| /external/tensorflow/tensorflow/core/api_def/base_api/ |
| D | api_def_NearestNeighbors.pbtxt | 11 name: "centers"
13 Matrix of shape (m, d). Rows are assumed to be centers.
19 Number of nearest centers to return for each point. If k is larger than m, then
20 only m centers are returned.
26 Matrix of shape (n, min(m, k)). Each row contains the indices of the centers
37 summary: "Selects the k nearest centers for each point."
39 Rows of points are assumed to be input points. Rows of centers are assumed to be
40 the list of candidate centers. For each point, the k centers that have least L2
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| D | api_def_ResizeNearestNeighbor.pbtxt | 26 If true, the centers of the 4 corner pixels of the input and output tensors are
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| D | api_def_ResizeNearestNeighborGrad.pbtxt | 27 If true, the centers of the 4 corner pixels of the input and grad tensors are
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| D | api_def_ResizeBilinearGrad.pbtxt | 28 If true, the centers of the 4 corner pixels of the input and grad tensors are
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| D | api_def_ResizeBicubicGrad.pbtxt | 28 If true, the centers of the 4 corner pixels of the input and grad tensors are
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| D | api_def_QuantizedResizeBilinear.pbtxt | 26 If true, the centers of the 4 corner pixels of the input and output tensors are
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| D | api_def_ResizeBicubic.pbtxt | 26 If true, the centers of the 4 corner pixels of the input and output tensors are
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| D | api_def_ResizeBilinear.pbtxt | 26 If true, the centers of the 4 corner pixels of the input and output tensors are
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| D | api_def_KMC2ChainInitialization.pbtxt | 26 the already sampled centers in the seed set. The op constructs one Markov chain
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| D | api_def_ExtractVolumePatches.pbtxt | 28 1-D of length 5. How far the centers of two consecutive patches are in
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| D | api_def_ExtractImagePatches.pbtxt | 27 How far the centers of two consecutive patches are in
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| D | api_def_ResizeArea.pbtxt | 26 If true, the centers of the 4 corner pixels of the input and output tensors are
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| /external/webp/src/enc/ |
| D | analysis_enc.c | 77 const int centers[NUM_MB_SEGMENTS], in SetSegmentAlphas()
80 int min = centers[0], max = centers[0]; in SetSegmentAlphas()
85 if (min > centers[n]) min = centers[n]; in SetSegmentAlphas()
86 if (max < centers[n]) max = centers[n]; in SetSegmentAlphas()
92 const int alpha = 255 * (centers[n] - mid) / (max - min); in SetSegmentAlphas()
93 const int beta = 255 * (centers[n] - min) / (max - min); in SetSegmentAlphas()
139 int centers[NUM_MB_SEGMENTS]; in AssignSegments() local
160 centers[k] = min_a + (n * range_a) / (2 * nb); in AssignSegments()
175 while (n + 1 < nb && abs(a - centers[n + 1]) < abs(a - centers[n])) { in AssignSegments()
192 displaced += abs(centers[n] - new_center); in AssignSegments()
[all …]
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| /external/tensorflow/tensorflow/core/kernels/ |
| D | clustering_ops.cc | 316 const Eigen::Map<const MatrixXfRowMajor> centers( in Compute() local
339 0.5 * centers.rowwise().squaredNorm(); in Compute()
404 FindKNearestCenters(k, points_shard, points_half_squared_norm, centers, in Compute()
429 const Eigen::Ref<const MatrixXfRowMajor>& centers, in FindKNearestCenters() argument
433 DCHECK_LE(k, centers.rows()); in FindKNearestCenters()
434 if (centers.rows() <= kNearestNeighborsCentersMaxBlockSize) { in FindKNearestCenters()
435 FindKNearestCentersOneBlock(k, points, points_half_squared_norm, centers, in FindKNearestCenters()
440 FindKNearestCentersBlockwise(k, points, points_half_squared_norm, centers, in FindKNearestCenters()
450 const Eigen::Ref<const MatrixXfRowMajor>& centers, in FindKNearestCentersOneBlock() argument
454 DCHECK_LE(k, centers.rows()); in FindKNearestCentersOneBlock()
[all …]
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| D | clustering_ops_test.cc | 176 Tensor centers(DT_FLOAT, TensorShape({num_centers, num_dims})); in SetUpNearestNeighbors() local
179 centers.flat<float>().setRandom(); in SetUpNearestNeighbors()
184 .Input(test::graph::Constant(g, centers)) in SetUpNearestNeighbors()
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| /external/tensorflow/tensorflow/core/ops/compat/ops_history_v1/ |
| D | NearestNeighbors.pbtxt | 8 name: "centers"
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| /external/tensorflow/tensorflow/core/ops/compat/ops_history_v2/ |
| D | NearestNeighbors.pbtxt | 8 name: "centers"
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| /external/tensorflow/tensorflow/python/keras/distribute/ |
| D | keras_image_model_correctness_test.py | 75 centers = np.random.randn(num_classes, *shape)
84 features.append(centers[label] + offset)
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| /external/libjpeg-turbo/simd/x86_64/ |
| D | jdsample-sse2.asm | 42 ; The upsampling algorithm is linear interpolation between pixel centers,
44 ; speed and visual quality. The centers of the output pixels are 1/4 and 3/4
45 ; of the way between input pixel centers.
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| /external/libjpeg-turbo/simd/i386/ |
| D | jdsample-sse2.asm | 41 ; The upsampling algorithm is linear interpolation between pixel centers,
43 ; speed and visual quality. The centers of the output pixels are 1/4 and 3/4
44 ; of the way between input pixel centers.
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| D | jdsample-mmx.asm | 41 ; The upsampling algorithm is linear interpolation between pixel centers,
43 ; speed and visual quality. The centers of the output pixels are 1/4 and 3/4
44 ; of the way between input pixel centers.
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| /external/skqp/src/shaders/gradients/ |
| D | SkTwoPointConicalGradient.cpp | 70 const SkPoint centers[2] = { c0 , c1 }; in Create() local
73 if (!gradientMatrix.setPolyToPoly(centers, unitvec, 2)) { in Create()
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| /external/skia/src/gpu/gradients/ |
| D | GrTwoPointConicalGradientLayout.fp | 234 // radii and centers.
247 // Make sure that the centers are different
252 // Make sure that the centers are different
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| /external/skia/src/shaders/gradients/ |
| D | SkTwoPointConicalGradient.cpp | 70 const SkPoint centers[2] = { c0 , c1 }; in Create() local
73 if (!gradientMatrix.setPolyToPoly(centers, unitvec, 2)) { in Create()
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| /external/skqp/src/gpu/gradients/ |
| D | GrTwoPointConicalGradientLayout.fp | 244 // radii and centers.
257 // Make sure that the centers are different
262 // Make sure that the centers are different
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