Can someone point me to one, because our high-school library is way too old. And it wont work with Dev-C(we using in it in CS class). /. Lawrenceville Press Random Library./ /. October 1997./ /. The following code works correctly under BOTH./ /. Borland and MSC. 19.8.1 ISO C Random Number Functions. This section describes the random number functions that are part of the ISO C. To use these facilities, you should include the header file stdlib.h in your program. Macro: int RANDMAX. The value of this macro is an integer constant representing the largest value the rand function can return. These are not standard C or C functions. In standard C and C there are srand and rand which are part of the header. These functions are probably part of Turbo C to improve compatibility with Turbo Pascal, where randomize and random are the default random number generator.

1. Using Dev Random
2. Dev C Random Library In Windows 10
3. Dev Random Example
4. Dev Random Dev Urandom

Source code:Lib/random.py

This module implements pseudo-random number generators for variousdistributions.

For integers, there is uniform selection from a range. For sequences, there isuniform selection of a random element, a function to generate a randompermutation of a list in-place, and a function for random sampling withoutreplacement.

On the real line, there are functions to compute uniform, normal (Gaussian),lognormal, negative exponential, gamma, and beta distributions. For generatingdistributions of angles, the von Mises distribution is available.

Almost all module functions depend on the basic function random(), whichgenerates a random float uniformly in the semi-open range [0.0, 1.0). Pythonuses the Mersenne Twister as the core generator. It produces 53-bit precisionfloats and has a period of 2**19937-1. The underlying implementation in C isboth fast and threadsafe. The Mersenne Twister is one of the most extensivelytested random number generators in existence. However, being completelydeterministic, it is not suitable for all purposes, and is completely unsuitablefor cryptographic purposes.

The functions supplied by this module are actually bound methods of a hiddeninstance of the random.Random class. You can instantiate your owninstances of Random to get generators that don’t share state.

Class Random can also be subclassed if you want to use a differentbasic generator of your own devising: in that case, override the random(),seed(), getstate(), and setstate() methods.Optionally, a new generator can supply a getrandbits() method — thisallows randrange() to produce selections over an arbitrarily large range.

The random module also provides the SystemRandom class whichuses the system function os.urandom() to generate random numbersfrom sources provided by the operating system.

Warning

The pseudo-random generators of this module should not be used forsecurity purposes. For security or cryptographic uses, see thesecrets module.

See also

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M. Matsumoto and T. Nishimura, “Mersenne Twister: A 623-dimensionallyequidistributed uniform pseudorandom number generator”, ACM Transactions onModeling and Computer Simulation Vol. 8, No. 1, January pp.3–30 1998.

Complementary-Multiply-with-Carry recipe for a compatible alternativerandom number generator with a long period and comparatively simple updateoperations.

Bookkeeping functions¶

Initialize the random number generator.

If a is omitted or None, the current system time is used. Ifrandomness sources are provided by the operating system, they are usedinstead of the system time (see the os.urandom() function for detailson availability).

If a is an int, it is used directly.

With version 2 (the default), a str, bytes, or bytearrayobject gets converted to an int and all of its bits are used.

With version 1 (provided for reproducing random sequences from older versionsof Python), the algorithm for str and bytes generates anarrower range of seeds.

Changed in version 3.2: Moved to the version 2 scheme which uses all of the bits in a string seed.

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Return an object capturing the current internal state of the generator. Thisobject can be passed to setstate() to restore the state.

state should have been obtained from a previous call to getstate(), andsetstate() restores the internal state of the generator to what it was atthe time getstate() was called.

Returns a Python integer with k random bits. This method is supplied withthe MersenneTwister generator and some other generators may also provide itas an optional part of the API. When available, getrandbits() enablesrandrange() to handle arbitrarily large ranges.

Functions for integers¶

Return a randomly selected element from range(start,stop,step). This isequivalent to choice(range(start,stop,step)), but doesn’t actually build arange object.

The positional argument pattern matches that of range(). Keyword argumentsshould not be used because the function may use them in unexpected ways.

Changed in version 3.2: randrange() is more sophisticated about producing equally distributedvalues. Formerly it used a style like int(random()*n) which could produceslightly uneven distributions.

Return a random integer N such that a<=N<=b. Alias forrandrange(a,b+1).

Functions for sequences¶

Return a random element from the non-empty sequence seq. If seq is empty,raises IndexError.

Return a k sized list of elements chosen from the population with replacement.If the population is empty, raises IndexError.

If a weights sequence is specified, selections are made according to therelative weights. Alternatively, if a cum_weights sequence is given, theselections are made according to the cumulative weights (perhaps computedusing itertools.accumulate()). For example, the relative weights[10,5,30,5] are equivalent to the cumulative weights[10,15,45,50]. Internally, the relative weights are converted tocumulative weights before making selections, so supplying the cumulativeweights saves work.

If neither weights nor cum_weights are specified, selections are madewith equal probability. If a weights sequence is supplied, it must bethe same length as the population sequence. It is a TypeErrorto specify both weights and cum_weights.

The weights or cum_weights can use any numeric type that interoperateswith the float values returned by random() (that includesintegers, floats, and fractions but excludes decimals). Weights areassumed to be non-negative.

For a given seed, the choices() function with equal weightingtypically produces a different sequence than repeated calls tochoice(). The algorithm used by choices() uses floatingpoint arithmetic for internal consistency and speed. The algorithm usedby choice() defaults to integer arithmetic with repeated selectionsto avoid small biases from round-off error.

Shuffle the sequence x in place.

The optional argument random is a 0-argument function returning a randomfloat in [0.0, 1.0); by default, this is the function random().

To shuffle an immutable sequence and return a new shuffled list, usesample(x,k=len(x)) instead.

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Note that even for small len(x), the total number of permutations of xcan quickly grow larger than the period of most random number generators.This implies that most permutations of a long sequence can never begenerated. For example, a sequence of length 2080 is the largest thatcan fit within the period of the Mersenne Twister random number generator.

Return a k length list of unique elements chosen from the population sequenceor set. Used for random sampling without replacement.

Returns a new list containing elements from the population while leaving theoriginal population unchanged. The resulting list is in selection order so thatall sub-slices will also be valid random samples. This allows raffle winners(the sample) to be partitioned into grand prize and second place winners (thesubslices).

Members of the population need not be hashable or unique. If the populationcontains repeats, then each occurrence is a possible selection in the sample.

To choose a sample from a range of integers, use a range() object as anargument. This is especially fast and space efficient for sampling from a largepopulation: sample(range(10000000),k=60).

If the sample size is larger than the population size, a ValueErroris raised.

Real-valued distributions¶

The following functions generate specific real-valued distributions. Functionparameters are named after the corresponding variables in the distribution’sequation, as used in common mathematical practice; most of these equations canbe found in any statistics text.

Return the next random floating point number in the range [0.0, 1.0).

Return a random floating point number N such that a<=N<=b fora<=b and b<=N<=a for b<a.

The end-point value b may or may not be included in the rangedepending on floating-point rounding in the equation a+(b-a)*random().

Return a random floating point number N such that low<=N<=high andwith the specified mode between those bounds. The low and high boundsdefault to zero and one. The mode argument defaults to the midpointbetween the bounds, giving a symmetric distribution.

Beta distribution. Conditions on the parameters are alpha>0 andbeta>0. Returned values range between 0 and 1.

Exponential distribution. lambd is 1.0 divided by the desiredmean. It should be nonzero. (The parameter would be called“lambda”, but that is a reserved word in Python.) Returned valuesrange from 0 to positive infinity if lambd is positive, and fromnegative infinity to 0 if lambd is negative.

Gamma distribution. (Not the gamma function!) Conditions on theparameters are alpha>0 and beta>0.

The probability distribution function is:

Gaussian distribution. mu is the mean, and sigma is the standarddeviation. This is slightly faster than the normalvariate() functiondefined below.

Log normal distribution. If you take the natural logarithm of thisdistribution, you’ll get a normal distribution with mean mu and standarddeviation sigma. mu can have any value, and sigma must be greater thanzero.

Normal distribution. mu is the mean, and sigma is the standard deviation.

mu is the mean angle, expressed in radians between 0 and 2*pi, and kappais the concentration parameter, which must be greater than or equal to zero. Ifkappa is equal to zero, this distribution reduces to a uniform random angleover the range 0 to 2*pi.

Pareto distribution. alpha is the shape parameter.

Weibull distribution. alpha is the scale parameter and beta is the shapeparameter.

Alternative Generator¶

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Class that implements the default pseudo-random number generator used by therandom module.

Class that uses the os.urandom() function for generating random numbersfrom sources provided by the operating system. Not available on all systems.Does not rely on software state, and sequences are not reproducible. Accordingly,the seed() method has no effect and is ignored.The getstate() and setstate() methods raiseNotImplementedError if called.

Notes on Reproducibility¶

Sometimes it is useful to be able to reproduce the sequences given by a pseudorandom number generator. By re-using a seed value, the same sequence should bereproducible from run to run as long as multiple threads are not running.

Most of the random module’s algorithms and seeding functions are subject tochange across Python versions, but two aspects are guaranteed not to change:

• If a new seeding method is added, then a backward compatible seeder will beoffered.

• The generator’s random() method will continue to produce the samesequence when the compatible seeder is given the same seed.

Examples and Recipes¶

Basic examples:

Simulations:

Example of statistical bootstrapping using resamplingwith replacement to estimate a confidence interval for the mean of a sample ofsize five:

Example of a resampling permutation testto determine the statistical significance or p-value of an observed differencebetween the effects of a drug versus a placebo:

Simulation of arrival times and service deliveries in a single server queue:

See also

Statistics for Hackersa video tutorial byJake Vanderplason statistical analysis using just a few fundamental conceptsincluding simulation, sampling, shuffling, and cross-validation.

Dev Random Example

Economics Simulationa simulation of a marketplace byPeter Norvig that shows effectiveuse of many of the tools and distributions provided by this module(gauss, uniform, sample, betavariate, choice, triangular, and randrange).

Dev Random Dev Urandom

A Concrete Introduction to Probability (using Python)a tutorial by Peter Norvig coveringthe basics of probability theory, how to write simulations, andhow to perform data analysis using Python.