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/external/tensorflow/tensorflow/python/kernel_tests/distributions/
Dexponential_test.py29 from tensorflow.python.ops.distributions import exponential as exponential_lib
54 exponential = exponential_lib.Exponential(rate=lam)
56 log_pdf = exponential.log_prob(x)
59 pdf = exponential.prob(x)
71 exponential = exponential_lib.Exponential(rate=rate)
72 log_pdf = exponential.log_prob(0.)
81 exponential = exponential_lib.Exponential(rate=lam)
83 cdf = exponential.cdf(x)
97 exponential = exponential_lib.Exponential(rate=lam)
99 log_survival = exponential.log_survival_function(x)
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/external/tensorflow/tensorflow/core/api_def/base_api/
Dapi_def_Exp.pbtxt3 summary: "Computes exponential of x element-wise. \\\\(y = e^x\\\\)."
5 This function computes the exponential of every element in the input tensor.
18 For complex numbers, the exponential value is calculated as follows:
Dapi_def_Elu.pbtxt3 summary: "Computes exponential linear: `exp(features) - 1` if < 0, `features` otherwise."
/external/mesa3d/.gitlab-ci/
Ddeqp-lima-fails.txt42 dEQP-GLES2.functional.shaders.random.exponential.fragment.11,Fail
43 dEQP-GLES2.functional.shaders.random.exponential.fragment.12,Fail
44 dEQP-GLES2.functional.shaders.random.exponential.fragment.14,Fail
45 dEQP-GLES2.functional.shaders.random.exponential.fragment.37,Fail
46 dEQP-GLES2.functional.shaders.random.exponential.fragment.5,Fail
47 dEQP-GLES2.functional.shaders.random.exponential.fragment.74,Fail
/external/tensorflow/tensorflow/compiler/mlir/xla/tests/translate/
Dif_conditional.hlotxt15 …%exponential.14 = f32[] exponential(%get-tuple-element.13), metadata={op_type="Exp" op_name="cond/…
16 ROOT %tuple.15 = (f32[]) tuple(%exponential.14), metadata={op_name="XLA_Retvals"}
41 // CHECK: [[R8:%.+]] = "mhlo.exponential"([[R7]])
Dif.mlir23 // CHECK: %[[VAL1:.+]] = f32[] exponential(f32[] %[[VAL0]])
24 %1 = "mhlo.exponential"(%0) : (tensor<f32>) -> tensor<f32>
53 %7 = "mhlo.exponential"(%6) : (tensor<f32>) -> tensor<f32>
/external/oboe/apps/OboeTester/app/src/main/java/com/google/sample/oboe/manualtest/
DExponentialTaper.java64 public double exponentialToLinear(double exponential) { in exponentialToLinear() argument
65 return Math.log((exponential + offset) / a) / (b * Math.log(ROOT)); in exponentialToLinear()
/external/tensorflow/tensorflow/compiler/mlir/hlo/tests/
Dinlining.mlir8 // CHECK: "mhlo.exponential"
26 %0 = "mhlo.exponential"(%arg0) : (tensor<f32>) -> tensor<f32>
Dlegalize-control-flow.mlir50 // CHECK: [[VAL5:%.+]] = "mhlo.exponential"([[VAL4]]) : (tensor<f32>) -> tensor<f32>
52 %2 = "mhlo.exponential"(%arg1) : (tensor<f32>) -> tensor<f32>
129 // CHECK: %5 = "mhlo.exponential"(%4) : (tensor<f32>) -> tensor<f32>
142 %2 = "mhlo.exponential"(%arg2) : (tensor<f32>) -> tensor<f32>
Dlower-complex.mlir196 // CHECK-DAG: [[EXP:%.+]] = "mhlo.exponential"(%arg0)
201 %1 = "mhlo.exponential"(%0) : (tensor<2xcomplex<f32>>) -> (tensor<2xcomplex<f32>>)
214 // CHECK-DAG: [[EXP:%.+]] = "mhlo.exponential"([[REAL]])
220 %0 = "mhlo.exponential"(%arg0) : (tensor<2xcomplex<f32>>) -> (tensor<2xcomplex<f32>>)
230 // CHECK-DAG: [[EXP:%.+]] = "mhlo.exponential"([[REAL]])
236 %0 = "mhlo.exponential"(%arg0) : (tensor<*xcomplex<f32>>) -> (tensor<*xcomplex<f32>>)
/external/perfetto/docs/design-docs/
Dheapprofd-sampling.md41 then repeatedly draw from the exponential distribution (which is the
43 above 0. The amount of times we had to draw from the exponential
53 exponential draw approach, as for a non-sample, we only need to decrement a
55 from exponential for every sample) is more expensive.
70 Because the exponential distribution is memoryless, we can add together
87 sum. This is because the exponential distribution we use is memoryless.
122 estimating the geometric distribution using an exponential distribution, as its
126 Draw sample from exponential distribution with p = 1 / 32000:
163 time, then the memorylessness property of the exponential distribution would
/external/icu/icu4c/source/data/locales/
Dgd.txt30 exponential{"E"}
47 exponential{"اس"}
64 exponential{"×۱۰^"}
81 exponential{"E"}
98 exponential{"E"}
115 exponential{"E"}
132 exponential{"E"}
149 exponential{"E"}
167 exponential{"E"}
347 exponential{"E"}
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Dzh_Hant.txt106 exponential{"اس"}
133 exponential{"×۱۰^"}
160 exponential{"E"}
187 exponential{"E"}
214 exponential{"E"}
241 exponential{"E"}
268 exponential{"E"}
296 exponential{"E"}
324 exponential{"E"}
351 exponential{"E"}
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Den_SI.txt13 exponential{"e"}
/external/eigen/unsupported/Eigen/
DMatrixFunctions35 * - \ref matrixbase_exp "MatrixBase::exp()", for computing the matrix exponential
112 Compute the matrix exponential.
118 \param[in] M matrix whose exponential is to be computed.
119 \returns expression representing the matrix exponential of \p M.
121 The matrix exponential of \f$ M \f$ is defined by
123 The matrix exponential can be used to solve linear ordinary
128 The matrix exponential is different from applying the exp function to all the entries in the matrix.
135 The matrix exponential is computed using the scaling-and-squaring
137 rescaled, then the exponential of the reduced matrix is computed
144 scaling and squaring method for the matrix exponential revisited,"
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/external/python/cpython3/Doc/library/
Dxml.rst82 billion laughs / exponential entity expansion
83 The `Billion Laughs`_ attack -- also known as exponential entity expansion --
86 The exponential expansion results in several gigabytes of text and
93 efficient as the exponential case but it avoids triggering parser countermeasures
/external/tensorflow/tensorflow/compiler/mlir/xla/tests/
Dlegalize-tf-control-flow.mlir33 %0 = "mhlo.exponential"(%arg1) : (tensor<f32>) -> tensor<f32>
63 // CHECK: [[VAL6:%.+]] = "mhlo.exponential"([[VAL5]])
64 %2 = "mhlo.exponential"(%arg1) : (tensor<f32>) -> tensor<f32>
79 …%0:2 = "tf.Case"(%index, %arg0, %arg1) {branches = [@exponential, @log, @floor], is_stateless = tr…
85 …// CHECK: %[[CALL_EXP:.*]]:2 = call @exponential(%[[TUPLE_ELEMENT_0]], %[[TUPLE_ELEMENT_1]]) :…
104 func @exponential(%arg0: tensor<f32>, %arg1: tensor<f32>) -> (tensor<f32>, tensor<f32>) {
105 %0 = "mhlo.exponential"(%arg1) : (tensor<f32>) -> tensor<f32>
130 // CHECK: [[VAL5:%.+]] = "mhlo.exponential"([[VAL4]])
131 %1 = "mhlo.exponential"(%arg1) : (tensor<f32>) -> tensor<f32>
/external/python/cpython2/Doc/library/
Dxml.rst76 billion laughs / exponential entity expansion
77 The `Billion Laughs`_ attack -- also known as exponential entity expansion --
80 the small string is expanded to several gigabytes. The exponential expansion
87 efficient as the exponential case but it avoids triggering countermeasures of
/external/snakeyaml/src/test/resources/pyyaml/
Dspec-02-20.data2 exponential: 12.3015e+02
Dconstruct-float.data2 exponential: 685.230_15e+03
/external/llvm-project/llvm/test/YAMLParser/
Dspec-02-20.test4 exponential: 12.3015e+02
Dconstruct-float.test4 exponential: 685.230_15e+03
/external/llvm/test/YAMLParser/
Dspec-02-20.test4 exponential: 12.3015e+02
Dconstruct-float.test4 exponential: 685.230_15e+03
/external/deqp/android/cts/master/
Dgles2-master.txt5129 dEQP-GLES2.functional.shaders.operator.exponential.pow.mediump_float_vertex
5130 dEQP-GLES2.functional.shaders.operator.exponential.pow.mediump_float_fragment
5131 dEQP-GLES2.functional.shaders.operator.exponential.pow.highp_float_vertex
5132 dEQP-GLES2.functional.shaders.operator.exponential.pow.highp_float_fragment
5133 dEQP-GLES2.functional.shaders.operator.exponential.pow.mediump_vec2_vertex
5134 dEQP-GLES2.functional.shaders.operator.exponential.pow.mediump_vec2_fragment
5135 dEQP-GLES2.functional.shaders.operator.exponential.pow.highp_vec2_vertex
5136 dEQP-GLES2.functional.shaders.operator.exponential.pow.highp_vec2_fragment
5137 dEQP-GLES2.functional.shaders.operator.exponential.pow.mediump_vec3_vertex
5138 dEQP-GLES2.functional.shaders.operator.exponential.pow.mediump_vec3_fragment
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