| /external/apache-commons-math/src/main/java/org/apache/commons/math/special/ |
| Erf.java | 42 * {@link Gamma#regularizedGammaP(double, double, double, int) regularized gamma function},
52 * @see Gamma#regularizedGammaP(double, double, double, int)
58 double ret = Gamma.regularizedGammaP(0.5, x * x, 1.0e-15, 10000);
71 * {@link Gamma#regularizedGammaQ(double, double, double, int) regularized gamma function},
81 * @see Gamma#regularizedGammaQ(double, double, double, int)
88 final double ret = Gamma.regularizedGammaQ(0.5, x * x, 1.0e-15, 10000);
|
| Beta.java | 196 ret = Gamma.logGamma(a) + Gamma.logGamma(b) -
197 Gamma.logGamma(a + b);
|
| Gamma.java | 26 * Gamma family of functions.
30 public class Gamma {
36 public static final double GAMMA = 0.577215664901532860606512090082;
74 private Gamma() {
79 * Returns the natural logarithm of the gamma function Γ(x).
84 * Gamma Function</a>, equation (28).</li>
87 * <li><a href="http://my.fit.edu/~gabdo/gamma.txt">Paul Godfrey, A note on
88 * the computation of the convergent Lanczos complex Gamma approximation
118 * Returns the regularized gamma function P(a, x).
122 * @return the regularized gamma function P(a, x
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| /external/apache-commons-math/src/main/java/org/apache/commons/math/distribution/ |
| BetaDistributionImpl.java | 22 import org.apache.commons.math.special.Gamma;
121 z = Gamma.logGamma(alpha) + Gamma.logGamma(beta) - Gamma.logGamma(alpha + beta);
|
| GammaDistributionImpl.java | 24 import org.apache.commons.math.special.Gamma;
54 * Create a new gamma distribution with the given alpha and beta values.
63 * Create a new gamma distribution with the given alpha and beta values.
100 ret = Gamma.regularizedGammaP(alpha, x / beta);
206 return FastMath.pow(x / beta, alpha - 1) / beta * FastMath.exp(-x / beta) / FastMath.exp(Gamma.logGamma(alpha));
248 // NOTE: gamma is skewed to the left
275 // Gamma is skewed to the left, therefore, P(X < μ) > .5
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| TDistributionImpl.java | 25 import org.apache.commons.math.special.Gamma;
118 return FastMath.exp(Gamma.logGamma(nPlus1Over2) - 0.5 * (FastMath.log(FastMath.PI) + FastMath.log(n)) -
119 Gamma.logGamma(n/2) - nPlus1Over2 * FastMath.log(1 + x * x /n));
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| WeibullDistributionImpl.java | 24 import org.apache.commons.math.special.Gamma;
306 * The mean is <code>scale * Gamma(1 + (1 / shape))</code>
307 * where <code>Gamma(...)</code> is the Gamma-function
316 return sc * FastMath.exp(Gamma.logGamma(1 + (1 / sh)));
323 * <code>scale^2 * Gamma(1 + (2 / shape)) - mean^2</code>
324 * where <code>Gamma(...)</code> is the Gamma-function
335 FastMath.exp(Gamma.logGamma(1 + (2 / sh))) -
|
| PoissonDistributionImpl.java | 24 import org.apache.commons.math.special.Gamma;
63 * Gamma#regularizedGammaP or continued fraction approximation of Gamma#regularizedGammaQ.
219 return Gamma.regularizedGammaQ((double) x + 1, mean, epsilon, maxIterations);
|
| SaddlePointExpansion.java | 19 import org.apache.commons.math.special.Gamma;
114 ret = Gamma.logGamma(z + 1.0) - (z + 0.5) * FastMath.log(z) +
|
| /external/tensorflow/tensorflow/python/kernel_tests/distributions/ |
| gamma_test.py | 30 from tensorflow.python.ops.distributions import gamma as gamma_lib
55 gamma = gamma_lib.Gamma(concentration=alpha, rate=beta)
57 self.assertEqual(self.evaluate(gamma.batch_shape_tensor()), (5,))
58 self.assertEqual(gamma.batch_shape, tensor_shape.TensorShape([5]))
59 self.assertAllEqual(self.evaluate(gamma.event_shape_tensor()), [])
60 self.assertEqual(gamma.event_shape, tensor_shape.TensorShape([]))
69 gamma = gamma_lib.Gamma(concentration=alpha, rate=beta)
70 log_pdf = gamma.log_prob(x
[all...] |
| /external/clang/test/Sema/ |
| exprs.c | 80 void test7(int *P, _Complex float Gamma) {
81 P = (P-42) + Gamma*4; // expected-error {{invalid operands to binary expression ('int *' and '_Complex float')}}
|
| /external/tensorflow/tensorflow/python/ops/distributions/ |
| gamma.py | 15 """The Gamma distribution class."""
41 "Gamma",
46 @tf_export(v1=["distributions.Gamma"])
47 class Gamma(distribution.Distribution):
48 """Gamma distribution.
50 The Gamma distribution is defined over positive real numbers using
59 Z = Gamma(alpha) beta**(-alpha)
67 * `Gamma` is the [gamma function](
73 cdf(x; alpha, beta, x > 0) = GammaInc(alpha, beta x) / Gamma(alpha
[all...] |
| distributions.py | 32 from tensorflow.python.ops.distributions.gamma import Gamma
|
| exponential.py | 29 from tensorflow.python.ops.distributions import gamma
41 class Exponential(gamma.Gamma):
57 The Exponential distribution is a special case of the Gamma distribution,
61 Exponential(rate) = Gamma(concentration=1., rate)
104 # true in the parent class "Gamma." Therefore, passing
|
| /external/tensorflow/tensorflow/contrib/distributions/python/kernel_tests/bijectors/ |
| invert_test.py | 24 from tensorflow.python.ops.distributions import gamma as gamma_lib
79 distribution=gamma_lib.Gamma(concentration=1., rate=2.),
|
| /external/tensorflow/tensorflow/contrib/distributions/python/ops/ |
| chi2.py | 27 from tensorflow.python.ops.distributions import gamma
37 class Chi2(gamma.Gamma):
49 Z = 2**(0.5 df) Gamma(0.5 df)
56 * `Gamma` is the [gamma function](
59 The Chi2 distribution is a special case of the Gamma distribution, i.e.,
62 Chi2(df) = Gamma(concentration=0.5 * df, rate=0.5)
97 # not true in the parent class "gamma." therefore, passing
|
| /external/libxcam/ |
| README.md | 34 - Gamma correction, MACC, color space, demosaicing, simple bilateral
|
| /external/clang/test/Misc/ |
| diag-template-diffing.cpp | 119 class Gamma{};
136 void set5(Alpha<Beta<Gamma<Delta<int, int>, int>, int>, int>);
138 set5(Alpha<Beta<Gamma<void, void>, double>, double>());
141 // CHECK-ELIDE-NOTREE: candidate function not viable: no known conversion from 'Alpha<Beta<Gamma<void, void>, double>, double>' to 'Alpha<Beta<Gamma<Delta<int, int>, int>, int>, int>' for 1st argument
143 // CHECK-NOELIDE-NOTREE: candidate function not viable: no known conversion from 'Alpha<Beta<Gamma<void, void>, double>, double>' to 'Alpha<Beta<Gamma<Delta<int, int>, int>, int>, int>' for 1st argument
148 // CHECK-ELIDE-TREE: Gamma<
157 // CHECK-NOELIDE-TREE: Gamma<
167 // CHECK-ELIDE-NOTREE: candidate function not viable: no known conversion from 'Alpha<Beta<Delta<int, int>, [...]>, [...]>' to 'Alpha<Beta<Gamma<Delta<int, int>, int>, [...]>, [...]>' for 1st argumen
[all...] |
| /external/pdfium/third_party/lcms/src/ |
| cmsgamma.c | 318 // X = Y ^ Gamma
331 // Type 1 Reversed: X = Y ^1/gamma
345 // Y = (aX + b)^Gamma | X >= -b/a
377 // Y = (aX + b)^Gamma | X <= -b/a
418 // Y = (aX + b)^Gamma | X >= d
454 // Y = (aX + b)^Gamma + e | X >= d
494 // Y = (a * X + b) ^ Gamma + c
504 // ((Y - c) ^1/Gamma - b) / a
514 // Y = a * log (b * X^Gamma + c) + d
524 // (Y - d) / a = log(b * X ^Gamma + c
1260 cmsFloat64Number gamma, sum, sum2; local
[all...] |
| cmstypes.c | 1175 cmsToneCurve* gamma = (cmsToneCurve*) Ptr; local
1285 cmsToneCurve* gamma = (cmsToneCurve*) Ptr; local
[all...] |
| /external/tensorflow/tensorflow/contrib/distributions/python/kernel_tests/ |
| distribution_test.py | 42 tfd.Gamma,
|
| mixture_test.py | [all...] |
| /external/ImageMagick/PerlMagick/demo/ |
| demo.pl | 70 print "Auto-gamma...\n";
72 $example->Label('Auto Gamma');
229 print "Gamma...\n";
231 $example->Label('Gamma');
232 $example->Gamma(1.6);
|
| /external/ImageMagick/www/source/ |
| examples.pl | 70 print "Auto-gamma...\n";
72 $example->Label('Auto Gamma');
209 print "Gamma...\n";
211 $example->Label('Gamma');
212 $example->Gamma(1.6);
|
| /external/fonttools/Lib/fontTools/ |
| agl.py | 169 0393;Gamma;GREEK CAPITAL LETTER GAMMA
461 03B3;gamma;GREEK SMALL LETTER GAMMA
[all...] |
