/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);
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Beta.java | 196 ret = Gamma.logGamma(a) + Gamma.logGamma(b) - 197 Gamma.logGamma(a + b);
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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 [all...] |
/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);
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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))) -
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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);
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SaddlePointExpansion.java | 19 import org.apache.commons.math.special.Gamma; 114 ret = Gamma.logGamma(z + 1.0) - (z + 0.5) * FastMath.log(z) +
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/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')}}
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/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
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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
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/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.),
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/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
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/external/libxcam/ |
README.md | 34 - Gamma correction, MACC, color space, demosaicing, simple bilateral
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/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,
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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);
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/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);
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/external/fonttools/Lib/fontTools/ |
agl.py | 169 0393;Gamma;GREEK CAPITAL LETTER GAMMA 461 03B3;gamma;GREEK SMALL LETTER GAMMA [all...] |