| /third_party/mindspore/tests/st/probability/distribution/ |
| D | test_categorical.py | 37 def construct(self, x_): argument
38 return self.c.prob(x_)
47 x_ = Tensor(np.array([0, 1, 0, 1, 1]).astype(
49 output = pmf(x_)
63 def construct(self, x_): argument
64 return self.c.log_prob(x_)
73 x_ = Tensor(np.array([0, 1, 0, 1, 1]).astype(
75 output = logprob(x_)
89 def construct(self, x_): argument
90 return self.c.kl_loss('Categorical', x_)
[all …]
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| D | test_uniform.py | 34 def construct(self, x_): argument
35 return self.u.prob(x_)
44 x_ = Tensor(np.array([-1.0, 0.0, 0.5, 1.0, 1.5, 3.0]).astype(np.float32), dtype=dtype.float32)
45 output = pdf(x_)
57 def construct(self, x_): argument
58 return self.u.log_prob(x_)
67 x_ = Tensor(np.array([0.5]).astype(np.float32), dtype=dtype.float32)
68 output = logprob(x_)
80 def construct(self, x_, y_): argument
81 return self.u.kl_loss('Uniform', x_, y_)
[all …]
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| D | test_exponential.py | 34 def construct(self, x_): argument
35 return self.e.prob(x_)
44 x_ = Tensor(np.array([-1.0, 0.0, 1.0]).astype(np.float32), dtype=dtype.float32)
45 output = pdf(x_)
57 def construct(self, x_): argument
58 return self.e.log_prob(x_)
67 x_ = Tensor(np.array([0.5, 1.0, 2.0]).astype(np.float32), dtype=dtype.float32)
68 output = logprob(x_)
80 def construct(self, x_): argument
81 return self.e.kl_loss('Exponential', x_)
[all …]
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| D | test_bernoulli.py | 36 def construct(self, x_): argument
37 return self.b.prob(x_)
47 x_ = Tensor(np.array([0, 1, 0, 1, 1]).astype(
49 output = pmf(x_)
63 def construct(self, x_): argument
64 return self.b.log_prob(x_)
75 x_ = Tensor(np.array([0, 1, 0, 1, 1]).astype(
77 output = logprob(x_)
91 def construct(self, x_): argument
92 return self.b.kl_loss('Bernoulli', x_)
[all …]
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| D | test_geometric.py | 36 def construct(self, x_): argument
37 return self.g.prob(x_)
47 x_ = Tensor(np.array([-1, 0, 1, 2, 3]
49 output = pdf(x_)
63 def construct(self, x_): argument
64 return self.g.log_prob(x_)
74 x_ = Tensor(np.array([0, 1, 2, 3, 4]).astype(
76 output = logprob(x_)
90 def construct(self, x_): argument
91 return self.g.kl_loss('Geometric', x_)
[all …]
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| D | test_poisson.py | 34 def construct(self, x_): argument
35 return self.p.prob(x_)
44 x_ = Tensor(np.array([-1.0, 0.0, 1.0]).astype(np.float32), dtype=dtype.float32)
45 output = pdf(x_)
57 def construct(self, x_): argument
58 return self.p.log_prob(x_)
67 x_ = Tensor(np.array([1.0, 2.0]).astype(np.float32), dtype=dtype.float32)
68 output = logprob(x_)
128 def construct(self, x_): argument
129 return self.p.cdf(x_)
[all …]
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| D | test_gamma.py | 35 def construct(self, x_): argument
36 return self.g.prob(x_)
57 def construct(self, x_): argument
58 return self.g.log_prob(x_)
80 def construct(self, x_, y_): argument
81 return self.g.kl_loss('Gamma', x_, y_)
164 def construct(self, x_): argument
165 return self.g.cdf(x_)
187 def construct(self, x_): argument
188 return self.g.log_cdf(x_)
[all …]
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| D | test_normal.py | 34 def construct(self, x_): argument
35 return self.n.prob(x_)
56 def construct(self, x_): argument
57 return self.n.log_prob(x_)
79 def construct(self, x_, y_): argument
80 return self.n.kl_loss('Normal', x_, y_)
160 def construct(self, x_): argument
161 return self.n.cdf(x_)
183 def construct(self, x_): argument
184 return self.n.log_cdf(x_)
[all …]
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| /third_party/mindspore/tests/ut/python/nn/probability/bijector/ |
| D | test_softplus.py | 46 def construct(self, x_): argument
47 ans1 = self.b1.inverse(self.b1.forward(x_))
48 ans2 = self.b2.inverse(self.b2.forward(x_))
69 def construct(self, x_): argument
70 ans1 = self.b1.forward_log_jacobian(x_)
71 ans2 = self.b2.forward_log_jacobian(x_)
93 def construct(self, x_): argument
94 ans1 = self.b1.inverse_log_jacobian(x_)
95 ans2 = self.b2.inverse_log_jacobian(x_)
117 def construct(self, x_): argument
[all …]
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| D | test_scalar_affine.py | 52 def construct(self, x_): argument
53 ans1 = self.b1.inverse(self.b1.forward(x_))
54 ans2 = self.b2.inverse(self.b2.forward(x_))
75 def construct(self, x_): argument
76 ans1 = self.b1.forward_log_jacobian(x_)
77 ans2 = self.b2.forward_log_jacobian(x_)
99 def construct(self, x_): argument
100 ans1 = self.b1.inverse_log_jacobian(x_)
101 ans2 = self.b2.inverse_log_jacobian(x_)
123 def construct(self, x_): argument
[all …]
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| D | test_gumbel_cdf.py | 61 def construct(self, x_): argument
62 ans1 = self.b1.inverse(self.b1.forward(x_))
63 ans2 = self.b2.inverse(self.b2.forward(x_))
84 def construct(self, x_): argument
85 ans1 = self.b1.forward_log_jacobian(x_)
86 ans2 = self.b2.forward_log_jacobian(x_)
108 def construct(self, x_): argument
109 ans1 = self.b1.inverse_log_jacobian(x_)
110 ans2 = self.b2.inverse_log_jacobian(x_)
132 def construct(self, x_): argument
[all …]
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| D | test_invert_bijector.py | 51 def construct(self, x_): argument
52 ans1 = self.inv1.inverse(x_) + self.inv1.inverse(x_)
53 ans2 = self.inv2.inverse(x_) + self.inv2.forward(x_)
75 def construct(self, x_): argument
76 ans1 = self.inv1.forward_log_jacobian(x_)
77 ans2 = self.inv2.forward_log_jacobian(x_)
99 def construct(self, x_): argument
100 ans1 = self.inv1.inverse_log_jacobian(x_)
101 ans2 = self.inv2.inverse_log_jacobian(x_)
123 def construct(self, x_): argument
[all …]
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| D | test_power_transform.py | 43 def construct(self, x_): argument
44 ans1 = self.b1.inverse(self.b1.forward(x_))
45 ans2 = self.b2.inverse(self.b2.forward(x_))
66 def construct(self, x_): argument
67 ans1 = self.b1.forward_log_jacobian(x_)
68 ans2 = self.b2.forward_log_jacobian(x_)
69 ans3 = self.b1.inverse_log_jacobian(x_)
70 ans4 = self.b2.inverse_log_jacobian(x_)
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| /third_party/boost/boost/math/interpolators/detail/ |
| D | cubic_hermite_detail.hpp | 24 : x_{std::move(x)}, y_{std::move(y)}, dydx_{std::move(dydx)} in cubic_hermite_detail()
28 if (x_.size() != y_.size())
32 if (x_.size() != dydx_.size())
36 if (x_.size() < 2)
40 Real x0 = x_[0];
41 for (size_t i = 1; i < x_.size(); ++i)
43 Real x1 = x_[i];
60 if (x <= x_.back()) in push_back()
64 x_.push_back(x); in push_back()
71 if (x < x_[0] || x > x_.back()) in operator ()()
[all …]
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| D | quintic_hermite_detail.hpp | 20 …er && y, RandomAccessContainer && dydx, RandomAccessContainer && d2ydx2) : x_{std::move(x)}, y_{st… in quintic_hermite_detail()
22 if (x_.size() != y_.size())
26 if (x_.size() != dydx_.size())
30 if (x_.size() != d2ydx2_.size())
34 if (x_.size() < 2)
38 Real x0 = x_[0];
39 for (decltype(x_.size()) i = 1; i < x_.size(); ++i)
41 Real x1 = x_[i];
54 if (x <= x_.back()) in push_back()
58 x_.push_back(x); in push_back()
[all …]
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| D | septic_hermite_detail.hpp | 21 …: x_{std::move(x)}, y_{std::move(y)}, dydx_{std::move(dydx)}, d2ydx2_{std::move(d2ydx2)}, d3ydx3_{… in septic_hermite_detail()
23 if (x_.size() != y_.size())
27 if (x_.size() != dydx_.size())
31 if (x_.size() != d2ydx2_.size())
35 if (x_.size() != d3ydx3_.size())
40 if (x_.size() < 2)
44 Real x0 = x_[0];
45 for (decltype(x_.size()) i = 1; i < x_.size(); ++i)
47 Real x1 = x_[i];
60 if (x <= x_.back()) { in push_back()
[all …]
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| /third_party/boost/boost/utility/detail/ |
| D | minstd_rand.hpp | 25 boost::uint_least32_t x_; member in boost::detail::minstd_rand
31 minstd_rand(): x_( 1 ) in minstd_rand()
35 explicit minstd_rand( boost::uint_least32_t x ): x_( x % m ) in minstd_rand()
37 if( x_ == 0 ) in minstd_rand()
39 x_ = 1; in minstd_rand()
45 boost::uint_least64_t y = x_; in operator ()()
49 x_ = static_cast<boost::uint_least32_t>( y ); in operator ()()
51 return x_; in operator ()()
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| /third_party/libinput/src/ |
| D | evdev-tablet-pad.h | 54 #define map_init(x_) ((x_).value = (uint32_t)-1) argument
55 #define map_is_unmapped(x_) ((x_).value == (uint32_t)-1) argument
56 #define map_is_button(x_) (((x_).value & 0xFF000000) == 0) argument
57 #define map_is_key(x_) (((x_).value & 0xFF000000) != 0) argument
60 #define map_value(x_) ((x_).value & 0x00FFFFFF) argument
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| /third_party/mindspore/tests/st/probability/bijector/ |
| D | test_invert.py | 34 def construct(self, x_): argument
35 return self.invert.forward(x_), self.origin.inverse(x_)
53 def construct(self, x_): argument
54 return self.invert.inverse(x_), self.origin.forward(x_)
72 def construct(self, x_): argument
73 return self.invert.forward_log_jacobian(x_),\
74 self.origin.inverse_log_jacobian(x_)
92 def construct(self, x_): argument
93 return self.invert.inverse_log_jacobian(x_),\
94 self.origin.forward_log_jacobian(x_)
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| D | test_scalar_affine.py | 33 def construct(self, x_): argument
34 return self.bijector.forward(x_)
52 def construct(self, x_): argument
53 return self.bijector.inverse(x_)
71 def construct(self, x_): argument
72 return self.bijector.forward_log_jacobian(x_)
90 def construct(self, x_): argument
91 return self.bijector.inverse_log_jacobian(x_)
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| /third_party/boost/libs/phoenix/test/bind/ |
| D | bind_member_variable_tests.cpp | 33 T x_; in write_test() local
35 bind(&T::m, f(x_))() = 122; in write_test()
36 BOOST_TEST(x_.m == 122); in write_test()
37 bind(&T::m, arg1)(f(x_)) = 123; in write_test()
38 BOOST_TEST(x_.m == 123); in write_test()
48 T x_; in read_test() local
49 x_.m = 123; in read_test()
51 BOOST_TEST(bind(&T::m, f(x_))() == 123); in read_test()
52 BOOST_TEST(bind(&T::m, arg1)(f(x_)) == 123); in read_test()
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| /third_party/mindspore/mindspore/ccsrc/frontend/optimizer/irpass/ |
| D | reshape_eliminate.h | 48 auto src_shape_abs = x_->abstract(); in operator()
63 return x_; in operator()
71 if (x_ == nullptr) { in Visit()
72 x_ = node; in Visit()
79 x_ = nullptr; in Reset()
84 AnfNodePtr x_{nullptr}, shape_{nullptr};
95 if (fg != nullptr && x_ != nullptr && shape_ != nullptr) { in operator()
96 auto new_node = fg->NewCNode({NewValueNode(prim_), x_, shape_}); in operator()
114 x_ = inputs[1]; in Visit()
122 x_ = nullptr; in Reset()
[all …]
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| D | tile_eliminate.h | 48 return x_; in operator()
53 return x_; in operator()
60 if (x_ == nullptr) { in Visit()
61 x_ = node; in Visit()
68 x_ = nullptr; in Reset()
73 AnfNodePtr x_{nullptr}, tuple_{nullptr};
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| /third_party/boost/boost/math/interpolators/ |
| D | makima.hpp | 127 if (x <= impl_->x_.back()) { in push_back()
130 impl_->x_.push_back(x); in push_back()
136 Real mim2 = (impl_->y_[i-1]-impl_->y_[i-2])/(impl_->x_[i-1]-impl_->x_[i-2]); in push_back()
137 Real mim1 = (impl_->y_[i ]-impl_->y_[i-1])/(impl_->x_[i ]-impl_->x_[i-1]); in push_back()
138 Real mi = (impl_->y_[i+1]-impl_->y_[i ])/(impl_->x_[i+1]-impl_->x_[i ]); in push_back()
139 Real mip1 = (impl_->y_[i+2]-impl_->y_[i+1])/(impl_->x_[i+2]-impl_->x_[i+1]); in push_back()
147 Real mnm4 = (impl_->y_[n-3]-impl_->y_[n-4])/(impl_->x_[n-3]-impl_->x_[n-4]); in push_back()
148 Real mnm3 = (impl_->y_[n-2]-impl_->y_[n-3])/(impl_->x_[n-2]-impl_->x_[n-3]); in push_back()
149 Real mnm2 = (impl_->y_[n-1]-impl_->y_[n-2])/(impl_->x_[n-1]-impl_->x_[n-2]); in push_back()
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| /third_party/boost/libs/math/test/ |
| D | test_beta_hooks.hpp | 79 double x_ = x; in ibeta() local
80 double y_ = 1-x_; in ibeta()
83 bratio_(&a_, &b_, &x_, &y_, &w, &w1, &ierr); in ibeta()
90 double x_ = x; in ibeta() local
91 double y_ = 1-x_; in ibeta()
94 bratio_(&a_, &b_, &x_, &y_, &w, &w1, &ierr); in ibeta()
101 double x_ = x; in ibeta() local
102 double y_ = 1-x_; in ibeta()
105 bratio_(&a_, &b_, &x_, &y_, &w, &w1, &ierr); in ibeta()
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