| /external/llvm/test/MC/ARM/ |
| thumb_set-diagnostics.s | 48 .type beta,%function
49 beta: label
52 .thumb_set beta, alpha
54 @ CHECK: error: redefinition of 'beta'
55 @ CHECK: .thumb_set beta, alpha
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| /frameworks/ml/nn/runtime/test/generated/models/ |
| softmax_float_1.model.cpp | 7 auto beta = model->addOperand(&type1); local
11 model->setOperandValue(beta, beta_init, sizeof(float) * 1);
12 model->addOperation(ANEURALNETWORKS_SOFTMAX, {input, beta}, {output});
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| softmax_float_2.model.cpp | 7 auto beta = model->addOperand(&type1); local
11 model->setOperandValue(beta, beta_init, sizeof(float) * 1);
12 model->addOperation(ANEURALNETWORKS_SOFTMAX, {input, beta}, {output});
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| softmax_quant8_1.model.cpp | 8 auto beta = model->addOperand(&type1); local
12 model->setOperandValue(beta, beta_init, sizeof(float) * 1);
13 model->addOperation(ANEURALNETWORKS_SOFTMAX, {input, beta}, {output});
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| softmax_quant8_2.model.cpp | 8 auto beta = model->addOperand(&type1); local
12 model->setOperandValue(beta, beta_init, sizeof(float) * 1);
13 model->addOperation(ANEURALNETWORKS_SOFTMAX, {input, beta}, {output});
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| local_response_norm_float_1.model.cpp | 11 auto beta = model->addOperand(&type2); local
21 model->setOperandValue(beta, beta_init, sizeof(float) * 1);
22 model->addOperation(ANEURALNETWORKS_LOCAL_RESPONSE_NORMALIZATION, {input, radius, bias, alpha, beta}, {output});
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| local_response_norm_float_2.model.cpp | 11 auto beta = model->addOperand(&type2); local
21 model->setOperandValue(beta, beta_init, sizeof(float) * 1);
22 model->addOperation(ANEURALNETWORKS_LOCAL_RESPONSE_NORMALIZATION, {input, radius, bias, alpha, beta}, {output});
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| local_response_norm_float_3.model.cpp | 11 auto beta = model->addOperand(&type2); local
21 model->setOperandValue(beta, beta_init, sizeof(float) * 1);
22 model->addOperation(ANEURALNETWORKS_LOCAL_RESPONSE_NORMALIZATION, {input, radius, bias, alpha, beta}, {output});
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| local_response_norm_float_4.model.cpp | 11 auto beta = model->addOperand(&type2); local
21 model->setOperandValue(beta, beta_init, sizeof(float) * 1);
22 model->addOperation(ANEURALNETWORKS_LOCAL_RESPONSE_NORMALIZATION, {input, radius, bias, alpha, beta}, {output});
|
| /frameworks/ml/nn/runtime/test/specs/ |
| softmax_float_1.mod.py | 5 beta = Float32Scalar("beta", 0.000001) variable
9 model = model.Operation("SOFTMAX", i1, beta).To(output)
|
| softmax_float_2.mod.py | 5 beta = Float32Scalar("beta", 1.) variable
9 model = model.Operation("SOFTMAX", i1, beta).To(output)
|
| softmax_quant8_1.mod.py | 5 beta = Float32Scalar("beta", 0.00001) # close to 0 variable
9 model = model.Operation("SOFTMAX", i1, beta).To(output)
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| softmax_quant8_2.mod.py | 5 beta = Float32Scalar("beta", 1.) variable
9 model = model.Operation("SOFTMAX", i1, beta).To(output)
|
| local_response_norm_float_1.mod.py | 6 beta = Float32Scalar("beta", .5) variable
9 model = model.Operation("LOCAL_RESPONSE_NORMALIZATION", i1, radius, bias, alpha, beta).To(output)
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| local_response_norm_float_2.mod.py | 6 beta = Float32Scalar("beta", .5) variable
9 model = model.Operation("LOCAL_RESPONSE_NORMALIZATION", i1, radius, bias, alpha, beta).To(output)
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| local_response_norm_float_3.mod.py | 6 beta = Float32Scalar("beta", .5) variable
9 model = model.Operation("LOCAL_RESPONSE_NORMALIZATION", i1, radius, bias, alpha, beta).To(output)
|
| local_response_norm_float_4.mod.py | 6 beta = Float32Scalar("beta", .5) variable
9 model = model.Operation("LOCAL_RESPONSE_NORMALIZATION", i1, radius, bias, alpha, beta).To(output)
|
| /bionic/libm/upstream-freebsd/lib/msun/src/ |
| s_ctanh.c | 39 * beta = 1/cos^2(y)
55 * beta rho s + I t
57 * 1 + beta s^2
78 double t, beta, s, rho, denom; local
131 beta = 1.0 + t * t; /* = 1 / cos^2(y) */
134 denom = 1 + beta * s * s;
135 return (CMPLX((beta * rho * s) / denom, t / denom));
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| s_ctanhf.c | 43 float t, beta, s, rho, denom; local
71 beta = 1.0 + t * t;
74 denom = 1 + beta * s * s;
75 return (CMPLXF((beta * rho * s) / denom, t / denom));
|
| /device/google/contexthub/firmware/os/inc/algos/ |
| time_sync.h | 41 float alpha, beta; member in struct:__anon3280
|
| /external/boringssl/src/crypto/fipsmodule/bn/ |
| montgomery_inv.c | 110 const uint64_t beta = n; local
116 * 2**(lg r - i) == u*2*alpha - v*beta. */
120 ((BN_ULLONG)u * 2 * alpha) - ((BN_ULLONG)v * beta));
124 * |u = (u + beta) / 2| and |v = (v / 2) + alpha|. */
147 uint64_t beta_if_u_is_odd = beta & u_is_odd; /* Either |beta| or 0. */
156 assert(1 == ((BN_ULLONG)u * 2 * alpha) - ((BN_ULLONG)v * beta));
|
| /external/cblas/examples/ |
| cblas_example1.c | 13 double alpha, beta; local
25 beta = 0;
60 cblas_dgemv( order, transa, m, n, alpha, a, lda, x, incx, beta,
|
| /external/drrickorang/LoopbackApp/app/src/main/java/org/drrickorang/loopback/ |
| Utilities.java | 31 final double beta = 0.5; local
35 samples[i] *= alpha - beta * Math.cos(coefficient);
|
| /external/eigen/unsupported/test/ |
| cxx11_tensor_sugar.cpp | 43 const float beta = 0.21f; local
46 Tensor<float, 3> R = A.constant(gamma) + A * A.constant(alpha) + B * B.constant(beta);
47 Tensor<float, 3> S = A * alpha + B * beta + gamma;
48 Tensor<float, 3> T = gamma + alpha * A + beta * B;
63 const float beta = 0.21f; local
68 - B.constant(beta) / B - A.constant(delta);
69 Tensor<float, 3> S = gamma - A / alpha - beta / B - delta;
|
| /external/eigen/blas/ |
| level2_cplx_impl.h | 14 * y := alpha*A*x + beta*y,
16 * where alpha and beta are scalars, x and y are n element vectors and
34 Scalar beta = *reinterpret_cast<const Scalar*>(pbeta); local
52 if(beta!=Scalar(1))
54 if(beta==Scalar(0)) make_vector(actual_y, *n).setZero();
55 else make_vector(actual_y, *n) *= beta;
75 * y := alpha*A*x + beta*y,
77 * where alpha and beta are scalars, x and y are n element vectors and
81 // RealScalar *x, int *incx, RealScalar *beta, RealScalar *y, int *incy)
88 * y := alpha*A*x + beta*y
[all...] |
