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  /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
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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
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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.
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
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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
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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;
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);
  /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;
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);
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);
  /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:
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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:
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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)
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  /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
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  /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)
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  /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));
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));
  /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));*/
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);
  /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;
  /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);
  /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);
  /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 *
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  /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);
  /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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