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  /external/llvm/utils/lit/utils/
README.txt 2 distribution.
  /external/pcre/dist/
COPYING 3 Please see the file LICENCE in the PCRE distribution for licensing details.
  /prebuilts/eclipse/maven/apache-maven-3.2.1/
NOTICE 2 Apache Maven Distribution
  /external/apache-commons-math/src/main/java/org/apache/commons/math/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
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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
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.
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
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
Distribution.java 17 package org.apache.commons.math.distribution;
26 public interface Distribution {
29 * to this distribution, this method returns P(X &le; 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 &le; X &le; x1).
47 * @return the probability that a random variable with this distribution
CauchyDistributionImpl.java 18 package org.apache.commons.math.distribution;
28 * {@link org.apache.commons.math.distribution.CauchyDistribution}.
45 /** The median of this distribution. */
48 /** The scale of this distribution. */
55 * Creates cauchy distribution with the medain equal to zero and scale
63 * Create a cauchy distribution using the given median and scale.
64 * @param median median for this distribution
65 * @param s scale parameter for this distribution
72 * Create a cauchy distribution using the given median and scale.
73 * @param median median for this distribution
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PoissonDistributionImpl.java 17 package org.apache.commons.math.distribution;
51 /** Distribution used to compute normal approximation. */
55 * Holds the Poisson mean for the distribution.
73 * Create a new Poisson distribution with the given the mean. The mean value
84 * Create a new Poisson distribution with the given mean, convergence criterion
99 * Create a new Poisson distribution with the given mean and convergence criterion.
111 * Create a new Poisson distribution with the given mean and maximum number of iterations.
124 * Create a new Poisson distribution with the given the mean. The mean value
128 * @param z a normal distribution used to compute normal approximations.
141 * Get the Poisson mean for the distribution
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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.
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>
  /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
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  /external/iproute2/netem/
pareto.c 2 * Pareto distribution table generator
23 printf("# This is the distribution table for the pareto distribution.\n");
README.distribution 1 Notes about distribution tables from Nistnet
3 I. About the distribution tables
5 The table used for "synthesizing" the distribution is essentially a scaled,
6 translated, inverse to the cumulative distribution function.
8 Here's how to think about it: Let F() be the cumulative distribution
9 function for a probability distribution X. We'll assume we've scaled
26 distribution has the same approximate "shape" as X, simply by letting
28 To see this, it's enough to show that Y's cumulative distribution function,
41 II. How to create distribution tables (in theory)
45 pareto distribution is one example of this. In other cases, an
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  /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());
  /prebuilts/tools/common/m2/repository/javax/inject/javax.inject/1/
javax.inject-1.pom 14 <distribution>repo</distribution>
  /external/apache-commons-math/src/main/java/org/apache/commons/math/random/
EmpiricalDistribution.java 30 * empirical probability distribution</a> -- a probability distribution derived
32 * of the population distribution that the data come from.<p>
34 * <i>distribution digests</i>, that describe empirical distributions and
36 * <li>loading the distribution from a file of observed data values</li>
41 * <li>generating random values from the distribution</li>
46 * generated will follow the distribution of the values in the file.</p>
53 * Computes the empirical distribution from the provided
61 * Computes the empirical distribution from the input file.
69 * Computes the empirical distribution using data read from a URL
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  /prebuilts/python/linux-x86/2.7.5/lib/python2.7/site-packages/setoolsgui/networkx/utils/
random_sequence.py 66 Return sample sequence of length n from a Pareto distribution.
73 Return sample sequence of length n from a power law distribution.
78 r"""Return a random value chosen from the Zipf distribution.
80 The return value is an integer drawn from the probability distribution
90 Exponent value of the distribution
99 Random value from Zipf distribution
110 distribution in uniformly bounded expected time dependent on
140 """Return a sample sequence of length n from a Zipf distribution with
151 Return sample sequence of length n from a uniform distribution.
156 def cumulative_distribution(distribution)
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  /external/clang/test/Index/Inputs/CommentXML/
valid-availability-attr-01.xml 5 <Availability distribution="OS X">
valid-availability-attr-02.xml 5 <Availability distribution="OS X">
  /external/fio/examples/
zipf.fio 1 # Example job file for using a zipf distribution instead
  /external/hyphenation-patterns/en-US/
NOTICE 2 % Copying and distribution of this file, with or without modification,
  /external/iputils/
in6_flowlabel.h 1 /* The in6_flowlabel.h file in the iputils distribution exists to provide

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