| /third_party/mindspore/mindspore-src/source/docs/api/api_python/nn_probability/ |
| D | mindspore.nn.probability.distribution.Beta.rst | 1 mindspore.nn.probability.distribution.Beta
4 …re.nn.probability.distribution.Beta(concentration1=None, concentration0=None, seed=None, dtype=mst…
6 Beta 分布（Beta Distribution）。
10 f(x, \alpha, \beta) = x^\alpha (1-x)^{\beta - 1} / B(\alpha, \beta)
12 其中 :math:`B` 为 Beta 函数。
15 … - **concentration1** (int, float, list, numpy.ndarray, Tensor) - Beta 分布的alpha。默认值： ``None`` 。
16 … - **concentration0** (int, float, list, numpy.ndarray, Tensor) - Beta 分布的beta。默认值： ``None`` 。
19 - **name** (str) - 分布的名称。默认值： ``'Beta'`` 。
24 - `dtype` 必须是float，因为 Beta 分布是连续的。
33 返回concentration0（也称为 Beta 分布的 beta）。
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| /third_party/rust/rust/tests/ui/associated-type-bounds/ |
| D | fn-apit.rs | 11 fn apit_bound(beta: impl Beta<Gamma: Alpha>) -> usize { in apit_bound()
12 desugared_bound(beta) in apit_bound()
15 fn apit_bound_region(beta: impl Beta<Gamma: 'static>) -> usize { in apit_bound_region()
16 desugared_bound_region(beta) in apit_bound_region()
20 beta: impl Copy + Beta<Gamma: Alpha + 'static + Delta> in apit_bound_multi()
22 desugared_bound_multi(beta) in apit_bound_multi()
26 beta: impl Beta<Gamma: Copy + for<'a> Epsilon<'a>> in apit_bound_region_forall()
28 desugared_bound_region_forall(beta) in apit_bound_region_forall()
32 beta: impl Beta<Gamma: Copy + for<'a> Epsilon<'a, Zeta: Eta>> in apit_bound_region_forall2()
34 desugared_bound_region_forall2(beta) in apit_bound_region_forall2()
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| D | fn-dyn-apit.rs | 13 fn dyn_apit_bound(beta: &dyn Beta<Gamma: Alpha>) -> usize { in dyn_apit_bound()
14 desugared_bound(beta) in dyn_apit_bound()
17 fn dyn_apit_bound_region(beta: &dyn Beta<Gamma: 'static>) -> usize { in dyn_apit_bound_region()
18 desugared_bound_region(beta) in dyn_apit_bound_region()
22 beta: &(dyn Beta<Gamma: Alpha + 'static + Delta> + Send) in dyn_apit_bound_multi()
24 desugared_bound_multi(beta) in dyn_apit_bound_multi()
28 beta: &dyn Beta<Gamma: Copy + for<'a> Epsilon<'a>> in dyn_apit_bound_region_forall()
30 desugared_bound_region_forall(beta) in dyn_apit_bound_region_forall()
34 beta: &dyn Beta<Gamma: Copy + for<'a> Epsilon<'a, Zeta: Eta>> in dyn_apit_bound_region_forall2()
36 desugared_bound_region_forall2(beta) in dyn_apit_bound_region_forall2()
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| D | fn-wrap-apit.rs | 15 fn wrap_apit_bound(beta: Wrap<impl Beta<Gamma: Alpha>>) -> usize { in wrap_apit_bound()
16 desugared_bound(beta.0) in wrap_apit_bound()
19 fn wrap_apit_bound_region(beta: Wrap<impl Beta<Gamma: 'static>>) -> usize { in wrap_apit_bound_region()
20 desugared_bound_region(beta.0) in wrap_apit_bound_region()
24 beta: Wrap<impl Copy + Beta<Gamma: Alpha + 'static + Delta>> in wrap_apit_bound_multi()
26 desugared_bound_multi(beta.0) in wrap_apit_bound_multi()
30 beta: Wrap<impl Beta<Gamma: Copy + for<'a> Epsilon<'a>>> in wrap_apit_bound_region_forall()
32 desugared_bound_region_forall(beta.0) in wrap_apit_bound_region_forall()
36 beta: Wrap<impl Beta<Gamma: Copy + for<'a> Epsilon<'a, Zeta: Eta>>> in wrap_apit_bound_region_forall2()
38 desugared_bound_region_forall2(beta.0) in wrap_apit_bound_region_forall2()
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| D | fn-where.rs | 13 fn where_bound<B>(beta: B) -> usize in where_bound()
15 B: Beta<Gamma: Alpha> in where_bound()
17 desugared_bound(beta) in where_bound()
20 fn where_bound_region<B>(beta: B) -> usize in where_bound_region()
22 B: Beta<Gamma: 'static> in where_bound_region()
24 desugared_bound_region(beta) in where_bound_region()
27 fn where_bound_multi<B>(beta: B) -> usize in where_bound_multi()
29 B: Copy + Beta<Gamma: Alpha + 'static + Delta>, in where_bound_multi()
31 desugared_bound_multi(beta) in where_bound_multi()
36 B: Beta<Gamma: 'a + Epsilon<'a>>, in where_bound_region_specific()
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| D | fn-inline.rs | 13 fn inline_bound<B: Beta<Gamma: Alpha>>(beta: B) -> usize { in inline_bound()
14 desugared_bound(beta) in inline_bound()
17 fn inline_bound_region<B: Beta<Gamma: 'static>>(beta: B) -> usize { in inline_bound_region()
18 desugared_bound_region(beta) in inline_bound_region()
21 fn inline_bound_multi<B: Copy + Beta<Gamma: Alpha + 'static + Delta>>( in inline_bound_multi()
22 beta: B in inline_bound_multi()
24 desugared_bound_multi(beta) in inline_bound_multi()
27 fn inline_bound_region_specific<'a, B: Beta<Gamma: 'a + Epsilon<'a>>>( in inline_bound_region_specific()
33 fn inline_bound_region_forall<B: Beta<Gamma: Copy + for<'a> Epsilon<'a>>>( in inline_bound_region_forall()
34 beta: B in inline_bound_region_forall()
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| /third_party/mindspore/mindspore-src/source/docs/api/api_python_en/nn_probability_en/ |
| D | mindspore.nn.probability.distribution.Beta.txt | 4 Return concentration0, aka the beta parameter of the Beta distribution.
12 Return concentration1, aka the alpha parameter of the Beta distribution.
23 … - **concentration1** (Tensor) - the alpha parameter of the Beta distribution. Default: ``None`` .
24 … - **concentration0** (Tensor) - the beta parameter of the Beta distribution. Default: ``None`` .
35 - **concentration1_b** (Tensor) - the alpha parameter of the other Beta distribution.
36 - **concentration0_b** (Tensor) - the beta parameter of the other Beta distribution.
37 … - **concentration1** (Tensor) - the alpha parameter of the Beta distribution. Default: ``None`` .
38 … - **concentration0** (Tensor) - the beta parameter of the Beta distribution. Default: ``None`` .
48 … - **concentration1** (Tensor) - the alpha parameter of the Beta distribution. Default: ``None`` .
49 … - **concentration0** (Tensor) - the beta parameter of the Beta distribution. Default: ``None`` .
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| /third_party/rust/rust/tests/ui/associated-type-bounds/auxiliary/ |
| D | fn-aux.rs | 7 pub trait Beta { trait
53 impl Beta for BetaType {
58 impl<'a> Beta for &'a BetaType { impl
63 impl Beta for GammaType {
89 pub fn desugared_bound<B>(beta: B) -> usize in desugared_bound()
91 B: Beta, in desugared_bound()
94 let gamma: B::Gamma = beta.gamma(); in desugared_bound()
98 pub fn desugared_bound_region<B>(beta: B) -> usize in desugared_bound_region()
100 B: Beta, in desugared_bound_region()
103 assert_static::<B::Gamma>(beta.gamma()) in desugared_bound_region()
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| D | fn-dyn-aux.rs | 7 pub trait Beta { trait
53 impl<T> Beta for &(dyn Beta<Gamma = T> + Send) { implementation
58 impl Beta for BetaType {
63 impl<'a> Beta for &'a BetaType { impl
68 impl Beta for GammaType {
94 pub fn desugared_bound<B: ?Sized>(beta: &B) -> usize in desugared_bound()
96 B: Beta, in desugared_bound()
99 let gamma: B::Gamma = beta.gamma(); in desugared_bound()
103 pub fn desugared_bound_region<B: ?Sized>(beta: &B) -> usize in desugared_bound_region()
105 B: Beta, in desugared_bound_region()
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| /third_party/mindspore/mindspore-src/source/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/cuda_ops/ |
| D | smooth_l1_loss_impl.cu | 22 __global__ void SmoothL1LossNoReduce(const int64_t input_size, const float beta, const T *predictio… in SmoothL1LossNoReduce() argument
26 if (value < beta) { in SmoothL1LossNoReduce()
27 loss[i] = 0.5 * value * value / beta; in SmoothL1LossNoReduce()
29 loss[i] = value - (0.5 * beta); in SmoothL1LossNoReduce()
35 __global__ void SmoothL1LossNoReduce(const int64_t input_size, const float beta, const half *predic… in SmoothL1LossNoReduce() argument
39 half h_beta = __float2half(beta); in SmoothL1LossNoReduce()
49 __global__ void SmoothL1LossSum(const int64_t input_size, const float beta, const T *prediction, co… in SmoothL1LossSum() argument
54 if (value < beta) { in SmoothL1LossSum()
55 tmp = 0.5 * value * value / beta; in SmoothL1LossSum()
57 tmp = value - (0.5 * beta); in SmoothL1LossSum()
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| D | hamming_window_impl.cu | 21 const float beta, S *output) { in HammingWindowOne() argument
29 …mingWindow(const size_t size, const double N, const double PI, const float alpha, const float beta, in HammingWindow() argument
32 double out = alpha - beta * cos((2 * pos * PI) / (N - 1)); in HammingWindow()
39 cudaError_t HammingWindow(const size_t size, T N, const float alpha, const float beta, const bool p… in HammingWindow() argument
44 … beta, output); in HammingWindow()
47 …<CUDA_BLOCKS(device_id, size), CUDA_THREADS(device_id), 0, cuda_stream>>>(size, N, PI, alpha, beta, in HammingWindow()
54 … const float beta, const bool periodic, half *output,
57 … const float beta, const bool periodic, half *output,
60 … const float beta, const bool periodic, half *output,
63 … const float beta, const bool periodic, half *output,
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| D | apply_add_sign_impl.cu | 27 … const S *alpha, const S *sign_decay, const S *beta, const G *gradient) { in ApplyAddSignKernel() argument
29 accumulation[i] = (beta[0] * accumulation[i]) + ((static_cast<T>(1.) - beta[0]) * gradient[i]); in ApplyAddSignKernel()
37 const float *alpha, const float *sign_decay, const float *beta, in ApplyAddSignKernel() argument
41 …(beta[0] * __half2float(accumulation[i])) + ((static_cast<float>(1.) - beta[0]) * __half2float(gra… in ApplyAddSignKernel()
52 const float *alpha, const float *sign_decay, const float *beta, in ApplyAddSignKernel() argument
55 …accumulation[i] = (beta[0] * accumulation[i]) + ((static_cast<float>(1.) - beta[0]) * __half2float… in ApplyAddSignKernel()
64 … const half *alpha, const half *sign_decay, const half *beta, const float *gradient) { in ApplyAddSignKernel() argument
67 …(__half2float(beta[0]) * accumulation[i]) + ((static_cast<float>(1.) - __half2float(beta[0])) * gr… in ApplyAddSignKernel()
76 … const half *alpha, const half *sign_decay, const half *beta, const half *gradient) { in ApplyAddSignKernel() argument
78 accumulation[i] = (__half2float(beta[0]) * accumulation[i]) + in ApplyAddSignKernel()
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| D | apply_power_sign_impl.cu | 43 … const S logbase, const S sign_decay, const S beta, const G *gradient) { in ApplyPowerSignKernel() argument
45 accumulation[i] = (beta * accumulation[i]) + ((static_cast<T>(1.) - beta) * gradient[i]); in ApplyPowerSignKernel()
53 const float logbase, const float sign_decay, const float beta, in ApplyPowerSignKernel() argument
57 …(beta * __half2float(accumulation[i])) + ((static_cast<float>(1.) - beta) * __half2float(gradient[… in ApplyPowerSignKernel()
68 const float logbase, const float sign_decay, const float beta, in ApplyPowerSignKernel() argument
71 …accumulation[i] = (beta * accumulation[i]) + ((static_cast<float>(1.) - beta) * __half2float(gradi… in ApplyPowerSignKernel()
80 … const half logbase, const half sign_decay, const half beta, const half *gradient) { in ApplyPowerSignKernel() argument
82 accumulation[i] = (__half2float(beta) * __half2float(accumulation[i])) + in ApplyPowerSignKernel()
83 ((static_cast<float>(1.) - __half2float(beta)) * __half2float(gradient[i])); in ApplyPowerSignKernel()
93 const half logbase, const half sign_decay, const half beta, in ApplyPowerSignKernel() argument
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| /third_party/ffmpeg/libavcodec/ |
| D | h264dsp_template.c | 104 …_t *p_pix, ptrdiff_t xstride, ptrdiff_t ystride, int inner_iters, int alpha, int beta, int8_t *tc0) in FUNCC()
111 beta <<= BIT_DEPTH - 8; in FUNCC()
127 FFABS( p1 - p0 ) < beta && in FUNCC()
128 FFABS( q1 - q0 ) < beta ) { in FUNCC()
133 if( FFABS( p2 - p0 ) < beta ) { in FUNCC()
138 if( FFABS( q2 - q0 ) < beta ) { in FUNCC()
152 static void FUNCC(h264_v_loop_filter_luma)(uint8_t *pix, ptrdiff_t stride, int alpha, int beta, int… in FUNCC()
154 FUNCC(h264_loop_filter_luma)(pix, stride, sizeof(pixel), 4, alpha, beta, tc0); in FUNCC()
156 static void FUNCC(h264_h_loop_filter_luma)(uint8_t *pix, ptrdiff_t stride, int alpha, int beta, int… in FUNCC()
158 FUNCC(h264_loop_filter_luma)(pix, sizeof(pixel), stride, 4, alpha, beta, tc0); in FUNCC()
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| /third_party/typescript/tests/baselines/reference/ |
| D | moduleOuterQualification.symbols | 5 interface Beta { }
6 >Beta : Symbol(Beta, Decl(moduleOuterQualification.ts, 0, 22))
11 // .d.ts emit: should be 'extends outer.Beta'
12 export interface Beta extends outer.Beta { }
13 >Beta : Symbol(Beta, Decl(moduleOuterQualification.ts, 2, 16))
14 >outer.Beta : Symbol(Beta, Decl(moduleOuterQualification.ts, 0, 22))
16 >Beta : Symbol(Beta, Decl(moduleOuterQualification.ts, 0, 22))
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| D | library-reference-8.trace.json | 8 …"======== Resolving type reference directive 'beta', containing file '/test/foo.ts', root director…
10 "File '/test/types/beta/package.json' does not exist.",
11 "File '/test/types/beta/index.d.ts' exist - use it as a name resolution result.",
12 "Resolving real path for '/test/types/beta/index.d.ts', result '/test/types/beta/index.d.ts'.",
13 …"======== Type reference directive 'beta' was successfully resolved to '/test/types/beta/index.d.t…
14 …"======== Resolving type reference directive 'beta', containing file '/test/types/alpha/index.d.ts…
16 "File '/test/types/beta/package.json' does not exist according to earlier cached lookups.",
17 "File '/test/types/beta/index.d.ts' exist - use it as a name resolution result.",
18 "Resolving real path for '/test/types/beta/index.d.ts', result '/test/types/beta/index.d.ts'.",
19 …"======== Type reference directive 'beta' was successfully resolved to '/test/types/beta/index.d.t…
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| D | library-reference-8.types | 3 /// <reference types="beta" />
4 var x: string = alpha.a + beta.b;
6 >alpha.a + beta.b : string
10 >beta.b : string
11 >beta : { b: string; }
18 /// <reference types="beta" />
23 === /test/types/beta/index.d.ts ===
25 declare var beta: { b: string };
26 >beta : { b: string; }
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| /third_party/mindspore/mindspore-src/source/mindspore/python/mindspore/nn/probability/distribution/ |
| D | beta.py | 15 """Beta Distribution"""
28 class Beta(Distribution): class
30 Beta distribution.
31 …A Beta distributio is a continuous distribution with the range :math:`[0, 1]` and the probability …
34 f(x, \alpha, \beta) = x^\alpha (1-x)^{\beta - 1} / B(\alpha, \beta)
36 Where :math:`B` is the Beta function.
40 also know as alpha of the Beta distribution. Default: ``None`` .
42 beta of the Beta distribution. Default: ``None`` .
45 name (str): The name of the distribution. Default: ``'Beta'`` .
50 - `dtype` must be a float type because Beta distributions are continuous.
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| /third_party/mindspore/mindspore-src/source/tests/st/probability/distribution/ |
| D | test_beta.py | 15 """test cases for Beta distribution"""
29 Test class: probability of Beta distribution.
33 self.b = msd.Beta(np.array([3.0]), np.array([1.0]), dtype=dtype.float32)
42 beta_benchmark = stats.beta(np.array([3.0]), np.array([1.0]))
51 Test class: log probability of Beta distribution.
55 self.b = msd.Beta(np.array([3.0]), np.array([1.0]), dtype=dtype.float32)
64 beta_benchmark = stats.beta(np.array([3.0]), np.array([1.0]))
73 Test class: kl_loss of Beta distribution.
77 self.b = msd.Beta(np.array([3.0]), np.array([4.0]), dtype=dtype.float32)
80 return self.b.kl_loss('Beta', x_, y_)
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| /third_party/ffmpeg/libavcodec/aarch64/ |
| D | h264dsp_init_aarch64.c | 29 int beta, int8_t *tc0);
31 int beta, int8_t *tc0);
33 int beta);
35 int beta);
37 int beta, int8_t *tc0);
39 int beta, int8_t *tc0);
41 int beta, int8_t *tc0);
43 int alpha, int beta);
45 int alpha, int beta);
47 int alpha, int beta);
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| /third_party/rust/rust/src/tools/clippy/book/src/development/infrastructure/ |
| D | release.md | 10 1. [Remerge the `beta` branch](#remerge-the-beta-branch)
11 2. [Update the `beta` branch](#update-the-beta-branch)
21 ## Remerge the `beta` branch
24 to the beta Rust release. The remerge is then necessary, to make sure that the
33 $ git branch master --contains upstream/beta
41 $ git merge upstream/beta
47 `HEAD` of the `beta` branch must exist. In addition to that, no files should be
50 ## Update the `beta` branch
54 First, the Clippy commit of the `beta` branch of the Rust repository has to be
60 $ git checkout upstream/beta
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| D | backport.md | 3 Sometimes it is necessary to backport changes to the beta release of Clippy.
8 Backports are done to the `beta` branch of Clippy. Backports to stable Clippy
15 Backports are done on the beta branch of the Clippy repository.
19 $ git checkout beta
30 $ git checkout beta
37 has to be first applied to the Clippy beta branch and then again synced to the
50 After this, you can open a PR to the `beta` branch of the Clippy repository.
58 back to the beta branch of the Rust repository.
62 $ git checkout beta
64 $ git subtree pull -p src/tools/clippy https://github.com/rust-lang/rust-clippy beta
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| /third_party/rust/crates/version_check/src/ |
| D | channel.rs | 7 Beta, enumerator
11 /// Release channel: "dev", "nightly", "beta", or "stable".
50 /// let beta = Channel::parse("1.32.0-beta").unwrap();
51 /// assert!(beta.is_beta());
62 } else if version.contains("-beta") || version == "beta" { in parse()
63 Some(Channel(Kind::Beta)) in parse()
75 Kind::Beta => "beta", in as_str()
95 /// let beta = Channel::parse("1.32.0-beta").unwrap();
96 /// assert!(!beta.supports_features());
104 Kind::Beta | Kind::Stable => false in supports_features()
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| /third_party/protobuf/third_party/abseil-cpp/absl/random/ |
| D | beta_distribution.h | 37 // Generate a floating-point variate conforming to a Beta distribution:
38 // pdf(x) \propto x^(alpha-1) * (1-x)^(beta-1),
39 // where the params alpha and beta are both strictly positive real values.
42 // to 0 or 1, due to numerical errors when alpha and beta are very different.
44 // Usage note: One usage is that alpha and beta are counts of number of
46 // approximating a beta distribution with a Gaussian distribution with the same
48 // smaller of alpha and beta when the number of trials are sufficiently large,
49 // to quantify how far a beta distribution is from the normal distribution.
59 explicit param_type(result_type alpha, result_type beta) in param_type() argument
60 : alpha_(alpha), beta_(beta) { in param_type()
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| /third_party/skia/third_party/externals/abseil-cpp/absl/random/ |
| D | beta_distribution.h | 35 // Generate a floating-point variate conforming to a Beta distribution:
36 // pdf(x) \propto x^(alpha-1) * (1-x)^(beta-1),
37 // where the params alpha and beta are both strictly positive real values.
40 // to 0 or 1, due to numerical errors when alpha and beta are very different.
42 // Usage note: One usage is that alpha and beta are counts of number of
44 // approximating a beta distribution with a Gaussian distribution with the same
46 // smaller of alpha and beta when the number of trials are sufficiently large,
47 // to quantify how far a beta distribution is from the normal distribution.
57 explicit param_type(result_type alpha, result_type beta) in param_type() argument
58 : alpha_(alpha), beta_(beta) { in param_type()
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