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/external/libaom/libaom/tools/
Dgen_constrained_tokenset.py16 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
45 parray[0] = 2 * (cdf(0.5, alpha, beta) - 0.5)
46 parray[1] = (2 * (cdf(1.5, alpha, beta) - cdf(0.5, alpha, beta)))
[all …]
/external/cblas/testing/
Dc_d3chke.c32 ALPHA=0.0, BETA=0.0; in F77_d3chke() local
51 ALPHA, A, 1, B, 1, BETA, C, 1 ); in F77_d3chke()
55 ALPHA, A, 1, B, 1, BETA, C, 1 ); in F77_d3chke()
59 ALPHA, A, 1, B, 1, BETA, C, 1 ); in F77_d3chke()
63 ALPHA, A, 1, B, 1, BETA, C, 1 ); in F77_d3chke()
67 ALPHA, A, 1, B, 1, BETA, C, 1 ); in F77_d3chke()
71 ALPHA, A, 1, B, 1, BETA, C, 1 ); in F77_d3chke()
75 ALPHA, A, 1, B, 1, BETA, C, 1 ); in F77_d3chke()
79 ALPHA, A, 1, B, 1, BETA, C, 1 ); in F77_d3chke()
83 ALPHA, A, 1, B, 1, BETA, C, 1 ); in F77_d3chke()
[all …]
Dc_s3chke.c32 ALPHA=0.0, BETA=0.0; in F77_s3chke() local
50 ALPHA, A, 1, B, 1, BETA, C, 1 ); in F77_s3chke()
54 ALPHA, A, 1, B, 1, BETA, C, 1 ); in F77_s3chke()
58 ALPHA, A, 1, B, 1, BETA, C, 1 ); in F77_s3chke()
62 ALPHA, A, 1, B, 1, BETA, C, 1 ); in F77_s3chke()
66 ALPHA, A, 1, B, 1, BETA, C, 1 ); in F77_s3chke()
70 ALPHA, A, 1, B, 1, BETA, C, 1 ); in F77_s3chke()
74 ALPHA, A, 1, B, 1, BETA, C, 1 ); in F77_s3chke()
78 ALPHA, A, 1, B, 1, BETA, C, 1 ); in F77_s3chke()
82 ALPHA, A, 1, B, 1, BETA, C, 1 ); in F77_s3chke()
[all …]
Dc_z3chke.c33 BETA[2] = {0.0,0.0}, in F77_z3chke() local
53 ALPHA, A, 1, B, 1, BETA, C, 1 ); in F77_z3chke()
57 ALPHA, A, 1, B, 1, BETA, C, 1 ); in F77_z3chke()
61 ALPHA, A, 1, B, 1, BETA, C, 1 ); in F77_z3chke()
65 ALPHA, A, 1, B, 1, BETA, C, 1 ); in F77_z3chke()
69 ALPHA, A, 1, B, 1, BETA, C, 1 ); in F77_z3chke()
73 ALPHA, A, 1, B, 1, BETA, C, 1 ); in F77_z3chke()
77 ALPHA, A, 1, B, 1, BETA, C, 1 ); in F77_z3chke()
81 ALPHA, A, 1, B, 1, BETA, C, 1 ); in F77_z3chke()
85 ALPHA, A, 1, B, 1, BETA, C, 1 ); in F77_z3chke()
[all …]
Dc_c3chke.c33 BETA[2] = {0.0,0.0}, in F77_c3chke() local
53 ALPHA, A, 1, B, 1, BETA, C, 1 ); in F77_c3chke()
57 ALPHA, A, 1, B, 1, BETA, C, 1 ); in F77_c3chke()
61 ALPHA, A, 1, B, 1, BETA, C, 1 ); in F77_c3chke()
65 ALPHA, A, 1, B, 1, BETA, C, 1 ); in F77_c3chke()
69 ALPHA, A, 1, B, 1, BETA, C, 1 ); in F77_c3chke()
73 ALPHA, A, 1, B, 1, BETA, C, 1 ); in F77_c3chke()
77 ALPHA, A, 1, B, 1, BETA, C, 1 ); in F77_c3chke()
81 ALPHA, A, 1, B, 1, BETA, C, 1 ); in F77_c3chke()
85 ALPHA, A, 1, B, 1, BETA, C, 1 ); in F77_c3chke()
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Dc_z2chke.c33 BETA[2] = {0.0,0.0}, in F77_z2chke() local
52 ALPHA, A, 1, X, 1, BETA, Y, 1 ); in F77_z2chke()
56 ALPHA, A, 1, X, 1, BETA, Y, 1 ); in F77_z2chke()
60 ALPHA, A, 1, X, 1, BETA, Y, 1 ); in F77_z2chke()
64 ALPHA, A, 1, X, 1, BETA, Y, 1 ); in F77_z2chke()
68 ALPHA, A, 1, X, 1, BETA, Y, 1 ); in F77_z2chke()
72 ALPHA, A, 1, X, 0, BETA, Y, 1 ); in F77_z2chke()
76 ALPHA, A, 1, X, 1, BETA, Y, 0 ); in F77_z2chke()
81 ALPHA, A, 1, X, 1, BETA, Y, 1 ); in F77_z2chke()
85 ALPHA, A, 1, X, 1, BETA, Y, 1 ); in F77_z2chke()
[all …]
Dc_c2chke.c33 BETA[2] = {0.0,0.0}, in F77_c2chke() local
52 ALPHA, A, 1, X, 1, BETA, Y, 1 ); in F77_c2chke()
56 ALPHA, A, 1, X, 1, BETA, Y, 1 ); in F77_c2chke()
60 ALPHA, A, 1, X, 1, BETA, Y, 1 ); in F77_c2chke()
64 ALPHA, A, 1, X, 1, BETA, Y, 1 ); in F77_c2chke()
68 ALPHA, A, 1, X, 1, BETA, Y, 1 ); in F77_c2chke()
72 ALPHA, A, 1, X, 0, BETA, Y, 1 ); in F77_c2chke()
76 ALPHA, A, 1, X, 1, BETA, Y, 0 ); in F77_c2chke()
81 ALPHA, A, 1, X, 1, BETA, Y, 1 ); in F77_c2chke()
85 ALPHA, A, 1, X, 1, BETA, Y, 1 ); in F77_c2chke()
[all …]
Dc_s2chke.c32 ALPHA=0.0, BETA=0.0; in F77_s2chke() local
50 ALPHA, A, 1, X, 1, BETA, Y, 1 ); in F77_s2chke()
54 ALPHA, A, 1, X, 1, BETA, Y, 1 ); in F77_s2chke()
58 ALPHA, A, 1, X, 1, BETA, Y, 1 ); in F77_s2chke()
62 ALPHA, A, 1, X, 1, BETA, Y, 1 ); in F77_s2chke()
66 ALPHA, A, 1, X, 1, BETA, Y, 1 ); in F77_s2chke()
70 ALPHA, A, 1, X, 0, BETA, Y, 1 ); in F77_s2chke()
74 ALPHA, A, 1, X, 1, BETA, Y, 0 ); in F77_s2chke()
79 ALPHA, A, 1, X, 1, BETA, Y, 1 ); in F77_s2chke()
83 ALPHA, A, 1, X, 1, BETA, Y, 1 ); in F77_s2chke()
[all …]
Dc_d2chke.c32 ALPHA=0.0, BETA=0.0; in F77_d2chke() local
50 ALPHA, A, 1, X, 1, BETA, Y, 1 ); in F77_d2chke()
54 ALPHA, A, 1, X, 1, BETA, Y, 1 ); in F77_d2chke()
58 ALPHA, A, 1, X, 1, BETA, Y, 1 ); in F77_d2chke()
62 ALPHA, A, 1, X, 1, BETA, Y, 1 ); in F77_d2chke()
66 ALPHA, A, 1, X, 1, BETA, Y, 1 ); in F77_d2chke()
70 ALPHA, A, 1, X, 0, BETA, Y, 1 ); in F77_d2chke()
74 ALPHA, A, 1, X, 1, BETA, Y, 0 ); in F77_d2chke()
79 ALPHA, A, 1, X, 1, BETA, Y, 1 ); in F77_d2chke()
83 ALPHA, A, 1, X, 1, BETA, Y, 1 ); in F77_d2chke()
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/external/ImageMagick/MagickCore/
Dfx.c524 static inline double FxGCD(const double alpha,const double beta) in FxGCD() argument
526 if (alpha < beta) in FxGCD()
527 return(FxGCD(beta,alpha)); in FxGCD()
528 if (fabs(beta) < 0.001) in FxGCD()
530 return(FxGCD(beta,alpha-beta*floor(alpha/beta))); in FxGCD()
577 beta; in FxGetSymbol() local
641 depth,&beta,exception); in FxGetSymbol()
671 depth,&beta,exception); in FxGetSymbol()
673 point.y=beta; in FxGetSymbol()
697 depth,&beta,exception); in FxGetSymbol()
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Dcomposite-private.h36 const double q,const double beta) in MagickOver_() argument
43 Da=QuantumScale*beta; in MagickOver_()
53 const double alpha,const Quantum *q,const double beta,Quantum *composite) in CompositePixelOver() argument
67 Da=QuantumScale*beta; in CompositePixelOver()
87 (double) q[i],beta)); in CompositePixelOver()
93 (double) q[i],beta)); in CompositePixelOver()
99 (double) q[i],beta)); in CompositePixelOver()
105 (double) q[i],beta)); in CompositePixelOver()
123 const PixelInfo *q,const double beta,PixelInfo *composite) in CompositePixelInfoOver() argument
134 Da=QuantumScale*beta, in CompositePixelInfoOver()
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/external/tensorflow/tensorflow/python/ops/distributions/
Dbeta.py15 """The Beta distribution class."""
41 "Beta",
50 @tf_export(v1=["distributions.Beta"])
51 class Beta(distribution.Distribution): class
52 """Beta distribution.
54 The Beta distribution is defined over the `(0, 1)` interval using parameters
55 `concentration1` (aka "alpha") and `concentration0` (aka "beta").
62 pdf(x; alpha, beta) = x**(alpha - 1) (1 - x)**(beta - 1) / Z
63 Z = Gamma(alpha) Gamma(beta) / Gamma(alpha + beta)
69 * `concentration0 = beta`,
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/external/tensorflow/tensorflow/python/kernel_tests/distributions/
Dbeta_test.py30 from tensorflow.python.ops.distributions import beta as beta_lib
55 dist = beta_lib.Beta(a, b)
64 dist = beta_lib.Beta(a, b)
73 dist = beta_lib.Beta(a, b)
82 dist = beta_lib.Beta(a, b)
89 dist = beta_lib.Beta(a, b)
96 dist = beta_lib.Beta(a, b, validate_args=True)
113 dist = beta_lib.Beta(a, b)
122 dist = beta_lib.Beta(a, b)
132 dist = beta_lib.Beta(a, b)
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/external/apache-commons-math/src/main/java/org/apache/commons/math/distribution/
DBetaDistributionImpl.java23 import org.apache.commons.math.special.Beta;
27 * Implements the Beta distribution.
32 * Beta distribution</a></li>
54 private double beta; field in BetaDistributionImpl
57 * updated whenever alpha or beta are changed.
67 * @param beta second shape parameter (must be positive)
72 public BetaDistributionImpl(double alpha, double beta, double inverseCumAccuracy) { in BetaDistributionImpl() argument
74 this.beta = beta; in BetaDistributionImpl()
82 * @param beta second shape parameter (must be positive)
84 public BetaDistributionImpl(double alpha, double beta) { in BetaDistributionImpl() argument
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DGammaDistributionImpl.java48 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.
65 * @param beta the scale parameter.
70 public GammaDistributionImpl(double alpha, double beta, double inverseCumAccuracy) { in GammaDistributionImpl() argument
73 setBetaInternal(beta); in GammaDistributionImpl()
100 ret = Gamma.regularizedGammaP(alpha, x / beta); in cumulativeProbability()
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/external/openscreen/third_party/abseil/src/absl/random/
Dbeta_distribution.h35 // 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.
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.
57 explicit param_type(result_type alpha, result_type beta) in param_type() argument
58 : alpha_(alpha), beta_(beta) { in param_type()
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/external/rust/crates/grpcio-sys/grpc/third_party/abseil-cpp/absl/random/
Dbeta_distribution.h35 // 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.
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.
57 explicit param_type(result_type alpha, result_type beta) in param_type() argument
58 : alpha_(alpha), beta_(beta) { in param_type()
[all …]
/external/webrtc/third_party/abseil-cpp/absl/random/
Dbeta_distribution.h35 // 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.
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.
57 explicit param_type(result_type alpha, result_type beta) in param_type() argument
58 : alpha_(alpha), beta_(beta) { in param_type()
[all …]
/external/abseil-cpp/absl/random/
Dbeta_distribution.h35 // 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.
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.
57 explicit param_type(result_type alpha, result_type beta) in param_type() argument
58 : alpha_(alpha), beta_(beta) { in param_type()
[all …]
/external/libtextclassifier/abseil-cpp/absl/random/
Dbeta_distribution.h35 // 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.
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.
57 explicit param_type(result_type alpha, result_type beta) in param_type() argument
58 : alpha_(alpha), beta_(beta) { in param_type()
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/external/XNNPACK/test/
Df32-velu.cc89 TEST(F32_VELU__NEON_RR2_LUT16_P3_X4, beta) { in TEST() argument
91 for (float beta : std::vector<float>({0.3f, 3.0f})) { in TEST() local
95 .beta(beta) in TEST()
172 TEST(F32_VELU__NEON_RR2_LUT16_P3_X8, beta) { in TEST() argument
174 for (float beta : std::vector<float>({0.3f, 3.0f})) { in TEST() local
178 .beta(beta) in TEST()
255 TEST(F32_VELU__NEON_RR2_LUT16_P3_X12, beta) { in TEST() argument
257 for (float beta : std::vector<float>({0.3f, 3.0f})) { in TEST() local
261 .beta(beta) in TEST()
338 TEST(F32_VELU__NEON_RR2_LUT16_P3_X16, beta) { in TEST() argument
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/external/protobuf/python/compatibility_tests/v2.5.0/
Dtest.sh26 3.0.0-beta-1)
27 OLD_VERSION=3.0.0-beta-1
28 …=http://repo1.maven.org/maven2/com/google/protobuf/protoc/3.0.0-beta-1/protoc-3.0.0-beta-1-linux-x…
30 3.0.0-beta-2)
31 OLD_VERSION=3.0.0-beta-2
32 …=http://repo1.maven.org/maven2/com/google/protobuf/protoc/3.0.0-beta-2/protoc-3.0.0-beta-2-linux-x…
34 3.0.0-beta-3)
35 OLD_VERSION=3.0.0-beta-3
36 …=http://repo1.maven.org/maven2/com/google/protobuf/protoc/3.0.0-beta-3/protoc-3.0.0-beta-3-linux-x…
38 3.0.0-beta-4)
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/external/eigen/lapack/
Dzlarfg.f40 *> H**H * ( alpha ) = ( beta ), H**H * H = I.
43 *> where alpha and beta are scalars, with beta real, and x is an
71 *> On exit, it is overwritten with the value beta.
130 DOUBLE PRECISION ALPHI, ALPHR, BETA, RSAFMN, SAFMIN, XNORM local
163 BETA = -SIGN( DLAPY3( ALPHR, ALPHI, XNORM ), ALPHR )
168 IF( ABS( BETA ).LT.SAFMIN ) THEN
170 * XNORM, BETA may be inaccurate; scale X and recompute them
175 BETA = BETA*RSAFMN
178 IF( ABS( BETA ).LT.SAFMIN )
181 * New BETA is at most 1, at least SAFMIN
[all …]
Dclarfg.f40 *> H**H * ( alpha ) = ( beta ), H**H * H = I.
43 *> where alpha and beta are scalars, with beta real, and x is an
71 *> On exit, it is overwritten with the value beta.
130 REAL ALPHI, ALPHR, BETA, RSAFMN, SAFMIN, XNORM local
163 BETA = -SIGN( SLAPY3( ALPHR, ALPHI, XNORM ), ALPHR )
168 IF( ABS( BETA ).LT.SAFMIN ) THEN
170 * XNORM, BETA may be inaccurate; scale X and recompute them
175 BETA = BETA*RSAFMN
178 IF( ABS( BETA ).LT.SAFMIN )
181 * New BETA is at most 1, at least SAFMIN
[all …]
Ddlarfg.f40 *> H * ( alpha ) = ( beta ), H**T * H = I.
43 *> where alpha and beta are scalars, and x is an (n-1)-element real
71 *> On exit, it is overwritten with the value beta.
130 DOUBLE PRECISION BETA, RSAFMN, SAFMIN, XNORM local
160 BETA = -SIGN( DLAPY2( ALPHA, XNORM ), ALPHA )
163 IF( ABS( BETA ).LT.SAFMIN ) THEN
165 * XNORM, BETA may be inaccurate; scale X and recompute them
171 BETA = BETA*RSAFMN
173 IF( ABS( BETA ).LT.SAFMIN )
176 * New BETA is at most 1, at least SAFMIN
[all …]

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