| /external/apache-commons-math/src/main/java/org/apache/commons/math/distribution/ |
| DiscreteDistribution.java | 17 package org.apache.commons.math.distribution;
25 public interface DiscreteDistribution extends Distribution {
28 * to this distribution, this method returns P(X = x). In other words, this
29 * method represents the probability mass function, or PMF for the distribution.
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| TDistribution.java | 17 package org.apache.commons.math.distribution;
20 * Student's t-Distribution.
25 * <li><a href="http://mathworld.wolfram.com/Studentst-Distribution.html">
26 * Student's t-Distribution</a></li>
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| BinomialDistribution.java | 17 package org.apache.commons.math.distribution;
20 * The Binomial Distribution.
26 * Binomial Distribution</a></li>
34 * Access the number of trials for this distribution.
40 * Access the probability of success for this distribution.
46 * Change the number of trials for this distribution.
54 * Change the probability of success for this distribution.
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| CauchyDistribution.java | 18 package org.apache.commons.math.distribution;
21 * Cauchy Distribution.
27 * Cauchy Distribution</a></li>
38 * @return median for this distribution
44 * @return scale parameter for this distribution
50 * @param median for this distribution
58 * @param s scale parameter for this distribution
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| ChiSquaredDistribution.java | 17 package org.apache.commons.math.distribution;
20 * The Chi-Squared Distribution.
26 * Chi-Squared Distribution</a></li>
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| ContinuousDistribution.java | 17 package org.apache.commons.math.distribution;
28 * 2.1, all continuous distribution implementations included in commons-math
33 public interface ContinuousDistribution extends Distribution {
36 * For this distribution, X, this method returns x such that P(X < x) = p.
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| Distribution.java | 17 package org.apache.commons.math.distribution;
26 public interface Distribution {
29 * to this distribution, this method returns P(X ≤ x). In other words,
30 * this method represents the (cumulative) distribution function, or
31 * CDF, for this distribution.
33 * @param x the value at which the distribution function is evaluated.
35 * distribution takes a value less than or equal to <code>x</code>
43 * to this distribution, this method returns P(x0 ≤ X ≤ x1).
47 * @return the probability that a random variable with this distribution
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| ExponentialDistribution.java | 17 package org.apache.commons.math.distribution;
20 * The Exponential Distribution.
26 * Exponential Distribution</a></li>
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| FDistribution.java | 17 package org.apache.commons.math.distribution;
20 * F-Distribution.
25 * <li><a href="http://mathworld.wolfram.com/F-Distribution.html">
26 * F-Distribution</a></li>
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| GammaDistribution.java | 17 package org.apache.commons.math.distribution;
20 * The Gamma Distribution.
26 * Gamma Distribution</a></li>
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| HasDensity.java | 18 package org.apache.commons.math.distribution;
23 * <p>Interface that signals that a distribution can compute the probability density function
|
| HypergeometricDistribution.java | 18 package org.apache.commons.math.distribution;
21 * The Hypergeometric Distribution.
27 * Hypergeometric Distribution</a></li>
|
| NormalDistribution.java | 18 package org.apache.commons.math.distribution;
21 * Normal (Gauss) Distribution.
27 * Normal Distribution</a></li>
36 * @return mean for this distribution
41 * @param mean for this distribution
48 * @return standard deviation for this distribution
53 * @param sd standard deviation for this distribution
|
| PascalDistribution.java | 17 package org.apache.commons.math.distribution;
20 * The Pascal distribution. The Pascal distribution is a special case of the
21 * Negative Binomial distribution where the number of successes parameter is an
24 * There are various ways to express the probability mass and distribution
25 * functions for the Pascal distribution. The convention employed by the
33 * Negative Binomial Distribution</a></li>
43 * Access the number of successes for this distribution.
50 * Access the probability of success for this distribution.
57 * Change the number of successes for this distribution
[all...] |
| PoissonDistribution.java | 17 package org.apache.commons.math.distribution;
22 * Interface representing the Poisson Distribution.
28 * Poisson distribution</a></li>
37 * Get the mean for the distribution.
39 * @return the mean for the distribution.
44 * Set the mean for the distribution.
56 * Calculates the Poisson distribution function using a normal approximation.
59 * @return the distribution function value calculated using a normal approximation
|
| WeibullDistribution.java | 18 package org.apache.commons.math.distribution;
21 * Weibull Distribution. This interface defines the two parameter form of the
22 * distribution as defined by
24 * Weibull Distribution</a>, equations (1) and (2).
30 * Weibull Distribution</a></li>
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| ZipfDistribution.java | 18 package org.apache.commons.math.distribution;
21 * The Zipf (or zeta) Distribution.
26 * Distribution</a></li>
35 * Get the number of elements (e.g. corpus size) for the distribution.
42 * Set the number of elements (e.g. corpus size) for the distribution.
54 * Get the exponent characterising the distribution.
61 * Set the exponent characterising the distribution.
|
| BetaDistribution.java | 17 package org.apache.commons.math.distribution;
|
| IntegerDistribution.java | 17 package org.apache.commons.math.distribution;
29 * to this distribution, this method returns P(X = x). In other words, this
30 * method represents the probability mass function for the distribution.
39 * to this distribution, this method returns P(X ≤ x). In other words,
40 * this method represents the probability distribution function, or PDF
41 * for the distribution.
44 * @return PDF for this distribution.
51 * For this distribution, X, this method returns P(x0 ≤ X ≤ x1).
62 * For this distribution, X, this method returns the largest x such that
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| /external/eigen/doc/special_examples/ |
| random_cpp11.cpp | 9 std::poisson_distribution<int> distribution(4.1);
10 auto poisson = [&] () {return distribution(generator);};
|
| /packages/inputmethods/LatinIME/native/jni/tests/suggest/core/layout/ |
| normal_distribution_2d_test.cpp | 33 const NormalDistribution2D distribution(ORIGIN_X, LARGE_STANDARD_DEVIATION, ORIGIN_Y,
38 // The probability density of the point near the distribution center is larger than the
39 // probability density of the point that is far from distribution center.
40 EXPECT_GE(distribution.getProbabilityDensity(SMALL_COORDINATE, SMALL_COORDINATE),
41 distribution.getProbabilityDensity(LARGE_COORDINATE, LARGE_COORDINATE));
45 EXPECT_GE(distribution.getProbabilityDensity(LARGE_COORDINATE, SMALL_COORDINATE),
46 distribution.getProbabilityDensity(SMALL_COORDINATE, LARGE_COORDINATE));
52 const NormalDistribution2D distribution(ORIGIN_X, LARGE_STANDARD_DEVIATION, ORIGIN_Y,
58 // The probability density of the rotated distribution at the point and the probability
59 // density of the original distribution at the rotated point are the same
[all...] |
| /external/tensorflow/tensorflow/contrib/distributions/python/ops/ |
| independent.py | 15 """The Independent distribution class."""
30 from tensorflow.python.ops.distributions import distribution as distribution_lib
33 class Independent(distribution_lib.Distribution):
34 """Independent distribution from batch of distributions.
36 This distribution is useful for regarding a collection of independent,
38 `Indpendent` distribution composed of a collection of `Bernoulli`
39 distributions might define a distribution over an image (where each
40 `Bernoulli` is a distribution over each pixel).
48 Similarly, the `Independent` distribution specifies a distribution over `[B
148 def distribution(self): member in class:Independent
[all...] |
| /packages/inputmethods/LatinIME/native/jni/tests/dictionary/utils/ |
| bloom_filter_test.cpp | 38 // Use the uniform integer distribution [0, TEST_RANDOM_DATA_MAX].
39 std::uniform_int_distribution<int> distribution(0, TEST_RANDOM_DATA_MAX);
40 auto randomNumberGenerator = std::bind(distribution, std::mt19937());
55 // Use the uniform integer distribution [0, 1].
56 std::uniform_int_distribution<int> distribution(0, 1);
57 auto randomBitGenerator = std::bind(distribution, std::mt19937());
|
| /build/kati/ |
| affinity.cc | 34 std::uniform_int_distribution<int> distribution(0, g_flags.num_cpus - 1);
35 int cpu = distribution(generator);
|
| /device/linaro/bootloader/edk2/AppPkg/Applications/Python/Python-2.7.2/Lib/distutils/command/ |
| install_headers.py | 36 headers = self.distribution.headers
46 return self.distribution.headers or []
|
