| /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_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_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_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_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_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_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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| /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()
149 int centers[NUM_MB_SEGMENTS]; in AssignSegments() local
170 centers[k] = min_a + (n * range_a) / (2 * nb); in AssignSegments()
185 while (n + 1 < nb && abs(a - centers[n + 1]) < abs(a - centers[n])) { in AssignSegments()
202 displaced += abs(centers[n] - new_center); in AssignSegments()
[all …]
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| /external/tensorflow/tensorflow/contrib/learn/python/learn/estimators/ |
| D | kmeans_test.py | 61 def make_random_points(centers, num_points, max_offset=20): argument
62 num_centers, num_dims = centers.shape
66 return (centers[assignments] + offsets, assignments, np.add.reduce(
349 centers = normalize(self.kmeans.clusters())
350 centers = centers[centers[:, 0].argsort()]
352 self.assertAllClose(centers, true_centers, atol=0.04)
356 centers = normalize(self.kmeans.clusters())
357 true_transform = 1 - cosine_similarity(self.points, centers)
365 centers = normalize(self.kmeans.clusters())
371 centers[assignments],
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| /external/tensorflow/tensorflow/contrib/factorization/python/ops/ |
| D | kmeans_test.py | 61 def make_random_points(centers, num_points, max_offset=20): argument
62 num_centers, num_dims = centers.shape
66 return (centers[assignments] + offsets, assignments, np.add.reduce(
381 centers = normalize(self.kmeans.cluster_centers())
382 centers = centers[centers[:, 0].argsort()]
384 self.assertAllClose(centers, true_centers, atol=0.04)
388 centers = normalize(self.kmeans.cluster_centers())
389 true_transform = 1 - cosine_similarity(self.points, centers)
398 centers = normalize(self.kmeans.cluster_centers())
404 centers[assignments],
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| D | gmm_ops_test.py | 46 self.centers = [[1, 1], [-1, 0.5], [2, 1]]
48 self.num_examples, self.centers)
73 def make_data_from_centers(num_vectors, centers): argument
86 current_class = np.random.random_integers(0, len(centers) - 1)
88 np.random.normal(centers[current_class][0],
90 np.random.normal(centers[current_class][1], np.random.random_sample())
93 return np.asarray(vectors), len(centers)
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| D | gmm_test.py | 78 def make_random_points(centers, num_points): argument
79 num_centers, num_dims = centers.shape
83 points = centers[assignments] + offsets
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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()
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| D | clustering_ops_test.cc | 179 Tensor centers(DT_FLOAT, TensorShape({num_centers, num_dims})); in SetUpNearestNeighbors() local
182 centers.flat<float>().setRandom(); in SetUpNearestNeighbors()
187 .Input(test::graph::Constant(g, centers)) in SetUpNearestNeighbors()
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| /external/tensorflow/tensorflow/contrib/factorization/g3doc/ |
| D | kmeans.md | 4 centers that minimizes the inertia, defined as $$ \sum_{i=0}^{n} \min_{\mu_j \in
13 through the full set of inputs to compute a new set of cluster centers.
18 is a stochastic variant of Lloyd's algorithm which updates the cluster centers
28 provides a very fast seeding method that provides high quality centers
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| /external/tensorflow/tensorflow/contrib/distribute/python/ |
| D | keras_image_model_correctness_test.py | 63 centers = np.random.randn(num_classes, *shape)
72 features.append(centers[label] + offset)
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| /external/libjpeg-turbo/simd/x86_64/ |
| D | jdsample-sse2.asm | 43 ; The upsampling algorithm is linear interpolation between pixel centers,
45 ; speed and visual quality. The centers of the output pixels are 1/4 and 3/4
46 ; of the way between input pixel centers.
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| /external/libjpeg-turbo/simd/i386/ |
| D | jdsample-sse2.asm | 43 ; The upsampling algorithm is linear interpolation between pixel centers,
45 ; speed and visual quality. The centers of the output pixels are 1/4 and 3/4
46 ; of the way between input pixel centers.
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| D | jdsample-mmx.asm | 43 ; The upsampling algorithm is linear interpolation between pixel centers,
45 ; speed and visual quality. The centers of the output pixels are 1/4 and 3/4
46 ; 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/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 | 244 // radii and centers.
257 // Make sure that the centers are different
262 // Make sure that the centers are different
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