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/external/libaom/tools/
Dgen_constrained_tokenset.py14 Model obtained from a 2-sided zero-centered distribution derived
16 cdf(x) = 0.5 + 0.5 * sgn(x) * [1 - {alpha/(alpha + |x|)} ^ beta]
18 For a given beta and a given probability of the 1-node, the alpha
19 is first solved, and then the {alpha, beta} pair is used to generate
30 def cdf_spareto(x, xm, beta): argument
31 p = 1 - (xm / (np.abs(x) + xm))**beta
36 def get_spareto(p, beta): argument
40 return ((cdf(1.5, x, beta) - cdf(0.5, x, beta)) /
41 (1 - cdf(0.5, x, beta)) - p)**2
43 alpha = scipy.optimize.fminbound(func, 1e-12, 10000, xtol=1e-12)
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/external/apache-commons-math/src/main/java/org/apache/commons/math3/distribution/
DBetaDistribution.java9 * http://www.apache.org/licenses/LICENSE-2.0
23 import org.apache.commons.math3.special.Beta;
29 * Implements the Beta distribution.
31 * @see <a href="http://en.wikipedia.org/wiki/Beta_distribution">Beta distribution</a>
40 public static final double DEFAULT_INVERSE_ABSOLUTE_ACCURACY = 1e-9;
43 private static final long serialVersionUID = -1221965979403477668L;
49 private final double beta; field in BetaDistribution
52 * Normalizing factor used in density computations. updated whenever alpha or beta are changed.
69 * @param beta Second shape parameter (must be positive).
71 public BetaDistribution(double alpha, double beta) { in BetaDistribution() argument
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DGumbelDistribution.java9 * http://www.apache.org/licenses/LICENSE-2.0
43 * http://mathworld.wolfram.com/Euler-MascheroniConstantApproximations.html
51 private final double beta; field in GumbelDistribution
63 * @param beta scale parameter (must be positive)
64 * @throws NotStrictlyPositiveException if {@code beta <= 0}
66 public GumbelDistribution(double mu, double beta) { in GumbelDistribution() argument
67 this(new Well19937c(), mu, beta); in GumbelDistribution()
75 * @param beta scale parameter (must be positive)
76 * @throws NotStrictlyPositiveException if {@code beta <= 0}
78 public GumbelDistribution(RandomGenerator rng, double mu, double beta) { in GumbelDistribution() argument
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/external/apache-commons-math/src/main/java/org/apache/commons/math/distribution/
DBetaDistributionImpl.java9 * http://www.apache.org/licenses/LICENSE-2.0
23 import org.apache.commons.math.special.Beta;
27 * Implements the Beta distribution.
32 * Beta distribution</a></li>
35 * @version $Revision: 1054524 $ $Date: 2011-01-03 05:59:18 +0100 (lun. 03 janv. 2011) $
45 public static final double DEFAULT_INVERSE_ABSOLUTE_ACCURACY = 1e-9;
48 private static final long serialVersionUID = -1221965979403477668L;
54 private double beta; field in BetaDistributionImpl
57 * updated whenever alpha or beta are changed.
67 * @param beta second shape parameter (must be positive)
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DGammaDistributionImpl.java9 * http://www.apache.org/licenses/LICENSE-2.0
30 * @version $Revision: 1054524 $ $Date: 2011-01-03 05:59:18 +0100 (lun. 03 janv. 2011) $
39 public static final double DEFAULT_INVERSE_ABSOLUTE_ACCURACY = 1e-9;
42 private static final long serialVersionUID = -3239549463135430361L;
48 private double beta; field in GammaDistributionImpl
54 * Create a new gamma distribution with the given alpha and beta values.
56 * @param beta the scale parameter.
58 public GammaDistributionImpl(double alpha, double beta) { in GammaDistributionImpl() argument
59 this(alpha, beta, DEFAULT_INVERSE_ABSOLUTE_ACCURACY); in GammaDistributionImpl()
63 * Create a new gamma distribution with the given alpha and beta values.
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/external/cblas/testing/
Dc_d3chke.c22 cblas_ok = 0 ; in chkxer()
32 ALPHA=0.0, BETA=0.0; in F77_d3chke() local
46 if (strncmp( sf,"cblas_dgemm" ,11)==0) { in F77_d3chke()
50 cblas_dgemm( INVALID, CblasNoTrans, CblasNoTrans, 0, 0, 0, in F77_d3chke()
51 ALPHA, A, 1, B, 1, BETA, C, 1 ); in F77_d3chke()
54 cblas_dgemm( INVALID, CblasNoTrans, CblasTrans, 0, 0, 0, in F77_d3chke()
55 ALPHA, A, 1, B, 1, BETA, C, 1 ); in F77_d3chke()
58 cblas_dgemm( INVALID, CblasTrans, CblasNoTrans, 0, 0, 0, in F77_d3chke()
59 ALPHA, A, 1, B, 1, BETA, C, 1 ); in F77_d3chke()
62 cblas_dgemm( INVALID, CblasTrans, CblasTrans, 0, 0, 0, in F77_d3chke()
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Dc_s3chke.c22 cblas_ok = 0 ; in chkxer()
32 ALPHA=0.0, BETA=0.0; in F77_s3chke() local
46 if (strncmp( sf,"cblas_sgemm" ,11)==0) { in F77_s3chke()
49 cblas_sgemm( INVALID, CblasNoTrans, CblasNoTrans, 0, 0, 0, in F77_s3chke()
50 ALPHA, A, 1, B, 1, BETA, C, 1 ); in F77_s3chke()
53 cblas_sgemm( INVALID, CblasNoTrans, CblasTrans, 0, 0, 0, in F77_s3chke()
54 ALPHA, A, 1, B, 1, BETA, C, 1 ); in F77_s3chke()
57 cblas_sgemm( INVALID, CblasTrans, CblasNoTrans, 0, 0, 0, in F77_s3chke()
58 ALPHA, A, 1, B, 1, BETA, C, 1 ); in F77_s3chke()
61 cblas_sgemm( INVALID, CblasTrans, CblasTrans, 0, 0, 0, in F77_s3chke()
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Dc_c3chke.c22 cblas_ok = 0 ; in chkxer()
33 BETA[2] = {0.0,0.0}, in F77_c3chke() local
48 if (strncmp( sf,"cblas_cgemm" ,11)==0) { in F77_c3chke()
52 cblas_cgemm( INVALID, CblasNoTrans, CblasNoTrans, 0, 0, 0, in F77_c3chke()
53 ALPHA, A, 1, B, 1, BETA, C, 1 ); in F77_c3chke()
56 cblas_cgemm( INVALID, CblasNoTrans, CblasTrans, 0, 0, 0, in F77_c3chke()
57 ALPHA, A, 1, B, 1, BETA, C, 1 ); in F77_c3chke()
60 cblas_cgemm( INVALID, CblasTrans, CblasNoTrans, 0, 0, 0, in F77_c3chke()
61 ALPHA, A, 1, B, 1, BETA, C, 1 ); in F77_c3chke()
64 cblas_cgemm( INVALID, CblasTrans, CblasTrans, 0, 0, 0, in F77_c3chke()
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Dc_z3chke.c22 cblas_ok = 0 ; in chkxer()
33 BETA[2] = {0.0,0.0}, in F77_z3chke() local
48 if (strncmp( sf,"cblas_zgemm" ,11)==0) { in F77_z3chke()
52 cblas_zgemm( INVALID, CblasNoTrans, CblasNoTrans, 0, 0, 0, in F77_z3chke()
53 ALPHA, A, 1, B, 1, BETA, C, 1 ); in F77_z3chke()
56 cblas_zgemm( INVALID, CblasNoTrans, CblasTrans, 0, 0, 0, in F77_z3chke()
57 ALPHA, A, 1, B, 1, BETA, C, 1 ); in F77_z3chke()
60 cblas_zgemm( INVALID, CblasTrans, CblasNoTrans, 0, 0, 0, in F77_z3chke()
61 ALPHA, A, 1, B, 1, BETA, C, 1 ); in F77_z3chke()
64 cblas_zgemm( INVALID, CblasTrans, CblasTrans, 0, 0, 0, in F77_z3chke()
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/external/webrtc/third_party/abseil-cpp/absl/random/
Dbeta_distribution.h7 // https://www.apache.org/licenses/LICENSE-2.0
35 // Generate a floating-point variate conforming to a Beta distribution:
36 // pdf(x) \propto x^(alpha-1) * (1-x)^(beta-1),
37 // where the params alpha and beta are both strictly positive real values.
39 // The support is the open interval (0, 1), but the return value might be equal
40 // to 0 or 1, due to numerical errors when alpha and beta are very different.
42 // Usage note: One usage is that alpha and beta are counts of number of
44 // approximating a beta distribution with a Gaussian distribution with the same
46 // smaller of alpha and beta when the number of trials are sufficiently large,
47 // to quantify how far a beta distribution is from the normal distribution.
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/external/angle/third_party/abseil-cpp/absl/random/
Dbeta_distribution.h7 // https://www.apache.org/licenses/LICENSE-2.0
35 // Generate a floating-point variate conforming to a Beta distribution:
36 // pdf(x) \propto x^(alpha-1) * (1-x)^(beta-1),
37 // where the params alpha and beta are both strictly positive real values.
39 // The support is the open interval (0, 1), but the return value might be equal
40 // to 0 or 1, due to numerical errors when alpha and beta are very different.
42 // Usage note: One usage is that alpha and beta are counts of number of
44 // approximating a beta distribution with a Gaussian distribution with the same
46 // smaller of alpha and beta when the number of trials are sufficiently large,
47 // to quantify how far a beta distribution is from the normal distribution.
[all …]
/external/rust/crates/grpcio-sys/grpc/third_party/abseil-cpp/absl/random/
Dbeta_distribution.h7 // https://www.apache.org/licenses/LICENSE-2.0
35 // Generate a floating-point variate conforming to a Beta distribution:
36 // pdf(x) \propto x^(alpha-1) * (1-x)^(beta-1),
37 // where the params alpha and beta are both strictly positive real values.
39 // The support is the open interval (0, 1), but the return value might be equal
40 // to 0 or 1, due to numerical errors when alpha and beta are very different.
42 // Usage note: One usage is that alpha and beta are counts of number of
44 // approximating a beta distribution with a Gaussian distribution with the same
46 // smaller of alpha and beta when the number of trials are sufficiently large,
47 // to quantify how far a beta distribution is from the normal distribution.
[all …]
/external/openscreen/third_party/abseil/src/absl/random/
Dbeta_distribution.h7 // https://www.apache.org/licenses/LICENSE-2.0
35 // Generate a floating-point variate conforming to a Beta distribution:
36 // pdf(x) \propto x^(alpha-1) * (1-x)^(beta-1),
37 // where the params alpha and beta are both strictly positive real values.
39 // The support is the open interval (0, 1), but the return value might be equal
40 // to 0 or 1, due to numerical errors when alpha and beta are very different.
42 // Usage note: One usage is that alpha and beta are counts of number of
44 // approximating a beta distribution with a Gaussian distribution with the same
46 // smaller of alpha and beta when the number of trials are sufficiently large,
47 // to quantify how far a beta distribution is from the normal distribution.
[all …]
Dbeta_distribution_test.cc7 // https://www.apache.org/licenses/LICENSE-2.0
54 std::exp(std::log((std::numeric_limits<TypeParam>::max)()) - in TYPED_TEST()
57 std::log((std::numeric_limits<TypeParam>::max)()) - in TYPED_TEST()
58 std::log(std::log((std::numeric_limits<TypeParam>::max)())) - 10.0f); in TYPED_TEST()
64 TypeParam(1e-20), TypeParam(1e-12), TypeParam(1e-8), TypeParam(1e-4), in TYPED_TEST()
65 TypeParam(1e-3), TypeParam(0.1), TypeParam(0.25), in TYPED_TEST()
66 std::nextafter(TypeParam(0.5), TypeParam(0)), // 0.5 - epsilon in TYPED_TEST()
69 std::nextafter(TypeParam(1), TypeParam(0)), // 1 - epsilon in TYPED_TEST()
74 std::nextafter(kSmallA, TypeParam(0)), // in TYPED_TEST()
77 std::nextafter(kLargeA, TypeParam(0)), // in TYPED_TEST()
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/external/cronet/third_party/abseil-cpp/absl/random/
Dbeta_distribution.h7 // https://www.apache.org/licenses/LICENSE-2.0
35 // Generate a floating-point variate conforming to a Beta distribution:
36 // pdf(x) \propto x^(alpha-1) * (1-x)^(beta-1),
37 // where the params alpha and beta are both strictly positive real values.
39 // The support is the open interval (0, 1), but the return value might be equal
40 // to 0 or 1, due to numerical errors when alpha and beta are very different.
42 // Usage note: One usage is that alpha and beta are counts of number of
44 // approximating a beta distribution with a Gaussian distribution with the same
46 // smaller of alpha and beta when the number of trials are sufficiently large,
47 // to quantify how far a beta distribution is from the normal distribution.
[all …]
/external/private-join-and-compute/third_party/abseil-cpp-20230125.2/absl/random/
Dbeta_distribution.h7 // https://www.apache.org/licenses/LICENSE-2.0
35 // Generate a floating-point variate conforming to a Beta distribution:
36 // pdf(x) \propto x^(alpha-1) * (1-x)^(beta-1),
37 // where the params alpha and beta are both strictly positive real values.
39 // The support is the open interval (0, 1), but the return value might be equal
40 // to 0 or 1, due to numerical errors when alpha and beta are very different.
42 // Usage note: One usage is that alpha and beta are counts of number of
44 // approximating a beta distribution with a Gaussian distribution with the same
46 // smaller of alpha and beta when the number of trials are sufficiently large,
47 // to quantify how far a beta distribution is from the normal distribution.
[all …]
/external/abseil-cpp/absl/random/
Dbeta_distribution.h7 // https://www.apache.org/licenses/LICENSE-2.0
35 // Generate a floating-point variate conforming to a Beta distribution:
36 // pdf(x) \propto x^(alpha-1) * (1-x)^(beta-1),
37 // where the params alpha and beta are both strictly positive real values.
39 // The support is the open interval (0, 1), but the return value might be equal
40 // to 0 or 1, due to numerical errors when alpha and beta are very different.
42 // Usage note: One usage is that alpha and beta are counts of number of
44 // approximating a beta distribution with a Gaussian distribution with the same
46 // smaller of alpha and beta when the number of trials are sufficiently large,
47 // to quantify how far a beta distribution is from the normal distribution.
[all …]
/external/tensorflow/third_party/absl/abseil-cpp/absl/random/
Dbeta_distribution.h7 // https://www.apache.org/licenses/LICENSE-2.0
35 // Generate a floating-point variate conforming to a Beta distribution:
36 // pdf(x) \propto x^(alpha-1) * (1-x)^(beta-1),
37 // where the params alpha and beta are both strictly positive real values.
39 // The support is the open interval (0, 1), but the return value might be equal
40 // to 0 or 1, due to numerical errors when alpha and beta are very different.
42 // Usage note: One usage is that alpha and beta are counts of number of
44 // approximating a beta distribution with a Gaussian distribution with the same
46 // smaller of alpha and beta when the number of trials are sufficiently large,
47 // to quantify how far a beta distribution is from the normal distribution.
[all …]
/external/libtextclassifier/abseil-cpp/absl/random/
Dbeta_distribution.h7 // https://www.apache.org/licenses/LICENSE-2.0
35 // Generate a floating-point variate conforming to a Beta distribution:
36 // pdf(x) \propto x^(alpha-1) * (1-x)^(beta-1),
37 // where the params alpha and beta are both strictly positive real values.
39 // The support is the open interval (0, 1), but the return value might be equal
40 // to 0 or 1, due to numerical errors when alpha and beta are very different.
42 // Usage note: One usage is that alpha and beta are counts of number of
44 // approximating a beta distribution with a Gaussian distribution with the same
46 // smaller of alpha and beta when the number of trials are sufficiently large,
47 // to quantify how far a beta distribution is from the normal distribution.
[all …]
/external/armnn/src/backends/backendsCommon/test/layerTests/
DSoftmaxTestImpl.cpp3 // SPDX-License-Identifier: MIT
63 float beta, in SimpleSoftmaxBaseTestImpl() argument
67 int axis = -1) in SimpleSoftmaxBaseTestImpl()
72 const int qOffset = 0; in SimpleSoftmaxBaseTestImpl()
94 data.m_Parameters.m_Beta = beta; in SimpleSoftmaxBaseTestImpl()
103 inputHandle->Allocate(); in SimpleSoftmaxBaseTestImpl()
104 outputHandle->Allocate(); in SimpleSoftmaxBaseTestImpl()
115 outputHandle->GetShape(), in SimpleSoftmaxBaseTestImpl()
124 float beta) in SimpleSoftmaxTestImpl() argument
129 float x0[4] = { exp((0.f - 1.0f) * beta), exp((1.0f - 1.0f) * beta), in SimpleSoftmaxTestImpl()
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/external/tensorflow/tensorflow/python/kernel_tests/distributions/
Dbeta_test.py7 # http://www.apache.org/licenses/LICENSE-2.0
26 from tensorflow.python.ops.distributions import beta as beta_lib
32 def try_import(name): # pylint: disable=invalid-name
51 dist = beta_lib.Beta(a, b)
60 dist = beta_lib.Beta(a, b)
69 dist = beta_lib.Beta(a, b)
78 dist = beta_lib.Beta(a, b)
85 dist = beta_lib.Beta(a, b)
92 dist = beta_lib.Beta(a, b, validate_args=True)
97 self.evaluate(dist.prob([-1., 0.1, 0.5]))
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/external/apache-commons-math/src/main/java/org/apache/commons/math3/random/
DStableRandomGenerator.java9 * http://www.apache.org/licenses/LICENSE-2.0
26 * with location parameter 0 and scale 1.
28 * <p>The implementation uses the Chambers-Mallows-Stuck method as described in <i>Handbook of
42 private final double beta; field in StableRandomGenerator
51 * @param alpha Stability parameter. Must be in range (0, 2]
52 * @param beta Skewness parameter. Must be in range [-1, 1]
54 * @throws OutOfRangeException if {@code alpha <= 0} or {@code alpha > 2} or {@code beta < -1}
55 * or {@code beta > 1}
58 final RandomGenerator generator, final double alpha, final double beta) in StableRandomGenerator() argument
64 if (!(alpha > 0d && alpha <= 2d)) { in StableRandomGenerator()
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/external/libavc/common/arm/
Dih264_deblk_chroma_a9.s9 @ * http://www.apache.org/licenses/LICENSE-2.0
47 @/* 05 01 2015 Kaushik Added double-call functions for */
68 @* @param[in] r0 - pu1_src
71 @* @param[in] r1 - src_strd
74 @* @param[in] r2 - alpha
77 @* @param[in] r3 - beta
78 @* Beta Value for the boundary
94 vpush {d8 - d15}
105 vabd.u8 q13, q3, q2 @Q13 = ABS(p1 - p0)
108 vabd.u8 q11, q2, q0 @Q11 = ABS(p0 - q0)
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/external/eigen/lapack/
Dzlarfg.f6 * http://www.netlib.org/lapack/explore-html/
10 *> <a href="http://www.netlib.org/cgi-bin/netlibfiles.tgz?format=tgz&filename=/lapack/lapack_routin…
12 *> <a href="http://www.netlib.org/cgi-bin/netlibfiles.zip?format=zip&filename=/lapack/lapack_routin…
14 *> <a href="http://www.netlib.org/cgi-bin/netlibfiles.txt?format=txt&filename=/lapack/lapack_routin…
40 *> H**H * ( alpha ) = ( beta ), H**H * H = I.
41 *> ( x ) ( 0 )
43 *> where alpha and beta are scalars, with beta real, and x is an
44 *> (n-1)-element complex vector. H is represented in the form
46 *> H = I - tau * ( 1 ) * ( 1 v**H ) ,
49 *> where tau is a complex scalar and v is a complex (n-1)-element
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Dclarfg.f6 * http://www.netlib.org/lapack/explore-html/
10 *> <a href="http://www.netlib.org/cgi-bin/netlibfiles.tgz?format=tgz&filename=/lapack/lapack_routin…
12 *> <a href="http://www.netlib.org/cgi-bin/netlibfiles.zip?format=zip&filename=/lapack/lapack_routin…
14 *> <a href="http://www.netlib.org/cgi-bin/netlibfiles.txt?format=txt&filename=/lapack/lapack_routin…
40 *> H**H * ( alpha ) = ( beta ), H**H * H = I.
41 *> ( x ) ( 0 )
43 *> where alpha and beta are scalars, with beta real, and x is an
44 *> (n-1)-element complex vector. H is represented in the form
46 *> H = I - tau * ( 1 ) * ( 1 v**H ) ,
49 *> where tau is a complex scalar and v is a complex (n-1)-element
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