| /external/python/google-api-python-client/docs/dyn/ |
| D | analytics_v3.management.customDimensions.html | 79 <p class="firstline">Get a custom dimension to which the user has access.</p>
82 <p class="firstline">Create a new custom dimension.</p>
88 <p class="firstline">Updates an existing custom dimension. This method supports patch semantics.</p>
91 <p class="firstline">Updates an existing custom dimension.</p>
95 <pre>Get a custom dimension to which the user has access.
98 accountId: string, Account ID for the custom dimension to retrieve. (required)
99 webPropertyId: string, Web property ID for the custom dimension to retrieve. (required)
100 customDimensionId: string, The ID of the custom dimension to retrieve. (required)
105 { # JSON template for Analytics Custom Dimension.
106 "index": 42, # Index of the custom dimension.
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| D | dfareporting_v2_7.reports.compatibleFields.html | 93 { # Represents a sorted dimension.
95 "name": "A String", # The name of the dimension.
96 "sortOrder": "A String", # An optional sort order for the dimension column.
120 { # Represents a sorted dimension.
122 "name": "A String", # The name of the dimension.
123 "sortOrder": "A String", # An optional sort order for the dimension column.
129 "value": "A String", # The value of the dimension.
130 "dimensionName": "A String", # The name of the dimension.
139 "value": "A String", # The value of the dimension.
140 "dimensionName": "A String", # The name of the dimension.
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| D | dfareporting_v2_8.reports.compatibleFields.html | 93 { # Represents a sorted dimension.
95 "name": "A String", # The name of the dimension.
96 "sortOrder": "A String", # An optional sort order for the dimension column.
120 { # Represents a sorted dimension.
122 "name": "A String", # The name of the dimension.
123 "sortOrder": "A String", # An optional sort order for the dimension column.
129 "value": "A String", # The value of the dimension.
130 "dimensionName": "A String", # The name of the dimension.
139 "value": "A String", # The value of the dimension.
140 "dimensionName": "A String", # The name of the dimension.
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| D | dfareporting_v2_7.reports.html | 136 { # Represents a sorted dimension.
138 "name": "A String", # The name of the dimension.
139 "sortOrder": "A String", # An optional sort order for the dimension column.
163 { # Represents a sorted dimension.
165 "name": "A String", # The name of the dimension.
166 "sortOrder": "A String", # An optional sort order for the dimension column.
172 "value": "A String", # The value of the dimension.
173 "dimensionName": "A String", # The name of the dimension.
182 "value": "A String", # The value of the dimension.
183 "dimensionName": "A String", # The name of the dimension.
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| D | dfareporting_v2_8.reports.html | 136 { # Represents a sorted dimension.
138 "name": "A String", # The name of the dimension.
139 "sortOrder": "A String", # An optional sort order for the dimension column.
163 { # Represents a sorted dimension.
165 "name": "A String", # The name of the dimension.
166 "sortOrder": "A String", # An optional sort order for the dimension column.
172 "value": "A String", # The value of the dimension.
173 "dimensionName": "A String", # The name of the dimension.
182 "value": "A String", # The value of the dimension.
183 "dimensionName": "A String", # The name of the dimension.
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| D | analyticsreporting_v4.reports.html | 97 # by pivoting your data on a second dimension.
126 # are attributes of your data. For example, the dimension `ga:city`
129 "name": "A String", # Name of the dimension to fetch, for example `ga:browser`.
130 … "histogramBuckets": [ # If non-empty, we place dimension values into buckets after string to
131 # int64. Dimension values that are not the string representation of an
135 # boundary, the "last" bucket includes all values up to infinity. Dimension
136 # values that fall in a bucket get transformed to a new dimension value. For
140 # - bucket #1: values < 0, dimension value "<0"
141 # - bucket #2: values in [0,1), dimension value "0"
142 # - bucket #3: values in [1,3), dimension value "1-2"
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| /external/tensorflow/tensorflow/python/framework/ |
| D | tensor_shape_test.py | 31 dim = tensor_shape.Dimension(12)
34 self.assertEqual(dim, tensor_shape.Dimension(12))
35 self.assertEqual(tensor_shape.Dimension(15),
36 dim + tensor_shape.Dimension(3))
37 self.assertEqual(tensor_shape.Dimension(15), dim + 3)
38 self.assertEqual(tensor_shape.Dimension(15), 3 + dim)
39 self.assertEqual(tensor_shape.Dimension(9), dim - 3)
40 self.assertEqual(tensor_shape.Dimension(1), 13 - dim)
41 self.assertEqual(tensor_shape.Dimension(24),
42 dim * tensor_shape.Dimension(2))
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| D | tensor_shape.py | 36 Concretely, `tensor_shape[i]` returned a Dimension instance in V1, but
66 dim = Dimension(None)
93 def dimension_value(dimension): argument
99 When accessing the value of a TensorShape dimension,
114 dimension: Either a `Dimension` instance, an integer, or None.
119 if isinstance(dimension, Dimension):
120 return dimension.value
121 return dimension
132 If you want to retrieve the Dimension instance corresponding to a certain
148 dim = Dimension(None)
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| /external/fonttools/Tests/varLib/data/ |
| D | Build.designspace | 14 <dimension name="weight" xvalue="0" />
15 <dimension name="contrast" xvalue="0" />
23 <dimension name="weight" xvalue="368" />
24 <dimension name="contrast" xvalue="0" />
29 <dimension name="weight" xvalue="1000" />
30 <dimension name="contrast" xvalue="0" />
35 <dimension name="weight" xvalue="1000" />
36 <dimension name="contrast" xvalue="100" />
41 <dimension name="weight" xvalue="0" />
42 <dimension name="contrast" xvalue="100" />
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| /external/eigen/unsupported/test/ |
| D | cxx11_tensor_image_patch.cpp | 21 VERIFY_IS_EQUAL(tensor.dimension(0), tensor_row_major.dimension(3)); in test_simple_patch()
22 VERIFY_IS_EQUAL(tensor.dimension(1), tensor_row_major.dimension(2)); in test_simple_patch()
23 VERIFY_IS_EQUAL(tensor.dimension(2), tensor_row_major.dimension(1)); in test_simple_patch()
24 VERIFY_IS_EQUAL(tensor.dimension(3), tensor_row_major.dimension(0)); in test_simple_patch()
29 VERIFY_IS_EQUAL(single_pixel_patch.dimension(0), 2); in test_simple_patch()
30 VERIFY_IS_EQUAL(single_pixel_patch.dimension(1), 1); in test_simple_patch()
31 VERIFY_IS_EQUAL(single_pixel_patch.dimension(2), 1); in test_simple_patch()
32 VERIFY_IS_EQUAL(single_pixel_patch.dimension(3), 3*5); in test_simple_patch()
33 VERIFY_IS_EQUAL(single_pixel_patch.dimension(4), 7); in test_simple_patch()
38 VERIFY_IS_EQUAL(single_pixel_patch_row_major.dimension(0), 7); in test_simple_patch()
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| D | cxx11_tensor_patch.cpp | 32 VERIFY_IS_EQUAL(no_patch.dimension(0), 1); in test_simple_patch()
33 VERIFY_IS_EQUAL(no_patch.dimension(1), 1); in test_simple_patch()
34 VERIFY_IS_EQUAL(no_patch.dimension(2), 1); in test_simple_patch()
35 VERIFY_IS_EQUAL(no_patch.dimension(3), 1); in test_simple_patch()
36 VERIFY_IS_EQUAL(no_patch.dimension(4), tensor.size()); in test_simple_patch()
38 VERIFY_IS_EQUAL(no_patch.dimension(0), tensor.size()); in test_simple_patch()
39 VERIFY_IS_EQUAL(no_patch.dimension(1), 1); in test_simple_patch()
40 VERIFY_IS_EQUAL(no_patch.dimension(2), 1); in test_simple_patch()
41 VERIFY_IS_EQUAL(no_patch.dimension(3), 1); in test_simple_patch()
42 VERIFY_IS_EQUAL(no_patch.dimension(4), 1); in test_simple_patch()
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| D | cxx11_tensor_volume_patch.cpp | 15 VERIFY_IS_EQUAL(single_voxel_patch.dimension(0), 4); in test_single_voxel_patch()
16 VERIFY_IS_EQUAL(single_voxel_patch.dimension(1), 1); in test_single_voxel_patch()
17 VERIFY_IS_EQUAL(single_voxel_patch.dimension(2), 1); in test_single_voxel_patch()
18 VERIFY_IS_EQUAL(single_voxel_patch.dimension(3), 1); in test_single_voxel_patch()
19 VERIFY_IS_EQUAL(single_voxel_patch.dimension(4), 2 * 3 * 5); in test_single_voxel_patch()
20 VERIFY_IS_EQUAL(single_voxel_patch.dimension(5), 7); in test_single_voxel_patch()
24 VERIFY_IS_EQUAL(single_voxel_patch_row_major.dimension(0), 7); in test_single_voxel_patch()
25 VERIFY_IS_EQUAL(single_voxel_patch_row_major.dimension(1), 2 * 3 * 5); in test_single_voxel_patch()
26 VERIFY_IS_EQUAL(single_voxel_patch_row_major.dimension(2), 1); in test_single_voxel_patch()
27 VERIFY_IS_EQUAL(single_voxel_patch_row_major.dimension(3), 1); in test_single_voxel_patch()
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| D | cxx11_tensor_shuffling.cpp | 31 VERIFY_IS_EQUAL(no_shuffle.dimension(0), 2); in test_simple_shuffling()
32 VERIFY_IS_EQUAL(no_shuffle.dimension(1), 3); in test_simple_shuffling()
33 VERIFY_IS_EQUAL(no_shuffle.dimension(2), 5); in test_simple_shuffling()
34 VERIFY_IS_EQUAL(no_shuffle.dimension(3), 7); in test_simple_shuffling()
53 VERIFY_IS_EQUAL(shuffle.dimension(0), 5); in test_simple_shuffling()
54 VERIFY_IS_EQUAL(shuffle.dimension(1), 7); in test_simple_shuffling()
55 VERIFY_IS_EQUAL(shuffle.dimension(2), 3); in test_simple_shuffling()
56 VERIFY_IS_EQUAL(shuffle.dimension(3), 2); in test_simple_shuffling()
98 VERIFY_IS_EQUAL(result.dimension(0), 5); in test_expr_shuffling()
99 VERIFY_IS_EQUAL(result.dimension(1), 7); in test_expr_shuffling()
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| D | cxx11_tensor_reverse.cpp | 33 VERIFY_IS_EQUAL(reversed_tensor.dimension(0), 2); in test_simple_reverse()
34 VERIFY_IS_EQUAL(reversed_tensor.dimension(1), 3); in test_simple_reverse()
35 VERIFY_IS_EQUAL(reversed_tensor.dimension(2), 5); in test_simple_reverse()
36 VERIFY_IS_EQUAL(reversed_tensor.dimension(3), 7); in test_simple_reverse()
55 VERIFY_IS_EQUAL(reversed_tensor.dimension(0), 2); in test_simple_reverse()
56 VERIFY_IS_EQUAL(reversed_tensor.dimension(1), 3); in test_simple_reverse()
57 VERIFY_IS_EQUAL(reversed_tensor.dimension(2), 5); in test_simple_reverse()
58 VERIFY_IS_EQUAL(reversed_tensor.dimension(3), 7); in test_simple_reverse()
78 VERIFY_IS_EQUAL(reversed_tensor.dimension(0), 2); in test_simple_reverse()
79 VERIFY_IS_EQUAL(reversed_tensor.dimension(1), 3); in test_simple_reverse()
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| /external/tensorflow/tensorflow/compiler/xla/g3doc/ |
| D | shapes.md | 14 The dimension numbers are arbitrary labels for convenience. The order of
15 these dimension numbers does not imply a particular minor/major ordering in
18 * By convention, dimensions are listed in increasing order of dimension
20 dimension 0 has size `A`, dimension 1 has size `B` and dimension 2 has size
24 dimension -1 is the last dimension (equivalent to `N-1` for an `N`
26 above, dimension -1 has size `C`, dimension -2 has size `B` and so on.
31 * dimension 0: `y`
32 * dimension 1: `x`
36 * dimension 0: `z`
37 * dimension 1: `y`
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| D | broadcasting.md | 60 broadcasting tuple specifies which dimension(s) in the **higher-rank** array to
64 a vector of dimension (3) to a matrix of dimensions (2,3). *Without specifying
66 addition, specify the broadcasting dimension to be (1), meaning the vector's
67 dimension is matched to dimension 1 of the matrix. In 2D, if dimension 0 is
68 considered as rows and dimension 1 as columns, this means that each element of
74 As a more complex example, consider adding a 3-element vector (dimension (3)) to
78 (1) A broadcasting dimension of 1 can be used. Each vector element becomes a
85 (2) A broadcasting dimension of 0 can be used. Each vector element becomes a row
92 > Note: when adding a 2x3 matrix to a 3-element vector, a broadcasting dimension
110 example, for an array with dimensions MxNxPxQ, a vector with dimension T can be
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| /external/google-fruit/extras/benchmark/tables/ |
| D | fruit_wiki.yml | 5 dimension: "num_bindings"
10 dimension: "num_classes"
15 dimension: "compiler_name"
30 dimension: "compile_time"
41 dimension: "compile_time"
52 dimension: "Full injection time"
63 dimension: "Total for setup"
74 dimension: "Total per request"
85 dimension: "Total"
96 dimension: "name"
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| D | fruit_internal.yml | 5 dimension: "num_bindings"
10 dimension: "num_classes"
15 dimension: "compiler_name"
28 dimension: "compile_time"
39 dimension: "compile_time"
50 dimension: "compile_time"
61 dimension: "Full injection time"
72 dimension: "componentNormalizationTime"
83 dimension: "Total for setup"
94 dimension: "Total per request"
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| /external/tensorflow/tensorflow/core/util/ |
| D | tensor_format.h | 29 // The mnemonics specify the meaning of each tensor dimension sorted from
44 // as NCHW, except that the size of the Channels dimension is divided by 4,
45 // and a new dimension of size 4 is appended, which packs 4 adjacent channel
52 // Similar to NHWC, but the size of the W dimension is divided by 4, and a
53 // new dimension of size 4 is appended, which packs 4 adjacent activations
54 // in the width dimension.
71 // The mnemonics specify the meaning of each tensor dimension sorted
86 // of the Input Channels dimension is divided by 4, and a new dimension of
121 // since it just a component of the width dimension. in GetTensorSpatialDims()
161 // Returns the index of the batch dimension.
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| /external/libxml2/test/schemas/ |
| D | bug306806_0.xml | 9 <radius dimension='L' Length='inch'> 1.375 </radius>
10 <length dimension='L' Length='inch'> 30.250 </length>
12 <translation dimension='L' Length='inch'> -13.125 0.000 0.000</translation>
13 <rotation dimension='L' Length='degree'> -0.000 90.000 0.000</rotation>
24 <radius dimension='L' Length='inch'> 1.375 </radius>
25 <base1 dimension='L' Length='inch'> 0 0 0 </base1>
26 <base2 dimension='L' Length='inch'> 1.1 1.1 1.1 </base2>
36 <radius dimension='L' Length='inch'> 1.375 </radius>
37 <base1 dimension='L' Length='inch'> 0 0 0 </base1>
38 <base2 dimension='L' Length='inch'> 1.1 1.1 1.1 </base2>
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| /external/tensorflow/tensorflow/compiler/xla/service/ |
| D | dynamic_dimension_inference.cc | 80 HloInstruction* operand, ShapeIndex index, int64 dimension,
86 // Pass through a dynamic dimension from the input to the output with the same
100 hlo, [&](HloInstruction* operand, ShapeIndex index, int64 dimension, in DefaultAction()
103 "Asked to propagate a dynamic dimension from hlo ", in DefaultAction()
104 operand->ToString(), "@", index.ToString(), "@", dimension, in DefaultAction()
112 hlo, [&](HloInstruction* operand, ShapeIndex index, int64 dimension, in HandleGetTupleElement() argument
117 parent_->SetDynamicSize(hlo, new_index, dimension, dynamic_size); in HandleGetTupleElement()
125 hlo, [&](HloInstruction*, ShapeIndex index, int64 dimension, in HandleTuple() argument
128 parent_->SetDynamicSize(hlo, index, dimension, dynamic_size); in HandleTuple()
135 hlo, [&](HloInstruction* operand, ShapeIndex index, int64 dimension, in HandleBroadcast() argument
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| /external/javaparser/javaparser-core/src/main/java/com/github/javaparser/ast/ |
| D | ArrayCreationLevel.java | 50 private Expression dimension; field in ArrayCreationLevel
58 public ArrayCreationLevel(int dimension) { in ArrayCreationLevel() argument
59 this(null, new IntegerLiteralExpr("" + dimension), new NodeList<>()); in ArrayCreationLevel()
62 public ArrayCreationLevel(Expression dimension) { in ArrayCreationLevel() argument
63 this(null, dimension, new NodeList<>()); in ArrayCreationLevel()
67 public ArrayCreationLevel(Expression dimension, NodeList<AnnotationExpr> annotations) { in ArrayCreationLevel() argument
68 this(null, dimension, annotations); in ArrayCreationLevel()
75 …public ArrayCreationLevel(TokenRange tokenRange, Expression dimension, NodeList<AnnotationExpr> an… in ArrayCreationLevel() argument
77 setDimension(dimension); in ArrayCreationLevel()
95 * Sets the dimension
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| /external/tensorflow/tensorflow/contrib/timeseries/python/timeseries/state_space_models/ |
| D | kalman_filter.py | 88 estimated_state: A prior mean over states [batch size x state dimension]
91 the state dimension).
98 given [batch size x observation dimension]
99 observation_model: The [batch size x observation dimension x model state
100 dimension] Tensor indicating how a particular state is mapped to
102 observation_noise: A [batch size x observation dimension x observation
103 dimension] Tensor or [observation dimension x observation dimension]
158 transition_matrices: A [batch size, state dimension, state dimension]
184 estimate [batch size x state dimension x state dimension]
185 transition_matrices: A [batch size, state dimension, state dimension]
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| /external/tensorflow/tensorflow/compiler/xla/ |
| D | layout_util.h | 37 // Creates a layout with the given minor-to-major dimension order. (This is a
101 // * R2+: equivalent to column-major. Dimension 0 is the minor, dimension 1 is
102 // more major, and so on until dimension N-1 which is the major.
107 // * R2+: equivalent to row-major. Dimension 0 is the major, dimension 1 is
108 // more minor, and so on until dimension N-1 which is the minor.
137 // Major(0) is the most major logical dimension number, Major(1) is the
138 // second-most-major logical dimension number and so on.
140 // This can be used to translate physical dimension numbers to logical
141 // dimension numbers. Assume that we are numbering the physical dimensions so
142 // that the most major physical dimension has physical dimension number 0 and
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| /external/apache-commons-math/src/main/java/org/apache/commons/math/ode/ |
| D | FirstOrderConverter.java | 29 * <p>The transformation is done by changing the n dimension state
30 * vector to a 2n dimension vector, where the first n components are
63 /** second order problem dimension. */
64 private final int dimension; field in FirstOrderConverter
81 dimension = equations.getDimension(); in FirstOrderConverter()
82 z = new double[dimension]; in FirstOrderConverter()
83 zDot = new double[dimension]; in FirstOrderConverter()
84 zDDot = new double[dimension]; in FirstOrderConverter()
87 /** Get the dimension of the problem.
88 * <p>The dimension of the first order problem is twice the
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