::testing::AssertionResult array_are_close(X const &x1, Y const &y1,double precision = 1.e-10) {
triqs::arrays::array<typename X::value_type, X::rank> x = x1;
triqs::arrays::array<typename X::value_type, X::rank> y = y1;
if (x.domain() != y.domain())
return ::testing::AssertionFailure() << "Comparing two arrays of different size "
<< "\n X = "<< x << "\n Y = "<< y;
if (max_element(abs(x - y)) < precision)
return ::testing::AssertionSuccess();
else
return ::testing::AssertionFailure() << "max_element(abs(x-y)) = " << max_element(abs(x - y)) << "\n X = "<< x << "\n Y = "<< y;
}
float Detector::sizeOfBlackWhiteBlackRun(int fromX, int fromY, int toX, int toY) {
// Mild variant of Bresenham's algorithm;
// see http://en.wikipedia.org/wiki/Bresenham's_line_algorithm
bool steep = abs(toY - fromY) > abs(toX - fromX);
if (steep) {
int temp = fromX;
fromX = fromY;
fromY = temp;
temp = toX;
toX = toY;
toY = temp;
}
int dx = abs(toX - fromX);
int dy = abs(toY - fromY);
int error = -dx >> 1;
int xstep = fromX < toX ? 1 : -1;
int ystep = fromY < toY ? 1 : -1;
// In black pixels, looking for white, first or second time.
int state = 0;
// Loop up until x == toX, but not beyond
int xLimit = toX + xstep;
for (int x = fromX, y = fromY; x != xLimit; x += xstep) {
int realX = steep ? y : x;
int realY = steep ? x : y;
// Does current pixel mean we have moved white to black or vice versa?
if (!((state == 1) ^ image_->get(realX, realY))) {
if (state == 2) {
return MathUtils::distance(x, y, fromX, fromY);
}
state++;
}
error += dy;
if (error > 0) {
if (y == toY) {
break;
}
y += ystep;
error -= dx;
}
}
// Found black-white-black; give the benefit of the doubt that the next pixel outside the image
// is "white" so this last point at (toX+xStep,toY) is the right ending. This is really a
// small approximation; (toX+xStep,toY+yStep) might be really correct. Ignore this.
if (state == 2) {
return MathUtils::distance(toX + xstep, toY, fromX, fromY);
}
// else we didn't find even black-white-black; no estimate is really possible
return nan();
}
int gcd_iterative(int a, int b) {
a = abs(a);
b = abs(b);
while (a && b) {
if (a > b)
a -= b;
else
b -= a;
}
return a+b; // at least one of these will be 0
}
int gcd(int a, int b) {
a = abs(a);
b = abs(b);
if (a == 0 || b == 0)
return a+b;
if (a > b)
return gcd(a-b, b);
return gcd(a, b-a);
}
void operator()(T1 &t1, const T2 &t2, const T3 &t3, const T4 &t4)
{
using std::abs;
t1 = adapted_pow(abs(t2), -beta1/(m_steps + 1)) *
adapted_pow(abs(t3), -beta2/(m_steps + 1)) *
adapted_pow(abs(t4), -beta3/(m_steps + 1)) *
adapted_pow(abs(dt1/dt2), -alpha1/(m_steps + 1))*
adapted_pow(abs(dt2/dt3), -alpha2/(m_steps + 1));
t1 = 1/t1;
};
float CollisionDetector::collideByDist(Vector3 object, Vector3 target)
{
//A
diffInX = abs(target.x - object.x);
//B
diffInZ = abs(target.z - object.z);
//TOA CAH SOH A square + B square = C square
dist = sqrt(diffInX * diffInX + diffInZ * diffInZ);
return dist;
}
static bool roughly_equal_to (const T &lhs, const T &rhs)
{
assert(!std::numeric_limits<T>::is_integer);
using std::max; using std::pow; using std::abs;
// last two digits of largest number
const T epsilon = max(abs(lhs), abs(rhs)) * pow(T(2), -T(std::numeric_limits<T>::digits-2));
assert(epsilon >= T(0));
assert(abs(lhs) + epsilon >= lhs);
assert(abs(rhs) + epsilon >= rhs);
//std::cerr << lhs << " " << rhs << " " << epsilon << " " << std::numeric_limits<T>::epsilon() << std::endl;
return abs(rhs-lhs) <= epsilon; // "<=", should also work for zero
}
bool GameState::canMove(const pos& i_s,const pos& j_s,const pos& i_f,const pos& j_f) const {
if(!gameStarted) { DEBUG("game hasn't started"); return false; } //are we moving pieces?
if(i_s < 0 || j_s < 0 || i_s > 7 || j_s > 7 || i_f < 0 || j_f < 0 || i_f > 7 || j_f > 7) { DEBUG("not in board"); return false; } //is this inside the board?
if(!piece(i_s,j_s)) { DEBUG("no piece"); return false; }
if((piece(i_s,j_s) & COLOR_MASK) != toMove) { DEBUG("wrong color"); return false; } //is it this person's turn?
if(piece(i_f,j_f)!=0) { DEBUG("moving to occupied"); return false; } // is there something occupying the spot to move to?
if(frozen(i_s,j_s)) { DEBUG("frozen"); return false; }
if(abs(i_f-i_s)+abs(j_f-j_s)!=1) { DEBUG("too far"); return false; }
if(isPawn(piece(i_s,j_s))) {
if((i_f-i_s) == (getColor(piece(i_s,j_s)) * 2 - 1)) { DEBUG("pawns don't move back"); return false; } //pawns going in the right direction?
}
return true;
}
void test_runge()
{
std::cout << "Testing interpolation of Runge's 1/(1+25x^2) function using barycentric interpolation on type " << boost::typeindex::type_id<Real>().pretty_name() << "\n";
std::mt19937 gen(8);
boost::random::uniform_real_distribution<Real> dis(0.005f, 0.01f);
std::vector<Real> x(100);
std::vector<Real> y(100);
x[0] = -2;
y[0] = 1/(1+25*x[0]*x[0]);
for (size_t i = 1; i < x.size(); ++i)
{
x[i] = x[i-1] + dis(gen);
y[i] = 1/(1+25*x[i]*x[i]);
}
boost::math::barycentric_rational<Real> interpolator(x.data(), y.data(), y.size(), 5);
for (size_t i = 0; i < x.size(); ++i)
{
Real t = x[i];
Real z = interpolator(t);
BOOST_CHECK_CLOSE(z, y[i], 0.03);
Real z_prime = interpolator.prime(t);
Real num = -50*t;
Real denom = (1+25*t*t)*(1+25*t*t);
if (abs(num/denom) > 0.00001)
{
BOOST_CHECK_CLOSE_FRACTION(z_prime, num/denom, 0.03);
}
}
Real tol = 0.0001;
for (size_t i = 0; i < x.size(); ++i)
{
Real t = x[i] + dis(gen);
Real z = interpolator(t);
BOOST_CHECK_CLOSE(z, 1/(1+25*t*t), tol);
Real z_prime = interpolator.prime(t);
Real num = -50*t;
Real denom = (1+25*t*t)*(1+25*t*t);
Real runge_prime = num/denom;
if (abs(runge_prime) > 0 && abs(z_prime - runge_prime)/abs(runge_prime) > tol)
{
std::cout << "Error too high for t = " << t << " which is a distance " << t - x[i] << " from node " << i << "/" << x.size() << " associated with data (" << x[i] << ", " << y[i] << ")\n";
BOOST_CHECK_CLOSE_FRACTION(z_prime, runge_prime, tol);
}
}
}
//added by ali 24 Jul 2011
Float analytic_straight_velocity2::operator() (const point & x)
{
point u;
assert(x[1]<=1.);
if (abs(x[1]) <= _ys)
{
u[0]=_Bn/(2.*_ys)*sqr(1.-_ys);
}
else
{
u[0]=_Bn/(2.*_ys)*(sqr(1.-_ys)-sqr(abs(x[1])-_ys));
}
return u[0];
}
/**
* @brief This defines the functionpart of the whole thing. Needed for rheolef::interpolate
*
* Let \f$ x = (x_0,x_1)\f$
\f[
u(x) = (u_0(x_1), 0)
\f]
\f{eqnarray}
\begin{cases}
\frac{f}{2}
\left[
|y_w|-\frac{Bn}{|f|}
\right]^2
&
x_1 \in [\pm Bn / |f|] \\
\frac{f}{2}
\left[
\left( |y_w| - \frac{Bn}{|f|} \right )^2-
\left( |y| - \frac{Bn}{|f|} \right )^2
\right]
&
|x_1| > Bn / |f|
\end{cases}
\f}
* @param x point position
* @return analytic velocity
*/
point analytic_straight_velocity ::operator() (const point & x)
{
point u;
Float yc = _Bn / abs(_dpdx);
if (abs(x[1]) <= yc)
{
u[0]=(_dpdx/2)*sqr(abs(_ywall)-yc);
}
else
{
u[0]=(_dpdx/2)*(sqr(abs(_ywall)-yc)-sqr(abs(x[1])-yc));
}
return u;
}
void Elevator::setDestinationBasedOnRiders() // reset toFloor based on riders' destinations
{
if (hasRiders()==true)
{
setDestination(&(r[0].getDestination()));
}
for (int i=0; i<r.size(); i++) //1) Query each rider in vector
{
if (abs(toFloor->getLocation() - location) >
abs( r[i].getDestination().getLocation() - location))
{
setDestination(&(r[i].getDestination()));
}
}
}
size_t
seqElements(const af_seq &seq) {
size_t out = 0;
if (seq.step > DBL_MIN) {
out = ((seq.end - seq.begin) / abs(seq.step)) + 1;
}
else if (seq.step < -DBL_MIN) {
out = ((seq.begin - seq.end) / abs(seq.step)) + 1;
}
else {
out = numeric_limits<size_t>::max();
}
return out;
}
//#####################################################################
// Function Line_Search_Quadratic_Golden_Section
//#####################################################################
template<class T> bool LINE_SEARCH<T>::
Line_Search_Golden_Section(NONLINEAR_FUNCTION<T(T)>& F,T a,T b,T& x,int max_iterations,T interval_tolerance) const
{
PHYSBAM_ASSERT(a<=b);
T tau=(T).5*(sqrt((T)5)-1),m=a+tau*(b-a),t;
BRACKET s={a,m,b,F(a),F(m),F(b)};
int i;
interval_tolerance*=abs(s.a)+abs(s.b);
for(i=1;i<=max_iterations;i++){
if(abs(s.b-s.a)<interval_tolerance) break;
t=s.a+s.b-s.m;
Update_Interval(s,t,F(t));}
x=Best_Value(s);
return i<=max_iterations;
}
vector<uint> matrix_utils::remove_linear_dependence_rows(Matrix &matrix) {
vector<uint> removed_rows;
//need to prevent swap whole columns, just swap indexes
vector<uint> columns(matrix[0].size());
for (uint i = 0; i != columns.size(); i++) {
columns[i] = i;
}
Matrix temp_matrix;
temp_matrix.reserve(matrix.size());
for (uint i = 0; i != matrix.size(); i++) {
temp_matrix.push_back(vector<double>(matrix[i].begin(), matrix[i].end()));
}
for (uint i = 0; i != temp_matrix.size(); i++) {
uint index_max = i;
for (uint j = i + 1; j != temp_matrix[0].size(); j++) {
if (abs(temp_matrix[i][columns[index_max]]) < abs(temp_matrix[i][columns[j]])) {
index_max = j;
}
}
if (index_max != i) {
uint tmp = columns[i];
columns[i] = columns[index_max];
columns[index_max] = tmp;
}
index_max = columns[i];
if (abs(temp_matrix[i][index_max]) < 1e-7) {
temp_matrix.erase(temp_matrix.begin() + i);
matrix.erase(matrix.begin() + i);
removed_rows.push_back(i);
i--;
continue;
}
double divisor = temp_matrix[i][index_max];
for (uint k = 0; k != temp_matrix[0].size(); k++) {
temp_matrix[i][k] /= divisor;
}
for (uint j = i + 1; j != temp_matrix.size(); j++) {
double multiplier = -temp_matrix[j][index_max];
for (uint k = 0; k != temp_matrix[0].size(); k++) {
temp_matrix[j][k] += temp_matrix[i][k] * multiplier;
}
}
}
return removed_rows;
}
int test_cp( const std::string& species_name, unsigned int species, Scalar cp_exact, Scalar T,
const Antioch::CEAEvaluator<Scalar>& thermo )
{
using std::abs;
int return_flag = 0;
const Scalar tol = std::numeric_limits<Scalar>::epsilon() * 5;
typedef typename Antioch::template TempCache<Scalar> Cache;
const Scalar cp = thermo.cp(Cache(T), species);
if( abs( (cp_exact - cp)/cp_exact ) > tol )
{
std::cerr << "Error: Mismatch in species specific heat."
<< "\nspecies = " << species_name
<< "\ncp = " << cp
<< "\ncp_exact = " << cp_exact
<< "\ndifference = " << (cp_exact - cp)
<< "\ntolerance = " << tol
<< "\nT = " << T << std::endl;
return_flag = 1;
}
return return_flag;
}
void run_test(int dim, int num_elements)
{
using std::abs;
typedef typename internal::traits<MatrixType>::Scalar Scalar;
typedef Matrix<Scalar, Eigen::Dynamic, Eigen::Dynamic> MatrixX;
typedef Matrix<Scalar, Eigen::Dynamic, 1> VectorX;
// MUST be positive because in any other case det(cR_t) may become negative for
// odd dimensions!
const Scalar c = abs(internal::random<Scalar>());
MatrixX R = randMatrixSpecialUnitary<Scalar>(dim);
VectorX t = Scalar(50)*VectorX::Random(dim,1);
MatrixX cR_t = MatrixX::Identity(dim+1,dim+1);
cR_t.block(0,0,dim,dim) = c*R;
cR_t.block(0,dim,dim,1) = t;
MatrixX src = MatrixX::Random(dim+1, num_elements);
src.row(dim) = Matrix<Scalar, 1, Dynamic>::Constant(num_elements, Scalar(1));
MatrixX dst = cR_t*src;
MatrixX cR_t_umeyama = umeyama(src.block(0,0,dim,num_elements), dst.block(0,0,dim,num_elements));
const Scalar error = ( cR_t_umeyama*src - dst ).norm() / dst.norm();
VERIFY(error < Scalar(40)*std::numeric_limits<Scalar>::epsilon());
}
void run_fixed_size_test(int num_elements)
{
using std::abs;
typedef Matrix<Scalar, Dimension+1, Dynamic> MatrixX;
typedef Matrix<Scalar, Dimension+1, Dimension+1> HomMatrix;
typedef Matrix<Scalar, Dimension, Dimension> FixedMatrix;
typedef Matrix<Scalar, Dimension, 1> FixedVector;
const int dim = Dimension;
// MUST be positive because in any other case det(cR_t) may become negative for
// odd dimensions!
const Scalar c = abs(internal::random<Scalar>());
FixedMatrix R = randMatrixSpecialUnitary<Scalar>(dim);
FixedVector t = Scalar(50)*FixedVector::Random(dim,1);
HomMatrix cR_t = HomMatrix::Identity(dim+1,dim+1);
cR_t.block(0,0,dim,dim) = c*R;
cR_t.block(0,dim,dim,1) = t;
MatrixX src = MatrixX::Random(dim+1, num_elements);
src.row(dim) = Matrix<Scalar, 1, Dynamic>::Constant(num_elements, Scalar(1));
MatrixX dst = cR_t*src;
Block<MatrixX, Dimension, Dynamic> src_block(src,0,0,dim,num_elements);
Block<MatrixX, Dimension, Dynamic> dst_block(dst,0,0,dim,num_elements);
HomMatrix cR_t_umeyama = umeyama(src_block, dst_block);
const Scalar error = ( cR_t_umeyama*src - dst ).array().square().sum();
VERIFY(error < Scalar(10)*std::numeric_limits<Scalar>::epsilon());
}
inline double rng_hcauchy(double sigma, bool& throw_warning) {
if (ISNAN(sigma) || sigma <= 0.0) {
throw_warning = true;
return NA_REAL;
}
return abs(R::rcauchy(0.0, sigma));
}
// Sets the initial step size automatically. The function and initial
// y data must be set for this to work. f, t0 and y0 must be set
// before you can call this method. Also sets the max dt to the same
// value.
void RKF45::set_dt0() {
// locals
double yprime = norm(f(t0,y0));
double y = norm(y0);
double length_scale;
if ( debug_level >= 3) {
cout << "y = " << y << "\n" << "yprime = " << yprime << endl;
}
// y/y' = <change in t> * y/<change in y>. A good approximation of dt0.
length_scale = y/yprime;
if ( debug_level >= 3 ) {
cout << "length scale = " << length_scale << endl;
}
dt0 = abs(get_relative_error_factor() * length_scale);
if ( debug_level >= 1 ) {
cout << "dt0 = " << dt0 << endl;
// cout << "Max step size: " << get_max_dt() << endl;
}
// dt0 must be a finite, positive number. If it's zero or NaN, make
// it sqrt(machine epsilon).
if ( !(dt0 > 0) ) {
if ( debug_level >= 1 ) {
cout << "WARNING: dt0 non-finite. Setting it to the default minimum dt"
<< endl;
}
set_dt0(default_min_dt());
}
else {
set_dt0(dt0);
}
}
static typename Function::value_type derivative(
const Function &f,
const typename Function::value_type &x)
{
typedef typename Function::value_type value_type;
typedef typename ValueType<value_type>::value_type T;
// Find minimum value for h
const std::size_t digits = std::numeric_limits<T>::digits;
const std::size_t bits = CHAR_BIT * sizeof(std::size_t);
const std::size_t N = std::min(digits, bits-1);
assert(1ul << N);
typename Function::value_type x_min_h, x_plus_h, deriv;
for (std::size_t i = 0; i < x.size(); ++i)
{
using std::abs;
const T h = (x[i] != 0) ? abs(x[i]) / T(1ul << (1ul+N/2)) :
T(1) / T(1ul << (1ul+N/2));
assert(h > 0);
x_min_h = x; x_min_h[i] -= h;
x_plus_h = x; x_plus_h[i] += h;
deriv[i] = (f(x_plus_h) - f(x_min_h)) / (h+h);
}
return deriv;
}
AntennaId User::get_smoothed_antenna(time_duration time)
{
unsigned next_event_minute = to_minutes(time);
float smoothed_lat = 0;
float smoothed_lon = 0;
float weight_sum = 0;
for(unsigned i = 0; i < events.size(); i++) {
Event* event = events.at(i);
unsigned event_minute = to_minutes(event->time.time_of_day());
Antenna* antenna = AntennaModel::find_antenna_by_id(event->antenna_id);
int diff = abs(event_minute - next_event_minute);
if (diff > 12 * 60) {
diff = 24 * 60 - diff;
}
float weight = pdf(normal(0, AntennaModel::timestep), diff);
weight_sum += weight;
smoothed_lat += weight * antenna->get_latitude();
smoothed_lon += weight * antenna->get_longitude();
}
smoothed_lat /= weight_sum;
smoothed_lon /= weight_sum;
return AntennaModel::find_nearest_antenna(smoothed_lat,
smoothed_lon)->get_id();
}
array mandelbrot(const array &in, int iter, float maxval)
{
array C = in;
array Z = C;
array mag = constant(0, C.dims());
for (int ii = 1; ii < iter; ii++) {
// Do the calculation
Z = Z * Z + C;
// Get indices where abs(Z) crosses maxval
array cond = (abs(Z) > maxval).as(f32);
mag = af::max(mag, cond * ii);
// If abs(Z) cross maxval, turn off those locations
C = C * (1 - cond);
Z = Z * (1 - cond);
// Ensuring the JIT does not become too large
af::eval(C, Z);
mag.eval();
}
// Normalize
return mag / maxval;
}
int tester()
{
using std::abs;
int return_flag = 0;
const Scalar tol = (std::numeric_limits<Scalar>::epsilon() * 10 < 5e-17)?5e-17:
std::numeric_limits<Scalar>::epsilon() * 10;
const Scalar eps_kb = 97.53L; // N2 value
const Scalar z_298 = 4.0L; // N2 value
Antioch::RotationalRelaxation<Scalar> rot(z_298,eps_kb);
for(Scalar T = 300.1; T <= 2500.1; T += 10.)
{
Scalar z = rot(T);
Scalar z_exact = Z(T,eps_kb,z_298);
if( abs( (z - z_exact)/z_exact) > tol )
{
std::cout << std::scientific << std::setprecision(16)
<< "Error: Mismatch in rotational relaxation values." << std::endl
<< " T = " << T << std::endl
<< " z = " << z << std::endl
<< " z_exact = " << z_exact << std::endl
<< " relative error = " << std::abs(z - z_exact)/z_exact << std::endl
<< " tolerance = " << tol << std::endl;
return_flag = 1;
}
}
return return_flag;
}
int main( int argc , char *argv[] )
{
using namespace std;
cout.precision( 14 );
auto f = []( auto x ) { return exp( - x * x ) - 0.5; };
auto df = []( auto x ) { return -2.0 * x * exp( -x * x ); };
{ // example 1 without ranges
double x = 1.0;
double root = newton( x , f , df );
cout << "Solution: " << root << " " << f( root ) << endl;
}
{ // example 2 with ranges
double x = 1.0;
auto r = make_newton_range( x , f , df );
auto r2 = ranges::view::take_while( r , [f]( auto x ) {
using std::abs;
return abs( f(x) ) > 1.0e-12; } );
ranges::for_each( r2 , [f]( auto x ) {
cout << x << " " << f(x) << "\n";
} );
cout << "Solution: " << r.x() << " " << r.y() << endl;
}
return 0;
}
bool FinderPatternFinder::haveMultiplyConfirmedCenters() {
int confirmedCount = 0;
float totalModuleSize = 0.0f;
size_t max = possibleCenters_.size();
for (size_t i = 0; i < max; i++) {
Ref<FinderPattern> pattern = possibleCenters_[i];
if (pattern->getCount() >= CENTER_QUORUM) {
confirmedCount++;
totalModuleSize += pattern->getEstimatedModuleSize();
}
}
if (confirmedCount < 3) {
return false;
}
// OK, we have at least 3 confirmed centers, but, it's possible that one is a "false positive"
// and that we need to keep looking. We detect this by asking if the estimated module sizes
// vary too much. We arbitrarily say that when the total deviation from average exceeds
// 5% of the total module size estimates, it's too much.
float average = totalModuleSize / max;
float totalDeviation = 0.0f;
for (size_t i = 0; i < max; i++) {
Ref<FinderPattern> pattern = possibleCenters_[i];
totalDeviation += abs(pattern->getEstimatedModuleSize() - average);
}
return totalDeviation <= 0.05f * totalModuleSize;
}
int test_h( const std::string& species_name, unsigned int species, Scalar h_exact, Scalar T,
const Antioch::CEAEvaluator<Scalar>& thermo )
{
using std::abs;
int return_flag = 0;
const Scalar tol = std::numeric_limits<Scalar>::epsilon() * 5;
typedef typename Antioch::template TempCache<Scalar> Cache;
const Scalar h = thermo.h(Cache(T), species);
if( abs( (h_exact - h)/h_exact ) > tol )
{
std::cerr << std::scientific << std::setprecision(16)
<< "Error: Mismatch in species total enthalpy."
<< "\nspecies = " << species_name
<< "\nh = " << h
<< "\nh_exact = " << h_exact
<< "\ndifference = " << (h_exact - h)
<< "\ntolerance = " << tol
<< "\nT = " << T << std::endl;
return_flag = 1;
}
return return_flag;
}
/**
* The GARS precision required to meet a given geographic resolution.
*
* @param[in] res the minimum of resolution in latitude and longitude
* (degrees).
* @return GARS precision.
*
* The returned length is in the range [0, 2].
**********************************************************************/
static int Precision(real res) {
using std::abs; res = abs(res);
for (int prec = 0; prec < maxprec_; ++prec)
if (Resolution(prec) <= res)
return prec;
return maxprec_;
}
T log1p_imp(T const & x, const Policy& pol, const mpl::int_<0>&)
{ // The function returns the natural logarithm of 1 + x.
typedef typename tools::promote_args<T>::type result_type;
BOOST_MATH_STD_USING
using std::abs;
static const char* function = "boost::math::log1p<%1%>(%1%)";
if(x < -1)
return policies::raise_domain_error<T>(
function, "log1p(x) requires x > -1, but got x = %1%.", x, pol);
if(x == -1)
return -policies::raise_overflow_error<T>(
function, 0, pol);
result_type a = abs(result_type(x));
if(a > result_type(0.5L))
return log(1 + result_type(x));
// Note that without numeric_limits specialisation support,
// epsilon just returns zero, and our "optimisation" will always fail:
if(a < tools::epsilon<result_type>())
return x;
detail::log1p_series<result_type> s(x);
boost::uintmax_t max_iter = policies::get_max_series_iterations<Policy>();
#if !BOOST_WORKAROUND(__BORLANDC__, BOOST_TESTED_AT(0x582))
result_type result = tools::sum_series(s, policies::digits<result_type, Policy>(), max_iter);
#else
result_type zero = 0;
result_type result = tools::sum_series(s, policies::digits<result_type, Policy>(), max_iter, zero);
#endif
policies::check_series_iterations(function, max_iter, pol);
return result;
}
ListNode *getIntersectionNode(ListNode *headA, ListNode *headB) {
int lena = 0;
int lenb = 0;
ListNode* tempa = headA;
ListNode* tempb = headB;
while (tempa) {
lena++;
tempa = tempa->next;
}
while (tempb) {
lenb++;
tempb = tempb->next;
}
int diff = abs(lena - lenb);
while (diff--) {
if (lena > lenb) {
headA = headA->next;
} else {
headB = headB->next;
}
}
while (headA and headB) {
if (headA == headB) return headA;
headA = headA->next;
headB = headB->next;
}
return NULL;
}
