--- rest25/library/random.rst	2008-12-19 16:58:07.000000000 +0900
+++ rest262/library/random.rst	2009-04-05 05:02:09.000000000 +0900
@@ -1,118 +1,121 @@
 
 :mod:`random` --- Generate pseudo-random numbers
 ================================================
 
 .. module:: random
    :synopsis: Generate pseudo-random numbers with various common distributions.
 
 
 This module implements pseudo-random number generators for various
 distributions.
 
 For integers, uniform selection from a range. For sequences, uniform selection
-of a random element, a function to generate a random permutation of a list in-
-place, and a function for random sampling without replacement.
+of a random element, a function to generate a random permutation of a list
+in-place, and a function for random sampling without replacement.
 
 On the real line, there are functions to compute uniform, normal (Gaussian),
 lognormal, negative exponential, gamma, and beta distributions. For generating
 distributions of angles, the von Mises distribution is available.
 
 Almost all module functions depend on the basic function :func:`random`, which
 generates a random float uniformly in the semi-open range [0.0, 1.0).  Python
 uses the Mersenne Twister as the core generator.  It produces 53-bit precision
 floats and has a period of 2\*\*19937-1.  The underlying implementation in C is
 both fast and threadsafe.  The Mersenne Twister is one of the most extensively
 tested random number generators in existence.  However, being completely
 deterministic, it is not suitable for all purposes, and is completely unsuitable
 for cryptographic purposes.
 
 The functions supplied by this module are actually bound methods of a hidden
 instance of the :class:`random.Random` class.  You can instantiate your own
 instances of :class:`Random` to get generators that don't share state.  This is
 especially useful for multi-threaded programs, creating a different instance of
 :class:`Random` for each thread, and using the :meth:`jumpahead` method to make
 it likely that the generated sequences seen by each thread don't overlap.
 
 Class :class:`Random` can also be subclassed if you want to use a different
 basic generator of your own devising: in that case, override the :meth:`random`,
 :meth:`seed`, :meth:`getstate`, :meth:`setstate` and :meth:`jumpahead` methods.
-Optionally, a new generator can supply a :meth:`getrandombits` method --- this
+Optionally, a new generator can supply a :meth:`getrandbits` method --- this
 allows :meth:`randrange` to produce selections over an arbitrarily large range.
 
 .. versionadded:: 2.4
-   the :meth:`getrandombits` method.
+   the :meth:`getrandbits` method.
 
 As an example of subclassing, the :mod:`random` module provides the
 :class:`WichmannHill` class that implements an alternative generator in pure
 Python.  The class provides a backward compatible way to reproduce results from
 earlier versions of Python, which used the Wichmann-Hill algorithm as the core
 generator.  Note that this Wichmann-Hill generator can no longer be recommended:
 its period is too short by contemporary standards, and the sequence generated is
 known to fail some stringent randomness tests.  See the references below for a
 recent variant that repairs these flaws.
 
 .. versionchanged:: 2.3
    Substituted MersenneTwister for Wichmann-Hill.
 
 Bookkeeping functions:
 
 
 .. function:: seed([x])
 
    Initialize the basic random number generator. Optional argument *x* can be any
-   hashable object. If *x* is omitted or ``None``, current system time is used;
+   :term:`hashable` object. If *x* is omitted or ``None``, current system time is used;
    current system time is also used to initialize the generator when the module is
    first imported.  If randomness sources are provided by the operating system,
    they are used instead of the system time (see the :func:`os.urandom` function
    for details on availability).
 
    .. versionchanged:: 2.4
       formerly, operating system resources were not used.
 
    If *x* is not ``None`` or an int or long, ``hash(x)`` is used instead. If *x* is
    an int or long, *x* is used directly.
 
 
 .. function:: getstate()
 
    Return an object capturing the current internal state of the generator.  This
    object can be passed to :func:`setstate` to restore the state.
 
    .. versionadded:: 2.1
 
+   .. versionchanged:: 2.6
+      State values produced in Python 2.6 cannot be loaded into earlier versions.
+
 
 .. function:: setstate(state)
 
    *state* should have been obtained from a previous call to :func:`getstate`, and
    :func:`setstate` restores the internal state of the generator to what it was at
    the time :func:`setstate` was called.
 
    .. versionadded:: 2.1
 
 
 .. function:: jumpahead(n)
 
    Change the internal state to one different from and likely far away from the
    current state.  *n* is a non-negative integer which is used to scramble the
    current state vector.  This is most useful in multi-threaded programs, in
-   conjuction with multiple instances of the :class:`Random` class:
+   conjunction with multiple instances of the :class:`Random` class:
    :meth:`setstate` or :meth:`seed` can be used to force all instances into the
    same internal state, and then :meth:`jumpahead` can be used to force the
    instances' states far apart.
 
    .. versionadded:: 2.1
 
    .. versionchanged:: 2.3
-      Instead of jumping to a specific state, *n* steps ahead, :meth:`jumpahead(n)`
+      Instead of jumping to a specific state, *n* steps ahead, ``jumpahead(n)``
       jumps to another state likely to be separated by many steps.
 
 
 .. function:: getrandbits(k)
 
    Returns a python :class:`long` int with *k* random bits. This method is supplied
    with the MersenneTwister generator and some other generators may also provide it
    as an optional part of the API. When available, :meth:`getrandbits` enables
    :meth:`randrange` to handle arbitrarily large ranges.
 
    .. versionadded:: 2.4
 
@@ -156,105 +159,119 @@
 
    Return a *k* length list of unique elements chosen from the population sequence.
    Used for random sampling without replacement.
 
    .. versionadded:: 2.3
 
    Returns a new list containing elements from the population while leaving the
    original population unchanged.  The resulting list is in selection order so that
    all sub-slices will also be valid random samples.  This allows raffle winners
    (the sample) to be partitioned into grand prize and second place winners (the
    subslices).
 
-   Members of the population need not be hashable or unique.  If the population
+   Members of the population need not be :term:`hashable` or unique.  If the population
    contains repeats, then each occurrence is a possible selection in the sample.
 
    To choose a sample from a range of integers, use an :func:`xrange` object as an
    argument.  This is especially fast and space efficient for sampling from a large
    population:  ``sample(xrange(10000000), 60)``.
 
 The following functions generate specific real-valued distributions. Function
 parameters are named after the corresponding variables in the distribution's
 equation, as used in common mathematical practice; most of these equations can
 be found in any statistics text.
 
 
 .. function:: random()
 
    Return the next random floating point number in the range [0.0, 1.0).
 
 
 .. function:: uniform(a, b)
 
-   Return a random real number *N* such that ``a <= N < b``.
+   Return a random floating point number *N* such that ``a <= N <= b`` for
+   ``a <= b`` and ``b <= N <= a`` for ``b < a``.
+
+
+.. function:: triangular(low, high, mode)
+
+   Return a random floating point number *N* such that ``low <= N <= high`` and
+   with the specified *mode* between those bounds.  The *low* and *high* bounds
+   default to zero and one.  The *mode* argument defaults to the midpoint
+   between the bounds, giving a symmetric distribution.
+
+   .. versionadded:: 2.6
 
 
 .. function:: betavariate(alpha, beta)
 
-   Beta distribution.  Conditions on the parameters are ``alpha > -1`` and ``beta >
-   -1``. Returned values range between 0 and 1.
+   Beta distribution.  Conditions on the parameters are ``alpha > 0`` and
+   ``beta > 0``. Returned values range between 0 and 1.
 
 
 .. function:: expovariate(lambd)
 
-   Exponential distribution.  *lambd* is 1.0 divided by the desired mean.  (The
-   parameter would be called "lambda", but that is a reserved word in Python.)
-   Returned values range from 0 to positive infinity.
+   Exponential distribution.  *lambd* is 1.0 divided by the desired
+   mean.  It should be nonzero.  (The parameter would be called
+   "lambda", but that is a reserved word in Python.)  Returned values
+   range from 0 to positive infinity if *lambd* is positive, and from
+   negative infinity to 0 if *lambd* is negative.
 
 
 .. function:: gammavariate(alpha, beta)
 
-   Gamma distribution.  (*Not* the gamma function!)  Conditions on the parameters
-   are ``alpha > 0`` and ``beta > 0``.
+   Gamma distribution.  (*Not* the gamma function!)  Conditions on the
+   parameters are ``alpha > 0`` and ``beta > 0``.
 
 
 .. function:: gauss(mu, sigma)
 
-   Gaussian distribution.  *mu* is the mean, and *sigma* is the standard deviation.
-   This is slightly faster than the :func:`normalvariate` function defined below.
+   Gaussian distribution.  *mu* is the mean, and *sigma* is the standard
+   deviation.  This is slightly faster than the :func:`normalvariate` function
+   defined below.
 
 
 .. function:: lognormvariate(mu, sigma)
 
    Log normal distribution.  If you take the natural logarithm of this
    distribution, you'll get a normal distribution with mean *mu* and standard
    deviation *sigma*.  *mu* can have any value, and *sigma* must be greater than
    zero.
 
 
 .. function:: normalvariate(mu, sigma)
 
    Normal distribution.  *mu* is the mean, and *sigma* is the standard deviation.
 
 
 .. function:: vonmisesvariate(mu, kappa)
 
-   *mu* is the mean angle, expressed in radians between 0 and 2\**pi*, and *kappa*
+   *mu* is the mean angle, expressed in radians between 0 and 2\*\ *pi*, and *kappa*
    is the concentration parameter, which must be greater than or equal to zero.  If
    *kappa* is equal to zero, this distribution reduces to a uniform random angle
-   over the range 0 to 2\**pi*.
+   over the range 0 to 2\*\ *pi*.
 
 
 .. function:: paretovariate(alpha)
 
    Pareto distribution.  *alpha* is the shape parameter.
 
 
 .. function:: weibullvariate(alpha, beta)
 
    Weibull distribution.  *alpha* is the scale parameter and *beta* is the shape
    parameter.
 
-Alternative Generators:
 
+Alternative Generators:
 
 .. class:: WichmannHill([seed])
 
    Class that implements the Wichmann-Hill algorithm as the core generator. Has all
    of the same methods as :class:`Random` plus the :meth:`whseed` method described
    below.  Because this class is implemented in pure Python, it is not threadsafe
    and may require locks between calls.  The period of the generator is
    6,953,607,871,644 which is small enough to require care that two independent
    random sequences do not overlap.
 
 
 .. function:: whseed([x])
@@ -299,17 +316,16 @@
 
 
 
 .. seealso::
 
    M. Matsumoto and T. Nishimura, "Mersenne Twister: A 623-dimensionally
    equidistributed uniform pseudorandom number generator", ACM Transactions on
    Modeling and Computer Simulation Vol. 8, No. 1, January pp.3-30 1998.
 
    Wichmann, B. A. & Hill, I. D., "Algorithm AS 183: An efficient and portable
    pseudo-random number generator", Applied Statistics 31 (1982) 188-190.
 
-   http://www.npl.co.uk/ssfm/download/abstracts.html#196
-      A modern variation of the Wichmann-Hill generator that greatly increases the
-      period, and passes now-standard statistical tests that the original generator
-      failed.
-
+   `Complementary-Multiply-with-Carry recipe
+   <http://code.activestate.com/recipes/576707/>`_ for a compatible alternative
+   random number generator with a long period and comparatively simple update
+   operations.
