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  /external/libcxx/test/std/containers/unord/unord.multiset/unord.multiset.cnstr/
assign_copy.pass.cpp 77 assert(std::distance(c.begin(), c.end()) == c.size());
78 assert(std::distance(c.cbegin(), c.cend()) == c.size());
149 assert(std::distance(c.begin(), c.end()) == c.size());
150 assert(std::distance(c.cbegin(), c.cend()) == c.size());
203 assert(std::distance(c.begin(), c.end()) == c.size());
204 assert(std::distance(c.cbegin(), c.cend()) == c.size());
  /external/selinux/sepolgen/src/sepolgen/
matching.py 125 """Determine the 'distance' between 2 access vectors.
170 # Object class distance
174 # Permission distance
178 # distance and dir.
184 # Combine the perm and other distance
202 # Get the distance for each access vector
  /external/skia/src/core/
SkPoint.cpp 81 // Returns the square of the Euclidian distance to (dx,dy).
86 // Calculates the square of the Euclidian distance to (dx,dy) and stores it in
87 // *lengthSquared. Returns true if the distance is judged to be "nearly zero".
235 // function. Otherwise, it returns the distance to the closer of a and
240 // a and b and the distance to the segment is the same as distance
  /ndk/sources/cxx-stl/llvm-libc++/libcxx/test/containers/unord/unord.map/unord.map.cnstr/
assign_copy.pass.cpp 70 assert(std::distance(c.begin(), c.end()) == c.size());
71 assert(std::distance(c.cbegin(), c.cend()) == c.size());
133 assert(std::distance(c.begin(), c.end()) == c.size());
134 assert(std::distance(c.cbegin(), c.cend()) == c.size());
179 assert(std::distance(c.begin(), c.end()) == c.size());
180 assert(std::distance(c.cbegin(), c.cend()) == c.size());
  /ndk/sources/cxx-stl/llvm-libc++/libcxx/test/containers/unord/unord.multimap/unord.multimap.cnstr/
copy.pass.cpp 78 assert(std::distance(c.begin(), c.end()) == c.size());
79 assert(std::distance(c.cbegin(), c.cend()) == c.size());
132 assert(std::distance(c.begin(), c.end()) == c.size());
133 assert(std::distance(c.cbegin(), c.cend()) == c.size());
187 assert(std::distance(c.begin(), c.end()) == c.size());
188 assert(std::distance(c.cbegin(), c.cend()) == c.size());
  /ndk/sources/cxx-stl/llvm-libc++/libcxx/test/containers/unord/unord.multiset/unord.multiset.cnstr/
assign_copy.pass.cpp 77 assert(std::distance(c.begin(), c.end()) == c.size());
78 assert(std::distance(c.cbegin(), c.cend()) == c.size());
149 assert(std::distance(c.begin(), c.end()) == c.size());
150 assert(std::distance(c.cbegin(), c.cend()) == c.size());
203 assert(std::distance(c.begin(), c.end()) == c.size());
204 assert(std::distance(c.cbegin(), c.cend()) == c.size());
  /prebuilts/misc/common/swig/include/2.0.11/octave/
octiterators.swg 38 virtual ptrdiff_t distance(const OctSwigIterator &x) const
100 return x.distance(*this);
147 ptrdiff_t distance(const OctSwigIterator &iter) const
151 return std::distance(current, iters->get_current());
313 %catches(std::invalid_argument) OctSwigIterator::distance(const OctSwigIterator &x) const;
338 virtual ptrdiff_t distance(const OctSwigIterator &x) const;
  /prebuilts/python/linux-x86/2.7.5/lib/python2.7/site-packages/sepolgen/
matching.py 125 """Determine the 'distance' between 2 access vectors.
170 # Object class distance
174 # Permission distance
178 # distance and dir.
184 # Combine the perm and other distance
202 # Get the distance for each access vector
  /system/keymaster/include/keymaster/
serializable.h 230 bool advance_read(int distance) {
231 if (static_cast<size_t>(read_position_ + distance) <= write_position_) {
232 read_position_ += distance;
238 bool advance_write(int distance) {
239 if (static_cast<size_t>(write_position_ + distance) <= buffer_size_) {
240 write_position_ += distance;
  /external/ImageMagick/ImageMagick/api/
feature.html 103 <p>GetImageFeatures() returns features for each channel in the image in each of four directions (horizontal, vertical, left and right diagonals) for the specified distance. The features include the angular second moment, contrast, correlation, sum of squares: variance, inverse difference moment, sum average, sum varience, sum entropy, entropy, difference variance, difference entropy, information measures of correlation 1, information measures of correlation 2, and maximum correlation coefficient. You can access the red channel contrast, for example, like this:</p>
116 const size_t distance,ExceptionInfo *exception)
130 <dt>distance</dt>
131 <dd>the distance. </dd>
141 <p>Use HoughLineImage() in conjunction with any binary edge extracted image (we recommand Canny) to identify lines in the image. The algorithm accumulates counts for every white pixel for every possible orientation (for angles from 0 to 179 in 1 degree increments) and distance from the center of the image to the corner (in 1 px increments) and stores the counts in an accumulator matrix of angle vs distance. The size of the accumulator is 180x(diagonal/2). Next it searches this space for peaks in counts and converts the locations of the peaks to slope and intercept in the normal x,y input image space. Use the slope/intercepts to find the endpoints clipped to the bounds of the image. The lines are then drawn. The counts are a measure of the length of the lines</p>
176 <p>MeanShiftImage() delineate arbitrarily shaped clusters in the image. For each pixel, it visits all the pixels in the neighborhood specified by the window centered at the pixel and excludes those that are outside the radius=(window-1)/2 surrounding the pixel. From those pixels, it finds those that are within the specified color distance from the current mean, and computes a new x,y centroid from those coordinates and a new mean. This new x,y centroid is used as the center for a new window. This process iterates until it converges and the final mean is replaces the (original window center) pixel value. It repeats this process for the next pixel, etc., until it processes all pixels in the image. Results are typically better with colorspaces other than sRGB. We recommend YIQ, YUV or YCbCr.</p>
202 <dd>the color distance. </dd>
  /external/ImageMagick/MagickCore/
morphology.h 62 ChebyshevKernel, /* Distance Measuring Kernels */
99 VoronoiMorphology /* Distance matte channel copy nearest color */
  /external/ImageMagick/www/api/
feature.html 107 <p>GetImageFeatures() returns features for each channel in the image in each of four directions (horizontal, vertical, left and right diagonals) for the specified distance. The features include the angular second moment, contrast, correlation, sum of squares: variance, inverse difference moment, sum average, sum varience, sum entropy, entropy, difference variance, difference entropy, information measures of correlation 1, information measures of correlation 2, and maximum correlation coefficient. You can access the red channel contrast, for example, like this:</p>
120 const size_t distance,ExceptionInfo *exception)
134 <dt>distance</dt>
135 <dd>the distance. </dd>
145 <p>Use HoughLineImage() in conjunction with any binary edge extracted image (we recommand Canny) to identify lines in the image. The algorithm accumulates counts for every white pixel for every possible orientation (for angles from 0 to 179 in 1 degree increments) and distance from the center of the image to the corner (in 1 px increments) and stores the counts in an accumulator matrix of angle vs distance. The size of the accumulator is 180x(diagonal/2). Next it searches this space for peaks in counts and converts the locations of the peaks to slope and intercept in the normal x,y input image space. Use the slope/intercepts to find the endpoints clipped to the bounds of the image. The lines are then drawn. The counts are a measure of the length of the lines</p>
180 <p>MeanShiftImage() delineate arbitrarily shaped clusters in the image. For each pixel, it visits all the pixels in the neighborhood specified by the window centered at the pixel and excludes those that are outside the radius=(window-1)/2 surrounding the pixel. From those pixels, it finds those that are within the specified color distance from the current mean, and computes a new x,y centroid from those coordinates and a new mean. This new x,y centroid is used as the center for a new window. This process iterates until it converges and the final mean is replaces the (original window center) pixel value. It repeats this process for the next pixel, etc., until it processes all pixels in the image. Results are typically better with colorspaces other than sRGB. We recommend YIQ, YUV or YCbCr.</p>
206 <dd>the color distance. </dd>
feature.php 103 <p>GetImageFeatures() returns features for each channel in the image in each of four directions (horizontal, vertical, left and right diagonals) for the specified distance. The features include the angular second moment, contrast, correlation, sum of squares: variance, inverse difference moment, sum average, sum varience, sum entropy, entropy, difference variance, difference entropy, information measures of correlation 1, information measures of correlation 2, and maximum correlation coefficient. You can access the red channel contrast, for example, like this:</p>
116 const size_t distance,ExceptionInfo *exception)
130 <dt>distance</dt>
131 <dd>the distance. </dd>
141 <p>Use HoughLineImage() in conjunction with any binary edge extracted image (we recommand Canny) to identify lines in the image. The algorithm accumulates counts for every white pixel for every possible orientation (for angles from 0 to 179 in 1 degree increments) and distance from the center of the image to the corner (in 1 px increments) and stores the counts in an accumulator matrix of angle vs distance. The size of the accumulator is 180x(diagonal/2). Next it searches this space for peaks in counts and converts the locations of the peaks to slope and intercept in the normal x,y input image space. Use the slope/intercepts to find the endpoints clipped to the bounds of the image. The lines are then drawn. The counts are a measure of the length of the lines</p>
176 <p>MeanShiftImage() delineate arbitrarily shaped clusters in the image. For each pixel, it visits all the pixels in the neighborhood specified by the window centered at the pixel and excludes those that are outside the radius=(window-1)/2 surrounding the pixel. From those pixels, it finds those that are within the specified color distance from the current mean, and computes a new x,y centroid from those coordinates and a new mean. This new x,y centroid is used as the center for a new window. This process iterates until it converges and the final mean is replaces the (original window center) pixel value. It repeats this process for the next pixel, etc., until it processes all pixels in the image. Results are typically better with colorspaces other than sRGB. We recommend YIQ, YUV or YCbCr.</p>
202 <dd>the color distance. </dd>
  /external/ceres-solver/data/nist/
Chwirut2.dat 14 metal distance.
26 1 Predictor (x = metal distance)
  /external/compiler-rt/lib/builtins/arm/
switch16.S 22 // the distance from lr to the label, thus making the tables PIC.
29 // The table contains signed 2-byte sized elements which are 1/2 the distance
switch32.S 22 // the distance from lr to the label, thus making the tables PIC.
29 // The table contains signed 4-byte sized elements which are the distance
switch8.S 22 // the distance from lr to the label, thus making the tables PIC.
29 // The table contains signed byte sized elements which are 1/2 the distance
switchu8.S 22 // the distance from lr to the label, thus making the tables PIC.
29 // The table contains unsigned byte sized elements which are 1/2 the distance
  /external/libcxx/test/std/containers/associative/map/map.cons/
assign_initializer_list.pass.cpp 43 assert(distance(m.begin(), m.end()) == 3);
68 assert(distance(m.begin(), m.end()) == 3);
initializer_list.pass.cpp 39 assert(distance(m.begin(), m.end()) == 3);
60 assert(distance(m.begin(), m.end()) == 3);
initializer_list_compare.pass.cpp 39 assert(distance(m.begin(), m.end()) == 3);
61 assert(distance(m.begin(), m.end()) == 3);
iter_iter.pass.cpp 40 assert(distance(m.begin(), m.end()) == 3);
62 assert(distance(m.begin(), m.end()) == 3);
  /external/libcxx/test/std/containers/associative/map/map.modifiers/
insert_initializer_list.pass.cpp 41 assert(distance(m.begin(), m.end()) == 3);
64 assert(distance(m.begin(), m.end()) == 3);
  /external/libcxx/test/std/containers/associative/multiset/
insert_initializer_list.pass.cpp 30 assert(distance(m.begin(), m.end()) == m.size());
49 assert(distance(m.begin(), m.end()) == m.size());
  /external/libcxx/test/std/containers/associative/multiset/multiset.cons/
assign_initializer_list.pass.cpp 30 assert(distance(m.begin(), m.end()) == 6);
47 assert(distance(m.begin(), m.end()) == 6);

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