Package org.apache.commons.math3.random

Class ValueServer

java.lang.Object

org.apache.commons.math3.random.ValueServer

public class ValueServer
extends Object

Generates values for use in simulation applications.

How values are generated is determined by the mode property.

Supported mode values are:

• DIGEST_MODE -- uses an empirical distribution
• REPLAY_MODE -- replays data from valuesFileURL
• UNIFORM_MODE -- generates uniformly distributed random values with mean = mu
• EXPONENTIAL_MODE -- generates exponentially distributed random values with mean = mu
• GAUSSIAN_MODE -- generates Gaussian distributed random values with mean = mu and standard deviation = sigma
• CONSTANT_MODE -- returns mu every time.

Field Summary

Fields

Modifier and Type	Field	Description
static final int	CONSTANT_MODE	Always return mu
static final int	DIGEST_MODE	Use empirical distribution.
static final int	EXPONENTIAL_MODE	Exponential random deviates with mean = μ.
static final int	GAUSSIAN_MODE	Gaussian random deviates with mean = μ, std dev = σ.
static final int	REPLAY_MODE	Replay data from valuesFilePath.
static final int	UNIFORM_MODE	Uniform random deviates with mean = μ.

Constructor Summary

Constructors

Constructor	Description
ValueServer()	Creates new ValueServer
ValueServer(RandomDataImpl randomData)	Deprecated. use ValueServer(RandomGenerator)
ValueServer(RandomGenerator generator)	Construct a ValueServer instance using a RandomGenerator as its source of random data.

Method Summary

Modifier and Type	Method	Description
void	closeReplayFile()	Closes valuesFileURL after use in REPLAY_MODE.
void	computeDistribution()	Computes the empirical distribution using values from the file in valuesFileURL, using the default number of bins.
void	computeDistribution(int binCount)	Computes the empirical distribution using values from the file in valuesFileURL and binCount bins.
void	fill(double[] values)	Fills the input array with values generated using getNext() repeatedly.
double[]	fill(int length)	Returns an array of length length with values generated using getNext() repeatedly.
EmpiricalDistribution	getEmpiricalDistribution()	Returns the EmpiricalDistribution used when operating in 0.
int	getMode()	Returns the data generation mode.
double	getMu()	Returns the mean used when operating in GAUSSIAN_MODE, EXPONENTIAL_MODE or UNIFORM_MODE.
double	getNext()	Returns the next generated value, generated according to the mode value (see MODE constants).
double	getSigma()	Returns the standard deviation used when operating in GAUSSIAN_MODE.
URL	getValuesFileURL()	Returns the URL for the file used to build the empirical distribution when using DIGEST_MODE.
void	reSeed(long seed)	Reseeds the random data generator.
void	resetReplayFile()	Resets REPLAY_MODE file pointer to the beginning of the valuesFileURL.
void	setMode(int mode)	Sets the data generation mode.
void	setMu(double mu)	Sets the mean used in data generation.
void	setSigma(double sigma)	Sets the standard deviation used in GAUSSIAN_MODE.
void	setValuesFileURL(String url)	Sets the values file URL using a string URL representation.
void	setValuesFileURL(URL url)	Sets the the values file URL.

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Field Details

DIGEST_MODE

public static final int DIGEST_MODE

Use empirical distribution.

See Also:

• Constant Field Values

REPLAY_MODE

public static final int REPLAY_MODE

Replay data from valuesFilePath.

See Also:

• Constant Field Values

UNIFORM_MODE

public static final int UNIFORM_MODE

Uniform random deviates with mean = μ.

See Also:

• Constant Field Values

EXPONENTIAL_MODE

public static final int EXPONENTIAL_MODE

Exponential random deviates with mean = μ.

See Also:

• Constant Field Values

GAUSSIAN_MODE

public static final int GAUSSIAN_MODE

Gaussian random deviates with mean = μ, std dev = σ.

See Also:

• Constant Field Values

CONSTANT_MODE

public static final int CONSTANT_MODE

Always return mu

See Also:

• Constant Field Values

Constructor Details

ValueServer

public ValueServer()

Creates new ValueServer

ValueServer

@Deprecated
public ValueServer(RandomDataImpl randomData)

Deprecated.

use ValueServer(RandomGenerator)

Construct a ValueServer instance using a RandomDataImpl as its source of random data.

Parameters:

randomData - the RandomDataImpl instance used to source random data

Since:

3.0

ValueServer

public ValueServer(RandomGenerator generator)

Construct a ValueServer instance using a RandomGenerator as its source of random data.

Parameters:

generator - source of random data

Since:

3.1

Method Details

getNext

public double getNext()
               throws IOException,
MathIllegalStateException,
MathIllegalArgumentException

Returns the next generated value, generated according to the mode value (see MODE constants).

Returns:

generated value

Throws:

IOException - in REPLAY_MODE if a file I/O error occurs

MathIllegalStateException - if mode is not recognized

MathIllegalArgumentException - if the underlying random generator thwrows one

fill

public void fill(double[] values)
          throws IOException,
MathIllegalStateException,
MathIllegalArgumentException

Fills the input array with values generated using getNext() repeatedly.

Parameters:

values - array to be filled

Throws:

IOException - in REPLAY_MODE if a file I/O error occurs

MathIllegalStateException - if mode is not recognized

MathIllegalArgumentException - if the underlying random generator thwrows one

fill

public double[] fill(int length)
              throws IOException,
MathIllegalStateException,
MathIllegalArgumentException

Returns an array of length length with values generated using getNext() repeatedly.

Parameters:

length - length of output array

Returns:

array of generated values

Throws:

IOException - in REPLAY_MODE if a file I/O error occurs

MathIllegalStateException - if mode is not recognized

MathIllegalArgumentException - if the underlying random generator thwrows one

computeDistribution

public void computeDistribution()
                         throws IOException,
ZeroException,
NullArgumentException

Computes the empirical distribution using values from the file in valuesFileURL, using the default number of bins.

valuesFileURL must exist and be readable by *this at runtime.

This method must be called before using getNext() with mode = DIGEST_MODE

Throws:

IOException - if an I/O error occurs reading the input file

NullArgumentException - if the valuesFileURL has not been set

ZeroException - if URL contains no data

computeDistribution

public void computeDistribution(int binCount)
                         throws NullArgumentException,
IOException,
ZeroException

Computes the empirical distribution using values from the file in valuesFileURL and binCount bins.

valuesFileURL must exist and be readable by this process at runtime.

This method must be called before using getNext() with mode = DIGEST_MODE

Parameters:

binCount - the number of bins used in computing the empirical distribution

Throws:

NullArgumentException - if the valuesFileURL has not been set

IOException - if an error occurs reading the input file

ZeroException - if URL contains no data

getMode

public int getMode()

Returns the data generation mode. See the class javadoc for description of the valid values of this property.

Returns:

Value of property mode.

setMode

public void setMode(int mode)

Sets the data generation mode.

Parameters:

mode - New value of the data generation mode.

getValuesFileURL

public URL getValuesFileURL()

Returns the URL for the file used to build the empirical distribution when using DIGEST_MODE.

Returns:

Values file URL.

setValuesFileURL

public void setValuesFileURL(String url)
                      throws MalformedURLException

Sets the values file URL using a string URL representation.

Parameters:

url - String representation for new valuesFileURL.

Throws:

MalformedURLException - if url is not well formed

setValuesFileURL

public void setValuesFileURL(URL url)

Sets the the values file URL.

The values file must be an ASCII text file containing one valid numeric entry per line.

Parameters:

url - URL of the values file.

getEmpiricalDistribution

public EmpiricalDistribution getEmpiricalDistribution()

Returns the EmpiricalDistribution used when operating in 0.

Returns:

EmpircalDistribution built by computeDistribution()

resetReplayFile

public void resetReplayFile()
                     throws IOException

Resets REPLAY_MODE file pointer to the beginning of the valuesFileURL.

Throws:

IOException - if an error occurs opening the file

NullPointerException - if the valuesFileURL has not been set.

closeReplayFile

public void closeReplayFile()
                     throws IOException

Closes valuesFileURL after use in REPLAY_MODE.

Throws:

IOException - if an error occurs closing the file

getMu

public double getMu()

Returns the mean used when operating in GAUSSIAN_MODE, EXPONENTIAL_MODE or UNIFORM_MODE. When operating in CONSTANT_MODE, this is the constant value always returned. Calling computeDistribution() sets this value to the overall mean of the values in the values file.

Returns:

Mean used in data generation.

setMu

public void setMu(double mu)

Sets the mean used in data generation. Note that calling this method after computeDistribution() has been called will have no effect on data generated in DIGEST_MODE.

Parameters:

mu - new Mean value.

getSigma

public double getSigma()

Returns the standard deviation used when operating in GAUSSIAN_MODE. Calling computeDistribution() sets this value to the overall standard deviation of the values in the values file. This property has no effect when the data generation mode is not GAUSSIAN_MODE.

Returns:

Standard deviation used when operating in GAUSSIAN_MODE.

setSigma

public void setSigma(double sigma)

Sets the standard deviation used in GAUSSIAN_MODE.

Parameters:

sigma - New standard deviation.

reSeed

public void reSeed(long seed)

Reseeds the random data generator.

Parameters:

seed - Value with which to reseed the RandomDataImpl used to generate random data.