| /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.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.
|
| corrector.cc | 40 Corrector::Corrector(double sq_norm, const double rho[3]) {
42 CHECK_GT(rho[1], 0.0);
43 sqrt_rho1_ = sqrt(rho[1]);
47 // of rho. Handling this case explicitly avoids the divide by zero
50 // The case where rho'' < 0 also gets special handling. Technically
53 // curvature correction when rho'' < 0, which is the case when we
60 // square root of the derivative of rho, and the Gauss-Newton
81 if ((sq_norm == 0.0) || (rho[2] <= 0.0)) {
89 // 0.5 * alpha^2 - alpha - rho'' / rho' * z'z = 0
[all...] |
| 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;
|
| residual_block.cc | 163 double rho[3]; local
164 loss_function_->Evaluate(squared_norm, rho);
165 *cost = 0.5 * rho[0];
175 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/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));
|
| 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;
|
| /external/chromium_org/third_party/mesa/src/src/mesa/drivers/dri/i965/ |
| brw_lower_texture_gradients.cpp | 127 /* Calculate rho from equation 3.20 of the GL 3.0 specification. */
128 ir_rvalue *rho; local
130 rho = expr(ir_binop_max, expr(ir_unop_abs, dPdx),
133 rho = expr(ir_binop_max, expr(ir_unop_sqrt, dot(dPdx, dPdx)),
137 /* lambda_base = log2(rho). We're ignoring GL state biases for now. */
139 ir->lod_info.lod = expr(ir_unop_log2, rho);
|
| /external/mesa3d/src/mesa/drivers/dri/i965/ |
| brw_lower_texture_gradients.cpp | 127 /* Calculate rho from equation 3.20 of the GL 3.0 specification. */
128 ir_rvalue *rho; local
130 rho = expr(ir_binop_max, expr(ir_unop_abs, dPdx),
133 rho = expr(ir_binop_max, expr(ir_unop_sqrt, dot(dPdx, dPdx)),
137 /* lambda_base = log2(rho). We're ignoring GL state biases for now. */
139 ir->lod_info.lod = expr(ir_unop_log2, rho);
|
| /ndk/sources/cxx-stl/llvm-libc++/libcxx/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 *
[all...] |
| /external/chromium_org/third_party/mesa/src/src/gallium/auxiliary/gallivm/ |
| lp_bld_sample.c | 178 * Generate code to compute coordinate gradient (rho).
181 * The resulting rho is scalar per quad.
203 LLVMValueRef rho; local
289 rho = rho_vec;
306 rho = lp_build_max(coord_bld, rho_s, rho_t);
314 rho = lp_build_max(coord_bld, rho, rho_r);
318 rho = lp_build_pack_aos_scalars(bld->gallivm, coord_bld->type,
319 perquadf_bld->type, rho);
328 rho = rho_vec
523 LLVMValueRef rho; local
[all...] |
| /external/mesa3d/src/gallium/auxiliary/gallivm/ |
| lp_bld_sample.c | 178 * Generate code to compute coordinate gradient (rho).
181 * The resulting rho is scalar per quad.
203 LLVMValueRef rho; local
289 rho = rho_vec;
306 rho = lp_build_max(coord_bld, rho_s, rho_t);
314 rho = lp_build_max(coord_bld, rho, rho_r);
318 rho = lp_build_pack_aos_scalars(bld->gallivm, coord_bld->type,
319 perquadf_bld->type, rho);
328 rho = rho_vec
523 LLVMValueRef rho; local
[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);
|
