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https://jucor.github.io/torch-randomkit
Provides and wraps the Randomkit library, copied from Numpy.
https://jucor.github.io/torch-randomkit
Last synced: 2 months ago
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Provides and wraps the Randomkit library, copied from Numpy.
- Host: GitHub
- URL: https://jucor.github.io/torch-randomkit
- Owner: google-deepmind
- License: bsd-3-clause
- Created: 2013-10-25T17:02:43.000Z (about 11 years ago)
- Default Branch: master
- Last Pushed: 2019-04-23T10:14:40.000Z (over 5 years ago)
- Last Synced: 2024-04-16T04:53:42.432Z (8 months ago)
- Language: Lua
- Homepage:
- Size: 286 KB
- Stars: 34
- Watchers: 16
- Forks: 26
- Open Issues: 9
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE.txt
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- awesome-machine-learning-cn - 官网
- awesome-machine-master - randomkit - Numpy's randomkit, wrapped for Torch (Lua)
README
[](https://travis-ci.org/deepmind/torch-randomkit)
#Randomkit random number generators, wrapped for Torch
**NOTE: THIS PACKAGE IS NOT ACTIVELY MAINTAINED**
Provides and wraps the Randomkit library, copied from Numpy. Please check-out its [website](http://deepmind.github.io/torch-randomkit) for up-to-date documentation or read below.
##Example
###Single sample
You can call any of the wrapped functions with just the distribution's parameters to generate a single sample and return a number:
require 'randomkit'
randomkit.poisson(5)
###Multiple samples from one distribution
Often, you might want to generate many samples identically distributed. Simply pass as a first argument a tensor of the proper dimension, into which the samples will be stored:
x = torch.Tensor(10000)
randomkit.poisson(x, 5)
The sampler returns the tensor, so you can shorten the above in:
x = randomkit.poisson(torch.Tensor(10000), 5)
###Multiple samples from multiple distributions
Finally, you might want to generate many samples, each from a distribution with different parameters. This is achieved by passing a Tensor as the parameter of the distribution:
many_lambda = torch.Tensor{5, 3, 40, 60}
x = randomkit.poisson(many_lambda)
Of course, this can be combined with passing a result Tensor as an optional first element, to re-use memory and avoid creating a new Tensor at each call:
many_lambda = torch.Tensor{5, 3, 40, 60}
x = torch.Tensor(many_lambda:size())
randomkit.poisson(x, many_lambda)
Note: in the latter case, the size of the result Tensor must correspond to the size of the parameter tensor -- we do not resize the result tensor automatically, yet:
###Getting/setting the seed and the state
Randomkit is transparently integrated with Torch's random stream: just use torch.manualSeed(seed), torch.getRNGState(), and torch.setRNGState(state) as usual.
Specifying an (optional) torch.Generator instance as the first argument will only influence the state of that generator, leaving the state of randomkit unchanged.
##Installation
From a terminal:
luarocks install randomkit
##Unit Tests
Last but not least, the unit tests are in the folder
luasrc/tests. You can run them from your local clone of the repository with:
git clone https://www.github.com/jucor/torch-randomkit
find torch-randomkit/luasrc/tests -name "test*lua" -exec torch {} \;
##Direct access to FFI
randomkit.ffi.*
Functions directly accessible at the top of the randomkit table are Lua wrappers to the actual C functions from Randomkit, with extra error checking. If, for any reason, you want to get rid of this error checking and of a possible overhead, the FFI-wrapper functions can be called directly via randomkit.ffi.myfunction() instead of randomkit.myfunction().
#List of distributions
##beta
randomkit.beta([output], a, b)
The Beta distribution over ``[0, 1]``.
The Beta distribution is a special case of the Dirichlet distribution,
and is related to the Gamma distribution. It has the probability
distribution function
$$ f(x; a,b) = \frac{1}{B(\alpha, \beta)} x^{\alpha - 1}
(1 - x)^{\beta - 1},$$
where the normalisation, B, is the beta function,
$$ B(\alpha, \beta) = \int_0^1 t^{\alpha - 1}
(1 - t)^{\beta - 1} dt.$$
It is often seen in Bayesian inference and order statistics.
####Parameters
* a : float
Alpha, non-negative.
* b : float
Beta, non-negative.
* size : tuple of ints, optional
The number of samples to draw. The output is packed according to
the size given.
####Returns
* out : ndarray
Array of the given shape, containing values drawn from a
Beta distribution.
##binomial
randomkit.binomial([output], n, p)
Draw samples from a binomial distribution.
Samples are drawn from a Binomial distribution with specified
parameters, n trials and p probability of success where
n an integer >= 0 and p is in the interval [0,1]. (n may be
input as a float, but it is truncated to an integer in use)
####Parameters
* n : float (but truncated to an integer)
parameter, >= 0.
* p : float
parameter, >= 0 and <=1.
* size : {tuple, int}
Output shape. If the given shape is, e.g., ``(m, n, k)``, then
``m * n * k`` samples are drawn.
####Returns
* samples : {ndarray, scalar}
where the values are all integers in [0, n].
####See Also
* scipy.stats.distributions.binom : probability density function,
distribution or cumulative density function, etc.
####Notes
The probability density for the Binomial distribution is
$$ P(N) = \binom{n}{N}p^N(1-p)^{n-N},$$
where \\(n\\) is the number of trials, \\(p\\) is the probability
of success, and \\(N\\) is the number of successes.
When estimating the standard error of a proportion in a population by
using a random sample, the normal distribution works well unless the
product p*n <=5, where p = population proportion estimate, and n =
number of samples, in which case the binomial distribution is used
instead. For example, a sample of 15 people shows 4 who are left
handed, and 11 who are right handed. Then p = 4/15 = 27%. 0.27*15 = 4,
so the binomial distribution should be used in this case.
####References
1. Dalgaard, Peter, "Introductory Statistics with R",
Springer-Verlag, 2002.
2. Glantz, Stanton A. "Primer of Biostatistics.", McGraw-Hill,
Fifth Edition, 2002.
3. Lentner, Marvin, "Elementary Applied Statistics", Bogden
and Quigley, 1972.
4. Weisstein, Eric W. "Binomial Distribution." From MathWorld--A
Wolfram Web Resource.
http://mathworld.wolfram.com/BinomialDistribution.html
5. Wikipedia, "Binomial-distribution",
http://en.wikipedia.org/wiki/Binomial_distribution
####Examples
Draw samples from the distribution:
$ n, p = 10, .5 number of trials, probability of each trial
$ s = np.random.binomial(n, p, 1000)
result of flipping a coin 10 times, tested 1000 times.
A real world example. A company drills 9 wild-cat oil exploration
wells, each with an estimated probability of success of 0.1. All nine
wells fail. What is the probability of that happening?
Let's do 20,000 trials of the model, and count the number that
generate zero positive results.
$ sum(np.random.binomial(9,0.1,20000)==0)/20000.
answer = 0.38885, or 38%.
##bytes
randomkit.bytes([output], length)
Return random bytes.
####Parameters
* length : int
Number of random bytes.
####Returns
* out : str
String of length `length`.
####Examples
$ np.random.bytes(10)
' eh\x85\x022SZ\xbf\xa4' random
##chisquare
randomkit.chisquare([output], df)
Draw samples from a chi-square distribution.
When `df` independent random variables, each with standard normal
distributions (mean 0, variance 1), are squared and summed, the
resulting distribution is chi-square (see Notes). This distribution
is often used in hypothesis testing.
####Parameters
* df : int
Number of degrees of freedom.
* size : tuple of ints, int, optional
Size of the returned array. By default, a scalar is
returned.
####Returns
* output : ndarray
Samples drawn from the distribution, packed in a `size`-shaped
array.
####Raises
ValueError
When `df` <= 0 or when an inappropriate `size` (e.g. ``size=-1``)
is given.
####Notes
The variable obtained by summing the squares of `df` independent,
standard normally distributed random variables:
$$ Q = \sum_{i=0}^{\mathtt{df}} X^2_i$$
is chi-square distributed, denoted
$$ Q \sim \chi^2_k.$$
The probability density function of the chi-squared distribution is
$$ p(x) = \frac{(1/2)^{k/2}}{\Gamma(k/2)}
x^{k/2 - 1} e^{-x/2},$$
where \\(\Gamma\\) is the gamma function,
$$ \Gamma(x) = \int_0^{-\infty} t^{x - 1} e^{-t} dt.$$
####References
1. NIST/SEMATECH e-Handbook of Statistical Methods, http://www.itl.nist.gov/div898/handbook/eda/section3/eda3666.htm
####Examples
$ np.random.chisquare(2,4)
array([ 1.89920014, 9.00867716, 3.13710533, 5.62318272])
##dirichlet
randomkit.dirichlet([output], alpha)
Draw samples from the Dirichlet distribution.
Draw `size` samples of dimension k from a Dirichlet distribution. A
Dirichlet-distributed random variable can be seen as a multivariate
generalization of a Beta distribution. Dirichlet pdf is the conjugate
prior of a multinomial in Bayesian inference.
####Parameters
* alpha : array
Parameter of the distribution (k dimension for sample of
dimension k).
* size : array
Number of samples to draw.
####Returns
* samples : ndarray,
The drawn samples, of shape (alpha.ndim, size).
####Notes
$$ X \approx \prod_{i=1}^{k}{x^{\alpha_i-1}_i}$$
Uses the following property for computation: for each dimension,
draw a random sample y_i from a standard gamma generator of shape
`alpha_i`, then
\\(X = \frac{1}{\sum_{i=1}^k{y_i}} (y_1, \ldots, y_n)\\) is
Dirichlet distributed.
####References
1. David McKay, "Information Theory, Inference and Learning
Algorithms," chapter 23,
http://www.inference.phy.cam.ac.uk/mackay/
2. Wikipedia, "Dirichlet distribution",
http://en.wikipedia.org/wiki/Dirichlet_distribution
####Examples
Taking an example cited in Wikipedia, this distribution can be used if
one wanted to cut strings (each of initial length 1.0) into K pieces
with different lengths, where each piece had, on average, a designated
average length, but allowing some variation in the relative sizes of the
pieces.
$ s = np.random.dirichlet((10, 5, 3), 20).transpose()
$ plt.barh(range(20), s[0])
$ plt.barh(range(20), s[1], left=s[0], color='g')
$ plt.barh(range(20), s[2], left=s[0]+s[1], color='r')
$ plt.title("Lengths of Strings")
##exponential
randomkit.exponential([output], scale)
Exponential distribution.
Its probability density function is
$$ f(x; \frac{1}{\beta}) = \frac{1}{\beta} \exp(-\frac{x}{\beta}),$$
for ``x > 0`` and 0 elsewhere. \\(\beta\\) is the scale parameter,
which is the inverse of the rate parameter \\(\lambda = 1/\beta\\).
The rate parameter is an alternative, widely used parameterization
of the exponential distribution [3].
The exponential distribution is a continuous analogue of the
geometric distribution. It describes many common situations, such as
the size of raindrops measured over many rainstorms [1], or the time
between page requests to Wikipedia [2].
####Parameters
* scale : float
The scale parameter, \\(\beta = 1/\lambda\\).
* size : tuple of ints
Number of samples to draw. The output is shaped
according to `size`.
####References
1. Peyton Z. Peebles Jr., "Probability, Random Variables and
Random Signal Principles", 4th ed, 2001, p. 57.
2. "Poisson Process", Wikipedia,
http://en.wikipedia.org/wiki/Poisson_process
3. "Exponential Distribution, Wikipedia,
http://en.wikipedia.org/wiki/Exponential_distribution
##f
randomkit.f([output], dfnum, dfden)
Draw samples from a F distribution.
Samples are drawn from an F distribution with specified parameters,
`dfnum` (degrees of freedom in numerator) and `dfden` (degrees of freedom
in denominator), where both parameters should be greater than zero.
The random variate of the F distribution (also known as the
Fisher distribution) is a continuous probability distribution
that arises in ANOVA tests, and is the ratio of two chi-square
variates.
####Parameters
* dfnum : float
Degrees of freedom in numerator. Should be greater than zero.
* dfden : float
Degrees of freedom in denominator. Should be greater than zero.
* size : {tuple, int}, optional
Output shape. If the given shape is, e.g., ``(m, n, k)``,
then ``m * n * k`` samples are drawn. By default only one sample
is returned.
####Returns
* samples : {ndarray, scalar}
Samples from the Fisher distribution.
####See Also
* scipy.stats.distributions.f : probability density function,
distribution or cumulative density function, etc.
####Notes
The F statistic is used to compare in-group variances to between-group
variances. Calculating the distribution depends on the sampling, and
so it is a function of the respective degrees of freedom in the
problem. The variable `dfnum` is the number of samples minus one, the
between-groups degrees of freedom, while `dfden` is the within-groups
degrees of freedom, the sum of the number of samples in each group
minus the number of groups.
####References
1. Glantz, Stanton A. "Primer of Biostatistics.", McGraw-Hill,
Fifth Edition, 2002.
2. Wikipedia, "F-distribution",
http://en.wikipedia.org/wiki/F-distribution
####Examples
An example from Glantz[1], pp 47-40.
Two groups, children of diabetics (25 people) and children from people
without diabetes (25 controls). Fasting blood glucose was measured,
case group had a mean value of 86.1, controls had a mean value of
82.2. Standard deviations were 2.09 and 2.49 respectively. Are these
data consistent with the null hypothesis that the parents diabetic
status does not affect their children's blood glucose levels?
Calculating the F statistic from the data gives a value of 36.01.
Draw samples from the distribution:
$ dfnum = 1. between group degrees of freedom
$ dfden = 48. within groups degrees of freedom
$ s = np.random.f(dfnum, dfden, 1000)
The lower bound for the top 1% of the samples is :
$ sort(s)[-10]
7.61988120985
So there is about a 1% chance that the F statistic will exceed 7.62,
the measured value is 36, so the null hypothesis is rejected at the 1%
level.
##gamma
randomkit.gamma([output], shape, scale)
Draw samples from a Gamma distribution.
Samples are drawn from a Gamma distribution with specified parameters,
`shape` (sometimes designated "k") and `scale` (sometimes designated
"theta"), where both parameters are > 0.
####Parameters
* shape : scalar > 0
The shape of the gamma distribution.
* scale : scalar > 0, optional
The scale of the gamma distribution. Default is equal to 1.
* size : shape_tuple, optional
Output shape. If the given shape is, e.g., ``(m, n, k)``, then
``m * n * k`` samples are drawn.
####Returns
* out : ndarray, float
Returns one sample unless `size` parameter is specified.
####See Also
* scipy.stats.distributions.gamma : probability density function,
distribution or cumulative density function, etc.
####Notes
The probability density for the Gamma distribution is
$$ p(x) = x^{k-1}\frac{e^{-x/\theta}}{\theta^k\Gamma(k)},$$
where \\(k\\) is the shape and \\(\theta\\) the scale,
and \\(\Gamma\\) is the Gamma function.
The Gamma distribution is often used to model the times to failure of
electronic components, and arises naturally in processes for which the
waiting times between Poisson distributed events are relevant.
####References
1. Weisstein, Eric W. "Gamma Distribution." From MathWorld--A
Wolfram Web Resource.
http://mathworld.wolfram.com/GammaDistribution.html
2. Wikipedia, "Gamma-distribution",
http://en.wikipedia.org/wiki/Gamma-distribution
####Examples
Draw samples from the distribution:
$ shape, scale = 2., 2. mean and dispersion
$ s = np.random.gamma(shape, scale, 1000)
Display the histogram of the samples, along with
the probability density function:
$ import matplotlib.pyplot as plt
$ import scipy.special as sps
$ count, bins, ignored = plt.hist(s, 50, normed=True)
$ y = bins**(shape-1)*(np.exp(-bins/scale) /
(sps.gamma(shape)*scale**shape))
$ plt.plot(bins, y, linewidth=2, color='r')
$ plt.show()
##geometric
randomkit.geometric([output], p)
Draw samples from the geometric distribution.
Bernoulli trials are experiments with one of two outcomes:
success or failure (an example of such an experiment is flipping
a coin). The geometric distribution models the number of trials
that must be run in order to achieve success. It is therefore
supported on the positive integers, ``k = 1, 2, ...``.
The probability mass function of the geometric distribution is
$$ f(k) = (1 - p)^{k - 1} p$$
where `p` is the probability of success of an individual trial.
####Parameters
* p : float
The probability of success of an individual trial.
* size : tuple of ints
Number of values to draw from the distribution. The output
is shaped according to `size`.
####Returns
* out : ndarray
Samples from the geometric distribution, shaped according to
`size`.
####Examples
Draw ten thousand values from the geometric distribution,
with the probability of an individual success equal to 0.35:
$ z = np.random.geometric(p=0.35, size=10000)
How many trials succeeded after a single run?
$ (z == 1).sum() / 10000.
0.34889999999999999 random
##gumbel
randomkit.gumbel([output], loc, scale)
Gumbel distribution.
Draw samples from a Gumbel distribution with specified location and scale.
For more information on the Gumbel distribution, see Notes and References
below.
####Parameters
* loc : float
The location of the mode of the distribution.
* scale : float
The scale parameter of the distribution.
* size : tuple of ints
Output shape. If the given shape is, e.g., ``(m, n, k)``, then
``m * n * k`` samples are drawn.
####Returns
* out : ndarray
The samples
####See Also
scipy.stats.gumbel_l
scipy.stats.gumbel_r
scipy.stats.genextreme
probability density function, distribution, or cumulative density
function, etc. for each of the above
weibull
####Notes
The Gumbel (or Smallest Extreme Value (SEV) or the Smallest Extreme Value
Type I) distribution is one of a class of Generalized Extreme Value (GEV)
distributions used in modeling extreme value problems. The Gumbel is a
special case of the Extreme Value Type I distribution for maximums from
distributions with "exponential-like" tails.
The probability density for the Gumbel distribution is
$$ p(x) = \frac{e^{-(x - \mu)/ \beta}}{\beta} e^{ -e^{-(x - \mu)/
\beta}},$$
where \\(\mu\\) is the mode, a location parameter, and \\(\beta\\) is
the scale parameter.
The Gumbel (named for German mathematician Emil Julius Gumbel) was used
very early in the hydrology literature, for modeling the occurrence of
flood events. It is also used for modeling maximum wind speed and rainfall
rates. It is a "fat-tailed" distribution - the probability of an event in
the tail of the distribution is larger than if one used a Gaussian, hence
the surprisingly frequent occurrence of 100-year floods. Floods were
initially modeled as a Gaussian process, which underestimated the frequency
of extreme events.
It is one of a class of extreme value distributions, the Generalized
Extreme Value (GEV) distributions, which also includes the Weibull and
Frechet.
The function has a mean of \\(\mu + 0.57721\beta\\) and a variance of
\\(\frac{\pi^2}{6}\beta^2\\).
####References
Gumbel, E. J., *Statistics of Extremes*, New York: Columbia University
Press, 1958.
Reiss, R.-D. and Thomas, M., *Statistical Analysis of Extreme Values from
Insurance, Finance, Hydrology and Other Fields*, Basel: Birkhauser Verlag,
2001.
####Examples
Draw samples from the distribution:
$ mu, beta = 0, 0.1 location and scale
$ s = np.random.gumbel(mu, beta, 1000)
Display the histogram of the samples, along with
the probability density function:
$ import matplotlib.pyplot as plt
$ count, bins, ignored = plt.hist(s, 30, normed=True)
$ plt.plot(bins, (1/beta)*np.exp(-(bins - mu)/beta)
* np.exp( -np.exp( -(bins - mu) /beta) ),
linewidth=2, color='r')
$ plt.show()
Show how an extreme value distribution can arise from a Gaussian process
and compare to a Gaussian:
$ means = []
$ maxima = []
$ for i in range(0,1000) :
a = np.random.normal(mu, beta, 1000)
means.append(a.mean())
maxima.append(a.max())
$ count, bins, ignored = plt.hist(maxima, 30, normed=True)
$ beta = np.std(maxima)*np.pi/np.sqrt(6)
$ mu = np.mean(maxima) - 0.57721*beta
$ plt.plot(bins, (1/beta)*np.exp(-(bins - mu)/beta)
* np.exp(-np.exp(-(bins - mu)/beta)),
linewidth=2, color='r')
$ plt.plot(bins, 1/(beta * np.sqrt(2 * np.pi))
* np.exp(-(bins - mu)**2 / (2 * beta**2)),
linewidth=2, color='g')
$ plt.show()
##hypergeometric
randomkit.hypergeometric([output], ngood, nbad, nsample)
Draw samples from a Hypergeometric distribution.
Samples are drawn from a Hypergeometric distribution with specified
parameters, ngood (ways to make a good selection), nbad (ways to make
a bad selection), and nsample = number of items sampled, which is less
than or equal to the sum ngood + nbad.
####Parameters
* ngood : int or array_like
Number of ways to make a good selection. Must be nonnegative.
* nbad : int or array_like
Number of ways to make a bad selection. Must be nonnegative.
* nsample : int or array_like
Number of items sampled. Must be at least 1 and at most
``ngood + nbad``.
* size : int or tuple of int
Output shape. If the given shape is, e.g., ``(m, n, k)``, then
``m * n * k`` samples are drawn.
####Returns
* samples : ndarray or scalar
The values are all integers in [0, n].
####See Also
* scipy.stats.distributions.hypergeom : probability density function,
distribution or cumulative density function, etc.
####Notes
The probability density for the Hypergeometric distribution is
$$ P(x) = \frac{\binom{m}{n}\binom{N-m}{n-x}}{\binom{N}{n}},$$
where \\(0 \le x \le m\\) and \\(n+m-N \le x \le n\\)
for P(x) the probability of x successes, n = ngood, m = nbad, and
N = number of samples.
Consider an urn with black and white marbles in it, ngood of them
black and nbad are white. If you draw nsample balls without
replacement, then the Hypergeometric distribution describes the
distribution of black balls in the drawn sample.
Note that this distribution is very similar to the Binomial
distribution, except that in this case, samples are drawn without
replacement, whereas in the Binomial case samples are drawn with
replacement (or the sample space is infinite). As the sample space
becomes large, this distribution approaches the Binomial.
####References
1. Lentner, Marvin, "Elementary Applied Statistics", Bogden
and Quigley, 1972.
2. Weisstein, Eric W. "Hypergeometric Distribution." From
MathWorld--A Wolfram Web Resource.
http://mathworld.wolfram.com/HypergeometricDistribution.html
3. Wikipedia, "Hypergeometric-distribution",
http://en.wikipedia.org/wiki/Hypergeometric-distribution
####Examples
Draw samples from the distribution:
$ ngood, nbad, nsamp = 100, 2, 10
number of good, number of bad, and number of samples
$ s = np.random.hypergeometric(ngood, nbad, nsamp, 1000)
$ hist(s)
note that it is very unlikely to grab both bad items
Suppose you have an urn with 15 white and 15 black marbles.
If you pull 15 marbles at random, how likely is it that
12 or more of them are one color?
$ s = np.random.hypergeometric(15, 15, 15, 100000)
$ sum(s>=12)/100000. + sum(s<=3)/100000.
answer = 0.003 pretty unlikely!
##laplace
randomkit.laplace([output], loc, scale)
Draw samples from the Laplace or double exponential distribution with
specified location (or mean) and scale (decay).
The Laplace distribution is similar to the Gaussian/normal distribution,
but is sharper at the peak and has fatter tails. It represents the
difference between two independent, identically distributed exponential
random variables.
####Parameters
* loc : float
The position, \\(\mu\\), of the distribution peak.
* scale : float
\\(\lambda\\), the exponential decay.
####Notes
It has the probability density function
$$ f(x; \mu, \lambda) = \frac{1}{2\lambda}
\exp\left(-\frac{|x - \mu|}{\lambda}\right).$$
The first law of Laplace, from 1774, states that the frequency of an error
can be expressed as an exponential function of the absolute magnitude of
the error, which leads to the Laplace distribution. For many problems in
Economics and Health sciences, this distribution seems to model the data
better than the standard Gaussian distribution
####References
1. Abramowitz, M. and Stegun, I. A. (Eds.). Handbook of Mathematical
Functions with Formulas, Graphs, and Mathematical Tables, 9th
printing. New York: Dover, 1972.
2. The Laplace distribution and generalizations
By Samuel Kotz, Tomasz J. Kozubowski, Krzysztof Podgorski,
Birkhauser, 2001.
3. Weisstein, Eric W. "Laplace Distribution."
From MathWorld--A Wolfram Web Resource.
http://mathworld.wolfram.com/LaplaceDistribution.html
4. Wikipedia, "Laplace distribution",
http://en.wikipedia.org/wiki/Laplace_distribution
####Examples
Draw samples from the distribution
$ loc, scale = 0., 1.
$ s = np.random.laplace(loc, scale, 1000)
Display the histogram of the samples, along with
the probability density function:
$ import matplotlib.pyplot as plt
$ count, bins, ignored = plt.hist(s, 30, normed=True)
$ x = np.arange(-8., 8., .01)
$ pdf = np.exp(-abs(x-loc/scale))/(2.*scale)
$ plt.plot(x, pdf)
Plot Gaussian for comparison:
$ g = (1/(scale * np.sqrt(2 * np.pi)) *
np.exp( - (x - loc)**2 / (2 * scale**2) ))
$ plt.plot(x,g)
##logistic
randomkit.logistic([output], loc, scale)
Draw samples from a Logistic distribution.
Samples are drawn from a Logistic distribution with specified
parameters, loc (location or mean, also median), and scale (>0).
####Parameters
* loc : float
* scale : float > 0.
* size : {tuple, int}
Output shape. If the given shape is, e.g., ``(m, n, k)``, then
``m * n * k`` samples are drawn.
####Returns
* samples : {ndarray, scalar}
where the values are all integers in [0, n].
####See Also
* scipy.stats.distributions.logistic : probability density function,
distribution or cumulative density function, etc.
####Notes
The probability density for the Logistic distribution is
$$ P(x) = P(x) = \frac{e^{-(x-\mu)/s}}{s(1+e^{-(x-\mu)/s})^2},$$
where \\(\mu\\) = location and \\(s\\) = scale.
The Logistic distribution is used in Extreme Value problems where it
can act as a mixture of Gumbel distributions, in Epidemiology, and by
the World Chess Federation (FIDE) where it is used in the Elo ranking
system, assuming the performance of each player is a logistically
distributed random variable.
####References
1. Reiss, R.-D. and Thomas M. (2001), Statistical Analysis of Extreme
Values, from Insurance, Finance, Hydrology and Other Fields,
Birkhauser Verlag, Basel, pp 132-133.
2. Weisstein, Eric W. "Logistic Distribution." From
MathWorld--A Wolfram Web Resource.
http://mathworld.wolfram.com/LogisticDistribution.html
3. Wikipedia, "Logistic-distribution",
http://en.wikipedia.org/wiki/Logistic-distribution
####Examples
Draw samples from the distribution:
$ loc, scale = 10, 1
$ s = np.random.logistic(loc, scale, 10000)
$ count, bins, ignored = plt.hist(s, bins=50)
plot against distribution
$ def logist(x, loc, scale):
return exp((loc-x)/scale)/(scale*(1+exp((loc-x)/scale))**2)
$ plt.plot(bins, logist(bins, loc, scale)*count.max()/\
logist(bins, loc, scale).max())
$ plt.show()
##lognormal
randomkit.lognormal([output], mean, sigma)
Return samples drawn from a log-normal distribution.
Draw samples from a log-normal distribution with specified mean,
standard deviation, and array shape. Note that the mean and standard
deviation are not the values for the distribution itself, but of the
underlying normal distribution it is derived from.
####Parameters
* mean : float
Mean value of the underlying normal distribution
* sigma : float, > 0.
Standard deviation of the underlying normal distribution
* size : tuple of ints
Output shape. If the given shape is, e.g., ``(m, n, k)``, then
``m * n * k`` samples are drawn.
####Returns
* samples : ndarray or float
The desired samples. An array of the same shape as `size` if given,
if `size` is None a float is returned.
####See Also
* scipy.stats.lognorm : probability density function, distribution,
cumulative density function, etc.
####Notes
A variable `x` has a log-normal distribution if `log(x)` is normally
distributed. The probability density function for the log-normal
distribution is:
$$ p(x) = \frac{1}{\sigma x \sqrt{2\pi}}
e^{(-\frac{(ln(x)-\mu)^2}{2\sigma^2})}$$
where \\(\mu\\) is the mean and \\(\sigma\\) is the standard
deviation of the normally distributed logarithm of the variable.
A log-normal distribution results if a random variable is the *product*
of a large number of independent, identically-distributed variables in
the same way that a normal distribution results if the variable is the
*sum* of a large number of independent, identically-distributed
variables.
####References
Limpert, E., Stahel, W. A., and Abbt, M., "Log-normal Distributions
across the Sciences: Keys and Clues," *BioScience*, Vol. 51, No. 5,
May, 2001. http://stat.ethz.ch/~stahel/lognormal/bioscience.pdf
Reiss, R.D. and Thomas, M., *Statistical Analysis of Extreme Values*,
Basel: Birkhauser Verlag, 2001, pp. 31-32.
####Examples
Draw samples from the distribution:
$ mu, sigma = 3., 1. mean and standard deviation
$ s = np.random.lognormal(mu, sigma, 1000)
Display the histogram of the samples, along with
the probability density function:
$ import matplotlib.pyplot as plt
$ count, bins, ignored = plt.hist(s, 100, normed=True, align='mid')
$ x = np.linspace(min(bins), max(bins), 10000)
$ pdf = (np.exp(-(np.log(x) - mu)**2 / (2 * sigma**2))
/ (x * sigma * np.sqrt(2 * np.pi)))
$ plt.plot(x, pdf, linewidth=2, color='r')
$ plt.axis('tight')
$ plt.show()
Demonstrate that taking the products of random samples from a uniform
distribution can be fit well by a log-normal probability density function.
$ Generate a thousand samples: each is the product of 100 random
$ values, drawn from a normal distribution.
$ b = []
$ for i in range(1000):
a = 10. + np.random.random(100)
b.append(np.product(a))
$ b = np.array(b) / np.min(b) scale values to be positive
$ count, bins, ignored = plt.hist(b, 100, normed=True, align='center')
$ sigma = np.std(np.log(b))
$ mu = np.mean(np.log(b))
$ x = np.linspace(min(bins), max(bins), 10000)
$ pdf = (np.exp(-(np.log(x) - mu)**2 / (2 * sigma**2))
/ (x * sigma * np.sqrt(2 * np.pi)))
$ plt.plot(x, pdf, color='r', linewidth=2)
$ plt.show()
##logseries
randomkit.logseries([output], p)
Draw samples from a Logarithmic Series distribution.
Samples are drawn from a Log Series distribution with specified
parameter, p (probability, 0 < p < 1).
####Parameters
* loc : float
* scale : float > 0.
* size : {tuple, int}
Output shape. If the given shape is, e.g., ``(m, n, k)``, then
``m * n * k`` samples are drawn.
####Returns
* samples : {ndarray, scalar}
where the values are all integers in [0, n].
####See Also
* scipy.stats.distributions.logser : probability density function,
distribution or cumulative density function, etc.
####Notes
The probability density for the Log Series distribution is
$$ P(k) = \frac{-p^k}{k \ln(1-p)},$$
where p = probability.
The Log Series distribution is frequently used to represent species
richness and occurrence, first proposed by Fisher, Corbet, and
Williams in 1943 [2]. It may also be used to model the numbers of
occupants seen in cars [3].
####References
1. Buzas, Martin A.; Culver, Stephen J., Understanding regional
species diversity through the log series distribution of
occurrences: BIODIVERSITY RESEARCH Diversity & Distributions,
Volume 5, Number 5, September 1999 , pp. 187-195(9).
2. Fisher, R.A,, A.S. Corbet, and C.B. Williams. 1943. The
relation between the number of species and the number of
individuals in a random sample of an animal population.
Journal of Animal Ecology, 12:42-58.
3. D. J. Hand, F. Daly, D. Lunn, E. Ostrowski, A Handbook of Small
Data Sets, CRC Press, 1994.
4. Wikipedia, "Logarithmic-distribution",
http://en.wikipedia.org/wiki/Logarithmic-distribution
####Examples
Draw samples from the distribution:
$ a = .6
$ s = np.random.logseries(a, 10000)
$ count, bins, ignored = plt.hist(s)
plot against distribution
$ def logseries(k, p):
return -p**k/(k*log(1-p))
$ plt.plot(bins, logseries(bins, a)*count.max()/
logseries(bins, a).max(), 'r')
$ plt.show()
##multivariate_normal
randomkit.multivariate_normal([output], mean, cov[])
Draw random samples from a multivariate normal distribution.
The multivariate normal, multinormal or Gaussian distribution is a
generalization of the one-dimensional normal distribution to higher
dimensions. Such a distribution is specified by its mean and
covariance matrix. These parameters are analogous to the mean
(average or "center") and variance (standard deviation, or "width,"
squared) of the one-dimensional normal distribution.
####Parameters
* mean : 1-D array_like, of length N
Mean of the N-dimensional distribution.
* cov : 2-D array_like, of shape (N, N)
Covariance matrix of the distribution. Must be symmetric and
positive semi-definite for "physically meaningful" results.
* size : int or tuple of ints, optional
Given a shape of, for example, ``(m,n,k)``, ``m*n*k`` samples are
generated, and packed in an `m`-by-`n`-by-`k` arrangement. Because
each sample is `N`-dimensional, the output shape is ``(m,n,k,N)``.
If no shape is specified, a single (`N`-D) sample is returned.
####Returns
* out : ndarray
The drawn samples, of shape *size*, if that was provided. If not,
the shape is ``(N,)``.
In other words, each entry ``out[i,j,...,:]`` is an N-dimensional
value drawn from the distribution.
####Notes
The mean is a coordinate in N-dimensional space, which represents the
location where samples are most likely to be generated. This is
analogous to the peak of the bell curve for the one-dimensional or
univariate normal distribution.
Covariance indicates the level to which two variables vary together.
From the multivariate normal distribution, we draw N-dimensional
samples, \\(X = [x_1, x_2, x_N]\\). The covariance matrix
element \\(C_{ij}\\) is the covariance of \\(x_i\\) and \\(x_j\\).
The element \\(C_{ii}\\) is the variance of \\(x_i\\) (i.e. its
"spread").
Instead of specifying the full covariance matrix, popular
approximations include:
####- Spherical covariance (*cov* is a multiple of the identity matrix) Diagonal covariance (*cov* has non-negative elements, and only on
the diagonal)
This geometrical property can be seen in two dimensions by plotting
generated data-points:
$ mean = [0,0]
$ cov = [[1,0],[0,100]] diagonal covariance, points lie on x or y-axis
$ import matplotlib.pyplot as plt
$ x,y = np.random.multivariate_normal(mean,cov,5000).T
$ plt.plot(x,y,'x'); plt.axis('equal'); plt.show()
Note that the covariance matrix must be non-negative definite.
####References
Papoulis, A., *Probability, Random Variables, and Stochastic Processes*,
3rd ed., New York: McGraw-Hill, 1991.
Duda, R. O., Hart, P. E., and Stork, D. G., *Pattern Classification*,
2nd ed., New York: Wiley, 2001.
####Examples
$ mean = (1,2)
$ cov = [[1,0],[1,0]]
$ x = np.random.multivariate_normal(mean,cov,(3,3))
$ x.shape
(3, 3, 2)
The following is probably true, given that 0.6 is roughly twice the
standard deviation:
$ print list( (x[0,0,:] - mean) < 0.6 )
[True, True]
##negative_binomial
randomkit.negative_binomial([output], n, p)
Draw samples from a negative_binomial distribution.
Samples are drawn from a negative_Binomial distribution with specified
parameters, `n` trials and `p` probability of success where `n` is an
integer > 0 and `p` is in the interval [0, 1].
####Parameters
* n : int
Parameter, > 0.
* p : float
Parameter, >= 0 and <=1.
* size : int or tuple of ints
Output shape. If the given shape is, e.g., ``(m, n, k)``, then
``m * n * k`` samples are drawn.
####Returns
* samples : int or ndarray of ints
Drawn samples.
####Notes
The probability density for the Negative Binomial distribution is
$$ P(N;n,p) = \binom{N+n-1}{n-1}p^{n}(1-p)^{N},$$
where \\(n-1\\) is the number of successes, \\(p\\) is the probability
of success, and \\(N+n-1\\) is the number of trials.
The negative binomial distribution gives the probability of n-1 successes
and N failures in N+n-1 trials, and success on the (N+n)th trial.
If one throws a die repeatedly until the third time a "1" appears, then the
probability distribution of the number of non-"1"s that appear before the
third "1" is a negative binomial distribution.
####References
1. Weisstein, Eric W. "Negative Binomial Distribution." From
MathWorld--A Wolfram Web Resource.
http://mathworld.wolfram.com/NegativeBinomialDistribution.html
2. Wikipedia, "Negative binomial distribution",
http://en.wikipedia.org/wiki/Negative_binomial_distribution
####Examples
Draw samples from the distribution:
A real world example. A company drills wild-cat oil exploration wells, each
with an estimated probability of success of 0.1. What is the probability
of having one success for each successive well, that is what is the
probability of a single success after drilling 5 wells, after 6 wells,
etc.?
$ s = np.random.negative_binomial(1, 0.1, 100000)
$ for i in range(1, 11):
probability = sum(s 1.
`nonc` is the non-centrality parameter.
####Parameters
* dfnum : int
Parameter, should be > 1.
* dfden : int
Parameter, should be > 1.
* nonc : float
Parameter, should be >= 0.
* size : int or tuple of ints
Output shape. If the given shape is, e.g., ``(m, n, k)``, then
``m * n * k`` samples are drawn.
####Returns
* samples : scalar or ndarray
Drawn samples.
####Notes
When calculating the power of an experiment (power = probability of
rejecting the null hypothesis when a specific alternative is true) the
non-central F statistic becomes important. When the null hypothesis is
true, the F statistic follows a central F distribution. When the null
hypothesis is not true, then it follows a non-central F statistic.
####References
Weisstein, Eric W. "Noncentral F-Distribution." From MathWorld--A Wolfram
Web Resource. http://mathworld.wolfram.com/NoncentralF-Distribution.html
Wikipedia, "Noncentral F distribution",
http://en.wikipedia.org/wiki/Noncentral_F-distribution
####Examples
In a study, testing for a specific alternative to the null hypothesis
requires use of the Noncentral F distribution. We need to calculate the
area in the tail of the distribution that exceeds the value of the F
distribution for the null hypothesis. We'll plot the two probability
distributions for comparison.
$ dfnum = 3 between group deg of freedom
$ dfden = 20 within groups degrees of freedom
$ nonc = 3.0
$ nc_vals = np.random.noncentral_f(dfnum, dfden, nonc, 1000000)
$ NF = np.histogram(nc_vals, bins=50, normed=True)
$ c_vals = np.random.f(dfnum, dfden, 1000000)
$ F = np.histogram(c_vals, bins=50, normed=True)
$ plt.plot(F[1][1:], F[0])
$ plt.plot(NF[1][1:], NF[0])
$ plt.show()
##normal
randomkit.normal([output], loc, scale)
Draw random samples from a normal (Gaussian) distribution.
The probability density function of the normal distribution, first
derived by De Moivre and 200 years later by both Gauss and Laplace
independently [2], is often called the bell curve because of
its characteristic shape (see the example below).
The normal distributions occurs often in nature. For example, it
describes the commonly occurring distribution of samples influenced
by a large number of tiny, random disturbances, each with its own
unique distribution [2].
####Parameters
* loc : float
Mean ("centre") of the distribution.
* scale : float
Standard deviation (spread or "width") of the distribution.
* size : tuple of ints
Output shape. If the given shape is, e.g., ``(m, n, k)``, then
``m * n * k`` samples are drawn.
####See Also
* scipy.stats.distributions.norm : probability density function,
distribution or cumulative density function, etc.
####Notes
The probability density for the Gaussian distribution is
$$ p(x) = \frac{1}{\sqrt{ 2 \pi \sigma^2 }}
e^{ - \frac{ (x - \mu)^2 } {2 \sigma^2} },$$
where \\(\mu\\) is the mean and \\(\sigma\\) the standard deviation.
The square of the standard deviation, \\(\sigma^2\\), is called the
variance.
The function has its peak at the mean, and its "spread" increases with
the standard deviation (the function reaches 0.607 times its maximum at
\\(x + \sigma\\) and \\(x - \sigma\\) [2]). This implies that
`numpy.random.normal` is more likely to return samples lying close to the
mean, rather than those far away.
####References
1. Wikipedia, "Normal distribution",
http://en.wikipedia.org/wiki/Normal_distribution
2. P. R. Peebles Jr., "Central Limit Theorem" in "Probability, Random
Variables and Random Signal Principles", 4th ed., 2001,
pp. 51, 51, 125.
####Examples
Draw samples from the distribution:
$ mu, sigma = 0, 0.1 mean and standard deviation
$ s = np.random.normal(mu, sigma, 1000)
Verify the mean and the variance:
$ abs(mu - np.mean(s)) < 0.01
True
$ abs(sigma - np.std(s, ddof=1)) < 0.01
True
Display the histogram of the samples, along with
the probability density function:
$ import matplotlib.pyplot as plt
$ count, bins, ignored = plt.hist(s, 30, normed=True)
$ plt.plot(bins, 1/(sigma * np.sqrt(2 * np.pi)) *
np.exp( - (bins - mu)**2 / (2 * sigma**2) ),
linewidth=2, color='r')
$ plt.show()
##pareto
randomkit.pareto([output], a)
Draw samples from a Pareto II or Lomax distribution with specified shape.
The Lomax or Pareto II distribution is a shifted Pareto distribution. The
classical Pareto distribution can be obtained from the Lomax distribution
by adding the location parameter m, see below. The smallest value of the
Lomax distribution is zero while for the classical Pareto distribution it
is m, where the standard Pareto distribution has location m=1.
Lomax can also be considered as a simplified version of the Generalized
Pareto distribution (available in SciPy), with the scale set to one and
the location set to zero.
The Pareto distribution must be greater than zero, and is unbounded above.
It is also known as the "80-20 rule". In this distribution, 80 percent of
the weights are in the lowest 20 percent of the range, while the other 20
percent fill the remaining 80 percent of the range.
####Parameters
* shape : float, > 0.
Shape of the distribution.
* size : tuple of ints
Output shape. If the given shape is, e.g., ``(m, n, k)``, then
``m * n * k`` samples are drawn.
####See Also
* scipy.stats.distributions.lomax.pdf : probability density function,
distribution or cumulative density function, etc.
* scipy.stats.distributions.genpareto.pdf : probability density function,
distribution or cumulative density function, etc.
####Notes
The probability density for the Pareto distribution is
$$ p(x) = \frac{am^a}{x^{a+1}}$$
where \\(a\\) is the shape and \\(m\\) the location
The Pareto distribution, named after the Italian economist Vilfredo Pareto,
is a power law probability distribution useful in many real world problems.
Outside the field of economics it is generally referred to as the Bradford
distribution. Pareto developed the distribution to describe the
distribution of wealth in an economy. It has also found use in insurance,
web page access statistics, oil field sizes, and many other problems,
including the download frequency for projects in Sourceforge [1]. It is
one of the so-called "fat-tailed" distributions.
####References
1. Francis Hunt and Paul Johnson, On the Pareto Distribution of
Sourceforge projects.
2. Pareto, V. (1896). Course of Political Economy. Lausanne.
3. Reiss, R.D., Thomas, M.(2001), Statistical Analysis of Extreme
Values, Birkhauser Verlag, Basel, pp 23-30.
4. Wikipedia, "Pareto distribution",
http://en.wikipedia.org/wiki/Pareto_distribution
####Examples
Draw samples from the distribution:
$ a, m = 3., 1. shape and mode
$ s = np.random.pareto(a, 1000) + m
Display the histogram of the samples, along with
the probability density function:
$ import matplotlib.pyplot as plt
$ count, bins, ignored = plt.hist(s, 100, normed=True, align='center')
$ fit = a*m**a/bins**(a+1)
$ plt.plot(bins, max(count)*fit/max(fit),linewidth=2, color='r')
$ plt.show()
##poisson
randomkit.poisson([output], lam)
Draw samples from a Poisson distribution.
The Poisson distribution is the limit of the Binomial
distribution for large N.
####Parameters
* lam : float
Expectation of interval, should be >= 0.
* size : int or tuple of ints, optional
Output shape. If the given shape is, e.g., ``(m, n, k)``, then
``m * n * k`` samples are drawn.
####Notes
The Poisson distribution
$$ f(k; \lambda)=\frac{\lambda^k e^{-\lambda}}{k!}$$
For events with an expected separation \\(\lambda\\) the Poisson
distribution \\(f(k; \lambda)\\) describes the probability of
\\(k\\) events occurring within the observed interval \\(\lambda\\).
Because the output is limited to the range of the C long type, a
ValueError is raised when `lam` is within 10 sigma of the maximum
representable value.
####References
1. Weisstein, Eric W. "Poisson Distribution." From MathWorld--A Wolfram
Web Resource. http://mathworld.wolfram.com/PoissonDistribution.html
2. Wikipedia, "Poisson distribution",
http://en.wikipedia.org/wiki/Poisson_distribution
####Examples
Draw samples from the distribution:
$ import numpy as np
$ s = np.random.poisson(5, 10000)
Display histogram of the sample:
$ import matplotlib.pyplot as plt
$ count, bins, ignored = plt.hist(s, 14, normed=True)
$ plt.show()
##power
randomkit.power([output], a)
Draws samples in [0, 1] from a power distribution with positive
exponent a - 1.
Also known as the power function distribution.
####Parameters
* a : float
parameter, > 0
* size : tuple of ints
Output shape. If the given shape is, e.g., ``(m, n, k)``, then
``m * n * k`` samples are drawn.
####Returns
* samples : {ndarray, scalar}
The returned samples lie in [0, 1].
####Raises
ValueError
If a<1.
####Notes
The probability density function is
$$ P(x; a) = ax^{a-1}, 0 \le x \le 1, a>0.$$
The power function distribution is just the inverse of the Pareto
distribution. It may also be seen as a special case of the Beta
distribution.
It is used, for example, in modeling the over-reporting of insurance
claims.
####References
1. Christian Kleiber, Samuel Kotz, "Statistical size distributions
in economics and actuarial sciences", Wiley, 2003.
2. Heckert, N. A. and Filliben, James J. (2003). NIST Handbook 148:
Dataplot Reference Manual, Volume 2: Let Subcommands and Library
Functions", National Institute of Standards and Technology Handbook
Series, June 2003.
http://www.itl.nist.gov/div898/software/dataplot/refman2/auxillar/powpdf.pdf
####Examples
Draw samples from the distribution:
$ a = 5. shape
$ samples = 1000
$ s = np.random.power(a, samples)
Display the histogram of the samples, along with
the probability density function:
$ import matplotlib.pyplot as plt
$ count, bins, ignored = plt.hist(s, bins=30)
$ x = np.linspace(0, 1, 100)
$ y = a*x**(a-1.)
$ normed_y = samples*np.diff(bins)[0]*y
$ plt.plot(x, normed_y)
$ plt.show()
Compare the power function distribution to the inverse of the Pareto.
$ from scipy import stats
$ rvs = np.random.power(5, 1000000)
$ rvsp = np.random.pareto(5, 1000000)
$ xx = np.linspace(0,1,100)
$ powpdf = stats.powerlaw.pdf(xx,5)
$ plt.figure()
$ plt.hist(rvs, bins=50, normed=True)
$ plt.plot(xx,powpdf,'r-')
$ plt.title('np.random.power(5)')
$ plt.figure()
$ plt.hist(1./(1.+rvsp), bins=50, normed=True)
$ plt.plot(xx,powpdf,'r-')
$ plt.title('inverse of 1 + np.random.pareto(5)')
$ plt.figure()
$ plt.hist(1./(1.+rvsp), bins=50, normed=True)
$ plt.plot(xx,powpdf,'r-')
$ plt.title('inverse of stats.pareto(5)')
##randint
randomkit.randint(low, high)
Return random integers from `low` (inclusive) to `high` (inclusive).
Return random integers from the "discrete uniform" distribution in the
closed interval [`low`, `high`].
Note: This function behaves differently from the numpy version shown in
examples.
####Parameters
* low : int
Lowest (signed) integer to be drawn from the distribution.
* high : int
Largest (signed) integer to be drawn from the distribution.
####Returns
* int
####See Also
* random.random_integers : similar to `randint`, only for the closed
interval [`low`, `high`], and 1 is the lowest value if `high` is
omitted. In particular, this other one is the one to use to generate
uniformly distributed discrete non-integers.
####Examples
$ np.random.randint(2, size=10)
array([1, 0, 0, 0, 1, 1, 0, 0, 1, 0])
$ np.random.randint(1, size=10)
array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])
Generate a 2 x 4 array of ints between 0 and 4, inclusive:
$ np.random.randint(5, size=(2, 4))
array([[4, 0, 2, 1],
[3, 2, 2, 0]])
##random
randomkit.random_sample([output], )
Return random floats in the half-open interval [0.0, 1.0).
Results are from the "continuous uniform" distribution over the
stated interval. To sample \\(Unif[a, b), b > a\\) multiply
the output of `random_sample` by `(b-a)` and add `a`::
(b - a) * random_sample() + a
####Parameters
* size : int or tuple of ints, optional
Defines the shape of the returned array of random floats. If None
(the default), returns a single float.
####Returns
* out : float or ndarray of floats
Array of random floats of shape `size` (unless ``size=None``, in which
case a single float is returned).
####Examples
$ np.random.random_sample()
0.47108547995356098
$ type(np.random.random_sample())
$ np.random.random_sample((5,))
array([ 0.30220482, 0.86820401, 0.1654503 , 0.11659149, 0.54323428])
Three-by-two array of random numbers from [-5, 0):
$ 5 * np.random.random_sample((3, 2)) - 5
array([[-3.99149989, -0.52338984],
[-2.99091858, -0.79479508],
[-1.23204345, -1.75224494]])
##random_sample
randomkit.random_sample([output], )
Return random floats in the half-open interval [0.0, 1.0).
Results are from the "continuous uniform" distribution over the
stated interval. To sample \\(Unif[a, b), b > a\\) multiply
the output of `random_sample` by `(b-a)` and add `a`::
(b - a) * random_sample() + a
####Parameters
* size : int or tuple of ints, optional
Defines the shape of the returned array of random floats. If None
(the default), returns a single float.
####Returns
* out : float or ndarray of floats
Array of random floats of shape `size` (unless ``size=None``, in which
case a single float is returned).
####Examples
$ np.random.random_sample()
0.47108547995356098
$ type(np.random.random_sample())
$ np.random.random_sample((5,))
array([ 0.30220482, 0.86820401, 0.1654503 , 0.11659149, 0.54323428])
Three-by-two array of random numbers from [-5, 0):
$ 5 * np.random.random_sample((3, 2)) - 5
array([[-3.99149989, -0.52338984],
[-2.99091858, -0.79479508],
[-1.23204345, -1.75224494]])
##rayleigh
randomkit.rayleigh([output], scale)
Draw samples from a Rayleigh distribution.
The \\(\chi\\) and Weibull distributions are generalizations of the
Rayleigh.
####Parameters
* scale : scalar
Scale, also equals the mode. Should be >= 0.
* size : int or tuple of ints, optional
Shape of the output. Default is None, in which case a single
value is returned.
####Notes
The probability density function for the Rayleigh distribution is
$$ P(x;scale) = \frac{x}{scale^2}e^{\frac{-x^2}{2 \cdotp scale^2}}$$
The Rayleigh distribution arises if the wind speed and wind direction are
both gaussian variables, then the vector wind velocity forms a Rayleigh
distribution. The Rayleigh distribution is used to model the expected
output from wind turbines.
####References
1. Brighton Webs Ltd., Rayleigh Distribution,
http://www.brighton-webs.co.uk/distributions/rayleigh.asp
2. Wikipedia, "Rayleigh distribution"
http://en.wikipedia.org/wiki/Rayleigh_distribution
####Examples
Draw values from the distribution and plot the histogram
$ values = hist(np.random.rayleigh(3, 100000), bins=200, normed=True)
Wave heights tend to follow a Rayleigh distribution. If the mean wave
height is 1 meter, what fraction of waves are likely to be larger than 3
meters?
$ meanvalue = 1
$ modevalue = np.sqrt(2 / np.pi) * meanvalue
$ s = np.random.rayleigh(modevalue, 1000000)
The percentage of waves larger than 3 meters is:
$ 100.*sum(s>3)/1000000.
0.087300000000000003
##standard_cauchy
randomkit.standard_cauchy([output], )
Standard Cauchy distribution with mode = 0.
Also known as the Lorentz distribution.
####Parameters
* size : int or tuple of ints
Shape of the output.
####Returns
* samples : ndarray or scalar
The drawn samples.
####Notes
The probability density function for the full Cauchy distribution is
$$ P(x; x_0, \gamma) = \frac{1}{\pi \gamma \bigl[ 1+
(\frac{x-x_0}{\gamma})^2 \bigr] }$$
and the Standard Cauchy distribution just sets \\(x_0=0\\) and
\\(\gamma=1\\)
The Cauchy distribution arises in the solution to the driven harmonic
oscillator problem, and also describes spectral line broadening. It
also describes the distribution of values at which a line tilted at
a random angle will cut the x axis.
When studying hypothesis tests that assume normality, seeing how the
tests perform on data from a Cauchy distribution is a good indicator of
their sensitivity to a heavy-tailed distribution, since the Cauchy looks
very much like a Gaussian distribution, but with heavier tails.
####References
1. NIST/SEMATECH e-Handbook of Statistical Methods, "Cauchy
Distribution",
http://www.itl.nist.gov/div898/handbook/eda/section3/eda3663.htm
2. Weisstein, Eric W. "Cauchy Distribution." From MathWorld--A
Wolfram Web Resource.
http://mathworld.wolfram.com/CauchyDistribution.html
3. Wikipedia, "Cauchy distribution"
http://en.wikipedia.org/wiki/Cauchy_distribution
####Examples
Draw samples and plot the distribution:
$ s = np.random.standard_cauchy(1000000)
$ s = s[(s>-25) & (s<25)] truncate distribution so it plots well
$ plt.hist(s, bins=100)
$ plt.show()
##standard_exponential
randomkit.standard_exponential([output], )
Draw samples from the standard exponential distribution.
`standard_exponential` is identical to the exponential distribution
with a scale parameter of 1.
####Parameters
* size : int or tuple of ints
Shape of the output.
####Returns
* out : float or ndarray
Drawn samples.
####Examples
Output a 3x8000 array:
$ n = np.random.standard_exponential((3, 8000))
##standard_gamma
randomkit.standard_gamma([output], shape)
Draw samples from a Standard Gamma distribution.
Samples are drawn from a Gamma distribution with specified parameters,
shape (sometimes designated "k") and scale=1.
####Parameters
* shape : float
Parameter, should be > 0.
* size : int or tuple of ints
Output shape. If the given shape is, e.g., ``(m, n, k)``, then
``m * n * k`` samples are drawn.
####Returns
* samples : ndarray or scalar
The drawn samples.
####See Also
* scipy.stats.distributions.gamma : probability density function,
distribution or cumulative density function, etc.
####Notes
The probability density for the Gamma distribution is
$$ p(x) = x^{k-1}\frac{e^{-x/\theta}}{\theta^k\Gamma(k)},$$
where \\(k\\) is the shape and \\(\theta\\) the scale,
and \\(\Gamma\\) is the Gamma function.
The Gamma distribution is often used to model the times to failure of
electronic components, and arises naturally in processes for which the
waiting times between Poisson distributed events are relevant.
####References
1. Weisstein, Eric W. "Gamma Distribution." From MathWorld--A
Wolfram Web Resource.
http://mathworld.wolfram.com/GammaDistribution.html
2. Wikipedia, "Gamma-distribution",
http://en.wikipedia.org/wiki/Gamma-distribution
####Examples
Draw samples from the distribution:
$ shape, scale = 2., 1. mean and width
$ s = np.random.standard_gamma(shape, 1000000)
Display the histogram of the samples, along with
the probability density function:
$ import matplotlib.pyplot as plt
$ import scipy.special as sps
$ count, bins, ignored = plt.hist(s, 50, normed=True)
$ y = bins**(shape-1) * ((np.exp(-bins/scale))/ \
(sps.gamma(shape) * scale**shape))
$ plt.plot(bins, y, linewidth=2, color='r')
$ plt.show()
##standard_normal
randomkit.standard_normal([output], )
Returns samples from a Standard Normal distribution (mean=0, stdev=1).
####Parameters
* size : int or tuple of ints, optional
Output shape. Default is None, in which case a single value is
returned.
####Returns
* out : float or ndarray
Drawn samples.
####Examples
$ s = np.random.standard_normal(8000)
$ s
####array([ 0.6888893 , 0.78096262, -0.89086505, ..., 0.49876311, random0.38672696, -0.4685006 ]) random
$ s.shape
(8000,)
$ s = np.random.standard_normal(size=(3, 4, 2))
$ s.shape
(3, 4, 2)
##standard_t
randomkit.standard_t([output], df)
Standard Student's t distribution with df degrees of freedom.
A special case of the hyperbolic distribution.
As `df` gets large, the result resembles that of the standard normal
distribution (`standard_normal`).
####Parameters
* df : int
Degrees of freedom, should be > 0.
* size : int or tuple of ints, optional
Output shape. Default is None, in which case a single value is
returned.
####Returns
* samples : ndarray or scalar
Drawn samples.
####Notes
The probability density function for the t distribution is
$$ P(x, df) = \frac{\Gamma(\frac{df+1}{2})}{\sqrt{\pi df}
\Gamma(\frac{df}{2})}\Bigl( 1+\frac{x^2}{df} \Bigr)^{-(df+1)/2}$$
The t test is based on an assumption that the data come from a Normal
distribution. The t test provides a way to test whether the sample mean
(that is the mean calculated from the data) is a good estimate of the true
mean.
The derivation of the t-distribution was forst published in 1908 by William
Gisset while working for the Guinness Brewery in Dublin. Due to proprietary
issues, he had to publish under a pseudonym, and so he used the name
Student.
####References
1. Dalgaard, Peter, "Introductory Statistics With R",
Springer, 2002.
2. Wikipedia, "Student's t-distribution"
http://en.wikipedia.org/wiki/Student's_t-distribution
####Examples
From Dalgaard page 83 [1], suppose the daily energy intake for 11
women in Kj is:
$ intake = np.array([5260., 5470, 5640, 6180, 6390, 6515, 6805, 7515, \
7515, 8230, 8770])
Does their energy intake deviate systematically from the recommended
value of 7725 kJ?
We have 10 degrees of freedom, so is the sample mean within 95% of the
recommended value?
$ s = np.random.standard_t(10, size=100000)
$ np.mean(intake)
6753.636363636364
$ intake.std(ddof=1)
1142.1232221373727
Calculate the t statistic, setting the ddof parameter to the unbiased
value so the divisor in the standard deviation will be degrees of
freedom, N-1.
$ t = (np.mean(intake)-7725)/(intake.std(ddof=1)/np.sqrt(len(intake)))
$ import matplotlib.pyplot as plt
$ h = plt.hist(s, bins=100, normed=True)
For a one-sided t-test, how far out in the distribution does the t
statistic appear?
$ $ np.sum(s= -1)
True
$ np.all(s < 0)
True
Display the histogram of the samples, along with the
probability density function:
$ import matplotlib.pyplot as plt
$ count, bins, ignored = plt.hist(s, 15, normed=True)
$ plt.plot(bins, np.ones_like(bins), linewidth=2, color='r')
$ plt.show()
##vonmises
randomkit.vonmises([output], mu, kappa)
Draw samples from a von Mises distribution.
Samples are drawn from a von Mises distribution with specified mode
(mu) and dispersion (kappa), on the interval [-pi, pi].
The von Mises distribution (also known as the circular normal
distribution) is a continuous probability distribution on the unit
circle. It may be thought of as the circular analogue of the normal
distribution.
####Parameters
* mu : float
Mode ("center") of the distribution.
* kappa : float
Dispersion of the distribution, has to be >=0.
* size : int or tuple of int
Output shape. If the given shape is, e.g., ``(m, n, k)``, then
``m * n * k`` samples are drawn.
####Returns
* samples : scalar or ndarray
The returned samples, which are in the interval [-pi, pi].
####See Also
* scipy.stats.distributions.vonmises : probability density function,
distribution, or cumulative density function, etc.
####Notes
The probability density for the von Mises distribution is
$$ p(x) = \frac{e^{\kappa cos(x-\mu)}}{2\pi I_0(\kappa)},$$
where \\(\mu\\) is the mode and \\(\kappa\\) the dispersion,
and \\(I_0(\kappa)\\) is the modified Bessel function of order 0.
The von Mises is named for Richard Edler von Mises, who was born in
Austria-Hungary, in what is now the Ukraine. He fled to the United
States in 1939 and became a professor at Harvard. He worked in
probability theory, aerodynamics, fluid mechanics, and philosophy of
science.
####References
Abramowitz, M. and Stegun, I. A. (ed.), *Handbook of Mathematical
Functions*, New York: Dover, 1965.
von Mises, R., *Mathematical Theory of Probability and Statistics*,
New York: Academic Press, 1964.
####Examples
Draw samples from the distribution:
$ mu, kappa = 0.0, 4.0 mean and dispersion
$ s = np.random.vonmises(mu, kappa, 1000)
Display the histogram of the samples, along with
the probability density function:
$ import matplotlib.pyplot as plt
$ import scipy.special as sps
$ count, bins, ignored = plt.hist(s, 50, normed=True)
$ x = np.arange(-np.pi, np.pi, 2*np.pi/50.)
$ y = -np.exp(kappa*np.cos(x-mu))/(2*np.pi*sps.jn(0,kappa))
$ plt.plot(x, y/max(y), linewidth=2, color='r')
$ plt.show()
##wald
randomkit.wald([output], mean, scale)
Draw samples from a Wald, or Inverse Gaussian, distribution.
As the scale approaches infinity, the distribution becomes more like a
Gaussian.
Some references claim that the Wald is an Inverse Gaussian with mean=1, but
this is by no means universal.
The Inverse Gaussian distribution was first studied in relationship to
Brownian motion. In 1956 M.C.K. Tweedie used the name Inverse Gaussian
because there is an inverse relationship between the time to cover a unit
distance and distance covered in unit time.
####Parameters
* mean : scalar
Distribution mean, should be > 0.
* scale : scalar
Scale parameter, should be >= 0.
* size : int or tuple of ints, optional
Output shape. Default is None, in which case a single value is
returned.
####Returns
* samples : ndarray or scalar
Drawn sample, all greater than zero.
####Notes
The probability density function for the Wald distribution is
$$ P(x;mean,scale) = \sqrt{\frac{scale}{2\pi x^3}}e^
\frac{-scale(x-mean)^2}{2\cdotp mean^2x}$$
As noted above the Inverse Gaussian distribution first arise from attempts
to model Brownian Motion. It is also a competitor to the Weibull for use in
reliability modeling and modeling stock returns and interest rate
processes.
####References
1. Brighton Webs Ltd., Wald Distribution,
http://www.brighton-webs.co.uk/distributions/wald.asp
2. Chhikara, Raj S., and Folks, J. Leroy, "The Inverse Gaussian
* Distribution: Theory : Methodology, and Applications", CRC Press,
1988.
3. Wikipedia, "Wald distribution"
http://en.wikipedia.org/wiki/Wald_distribution
####Examples
Draw values from the distribution and plot the histogram:
$ import matplotlib.pyplot as plt
$ h = plt.hist(np.random.wald(3, 2, 100000), bins=200, normed=True)
$ plt.show()
##weibull
randomkit.weibull([output], a)
Weibull distribution.
Draw samples from a 1-parameter Weibull distribution with the given
shape parameter `a`.
$$ X = (-ln(U))^{1/a}$$
Here, U is drawn from the uniform distribution over (0,1].
The more common 2-parameter Weibull, including a scale parameter
\\(\lambda\\) is just \\(X = \lambda(-ln(U))^{1/a}\\).
####Parameters
* a : float
Shape of the distribution.
* size : tuple of ints
Output shape. If the given shape is, e.g., ``(m, n, k)``, then
``m * n * k`` samples are drawn.
####See Also
scipy.stats.distributions.weibull_max
scipy.stats.distributions.weibull_min
scipy.stats.distributions.genextreme
gumbel
####Notes
The Weibull (or Type III asymptotic extreme value distribution for smallest
values, SEV Type III, or Rosin-Rammler distribution) is one of a class of
Generalized Extreme Value (GEV) distributions used in modeling extreme
value problems. This class includes the Gumbel and Frechet distributions.
The probability density for the Weibull distribution is
$$ p(x) = \frac{a}
{\lambda}(\frac{x}{\lambda})^{a-1}e^{-(x/\lambda)^a},$$
where \\(a\\) is the shape and \\(\lambda\\) the scale.
The function has its peak (the mode) at
\\(\lambda(\frac{a-1}{a})^{1/a}\\).
When ``a = 1``, the Weibull distribution reduces to the exponential
distribution.
####References
1. Waloddi Weibull, Professor, Royal Technical University, Stockholm,
1939 "A Statistical Theory Of The Strength Of Materials",
Ingeniorsvetenskapsakademiens Handlingar Nr 151, 1939,
Generalstabens Litografiska Anstalts Forlag, Stockholm.
2. Waloddi Weibull, 1951 "A Statistical Distribution Function of Wide
Applicability", Journal Of Applied Mechanics ASME Paper.
3. Wikipedia, "Weibull distribution",
http://en.wikipedia.org/wiki/Weibull_distribution
####Examples
Draw samples from the distribution:
$ a = 5. shape
$ s = np.random.weibull(a, 1000)
Display the histogram of the samples, along with
the probability density function:
$ import matplotlib.pyplot as plt
$ x = np.arange(1,100.)/50.
$ def weib(x,n,a):
return (a / n) * (x / n)**(a - 1) * np.exp(-(x / n)**a)
$ count, bins, ignored = plt.hist(np.random.weibull(5.,1000))
$ x = np.arange(1,100.)/50.
$ scale = count.max()/weib(x, 1., 5.).max()
$ plt.plot(x, weib(x, 1., 5.)*scale)
$ plt.show()
##zipf
randomkit.zipf([output], a)
Draw samples from a Zipf distribution.
Samples are drawn from a Zipf distribution with specified parameter
`a` > 1.
The Zipf distribution (also known as the zeta distribution) is a
continuous probability distribution that satisfies Zipf's law: the
frequency of an item is inversely proportional to its rank in a
frequency table.
####Parameters
* a : float > 1
Distribution parameter.
* size : int or tuple of int, optional
Output shape. If the given shape is, e.g., ``(m, n, k)``, then
``m * n * k`` samples are drawn; a single integer is equivalent in
its result to providing a mono-tuple, i.e., a 1-D array of length
*size* is returned. The default is None, in which case a single
scalar is returned.
####Returns
* samples : scalar or ndarray
The returned samples are greater than or equal to one.
####See Also
* scipy.stats.distributions.zipf : probability density function,
distribution, or cumulative density function, etc.
####Notes
The probability density for the Zipf distribution is
$$ p(x) = \frac{x^{-a}}{\zeta(a)},$$
where \\(\zeta\\) is the Riemann Zeta function.
It is named for the American linguist George Kingsley Zipf, who noted
that the frequency of any word in a sample of a language is inversely
proportional to its rank in the frequency table.
####References
Zipf, G. K., *Selected Studies of the Principle of Relative Frequency
in Language*, Cambridge, MA: Harvard Univ. Press, 1932.
####Examples
Draw samples from the distribution:
$ a = 2. parameter
$ s = np.random.zipf(a, 1000)
Display the histogram of the samples, along with
the probability density function:
$ import matplotlib.pyplot as plt
$ import scipy.special as sps
Truncate s values at 50 so plot is interesting
$ count, bins, ignored = plt.hist(s[s<50], 50, normed=True)
$ x = np.arange(1., 50.)
$ y = x**(-a)/sps.zetac(a)
$ plt.plot(x, y/max(y), linewidth=2, color='r')
$ plt.show()