| /external/apache-commons-math/src/main/java/org/apache/commons/math/ode/ |
| 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 class:FirstOrderConverter
81 dimension = equations.getDimension();
82 z = new double[dimension];
83 zDot = new double[dimension];
84 zDDot = new double[dimension];
87 /** Get the dimension of the problem.
88 * <p>The dimension of the first order problem is twice th
[all...] |
| /external/apache-commons-math/src/main/java/org/apache/commons/math/random/ |
| UnitSphereRandomVectorGenerator.java | 37 * Space dimension.
39 private final int dimension; field in class:UnitSphereRandomVectorGenerator
42 * @param dimension Space dimension.
45 public UnitSphereRandomVectorGenerator(final int dimension,
47 this.dimension = dimension;
54 * @param dimension Space dimension.
56 public UnitSphereRandomVectorGenerator(final int dimension) {
[all...] |
| UncorrelatedRandomVectorGenerator.java | 67 * @param dimension dimension of the vectors to generate
71 public UncorrelatedRandomVectorGenerator(int dimension,
73 mean = new double[dimension];
74 standardDeviation = new double[dimension];
|
| /external/apache-commons-math/src/main/java/org/apache/commons/math/exception/ |
| DimensionMismatchException.java | 32 /** Correct dimension. */
33 private final int dimension; field in class:DimensionMismatchException
38 * @param wrong Wrong dimension.
39 * @param expected Expected dimension.
44 dimension = expected;
48 * @return the expected dimension.
51 return dimension;
|
| /external/eigen/unsupported/test/ |
| cxx11_tensor_layout_swap.cpp | 22 VERIFY_IS_EQUAL(tensor.dimension(0), tensor2.dimension(2));
23 VERIFY_IS_EQUAL(tensor.dimension(1), tensor2.dimension(1));
24 VERIFY_IS_EQUAL(tensor.dimension(2), tensor2.dimension(0));
43 VERIFY_IS_EQUAL(tensor.dimension(0), tensor2.dimension(2));
44 VERIFY_IS_EQUAL(tensor.dimension(1), tensor2.dimension(1))
[all...] |
| cxx11_tensor_patch.cpp | 32 VERIFY_IS_EQUAL(no_patch.dimension(0), 1);
33 VERIFY_IS_EQUAL(no_patch.dimension(1), 1);
34 VERIFY_IS_EQUAL(no_patch.dimension(2), 1);
35 VERIFY_IS_EQUAL(no_patch.dimension(3), 1);
36 VERIFY_IS_EQUAL(no_patch.dimension(4), tensor.size());
38 VERIFY_IS_EQUAL(no_patch.dimension(0), tensor.size());
39 VERIFY_IS_EQUAL(no_patch.dimension(1), 1);
40 VERIFY_IS_EQUAL(no_patch.dimension(2), 1);
41 VERIFY_IS_EQUAL(no_patch.dimension(3), 1);
42 VERIFY_IS_EQUAL(no_patch.dimension(4), 1)
[all...] |
| cxx11_tensor_volume_patch.cpp | 15 VERIFY_IS_EQUAL(single_voxel_patch.dimension(0), 4);
16 VERIFY_IS_EQUAL(single_voxel_patch.dimension(1), 1);
17 VERIFY_IS_EQUAL(single_voxel_patch.dimension(2), 1);
18 VERIFY_IS_EQUAL(single_voxel_patch.dimension(3), 1);
19 VERIFY_IS_EQUAL(single_voxel_patch.dimension(4), 2 * 3 * 5);
20 VERIFY_IS_EQUAL(single_voxel_patch.dimension(5), 7);
24 VERIFY_IS_EQUAL(single_voxel_patch_row_major.dimension(0), 7);
25 VERIFY_IS_EQUAL(single_voxel_patch_row_major.dimension(1), 2 * 3 * 5);
26 VERIFY_IS_EQUAL(single_voxel_patch_row_major.dimension(2), 1);
27 VERIFY_IS_EQUAL(single_voxel_patch_row_major.dimension(3), 1)
[all...] |
| cxx11_tensor_image_patch.cpp | 21 VERIFY_IS_EQUAL(tensor.dimension(0), tensor_row_major.dimension(3));
22 VERIFY_IS_EQUAL(tensor.dimension(1), tensor_row_major.dimension(2));
23 VERIFY_IS_EQUAL(tensor.dimension(2), tensor_row_major.dimension(1));
24 VERIFY_IS_EQUAL(tensor.dimension(3), tensor_row_major.dimension(0));
29 VERIFY_IS_EQUAL(single_pixel_patch.dimension(0), 2);
30 VERIFY_IS_EQUAL(single_pixel_patch.dimension(1), 1)
[all...] |
| cxx11_tensor_inflation.cpp | 31 VERIFY_IS_EQUAL(no_stride.dimension(0), 2);
32 VERIFY_IS_EQUAL(no_stride.dimension(1), 3);
33 VERIFY_IS_EQUAL(no_stride.dimension(2), 5);
34 VERIFY_IS_EQUAL(no_stride.dimension(3), 7);
53 VERIFY_IS_EQUAL(inflated.dimension(0), 3);
54 VERIFY_IS_EQUAL(inflated.dimension(1), 9);
55 VERIFY_IS_EQUAL(inflated.dimension(2), 9);
56 VERIFY_IS_EQUAL(inflated.dimension(3), 19);
|
| cxx11_tensor_shuffling.cpp | 31 VERIFY_IS_EQUAL(no_shuffle.dimension(0), 2);
32 VERIFY_IS_EQUAL(no_shuffle.dimension(1), 3);
33 VERIFY_IS_EQUAL(no_shuffle.dimension(2), 5);
34 VERIFY_IS_EQUAL(no_shuffle.dimension(3), 7);
53 VERIFY_IS_EQUAL(shuffle.dimension(0), 5);
54 VERIFY_IS_EQUAL(shuffle.dimension(1), 7);
55 VERIFY_IS_EQUAL(shuffle.dimension(2), 3);
56 VERIFY_IS_EQUAL(shuffle.dimension(3), 2);
98 VERIFY_IS_EQUAL(result.dimension(0), 5);
99 VERIFY_IS_EQUAL(result.dimension(1), 7)
[all...] |
| cxx11_tensor_reverse.cpp | 33 VERIFY_IS_EQUAL(reversed_tensor.dimension(0), 2);
34 VERIFY_IS_EQUAL(reversed_tensor.dimension(1), 3);
35 VERIFY_IS_EQUAL(reversed_tensor.dimension(2), 5);
36 VERIFY_IS_EQUAL(reversed_tensor.dimension(3), 7);
55 VERIFY_IS_EQUAL(reversed_tensor.dimension(0), 2);
56 VERIFY_IS_EQUAL(reversed_tensor.dimension(1), 3);
57 VERIFY_IS_EQUAL(reversed_tensor.dimension(2), 5);
58 VERIFY_IS_EQUAL(reversed_tensor.dimension(3), 7);
78 VERIFY_IS_EQUAL(reversed_tensor.dimension(0), 2);
79 VERIFY_IS_EQUAL(reversed_tensor.dimension(1), 3)
[all...] |
| cxx11_tensor_custom_op.cpp | 20 result[0] = input.dimension(0) * 2;
21 result[1] = input.dimension(1) * 2;
34 Eigen::DSizes<DenseIndex, 2> extents(output.dimension(0)-1, output.dimension(1)-1);
45 VERIFY_IS_EQUAL(result.dimension(0), 6);
46 VERIFY_IS_EQUAL(result.dimension(1), 10);
64 result[0] = input1.dimension(0);
65 result[1] = input2.dimension(1);
66 result[2] = input2.dimension(2);
77 for (int i = 0; i < output.dimension(2); ++i)
[all...] |
| cxx11_tensor_broadcasting.cpp | 30 VERIFY_IS_EQUAL(no_broadcast.dimension(0), 2);
31 VERIFY_IS_EQUAL(no_broadcast.dimension(1), 3);
32 VERIFY_IS_EQUAL(no_broadcast.dimension(2), 5);
33 VERIFY_IS_EQUAL(no_broadcast.dimension(3), 7);
52 VERIFY_IS_EQUAL(broadcast.dimension(0), 4);
53 VERIFY_IS_EQUAL(broadcast.dimension(1), 9);
54 VERIFY_IS_EQUAL(broadcast.dimension(2), 5);
55 VERIFY_IS_EQUAL(broadcast.dimension(3), 28);
82 VERIFY_IS_EQUAL(broadcast.dimension(0), 16);
83 VERIFY_IS_EQUAL(broadcast.dimension(1), 9)
[all...] |
| cxx11_tensor_ifft.cpp | 31 VERIFY_IS_EQUAL(tensor_after_fft.dimension(0), sequence_length);
32 VERIFY_IS_EQUAL(tensor_after_fft_ifft.dimension(0), sequence_length);
54 VERIFY_IS_EQUAL(tensor_after_fft.dimension(0), dim0);
55 VERIFY_IS_EQUAL(tensor_after_fft.dimension(1), dim1);
56 VERIFY_IS_EQUAL(tensor_after_fft_ifft.dimension(0), dim0);
57 VERIFY_IS_EQUAL(tensor_after_fft_ifft.dimension(1), dim1);
83 VERIFY_IS_EQUAL(tensor_after_fft.dimension(0), dim0);
84 VERIFY_IS_EQUAL(tensor_after_fft.dimension(1), dim1);
85 VERIFY_IS_EQUAL(tensor_after_fft.dimension(2), dim2);
86 VERIFY_IS_EQUAL(tensor_after_fft_ifft.dimension(0), dim0)
[all...] |
| cxx11_tensor_map.cpp | 52 VERIFY_IS_EQUAL(vec1.dimension(0), 6);
93 VERIFY_IS_EQUAL(mat3.dimension(0), 2);
94 VERIFY_IS_EQUAL(mat3.dimension(1), 3);
98 VERIFY_IS_EQUAL(mat4.dimension(0), 2);
99 VERIFY_IS_EQUAL(mat4.dimension(1), 3);
137 VERIFY_IS_EQUAL(mat3.dimension(0), 2);
138 VERIFY_IS_EQUAL(mat3.dimension(1), 3);
139 VERIFY_IS_EQUAL(mat3.dimension(2), 7);
143 VERIFY_IS_EQUAL(mat4.dimension(0), 2);
144 VERIFY_IS_EQUAL(mat4.dimension(1), 3)
[all...] |
| /external/webrtc/webrtc/modules/audio_processing/vad/ |
| gmm.cc | 24 int dimension,
26 for (int n = 0; n < dimension; ++n)
32 int dimension) {
34 for (int i = 0; i < dimension; ++i) {
36 for (int j = 0; j < dimension; j++)
45 if (gmm_parameters.dimension > kMaxDimension) {
54 RemoveMean(x, mean_vec, gmm_parameters.dimension, v);
55 double q = ComputeExponent(v, covar_inv, gmm_parameters.dimension) +
58 mean_vec += gmm_parameters.dimension;
59 covar_inv += gmm_parameters.dimension * gmm_parameters.dimension
[all...] |
| gmm.h | 23 // weight[n] = log(w[n]) - |dimension|/2 * log(2*pi) - 1/2 * log(det(cov[n]));
26 // pointer to the first element of a |num_mixtures|x|dimension| matrix
29 // pointer to the first element of a |num_mixtures|x|dimension|x|dimension|
34 int dimension; member in struct:webrtc::GmmParameters
41 // acceptable dimension by the following function -1 is returned.
|
| /external/javaparser/javaparser-core/src/main/java/com/github/javaparser/ast/ |
| ArrayCreationLevel.java | 50 private Expression dimension; field in class:ArrayCreationLevel
58 public ArrayCreationLevel(int dimension) {
59 this(null, new IntegerLiteralExpr("" + dimension), new NodeList<>());
62 public ArrayCreationLevel(Expression dimension) {
63 this(null, dimension, new NodeList<>());
67 public ArrayCreationLevel(Expression dimension, NodeList<AnnotationExpr> annotations) {
68 this(null, dimension, annotations);
75 public ArrayCreationLevel(TokenRange tokenRange, Expression dimension, NodeList<AnnotationExpr> annotations) {
77 setDimension(dimension);
95 * Sets the dimension
[all...] |
| /external/javaparser/javaparser-symbol-solver-testing/src/test/test_sourcecode/javaparser_new_src/javaparser-core/com/github/javaparser/ast/ |
| ArrayCreationLevel.java | 20 private Expression dimension; field in class:ArrayCreationLevel
23 public ArrayCreationLevel(Range range, Expression dimension, List<AnnotationExpr> annotations) {
25 setDimension(dimension);
37 public void setDimension(Expression dimension) {
38 this.dimension = dimension;
39 setAsParentNodeOf(dimension);
43 return dimension;
|
| /external/apache-commons-math/src/main/java/org/apache/commons/math/util/ |
| MultidimensionalCounter.java | 49 private final int dimension; field in class:MultidimensionalCounter
51 * Offset for each dimension.
63 * Index of last dimension.
74 private final int[] counter = new int[dimension];
92 for (int i = 0; i < dimension; i++) {
131 return /* Arrays.*/ copyOf(counter, dimension); // Java 1.5 does not support Arrays.copyOf()
135 * Get the current count in the selected dimension.
137 * @param dim Dimension index.
160 * @param size Counter sizes (number of slots in each dimension).
165 dimension = size.length
[all...] |
| /external/tensorflow/tensorflow/compiler/xla/ |
| index_util.cc | 35 << "indexing beyond extent in dimension " << i << ":"
49 // where L(0) is the most-minor dimension and L(n-1) the most-major. The
78 for (auto dimension : LayoutUtil::MinorToMajor(shape)) {
81 linear_index = multi_index[dimension];
82 scale = shape.dimensions(dimension);
85 linear_index += scale * multi_index[dimension];
86 scale *= shape.dimensions(dimension);
108 for (auto dimension : LayoutUtil::MinorToMajor(shape)) {
109 multi_index[dimension] =
110 (linear_index / divisor) % shape.dimensions(dimension);
[all...] |
| /external/libjpeg-turbo/java/org/libjpegturbo/turbojpeg/ |
| TJScalingFactor.java | 70 * Returns the scaled value of <code>dimension</code>. This function
72 * <code>ceil(dimension * scalingFactor)</code>.
74 * @param dimension width or height to multiply by this scaling factor
76 * @return the scaled value of <code>dimension</code>.
78 public int getScaled(int dimension) {
79 return (dimension * num + denom - 1) / denom;
|
| /external/tensorflow/tensorflow/core/util/ |
| 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
418 << dimension; local
443 << filter_tensor_format << ", " << dimension; local
[all...] |
| /external/tensorflow/tensorflow/lite/tools/optimize/ |
| quantization_utils.cc | 82 const std::vector<int>& dimension,
86 const int32_t channel_dim_size = dimension[channel_dim_index];
91 RuntimeShape tensor_dims{dimension[0], dimension[1], dimension[2],
92 dimension[3]};
95 for (indices[0] = 0; indices[0] < dimension[0]; indices[0]++) {
96 for (indices[1] = 0; indices[1] < dimension[1]; indices[1]++) {
97 for (indices[2] = 0; indices[2] < dimension[2]; indices[2]++) {
98 for (indices[3] = 0; indices[3] < dimension[3]; indices[3]++)
[all...] |
| /external/tensorflow/tensorflow/core/kernels/ |
| conv_2d.h | 107 kernel, output_backward, input_backward.dimension(2),
108 input_backward.dimension(1), col_stride, row_stride, col_dilation,
123 input, output_backward, kernel_backward.dimension(1),
124 kernel_backward.dimension(0), col_stride, row_stride, col_dilation,
158 merged_dims[0] = in.dimension(0); // spatial dimensions
160 merged_dims[0] *= in.dimension(i);
162 merged_dims[1] = in.dimension(NDIMS - 2); // input filters
163 merged_dims[2] = in.dimension(NDIMS - 1); // output filters
178 expanded_dims[out_index++] = in.dimension(spatial_dim);
183 in.dimension(kLastSpatialDim + shuffling_perm[merged_dim])
[all...] |
