metasyn.distribution.normal

Module implementing normal distributions.

Classes

`ContinuousNormalDistribution`(mean, sd)	Normal distribution for floating point type.
`ContinuousNormalFitter`(privacy)	Fitter for continuous normal distribution.
`ContinuousTruncatedNormalDistribution`(lower, ...)	Truncated normal distribution for floating point type.
`ContinuousTruncatedNormalFitter`(privacy)	Fitter for continuous truncated normal fitter.
`DiscreteNormalDistribution`(mean, sd)	Normal discrete distribution.
`DiscreteNormalFitter`(privacy)	Fitter for discrete normal distribution.
`DiscreteTruncatedNormalDistribution`(lower, ...)	Truncated normal discrete distribution.
`DiscreteTruncatedNormalFitter`(privacy)	Fitter for discrete truncated normal distribution.
`LogNormalDistribution`(mean, sd)	Log-normal distribution for floating point type.
`LogNormalFitter`(privacy)	Fitter for log normal distribution.

class metasyn.distribution.normal.ContinuousNormalDistribution(mean, sd)

Bases: ScipyDistribution

Normal distribution for floating point type.

This class implements the normal/gaussian distribution and takes the average and standard deviation as initialization input.

Parameters:

mean (float) – Mean of the normal distribution.
sd (float) – Standard deviation of the normal distribution.

Examples

>>> NormalDistribution(mean=1.3, sd=4.5)

name: core.normal

unique: False

version: 1.0

var_type: continuous

scipy_class = <scipy.stats._continuous_distns.norm_gen object>

classmethod default_distribution(var_type=None)

Get a distribution with default parameters.

Return type:: BaseDistribution

name: str = 'core.normal': The identifier for the implemented distribution

var_type: Union[str, Sequence[str]] = 'continuous': The variable type of the distribution

class metasyn.distribution.normal.ContinuousNormalFitter(privacy)

Bases: ScipyFitter

Fitter for continuous normal distribution.

Parameters:: privacy (BasePrivacy)

dist_class: <class ‘metasyn.distribution.normal.ContinuousNormalDistribution’>

version: 1.0

var_type: continuous

privacy: none

distribution: alias of ContinuousNormalDistribution

class metasyn.distribution.normal.ContinuousTruncatedNormalDistribution(lower, upper, mean, sd)

Bases: ScipyDistribution

Truncated normal distribution for floating point type.

Parameters:

lower (float) – Lower bound of the truncated normal distribution.
upper (float) – Upper bound of the truncated normal distribution.
mean (float) – Mean of the non-truncated normal distribution.
sd (float) – Standard deviation of the non-truncated normal distribution.

Examples

>>> TruncatedNormalDistribution(lower=1.0, upper=3.5, mean=2.3, sd=5)

name: core.truncated_normal

unique: False

version: 1.0

var_type: continuous

scipy_class = <scipy.stats._continuous_distns.truncnorm_gen object>

classmethod default_distribution(var_type=None)

Get a distribution with default parameters.

Return type:: BaseDistribution

name: str = 'core.truncated_normal': The identifier for the implemented distribution

var_type: Union[str, Sequence[str]] = 'continuous': The variable type of the distribution

class metasyn.distribution.normal.ContinuousTruncatedNormalFitter(privacy)

Bases: BaseFitter

Fitter for continuous truncated normal fitter.

Parameters:: privacy (BasePrivacy)

dist_class: <class ‘metasyn.distribution.normal.ContinuousTruncatedNormalDistribution’>

version: 1.0

var_type: continuous

privacy: none

distribution: alias of ContinuousTruncatedNormalDistribution

class metasyn.distribution.normal.DiscreteNormalDistribution(mean, sd)

Bases: ContinuousNormalDistribution

Normal discrete distribution.

This class implements the normal/gaussian distribution and takes the average and standard deviation as initialization input.

Parameters:

mean (float) – Mean of the normal distribution.
sd (float) – Standard deviation of the normal distribution.

Examples

>>> DiscreteNormalDistribution(mean=2.4, sd=1.2)

name: core.normal

unique: False

version: 1.0

var_type: discrete

draw(): Draw a random element from the fitted distribution.

classmethod default_distribution(var_type=None): Get a distribution with default parameters.

name: str = 'core.normal': The identifier for the implemented distribution

var_type: Union[str, Sequence[str]] = 'discrete': The variable type of the distribution

class metasyn.distribution.normal.DiscreteNormalFitter(privacy)

Bases: ScipyFitter

Fitter for discrete normal distribution.

Parameters:: privacy (BasePrivacy)

dist_class: <class ‘metasyn.distribution.normal.DiscreteNormalDistribution’>

version: 1.0

var_type: discrete

privacy: none

distribution: alias of DiscreteNormalDistribution

class metasyn.distribution.normal.DiscreteTruncatedNormalDistribution(lower, upper, mean, sd)

Bases: ContinuousTruncatedNormalDistribution

Truncated normal discrete distribution.

Parameters:

lower (float) – Lower bound of the truncated normal distribution.
upper (float) – Upper bound of the truncated normal distribution.
mean (float) – Mean of the non-truncated normal distribution.
sd (float) – Standard deviation of the non-truncated normal distribution.

Examples

>>> DiscreteTruncatedNormalDistribution(lower=1.2, upper=4.5, mean=2.3, sd=4.5)

name: core.truncated_normal

unique: False

version: 1.0

var_type: discrete

draw(): Draw a random element from the fitted distribution.

classmethod default_distribution(var_type=None): Get a distribution with default parameters.

name: str = 'core.truncated_normal': The identifier for the implemented distribution

var_type: Union[str, Sequence[str]] = 'discrete': The variable type of the distribution

class metasyn.distribution.normal.DiscreteTruncatedNormalFitter(privacy)

Bases: ContinuousTruncatedNormalFitter

Fitter for discrete truncated normal distribution.

Parameters:: privacy (BasePrivacy)

dist_class: <class ‘metasyn.distribution.normal.DiscreteTruncatedNormalDistribution’>

version: 1.0

var_type: discrete

privacy: none

distribution: alias of DiscreteTruncatedNormalDistribution

class metasyn.distribution.normal.LogNormalDistribution(mean, sd)

Bases: ScipyDistribution

Log-normal distribution for floating point type.

This class implements the log-normal mu and sigma as initialization input.

Parameters:

mean (float) – Controls the mean of the distribution.
sd (float) – Controls the width of the distribution.

Examples

>>> LogNormalDistribution(mean=-2.0, sd=4.5)

name: core.lognormal

unique: False

version: 1.0

var_type: continuous

scipy_class = <scipy.stats._continuous_distns.lognorm_gen object>

classmethod default_distribution(var_type=None)

Get a distribution with default parameters.

Return type:: BaseDistribution

name: str = 'core.lognormal': The identifier for the implemented distribution

var_type: Union[str, Sequence[str]] = 'continuous': The variable type of the distribution

class metasyn.distribution.normal.LogNormalFitter(privacy)

Bases: BaseFitter

Fitter for log normal distribution.

Parameters:: privacy (BasePrivacy)

dist_class: <class ‘metasyn.distribution.normal.LogNormalDistribution’>

version: 1.0

var_type: continuous

privacy: none

distribution: alias of LogNormalDistribution