Searched refs:noise_std (Results 1 – 7 of 7) sorted by relevance
| /external/pytorch/functorch/examples/ensembling/ |
| D | parallel_train.py | 38 def make_spirals(n_samples, noise_std=0.0, rotations=1.0): argument
47 + torch.randn(n_samples, device=DEVICE) * noise_std
51 + torch.randn(n_samples, device=DEVICE) * noise_std
57 points, labels = make_spirals(100, noise_std=0.05)
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| /external/libopus/dnn/ |
| D | dump_data.c | 71 void compute_noise(int *noise, float noise_std) { in compute_noise() argument
74 …noise[i] = (int)floor(.5 + noise_std*.707*(log_approx(rand()/(float)RAND_MAX)-log_approx(rand()/(f… in compute_noise()
132 float noise_std=0; in main() local
214 noise_std = ABS16(-1.5*log(1e-4+tmp1)-.5*log(1e-4+tmp2)); in main()
253 compute_noise(noisebuf, noise_std); in main()
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| /external/libaom/examples/ |
| D | noise_model.c | 198 double noise_std = 0, noise_mean = 0; in print_variance_y() local
207 noise_std += noise_v * noise_v; in print_variance_y()
222 noise_std = sqrt(noise_std / n - noise_mean * noise_mean); in print_variance_y()
225 flat_blocks[by * num_blocks_w + bx], block_mean, noise_std, in print_variance_y()
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| /external/webrtc/test/fuzzers/ |
| D | neteq_signal_fuzzer.cc | 50 const float noise_std = fuzz_data_.ReadOrDefaultValue<uint16_t>(0) % 2000; in Generate() local
53 noise_generator_.Gaussian(0, noise_std)); in Generate()
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| /external/libaom/aom_dsp/ |
| D | noise_model.h | 107 aom_noise_strength_solver_t *solver, double block_mean, double noise_std);
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| D | noise_model.c | 267 aom_noise_strength_solver_t *solver, double block_mean, double noise_std) { in aom_noise_strength_solver_add_measurement() argument
277 solver->eqns.b[bin_i0] += (1.0 - a) * noise_std; in aom_noise_strength_solver_add_measurement()
278 solver->eqns.b[bin_i1] += a * noise_std; in aom_noise_strength_solver_add_measurement()
279 solver->total += noise_std; in aom_noise_strength_solver_add_measurement()
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| /external/pytorch/test/functorch/ |
| D | test_eager_transforms.py | 4363 def make_spirals(n_samples, noise_std=0.0, rotations=1.0): argument
4370 xs = rs * signs * torch.cos(thetas) + torch.randn(n_samples) * noise_std
4371 ys = rs * signs * torch.sin(thetas) + torch.randn(n_samples) * noise_std
4375 points, labels = make_spirals(100, noise_std=0.05)
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