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/external/skia/site/docs/dev/design/conical/
D_index.md71 1. All centers $C_t = (x_t, 0)$ must be on the $x$ axis
72 2. The radius $r_t$ is $x_t r_1$.
73 3. Given $x_t$ , we can derive $t = f + (1 - f) x_t$
75 From now on, we'll focus on how to quickly computes $x_t$. Note that $r_t > 0$
76 so we're only interested positive solution $x_t$. Again, if there are multiple
77 $x_t$ solutions, we may want to find the bigger one if $1 - f > 0$, and smaller
83 **Theorem 1.** The solution to $x_t$ is
91 Case 2 always produces a valid $x_t$. Case 1 and 3 requires $x > 0$ to produce
92 valid $x_t > 0$. Case 3 may have no solution at all if
96 $(x_t - x)^2 + y^2 = (x_t r_1)^2$ and eliminate negative $x_t$ solutions get us
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/external/skqp/site/dev/design/conical/
Dindex.md61 1. All centers $C_t = (x_t, 0)$ must be on the $x$ axis
62 2. The radius $r_t$ is $x_t r_1$.
63 3. Given $x_t$ , we can derive $t = f + (1 - f) x_t$
65 From now on, we'll focus on how to quickly computes $x_t$. Note that $r_t > 0$ so we're only
66 interested positive solution $x_t$. Again, if there are multiple $x_t$ solutions, we may want to
72 **Theorem 1.** The solution to $x_t$ is
78 Case 2 always produces a valid $x_t$. Case 1 and 3 requires $x > 0$ to produce valid $x_t > 0$. Case
81 *Proof.* Algebriacally, solving the quadratic equation $(x_t - x)^2 + y^2 = (x_t r_1)^2$ and
82 eliminate negative $x_t$ solutions get us the theorem.
89 1. we still need to compute $t$ from $x_t$ (remember that $t = f + (1-f) x_t$);
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/external/tensorflow/tensorflow/core/grappler/optimizers/
Ddebug_stripper_test.cc126 Tensor x_t(DT_FLOAT, TensorShape({})); in TEST_F() local
128 x_t.flat<float>()(0) = 1.0f; in TEST_F()
131 EvaluateNodes(item.graph, {"z"}, {{"x", x_t}, {"y", y_t}}); in TEST_F()
133 EvaluateNodes(output, {"z"}, {{"x", x_t}, {"y", y_t}}); in TEST_F()
185 Tensor x_t(DT_FLOAT, TensorShape({})); in TEST_F() local
187 x_t.flat<float>()(0) = 1.0f; in TEST_F()
190 EvaluateNodes(item.graph, {"z"}, {{"x", x_t}, {"y", y_t}}); in TEST_F()
192 EvaluateNodes(output, {"z"}, {{"x", x_t}, {"y", y_t}}); in TEST_F()
223 Tensor x_t(DT_FLOAT, TensorShape({})); in TEST_F() local
224 x_t.flat<float>()(0) = 1.0f; in TEST_F()
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Dconstant_folding_test.cc49 Tensor x_t(DTYPE, TensorShape({2, 2})); in SimpleNeutralElementTest() local
53 x_t.flat<T>()(i) = T(i + 1); in SimpleNeutralElementTest()
120 EvaluateNodes(item.graph, item.fetch, {{"x", x_t}}); in SimpleNeutralElementTest()
121 auto tensors = EvaluateNodes(output, item.fetch, {{"x", x_t}}); in SimpleNeutralElementTest()
388 auto x_t = GenerateRandomTensor<DT_FLOAT>(TensorShape({2, 2})); in TEST_F() local
393 EvaluateNodes(item.graph, fetch, {{"x", x_t}, {"y", y_t}}); in TEST_F()
396 auto tensors = EvaluateNodes(output, fetch, {{"x", x_t}, {"y", y_t}}); in TEST_F()
445 auto x_t = GenerateRandomTensor<DT_FLOAT>(TensorShape({2, 2})); in TEST_F() local
448 auto tensor_expected = EvaluateNodes(item.graph, fetch, {{"x", x_t}}); in TEST_F()
451 auto tensors = EvaluateNodes(output, fetch, {{"x", x_t}}); in TEST_F()
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Darithmetic_optimizer_test.cc856 auto x_t = GenerateRandomTensor<DT_FLOAT>(TensorShape({3, 3, 28, 28})); in TEST_F() local
858 EvaluateNodes(item.graph, item.fetch, {{"Placeholder", x_t}}); in TEST_F()
868 auto tensors = EvaluateNodes(output, item.fetch, {{"Placeholder", x_t}}); in TEST_F()
895 auto x_t = GenerateRandomTensor<DT_FLOAT>(TensorShape({3, 3, 28, 28})); in TEST_F() local
898 item.feed = {{"Placeholder", x_t}}; in TEST_F()
924 auto x_t = GenerateRandomTensor<DT_FLOAT>(TensorShape({4, 3, 28, 28})); in TEST_F() local
927 item.feed = {{"Placeholder", x_t}}; in TEST_F()
956 auto x_t = GenerateRandomTensor<DT_FLOAT>(TensorShape({4, 3, 28, 28})); in TEST_F() local
959 item.feed = {{"Placeholder", x_t}}; in TEST_F()
988 auto x_t = GenerateRandomTensor<DT_FLOAT>(TensorShape({8, 3, 28, 28})); in TEST_F() local
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/external/llvm-project/compiler-rt/test/builtins/Unit/
Dcompiler_rt_logbl_test.c19 twords x_t, crt_value_t, libm_value_t; in test__compiler_rt_logbl() local
20 x_t.all = toRep(x); in test__compiler_rt_logbl()
26 x_t.s.high, x_t.s.low, crt_value_t.s.high, crt_value_t.s.low, in test__compiler_rt_logbl()
/external/skqp/src/gpu/gradients/
DGrTwoPointConicalGradientLayout.fp39 // calculations of t and x_t below overflow and produce an incorrect interpolant (which then
69 float x_t = -1;
71 x_t = dot(p, p) / p.x;
73 x_t = length(p) - p.x * invR1;
81 // is really critical, maybe we should just compute the area where temp and x_t are
85 x_t = -sqrt(temp) - p.x * invR1;
87 x_t = sqrt(temp) - p.x * invR1;
92 // The final calculation of t from x_t has lots of static optimizations but only do them
93 // when x_t is positive (which can be assumed true if isWellBehaved is true)
97 if (x_t <= 0.0) {
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/external/skia/src/gpu/gradients/
DGrTwoPointConicalGradientLayout.fp56 float x_t = -1;
58 x_t = dot(p, p) / p.x;
60 x_t = length(p) - p.x * invR1;
68 // is really critical, maybe we should just compute the area where temp and x_t are
72 x_t = -sqrt(temp) - p.x * invR1;
74 x_t = sqrt(temp) - p.x * invR1;
79 // The final calculation of t from x_t has lots of static optimizations but only do them
80 // when x_t is positive (which can be assumed true if isWellBehaved is true)
84 if (x_t <= 0.0) {
90 t = x_t;
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/external/tensorflow/tensorflow/core/kernels/
Dfused_batch_norm_op_test.cc209 Tensor x_t(dtype, data_format == FORMAT_NHWC ? TensorShape({n, h, w, c}) in FusedBatchNormInference() local
211 x_t.flat<T>().setRandom(); in FusedBatchNormInference()
218 Node* x = test::graph::Constant(g, x_t, "x"); in FusedBatchNormInference()
251 Tensor x_t(dtype, shape); in FusedBatchNormGrad() local
252 x_t.flat<T>().setRandom(); in FusedBatchNormGrad()
258 Node* x = test::graph::Constant(g, x_t, "x"); in FusedBatchNormGrad()
Dfused_batch_norm_ex_op_test.cc556 Tensor x_t(dtype, data_format == FORMAT_NHWC ? TensorShape({n, h, w, c}) in FusedBatchNormEx() local
558 x_t.flat<T>().setRandom(); in FusedBatchNormEx()
563 Node* x = test::graph::Constant(g, x_t, "x"); in FusedBatchNormEx()
/external/tensorflow/tensorflow/python/kernel_tests/
Dsparse_tensor_dense_matmul_op_test.py343 x_t = constant_op.constant(x)
346 x_t, y_t, adjoint_a, adjoint_b)
349 x_t = constant_op.constant(x)
352 x_t, y_t, adjoint_a, adjoint_b)
Dunique_op_test.py147 x_t = array_ops.placeholder(dtypes.int32, shape=None)
148 _, idx = gen_array_ops.unique_v2(x_t, axis=[0])
Dfunctional_ops_test.py394 x_t = array_ops.transpose(x)
398 result_t = functional_ops.scan(lambda a, x: a + x, x_t, infer_shape=False)
401 result_t_grad = gradients_impl.gradients(result_t, [x_t])[0]
/external/tensorflow/tensorflow/python/ops/distributions/
Dstudent_t.py285 x_t = self.df / (y**2. + self.df)
286 neg_cdf = 0.5 * math_ops.betainc(0.5 * self.df, 0.5, x_t)
/external/tensorflow/tensorflow/core/framework/
Dtensor_util_test.cc544 Tensor x_t; in CompareTensorValues() local
545 EXPECT_TRUE(x_t.FromProto(x)); in CompareTensorValues()
548 test::ExpectTensorEqual<T>(x_t, y_t); in CompareTensorValues()
/external/tensorflow/tensorflow/python/ops/
Drnn.py61 x_t = array_ops.transpose(
63 x_t.set_shape(
67 return x_t
/external/python/cpython3/Lib/test/
Dmime.types1192 model/vnd.parasolid.transmit.text x_t xmt_txt