/external/ceres-solver/internal/ceres/ |
loss_function.cc | 40 void TrivialLoss::Evaluate(double s, double rho[3]) const { 41 rho[0] = s; 42 rho[1] = 1; 43 rho[2] = 0; 46 void HuberLoss::Evaluate(double s, double rho[3]) const { 51 rho[0] = 2 * a_ * r - b_; 52 rho[1] = a_ / r; 53 rho[2] = - rho[1] / (2 * s); 56 rho[0] = s [all...] |
corrector.cc | 41 Corrector::Corrector(double sq_norm, const double rho[3]) { 43 CHECK_GT(rho[1], 0.0); 44 sqrt_rho1_ = sqrt(rho[1]); 48 // of rho. Handling this case explicitly avoids the divide by zero 51 // The case where rho'' < 0 also gets special handling. Technically 54 // curvature correction when rho'' < 0, which is the case when we 61 // square root of the derivative of rho, and the Gauss-Newton 82 if ((sq_norm == 0.0) || (rho[2] <= 0.0)) { 90 // 0.5 * alpha^2 - alpha - rho'' / rho' * z'z = 0 [all...] |
corrector.h | 60 // rho[1] needs to be positive. The constructor will crash if this 66 explicit Corrector(double sq_norm, const double rho[3]); 68 // residuals *= sqrt(rho[1]) / (1 - alpha) 71 // jacobian = sqrt(rho[1]) * jacobian - 72 // sqrt(rho[1]) * alpha / sq_norm * residuals residuals' * jacobian.
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loss_function_test.cc | 44 // Compares the values of rho'(s) and rho''(s) computed by the 46 // of rho(s). 50 // Evaluate rho(s), rho'(s) and rho''(s). 51 double rho[3]; local 52 loss.Evaluate(s, rho); 54 // Use symmetric finite differencing to estimate rho'(s) and 55 // rho''(s) 122 double rho[3]; local 200 double rho[3]; local [all...] |
corrector_test.cc | 44 // If rho[1] is zero, the Corrector constructor should crash. 51 // If rho[1] is negative, the Corrector constructor should crash. 65 // In light of the rho'' < 0 clamping now implemented in 66 // corrector.cc, alpha = 0 whenever rho'' < 0. 121 // rho[2] < 0 -> alpha = 0.0 130 // sqrt(rho[1]) * (1 - alpha) * J. 148 double rho[3]; local 178 rho[0] = sq_norm; 179 rho[1] = RandDouble(); 180 rho[2] = 2.0 * RandDouble() - 1.0 216 double rho[3]; local [all...] |
conjugate_gradients_solver.cc | 110 double rho = 1.0; local 128 double last_rho = rho; 129 rho = r.dot(z); 131 if (IsZeroOrInfinity(rho)) { 132 LOG(ERROR) << "Numerical failure. rho = " << rho; 140 double beta = rho / last_rho; 160 double alpha = rho / pq;
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residual_block.cc | 160 double rho[3]; local 161 loss_function_->Evaluate(squared_norm, rho); 162 *cost = 0.5 * rho[0]; 172 Corrector correct(squared_norm, rho);
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/external/dropbear/libtommath/ |
bn_mp_montgomery_setup.c | 20 mp_montgomery_setup (mp_int * n, mp_digit * rho) 50 /* rho = -1/m mod b */ 51 *rho = (unsigned long)(((mp_word)1 << ((mp_word) DIGIT_BIT)) - x) & MP_MASK;
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bn_mp_montgomery_reduce.c | 20 mp_montgomery_reduce (mp_int * x, mp_int * n, mp_digit rho) 35 return fast_mp_montgomery_reduce (x, n, rho); 47 /* mu = ai * rho mod b 49 * The value of rho must be precalculated via 55 mu = (mp_digit) (((mp_word)x->dp[ix]) * ((mp_word)rho) & MP_MASK);
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bn_fast_mp_montgomery_reduce.c | 26 int fast_mp_montgomery_reduce (mp_int * x, mp_int * n, mp_digit rho) 76 mu = (mp_digit) (((W[ix] & MP_MASK) * rho) & MP_MASK);
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/external/ceres-solver/docs/ |
curvefitting.tex | 50 0: f: 1.211734e+02 d: 0.00e+00 g: 3.61e+02 h: 0.00e+00 rho: 0.00e+00 mu: 1.00e-04 li: 0 51 1: f: 1.211734e+02 d:-2.21e+03 g: 3.61e+02 h: 7.52e-01 rho:-1.87e+01 mu: 2.00e-04 li: 1 52 2: f: 1.211734e+02 d:-2.21e+03 g: 3.61e+02 h: 7.51e-01 rho:-1.86e+01 mu: 8.00e-04 li: 1 53 3: f: 1.211734e+02 d:-2.19e+03 g: 3.61e+02 h: 7.48e-01 rho:-1.85e+01 mu: 6.40e-03 li: 1 54 4: f: 1.211734e+02 d:-2.02e+03 g: 3.61e+02 h: 7.22e-01 rho:-1.70e+01 mu: 1.02e-01 li: 1 55 5: f: 1.211734e+02 d:-7.34e+02 g: 3.61e+02 h: 5.78e-01 rho:-6.32e+00 mu: 3.28e+00 li: 1 56 6: f: 3.306595e+01 d: 8.81e+01 g: 4.10e+02 h: 3.18e-01 rho: 1.37e+00 mu: 1.09e+00 li: 1 57 7: f: 6.426770e+00 d: 2.66e+01 g: 1.81e+02 h: 1.29e-01 rho: 1.10e+00 mu: 3.64e-01 li: 1 58 8: f: 3.344546e+00 d: 3.08e+00 g: 5.51e+01 h: 3.05e-02 rho: 1.03e+00 mu: 1.21e-01 li: 1 59 9: f: 1.987485e+00 d: 1.36e+00 g: 2.33e+01 h: 8.87e-02 rho: 9.94e-01 mu: 4.05e-02 li: [all...] |
powell.tex | 84 0: f: 1.075000e+02 d: 0.00e+00 g: 1.55e+02 h: 0.00e+00 rho: 0.00e+00 mu: 1.00e-04 li: 0 85 1: f: 5.036190e+00 d: 1.02e+02 g: 2.00e+01 h: 2.16e+00 rho: 9.53e-01 mu: 3.33e-05 li: 1 86 2: f: 3.148168e-01 d: 4.72e+00 g: 2.50e+00 h: 6.23e-01 rho: 9.37e-01 mu: 1.11e-05 li: 1 87 3: f: 1.967760e-02 d: 2.95e-01 g: 3.13e-01 h: 3.08e-01 rho: 9.37e-01 mu: 3.70e-06 li: 1 88 4: f: 1.229900e-03 d: 1.84e-02 g: 3.91e-02 h: 1.54e-01 rho: 9.37e-01 mu: 1.23e-06 li: 1 89 5: f: 7.687123e-05 d: 1.15e-03 g: 4.89e-03 h: 7.69e-02 rho: 9.37e-01 mu: 4.12e-07 li: 1 90 6: f: 4.804625e-06 d: 7.21e-05 g: 6.11e-04 h: 3.85e-02 rho: 9.37e-01 mu: 1.37e-07 li: 1 91 7: f: 3.003028e-07 d: 4.50e-06 g: 7.64e-05 h: 1.92e-02 rho: 9.37e-01 mu: 4.57e-08 li: 1 92 8: f: 1.877006e-08 d: 2.82e-07 g: 9.54e-06 h: 9.62e-03 rho: 9.37e-01 mu: 1.52e-08 li: 1 93 9: f: 1.173223e-09 d: 1.76e-08 g: 1.19e-06 h: 4.81e-03 rho: 9.37e-01 mu: 5.08e-09 li: [all...] |
modeling.tex | 215 \texttt{out} = \begin{bmatrix}\rho(s), & \rho'(s), & \rho''(s)\end{bmatrix} 218 Here the convention is that the contribution of a term to the cost function is given by $\frac{1}{2}\rho(s)$, where $s = \|f_i\|^2$. Calling the method with a negative value of $s$ is an error and the implementations are not required to handle that case. 220 Most sane choices of $\rho$ satisfy: 222 \rho(0) &= 0\\ 223 \rho'(0) &= 1\\ 224 \rho'(s) &< 1 \text{ in the outlier region}\\ 225 \rho''(s) &< 0 \text{ in the outlier region} 230 Given one robustifier $\rho(s) [all...] |
/external/ceres-solver/include/ceres/ |
loss_function.h | 91 // function (rho in this example): 93 // out[0] = rho(sq_norm), 94 // out[1] = rho'(sq_norm), 95 // out[2] = rho''(sq_norm), 98 // cost function is given by 1/2 rho(s), where 105 // Most sane choices of rho() satisfy: 107 // rho(0) = 0, 108 // rho'(0) = 1, 109 // rho'(s) < 1 in outlier region, 110 // rho''(s) < 0 in outlier region [all...] |
/external/eigen/unsupported/Eigen/src/IterativeSolvers/ |
ConstrainedConjGrad.h | 62 Scalar rho, rho_1, alpha; local 69 rho = 1.0; 74 while (rho >= 1e-38) 79 alpha = rho / p.dot(q); 82 rho_1 = rho; 83 rho = r.dot(r); 84 p = (rho/rho_1) * p + r; 114 Scalar rho = 1.0, rho_1, lambda, gamma; local 157 rho_1 = rho; 158 rho = r.dot(z) [all...] |
/bionic/libm/upstream-freebsd/lib/msun/src/ |
s_ctanh.c | 41 * rho = cosh(x) 55 * beta rho s + i t 78 double t, beta, s, rho, denom; local 132 rho = sqrt(1 + s * s); /* = cosh(x) */ 134 return (cpack((beta * rho * s) / denom, t / denom));
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s_ctanhf.c | 43 float t, beta, s, rho, denom; local 72 rho = sqrtf(1 + s * s); 74 return (cpackf((beta * rho * s) / denom, t / denom));
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/external/iproute2/netem/ |
stats.c | 24 double mu=0.0, sigma=0.0, sumsquare=0.0, sum=0.0, top=0.0, rho=0.0; local 51 rho = top/sigma2; 55 printf("rho = %12.6f\n", rho); 57 /*printf("correlation rho = %10.6f\n", top/((double)(n-1)*sigma*sigma));*/
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maketable.c | 51 arraystats(double *x, int limit, double *mu, double *sigma, double *rho) 70 *rho = top/sigma2; 200 double mu, sigma, rho; local 219 arraystats(x, limit, &mu, &sigma, &rho); 221 fprintf(stderr, "%d values, mu %10.4f, sigma %10.4f, rho %10.4f\n", 222 limit, mu, sigma, rho);
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/external/ceres-solver/examples/ |
fields_of_experts.cc | 78 void FieldsOfExpertsLoss::Evaluate(double sq_norm, double rho[3]) const { 83 rho[0] = alpha_ * log(sum); 84 rho[1] = alpha_ * c * inv; 85 rho[2] = - alpha_ * c * c * inv * inv;
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/ndk/sources/cxx-stl/llvm-libc++/test/numerics/complex.number/complex.value.ops/ |
polar.pass.cpp | 14 // polar(const T& rho, const T& theta = 0); 23 test(const T& rho, std::complex<T> x) 25 assert(std::polar(rho) == x); 30 test(const T& rho, const T& theta, std::complex<T> x) 32 assert(std::polar(rho, theta) == x);
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/external/eigen/Eigen/src/IterativeLinearSolvers/ |
BiCGSTAB.h | 46 Scalar rho = 1; local 61 Scalar rho_old = rho; 63 rho = r0.dot(r); 64 if (rho == Scalar(0)) return false; /* New search directions cannot be found */ 65 Scalar beta = (rho/rho_old) * (alpha / w); 72 alpha = rho / r0.dot(v);
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/external/opencv/cv/src/ |
cvhough.cpp | 64 float rho; member in struct:CvLinePolar 78 rho and theta are discretization steps (in pixels and radians correspondingly). 81 array of (rho, theta) pairs. linesMax is the buffer size (number of pairs). 85 icvHoughLinesStandard( const CvMat* img, float rho, float theta, 104 float irho = 1 / rho; 115 numrho = cvRound(((width + height) * 2 + 1) / rho); 165 line.rho = (r - (numrho - 1)*0.5f) * rho; 192 float rho, float theta, int threshold, 211 int rn, tn; /* number of rho and theta discrete values * [all...] |
/external/bouncycastle/bcprov/src/main/java/org/bouncycastle/math/ec/ |
WTauNafMultiplier.java | 35 ZTauElement rho = Tnaf.partModReduction(k, m, a, s, mu, (byte)10); local 37 return multiplyWTnaf(p, rho, preCompInfo, a, mu);
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/external/eigen/bench/ |
eig33.cpp | 76 Scalar rho = internal::sqrt(-a_over_3); local 80 roots(0) = c2_over_3 + Scalar(2)*rho*cos_theta; 81 roots(1) = c2_over_3 - rho*(cos_theta + s_sqrt3*sin_theta); 82 roots(2) = c2_over_3 - rho*(cos_theta - s_sqrt3*sin_theta);
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