| /prebuilts/clang/host/linux-x86/clang-4691093/lib64/clang/6.0.2/include/ |
| tgmath.h | 1292 // conj
1312 __tg_conj(double _Complex __x) {return conj(__x);}
1318 #undef conj macro
1319 #define conj(__x) __tg_conj(__tg_promote1((__x))(__x)) macro
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| /prebuilts/misc/darwin-x86/analyzer/lib/clang/3.3/include/ |
| tgmath.h | 1283 // conj
1303 __tg_conj(double _Complex __x) {return conj(__x);}
1309 #undef conj macro
1310 #define conj(__x) __tg_conj(__tg_promote1((__x))(__x)) macro
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| /prebuilts/misc/linux-x86/analyzer/lib/clang/3.3/include/ |
| tgmath.h | 1283 // conj
1303 __tg_conj(double _Complex __x) {return conj(__x);}
1309 #undef conj macro
1310 #define conj(__x) __tg_conj(__tg_promote1((__x))(__x)) macro
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| /prebuilts/sdk/renderscript/clang-include/ |
| tgmath.h | 1283 // conj
1303 __tg_conj(double _Complex __x) {return conj(__x);}
1309 #undef conj macro
1310 #define conj(__x) __tg_conj(__tg_promote1((__x))(__x)) macro
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| /external/eigen/Eigen/src/Core/ |
| GlobalFunctions.h | 58 EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(conj,scalar_conjugate_op,complex conjugate,\sa ArrayBase::conjugate)
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| GenericPacketMath.h | 163 /** \internal \returns conj(a) (coeff-wise) */
166 pconj(const Packet& a) { return numext::conj(a); }
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| /external/eigen/Eigen/src/Core/products/ |
| SelfadjointMatrixVector.h | 63 Scalar cjAlpha = ConjugateRhs ? numext::conj(alpha) : alpha;
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| GeneralMatrixVector.h | 114 alpha = numext::conj(alpha);
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| /external/eigen/Eigen/src/IterativeLinearSolvers/ |
| IncompleteCholesky.h | 300 Scalar v_j_jk = numext::conj(vals[jk]);
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| /external/tensorflow/tensorflow/contrib/distributions/python/ops/ |
| sample_stats.py | 52 `RXX` may be defined as (with `E` expectation and `Conj` complex conjugate)
55 RXX[m] := E{ W[m] Conj(W[0]) } = E{ W[0] Conj(W[-m]) },
58 S**2 := E{ (X[0] - MU) Conj(X[0] - MU) }.
69 rxx[m] := (L - m)**-1 sum_n w[n + m] Conj(w[n]),
72 s**2 := L**-1 sum_n (x[n] - mu) Conj(x[n] - mu)
107 # F[x]_k Conj(F[x]_k) = F[R]_k, where
108 # R_m := sum_n x_n Conj(x_{(n - m) mod N}).
160 spectral_density = fft_x_rotated_pad * math_ops.conj(fft_x_rotated_pad)
162 # It is the inner product sum_n X[n] * Conj(X[n - m])
[all...] |
| /external/tensorflow/tensorflow/python/kernel_tests/ |
| matmul_op_test.py | 49 return np.conj(x.T)
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| svd_op_test.py | 179 np.conj(np.swapaxes(v_np, -2, -1)), v_tf_val,
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| fft_ops_test.py | 79 loss = math_ops.reduce_sum(math_ops.real(z * math_ops.conj(z)))
97 loss = math_ops.reduce_sum(math_ops.real(z * math_ops.conj(z)))
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| transpose_op_test.py | 44 np_ans = np.conj(np_ans)
70 np_ans = np.conj(np_ans)
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| /external/tensorflow/tensorflow/python/ops/ |
| gradient_checker_test.py | 151 y = math_ops.conj(x)
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| linalg_ops.py | 383 transpose(conj(v[..., :, :]))`
552 tensor * math_ops.conj(tensor), axis, keepdims=True))
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| math_ops.py | 103 @@conj
2480 def conj(x, name=None): function
[all...] |
| /frameworks/native/libs/math/tests/ |
| quat_test.cpp | 227 quatd qc(conj(q));
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| /external/eigen/Eigen/src/Core/functors/ |
| UnaryFunctors.h | 112 EIGEN_STRONG_INLINE const Scalar operator() (const Scalar& a) const { using numext::conj; return conj(a); }
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| /external/tensorflow/tensorflow/core/grappler/optimizers/ |
| arithmetic_optimizer_test.cc | 461 Output conj = ops::Conj(s.WithOpName("conj"), z); local
462 Output transp = ops::Transpose(s.WithOpName("trans"), conj, perm);
488 Output conj = ops::Conj(s.WithOpName("conj"), z); local
490 ops::ConjugateTranspose(s.WithOpName("conjugate_trans"), conj, perm);
513 Output conj = ops::Conj(s.WithOpName("conj"), trans) local
[all...] |
| /external/eigen/Eigen/src/Cholesky/ |
| LLT.h | 291 mat.col(j).tail(rs) = (nLjj/Ljj) * mat.col(j).tail(rs) + (nLjj * sigma*numext::conj(wj)/gamma)*temp.tail(rs);
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| /external/eigen/Eigen/src/SparseQR/ |
| SparseQR.h | 529 tau = numext::conj((beta-c0) / beta);
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| /external/tensorflow/tensorflow/python/ops/linalg/ |
| linear_operator_identity.py | 610 self._multiplier_matrix_conj = math_ops.conj(self._multiplier_matrix)
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| /external/eigen/test/ |
| array.cpp | 401 VERIFY_IS_APPROX(conj(m1.conjugate()), m1);
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| product_extra.cpp | 45 VERIFY_IS_APPROX(m3.noalias() = ((s1 * m1).adjoint()) * m2, (numext::conj(s1) * m1.adjoint()).eval() * m2);
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