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  /frameworks/base/core/java/android/hardware/camera2/
CaptureResult.java     [all...]
  /frameworks/base/core/java/android/widget/
LinearLayout.java 674 * Returns the desired weights sum.
685 * Defines the desired weights sum. If unspecified the weights sum is computed
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  /frameworks/support/v7/appcompat/src/main/java/androidx/appcompat/widget/
LinearLayoutCompat.java 522 * Returns the desired weights sum.
533 * Defines the desired weights sum. If unspecified the weights sum is computed
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  /external/tensorflow/tensorflow/contrib/bayesflow/python/ops/
layers_conv_variational.py     [all...]
  /external/freetype/include/freetype/
ftdriver.h 118 * be the more critical direction anyway; the weights and spacing of
126 * 2) Alignment in the vertical direction: Weights and spacing along the
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  /external/icu/android_icu4j/src/main/java/android/icu/text/
AlphabeticIndex.java     [all...]
  /external/icu/android_icu4j/src/main/tests/android/icu/dev/test/collator/
CollationRegressionTest.java     [all...]
  /external/icu/icu4c/source/test/intltest/
regcoll.cpp 1097 // Compares and prints only 16-bit primary weights.
1123 // Compares and prints only 16-bit primary weights.
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  /external/icu/icu4j/main/classes/collate/src/com/ibm/icu/text/
AlphabeticIndex.java     [all...]
  /external/icu/icu4j/main/tests/collate/src/com/ibm/icu/dev/test/collator/
CollationRegressionTest.java     [all...]
  /external/libavc/common/arm/
ih264_weighted_bi_pred_a9q.s 390 @* weights for the weighted prediction in U and V
393 @* weights for the weighted prediction in U and V
  /external/libavc/common/armv8/
ih264_weighted_bi_pred_av8.s 357 //* weights for the weighted prediction in U and V
360 //* weights for the weighted prediction in U and V
  /external/libxcam/modules/isp/
aiq_handler.cpp 849 unsigned char* weights_map_ptr = (*out_weight_grid)->weights;
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  /external/llvm/lib/IR/
Metadata.cpp     [all...]
  /external/llvm/test/CodeGen/X86/
switch.ll 632 ; equal-sized sub-trees. When taking weights into account, case 70 becomes the
731 ; the left and {200,300} on the right. However, the jump table weights as much
  /external/mesa3d/src/mesa/main/
texcompress_bptc.c 511 static const uint8_t *weights[] = { local
516 weight = weights[index_bits][index];
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  /external/tensorflow/tensorflow/compiler/xla/python/
xla_client.py     [all...]
  /external/tensorflow/tensorflow/contrib/eager/python/
network_test.py     [all...]
  /external/tensorflow/tensorflow/contrib/kfac/python/ops/
fisher_blocks.py 367 [input_size, output_size] corresponding to layer's weights. If not, a
385 [input_size, output_size] corresponding to layer's weights. If not, a
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  /external/tensorflow/tensorflow/contrib/timeseries/python/timeseries/state_space_models/
state_space_model.py 100 bayesian_prior_weighting: If True, weights the prior (covariance_prior_fn)
101 based on an estimate of the full dataset size. If False, weights it
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  /external/tensorflow/tensorflow/docs_src/tutorials/
audio_recognition.md 74 This is saving out the current trained weights to a checkpoint file. If your
595 will adjust the number of computations and weights automatically to fit with
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  /external/tensorflow/tensorflow/python/ops/
resource_variable_ops.py 249 constraints or value constraints for layer weights). The function must
322 constraints or value constraints for layer weights). The function must
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  /prebuilts/go/darwin-x86/src/cmd/vendor/github.com/google/pprof/internal/graph/
graph.go 334 // Update edge weights for all edges in stack, avoiding double counting.
1020 // at the flat and cum weights of the node and the incoming/outgoing
  /prebuilts/go/linux-x86/src/cmd/vendor/github.com/google/pprof/internal/graph/
graph.go 334 // Update edge weights for all edges in stack, avoiding double counting.
1020 // at the flat and cum weights of the node and the incoming/outgoing
  /external/ImageMagick/ImageMagick/api/
effect.html 577 <p>Using a separable kernel would be faster, but the negative weights cancel out on the corners of the kernel producing often undesirable ringing in the filtered result; this can be avoided by using a 2D gaussian shaped image sharpening kernel instead.</p>

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