| /external/libaom/libaom/tools/ |
| gen_constrained_tokenset.py | 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):
31 p = 1 - (xm / (np.abs(x) + xm))**beta
36 def get_spareto(p, beta):
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
[all...] |
| /external/libcxx/test/std/numerics/rand/rand.dis/rand.dist.pois/rand.dist.pois.gamma/ |
| ctor_double_double.pass.cpp | 15 // explicit gamma_distribution(result_type alpha = 0, result_type beta = 1);
26 assert(d.beta() == 1);
32 assert(d.beta() == 1);
38 assert(d.beta() == 5.25);
|
| param_ctor.pass.cpp | 28 assert(p.beta() == 1);
35 assert(p.beta() == 1);
42 assert(p.beta() == 5);
|
| ctor_param.pass.cpp | 28 assert(d.beta() == 10);
|
| param_assign.pass.cpp | 30 assert(p.beta() == 6);
|
| param_copy.pass.cpp | 29 assert(p.beta() == .125);
|
| /external/apache-commons-math/src/main/java/org/apache/commons/math/distribution/ |
| BetaDistributionImpl.java | 23 import org.apache.commons.math.special.Beta;
27 * Implements the Beta distribution.
32 * Beta distribution</a></li>
54 private double beta; field in class: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) {
74 this.beta = beta;
82 * @param beta second shape parameter (must be positive
[all...] |
| GammaDistributionImpl.java | 48 private double beta; field in class: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) {
59 this(alpha, beta, DEFAULT_INVERSE_ABSOLUTE_ACCURACY);
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) {
73 setBetaInternal(beta);
100 ret = Gamma.regularizedGammaP(alpha, x / beta);
[all...] |
| GammaDistribution.java | 48 * Modify the scale parameter, beta.
49 * @param beta the new scale parameter.
53 void setBeta(double beta);
56 * Access the scale parameter, beta
57 * @return beta.
|
| WeibullDistribution.java | 61 * @param beta The new scale parameter value.
65 void setScale(double beta);
|
| BetaDistribution.java | 22 * Computes the cumulative, inverse cumulative and density functions for the beta distribuiton.
44 * Modify the shape parameter, beta.
45 * @param beta the new scale parameter.
49 void setBeta(double beta);
52 * Access the shape parameter, beta
53 * @return beta.
|
| WeibullDistributionImpl.java | 71 * @param beta the scale parameter.
73 public WeibullDistributionImpl(double alpha, double beta){
74 this(alpha, beta, DEFAULT_INVERSE_ABSOLUTE_ACCURACY);
81 * @param beta the scale parameter.
86 public WeibullDistributionImpl(double alpha, double beta, double inverseCumAccuracy){
89 setScaleInternal(beta);
203 * @param beta the new scale parameter value.
207 public void setScale(double beta) {
208 setScaleInternal(beta);
213 * @param beta the new scale parameter value
[all...] |
| /external/eigen/Eigen/src/Householder/ |
| Householder.h | 26 * \f$ H *this = [ beta 0 ... 0]^T \f$
36 * \param beta the result of H * \c *this
42 void MatrixBase<Derived>::makeHouseholderInPlace(Scalar& tau, RealScalar& beta)
45 makeHouseholder(essentialPart, tau, beta);
49 * \f$ H *this = [ beta 0 ... 0]^T \f$
58 * \param beta the result of H * \c *this
68 RealScalar& beta) const
83 beta = numext::real(c0);
88 beta = sqrt(numext::abs2(c0) + tailSqNorm);
90 beta = -beta
[all...] |
| /external/tensorflow/tensorflow/lite/kernels/ |
| softmax_test.cc | 34 SoftmaxOpModel(int batches, int size, float beta)
35 : batches_(batches), input_size_(size), beta_(beta) {
63 SoftmaxOpModel m(/*batches=*/2, /*size=*/5, /*beta=*/1.0);
82 const float beta = 1.0; local
88 SoftmaxOpModel m(batch_size, input_size, beta);
97 params.beta = beta;
111 const float beta = 0.5; local
117 SoftmaxOpModel m(batch_size, input_size, beta);
126 params.beta = beta
[all...] |
| /external/eigen/unsupported/test/ |
| cxx11_tensor_sugar.cpp | 43 const float beta = 0.21f; local
46 Tensor<float, 3> R = A.constant(gamma) + A * A.constant(alpha) + B * B.constant(beta);
47 Tensor<float, 3> S = A * alpha + B * beta + gamma;
48 Tensor<float, 3> T = gamma + alpha * A + beta * B;
63 const float beta = 0.21f; local
68 - B.constant(beta) / B - A.constant(delta);
69 Tensor<float, 3> S = gamma - A / alpha - beta / B - delta;
|
| /external/ImageMagick/MagickCore/ |
| composite-private.h | 36 const double q,const double beta)
43 Da=QuantumScale*beta;
53 const double alpha,const Quantum *q,const double beta,Quantum *composite)
67 Da=QuantumScale*beta;
87 (double) q[i],beta));
93 (double) q[i],beta));
99 (double) q[i],beta));
105 (double) q[i],beta));
123 const double alpha,const PixelInfo *q,const double beta,PixelInfo *composite)
134 Da=QuantumScale*beta,
[all...] |
| /external/bouncycastle/bcprov/src/main/java/org/bouncycastle/math/ec/endo/ |
| GLVTypeBParameters.java | 15 protected final BigInteger beta; field in class:GLVTypeBParameters
21 public GLVTypeBParameters(BigInteger beta, BigInteger lambda, BigInteger[] v1, BigInteger[] v2, BigInteger g1,
27 this.beta = beta;
40 return beta;
|
| /external/bouncycastle/repackaged/bcprov/src/main/java/com/android/org/bouncycastle/math/ec/endo/ |
| GLVTypeBParameters.java | 19 protected final BigInteger beta; field in class:GLVTypeBParameters
25 public GLVTypeBParameters(BigInteger beta, BigInteger lambda, BigInteger[] v1, BigInteger[] v2, BigInteger g1,
31 this.beta = beta;
44 return beta;
|
| /external/vboot_reference/scripts/image_signing/sample-test-configs/ |
| ensure_sane_lsb-release.config | 20 beta-channel
|
| /external/tensorflow/tensorflow/compiler/tests/ |
| addsign_test.py | 44 beta=0.9,
47 m_t = beta * m + (1 - beta) * g_t
64 beta=0.9):
83 beta=beta,
111 beta=beta,
121 beta=beta,
[all...] |
| powersign_test.py | 45 beta=0.9,
48 m_t = beta * m + (1 - beta) * g_t
65 beta=0.9):
84 beta=beta,
112 beta=beta,
122 beta=beta,
[all...] |
| /bionic/libm/upstream-freebsd/lib/msun/src/ |
| s_ctanh.c | 41 * beta = 1/cos^2(y)
57 * beta rho s + I t
59 * 1 + beta s^2
80 double t, beta, s, rho, denom; local
133 beta = 1.0 + t * t; /* = 1 / cos^2(y) */
136 denom = 1 + beta * s * s;
137 return (CMPLX((beta * rho * s) / denom, t / denom));
|
| s_ctanhf.c | 45 float t, beta, s, rho, denom; local
73 beta = 1.0 + t * t;
76 denom = 1 + beta * s * s;
77 return (CMPLXF((beta * rho * s) / denom, t / denom));
|
| /external/grpc-grpc/src/python/grpcio_tests/tests/unit/beta/ |
| test_utilities.py | 14 """Test-appropriate entry points into the gRPC Python Beta API."""
17 from grpc.beta import implementations
|
| /external/tensorflow/tensorflow/contrib/solvers/python/ops/ |
| lanczos.py | 87 beta: A rank-1 `Tensor` of type `operator.dtype` and shape `[k]`.
107 # beta = subdiagonal of B_k.
112 ["u", "v", "alpha", "beta"])
114 def update_state(old, i, u, v, alpha, beta):
118 old.alpha.write(i, alpha), old.beta.write(i, beta))
159 i > 0, lambda: r - ls.beta.read(i - 1) * read_colvec(ls.v, i - 1),
168 u, beta = orthogonalize_(i, ls.u, p)
170 u, beta = util.l2normalize(p)
172 return i + 1, update_state(ls, i, u, v, alpha, beta)
[all...] |
