| /third_party/typescript/tests/baselines/reference/ |
| D | library-reference-8.trace.json | 2 …olving type reference directive 'alpha', containing file '/test/foo.ts', root directory '/test/typ…
3 "Resolving with primary search path '/test/types'.",
4 "File '/test/types/alpha/package.json' does not exist.",
5 "File '/test/types/alpha/index.d.ts' exist - use it as a name resolution result.",
6 … "Resolving real path for '/test/types/alpha/index.d.ts', result '/test/types/alpha/index.d.ts'.",
7 …"======== Type reference directive 'alpha' was successfully resolved to '/test/types/alpha/index.d…
8 …"======== Resolving type reference directive 'beta', containing file '/test/foo.ts', root director…
9 "Resolving with primary search path '/test/types'.",
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.",
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| /third_party/mindspore/mindspore-src/source/tests/st/probability/distribution/ |
| D | test_beta.py | 7 # http://www.apache.org/licenses/LICENSE-2.0
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)
40 Test pdf.
42 beta_benchmark = stats.beta(np.array([3.0]), np.array([1.0]))
46 tol = 1e-6
47 assert (np.abs(output.asnumpy() - expect_pdf) < tol).all()
51 Test class: log probability of Beta distribution.
55 self.b = msd.Beta(np.array([3.0]), np.array([1.0]), dtype=dtype.float32)
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| /third_party/mindspore/mindspore-src/source/tests/st/ops/ |
| D | test_func_layer_norm.py | 7 # http://www.apache.org/licenses/LICENSE-2.0
28 def layer_norm_forward_func(input_x, normalized_shape, gamma, beta, eps=1e-7): argument
29 return layer_norm(input_x, normalized_shape, gamma, beta, eps)
32 def layer_norm_backward_func(input_x, normalized_shape, gamma, beta, eps=1e-7): argument
33 return ops.grad(layer_norm_forward_func, (0, 2, 3))(input_x, normalized_shape, gamma, beta, eps)
38 def layer_norm_forward_func_np(input_x, normalized_shape, gamma, beta, eps=1e-7): argument
39 mean_np = np.mean(input_x, axis=-1, keepdims=True)
40 var_np = np.var(input_x, axis=-1, keepdims=True)
41 x_norm = (input_x - mean_np) / np.sqrt(var_np + eps)
42 return gamma * x_norm + beta
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| D | test_ops_softplus.py | 7 # http://www.apache.org/licenses/LICENSE-2.0
28 def generate_expect_forward_output(x, beta=1, threshold=20): argument
29 sacling_input = beta * x
30 output = (1 / beta) * np.log(1 + np.exp(sacling_input))
35 def softplus_forward_func(x, beta=1, threshold=20): argument
36 return softplus(x, beta, threshold)
40 def softplus_backward_func(x, beta=1, threshold=20): argument
41 return ops.grad(softplus_forward_func, (0))(x, beta, threshold)
45 def softplus_vmap_func(x, beta=1, threshold=20): argument
46 return ops.vmap(softplus_forward_func, in_axes=(0, None, None), out_axes=0)(x, beta, threshold)
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| /third_party/mindspore/mindspore-src/source/tests/st/ops/ascend/ |
| D | test_layer_norm_v3.py | 7 # http://www.apache.org/licenses/LICENSE-2.0
30 def construct(self, input_x, gamma, beta): argument
31 return self.layernorm(input_x, gamma, beta)
40 Feature: test LayerNormV3 forward.
41 Description: test LayerNormV3 inputs.
47 beta = Tensor(np.ones([3]), mindspore.float32)
49 output, mean, variance = net(input_x, gamma, beta)
51 expect_output = np.array([[-0.22474468, 1., 2.22474468], [-0.22474468, 1., 2.22474468]])
55 assert np.allclose(output.asnumpy(), expect_output, atol=1e-6)
56 assert np.allclose(mean.asnumpy(), expect_mean, atol=1e-6)
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| D | test_add_layernorm.py | 7 # http://www.apache.org/licenses/LICENSE-2.0
17 """test where"""
32 def generate_expect_forward_output(x1, x2, gamma, beta, eps=1e-5): argument
35 rstdOut = np.power((res.var(1).reshape(2, 1) + eps), -0.5)
36 y = rstdOut * (res - meanOut) * gamma + beta
42 self.layernorm = P.LayerNorm(begin_norm_axis=-1,
43 begin_params_axis=-1,
44 epsilon=1e-5)
46 def construct(self, x1, x2, gamma, beta): argument
48 y, meanOut, rstdOut = self.layernorm(res, gamma, beta)
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| /third_party/mindspore/mindspore-src/source/tests/st/sparse/ |
| D | test_csr_add.py | 7 # http://www.apache.org/licenses/LICENSE-2.0
59 Feature: Test function csr_add.
60 Description: Test CSRTensor matrix add.
65 beta = Tensor(1, mstype.float32)
67 c = csr_add(csra, csrb, alpha, beta)
75 beta = Tensor(-1, mstype.float32)
76 c = csr_add(csra, csrb, alpha, beta)
91 Feature: Test ops SparseMatrixAdd.
92 Description: Test CSRTensor matrix add.
97 beta = Tensor(1, mstype.float32)
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| /third_party/mindspore/mindspore-src/source/tests/ut/python/parallel/ |
| D | test_group_norm.py | 7 # http://www.apache.org/licenses/LICENSE-2.0
36 self.eps = 1e-5
38 def construct(self, x, num_groups, gamma, beta): argument
39 out = self.group_norm(x, num_groups, gamma, beta, self.eps)[0]
45 Feature: test GroupNorm parallel.
46 Description: test GroupNorm parallel
56 beta = Tensor(np.ones(shape=(num_channels,)), dtype=mstype.float32)
60 phase = compile_net(net, x, num_groups, gamma, beta)
62 assert validator.check_node_inputs_has('GroupNorm-0', ['StridedSlice-0', num_groups])
67 Feature: test GroupNorm parallel with input rank3(N,C,D).
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| D | test_gamma.py | 7 # http://www.apache.org/licenses/LICENSE-2.0
38 def construct(self, shape, alpha, beta): argument
39 out = self.uniform_real(shape, alpha, beta)
45 Features: test UniformReal auto parallel
54 beta = Tensor(np.array([1.0]), ms.float32)
55 compile_net(net, shape, alpha, beta)
60 Features: test UniformReal data parallel
68 beta = Tensor(np.array([1.0]), ms.float32)
69 phase = compile_net(net, shape, alpha, beta)
72 assert validator.check_node_attrs("Gamma-0", {"seed": 2, "seed2": 2})
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| D | test_layout_extend_layernorm.py | 7 # http://www.apache.org/licenses/LICENSE-2.0
67 self.beta = Parameter(x_beta, "beta")
70 out1, _, _ = self.layernorm(y, self.gamma, self.beta)
77 beta = Tensor(np.ones([16, 32]), dtype=ms.float32) variable
81 Feature: test layout extend
88 net = Net(gamma, beta, layout1, begin_norm_axis=2)
96 Feature: test layout extend for multi shard
103 net = Net(gamma, beta, layout1, begin_norm_axis=2)
111 Feature: test layout extend for multi shard
118 net = Net(gamma, beta, layout1, begin_norm_axis=2)
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| /third_party/skia/third_party/externals/abseil-cpp/absl/random/ |
| D | beta_distribution_test.cc | 7 // https://www.apache.org/licenses/LICENSE-2.0
46 class BetaDistributionInterfaceTest : public ::testing::Test {};
48 // double-double arithmetic is not supported well by either GCC or Clang; see
65 std::exp(std::log((std::numeric_limits<TypeParam>::max)()) - in TYPED_TEST()
72 TypeParam(1e-20), TypeParam(1e-12), TypeParam(1e-8), TypeParam(1e-4), in TYPED_TEST()
73 TypeParam(1e-3), TypeParam(0.1), TypeParam(0.25), in TYPED_TEST()
74 std::nextafter(TypeParam(0.5), TypeParam(0)), // 0.5 - epsilon in TYPED_TEST()
77 std::nextafter(TypeParam(1), TypeParam(0)), // 1 - epsilon in TYPED_TEST()
99 for (TypeParam beta : kValues) { in TYPED_TEST() local
101 INFO, absl::StrFormat("Smoke test for Beta(%a, %a)", alpha, beta)); in TYPED_TEST()
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| /third_party/rust/crates/bitflags/tests/ |
| D | compile.rs | 10 #[test]
12 prepare_stderr_files("tests/compile-fail").unwrap(); in fail()
15 t.compile_fail("tests/compile-fail/**/*.rs"); in fail()
18 #[test]
21 t.pass("tests/compile-pass/**/*.rs"); in pass()
26 // having some message to check makes sure user-facing errors are sensical.
28 // The approach we use is to run the test on all compilers, but only check stderr
29 // output on beta (which is the next stable release). We do this by default ignoring
30 // any `.stderr` files in the `compile-fail` directory, and copying `.stderr.beta` files
31 // when we happen to be running on a beta compiler.
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| /third_party/mindspore/mindspore-src/source/tests/st/dyn_shape_dev/ |
| D | test_layer_norm.py | 7 # http://www.apache.org/licenses/LICENSE-2.0
25 def layer_norm_forward_func(input_x, gamma, beta): argument
26 return ops.LayerNorm(begin_norm_axis=1, begin_params_axis=1, epsilon=1e-7)(input_x, gamma, beta)
30 def layer_norm_backward_func(input_x, gamma, beta): argument
31 return ops.grad(layer_norm_forward_func, (0, 1, 2))(input_x, gamma, beta)
34 def layer_norm_dyn_shape_func(input_x, gamma, beta): argument
35 return ops.LayerNorm(begin_norm_axis=1, begin_params_axis=1, epsilon=1e-7)(input_x, gamma, beta)
47 Description: test op layer norm.
53 beta = ms.Tensor(np.zeros([3]), ms.float32)
54 output, mean, variance = layer_norm_forward_func(input_x, gamma, beta)
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| /third_party/skia/third_party/externals/abseil-cpp/absl/random/internal/ |
| D | distribution_test_util.h | 7 // https://www.apache.org/licenses/LICENSE-2.0
25 // NOTE: The functions in this file are test only, and are should not be used in
26 // non-test code.
32 // http://webspace.ship.edu/pgmarr/Geo441/Lectures/Lec%205%20-%20Normality%20Testing.pdf
49 // Computes the Z-score for a set of data with the given distribution moments
58 // Computes the maximum distance from the mean tolerable, for Z-Tests that are
73 // Beta(p, q) = Gamma(p) * Gamma(q) / Gamma(p+q)
74 double beta(double p, double q);
82 // Implements the incomplete regularized beta function, AS63, BETAIN.
87 // `p` is beta parameter p, `q` is beta parameter q.
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| /third_party/mindspore/mindspore-src/source/tests/st/ops/cpu/ |
| D | test_smoothl1loss_op.py | 7 # http://www.apache.org/licenses/LICENSE-2.0
27 def smoothl1loss(beta, reduction): argument
32 net = nn.SmoothL1Loss(beta, reduction)
54 Description: test the rightness of SmoothL1Loss cpu kernel.
58 beta = 1.0
59 loss = smoothl1loss(beta, reduction)
67 beta = 1 / 9
68 loss = smoothl1loss(beta, reduction)
88 def smoothl1loss_grad(beta): argument
94 net = nn.SmoothL1Loss(beta)
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| D | test_apply_power_sign.py | 7 # http://www.apache.org/licenses/LICENSE-2.0
37 self.beta = 0.9
41 self.sign_decay, self.beta, grad)
47 Feature: test ops ApplyPowerSign.
54 expect_var = [[5.95575690e-01, 3.89676481e-01],
55 [9.85252112e-02, 4.88201708e-01]]
56 expect_m = [[5.70000052e-01, 5.19999981e-01],
57 [1.89999998e-01, 6.20000064e-01]]
72 def construct(self, lr, logbase, sign_decay, beta, grad): argument
73 return self.vmap_grad(self.var, self.m, lr, logbase, sign_decay, beta, grad)
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| /third_party/mindspore/mindspore-src/source/tests/st/ops/gpu/ |
| D | test_smoothl1loss_op.py | 7 # http://www.apache.org/licenses/LICENSE-2.0
27 def smoothl1loss(beta, reduction): argument
32 net = nn.SmoothL1Loss(beta, reduction)
54 Description: test the rightness of SmoothL1Loss cpu kernel.
58 beta = 1.0
59 loss = smoothl1loss(beta, reduction)
67 beta = 1 / 9
68 loss = smoothl1loss(beta, reduction)
88 def smoothl1loss_grad(beta): argument
94 net = nn.SmoothL1Loss(beta)
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| D | test_sparse_addmm_op.py | 7 # http://www.apache.org/licenses/LICENSE-2.0
30 def construct(self, input_indices, input_values, input_shape, x2_dense, x3_dense, alpha, beta): argument
31 …return self.sparse_addmm(input_indices, input_values, input_shape, x2_dense, x3_dense, alpha, beta)
39 Feature: SparseAddmm gpu TEST.
40 Description: 2d int32 test case for SparseAddmm
51 beta = Tensor(np.array([1]), mstype.int32)
54 y_dense = net(input_indices, input_values, input_shape, x2_dense, x3_dense, alpha, beta)
65 Feature: SparseAddmm gpu TEST.
66 Description: 2d int32 test case for SparseAddmm
77 beta = Tensor(np.array([1]), mstype.int64)
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| D | test_sspaddmm_op.py | 7 # http://www.apache.org/licenses/LICENSE-2.0
32 x2_shape, x3_dense, alpha, beta): argument
34 x2_values, x2_shape, x3_dense, alpha, beta)
42 Feature: test Sspaddmm ops in gpu.
43 Description: test the ops in dynamic shape.
57 beta = Tensor(1, dtype=mstype.int32)
60 x2_values_dyn, x2_shape_dyn, x3_dense_dyn, alpha, beta)
72 x3_dense, alpha, beta)
83 Feature: Sspaddmm gpu TEST.
84 Description: 2d int32 test case for Sspaddmm
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| /third_party/rust/crates/clap/.github/workflows/ |
| D | rust-next.yml | 1 name: rust-next
4 - cron: '3 3 3 * *'
9 test:
10 name: Test
15 - build: stable
16 os: ubuntu-latest
19 - build: linux
20 os: ubuntu-latest
21 rust: "beta"
23 - build: windows
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| /third_party/python/Lib/test/ |
| D | test_getopt.py | 2 # David Goodger <dgoodger@bigfoot.com> 2000-08-19
4 from test.support import verbose, run_doctest
5 from test.support.os_helper import EnvironmentVarGuard
45 self.assertEqual(opts, [('-a', '')])
49 self.assertEqual(opts, [('-a', '1')])
53 #self.assertEqual(opts, [('-a', '1')])
57 self.assertEqual(opts, [('-a', '1')])
61 self.assertEqual(opts, [('-a', '1')])
69 self.assertEqual(opts, [('--abc', '')])
73 self.assertEqual(opts, [('--abc', '1')])
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| /third_party/rust/crates/lazy-static.rs/ |
| D | appveyor.yml | 12 - TARGET: i686-pc-windows-gnu
14 - TARGET: i686-pc-windows-msvc
16 - TARGET: x86_64-pc-windows-gnu
18 - TARGET: x86_64-pc-windows-msvc
20 # Beta channel
21 - TARGET: i686-pc-windows-gnu
22 CHANNEL: beta
23 - TARGET: i686-pc-windows-msvc
24 CHANNEL: beta
25 - TARGET: x86_64-pc-windows-gnu
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| /third_party/mindspore/mindspore-src/source/tests/st/ops/ascend/test_tbe_ops/ |
| D | test_smooth_l1_loss_grad.py | 7 # http://www.apache.org/licenses/LICENSE-2.0
38 def smoothl1loss_grad(beta): argument
44 net = nn.SmoothL1Loss(beta)
56 Description: test the rightness of SmoothL1LossGrad cpu kernel.
60 epsilon = 1e-6
62 beta = 1.0
63 dx = smoothl1loss_grad(beta)
64 dx1_expect = np.array([-0.71552587, 0.01499678, -0.06709455, -0.30110368, -0.45868093,
65 0.24838912, -0.46063876, 0.41411355, 0.04507046, -1.4708229,
66 0.04481723, 0.38508227, -0.17292616, -0.52333146, -1.0309995,
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| /third_party/rust/crates/termcolor/.github/workflows/ |
| D | ci.yml | 6 - master
8 - cron: '00 01 * * *'
10 test:
11 name: test
12 runs-on: ${{ matrix.os }}
16 - pinned
17 - pinned-win
18 - stable
19 - beta
20 - nightly
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| /third_party/toybox/tests/ |
| D | cut.test | 7 [ -f testing.sh ] && . testing.sh
11 # Creating test file for testing cut
13 alpha:beta:gamma:delta:epsilon:zeta:eta:theta:iota:kappa:lambda:mu
16 testing "-b a,a,a" "cut -b 3,3,3 abc.txt" "e\np\ne\n" "" ""
17 testing "-b overlaps" "cut -b 1-3,2-5,7-9,9-10 abc.txt" \
19 testing "-b encapsulated" "cut -b 3-8,4-6 abc.txt" "e:two:\npha:be\ne quic\n" \
21 testing "-bO overlaps" \
22 "cut --output-delimiter ' ' -b 1-3,2-5,7-9,9-10 abc.txt" \
23 "one:t o:th\nalpha beta\nthe q ick \n" "" ""
24 testing "high-low error" "cut -b 8-3 abc.txt 2>/dev/null || echo err" "err\n" \
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