solve the symmetric system: At·A·x = At·b
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// Solve the symmetric system: At·A·x = At·b
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static bool LeastSquaresSolver(const sparse::Matrix &A, const FullVector &b, FullVector &x, float epsilon = 1e-5f)
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{
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xaDebugAssert(A.width() == x.dimension());
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@ -4477,22 +4477,22 @@ private:
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* Gradient method.
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*
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* Solving sparse linear systems:
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* (1) A·x = b
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* (1) A·x = b
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*
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* The conjugate gradient algorithm solves (1) only in the case that A is
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* symmetric and positive definite. It is based on the idea of minimizing the
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* function
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*
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* (2) f(x) = 1/2·x·A·x - b·x
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* (2) f(x) = 1/2·x·A·x - b·x
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*
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* This function is minimized when its gradient
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*
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* (3) df = A·x - b
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* (3) df = A·x - b
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*
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* is zero, which is equivalent to (1). The minimization is carried out by
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* generating a succession of search directions p.k and improved minimizers x.k.
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* At each stage a quantity alfa.k is found that minimizes f(x.k + alfa.k·p.k),
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* and x.k+1 is set equal to the new point x.k + alfa.k·p.k. The p.k and x.k are
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* At each stage a quantity alfa.k is found that minimizes f(x.k + alfa.k·p.k),
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* and x.k+1 is set equal to the new point x.k + alfa.k·p.k. The p.k and x.k are
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* built up in such a way that x.k+1 is also the minimizer of f over the whole
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* vector space of directions already taken, {p.1, p.2, . . . , p.k}. After N
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* iterations you arrive at the minimizer over the entire vector space, i.e., the
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@ -4520,7 +4520,7 @@ private:
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float delta_new;
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float alpha;
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float beta;
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// r = b - A·x;
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// r = b - A·x;
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sparse::copy(b, r);
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sparse::sgemv(-1, A, x, 1, r);
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// p = r;
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@ -4529,24 +4529,24 @@ private:
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delta_0 = delta_new;
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while (i < i_max && delta_new > epsilon * epsilon * delta_0) {
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i++;
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// q = A·p
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// q = A·p
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mult(A, p, q);
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// alpha = delta_new / p·q
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// alpha = delta_new / p·q
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alpha = delta_new / sparse::dot( p, q );
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// x = alfa·p + x
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// x = alfa·p + x
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sparse::saxpy(alpha, p, x);
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if ((i & 31) == 0) { // recompute r after 32 steps
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// r = b - A·x
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// r = b - A·x
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sparse::copy(b, r);
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sparse::sgemv(-1, A, x, 1, r);
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} else {
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// r = r - alpha·q
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// r = r - alpha·q
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sparse::saxpy(-alpha, q, r);
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}
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delta_old = delta_new;
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delta_new = sparse::dot( r, r );
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beta = delta_new / delta_old;
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// p = beta·p + r
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// p = beta·p + r
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sparse::scal(beta, p);
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sparse::saxpy(1, r, p);
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}
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@ -4572,35 +4572,35 @@ private:
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float delta_new;
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float alpha;
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float beta;
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// r = b - A·x
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// r = b - A·x
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sparse::copy(b, r);
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sparse::sgemv(-1, A, x, 1, r);
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// p = M^-1 · r
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// p = M^-1 · r
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preconditioner.apply(r, p);
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delta_new = sparse::dot(r, p);
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delta_0 = delta_new;
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while (i < i_max && delta_new > epsilon * epsilon * delta_0) {
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i++;
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// q = A·p
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// q = A·p
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mult(A, p, q);
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// alpha = delta_new / p·q
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// alpha = delta_new / p·q
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alpha = delta_new / sparse::dot(p, q);
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// x = alfa·p + x
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// x = alfa·p + x
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sparse::saxpy(alpha, p, x);
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if ((i & 31) == 0) { // recompute r after 32 steps
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// r = b - A·x
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// r = b - A·x
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sparse::copy(b, r);
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sparse::sgemv(-1, A, x, 1, r);
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} else {
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// r = r - alfa·q
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// r = r - alfa·q
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sparse::saxpy(-alpha, q, r);
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}
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// s = M^-1 · r
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// s = M^-1 · r
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preconditioner.apply(r, s);
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delta_old = delta_new;
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delta_new = sparse::dot( r, s );
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beta = delta_new / delta_old;
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// p = s + beta·p
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// p = s + beta·p
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sparse::scal(beta, p);
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sparse::saxpy(1, s, p);
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}
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