| /external/webp/src/enc/ |
| analysis_enc.c | 77 const int centers[NUM_MB_SEGMENTS],
80 int min = centers[0], max = centers[0];
85 if (min > centers[n]) min = centers[n];
86 if (max < centers[n]) max = centers[n];
92 const int alpha = 255 * (centers[n] - mid) / (max - min);
93 const int beta = 255 * (centers[n] - min) / (max - min);
146 // array bounds of 'centers' with some compilers (noticed with gcc-4.9)
149 int centers[NUM_MB_SEGMENTS]; local
[all...] |
| /external/tensorflow/tensorflow/contrib/factorization/kernels/ |
| clustering_ops.cc | 297 InvalidArgument("Input centers should be a matrix."));
315 const Eigen::Map<const MatrixXfRowMajor> centers(
338 0.5 * centers.rowwise().squaredNorm();
342 // sharding the points and centers as follows:
344 // 1. Centers are sharded such that each block of centers has at most
347 // block of centers. The block size of points is chosen such that the
351 // are reduced to a set of k nearest centers as soon as possible. This
368 num_centers) /* centers in a block */
371 // The memory needed for storing the centers being processed. This is share
[all...] |
| clustering_ops_test.cc | 35 // Number of centers for tests.
179 Tensor centers(DT_FLOAT, TensorShape({num_centers, num_dims}));
182 centers.flat<float>().setRandom();
187 .Input(test::graph::Constant(g, centers))
|
| /external/tensorflow/tensorflow/contrib/factorization/python/ops/ |
| 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):
74 """Generates 2-dimensional data with random centers.
78 centers: a list of random 2-dimensional centers.
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)
[all...] |
| kmeans_test.py | 60 def make_random_points(centers, num_points, max_offset=20):
61 num_centers, num_dims = centers.shape
65 return (centers[assignments] + offsets, assignments, np.add.reduce(
220 # Make a call to fit to initialize the cluster centers.
342 centers = normalize(self.kmeans.cluster_centers())
343 centers = centers[centers[:, 0].argsort()]
345 self.assertAllClose(centers, true_centers, atol=0.04)
349 centers = normalize(self.kmeans.cluster_centers()
[all...] |
| gmm_test.py | 78 def make_random_points(centers, num_points):
79 num_centers, num_dims = centers.shape
83 points = centers[assignments] + offsets
|
| /external/tensorflow/tensorflow/contrib/learn/python/learn/estimators/ |
| kmeans_test.py | 62 def make_random_points(centers, num_points, max_offset=20):
63 num_centers, num_dims = centers.shape
67 return (centers[assignments] + offsets, assignments, np.add.reduce(
228 # Make a call to fit to initialize the cluster centers.
350 centers = normalize(self.kmeans.clusters())
351 centers = centers[centers[:, 0].argsort()]
353 self.assertAllClose(centers, true_centers, atol=0.04)
357 centers = normalize(self.kmeans.clusters()
[all...] |
| dynamic_rnn_estimator_test.py | 657 # Create examples by choosing 'centers' and adding uniform noise.
658 centers = math_ops.matmul(
668 sequences = centers + noise
|
| /external/opencv/ml/src/ |
| ml_inner_functions.cpp | 267 CvMat* centers = _centers; local
286 CV_CALL(centers = cvCreateMat (num_of_clusters, dim, CV_32FC1));
295 CV_CALL(centers = cvCreateMat (num_of_clusters, dim, CV_64FC1));
308 CV_CALL(cvGetCol (centers, ¢ers_comp, i));
314 if( (cvGetErrStatus () < 0) || (centers != _centers) )
315 cvReleaseMat (¢ers);
317 return _centers ? _centers : centers;
[all...] |
| mlem.cpp | 576 CvMat* centers = 0; local
595 CV_CALL( centers = cvCreateMat( nclusters, dims, CV_64FC1 ));
602 CV_CALL( cvConvert( centers0, centers ));
625 const double* c = (double*)(centers->data.ptr + k*centers->step);
658 CV_SWAP( centers, old_centers, temp );
659 cvZero( centers );
662 // update centers
667 double* c = (double*)(centers->data.ptr + k*centers->step)
[all...] |
| _ml.h | 250 /* Generates a set of classes centers in quantity <num_of_clusters> that are generated as
252 <data> should have horizontal orientation. If <centers> != NULL, the function doesn't
253 allocate any memory and stores generated centers in <centers>, returns <centers>.
254 If <centers> == NULL, the function allocates memory and creates the matrice. Centers
259 CvMat* centers CV_DEFAULT(0));
318 (labels and/or centers and/or probs) back to the output arrays */
321 const CvMat* centers, CvMat* dst_centers
[all...] |
| /external/opencv/cxcore/src/ |
| cxutils.cpp | 48 CvMat* centers = 0; local
94 CV_CALL( centers = cvCreateMat( cluster_count, dims, CV_64FC1 ));
98 // init centers
107 // computer centers
108 cvZero( centers );
115 double* c = (double*)(centers->data.ptr + k*centers->step);
140 double* c = (double*)(centers->data.ptr + k*centers->step);
178 double* c = (double*)(centers->data.ptr + k*centers->step)
[all...] |
| /external/opencv/cv/src/ |
| cvhough.cpp | 872 CvSeq *nz, *centers; local
892 CV_CALL( centers = cvCreateSeq( CV_32SC1, sizeof(CvSeq), sizeof(int), storage ));
970 cvSeqPush(centers, &base);
974 center_count = centers->total;
979 cvCvtSeqToArray( centers, sort_buf );
982 cvClearSeq( centers );
983 cvSeqPushMulti( centers, sort_buf, center_count );
992 for( i = 0; i < centers->total; i++ )
994 int ofs = *(int*)cvGetSeqElem( centers, i );
[all...] |
| cvcalibinit.cpp | 1022 CvPoint2D32f *centers = 0; local
[all...] |
| /external/skia/src/shaders/gradients/ |
| SkTwoPointConicalGradient.cpp | 66 const SkPoint centers[2] = { c0 , c1 }; local
69 if (!gradientMatrix.setPolyToPoly(centers, unitvec, 2)) {
|
| /external/skqp/src/shaders/gradients/ |
| SkTwoPointConicalGradient.cpp | 66 const SkPoint centers[2] = { c0 , c1 }; local
69 if (!gradientMatrix.setPolyToPoly(centers, unitvec, 2)) {
|
| /cts/apps/CtsVerifier/src/com/android/cts/verifier/sensors/ |
| RVCVXCheckAnalyzer.java | 820 MatOfPoint2f centers = new MatOfPoint2f(); local
851 gray, patternSize, centers, Calib3d.CALIB_CB_ASYMMETRIC_GRID);
859 Calib3d.drawChessboardCorners(frame, patternSize, centers, true);
866 Calib3d.solvePnP(grid, centers, camMat, coeff, rvec, tvec,
881 double error = Core.norm(centers, reprojCenters, Core.NORM_L2);
[all...] |
| /external/libjpeg-turbo/simd/ |
| jdsample-mmx.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.
|
| jdsample-sse2-64.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.
|
| 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.
|
| /external/tensorflow/tensorflow/contrib/losses/python/metric_learning/ |
| metric_loss_ops_test.py | 411 # Expose cluster centers, i.e. medoids.
413 # Expose indices of chosen cluster centers.
521 n_samples=n_samples, centers=n_clusters)
|
| /prebuilts/gcc/linux-x86/host/x86_64-w64-mingw32-4.8/x86_64-w64-mingw32/include/ |
| d3d.h | [all...] |
| /cts/apps/CtsVerifier/libs/ |
| opencv3-android.jar | |
