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  /external/libcxx/test/std/numerics/rand/rand.dis/rand.dist.bern/rand.dist.bern.bernoulli/
eval.pass.cpp 58 double x_mean = d.p(); local
62 assert(std::abs((mean - x_mean) / x_mean) < 0.01);
94 double x_mean = d.p(); local
98 assert(std::abs((mean - x_mean) / x_mean) < 0.01);
eval_param.pass.cpp 60 double x_mean = p.p(); local
64 assert(std::abs((mean - x_mean) / x_mean) < 0.01);
98 double x_mean = p.p(); local
102 assert(std::abs((mean - x_mean) / x_mean) < 0.01);
  /external/libcxx/test/std/numerics/rand/rand.dis/rand.dist.norm/rand.dist.norm.t/
eval.pass.cpp 61 double x_mean = 0; local
65 assert(std::abs(mean - x_mean) < 0.01);
97 double x_mean = 0;
101 assert(std::abs(mean - x_mean) < 0.01);
133 double x_mean = 0;
137 assert(std::abs(mean - x_mean) < 0.01);
eval_param.pass.cpp 62 double x_mean = 0; local
66 assert(std::abs(mean - x_mean) < 0.01);
99 double x_mean = 0;
103 assert(std::abs(mean - x_mean) < 0.01);
136 double x_mean = 0;
140 assert(std::abs(mean - x_mean) < 0.01);
  /ndk/sources/cxx-stl/llvm-libc++/libcxx/test/numerics/rand/rand.dis/rand.dist.bern/rand.dist.bern.bernoulli/
eval.pass.cpp 58 double x_mean = d.p(); local
62 assert(std::abs((mean - x_mean) / x_mean) < 0.01);
94 double x_mean = d.p(); local
98 assert(std::abs((mean - x_mean) / x_mean) < 0.01);
eval_param.pass.cpp 60 double x_mean = p.p(); local
64 assert(std::abs((mean - x_mean) / x_mean) < 0.01);
98 double x_mean = p.p(); local
102 assert(std::abs((mean - x_mean) / x_mean) < 0.01);
  /ndk/sources/cxx-stl/llvm-libc++/libcxx/test/numerics/rand/rand.dis/rand.dist.norm/rand.dist.norm.t/
eval.pass.cpp 59 double x_mean = 0; local
63 assert(std::abs(mean - x_mean) < 0.01);
95 double x_mean = 0;
99 assert(std::abs(mean - x_mean) < 0.01);
131 double x_mean = 0;
135 assert(std::abs(mean - x_mean) < 0.01);
eval_param.pass.cpp 60 double x_mean = 0; local
64 assert(std::abs(mean - x_mean) < 0.01);
97 double x_mean = 0;
101 assert(std::abs(mean - x_mean) < 0.01);
134 double x_mean = 0;
138 assert(std::abs(mean - x_mean) < 0.01);
  /external/libcxx/test/std/numerics/rand/rand.dis/rand.dist.samp/rand.dist.samp.pconst/
eval_param.pass.cpp 86 double x_mean = (b[i+1] + b[i]) / 2;
90 assert(std::abs((mean - x_mean) / x_mean) < 0.01);
  /external/libcxx/test/std/numerics/rand/rand.dis/rand.dist.uni/rand.dist.uni.int/
eval_param.pass.cpp 65 double x_mean = ((double)p.a() + p.b()) / 2; local
70 assert(std::abs((mean - x_mean) / x_mean) < 0.01);
  /external/libcxx/test/std/numerics/rand/rand.dis/rand.dist.uni/rand.dist.uni.real/
eval_param.pass.cpp 65 D::result_type x_mean = (p.a() + p.b()) / 2; local
69 assert(std::abs((mean - x_mean) / x_mean) < 0.01);
  /ndk/sources/cxx-stl/llvm-libc++/libcxx/test/numerics/rand/rand.dis/rand.dist.samp/rand.dist.samp.pconst/
eval_param.pass.cpp 84 double x_mean = (b[i+1] + b[i]) / 2;
88 assert(std::abs((mean - x_mean) / x_mean) < 0.01);
  /ndk/sources/cxx-stl/llvm-libc++/libcxx/test/numerics/rand/rand.dis/rand.dist.uni/rand.dist.uni.int/
eval_param.pass.cpp 65 double x_mean = ((double)p.a() + p.b()) / 2; local
70 assert(std::abs((mean - x_mean) / x_mean) < 0.01);
  /ndk/sources/cxx-stl/llvm-libc++/libcxx/test/numerics/rand/rand.dis/rand.dist.uni/rand.dist.uni.real/
eval_param.pass.cpp 65 D::result_type x_mean = (p.a() + p.b()) / 2; local
69 assert(std::abs((mean - x_mean) / x_mean) < 0.01);
  /external/eigen/doc/
FunctionsTakingEigenTypes.dox 99 const RowVectorXf x_mean = x.colwise().sum() / num_observations;
101 C = (x.rowwise() - x_mean).transpose() * (y.rowwise() - y_mean) / num_observations;
119 const RowVectorXf x_mean = x.colwise().sum() / num_observations;
121 return (x.rowwise() - x_mean).transpose() * (y.rowwise() - y_mean) / num_observations;
142 const RowVectorXf x_mean = x.colwise().sum() / num_observations;
144 C = (x.rowwise() - x_mean).transpose() * (y.rowwise() - y_mean) / num_observations;
164 const RowVectorType x_mean = x.colwise().sum() / num_observations;
168 (x.rowwise() - x_mean).transpose() * (y.rowwise() - y_mean) / num_observations;
196 const RowVectorType x_mean = x.colwise().sum() / num_observations;
202 C = (x.rowwise() - x_mean).transpose() * (y.rowwise() - y_mean) / num_observations
    [all...]
  /external/ceres-solver/examples/
robot_pose_mle.cc 110 // p(x) \propto \exp{-((x - x_mean) / x_stddev)^2}
112 // where x refers to either the MLE odometry u* or range reading y, and x_mean
117 // x* = \arg\min{((x - x_mean) / x_stddev)^2}
120 // The non-linear component arise from the computation of x_mean. The residuals
121 // ((x - x_mean) / x_stddev) for the residuals that Ceres will optimize. As

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