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  /frameworks/rs/tests/java_api/ImageProcessing_jb/src/com/android/rs/image/
threshold.rs 30 // Store our coefficients here
65 //Now we need to normalize the weights because all our coefficients need to add up to one
threshold_half.rs 30 // Store our coefficients here
65 //Now we need to normalize the weights because all our coefficients need to add up to one
  /external/ImageMagick/MagickCore/
distort.h 28 coefficients for common methods.
  /external/adhd/cras/src/dsp/
biquad.h 18 * We use double during the coefficients calculation for better accurary, but
drc_math.h 96 /* Coefficients obtained from:
145 /* Coefficients obtained from:
169 /* Coefficients obtained from:
  /external/eigen/Eigen/src/SparseCore/
MappedSparseMatrix.h 20 * \param _Scalar the scalar type, i.e. the type of the coefficients
  /external/eigen/doc/
FixedSizeVectorizable.dox 28 The array of coefficients of a fixed-size Eigen object is a plain "static array", it is not dynamically allocated. For example, the data behind a Matrix4f is just a "float array[16]".
Pitfalls.dox 19 In this example, the type of C is not a MatrixXd but an abstract expression representing a matrix product and storing references to A and B. Therefore, the product of A*B will be carried out multiple times, once per iteration of the for loop. Moreover, if the coefficients of A or B change during the iteration, then C will evaluate to different values.
QuickStartGuide.dox 69 The second example starts by declaring a 3-by-3 matrix \c m which is initialized using the \link DenseBase::Random(Index,Index) Random() \endlink method with random values between -1 and 1. The next line applies a linear mapping such that the values are between 10 and 110. The function call \link DenseBase::Constant(Index,Index,const Scalar&) MatrixXd::Constant\endlink(3,3,1.2) returns a 3-by-3 matrix expression having all coefficients equal to 1.2. The rest is standard arithmetics.
71 The next line of the \c main function introduces a new type: \c VectorXd. This represents a (column) vector of arbitrary size. Here, the vector \c v is created to contain \c 3 coefficients which are left unitialized. The one but last line uses the so-called comma-initializer, explained in \ref TutorialAdvancedInitialization, to set all coefficients of the vector \c v to be as follows:
84 Now look back at the second example program. We presented two versions of it. In the version in the left column, the matrix is of type \c MatrixXd which represents matrices of arbitrary size. The version in the right column is similar, except that the matrix is of type \c Matrix3d, which represents matrices of a fixed size (here 3-by-3). Because the type already encodes the size of the matrix, it is not necessary to specify the size in the constructor; compare <tt>MatrixXd m(3,3)</tt> with <tt>Matrix3d m</tt>. Similarly, we have \c VectorXd on the left (arbitrary size) versus \c Vector3d on the right (fixed size). Note that here the coefficients of vector \c v are directly set in the constructor, though the same syntax of the left example could be used too.
  /external/eigen/unsupported/Eigen/src/Skyline/
SkylineMatrixBase.h 49 /**< This is equal to the number of coefficients, i.e. the number of
114 /** \returns the number of coefficients, which is \a rows()*cols().
120 /** \returns the number of nonzero coefficients which is in practice the number
121 * of stored coefficients. */
  /external/fec/
dtest.c 67 /* Generate set of coefficients
  /external/libopus/silk/
A2NLSF.c 28 /* Conversion between prediction filter coefficients and NLSFs */
125 /* Compute Normalized Line Spectral Frequencies (NLSFs) from whitening filter coefficients */
126 /* If not all roots are found, the a_Q16 coefficients are bandwidth expanded until convergence. */
129 opus_int32 *a_Q16, /* I/O Monic whitening filter coefficients in Q16 [d] */
  /external/libopus/silk/x86/
main_sse.h 43 opus_int *gain_Q7, /* O sum of absolute LTP coefficients */
50 const opus_int32 max_gain_Q7, /* I maximum sum of absolute LTP coefficients */
66 opus_int *gain_Q7, /* O sum of absolute LTP coefficients */
73 const opus_int32 max_gain_Q7, /* I maximum sum of absolute LTP coefficients */
  /external/libvpx/libvpx/vp9/encoder/
vp9_context_tree.h 46 // coefficients are quantized to 0, skippable is set to 0.
  /external/tensorflow/tensorflow/contrib/timeseries/python/timeseries/state_space_models/
varma.py 39 coefficients in the transition equation for the observed component, which
42 which further aids learning of the autoregressive coefficients when VARMA is in
89 # TODO(allenl): Evaluate parameter transformations for AR/MA coefficients
132 # Noise is broadcast, through the moving average coefficients, to
  /external/webrtc/webrtc/modules/audio_coding/codecs/ilbc/
chebyshev.h 34 int16_t *f /* (i) The coefficients in the polynomial */
decode_residual.h 35 coefficients */
simple_lsf_quant.c 28 int16_t *lsfdeq, /* (o) dequantized lsf coefficients
simple_lsf_quant.h 29 int16_t *lsfdeq, /* (o) dequantized lsf coefficients
  /external/webrtc/webrtc/modules/audio_processing/vad/
pitch_internal.cc 20 // The reason behind this is that LPC coefficients are computed for the first
  /frameworks/av/media/libstagefright/codecs/amrwbenc/src/
int_lpc.c 36 Word16 Az[] /* output: LP coefficients in 4 subframes */
  /prebuilts/go/darwin-x86/src/math/
sinh.go 28 // The coefficients are #2029 from Hart & Cheney. (20.36D)
  /prebuilts/go/linux-x86/src/math/
sinh.go 28 // The coefficients are #2029 from Hart & Cheney. (20.36D)
  /system/extras/libfec/include/fec/
ecc.h 32 0x11d, /* field generator polynomial coefficients */ \
  /external/apache-commons-math/src/main/java/org/apache/commons/math/optimization/direct/
NelderMead.java 47 /** Build a Nelder-Mead optimizer with default coefficients.
48 * <p>The default coefficients are 1.0 for rho, 2.0 for khi and 0.5
58 /** Build a Nelder-Mead optimizer with specified coefficients.

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