| /third_party/mindspore/mindspore/nn/probability/distribution/ |
| D | normal.py | 135 mean=None, argument
144 param['param_dict'] = {'mean': mean, 'sd': sd}
149 self._mean_value = self._add_parameter(mean, 'mean')
177 def _get_dist_args(self, mean=None, sd=None): argument
178 if mean is not None:
179 self.checktensor(mean, 'mean')
181 mean = self._mean_value
186 return mean, sd
188 def _mean(self, mean=None, sd=None): argument
192 mean, sd = self._check_param_type(mean, sd)
[all …]
|
| D | log_normal.py | 143 mean, sd = self._check_param_type(loc, scale)
144 var = self.distribution("var", mean=mean, sd=sd)
145 return self.exp(mean + 0.5 * var)
151 mean, sd = self._check_param_type(loc, scale)
152 var = self.distribution("var", mean=mean, sd=sd)
153 return self.exp(mean - var)
159 mean, sd = self._check_param_type(loc, scale)
160 var = self.distribution("var", mean=mean, sd=sd)
161 return self.expm1(var) * self.exp(2. * mean + var)
170 mean, sd = self._check_param_type(loc, scale)
[all …]
|
| /third_party/boost/boost/math/distributions/ |
| D | poisson.hpp | 62 …inline bool check_mean(const char* function, const RealType& mean, RealType* result, const Policy&… in check_mean() argument
64 if(!(boost::math::isfinite)(mean) || (mean < 0)) in check_mean()
68 "Mean argument is %1%, but must be >= 0 !", mean, pol); in check_mean()
75 …inline bool check_mean_NZ(const char* function, const RealType& mean, RealType* result, const Poli… in check_mean_NZ() argument
77 if( !(boost::math::isfinite)(mean) || (mean <= 0)) in check_mean_NZ()
81 "Mean argument is %1%, but must be > 0 !", mean, pol); in check_mean_NZ()
88 …inline bool check_dist(const char* function, const RealType& mean, RealType* result, const Policy&… in check_dist() argument
90 return check_mean_NZ(function, mean, result, pol); in check_dist()
107 …inline bool check_dist_and_k(const char* function, RealType mean, RealType k, RealType* result, co… in check_dist_and_k() argument
109 if((check_dist(function, mean, result, pol) == false) || in check_dist_and_k()
[all …]
|
| D | inverse_gaussian.hpp | 88 RealType mean()const in mean() function in boost::math::inverse_gaussian_distribution
139 RealType mean = dist.mean(); in pdf() local
146 if(false == detail::check_location(function, mean, &result, Policy())) in pdf()
150 if(false == detail::check_x_gt0(function, mean, &result, Policy())) in pdf()
166 * exp(-scale * (x - mean) * (x - mean) / (2 * x * mean * mean)); in pdf()
176 RealType mean = dist.mean(); in cdf() local
183 if(false == detail::check_location(function, mean, &result, Policy())) in cdf()
187 if (false == detail::check_x_gt0(function, mean, &result, Policy())) in cdf()
209 n0 *= ((x / mean) -1); in cdf()
211 RealType expfactor = exp(2 * scale / mean); in cdf()
[all …]
|
| D | normal.hpp | 44 RealType mean()const in mean() function in boost::math::normal_distribution
117 RealType mean = dist.mean(); in pdf() local
126 if(false == detail::check_location(function, mean, &result, Policy())) in pdf()
144 RealType exponent = x - mean; in pdf()
160 RealType mean = dist.mean(); in cdf() local
167 if(false == detail::check_location(function, mean, &result, Policy())) in cdf()
189 RealType diff = (x - mean) / (sd * constants::root_two<RealType>()); in cdf()
200 RealType mean = dist.mean(); in quantile() local
206 if(false == detail::check_location(function, mean, &result, Policy())) in quantile()
214 result += mean; in quantile()
[all …]
|
| /third_party/boost/libs/accumulators/test/ |
| D | droppable.cpp | 27 accumulator_set<int, stats<droppable<tag::mean> > > acc, test_acc(sample = 0); in test_stat()
30 BOOST_CHECK_CLOSE(1., mean(acc), 1e-5); in test_stat()
35 BOOST_CHECK_CLOSE(0.5, mean(acc), 1e-5); in test_stat()
39 acc.drop<tag::mean>(); in test_stat()
42 BOOST_CHECK_CLOSE(0.5, mean(acc), 1e-5); in test_stat()
46 assert_is_double(mean(acc)); in test_stat()
49 accumulator_set<int, stats<droppable<tag::mean(immediate)> > > acc2, test_acc2(sample = 0); in test_stat()
52 BOOST_CHECK_CLOSE(1., mean(acc2), 1e-5); in test_stat()
56 BOOST_CHECK_CLOSE(0.5, mean(acc2), 1e-5); in test_stat()
59 acc2.drop<tag::mean>(); in test_stat()
[all …]
|
| D | mean.cpp | 26 typedef accumulator_set<int, stats<tag::mean,
29 typedef accumulator_set<int, stats<tag::mean(immediate),
40 BOOST_CHECK_CLOSE(1., mean(acc), 1e-5); in test_stat()
46 BOOST_CHECK_CLOSE(0.5, mean(acc), 1e-5); in test_stat()
52 BOOST_CHECK_CLOSE(1., mean(acc), 1e-5); in test_stat()
57 assert_is_double(mean(acc)); in test_stat()
62 BOOST_CHECK_CLOSE(1., mean(acc2), 1e-5); in test_stat()
67 BOOST_CHECK_CLOSE(0.5, mean(acc2), 1e-5); in test_stat()
72 BOOST_CHECK_CLOSE(1., mean(acc2), 1e-5); in test_stat()
76 assert_is_double(mean(acc2)); in test_stat()
[all …]
|
| D | external_accumulator.cpp | 26 accumulator_set<int, stats<tag::mean, tag::external<tag::sum, my::tag::sum_acc> > > acc; in test_stat()
31 BOOST_CHECK_CLOSE(1., mean(acc, my::sum_acc = sum_acc), 1e-5); in test_stat()
37 BOOST_CHECK_CLOSE(0.5, mean(acc, my::sum_acc = sum_acc), 1e-5); in test_stat()
43 BOOST_CHECK_CLOSE(1., mean(acc, my::sum_acc = sum_acc), 1e-5); in test_stat()
58 tag::mean in test_reference()
66 BOOST_CHECK_CLOSE(1., mean(acc), 1e-5); in test_reference()
72 BOOST_CHECK_CLOSE(0.5, mean(acc), 1e-5); in test_reference()
78 BOOST_CHECK_CLOSE(1., mean(acc), 1e-5); in test_reference()
93 tag::mean in test_reference2()
100 BOOST_CHECK_CLOSE(1., mean(acc), 1e-5); in test_reference2()
[all …]
|
| /third_party/boost/libs/math/example/ |
| D | find_mean_and_sd_normal.cpp | 66 double mean = 3.; // kg in main() local
68 normal packs(mean, standard_deviation); in main()
84 cout <<"Fraction of packs <= " << minimum_weight << " with a mean of " << mean in main()
100 << " with a mean of " << xpacks.mean() in main()
108 double offset = mean - low_limit - quantile(packs, under_fraction); in main()
109 double nominal_mean = mean + offset; in main()
155 cauchy cpacks(mean, standard_deviation); in main()
156 cout << "Cauchy Setting the packer to " << mean << " will mean that " in main()
227 << ", mean = " << packs.mean() << ", sd = " << packs.standard_deviation() << endl; in main()
237 normal pack05(mean, 0.05); in main()
[all …]
|
| D | find_root_example.cpp | 52 double mean = 3.; // kg in main() local
54 normal packs(mean, standard_deviation); in main()
61 cout <<"fraction of packs <= " << under_weight << " with a mean of " << mean in main()
70 << " with a mean of " << xpacks.mean() in main()
75 double offset = mean - low_limit - quantile(packs, under_fraction); in main()
76 double nominal_mean = mean + offset; in main()
96 << ", mean = " << packs.mean() << ", sd = " << packs.standard_deviation() << endl; // in main()
106 normal pack05(mean, 0.05); in main()
108 << ", mean = " << pack05.mean() << ", sd = " << pack05.standard_deviation() << endl; in main()
110 cout <<"Fraction of packs >= " << under_weight << " with a mean of " << mean in main()
[all …]
|
| D | students_t_example1.cpp | 67 double mean = (value[0] + value[1] + value[2]) / static_cast<double>(values); in main() local
68 …cout << "Standard = " << standard << ", mean = " << mean << ", (mean - standard) = " << mean - sta… in main()
69 … sd = sqrt(((value[0] - mean) * (value[0] - mean) + (value[1] - mean) * (value[1] - mean) + (value… in main()
78 double t = (mean - standard) * std::sqrt(static_cast<double>(values)) / sd; in main()
|
| D | normal_misc_examples.cpp | 50 cout << "Standard normal distribution, mean = "<< s.mean() in main()
68 cout << "Standard normal mean = "<< s.mean() in main()
242 double mean = 140.; // sacks per week. in main() local
244 normal sacks(mean, standard_deviation); in main()
274 double mean = 3.; // kg in main() local
276 normal packs(mean, standard_deviation); in main()
283 cout <<"fraction of packs <= " << under_weight << " with a mean of " << mean in main()
292 << " with a mean of " << xpacks.mean() in main()
297 double offset = mean - low_limit - quantile(packs, under_fraction); in main()
298 double nominal_mean = mean + offset; in main()
[all …]
|
| /third_party/mindspore/mindspore/ccsrc/backend/kernel_compiler/cpu/nnacl/fp32/ |
| D | instance_norm_fp32.c | 37 double mean = 0.0f; in InstanceNorm() local
48 mean += MS_F32X4_GETI(src128, i); in InstanceNorm()
59 mean += vaddvq_f32(srcv); in InstanceNorm()
63 mean += MS_F32X4_GETI(srcv, i); in InstanceNorm()
69 mean += vget_lane_f32(src_add4, 0); in InstanceNorm()
77 mean += src[index]; in InstanceNorm()
81 mean /= (float)param->inner_size_; in InstanceNorm()
83 const double deno = gamma_data[c] / sqrt(squ_m - mean * mean + param->epsilon_); in InstanceNorm()
87 MS_FLOAT32X8 meanv8 = MS_MOV256_F32(mean); in InstanceNorm()
98 MS_FLOAT32X4 meanv4 = MS_MOVQ_F32(mean); in InstanceNorm()
[all …]
|
| /third_party/boost/boost/histogram/accumulators/ |
| D | mean.hpp | 27 class mean { class
32 mean() = default;
36 mean(const mean<T>& o) noexcept in mean() function in boost::histogram::accumulators::mean
40 mean(const_reference n, const_reference mean, const_reference variance) noexcept in mean() argument
41 : sum_(n), mean_(mean), sum_of_deltas_squared_(variance * (n - 1)) {} in mean()
60 mean& operator+=(const mean& rhs) noexcept { in operator +=()
74 mean& operator*=(const_reference s) noexcept { in operator *=()
80 bool operator==(const mean& rhs) const noexcept { in operator ==()
85 bool operator!=(const mean& rhs) const noexcept { return !operator==(rhs); } in operator !=()
130 struct version<boost::histogram::accumulators::mean<T>> : std::integral_constant<int, 1> {
[all …]
|
| /third_party/boost/boost/accumulators/statistics/ |
| D | kurtosis.hpp | 59 - 4. * accumulators::moment<3>(args) * mean(args) in result()
60 + 6. * accumulators::moment<2>(args) * mean(args) * mean(args) in result()
61 - 3. * mean(args) * mean(args) * mean(args) * mean(args) in result()
62 , ( accumulators::moment<2>(args) - mean(args) * mean(args) ) in result()
63 * ( accumulators::moment<2>(args) - mean(args) * mean(args) ) in result()
80 : depends_on<mean, moment<2>, moment<3>, moment<4> >
|
| D | mean.hpp | 59 : mean(numeric::fdiv(args[sample | Sample()], numeric::one<std::size_t>::value)) in immediate_mean_impl()
67 this->mean = numeric::fdiv( in operator ()()
68 (this->mean * (cnt - 1)) + args[parameter::keyword<Tag>::get()] in operator ()()
75 return this->mean; in result()
81 ar & mean; in serialize() local
85 result_type mean; member
100 struct mean struct
155 extractor<tag::mean> const mean = {}; variable
159 BOOST_ACCUMULATORS_IGNORE_GLOBAL(mean)
163 using extract::mean;
[all …]
|
| D | skewness.hpp | 62 - 3. * accumulators::moment<2>(args) * mean(args) in result()
63 + 2. * mean(args) * mean(args) * mean(args) in result()
64 , ( accumulators::moment<2>(args) - mean(args) * mean(args) ) in result()
65 * std::sqrt( accumulators::moment<2>(args) - mean(args) * mean(args) ) in result()
82 : depends_on<mean, moment<2>, moment<3> >
|
| /third_party/typescript/tests/baselines/reference/ |
| D | maximum10SpellingSuggestions.errors.txt | 1 …er/maximum10SpellingSuggestions.ts(4,1): error TS2552: Cannot find name 'bob'. Did you mean 'blob'?
2 …er/maximum10SpellingSuggestions.ts(4,6): error TS2552: Cannot find name 'bob'. Did you mean 'blob'?
3 …r/maximum10SpellingSuggestions.ts(4,11): error TS2552: Cannot find name 'bob'. Did you mean 'blob'?
4 …r/maximum10SpellingSuggestions.ts(4,16): error TS2552: Cannot find name 'bob'. Did you mean 'blob'?
5 …r/maximum10SpellingSuggestions.ts(4,21): error TS2552: Cannot find name 'bob'. Did you mean 'blob'?
6 …r/maximum10SpellingSuggestions.ts(4,26): error TS2552: Cannot find name 'bob'. Did you mean 'blob'?
7 …r/maximum10SpellingSuggestions.ts(4,31): error TS2552: Cannot find name 'bob'. Did you mean 'blob'?
8 …r/maximum10SpellingSuggestions.ts(4,36): error TS2552: Cannot find name 'bob'. Did you mean 'blob'?
9 …r/maximum10SpellingSuggestions.ts(4,41): error TS2552: Cannot find name 'bob'. Did you mean 'blob'?
10 …r/maximum10SpellingSuggestions.ts(4,46): error TS2552: Cannot find name 'bob'. Did you mean 'blob'?
[all …]
|
| D | noImplicitAnyNamelessParameter.errors.txt | 1 …essParameter.ts(2,17): error TS7051: Parameter has a name but no type. Did you mean 'arg0: string'?
2 …NamelessParameter.ts(2,25): error TS7051: Parameter has a name but no type. Did you mean 'arg1: C'?
3 …NamelessParameter.ts(3,19): error TS7051: Parameter has a name but no type. Did you mean 'arg0: C'?
4 …essParameter.ts(3,22): error TS7051: Parameter has a name but no type. Did you mean 'arg1: number'?
5 …ssParameter.ts(4,20): error TS7051: Parameter has a name but no type. Did you mean 'arg0: boolean'?
6 …NamelessParameter.ts(4,29): error TS7051: Parameter has a name but no type. Did you mean 'arg1: C'?
7 …essParameter.ts(4,32): error TS7051: Parameter has a name but no type. Did you mean 'arg2: object'?
8 …Parameter.ts(4,40): error TS7051: Parameter has a name but no type. Did you mean 'arg3: undefined'?
15 !!! error TS7051: Parameter has a name but no type. Did you mean 'arg0: string'?
17 !!! error TS7051: Parameter has a name but no type. Did you mean 'arg1: C'?
[all …]
|
| D | jsxParsingError3.errors.txt | 1 tests/cases/conformance/jsx/Error1.tsx(1,15): error TS1381: Unexpected token. Did you mean `{'}'}` …
2 tests/cases/conformance/jsx/Error2.tsx(1,15): error TS1382: Unexpected token. Did you mean `{'>'}` …
3 tests/cases/conformance/jsx/Error3.tsx(1,22): error TS1381: Unexpected token. Did you mean `{'}'}` …
4 tests/cases/conformance/jsx/Error4.tsx(1,22): error TS1382: Unexpected token. Did you mean `{'>'}` …
5 tests/cases/conformance/jsx/Error5.tsx(1,15): error TS1381: Unexpected token. Did you mean `{'}'}` …
6 tests/cases/conformance/jsx/Error6.tsx(1,15): error TS1382: Unexpected token. Did you mean `{'>'}` …
20 !!! error TS1381: Unexpected token. Did you mean `{'}'}` or `}`?
25 !!! error TS1382: Unexpected token. Did you mean `{'>'}` or `>`?
30 !!! error TS1381: Unexpected token. Did you mean `{'}'}` or `}`?
35 !!! error TS1382: Unexpected token. Did you mean `{'>'}` or `>`?
[all …]
|
| /third_party/mindspore/tests/ut/python/dataset/ |
| D | test_normalizeOp.py | 32 def normalize_np(image, mean, std): argument
39 image = image - np.array(mean)
44 def util_test_normalize(mean, std, op_type): argument
51 normalize_op = c_vision.Normalize(mean, std)
61 py_vision.Normalize(mean, std)
72 def util_test_normalize_grayscale(num_output_channels, mean, std): argument
80 py_vision.Normalize(mean, std)
94 mean = [121.0, 115.0, 100.0]
98 normalize_op = c_vision.Normalize(mean, std)
114 image_np_normalized = normalize_np(image_original, mean, std)
[all …]
|
| /third_party/mindspore/mindspore/ccsrc/backend/kernel_compiler/gpu/cuda_impl/ |
| D | layer_norm_impl.cu | 26 inline __device__ void MeanAndVarAccumulation(T *mean, T *var, T *num, const T &val) { in MeanAndVarAccumulation() argument
31 T mean_new = mean[0] + (val - mean[0]) / num[0]; in MeanAndVarAccumulation()
32 var[0] = var[0] + (val - mean[0]) * (val - mean_new); in MeanAndVarAccumulation()
33 mean[0] = mean_new; in MeanAndVarAccumulation()
50 inline __device__ void ThreadReduce(const int &col_dim, const T *block_addr, T *mean, T *var, T *nu… in ThreadReduce() argument
58 MeanAndVarAccumulation(mean, var, num, block_addr[pos]); in ThreadReduce()
64 inline __device__ void WarpReduce(T *mean, T *var, T *num) { in WarpReduce() argument
66 T mean_other = __shfl_down_sync(0xffffffff, mean[0], delta); in WarpReduce()
69 MeanAndVarMerge(mean, var, num, mean_other, var_other, num_other); in WarpReduce()
74 inline __device__ void BlockReduce(const int &col_dim, T *mean, T *var, T *num, T *mean_addr, T *va… in BlockReduce() argument
[all …]
|
| /third_party/mindspore/tests/st/ops/gpu/ |
| D | test_batchnorm_fold_op.py | 31 def __init__(self, mean, variance): argument
33 self.mean = mean
39 a, b, c, d = self.op(x, self.mean, self.variance, current_step)
43 def np_result(x, mean, var, momentum, epsilon): argument
44 np_mean = x.mean(axis=(0, 2, 3))
47 mean_update = (1 - momentum) * np_mean + momentum * mean
50 delay_mean = mean.copy()
61 mean = np.random.uniform(1, 10, size=[c]).astype('float32')
64 ms_mean_t = Tensor(mean)
71 expect1, expect2, expect3, expect4, expect5, expect6 = np_result(x, mean, variance, 0.9, 1e-12)
[all …]
|
| /third_party/abseil-cpp/absl/random/ |
| D | poisson_distribution_test.cc | 113 const double mean = std::min(kMax, m); in TYPED_TEST() local
114 const param_type param(mean); in TYPED_TEST()
117 absl::poisson_distribution<TypeParam> before(mean); in TYPED_TEST()
118 EXPECT_EQ(before.mean(), param.mean()); in TYPED_TEST()
137 ABSL_INTERNAL_LOG(INFO, absl::StrCat("Range {", param.mean(), "}: ", in TYPED_TEST()
146 EXPECT_NE(before.mean(), after.mean()); in TYPED_TEST()
152 EXPECT_EQ(before.mean(), after.mean()) // in TYPED_TEST()
165 explicit PoissonModel(double mean) : mean_(mean) {} in PoissonModel() argument
167 double mean() const { return mean_; } in mean() function in __anon10bc306f0111::PoissonModel
217 const size_t max_i = 50 * stddev() + mean(); in InitCDF()
[all …]
|
| /third_party/skia/third_party/externals/abseil-cpp/absl/random/ |
| D | poisson_distribution_test.cc | 113 const double mean = std::min(kMax, m); in TYPED_TEST() local
114 const param_type param(mean); in TYPED_TEST()
117 absl::poisson_distribution<TypeParam> before(mean); in TYPED_TEST()
118 EXPECT_EQ(before.mean(), param.mean()); in TYPED_TEST()
137 ABSL_INTERNAL_LOG(INFO, absl::StrCat("Range {", param.mean(), "}: ", in TYPED_TEST()
146 EXPECT_NE(before.mean(), after.mean()); in TYPED_TEST()
152 EXPECT_EQ(before.mean(), after.mean()) // in TYPED_TEST()
165 explicit PoissonModel(double mean) : mean_(mean) {} in PoissonModel() argument
167 double mean() const { return mean_; } in mean() function in __anon91352ae40111::PoissonModel
217 const size_t max_i = 50 * stddev() + mean(); in InitCDF()
[all …]
|