phitter.continuous.continuous_distributions package

Submodules

phitter.continuous.continuous_distributions.alpha module

class phitter.continuous.continuous_distributions.alpha.Alpha(parameters=None, continuous_measures=None, init_parameters_examples=False)

Bases: object

Alpha distribution - Parameters Alpha Distribution: {“alpha”: *, “loc”: *, “scale”: *} - https://phitter.io/distributions/continuous/alpha

cdf(x)

Cumulative distribution function

Return type:

float | ndarray

central_moments(k)

Parametric central moments. µ’[k] = E[(X - E[X])ᵏ] = ∫(x-µ[k])ᵏ∙f(x) dx

Return type:

float | None

get_parameters(continuous_measures)

Calculate proper parameters of the distribution from sample continuous_measures. The parameters are calculated by solving the equations of the measures expected for this distribution.The number of equations to consider is equal to the number of parameters.

Parameters:

continuous_measures (MEASUREMESTS) – attributes: mean, std, variance, skewness, kurtosis, median, mode, min, max, size, num_bins, data

Returns:

parameters

Return type:

{“alpha”: *, “loc”: *, “scale”: *}

property kurtosis: float

Parametric kurtosis

property mean: float

Parametric mean

property median: float

Parametric median

property mode: float

Parametric mode

property name

non_central_moments(k)

Parametric no central moments. µ[k] = E[Xᵏ] = ∫xᵏ∙f(x) dx

Return type:

float | None

property num_parameters: int

Number of parameters of the distribution

parameter_restrictions()

Check parameters restrictions

Return type:

bool

property parameters_example: dict[str, int | float]

pdf(x)

Probability density function

Return type:

float | ndarray

ppf(u)

Percent point function. Inverse of Cumulative distribution function. If CDF[x] = u => PPF[u] = x

Return type:

float | ndarray

sample(n, seed=None)

Sample of n elements of ditribution

Return type:

ndarray

property skewness: float

Parametric skewness

property standard_deviation: float

Parametric standard deviation

property variance: float

Parametric variance

phitter.continuous.continuous_distributions.arcsine module

class phitter.continuous.continuous_distributions.arcsine.Arcsine(parameters=None, continuous_measures=None, init_parameters_examples=False)

Bases: object

Arcsine distribution - Parameters Arcsine Distribution: {“a”: *, “b”: *} - https://phitter.io/distributions/continuous/arcsine

cdf(x)

Cumulative distribution function

Return type:

float | ndarray

central_moments(k)

Parametric central moments. µ’[k] = E[(X - E[X])ᵏ] = ∫(x-µ[k])ᵏ∙f(x) dx

Return type:

float | None

get_parameters(continuous_measures)

Calculate proper parameters of the distribution from sample continuous_measures. The parameters are calculated by formula.

Parameters:

continuous_measures (MEASUREMESTS) – attributes: mean, std, variance, skewness, kurtosis, median, mode, min, max, size, num_bins, data

Returns:

parameters

Return type:

{“a”: *, “b”: *}

property kurtosis: float

Parametric kurtosis

property mean: float

Parametric mean

property median: float

Parametric median

property mode: float

Parametric mode

property name

non_central_moments(k)

Parametric no central moments. µ[k] = E[Xᵏ] = ∫xᵏ∙f(x) dx

Return type:

float | None

property num_parameters: int

Number of parameters of the distribution

parameter_restrictions()

Check parameters restrictions

Return type:

bool

property parameters_example: dict[str, int | float]

pdf(x)

Probability density function

Return type:

float | ndarray

ppf(u)

Percent point function. Inverse of Cumulative distribution function. If CDF[x] = u => PPF[u] = x

Return type:

float | ndarray

sample(n, seed=None)

Sample of n elements of ditribution

Return type:

ndarray

property skewness: float

Parametric skewness

property standard_deviation: float

Parametric standard deviation

property variance: float

Parametric variance

phitter.continuous.continuous_distributions.argus module

class phitter.continuous.continuous_distributions.argus.Argus(parameters=None, continuous_measures=None, init_parameters_examples=False)

Bases: object

Argus distribution - Parameters Argus Distribution: {“chi”: *, “loc”: *, “scale”: *} - https://phitter.io/distributions/continuous/argus

cdf(x)

Cumulative distribution function

Return type:

float | ndarray

central_moments(k)

Parametric central moments. µ’[k] = E[(X - E[X])ᵏ] = ∫(x-µ[k])ᵏ∙f(x) dx

Return type:

float | None

get_parameters(continuous_measures)

Calculate proper parameters of the distribution from sample continuous_measures. The parameters are calculated by solving the equations of the measures expected for this distribution.The number of equations to consider is equal to the number of parameters.

Parameters:

continuous_measures (MEASUREMESTS) – attributes: mean, std, variance, skewness, kurtosis, median, mode, min, max, size, num_bins, data

Returns:

parameters

Return type:

{“chi”: *, “loc”: *, “scale”: *}

property kurtosis: float

Parametric kurtosis

property mean: float

Parametric mean

property median: float

Parametric median

property mode: float

Parametric mode

property name

non_central_moments(k)

Parametric no central moments. µ[k] = E[Xᵏ] = ∫xᵏ∙f(x) dx

Return type:

float | None

property num_parameters: int

Number of parameters of the distribution

parameter_restrictions()

Check parameters restrictions

Return type:

bool

property parameters_example: dict[str, int | float]

pdf(x)

Probability density function

Return type:

float | ndarray

ppf(u)

Percent point function. Inverse of Cumulative distribution function. If CDF[x] = u => PPF[u] = x

Return type:

float | ndarray

sample(n, seed=None)

Sample of n elements of ditribution

Return type:

ndarray

property skewness: float

Parametric skewness

property standard_deviation: float

Parametric standard deviation

property variance: float

Parametric variance

phitter.continuous.continuous_distributions.beta module

class phitter.continuous.continuous_distributions.beta.Beta(parameters=None, continuous_measures=None, init_parameters_examples=False)

Bases: object

Beta distribution - Parameters Beta Distribution: {“alpha”: *, “beta”: *, “A”: *, “B”: *} - https://phitter.io/distributions/continuous/beta

cdf(x)

Cumulative distribution function

Return type:

float | ndarray

central_moments(k)

Parametric central moments. µ’[k] = E[(X - E[X])ᵏ] = ∫(x-µ[k])ᵏ∙f(x) dx

Return type:

float | None

get_parameters(continuous_measures)

Calculate proper parameters of the distribution from sample continuous_measures. The parameters are calculated by solving the equations of the measures expected for this distribution.The number of equations to consider is equal to the number of parameters.

Parameters:

continuous_measures (MEASUREMESTS) – attributes: mean, std, variance, skewness, kurtosis, median, mode, min, max, size, num_bins, data

Returns:

parameters

Return type:

{“alpha”: *, “beta”: *, “A”: *, “B”: *}

property kurtosis: float

Parametric kurtosis

property mean: float

Parametric mean

property median: float

Parametric median

property mode: float

Parametric mode

property name

non_central_moments(k)

Parametric no central moments. µ[k] = E[Xᵏ] = ∫xᵏ∙f(x) dx

Return type:

float | None

property num_parameters: int

Number of parameters of the distribution

parameter_restrictions()

Check parameters restrictions

Return type:

bool

property parameters_example: dict[str, int | float]

pdf(x)

Probability density function

Return type:

float | ndarray

ppf(u)

Percent point function. Inverse of Cumulative distribution function. If CDF[x] = u => PPF[u] = x

Return type:

float | ndarray

sample(n, seed=None)

Sample of n elements of ditribution

Return type:

ndarray

property skewness: float

Parametric skewness

property standard_deviation: float

Parametric standard deviation

property variance: float

Parametric variance

phitter.continuous.continuous_distributions.beta_prime module

class phitter.continuous.continuous_distributions.beta_prime.BetaPrime(parameters=None, continuous_measures=None, init_parameters_examples=False)

Bases: object

Beta Prime Distribution - Parameters BetaPrime Distribution: {“alpha”: *, “beta”: *} - https://phitter.io/distributions/continuous/beta_prime

cdf(x)

Cumulative distribution function

Return type:

float | ndarray

central_moments(k)

Parametric central moments. µ’[k] = E[(X - E[X])ᵏ] = ∫(x-µ[k])ᵏ∙f(x) dx

Return type:

float | None

get_parameters(continuous_measures)

Calculate proper parameters of the distribution from sample continuous_measures. The parameters are calculated by solving the equations of the measures expected for this distribution.The number of equations to consider is equal to the number of parameters.

Parameters:

continuous_measures (MEASUREMESTS) – attributes: mean, std, variance, skewness, kurtosis, median, mode, min, max, size, num_bins, data

Returns:

parameters

Return type:

{“alpha”: *, “beta”: *}

property kurtosis: float

Parametric kurtosis

property mean: float

Parametric mean

property median: float

Parametric median

property mode: float

Parametric mode

property name

non_central_moments(k)

Parametric no central moments. µ[k] = E[Xᵏ] = ∫xᵏ∙f(x) dx

Return type:

float | None

property num_parameters: int

Number of parameters of the distribution

parameter_restrictions()

Check parameters restrictions

Return type:

bool

property parameters_example: dict[str, int | float]

pdf(x)

Probability density function

Return type:

float | ndarray

ppf(u)

Percent point function. Inverse of Cumulative distribution function. If CDF[x] = u => PPF[u] = x

Return type:

float | ndarray

sample(n, seed=None)

Sample of n elements of ditribution

Return type:

ndarray

property skewness: float

Parametric skewness

property standard_deviation: float

Parametric standard deviation

property variance: float

Parametric variance

phitter.continuous.continuous_distributions.beta_prime_4p module

class phitter.continuous.continuous_distributions.beta_prime_4p.BetaPrime4P(parameters=None, continuous_measures=None, init_parameters_examples=False)

Bases: object

Beta Prime 4P Distribution - Parameters BetaPrime4P Distribution: {“alpha”: *, “beta”: *, “loc”: *, “scale”: *} - https://phitter.io/distributions/continuous/beta_prime_4p

cdf(x)

Cumulative distribution function

Return type:

float | ndarray

central_moments(k)

Parametric central moments. µ’[k] = E[(X - E[X])ᵏ] = ∫(x-µ[k])ᵏ∙f(x) dx

Return type:

float | None

get_parameters(continuous_measures)

Calculate proper parameters of the distribution from sample continuous_measures. The parameters are calculated by solving the equations of the measures expected for this distribution.The number of equations to consider is equal to the number of parameters.

Parameters:

continuous_measures (MEASUREMESTS) – attributes: mean, std, variance, skewness, kurtosis, median, mode, min, max, size, num_bins, data

Returns:

parameters

Return type:

{“alpha”: *, “beta”: *, “loc”: *, “scale”: *}

property kurtosis: float

Parametric kurtosis

property mean: float

Parametric mean

property median: float

Parametric median

property mode: float

Parametric mode

property name

non_central_moments(k)

Parametric no central moments. µ[k] = E[Xᵏ] = ∫xᵏ∙f(x) dx

Return type:

float | None

property num_parameters: int

Number of parameters of the distribution

parameter_restrictions()

Check parameters restrictions

Return type:

bool

property parameters_example: dict[str, int | float]

pdf(x)

Probability density function

Return type:

float | ndarray

ppf(u)

Percent point function. Inverse of Cumulative distribution function. If CDF[x] = u => PPF[u] = x

Return type:

float | ndarray

sample(n, seed=None)

Sample of n elements of ditribution

Return type:

ndarray

property skewness: float

Parametric skewness

property standard_deviation: float

Parametric standard deviation

property variance: float

Parametric variance

phitter.continuous.continuous_distributions.bradford module

class phitter.continuous.continuous_distributions.bradford.Bradford(parameters=None, continuous_measures=None, init_parameters_examples=False)

Bases: object

Bradford distribution - Parameters Bradford Distribution: {“c”: *, “min”: *, “max”: *} - https://phitter.io/distributions/continuous/bradford

cdf(x)

Cumulative distribution function

Return type:

float | ndarray

central_moments(k)

Parametric central moments. µ’[k] = E[(X - E[X])ᵏ] = ∫(x-µ[k])ᵏ∙f(x) dx

Return type:

float | None

get_parameters(continuous_measures)

Calculate proper parameters of the distribution from sample continuous_measures. The parameters are calculated by formula.

Parameters:

continuous_measures (MEASUREMESTS) – attributes: mean, std, variance, skewness, kurtosis, median, mode, min, max, size, num_bins, data

Returns:

parameters

Return type:

{“c”: *, “min”: *, “max”: *}

property kurtosis: float

Parametric kurtosis

property mean: float

Parametric mean

property median: float

Parametric median

property mode: float

Parametric mode

property name

non_central_moments(k)

Parametric no central moments. µ[k] = E[Xᵏ] = ∫xᵏ∙f(x) dx

Return type:

float | None

property num_parameters: int

Number of parameters of the distribution

parameter_restrictions()

Check parameters restrictions

Return type:

bool

property parameters_example: dict[str, int | float]

pdf(x)

Probability density function

Return type:

float | ndarray

ppf(u)

Percent point function. Inverse of Cumulative distribution function. If CDF[x] = u => PPF[u] = x

Return type:

float | ndarray

sample(n, seed=None)

Sample of n elements of ditribution

Return type:

ndarray

property skewness: float

Parametric skewness

property standard_deviation: float

Parametric standard deviation

property variance: float

Parametric variance

phitter.continuous.continuous_distributions.burr module

class phitter.continuous.continuous_distributions.burr.Burr(parameters=None, continuous_measures=None, init_parameters_examples=False)

Bases: object

Burr distribution - Parameters Burr Distribution: {“A”: *, “B”: *, “C”: *} - https://phitter.io/distributions/continuous/burr

cdf(x)

Cumulative distribution function

Return type:

float | ndarray

central_moments(k)

Parametric central moments. µ’[k] = E[(X - E[X])ᵏ] = ∫(x-µ[k])ᵏ∙f(x) dx

Return type:

float | None

get_parameters(continuous_measures)

Calculate proper parameters of the distribution from sample continuous_measures. The parameters are calculated by formula.

Parameters:

continuous_measures (MEASUREMESTS) – attributes: mean, std, variance, skewness, kurtosis, median, mode, min, max, size, num_bins, data

Returns:

parameters

Return type:

{“A”: *, “B”: *, “C”: *}

property kurtosis: float

Parametric kurtosis

property mean: float

Parametric mean

property median: float

Parametric median

property mode: float

Parametric mode

property name

non_central_moments(k)

Parametric no central moments. µ[k] = E[Xᵏ] = ∫xᵏ∙f(x) dx

Return type:

float | None

property num_parameters: int

Number of parameters of the distribution

parameter_restrictions()

Check parameters restrictions

Return type:

bool

property parameters_example: dict[str, int | float]

pdf(x)

Probability density function

Return type:

float | ndarray

ppf(u)

Percent point function. Inverse of Cumulative distribution function. If CDF[x] = u => PPF[u] = x

Return type:

float | ndarray

sample(n, seed=None)

Sample of n elements of ditribution

Return type:

ndarray

property skewness: float

Parametric skewness

property standard_deviation: float

Parametric standard deviation

property variance: float

Parametric variance

phitter.continuous.continuous_distributions.burr_4p module

class phitter.continuous.continuous_distributions.burr_4p.Burr4P(parameters=None, continuous_measures=None, init_parameters_examples=False)

Bases: object

Burr distribution - Parameters Burr4P Distribution: {“A”: *, “B”: *, “C”: *, “loc”: *} - https://phitter.io/distributions/continuous/burr_4p

cdf(x)

Cumulative distribution function

Return type:

float | ndarray

central_moments(k)

Parametric central moments. µ’[k] = E[(X - E[X])ᵏ] = ∫(x-µ[k])ᵏ∙f(x) dx

Return type:

float | None

get_parameters(continuous_measures)

Calculate proper parameters of the distribution from sample continuous_measures. The parameters are calculated by formula.

Parameters:

continuous_measures (MEASUREMESTS) – attributes: mean, std, variance, skewness, kurtosis, median, mode, min, max, size, num_bins, data

Returns:

parameters

Return type:

{“A”: *, “B”: *, “C”: *, “loc”: *}

property kurtosis: float

Parametric kurtosis

property mean: float

Parametric mean

property median: float

Parametric median

property mode: float

Parametric mode

property name

non_central_moments(k)

Parametric no central moments. µ[k] = E[Xᵏ] = ∫xᵏ∙f(x) dx

Return type:

float | None

property num_parameters: int

Number of parameters of the distribution

parameter_restrictions()

Check parameters restrictions

Return type:

bool

property parameters_example: dict[str, int | float]

pdf(x)

Probability density function

Return type:

float | ndarray

ppf(u)

Percent point function. Inverse of Cumulative distribution function. If CDF[x] = u => PPF[u] = x

Return type:

float | ndarray

sample(n, seed=None)

Sample of n elements of ditribution

Return type:

ndarray

property skewness: float

Parametric skewness

property standard_deviation: float

Parametric standard deviation

property variance: float

Parametric variance

phitter.continuous.continuous_distributions.cauchy module

class phitter.continuous.continuous_distributions.cauchy.Cauchy(parameters=None, continuous_measures=None, init_parameters_examples=False)

Bases: object

Cauchy distribution - Parameters Cauchy Distribution: {“x0”: *, “gamma”: *} - https://phitter.io/distributions/continuous/cauchy

cdf(x)

Cumulative distribution function

Return type:

float | ndarray

central_moments(k)

Parametric central moments. µ’[k] = E[(X - E[X])ᵏ] = ∫(x-µ[k])ᵏ∙f(x) dx

Return type:

float | None

get_parameters(continuous_measures)

Calculate proper parameters of the distribution from sample continuous_measures. The parameters are calculated by formula.

Parameters:

continuous_measures (MEASUREMESTS) – attributes: mean, std, variance, skewness, kurtosis, median, mode, min, max, size, num_bins, data

Returns:

parameters

Return type:

{“x0”: *, “gamma”: *}

property kurtosis: float

Parametric kurtosis

property mean: float

Parametric mean

property median: float

Parametric median

property mode: float

Parametric mode

property name

non_central_moments(k)

Parametric no central moments. µ[k] = E[Xᵏ] = ∫xᵏ∙f(x) dx

Return type:

float | None

property num_parameters: int

Number of parameters of the distribution

parameter_restrictions()

Check parameters restrictions

Return type:

bool

property parameters_example: dict[str, int | float]

pdf(x)

Probability density function

Return type:

float | ndarray

ppf(u)

Percent point function. Inverse of Cumulative distribution function. If CDF[x] = u => PPF[u] = x

Return type:

float | ndarray

sample(n, seed=None)

Sample of n elements of ditribution

Return type:

ndarray

property skewness: float

Parametric skewness

property standard_deviation: float

Parametric standard deviation

property variance: float

Parametric variance

phitter.continuous.continuous_distributions.chi_square module

class phitter.continuous.continuous_distributions.chi_square.ChiSquare(parameters=None, continuous_measures=None, init_parameters_examples=False)

Bases: object

Chi Square distribution - Parameters ChiSquare Distribution: {“df”: *} - https://phitter.io/distributions/continuous/chi_square

cdf(x)

Cumulative distribution function

Return type:

float | ndarray

central_moments(k)

Parametric central moments. µ’[k] = E[(X - E[X])ᵏ] = ∫(x-µ[k])ᵏ∙f(x) dx

Return type:

float | None

get_parameters(continuous_measures)

Calculate proper parameters of the distribution from sample continuous_measures. The parameters are calculated by formula.

Parameters:

continuous_measures (MEASUREMESTS) – attributes: mean, std, variance, skewness, kurtosis, median, mode, min, max, size, num_bins, data

Returns:

parameters

Return type:

{“df”: *}

property kurtosis: float

Parametric kurtosis

property mean: float

Parametric mean

property median: float

Parametric median

property mode: float

Parametric mode

property name

non_central_moments(k)

Parametric no central moments. µ[k] = E[Xᵏ] = ∫xᵏ∙f(x) dx

Return type:

float | None

property num_parameters: int

Number of parameters of the distribution

parameter_restrictions()

Check parameters restrictions

Return type:

bool

property parameters_example: dict[str, int | float]

pdf(x)

Probability density function

Return type:

float | ndarray

ppf(u)

Percent point function. Inverse of Cumulative distribution function. If CDF[x] = u => PPF[u] = x

Return type:

float | ndarray

sample(n, seed=None)

Sample of n elements of ditribution

Return type:

ndarray

property skewness: float

Parametric skewness

property standard_deviation: float

Parametric standard deviation

property variance: float

Parametric variance

phitter.continuous.continuous_distributions.chi_square_3p module

class phitter.continuous.continuous_distributions.chi_square_3p.ChiSquare3P(parameters=None, continuous_measures=None, init_parameters_examples=False)

Bases: object

Chi Square distribution - Parameters ChiSquare3P Distribution: {“df”: *, “loc”: *, “scale”: *} - https://phitter.io/distributions/continuous/chi_square_3p

cdf(x)

Cumulative distribution function

Return type:

float | ndarray

central_moments(k)

Parametric central moments. µ’[k] = E[(X - E[X])ᵏ] = ∫(x-µ[k])ᵏ∙f(x) dx

Return type:

float | None

get_parameters(continuous_measures)

Calculate proper parameters of the distribution from sample continuous_measures. The parameters are calculated by formula.

Parameters:

continuous_measures (MEASUREMESTS) – attributes: mean, std, variance, skewness, kurtosis, median, mode, min, max, size, num_bins, data

Returns:

parameters

Return type:

{“df”: *, “loc”: *, “scale”: *}

property kurtosis: float

Parametric kurtosis

property mean: float

Parametric mean

property median: float

Parametric median

property mode: float

Parametric mode

property name

non_central_moments(k)

Parametric no central moments. µ[k] = E[Xᵏ] = ∫xᵏ∙f(x) dx

Return type:

float | None

property num_parameters: int

Number of parameters of the distribution

parameter_restrictions()

Check parameters restrictions

Return type:

bool

property parameters_example: dict[str, int | float]

pdf(x)

Probability density function

Return type:

float | ndarray

ppf(u)

Percent point function. Inverse of Cumulative distribution function. If CDF[x] = u => PPF[u] = x

Return type:

float | ndarray

sample(n, seed=None)

Sample of n elements of ditribution

Return type:

ndarray

property skewness: float

Parametric skewness

property standard_deviation: float

Parametric standard deviation

property variance: float

Parametric variance

phitter.continuous.continuous_distributions.dagum module

class phitter.continuous.continuous_distributions.dagum.Dagum(parameters=None, continuous_measures=None, init_parameters_examples=False)

Bases: object

Dagum distribution - Parameters Dagum Distribution: {“a”: *, “b”: *, “p”: *} - https://phitter.io/distributions/continuous/dagum

cdf(x)

Cumulative distribution function

Return type:

float | ndarray

central_moments(k)

Parametric central moments. µ’[k] = E[(X - E[X])ᵏ] = ∫(x-µ[k])ᵏ∙f(x) dx

Return type:

float | None

get_parameters(continuous_measures)

Calculate proper parameters of the distribution from sample continuous_measures. The parameters are calculated by formula.

Parameters:

continuous_measures (MEASUREMESTS) – attributes: mean, std, variance, skewness, kurtosis, median, mode, min, max, size, num_bins, data

Returns:

parameters

Return type:

{“a”: *, “b”: *, “p”: *}

property kurtosis: float

Parametric kurtosis

property mean: float

Parametric mean

property median: float

Parametric median

property mode: float

Parametric mode

property name

non_central_moments(k)

Parametric no central moments. µ[k] = E[Xᵏ] = ∫xᵏ∙f(x) dx

Return type:

float | None

property num_parameters: int

Number of parameters of the distribution

parameter_restrictions()

Check parameters restrictions

Return type:

bool

property parameters_example: dict[str, int | float]

pdf(x)

Probability density function

Return type:

float | ndarray

ppf(u)

Percent point function. Inverse of Cumulative distribution function. If CDF[x] = u => PPF[u] = x

Return type:

float | ndarray

sample(n, seed=None)

Sample of n elements of ditribution

Return type:

ndarray

property skewness: float

Parametric skewness

property standard_deviation: float

Parametric standard deviation

property variance: float

Parametric variance

phitter.continuous.continuous_distributions.dagum_4p module

class phitter.continuous.continuous_distributions.dagum_4p.Dagum4P(parameters=None, continuous_measures=None, init_parameters_examples=False)

Bases: object

Dagum distribution - Parameters Dagum4P Distribution: {“a”: *, “b”: *, “p”: *, “loc”: *} - https://phitter.io/distributions/continuous/dagum_4p

cdf(x)

Cumulative distribution function

Return type:

float | ndarray

central_moments(k)

Parametric central moments. µ’[k] = E[(X - E[X])ᵏ] = ∫(x-µ[k])ᵏ∙f(x) dx

Return type:

float | None

get_parameters(continuous_measures)

Calculate proper parameters of the distribution from sample continuous_measures. The parameters are calculated by formula.

Parameters:

continuous_measures (MEASUREMESTS) – attributes: mean, std, variance, skewness, kurtosis, median, mode, min, max, size, num_bins, data

Returns:

parameters

Return type:

{“a”: *, “b”: *, “p”: *, “loc”: *}

property kurtosis: float

Parametric kurtosis

property mean: float

Parametric mean

property median: float

Parametric median

property mode: float

Parametric mode

property name

non_central_moments(k)

Parametric no central moments. µ[k] = E[Xᵏ] = ∫xᵏ∙f(x) dx

Return type:

float | None

property num_parameters: int

Number of parameters of the distribution

parameter_restrictions()

Check parameters restrictions

Return type:

bool

property parameters_example: dict[str, int | float]

pdf(x)

Probability density function

Return type:

float | ndarray

ppf(u)

Percent point function. Inverse of Cumulative distribution function. If CDF[x] = u => PPF[u] = x

Return type:

float | ndarray

sample(n, seed=None)

Sample of n elements of ditribution

Return type:

ndarray

property skewness: float

Parametric skewness

property standard_deviation: float

Parametric standard deviation

property variance: float

Parametric variance

phitter.continuous.continuous_distributions.erlang module

class phitter.continuous.continuous_distributions.erlang.Erlang(parameters=None, continuous_measures=None, init_parameters_examples=False)

Bases: object

Erlang distribution - Parameters Erlang Distribution: {“k”: *, “beta”: *} - https://phitter.io/distributions/continuous/erlang

cdf(x)

Cumulative distribution function

Return type:

float | ndarray

central_moments(k)

Parametric central moments. µ’[k] = E[(X - E[X])ᵏ] = ∫(x-µ[k])ᵏ∙f(x) dx

Return type:

float | None

get_parameters(continuous_measures)

Calculate proper parameters of the distribution from sample continuous_measures. The parameters are calculated by formula.

Parameters:

continuous_measures (MEASUREMESTS) – attributes: mean, std, variance, skewness, kurtosis, median, mode, min, max, size, num_bins, data

Returns:

parameters

Return type:

{“k”: *, “beta”: *}

property kurtosis: float

Parametric kurtosis

property mean: float

Parametric mean

property median: float

Parametric median

property mode: float

Parametric mode

property name

non_central_moments(k)

Parametric no central moments. µ[k] = E[Xᵏ] = ∫xᵏ∙f(x) dx

Return type:

float | None

property num_parameters: int

Number of parameters of the distribution

parameter_restrictions()

Check parameters restriction

Return type:

bool

property parameters_example: dict[str, int | float]

pdf(x)

Probability density function

Return type:

float | ndarray

ppf(u)

Percent point function. Inverse of Cumulative distribution function. If CDF[x] = u => PPF[u] = x

Return type:

float | ndarray

sample(n, seed=None)

Sample of n elements of ditribution

Return type:

ndarray

property skewness: float

Parametric skewness

property standard_deviation: float

Parametric standard deviation

property variance: float

Parametric variance

phitter.continuous.continuous_distributions.erlang_3p module

class phitter.continuous.continuous_distributions.erlang_3p.Erlang3P(parameters=None, continuous_measures=None, init_parameters_examples=False)

Bases: object

Erlang 3p distribution - Parameters Erlang3P Distribution: {“k”: *, “beta”: *, “loc”: *} - https://phitter.io/distributions/continuous/erlang_3p

cdf(x)

Cumulative distribution function

Return type:

float | ndarray

central_moments(k)

Parametric central moments. µ’[k] = E[(X - E[X])ᵏ] = ∫(x-µ[k])ᵏ∙f(x) dx

Return type:

float | None

get_parameters(continuous_measures)

Calculate proper parameters of the distribution from sample continuous_measures. The parameters are calculated by formula.

Parameters:

continuous_measures (MEASUREMESTS) – attributes: mean, std, variance, skewness, kurtosis, median, mode, min, max, size, num_bins, data

Returns:

parameters

Return type:

{“k”: *, “beta”: *, “loc”: *}

property kurtosis: float

Parametric kurtosis

property mean: float

Parametric mean

property median: float

Parametric median

property mode: float

Parametric mode

property name

non_central_moments(k)

Parametric no central moments. µ[k] = E[Xᵏ] = ∫xᵏ∙f(x) dx

Return type:

float | None

property num_parameters: int

Number of parameters of the distribution

parameter_restrictions()

Check parameters restriction

Return type:

bool

property parameters_example: dict[str, int | float]

pdf(x)

Probability density function

Return type:

float | ndarray

ppf(u)

Percent point function. Inverse of Cumulative distribution function. If CDF[x] = u => PPF[u] = x

Return type:

float | ndarray

sample(n, seed=None)

Sample of n elements of ditribution

Return type:

ndarray

property skewness: float

Parametric skewness

property standard_deviation: float

Parametric standard deviation

property variance: float

Parametric variance

phitter.continuous.continuous_distributions.error_function module

class phitter.continuous.continuous_distributions.error_function.ErrorFunction(parameters=None, continuous_measures=None, init_parameters_examples=False)

Bases: object

Error Function distribution - Parameters ErrorFunction Distribution: {“h”: *} - https://phitter.io/distributions/continuous/error_function

cdf(x)

Cumulative distribution function

Return type:

float | ndarray

central_moments(k)

Parametric central moments. µ’[k] = E[(X - E[X])ᵏ] = ∫(x-µ[k])ᵏ∙f(x) dx

Return type:

float | None

get_parameters(continuous_measures)

Calculate proper parameters of the distribution from sample continuous_measures. The parameters are calculated by formula.

Parameters:

continuous_measures (MEASUREMESTS) – attributes: mean, std, variance, skewness, kurtosis, median, mode, min, max, size, num_bins, data

Returns:

parameters

Return type:

{“h”: *}

property kurtosis: float

Parametric kurtosis

property mean: float

Parametric mean

property median: float

Parametric median

property mode: float

Parametric mode

property name

non_central_moments(k)

Parametric no central moments. µ[k] = E[Xᵏ] = ∫xᵏ∙f(x) dx

Return type:

float | None

property num_parameters: int

Number of parameters of the distribution

parameter_restrictions()

Check parameters restrictions

Return type:

bool

property parameters_example: dict[str, int | float]

pdf(x)

Probability density function

Return type:

float | ndarray

ppf(u)

Percent point function. Inverse of Cumulative distribution function. If CDF[x] = u => PPF[u] = x

Return type:

float | ndarray

sample(n, seed=None)

Sample of n elements of ditribution

Return type:

ndarray

property skewness: float

Parametric skewness

property standard_deviation: float

Parametric standard deviation

property variance: float

Parametric variance

phitter.continuous.continuous_distributions.exponential module

class phitter.continuous.continuous_distributions.exponential.Exponential(parameters=None, continuous_measures=None, init_parameters_examples=False)

Bases: object

Exponential distribution - Parameters Exponential Distribution: {“lambda”: *} - https://phitter.io/distributions/continuous/exponential

cdf(x)

Cumulative distribution function. Calculated with known formula.

Return type:

float | ndarray

central_moments(k)

Parametric central moments. µ’[k] = E[(X - E[X])ᵏ] = ∫(x-µ[k])ᵏ∙f(x) dx

Return type:

float | None

get_parameters(continuous_measures)

Calculate proper parameters of the distribution from sample continuous_measures. The parameters are calculated by solving the equations of the measures expected for this distribution.The number of equations to consider is equal to the number of parameters.

Parameters:

continuous_measures (MEASUREMESTS) – attributes: mean, std, variance, skewness, kurtosis, median, mode, min, max, size, num_bins, data

Returns:

parameters

Return type:

{“lambda”: *}

property kurtosis: float

Parametric kurtosis

property mean: float

Parametric mean

property median: float

Parametric median

property mode: float

Parametric mode

property name

non_central_moments(k)

Parametric no central moments. µ[k] = E[Xᵏ] = ∫xᵏ∙f(x) dx

Return type:

float | None

property num_parameters: int

Number of parameters of the distribution

parameter_restrictions()

Check parameters restrictions

Return type:

bool

property parameters_example: dict[str, int | float]

pdf(x)

Probability density function

Return type:

float | ndarray

ppf(u)

Percent point function. Inverse of Cumulative distribution function. If CDF[x] = u => PPF[u] = x

Return type:

float | ndarray

sample(n, seed=None)

Sample of n elements of ditribution

Return type:

ndarray

property skewness: float

Parametric skewness

property standard_deviation: float

Parametric standard deviation

property variance: float

Parametric variance

phitter.continuous.continuous_distributions.exponential_2p module

class phitter.continuous.continuous_distributions.exponential_2p.Exponential2P(parameters=None, continuous_measures=None, init_parameters_examples=False)

Bases: object

Exponential distribution - Parameters Exponential2P Distribution: {“lambda”: *, “loc”: *} - https://phitter.io/distributions/continuous/exponential_2p

cdf(x)

Cumulative distribution function. Calculated with known formula.

Return type:

float | ndarray

central_moments(k)

Parametric central moments. µ’[k] = E[(X - E[X])ᵏ] = ∫(x-µ[k])ᵏ∙f(x) dx

Return type:

float | None

get_parameters(continuous_measures)

Calculate proper parameters of the distribution from sample continuous_measures. The parameters are calculated by solving the equations of the measures expected for this distribution.The number of equations to consider is equal to the number of parameters.

Parameters:

continuous_measures (MEASUREMESTS) – attributes: mean, std, variance, skewness, kurtosis, median, mode, min, max, size, num_bins, data

Returns:

parameters

Return type:

{“lambda”: *, “loc”: *}

property kurtosis: float

Parametric kurtosis

property mean: float

Parametric mean

property median: float

Parametric median

property mode: float

Parametric mode

property name

non_central_moments(k)

Parametric no central moments. µ[k] = E[Xᵏ] = ∫xᵏ∙f(x) dx

Return type:

float | None

property num_parameters: int

Number of parameters of the distribution

parameter_restrictions()

Check parameters restrictions

Return type:

bool

property parameters_example: dict[str, int | float]

pdf(x)

Probability density function

Return type:

float | ndarray

ppf(u)

Percent point function. Inverse of Cumulative distribution function. If CDF[x] = u => PPF[u] = x

Return type:

float | ndarray

sample(n, seed=None)

Sample of n elements of ditribution

Return type:

ndarray

property skewness: float

Parametric skewness

property standard_deviation: float

Parametric standard deviation

property variance: float

Parametric variance

phitter.continuous.continuous_distributions.f module

class phitter.continuous.continuous_distributions.f.F(parameters=None, continuous_measures=None, init_parameters_examples=False)

Bases: object

F distribution - Parameters F Distribution: {“df1”: *, “df2”: *} - https://phitter.io/distributions/continuous/f

cdf(x)

Cumulative distribution function

Return type:

float | ndarray

central_moments(k)

Parametric central moments. µ’[k] = E[(X - E[X])ᵏ] = ∫(x-µ[k])ᵏ∙f(x) dx

Return type:

float | None

get_parameters(continuous_measures)

Calculate proper parameters of the distribution from sample continuous_measures. The parameters are calculated by formula.

Parameters:

continuous_measures (MEASUREMESTS) – attributes: mean, std, variance, skewness, kurtosis, median, mode, min, max, size, num_bins, data

Returns:

parameters

Return type:

{“df1”: *, “df2”: *}

property kurtosis: float

Parametric kurtosis

property mean: float

Parametric mean

property median: float

Parametric median

property mode: float

Parametric mode

property name

non_central_moments(k)

Parametric no central moments. µ[k] = E[Xᵏ] = ∫xᵏ∙f(x) dx

Return type:

float | None

property num_parameters: int

Number of parameters of the distribution

parameter_restrictions()

Check parameters restrictions

Return type:

bool

property parameters_example: dict[str, int | float]

pdf(x)

Probability density function

Return type:

float | ndarray

ppf(u)

Percent point function. Inverse of Cumulative distribution function. If CDF[x] = u => PPF[u] = x

Return type:

float | ndarray

sample(n, seed=None)

Sample of n elements of ditribution

Return type:

ndarray

property skewness: float

Parametric skewness

property standard_deviation: float

Parametric standard deviation

property variance: float

Parametric variance

phitter.continuous.continuous_distributions.f_4p module

class phitter.continuous.continuous_distributions.f_4p.F4P(parameters=None, continuous_measures=None, init_parameters_examples=False)

Bases: object

F distribution - Parameters F4P Distribution: {“df1”: *, “df2”: *, “loc”: *, “scale”: *} - https://phitter.io/distributions/continuous/f_4p

cdf(x)

Cumulative distribution function

Return type:

float | ndarray

central_moments(k)

Parametric central moments. µ’[k] = E[(X - E[X])ᵏ] = ∫(x-µ[k])ᵏ∙f(x) dx

Return type:

float | None

get_parameters(continuous_measures)

Calculate proper parameters of the distribution from sample continuous_measures. The parameters are calculated by formula.

Parameters:

continuous_measures (MEASUREMESTS) – attributes: mean, std, variance, skewness, kurtosis, median, mode, min, max, size, num_bins, data

Returns:

parameters

Return type:

{“df1”: *, “df2”: *, “loc”: *, “scale”: *}

property kurtosis: float

Parametric kurtosis

property mean: float

Parametric mean

property median: float

Parametric median

property mode: float

Parametric mode

property name

non_central_moments(k)

Parametric no central moments. µ[k] = E[Xᵏ] = ∫xᵏ∙f(x) dx

Return type:

float | None

property num_parameters: int

Number of parameters of the distribution

parameter_restrictions()

Check parameters restrictions

Return type:

bool

property parameters_example: dict[str, int | float]

pdf(x)

Probability density function

Return type:

float | ndarray

ppf(u)

Percent point function. Inverse of Cumulative distribution function. If CDF[x] = u => PPF[u] = x

Return type:

float | ndarray

sample(n, seed=None)

Sample of n elements of ditribution

Return type:

ndarray

property skewness: float

Parametric skewness

property standard_deviation: float

Parametric standard deviation

property variance: float

Parametric variance

phitter.continuous.continuous_distributions.fatigue_life module

class phitter.continuous.continuous_distributions.fatigue_life.FatigueLife(parameters=None, continuous_measures=None, init_parameters_examples=False)

Bases: object

Fatigue life Distribution Also known as Birnbaum-Saunders distribution - Parameters FatigueLife Distribution: {“gamma”: *, “loc”: *, “scale”: *} - https://phitter.io/distributions/continuous/fatigue_life

cdf(x)

Cumulative distribution function

Return type:

float | ndarray

central_moments(k)

Parametric central moments. µ’[k] = E[(X - E[X])ᵏ] = ∫(x-µ[k])ᵏ∙f(x) dx

Return type:

float | None

get_parameters(continuous_measures)

Calculate proper parameters of the distribution from sample continuous_measures. The parameters are calculated by formula.

Parameters:

continuous_measures (MEASUREMESTS) – attributes: mean, std, variance, skewness, kurtosis, median, mode, min, max, size, num_bins, data

Returns:

parameters

Return type:

{“gamma”: *, “loc”: *, “scale”: *}

property kurtosis: float

Parametric kurtosis

property mean: float

Parametric mean

property median: float

Parametric median

property mode: float

Parametric mode

property name

non_central_moments(k)

Parametric no central moments. µ[k] = E[Xᵏ] = ∫xᵏ∙f(x) dx

Return type:

float | None

property num_parameters: int

Number of parameters of the distribution

parameter_restrictions()

Check parameters restrictions

Return type:

bool

property parameters_example: dict[str, int | float]

pdf(x)

Probability density function

Return type:

float | ndarray

ppf(u)

Percent point function. Inverse of Cumulative distribution function. If CDF[x] = u => PPF[u] = x

Return type:

float | ndarray

sample(n, seed=None)

Sample of n elements of ditribution

Return type:

ndarray

property skewness: float

Parametric skewness

property standard_deviation: float

Parametric standard deviation

property variance: float

Parametric variance

phitter.continuous.continuous_distributions.folded_normal module

class phitter.continuous.continuous_distributions.folded_normal.FoldedNormal(parameters=None, continuous_measures=None, init_parameters_examples=False)

Bases: object

Folded Normal Distribution - https://phitter.io/distributions/continuous/folded_normal

cdf(x)

Cumulative distribution function

Return type:

float | ndarray

central_moments(k)

Parametric central moments. µ’[k] = E[(X - E[X])ᵏ] = ∫(x-µ[k])ᵏ∙f(x) dx

Return type:

float | None

get_parameters(continuous_measures)

Calculate proper parameters of the distribution from sample continuous_measures. The parameters are calculated by formula.

Parameters:

continuous_measures (dict) – {“mu”: * , “variance”: * , “skewness”: * , “kurtosis”: * , “data”: * }

Returns:

parameters

Return type:

{“mu”: *, “sigma”: *}

property kurtosis: float

Parametric kurtosis

property mean: float

Parametric mean

property median: float

Parametric median

property mode: float

Parametric mode

property name

non_central_moments(k)

Parametric no central moments. µ[k] = E[Xᵏ] = ∫xᵏ∙f(x) dx

Return type:

float | None

property num_parameters: int

Number of parameters of the distribution

parameter_restrictions()

Check parameters restrictions

Return type:

bool

property parameters_example: dict[str, int | float]

pdf(x)

Probability density function

Return type:

float | ndarray

ppf(u)

Percent point function. Inverse of Cumulative distribution function. If CDF[x] = u => PPF[u] = x

Return type:

float | ndarray

sample(n, seed=None)

Sample of n elements of ditribution

Return type:

ndarray

property skewness: float

Parametric skewness

property standard_deviation: float

Parametric standard deviation

property variance: float

Parametric variance

phitter.continuous.continuous_distributions.frechet module

class phitter.continuous.continuous_distributions.frechet.Frechet(parameters=None, continuous_measures=None, init_parameters_examples=False)

Bases: object

Fréchet Distribution Also known as inverse Weibull distribution (Scipy name) - https://phitter.io/distributions/continuous/frechet

cdf(x)

Cumulative distribution function

Return type:

float | ndarray

central_moments(k)

Parametric central moments. µ’[k] = E[(X - E[X])ᵏ] = ∫(x-µ[k])ᵏ∙f(x) dx

Return type:

float | None

get_parameters(continuous_measures)

Calculate proper parameters of the distribution from sample continuous_measures. The parameters are calculated by formula.

Parameters:

continuous_measures (MEASUREMESTS) – attributes: mean, std, variance, skewness, kurtosis, median, mode, min, max, size, num_bins, data

Returns:

parameters

Return type:

{“alpha”: *, “loc”: *, “scale”: *}

property kurtosis: float

Parametric kurtosis

property mean: float

Parametric mean

property median: float

Parametric median

property mode: float

Parametric mode

property name

non_central_moments(k)

Parametric no central moments. µ[k] = E[Xᵏ] = ∫xᵏ∙f(x) dx

Return type:

float | None

property num_parameters: int

Number of parameters of the distribution

parameter_restrictions()

Check parameters restrictions

Return type:

bool

property parameters_example: dict[str, int | float]

pdf(x)

Probability density function

Return type:

float | ndarray

ppf(u)

Percent point function. Inverse of Cumulative distribution function. If CDF[x] = u => PPF[u] = x

Return type:

float | ndarray

sample(n, seed=None)

Sample of n elements of ditribution

Return type:

ndarray

property skewness: float

Parametric skewness

property standard_deviation: float

Parametric standard deviation

property variance: float

Parametric variance

phitter.continuous.continuous_distributions.gamma module

class phitter.continuous.continuous_distributions.gamma.Gamma(parameters=None, continuous_measures=None, init_parameters_examples=False)

Bases: object

Gamma distribution - Parameters Gamma Distribution: {“alpha”: *, “beta”: *} - https://phitter.io/distributions/continuous/gamma

cdf(x)

Cumulative distribution function

Return type:

float | ndarray

central_moments(k)

Parametric central moments. µ’[k] = E[(X - E[X])ᵏ] = ∫(x-µ[k])ᵏ∙f(x) dx

Return type:

float | None

get_parameters(continuous_measures)

Calculate proper parameters of the distribution from sample continuous_measures. The parameters are calculated by formula.

Parameters:

continuous_measures (MEASUREMESTS) – attributes: mean, std, variance, skewness, kurtosis, median, mode, min, max, size, num_bins, data

Returns:

parameters

Return type:

{“alpha”: *, “beta”: *}

property kurtosis: float

Parametric kurtosis

property mean: float

Parametric mean

property median: float

Parametric median

property mode: float

Parametric mode

property name

non_central_moments(k)

Parametric no central moments. µ[k] = E[Xᵏ] = ∫xᵏ∙f(x) dx

Return type:

float | None

property num_parameters: int

Number of parameters of the distribution

parameter_restrictions()

Check parameters restrictions

Return type:

bool

property parameters_example: dict[str, int | float]

pdf(x)

Probability density function

Return type:

float | ndarray

ppf(u)

Percent point function. Inverse of Cumulative distribution function. If CDF[x] = u => PPF[u] = x

Return type:

float | ndarray

sample(n, seed=None)

Sample of n elements of ditribution

Return type:

ndarray

property skewness: float

Parametric skewness

property standard_deviation: float

Parametric standard deviation

property variance: float

Parametric variance

phitter.continuous.continuous_distributions.gamma_3p module

class phitter.continuous.continuous_distributions.gamma_3p.Gamma3P(parameters=None, continuous_measures=None, init_parameters_examples=False)

Bases: object

Gamma distribution - Parameters Gamma3P Distribution: {“alpha”: *, “loc”: *, “beta”: *} - https://phitter.io/distributions/continuous/gamma_3p

cdf(x)

Cumulative distribution function

Return type:

float | ndarray

central_moments(k)

Parametric central moments. µ’[k] = E[(X - E[X])ᵏ] = ∫(x-µ[k])ᵏ∙f(x) dx

Return type:

float | None

get_parameters(continuous_measures)

Calculate proper parameters of the distribution from sample continuous_measures. The parameters are calculated by formula.

Parameters:

continuous_measures (MEASUREMESTS) – attributes: mean, std, variance, skewness, kurtosis, median, mode, min, max, size, num_bins, data

Returns:

parameters

Return type:

{“alpha”: *, “loc”: *, “beta”: *}

property kurtosis: float

Parametric kurtosis

property mean: float

Parametric mean

property median: float

Parametric median

property mode: float

Parametric mode

property name

non_central_moments(k)

Parametric no central moments. µ[k] = E[Xᵏ] = ∫xᵏ∙f(x) dx

Return type:

float | None

property num_parameters: int

Number of parameters of the distribution

parameter_restrictions()

Check parameters restrictions

Return type:

bool

property parameters_example: dict[str, int | float]

pdf(x)

Probability density function

Return type:

float | ndarray

ppf(u)

Percent point function. Inverse of Cumulative distribution function. If CDF[x] = u => PPF[u] = x

Return type:

float | ndarray

sample(n, seed=None)

Sample of n elements of ditribution

Return type:

ndarray

property skewness: float

Parametric skewness

property standard_deviation: float

Parametric standard deviation

property variance: float

Parametric variance

phitter.continuous.continuous_distributions.generalized_extreme_value module

class phitter.continuous.continuous_distributions.generalized_extreme_value.GeneralizedExtremeValue(parameters=None, continuous_measures=None, init_parameters_examples=False)

Bases: object

Generalized Extreme Value Distribution - https://phitter.io/distributions/continuous/generalized_extreme_value

cdf(x)

Cumulative distribution function

Return type:

float | ndarray

central_moments(k)

Parametric central moments. µ’[k] = E[(X - E[X])ᵏ] = ∫(x-µ[k])ᵏ∙f(x) dx

Return type:

float | None

get_parameters(continuous_measures)

Calculate proper parameters of the distribution from sample continuous_measures. The parameters are calculated by formula.

Parameters:

continuous_measures (MEASUREMESTS) – attributes: mean, std, variance, skewness, kurtosis, median, mode, min, max, size, num_bins, data

Returns:

parameters

Return type:

{“xi”: *, “mu”: *, “sigma”: *}

property kurtosis: float

Parametric kurtosis

property mean: float

Parametric mean

property median: float

Parametric median

property mode: float

Parametric mode

property name

non_central_moments(k)

Parametric no central moments. µ[k] = E[Xᵏ] = ∫xᵏ∙f(x) dx

Return type:

float | None

property num_parameters: int

Number of parameters of the distribution

parameter_restrictions()

Check parameters restrictions

Return type:

bool

property parameters_example: dict[str, int | float]

pdf(x)

Probability density function

Return type:

float | ndarray

ppf(u)

Percent point function. Inverse of Cumulative distribution function. If CDF[x] = u => PPF[u] = x

Return type:

float | ndarray

sample(n, seed=None)

Sample of n elements of ditribution

Return type:

ndarray

property skewness: float

Parametric skewness

property standard_deviation: float

Parametric standard deviation

property variance: float

Parametric variance

phitter.continuous.continuous_distributions.generalized_gamma module

class phitter.continuous.continuous_distributions.generalized_gamma.GeneralizedGamma(parameters=None, continuous_measures=None, init_parameters_examples=False)

Bases: object

Generalized Gamma Distribution - https://phitter.io/distributions/continuous/generalized_gamma

cdf(x)

Cumulative distribution function

Return type:

float | ndarray

central_moments(k)

Parametric central moments. µ’[k] = E[(X - E[X])ᵏ] = ∫(x-µ[k])ᵏ∙f(x) dx

Return type:

float | None

get_parameters(continuous_measures)

Calculate proper parameters of the distribution from sample continuous_measures. The parameters are calculated by formula.

Parameters:

continuous_measures (MEASUREMESTS) – attributes: mean, std, variance, skewness, kurtosis, median, mode, min, max, size, num_bins, data

Returns:

parameters

Return type:

{“a”: *, “d”: *, “p”: *}

property kurtosis: float

Parametric kurtosis

property mean: float

Parametric mean

property median: float

Parametric median

property mode: float

Parametric mode

property name

non_central_moments(k)

Parametric no central moments. µ[k] = E[Xᵏ] = ∫xᵏ∙f(x) dx

Return type:

float | None

property num_parameters: int

Number of parameters of the distribution

parameter_restrictions()

Check parameters restrictions

Return type:

bool

property parameters_example: dict[str, int | float]

pdf(x)

Probability density function

Return type:

float | ndarray

ppf(u)

Percent point function. Inverse of Cumulative distribution function. If CDF[x] = u => PPF[u] = x

Return type:

float | ndarray

sample(n, seed=None)

Sample of n elements of ditribution

Return type:

ndarray

property skewness: float

Parametric skewness

property standard_deviation: float

Parametric standard deviation

property variance: float

Parametric variance

phitter.continuous.continuous_distributions.generalized_gamma_4p module

class phitter.continuous.continuous_distributions.generalized_gamma_4p.GeneralizedGamma4P(parameters=None, continuous_measures=None, init_parameters_examples=False)

Bases: object

Generalized Gamma Distribution - https://phitter.io/distributions/continuous/generalized_gamma_4p

cdf(x)

Cumulative distribution function

Return type:

float | ndarray

central_moments(k)

Parametric central moments. µ’[k] = E[(X - E[X])ᵏ] = ∫(x-µ[k])ᵏ∙f(x) dx

Return type:

float | None

get_parameters(continuous_measures)

Calculate proper parameters of the distribution from sample continuous_measures. The parameters are calculated by formula.

Parameters:

continuous_measures (MEASUREMESTS) – attributes: mean, std, variance, skewness, kurtosis, median, mode, min, max, size, num_bins, data

Returns:

parameters

Return type:

{“a”: *, “d”: *, “p”: *, “loc”: *}

property kurtosis: float

Parametric kurtosis

property mean: float

Parametric mean

property median: float

Parametric median

property mode: float

Parametric mode

property name

non_central_moments(k)

Parametric no central moments. µ[k] = E[Xᵏ] = ∫xᵏ∙f(x) dx

Return type:

float | None

property num_parameters: int

Number of parameters of the distribution

parameter_restrictions()

Check parameters restrictions

Return type:

bool

property parameters_example: dict[str, int | float]

pdf(x)

Probability density function

Return type:

float | ndarray

ppf(u)

Percent point function. Inverse of Cumulative distribution function. If CDF[x] = u => PPF[u] = x

Return type:

float | ndarray

sample(n, seed=None)

Sample of n elements of ditribution

Return type:

ndarray

property skewness: float

Parametric skewness

property standard_deviation: float

Parametric standard deviation

property variance: float

Parametric variance

phitter.continuous.continuous_distributions.generalized_logistic module

class phitter.continuous.continuous_distributions.generalized_logistic.GeneralizedLogistic(parameters=None, continuous_measures=None, init_parameters_examples=False)

Bases: object

Generalized Logistic Distribution

• https://phitter.io/distributions/continuous/generalized_logistic

cdf(x)

Cumulative distribution function

Return type:

float | ndarray

central_moments(k)

Parametric central moments. µ’[k] = E[(X - E[X])ᵏ] = ∫(x-µ[k])ᵏ∙f(x) dx

Return type:

float | None

get_parameters(continuous_measures)

Calculate proper parameters of the distribution from sample continuous_measures. The parameters are calculated by formula.

Parameters:

continuous_measures (MEASUREMESTS) – attributes: mean, std, variance, skewness, kurtosis, median, mode, min, max, size, num_bins, data

Returns:

parameters

Return type:

{“loc”: *, “scale”: *, “c”: *}

property kurtosis: float

Parametric kurtosis

property mean: float

Parametric mean

property median: float

Parametric median

property mode: float

Parametric mode

property name

non_central_moments(k)

Parametric no central moments. µ[k] = E[Xᵏ] = ∫xᵏ∙f(x) dx

Return type:

float | None

property num_parameters: int

Number of parameters of the distribution

parameter_restrictions()

Check parameters restrictions

Return type:

bool

property parameters_example: dict[str, int | float]

pdf(x)

Probability density function

Return type:

float | ndarray

ppf(u)

Percent point function. Inverse of Cumulative distribution function. If CDF[x] = u => PPF[u] = x

Return type:

float | ndarray

sample(n, seed=None)

Sample of n elements of ditribution

Return type:

ndarray

property skewness: float

Parametric skewness

property standard_deviation: float

Parametric standard deviation

property variance: float

Parametric variance

phitter.continuous.continuous_distributions.generalized_normal module

class phitter.continuous.continuous_distributions.generalized_normal.GeneralizedNormal(parameters=None, continuous_measures=None, init_parameters_examples=False)

Bases: object

Generalized normal distribution - Parameters GeneralizedNormal Distribution: {“beta”: *, “mu”: *, “alpha”: *} - https://phitter.io/distributions/continuous/generalized_normal This distribution is known whit the following names: * Error Distribution * Exponential Power Distribution * Generalized Error Distribution (GED) * Generalized Gaussian distribution (GGD) * Subbotin distribution

cdf(x)

Cumulative distribution function

Return type:

float | ndarray

central_moments(k)

Parametric central moments. µ’[k] = E[(X - E[X])ᵏ] = ∫(x-µ[k])ᵏ∙f(x) dx

Return type:

float | None

get_parameters(continuous_measures)

Calculate proper parameters of the distribution from sample continuous_measures. The parameters are calculated by formula.

Parameters:

continuous_measures (MEASUREMESTS) – attributes: mean, std, variance, skewness, kurtosis, median, mode, min, max, size, num_bins, data

Returns:

parameters

Return type:

{“beta”: *, “mu”: *, “alpha”: *}

property kurtosis: float

Parametric kurtosis

property mean: float

Parametric mean

property median: float

Parametric median

property mode: float

Parametric mode

property name

non_central_moments(k)

Parametric no central moments. µ[k] = E[Xᵏ] = ∫xᵏ∙f(x) dx

Return type:

float | None

property num_parameters: int

Number of parameters of the distribution

parameter_restrictions()

Check parameters restriction

Return type:

bool

property parameters_example: dict[str, int | float]

pdf(x)

Probability density function

Return type:

float | ndarray

ppf(u)

Percent point function. Inverse of Cumulative distribution function. If CDF[x] = u => PPF[u] = x

Return type:

float | ndarray

sample(n, seed=None)

Sample of n elements of ditribution

Return type:

ndarray

property skewness: float

Parametric skewness

property standard_deviation: float

Parametric standard deviation

property variance: float

Parametric variance

phitter.continuous.continuous_distributions.generalized_pareto module

class phitter.continuous.continuous_distributions.generalized_pareto.GeneralizedPareto(parameters=None, continuous_measures=None, init_parameters_examples=False)

Bases: object

Generalized Pareto distribution - Parameters GeneralizedPareto Distribution: {“c”: *, “mu”: *, “sigma”: *} - https://phitter.io/distributions/continuous/generalized_pareto

cdf(x)

Cumulative distribution function

Return type:

float | ndarray

central_moments(k)

Parametric central moments. µ’[k] = E[(X - E[X])ᵏ] = ∫(x-µ[k])ᵏ∙f(x) dx

Return type:

float | None

get_parameters(continuous_measures)

Calculate proper parameters of the distribution from sample continuous_measures. The parameters are calculated by formula.

Parameters:

continuous_measures (MEASUREMESTS) – attributes: mean, std, variance, skewness, kurtosis, median, mode, min, max, size, num_bins, data

Returns:

parameters

Return type:

{“c”: *, “mu”: *, “sigma”: *}

property kurtosis: float

Parametric kurtosis

property mean: float

Parametric mean

property median: float

Parametric median

property mode: float

Parametric mode

property name

non_central_moments(k)

Parametric no central moments. µ[k] = E[Xᵏ] = ∫xᵏ∙f(x) dx

Return type:

float | None

property num_parameters: int

Number of parameters of the distribution

parameter_restrictions()

Check parameters restrictions

Return type:

bool

property parameters_example: dict[str, int | float]

pdf(x)

Probability density function

Return type:

float | ndarray

ppf(u)

Percent point function. Inverse of Cumulative distribution function. If CDF[x] = u => PPF[u] = x

Return type:

float | ndarray

sample(n, seed=None)

Sample of n elements of ditribution

Return type:

ndarray

property skewness: float

Parametric skewness

property standard_deviation: float

Parametric standard deviation

property variance: float

Parametric variance

phitter.continuous.continuous_distributions.gibrat module

class phitter.continuous.continuous_distributions.gibrat.Gibrat(parameters=None, continuous_measures=None, init_parameters_examples=False)

Bases: object

Gibrat distribution - Parameters Gibrat Distribution: {“loc”: *, “scale”: *} - https://phitter.io/distributions/continuous/gibrat

cdf(x)

Cumulative distribution function

Return type:

float | ndarray

central_moments(k)

Parametric central moments. µ’[k] = E[(X - E[X])ᵏ] = ∫(x-µ[k])ᵏ∙f(x) dx

Return type:

float | None

get_parameters(continuous_measures)

Calculate proper parameters of the distribution from sample continuous_measures. The parameters are calculated by solving the equations of the measures expected for this distribution.The number of equations to consider is equal to the number of parameters.

Parameters:

continuous_measures (MEASUREMESTS) – attributes: mean, std, variance, skewness, kurtosis, median, mode, min, max, size, num_bins, data

Returns:

parameters

Return type:

{“loc”: *, “scale”: *}

property kurtosis: float

Parametric kurtosis

property mean: float

Parametric mean

property median: float

Parametric median

property mode: float

Parametric mode

property name

non_central_moments(k)

Parametric no central moments. µ[k] = E[Xᵏ] = ∫xᵏ∙f(x) dx

Return type:

float | None

property num_parameters: int

Number of parameters of the distribution

parameter_restrictions()

Check parameters restrictions

Return type:

bool

property parameters_example: dict[str, int | float]

pdf(x)

Probability density function

Return type:

float | ndarray

ppf(u)

Percent point function. Inverse of Cumulative distribution function. If CDF[x] = u => PPF[u] = x

Return type:

float | ndarray

sample(n, seed=None)

Sample of n elements of ditribution

Return type:

ndarray

property skewness: float

Parametric skewness

property standard_deviation: float

Parametric standard deviation

property variance: float

Parametric variance

phitter.continuous.continuous_distributions.gumbel_left module

class phitter.continuous.continuous_distributions.gumbel_left.GumbelLeft(parameters=None, continuous_measures=None, init_parameters_examples=False)

Bases: object

Gumbel Left Distribution Gumbel Min Distribution Extreme Value Minimum Distribution - https://phitter.io/distributions/continuous/gumbel_left

cdf(x)

Cumulative distribution function

Return type:

float | ndarray

central_moments(k)

Parametric central moments. µ’[k] = E[(X - E[X])ᵏ] = ∫(x-µ[k])ᵏ∙f(x) dx

Return type:

float | None

get_parameters(continuous_measures)

Calculate proper parameters of the distribution from sample continuous_measures. The parameters are calculated by formula.

Parameters:

continuous_measures (MEASUREMESTS) – attributes: mean, std, variance, skewness, kurtosis, median, mode, min, max, size, num_bins, data

Returns:

parameters

Return type:

{“mu”: *, “sigma”: *}

property kurtosis: float

Parametric kurtosis

property mean: float

Parametric mean

property median: float

Parametric median

property mode: float

Parametric mode

property name

non_central_moments(k)

Parametric no central moments. µ[k] = E[Xᵏ] = ∫xᵏ∙f(x) dx

Return type:

float | None

property num_parameters: int

Number of parameters of the distribution

parameter_restrictions()

Check parameters restrictions

Return type:

bool

property parameters_example: dict[str, int | float]

pdf(x)

Probability density function

Return type:

float | ndarray

ppf(u)

Percent point function. Inverse of Cumulative distribution function. If CDF[x] = u => PPF[u] = x

Return type:

float | ndarray

sample(n, seed=None)

Sample of n elements of ditribution

Return type:

ndarray

property skewness: float

Parametric skewness

property standard_deviation: float

Parametric standard deviation

property variance: float

Parametric variance

phitter.continuous.continuous_distributions.gumbel_right module

class phitter.continuous.continuous_distributions.gumbel_right.GumbelRight(parameters=None, continuous_measures=None, init_parameters_examples=False)

Bases: object

Gumbel Right Distribution Gumbel Max Distribution Extreme Value Maximum Distribution

• https://phitter.io/distributions/continuous/gumbel_right

cdf(x)

Cumulative distribution function

Return type:

float | ndarray

central_moments(k)

Parametric central moments. µ’[k] = E[(X - E[X])ᵏ] = ∫(x-µ[k])ᵏ∙f(x) dx

Return type:

float | None

get_parameters(continuous_measures)

Calculate proper parameters of the distribution from sample continuous_measures. The parameters are calculated by formula.

Parameters:

continuous_measures (MEASUREMESTS) – attributes: mean, std, variance, skewness, kurtosis, median, mode, min, max, size, num_bins, data

Returns:

parameters

Return type:

{“mu”: *, “sigma”: *}

property kurtosis: float

Parametric kurtosis

property mean: float

Parametric mean

property median: float

Parametric median

property mode: float

Parametric mode

property name

non_central_moments(k)

Parametric no central moments. µ[k] = E[Xᵏ] = ∫xᵏ∙f(x) dx

Return type:

float | None

property num_parameters: int

Number of parameters of the distribution

parameter_restrictions()

Check parameters restrictions

Return type:

bool

property parameters_example: dict[str, int | float]

pdf(x)

Probability density function

Return type:

float | ndarray

ppf(u)

Percent point function. Inverse of Cumulative distribution function. If CDF[x] = u => PPF[u] = x

Return type:

float | ndarray

sample(n, seed=None)

Sample of n elements of ditribution

Return type:

ndarray

property skewness: float

Parametric skewness

property standard_deviation: float

Parametric standard deviation

property variance: float

Parametric variance

phitter.continuous.continuous_distributions.half_normal module

class phitter.continuous.continuous_distributions.half_normal.HalfNormal(parameters=None, continuous_measures=None, init_parameters_examples=False)

Bases: object

Half Normal Distribution - https://phitter.io/distributions/continuous/half_normal

cdf(x)

Cumulative distribution function

Return type:

float | ndarray

central_moments(k)

Parametric central moments. µ’[k] = E[(X - E[X])ᵏ] = ∫(x-µ[k])ᵏ∙f(x) dx

Return type:

float | None

get_parameters(continuous_measures)

Calculate proper parameters of the distribution from sample continuous_measures. The parameters are calculated by formula.

Parameters:

continuous_measures (dict) – {“mu”: * , “variance”: * , “skewness”: * , “kurtosis”: * , “data”: * }

Returns:

parameters

Return type:

{“mu”: *, “sigma”: *}

property kurtosis: float

Parametric kurtosis

property mean: float

Parametric mean

property median: float

Parametric median

property mode: float

Parametric mode

property name

non_central_moments(k)

Parametric no central moments. µ[k] = E[Xᵏ] = ∫xᵏ∙f(x) dx

Return type:

float | None

property num_parameters: int

Number of parameters of the distribution

parameter_restrictions()

Check parameters restrictions

Return type:

bool

property parameters_example: dict[str, int | float]

pdf(x)

Probability density function

Return type:

float | ndarray

ppf(u)

Percent point function. Inverse of Cumulative distribution function. If CDF[x] = u => PPF[u] = x

Return type:

float | ndarray

sample(n, seed=None)

Sample of n elements of ditribution

Return type:

ndarray

property skewness: float

Parametric skewness

property standard_deviation: float

Parametric standard deviation

property variance: float

Parametric variance

phitter.continuous.continuous_distributions.hyperbolic_secant module

class phitter.continuous.continuous_distributions.hyperbolic_secant.HyperbolicSecant(parameters=None, continuous_measures=None, init_parameters_examples=False)

Bases: object

Hyperbolic Secant distribution - Parameters HyperbolicSecant Distribution: {“mu”: *, “sigma”: *} - https://phitter.io/distributions/continuous/hyperbolic_secant

cdf(x)

Cumulative distribution function

Return type:

float | ndarray

central_moments(k)

Parametric central moments. µ’[k] = E[(X - E[X])ᵏ] = ∫(x-µ[k])ᵏ∙f(x) dx

Return type:

float | None

get_parameters(continuous_measures)

Calculate proper parameters of the distribution from sample continuous_measures. The parameters are calculated by formula.

Parameters:

continuous_measures (dict) – {“mu”: * , “variance”: * , “skewness”: * , “kurtosis”: * , “data”: * }

Returns:

parameters

Return type:

{“mu”: *, “sigma”: *}

property kurtosis: float

Parametric kurtosis

property mean: float

Parametric mean

property median: float

Parametric median

property mode: float

Parametric mode

property name

non_central_moments(k)

Parametric no central moments. µ[k] = E[Xᵏ] = ∫xᵏ∙f(x) dx

Return type:

float | None

property num_parameters: int

Number of parameters of the distribution

parameter_restrictions()

Check parameters restrictions

Return type:

bool

property parameters_example: dict[str, int | float]

pdf(x)

Probability density function

Return type:

float | ndarray

ppf(u)

Percent point function. Inverse of Cumulative distribution function. If CDF[x] = u => PPF[u] = x

Return type:

float | ndarray

sample(n, seed=None)

Sample of n elements of ditribution

Return type:

ndarray

property skewness: float

Parametric skewness

property standard_deviation: float

Parametric standard deviation

property variance: float

Parametric variance

phitter.continuous.continuous_distributions.inverse_gamma module

class phitter.continuous.continuous_distributions.inverse_gamma.InverseGamma(parameters=None, continuous_measures=None, init_parameters_examples=False)

Bases: object

Inverse Gamma Distribution Also known Pearson Type 5 distribution - Parameters InverseGamma Distribution: {“alpha”: *, “beta”: *} - https://phitter.io/distributions/continuous/inverse_gamma

cdf(x)

Cumulative distribution function

Return type:

float | ndarray

central_moments(k)

Parametric central moments. µ’[k] = E[(X - E[X])ᵏ] = ∫(x-µ[k])ᵏ∙f(x) dx

Return type:

float | None

get_parameters(continuous_measures)

Calculate proper parameters of the distribution from sample continuous_measures. The parameters are calculated by formula.

Parameters:

continuous_measures (MEASUREMESTS) – attributes: mean, std, variance, skewness, kurtosis, median, mode, min, max, size, num_bins, data

Returns:

parameters

Return type:

{“alpha”: *, “beta”: *}

property kurtosis: float

Parametric kurtosis

property mean: float

Parametric mean

property median: float

Parametric median

property mode: float

Parametric mode

property name

non_central_moments(k)

Parametric no central moments. µ[k] = E[Xᵏ] = ∫xᵏ∙f(x) dx

Return type:

float | None

property num_parameters: int

Number of parameters of the distribution

parameter_restrictions()

Check parameters restrictions

Return type:

bool

property parameters_example: dict[str, int | float]

pdf(x)

Probability density function

Return type:

float | ndarray

ppf(u)

Percent point function. Inverse of Cumulative distribution function. If CDF[x] = u => PPF[u] = x

Return type:

float | ndarray

sample(n, seed=None)

Sample of n elements of ditribution

Return type:

ndarray

property skewness: float

Parametric skewness

property standard_deviation: float

Parametric standard deviation

property variance: float

Parametric variance

phitter.continuous.continuous_distributions.inverse_gamma_3p module

class phitter.continuous.continuous_distributions.inverse_gamma_3p.InverseGamma3P(parameters=None, continuous_measures=None, init_parameters_examples=False)

Bases: object

Inverse Gamma Distribution Also known Pearson Type 5 distribution - Parameters InverseGamma3P Distribution: {“alpha”: *, “beta”: *, “loc”: *} - https://phitter.io/distributions/continuous/inverse_gamma_3p

cdf(x)

Cumulative distribution function

Return type:

float | ndarray

central_moments(k)

Parametric central moments. µ’[k] = E[(X - E[X])ᵏ] = ∫(x-µ[k])ᵏ∙f(x) dx

Return type:

float | None

get_parameters(continuous_measures)

Calculate proper parameters of the distribution from sample continuous_measures. The parameters are calculated by formula.

Parameters:

continuous_measures (MEASUREMESTS) – attributes: mean, std, variance, skewness, kurtosis, median, mode, min, max, size, num_bins, data

Returns:

parameters

Return type:

{“alpha”: *, “beta”: *, “loc”: *}

property kurtosis: float

Parametric kurtosis

property mean: float

Parametric mean

property median: float

Parametric median

property mode: float

Parametric mode

property name

non_central_moments(k)

Parametric no central moments. µ[k] = E[Xᵏ] = ∫xᵏ∙f(x) dx

Return type:

float | None

property num_parameters: int

Number of parameters of the distribution

parameter_restrictions()

Check parameters restrictions

Return type:

bool

property parameters_example: dict[str, int | float]

pdf(x)

Probability density function

Return type:

float | ndarray

ppf(u)

Percent point function. Inverse of Cumulative distribution function. If CDF[x] = u => PPF[u] = x

Return type:

float | ndarray

sample(n, seed=None)

Sample of n elements of ditribution

Return type:

ndarray

property skewness: float

Parametric skewness

property standard_deviation: float

Parametric standard deviation

property variance: float

Parametric variance

phitter.continuous.continuous_distributions.inverse_gaussian module

class phitter.continuous.continuous_distributions.inverse_gaussian.InverseGaussian(parameters=None, continuous_measures=None, init_parameters_examples=False)

Bases: object

Inverse Gaussian Distribution Also known like Wald distribution - Parameters InverseGaussian Distribution: {“mu”: *, “lambda”: *} - https://phitter.io/distributions/continuous/inverse_gaussian

cdf(x)

Cumulative distribution function

Return type:

float | ndarray

central_moments(k)

Parametric central moments. µ’[k] = E[(X - E[X])ᵏ] = ∫(x-µ[k])ᵏ∙f(x) dx

Return type:

float | None

get_parameters(continuous_measures)

Calculate proper parameters of the distribution from sample continuous_measures. The parameters are calculated by formula.

Parameters:

continuous_measures (dict) – {“mu”: * , “variance”: * , “skewness”: * , “kurtosis”: * , “data”: * }

Returns:

parameters

Return type:

{“mu”: *, “lambda”: *}

property kurtosis: float

Parametric kurtosis

property mean: float

Parametric mean

property median: float

Parametric median

property mode: float

Parametric mode

property name

non_central_moments(k)

Parametric no central moments. µ[k] = E[Xᵏ] = ∫xᵏ∙f(x) dx

Return type:

float | None

property num_parameters: int

Number of parameters of the distribution

parameter_restrictions()

Check parameters restrictions

Return type:

bool

property parameters_example: dict[str, int | float]

pdf(x)

Probability density function

Return type:

float | ndarray

ppf(u)

Percent point function. Inverse of Cumulative distribution function. If CDF[x] = u => PPF[u] = x

Return type:

float | ndarray

sample(n, seed=None)

Sample of n elements of ditribution

Return type:

ndarray

property skewness: float

Parametric skewness

property standard_deviation: float

Parametric standard deviation

property variance: float

Parametric variance

phitter.continuous.continuous_distributions.inverse_gaussian_3p module

class phitter.continuous.continuous_distributions.inverse_gaussian_3p.InverseGaussian3P(parameters=None, continuous_measures=None, init_parameters_examples=False)

Bases: object

Inverse Gaussian Distribution Also known like Wald distribution - Parameters InverseGaussian3P Distribution: {“mu”: *, “lambda”: *, “loc”: *} - https://phitter.io/distributions/continuous/inverse_gaussian_3p

cdf(x)

Cumulative distribution function

Return type:

float | ndarray

central_moments(k)

Parametric central moments. µ’[k] = E[(X - E[X])ᵏ] = ∫(x-µ[k])ᵏ∙f(x) dx

Return type:

float | None

get_parameters(continuous_measures)

Calculate proper parameters of the distribution from sample continuous_measures. The parameters are calculated by formula.

Parameters:

continuous_measures (dict) – {“mu”: * , “variance”: * , “skewness”: * , “kurtosis”: * , “data”: * }

Returns:

parameters

Return type:

{“mu”: *, “lambda”: *, “loc”: *}

property kurtosis: float

Parametric kurtosis

property mean: float

Parametric mean

property median: float

Parametric median

property mode: float

Parametric mode

property name

non_central_moments(k)

Parametric no central moments. µ[k] = E[Xᵏ] = ∫xᵏ∙f(x) dx

Return type:

float | None

property num_parameters: int

Number of parameters of the distribution

parameter_restrictions()

Check parameters restrictions

Return type:

bool

property parameters_example: dict[str, int | float]

pdf(x)

Probability density function

Return type:

float | ndarray

ppf(u)

Percent point function. Inverse of Cumulative distribution function. If CDF[x] = u => PPF[u] = x

Return type:

float | ndarray

sample(n, seed=None)

Sample of n elements of ditribution

Return type:

ndarray

property skewness: float

Parametric skewness

property standard_deviation: float

Parametric standard deviation

property variance: float

Parametric variance

phitter.continuous.continuous_distributions.johnson_sb module

class phitter.continuous.continuous_distributions.johnson_sb.JohnsonSB(parameters=None, continuous_measures=None, init_parameters_examples=False)

Bases: object

Johnson SB distribution - Parameters JohnsonSB Distribution: {“xi”: *, “lambda”: *, “gamma”: *, “delta”: *} - https://phitter.io/distributions/continuous/johnson_sb

cdf(x)

Cumulative distribution function

Return type:

float | ndarray

central_moments(k)

Parametric central moments. µ’[k] = E[(X - E[X])ᵏ] = ∫(x-µ[k])ᵏ∙f(x) dx

Return type:

float | None

get_parameters(continuous_measures)

Calculate proper parameters of the distribution from sample continuous_measures. The parameters are calculated with the method proposed in [1].

Parameters:

continuous_measures (MEASUREMESTS) – attributes: mean, std, variance, skewness, kurtosis, median, mode, min, max, size, num_bins, data

Returns:

parameters – {“xi”: * , “lambda”: * , “gamma”: * , “delta”: * }

Return type:

{“xi”: *, “lambda”: *, “gamma”: *, “delta”: *}

References

[1]

George, F., & Ramachandran, K. M. (2011). Estimation of parameters of Johnson’s system of distributions. Journal of Modern Applied Statistical Methods, 10(2), 9.

property kurtosis: float

Parametric kurtosis

property mean: float

Parametric mean

property median: float

Parametric median

property mode: float

Parametric mode

property name

non_central_moments(k)

Parametric no central moments. µ[k] = E[Xᵏ] = ∫xᵏ∙f(x) dx

Return type:

float | None

property num_parameters: int

Number of parameters of the distribution

parameter_restrictions()

Check parameters restrictions

Return type:

bool

property parameters_example: dict[str, int | float]

pdf(x)

Probability density function

Return type:

float | ndarray

ppf(u)

Percent point function. Inverse of Cumulative distribution function. If CDF[x] = u => PPF[u] = x

Return type:

float | ndarray

sample(n, seed=None)

Sample of n elements of ditribution

Return type:

ndarray

property skewness: float

Parametric skewness

property standard_deviation: float

Parametric standard deviation

property variance: float

Parametric variance

phitter.continuous.continuous_distributions.johnson_su module

class phitter.continuous.continuous_distributions.johnson_su.JohnsonSU(parameters=None, continuous_measures=None, init_parameters_examples=False)

Bases: object

Johnson SU distribution - Parameters JohnsonSU Distribution: {“xi”: *, “lambda”: *, “gamma”: *, “delta”: *} - https://phitter.io/distributions/continuous/johnson_su

cdf(x)

Cumulative distribution function

Return type:

float | ndarray

central_moments(k)

Parametric central moments. µ’[k] = E[(X - E[X])ᵏ] = ∫(x-µ[k])ᵏ∙f(x) dx

Return type:

float | None

get_parameters(continuous_measures)

Return type:

dict[str, float | int]

property kurtosis: float

Parametric kurtosis

property mean: float

Parametric mean

property median: float

Parametric median

property mode: float

Parametric mode

property name

non_central_moments(k)

Parametric no central moments. µ[k] = E[Xᵏ] = ∫xᵏ∙f(x) dx

Return type:

float | None

property num_parameters: int

Number of parameters of the distribution

parameter_restrictions()

Check parameters restrictions

Return type:

bool

property parameters_example: dict[str, int | float]

pdf(x)

Probability density function

Return type:

float | ndarray

ppf(u)

Percent point function. Inverse of Cumulative distribution function. If CDF[x] = u => PPF[u] = x

Return type:

float | ndarray

sample(n, seed=None)

Sample of n elements of ditribution

Return type:

ndarray

property skewness: float

Parametric skewness

property standard_deviation: float

Parametric standard deviation

property variance: float

Parametric variance

phitter.continuous.continuous_distributions.kumaraswamy module

class phitter.continuous.continuous_distributions.kumaraswamy.Kumaraswamy(parameters=None, continuous_measures=None, init_parameters_examples=False)

Bases: object

Kumaraswami distribution - Parameters Kumaraswamy Distribution: {“alpha”: *, “beta”: *, “min”: *, “max”: *} - https://phitter.io/distributions/continuous/kumaraswamy

cdf(x)

Cumulative distribution function

Return type:

float | ndarray

central_moments(k)

Parametric central moments. µ’[k] = E[(X - E[X])ᵏ] = ∫(x-µ[k])ᵏ∙f(x) dx

Return type:

float | None

get_parameters(continuous_measures)

Calculate proper parameters of the distribution from sample continuous_measures. The parameters are calculated by solving the equations of the measures expected for this distribution.The number of equations to consider is equal to the number of parameters.

Parameters:

continuous_measures (MEASUREMESTS) – attributes: mean, std, variance, skewness, kurtosis, median, mode, min, max, size, num_bins, data

Returns:

parameters

Return type:

{“alpha”: *, “beta”: *, “min”: *, “max”: *}

property kurtosis: float

Parametric kurtosis

property mean: float

Parametric mean

property median: float

Parametric median

property mode: float

Parametric mode

property name

non_central_moments(k)

Parametric no central moments. µ[k] = E[Xᵏ] = ∫xᵏ∙f(x) dx

Return type:

float | None

property num_parameters: int

Number of parameters of the distribution

parameter_restrictions()

Check parameters restrictions

Return type:

bool

property parameters_example: dict[str, int | float]

pdf(x)

Probability density function

Return type:

float | ndarray

ppf(u)

Percent point function. Inverse of Cumulative distribution function. If CDF[x] = u => PPF[u] = x

Return type:

float | ndarray

sample(n, seed=None)

Sample of n elements of ditribution

Return type:

ndarray

property skewness: float

Parametric skewness

property standard_deviation: float

Parametric standard deviation

property variance: float

Parametric variance

phitter.continuous.continuous_distributions.laplace module

class phitter.continuous.continuous_distributions.laplace.Laplace(parameters=None, continuous_measures=None, init_parameters_examples=False)

Bases: object

Laplace distribution - Parameters Laplace Distribution: {“mu”: *, “b”: *} - https://phitter.io/distributions/continuous/laplace

cdf(x)

Cumulative distribution function

Return type:

float | ndarray

central_moments(k)

Parametric central moments. µ’[k] = E[(X - E[X])ᵏ] = ∫(x-µ[k])ᵏ∙f(x) dx

Return type:

float | None

get_parameters(continuous_measures)

Calculate proper parameters of the distribution from sample continuous_measures. The parameters are calculated by formula.

Parameters:

continuous_measures (MEASUREMESTS) – attributes: mean, std, variance, skewness, kurtosis, median, mode, min, max, size, num_bins, data

Returns:

parameters

Return type:

{“mu”: *, “b”: *}

property kurtosis: float

Parametric kurtosis

property mean: float

Parametric mean

property median: float

Parametric median

property mode: float

Parametric mode

property name

non_central_moments(k)

Parametric no central moments. µ[k] = E[Xᵏ] = ∫xᵏ∙f(x) dx

Return type:

float | None

property num_parameters: int

Number of parameters of the distribution

parameter_restrictions()

Check parameters restrictions

Return type:

bool

property parameters_example: dict[str, int | float]

pdf(x)

Probability density function

Return type:

float | ndarray

ppf(u)

Percent point function. Inverse of Cumulative distribution function. If CDF[x] = u => PPF[u] = x

Return type:

float | ndarray

sample(n, seed=None)

Sample of n elements of ditribution

Return type:

ndarray

property skewness: float

Parametric skewness

property standard_deviation: float

Parametric standard deviation

property variance: float

Parametric variance

phitter.continuous.continuous_distributions.levy module

class phitter.continuous.continuous_distributions.levy.Levy(parameters=None, continuous_measures=None, init_parameters_examples=False)

Bases: object

Levy distribution - Parameters Levy Distribution: {“mu”: *, “c”: *} - https://phitter.io/distributions/continuous/levy

cdf(x)

Cumulative distribution function

Return type:

float | ndarray

central_moments(k)

Parametric central moments. µ’[k] = E[(X - E[X])ᵏ] = ∫(x-µ[k])ᵏ∙f(x) dx

Return type:

float | None

get_parameters(continuous_measures)

Calculate proper parameters of the distribution from sample continuous_measures. The parameters are calculated by formula.

Parameters:

continuous_measures (MEASUREMESTS) – attributes: mean, std, variance, skewness, kurtosis, median, mode, min, max, size, num_bins, data

Returns:

parameters

Return type:

{“mu”: *, “c”: *}

property kurtosis: float

Parametric kurtosis

property mean: float

Parametric mean

property median: float

Parametric median

property mode: float

Parametric mode

property name

non_central_moments(k)

Parametric no central moments. µ[k] = E[Xᵏ] = ∫xᵏ∙f(x) dx

Return type:

float | None

property num_parameters: int

Number of parameters of the distribution

parameter_restrictions()

Check parameters restrictions

Return type:

bool

property parameters_example: dict[str, int | float]

pdf(x)

Probability density function

Return type:

float | ndarray

ppf(u)

Percent point function. Inverse of Cumulative distribution function. If CDF[x] = u => PPF[u] = x

Return type:

float | ndarray

sample(n, seed=None)

Sample of n elements of ditribution

Return type:

ndarray

property skewness: float

Parametric skewness

property standard_deviation: float

Parametric standard deviation

property variance: float

Parametric variance

phitter.continuous.continuous_distributions.loggamma module

class phitter.continuous.continuous_distributions.loggamma.LogGamma(parameters=None, continuous_measures=None, init_parameters_examples=False)

Bases: object

LogGamma distribution - Parameters LogGamma Distribution: {“c”: *, “mu”: *, “sigma”: *} - https://phitter.io/distributions/continuous/loggamma

cdf(x)

Cumulative distribution function

Return type:

float | ndarray

central_moments(k)

Parametric central moments. µ’[k] = E[(X - E[X])ᵏ] = ∫(x-µ[k])ᵏ∙f(x) dx

Return type:

float | None

get_parameters(continuous_measures)

Calculate proper parameters of the distribution from sample continuous_measures. The parameters are calculated by formula.

Parameters:

continuous_measures (MEASUREMESTS) – attributes: mean, std, variance, skewness, kurtosis, median, mode, min, max, size, num_bins, data

Returns:

parameters

Return type:

{“c”: *, “mu”: *, “sigma”: *}

property kurtosis: float

Parametric kurtosis

property mean: float

Parametric mean

property median: float

Parametric median

property mode: float

Parametric mode

property name

non_central_moments(k)

Parametric no central moments. µ[k] = E[Xᵏ] = ∫xᵏ∙f(x) dx

Return type:

float | None

property num_parameters: int

Number of parameters of the distribution

parameter_restrictions()

Check parameters restrictions

Return type:

bool

property parameters_example: dict[str, int | float]

pdf(x)

Probability density function

Return type:

float | ndarray

ppf(u)

Percent point function. Inverse of Cumulative distribution function. If CDF[x] = u => PPF[u] = x

Return type:

float | ndarray

sample(n, seed=None)

Sample of n elements of ditribution

Return type:

ndarray

property skewness: float

Parametric skewness

property standard_deviation: float

Parametric standard deviation

property variance: float

Parametric variance

phitter.continuous.continuous_distributions.logistic module

class phitter.continuous.continuous_distributions.logistic.Logistic(parameters=None, continuous_measures=None, init_parameters_examples=False)

Bases: object

Logistic distribution - Parameters Logistic Distribution: {“mu”: *, “sigma”: *} - https://phitter.io/distributions/continuous/logistic

cdf(x)

Cumulative distribution function

Return type:

float | ndarray

central_moments(k)

Parametric central moments. µ’[k] = E[(X - E[X])ᵏ] = ∫(x-µ[k])ᵏ∙f(x) dx

Return type:

float | None

get_parameters(continuous_measures)

Calculate proper parameters of the distribution from sample continuous_measures. The parameters are calculated by formula.

Parameters:

continuous_measures (MEASUREMESTS) – attributes: mean, std, variance, skewness, kurtosis, median, mode, min, max, size, num_bins, data

Returns:

parameters

Return type:

{“mu”: *, “sigma”: *}

property kurtosis: float

Parametric kurtosis

property mean: float

Parametric mean

property median: float

Parametric median

property mode: float

Parametric mode

property name

non_central_moments(k)

Parametric no central moments. µ[k] = E[Xᵏ] = ∫xᵏ∙f(x) dx

Return type:

float | None

property num_parameters: int

Number of parameters of the distribution

parameter_restrictions()

Check parameters restrictions

Return type:

bool

property parameters_example: dict[str, int | float]

pdf(x)

Probability density function

Return type:

float | ndarray

ppf(u)

Percent point function. Inverse of Cumulative distribution function. If CDF[x] = u => PPF[u] = x

Return type:

float | ndarray

sample(n, seed=None)

Sample of n elements of ditribution

Return type:

ndarray

property skewness: float

Parametric skewness

property standard_deviation: float

Parametric standard deviation

property variance: float

Parametric variance

phitter.continuous.continuous_distributions.loglogistic module

class phitter.continuous.continuous_distributions.loglogistic.LogLogistic(parameters=None, continuous_measures=None, init_parameters_examples=False)

Bases: object

Loglogistic distribution - Parameters LogLogistic Distribution: {“alpha”: *, “beta”: *} - https://phitter.io/distributions/continuous/loglogistic

cdf(x)

Cumulative distribution function

Return type:

float | ndarray

central_moments(k)

Parametric central moments. µ’[k] = E[(X - E[X])ᵏ] = ∫(x-µ[k])ᵏ∙f(x) dx

Return type:

float | None

get_parameters(continuous_measures)

Calculate proper parameters of the distribution from sample continuous_measures. The parameters are calculated by formula.

Parameters:

continuous_measures (MEASUREMESTS) – attributes: mean, std, variance, skewness, kurtosis, median, mode, min, max, size, num_bins, data

Returns:

parameters

Return type:

{“alpha”: *, “beta”: *}

property kurtosis: float

Parametric kurtosis

property mean: float

Parametric mean

property median: float

Parametric median

property mode: float

Parametric mode

property name

non_central_moments(k)

Parametric no central moments. µ[k] = E[Xᵏ] = ∫xᵏ∙f(x) dx

Return type:

float | None

property num_parameters: int

Number of parameters of the distribution

parameter_restrictions()

Check parameters restrictions

Return type:

bool

property parameters_example: dict[str, int | float]

pdf(x)

Probability density function

Return type:

float | ndarray

ppf(u)

Percent point function. Inverse of Cumulative distribution function. If CDF[x] = u => PPF[u] = x

Return type:

float | ndarray

sample(n, seed=None)

Sample of n elements of ditribution

Return type:

ndarray

property skewness: float

Parametric skewness

property standard_deviation: float

Parametric standard deviation

property variance: float

Parametric variance

phitter.continuous.continuous_distributions.loglogistic_3p module

class phitter.continuous.continuous_distributions.loglogistic_3p.LogLogistic3P(parameters=None, continuous_measures=None, init_parameters_examples=False)

Bases: object

Loglogistic distribution - Parameters LogLogistic3P Distribution: {“loc”: *, “alpha”: *, “beta”: *} - https://phitter.io/distributions/continuous/loglogistic_3p

cdf(x)

Cumulative distribution function

Return type:

float | ndarray

central_moments(k)

Parametric central moments. µ’[k] = E[(X - E[X])ᵏ] = ∫(x-µ[k])ᵏ∙f(x) dx

Return type:

float | None

get_parameters(continuous_measures)

Calculate proper parameters of the distribution from sample continuous_measures. The parameters are calculated by formula.

Parameters:

continuous_measures (MEASUREMESTS) – attributes: mean, std, variance, skewness, kurtosis, median, mode, min, max, size, num_bins, data

Returns:

parameters

Return type:

{“loc”: *, “alpha”: *, “beta”: *}

property kurtosis: float

Parametric kurtosis

property mean: float

Parametric mean

property median: float

Parametric median

property mode: float

Parametric mode

property name

non_central_moments(k)

Parametric no central moments. µ[k] = E[Xᵏ] = ∫xᵏ∙f(x) dx

Return type:

float | None

property num_parameters: int

Number of parameters of the distribution

parameter_restrictions()

Check parameters restrictions

Return type:

bool

property parameters_example: dict[str, int | float]

pdf(x)

Probability density function

Return type:

float | ndarray

ppf(u)

Percent point function. Inverse of Cumulative distribution function. If CDF[x] = u => PPF[u] = x

Return type:

float | ndarray

sample(n, seed=None)

Sample of n elements of ditribution

Return type:

ndarray

property skewness: float

Parametric skewness

property standard_deviation: float

Parametric standard deviation

property variance: float

Parametric variance

phitter.continuous.continuous_distributions.lognormal module

class phitter.continuous.continuous_distributions.lognormal.LogNormal(parameters=None, continuous_measures=None, init_parameters_examples=False)

Bases: object

Lognormal distribution - Parameters LogNormal Distribution: {“mu”: *, “sigma”: *} - https://phitter.io/distributions/continuous/lognormal

cdf(x)

Cumulative distribution function

Return type:

float | ndarray

central_moments(k)

Parametric central moments. µ’[k] = E[(X - E[X])ᵏ] = ∫(x-µ[k])ᵏ∙f(x) dx

Return type:

float | None

get_parameters(continuous_measures)

Calculate proper parameters of the distribution from sample continuous_measures. The parameters are calculated by formula.

Parameters:

continuous_measures (dict) – {“mu”: * , “variance”: * , “skewness”: * , “kurtosis”: * , “data”: * }

Returns:

parameters

Return type:

{“mu”: *, “sigma”: *}

property kurtosis: float

Parametric kurtosis

property mean: float

Parametric mean

property median: float

Parametric median

property mode: float

Parametric mode

property name

non_central_moments(k)

Parametric no central moments. µ[k] = E[Xᵏ] = ∫xᵏ∙f(x) dx

Return type:

float | None

property num_parameters: int

Number of parameters of the distribution

parameter_restrictions()

Check parameters restrictions

Return type:

bool

property parameters_example: dict[str, int | float]

pdf(x)

Probability density function

Return type:

float | ndarray

ppf(u)

Percent point function. Inverse of Cumulative distribution function. If CDF[x] = u => PPF[u] = x

Return type:

float | ndarray

sample(n, seed=None)

Sample of n elements of ditribution

Return type:

ndarray

property skewness: float

Parametric skewness

property standard_deviation: float

Parametric standard deviation

property variance: float

Parametric variance

phitter.continuous.continuous_distributions.maxwell module

class phitter.continuous.continuous_distributions.maxwell.Maxwell(parameters=None, continuous_measures=None, init_parameters_examples=False)

Bases: object

Maxwell distribution - Parameters Maxwell Distribution: {“alpha”: *, “loc”: *} - https://phitter.io/distributions/continuous/maxwell

cdf(x)

Cumulative distribution function

Return type:

float | ndarray

central_moments(k)

Parametric central moments. µ’[k] = E[(X - E[X])ᵏ] = ∫(x-µ[k])ᵏ∙f(x) dx

Return type:

float | None

get_parameters(continuous_measures)

Calculate proper parameters of the distribution from sample continuous_measures. The parameters are calculated by formula.

Parameters:

continuous_measures (MEASUREMESTS) – attributes: mean, std, variance, skewness, kurtosis, median, mode, min, max, size, num_bins, data

Returns:

parameters

Return type:

{“alpha”: *, “loc”: *}

property kurtosis: float

Parametric kurtosis

property mean: float

Parametric mean

property median: float

Parametric median

property mode: float

Parametric mode

property name

non_central_moments(k)

Parametric no central moments. µ[k] = E[Xᵏ] = ∫xᵏ∙f(x) dx

Return type:

float | None

property num_parameters: int

Number of parameters of the distribution

parameter_restrictions()

Check parameters restrictions

Return type:

bool

property parameters_example: dict[str, int | float]

pdf(x)

Probability density function

Return type:

float | ndarray

ppf(u)

Percent point function. Inverse of Cumulative distribution function. If CDF[x] = u => PPF[u] = x

Return type:

float | ndarray

sample(n, seed=None)

Sample of n elements of ditribution

Return type:

ndarray

property skewness: float

Parametric skewness

property standard_deviation: float

Parametric standard deviation

property variance: float

Parametric variance

phitter.continuous.continuous_distributions.moyal module

class phitter.continuous.continuous_distributions.moyal.Moyal(parameters=None, continuous_measures=None, init_parameters_examples=False)

Bases: object

Moyal distribution - Parameters Moyal Distribution: {“mu”: *, “sigma”: *} - https://phitter.io/distributions/continuous/moyal

cdf(x)

Cumulative distribution function

Return type:

float | ndarray

central_moments(k)

Parametric central moments. µ’[k] = E[(X - E[X])ᵏ] = ∫(x-µ[k])ᵏ∙f(x) dx

Return type:

float | None

get_parameters(continuous_measures)

Calculate proper parameters of the distribution from sample continuous_measures. The parameters are calculated by formula.

Parameters:

continuous_measures (dict) – {“mu”: * , “variance”: * , “skewness”: * , “kurtosis”: * , “data”: * }

Returns:

parameters

Return type:

{“mu”: *, “sigma”: *}

property kurtosis: float

Parametric kurtosis

property mean: float

Parametric mean

property median: float

Parametric median

property mode: float

Parametric mode

property name

non_central_moments(k)

Parametric no central moments. µ[k] = E[Xᵏ] = ∫xᵏ∙f(x) dx

Return type:

float | None

property num_parameters: int

Number of parameters of the distribution

parameter_restrictions()

Check parameters restrictions

Return type:

bool

property parameters_example: dict[str, int | float]

pdf(x)

Probability density function

Return type:

float | ndarray

ppf(u)

Percent point function. Inverse of Cumulative distribution function. If CDF[x] = u => PPF[u] = x

Return type:

float | ndarray

sample(n, seed=None)

Sample of n elements of ditribution

Return type:

ndarray

property skewness: float

Parametric skewness

property standard_deviation: float

Parametric standard deviation

property variance: float

Parametric variance

phitter.continuous.continuous_distributions.nakagami module

class phitter.continuous.continuous_distributions.nakagami.Nakagami(parameters=None, continuous_measures=None, init_parameters_examples=False)

Bases: object

Nakagami distribution - Parameters Nakagami Distribution: {“m”: *, “omega”: *} - https://phitter.io/distributions/continuous/nakagami

cdf(x)

Cumulative distribution function

Return type:

float | ndarray

central_moments(k)

Parametric central moments. µ’[k] = E[(X - E[X])ᵏ] = ∫(x-µ[k])ᵏ∙f(x) dx

Return type:

float | None

get_parameters(continuous_measures)

Calculate proper parameters of the distribution from sample continuous_measures. The parameters are calculated by formula.

Parameters:

continuous_measures (MEASUREMESTS) – attributes: mean, std, variance, skewness, kurtosis, median, mode, min, max, size, num_bins, data

Returns:

parameters

Return type:

{“m”: *, “omega”: *}

property kurtosis: float

Parametric kurtosis

property mean: float

Parametric mean

property median: float

Parametric median

property mode: float

Parametric mode

property name

non_central_moments(k)

Parametric no central moments. µ[k] = E[Xᵏ] = ∫xᵏ∙f(x) dx

Return type:

float | None

property num_parameters: int

Number of parameters of the distribution

parameter_restrictions()

Check parameters restriction

Return type:

bool

property parameters_example: dict[str, int | float]

pdf(x)

Probability density function

Return type:

float | ndarray

ppf(u)

Percent point function. Inverse of Cumulative distribution function. If CDF[x] = u => PPF[u] = x

Return type:

float | ndarray

sample(n, seed=None)

Sample of n elements of ditribution

Return type:

ndarray

property skewness: float

Parametric skewness

property standard_deviation: float

Parametric standard deviation

property variance: float

Parametric variance

phitter.continuous.continuous_distributions.non_central_chi_square module

class phitter.continuous.continuous_distributions.non_central_chi_square.NonCentralChiSquare(parameters=None, continuous_measures=None, init_parameters_examples=False)

Bases: object

Non-Central Chi Square distribution - Parameters NonCentralChiSquare Distribution: {“lambda”: *, “n”: *} - https://phitter.io/distributions/continuous/non_central_chi_square

cdf(x)

Cumulative distribution function

Return type:

float | ndarray

central_moments(k)

Parametric central moments. µ’[k] = E[(X - E[X])ᵏ] = ∫(x-µ[k])ᵏ∙f(x) dx

Return type:

float | None

get_parameters(continuous_measures)

Calculate proper parameters of the distribution from sample continuous_measures. The parameters are calculated by formula.

Parameters:

continuous_measures (MEASUREMESTS) – attributes: mean, std, variance, skewness, kurtosis, median, mode, min, max, size, num_bins, data

Returns:

parameters

Return type:

{“lambda”: *, “n”: *}

property kurtosis: float

Parametric kurtosis

property mean: float

Parametric mean

property median: float

Parametric median

property mode: float

Parametric mode

property name

non_central_moments(k)

Parametric no central moments. µ[k] = E[Xᵏ] = ∫xᵏ∙f(x) dx

Return type:

float | None

property num_parameters: int

Number of parameters of the distribution

parameter_restrictions()

Check parameters restrictions

Return type:

bool

property parameters_example: dict[str, int | float]

pdf(x)

Probability density function

Return type:

float | ndarray

ppf(u)

Percent point function. Inverse of Cumulative distribution function. If CDF[x] = u => PPF[u] = x

Return type:

float | ndarray

sample(n, seed=None)

Sample of n elements of ditribution

Return type:

ndarray

property skewness: float

Parametric skewness

property standard_deviation: float

Parametric standard deviation

property variance: float

Parametric variance

phitter.continuous.continuous_distributions.non_central_f module

class phitter.continuous.continuous_distributions.non_central_f.NonCentralF(parameters=None, continuous_measures=None, init_parameters_examples=False)

Bases: object

Non-Central F distribution - Parameters NonCentralF Distribution: {“lambda”: *, “n1”: *, “n2”: *} - https://phitter.io/distributions/continuous/non_central_f

cdf(x)

Cumulative distribution function

Return type:

float | ndarray

central_moments(k)

Parametric central moments. µ’[k] = E[(X - E[X])ᵏ] = ∫(x-µ[k])ᵏ∙f(x) dx

Return type:

float | None

get_parameters(continuous_measures)

Calculate proper parameters of the distribution from sample continuous_measures. The parameters are calculated by formula.

Parameters:

continuous_measures (MEASUREMESTS) – attributes: mean, std, variance, skewness, kurtosis, median, mode, min, max, size, num_bins, data

Returns:

parameters

Return type:

{“lambda”: *, “n1”: *, “n2”: *}

property kurtosis: float

Parametric kurtosis

property mean: float

Parametric mean

property median: float

Parametric median

property mode: float

Parametric mode

property name

non_central_moments(k)

Parametric no central moments. µ[k] = E[Xᵏ] = ∫xᵏ∙f(x) dx

Return type:

float | None

property num_parameters: int

Number of parameters of the distribution

parameter_restrictions()

Check parameters restrictions

Return type:

bool

property parameters_example: dict[str, int | float]

pdf(x)

Probability density function

Return type:

float | ndarray

ppf(u)

Percent point function. Inverse of Cumulative distribution function. If CDF[x] = u => PPF[u] = x

Return type:

float | ndarray

sample(n, seed=None)

Sample of n elements of ditribution

Return type:

ndarray

property skewness: float

Parametric skewness

property standard_deviation: float

Parametric standard deviation

property variance: float

Parametric variance

phitter.continuous.continuous_distributions.non_central_t_student module

class phitter.continuous.continuous_distributions.non_central_t_student.NonCentralTStudent(parameters=None, continuous_measures=None, init_parameters_examples=False)

Bases: object

Non-Central T Student distribution - Parameters NonCentralTStudent Distribution: {“lambda”: *, “n”: *, “loc”: *, “scale”: *} - https://phitter.io/distributions/continuous/non_central_t_student Hand-book on Statistical Distributions (pag.116) … Christian Walck

cdf(x)

Cumulative distribution function

Return type:

float | ndarray

central_moments(k)

Parametric central moments. µ’[k] = E[(X - E[X])ᵏ] = ∫(x-µ[k])ᵏ∙f(x) dx

Return type:

float | None

get_parameters(continuous_measures)

Calculate proper parameters of the distribution from sample continuous_measures. The parameters are calculated by formula.

Parameters:

continuous_measures (MEASUREMESTS) – attributes: mean, std, variance, skewness, kurtosis, median, mode, min, max, size, num_bins, data

Returns:

parameters

Return type:

{“lambda”: *, “n”: *, “loc”: *, “scale”: *}

property kurtosis: float

Parametric kurtosis

property mean: float

Parametric mean

property median: float

Parametric median

property mode: float

Parametric mode

property name

non_central_moments(k)

Parametric no central moments. µ[k] = E[Xᵏ] = ∫xᵏ∙f(x) dx

Return type:

float | None

property num_parameters: int

Number of parameters of the distribution

parameter_restrictions()

Check parameters restrictions

Return type:

bool

property parameters_example: dict[str, int | float]

pdf(x)

Probability density function

Return type:

float | ndarray

ppf(u)

Percent point function. Inverse of Cumulative distribution function. If CDF[x] = u => PPF[u] = x

Return type:

float | ndarray

sample(n, seed=None)

Sample of n elements of ditribution

Return type:

ndarray

property skewness: float

Parametric skewness

property standard_deviation: float

Parametric standard deviation

property variance: float

Parametric variance

phitter.continuous.continuous_distributions.normal module

class phitter.continuous.continuous_distributions.normal.Normal(parameters=None, continuous_measures=None, init_parameters_examples=False)

Bases: object

Normal distribution - Parameters Normal Distribution: {“mu”: *, “sigma”: *} - https://phitter.io/distributions/continuous/normal

cdf(x)

Cumulative distribution function

Return type:

float | ndarray

central_moments(k)

Parametric central moments. µ’[k] = E[(X - E[X])ᵏ] = ∫(x-µ[k])ᵏ∙f(x) dx

Return type:

float | None

get_parameters(continuous_measures)

Calculate proper parameters of the distribution from sample continuous_measures. The parameters are calculated by formula.

Parameters:

continuous_measures (dict) – {“mu”: * , “variance”: * , “skewness”: * , “kurtosis”: * , “data”: * }

Returns:

parameters

Return type:

{“mu”: *, “sigma”: *}

property kurtosis: float

Parametric kurtosis

property mean: float

Parametric mean

property median: float

Parametric median

property mode: float

Parametric mode

property name

non_central_moments(k)

Parametric no central moments. µ[k] = E[Xᵏ] = ∫xᵏ∙f(x) dx

Return type:

float | None

property num_parameters: int

Number of parameters of the distribution

parameter_restrictions()

Check parameters restrictions

Return type:

bool

property parameters_example: dict[str, int | float]

pdf(x)

Probability density function

Return type:

float | ndarray

ppf(u)

Percent point function. Inverse of Cumulative distribution function. If CDF[x] = u => PPF[u] = x

Return type:

float | ndarray

sample(n, seed=None)

Sample of n elements of ditribution

Return type:

ndarray

property skewness: float

Parametric skewness

property standard_deviation: float

Parametric standard deviation

property variance: float

Parametric variance

phitter.continuous.continuous_distributions.pareto_first_kind module

class phitter.continuous.continuous_distributions.pareto_first_kind.ParetoFirstKind(parameters=None, continuous_measures=None, init_parameters_examples=False)

Bases: object

Pareto first kind distribution distribution - Parameters ParetoFirstKind Distribution: {“alpha”: *, “xm”: *, “loc”: *} - https://phitter.io/distributions/continuous/pareto_first_kind

cdf(x)

Cumulative distribution function

Return type:

float | ndarray

central_moments(k)

Parametric central moments. µ’[k] = E[(X - E[X])ᵏ] = ∫(x-µ[k])ᵏ∙f(x) dx

Return type:

float | None

get_parameters(continuous_measures)

Calculate proper parameters of the distribution from sample continuous_measures. The parameters are calculated by formula.

Parameters:

continuous_measures (MEASUREMESTS) – attributes: mean, std, variance, skewness, kurtosis, median, mode, min, max, size, num_bins, data

Returns:

parameters

Return type:

{“alpha”: *, “xm”: *, “loc”: *}

property kurtosis: float

Parametric kurtosis

property mean: float

Parametric mean

property median: float

Parametric median

property mode: float

Parametric mode

property name

non_central_moments(k)

Parametric no central moments. µ[k] = E[Xᵏ] = ∫xᵏ∙f(x) dx

Return type:

float | None

property num_parameters: int

Number of parameters of the distribution

parameter_restrictions()

Check parameters restriction

Return type:

bool

property parameters_example: dict[str, int | float]

pdf(x)

Probability density function

Return type:

float | ndarray

ppf(u)

Percent point function. Inverse of Cumulative distribution function. If CDF[x] = u => PPF[u] = x

Return type:

float | ndarray

sample(n, seed=None)

Sample of n elements of ditribution

Return type:

ndarray

property skewness: float

Parametric skewness

property standard_deviation: float

Parametric standard deviation

property variance: float

Parametric variance

phitter.continuous.continuous_distributions.pareto_second_kind module

class phitter.continuous.continuous_distributions.pareto_second_kind.ParetoSecondKind(parameters=None, continuous_measures=None, init_parameters_examples=False)

Bases: object

Pareto second kind distribution Distribution Also known as Lomax Distribution or Pareto Type II distributions - https://phitter.io/distributions/continuous/pareto_second_kind

cdf(x)

Cumulative distribution function

Return type:

float | ndarray

central_moments(k)

Parametric central moments. µ’[k] = E[(X - E[X])ᵏ] = ∫(x-µ[k])ᵏ∙f(x) dx

Return type:

float | None

get_parameters(continuous_measures)

Calculate proper parameters of the distribution from sample continuous_measures. The parameters are calculated by formula.

Parameters:

continuous_measures (MEASUREMESTS) – attributes: mean, std, variance, skewness, kurtosis, median, mode, min, max, size, num_bins, data

Returns:

parameters

Return type:

{“alpha”: *, “xm”: *, “loc”: *}

property kurtosis: float

Parametric kurtosis

property mean: float

Parametric mean

property median: float

Parametric median

property mode: float

Parametric mode

property name

non_central_moments(k)

Parametric no central moments. µ[k] = E[Xᵏ] = ∫xᵏ∙f(x) dx

Return type:

float | None

property num_parameters: int

Number of parameters of the distribution

parameter_restrictions()

Check parameters restriction

Return type:

bool

property parameters_example: dict[str, int | float]

pdf(x)

Probability density function

Return type:

float | ndarray

ppf(u)

Percent point function. Inverse of Cumulative distribution function. If CDF[x] = u => PPF[u] = x

Return type:

float | ndarray

sample(n, seed=None)

Sample of n elements of ditribution

Return type:

ndarray

property skewness: float

Parametric skewness

property standard_deviation: float

Parametric standard deviation

property variance: float

Parametric variance

phitter.continuous.continuous_distributions.pert module

class phitter.continuous.continuous_distributions.pert.Pert(parameters=None, continuous_measures=None, init_parameters_examples=False)

Bases: object

Pert distribution - Parameters Pert Distribution: {“a”: *, “b”: *, “c”: *} - https://phitter.io/distributions/continuous/pert

cdf(x)

Cumulative distribution function

Return type:

float | ndarray

central_moments(k)

Parametric central moments. µ’[k] = E[(X - E[X])ᵏ] = ∫(x-µ[k])ᵏ∙f(x) dx

Return type:

float | None

get_parameters(continuous_measures)

Calculate proper parameters of the distribution from sample continuous_measures. The parameters are calculated by solving the equations of the measures expected for this distribution.The number of equations to consider is equal to the number of parameters.

Parameters:

continuous_measures (dict) – {“mean”: * , “variance”: * , “skewness”: * , “kurtosis”: * , “median”: * , “b”: * }

Returns:

parameters

Return type:

{“a”: *, “b”: *, “c”: *}

property kurtosis: float

Parametric kurtosis

property mean: float

Parametric mean

property median: float

Parametric median

property mode: float

Parametric mode

property name

non_central_moments(k)

Parametric no central moments. µ[k] = E[Xᵏ] = ∫xᵏ∙f(x) dx

Return type:

float | None

property num_parameters: int

Number of parameters of the distribution

parameter_restrictions()

Check parameters restrictions

Return type:

bool

property parameters_example: dict[str, int | float]

pdf(x)

Probability density function

Return type:

float | ndarray

ppf(u)

Percent point function. Inverse of Cumulative distribution function. If CDF[x] = u => PPF[u] = x

Return type:

float | ndarray

sample(n, seed=None)

Sample of n elements of ditribution

Return type:

ndarray

property skewness: float

Parametric skewness

property standard_deviation: float

Parametric standard deviation

property variance: float

Parametric variance

phitter.continuous.continuous_distributions.power_function module

class phitter.continuous.continuous_distributions.power_function.PowerFunction(parameters=None, continuous_measures=None, init_parameters_examples=False)

Bases: object

Power function distribution - Parameters PowerFunction Distribution: {“alpha”: *, “a”: *, “b”: *} - https://phitter.io/distributions/continuous/power_function

cdf(x)

Cumulative distribution function

Return type:

float | ndarray

central_moments(k)

Parametric central moments. µ’[k] = E[(X - E[X])ᵏ] = ∫(x-µ[k])ᵏ∙f(x) dx

Return type:

float | None

get_parameters(continuous_measures)

Calculate proper parameters of the distribution from sample continuous_measures. The parameters are calculated by solving the equations of the measures expected for this distribution.The number of equations to consider is equal to the number of parameters.

Parameters:

continuous_measures (MEASUREMESTS) – attributes: mean, std, variance, skewness, kurtosis, median, mode, min, max, size, num_bins, data

Returns:

parameters

Return type:

{“alpha”: *, “a”: *, “b”: *}

property kurtosis: float

Parametric kurtosis

property mean: float

Parametric mean

property median: float

Parametric median

property mode: float

Parametric mode

property name

non_central_moments(k)

Parametric no central moments. µ[k] = E[Xᵏ] = ∫xᵏ∙f(x) dx

Return type:

float | None

property num_parameters: int

Number of parameters of the distribution

parameter_restrictions()

Check parameters restrictions

Return type:

bool

property parameters_example: dict[str, int | float]

pdf(x)

Probability density function

Return type:

float | ndarray

ppf(u)

Percent point function. Inverse of Cumulative distribution function. If CDF[x] = u => PPF[u] = x

Return type:

float | ndarray

sample(n, seed=None)

Sample of n elements of ditribution

Return type:

ndarray

property skewness: float

Parametric skewness

property standard_deviation: float

Parametric standard deviation

property variance: float

Parametric variance

phitter.continuous.continuous_distributions.rayleigh module

class phitter.continuous.continuous_distributions.rayleigh.Rayleigh(parameters=None, continuous_measures=None, init_parameters_examples=False)

Bases: object

Rayleigh distribution - Parameters Rayleigh Distribution: {“gamma”: *, “sigma”: *} - https://phitter.io/distributions/continuous/rayleigh

cdf(x)

Cumulative distribution function

Return type:

float | ndarray

central_moments(k)

Parametric central moments. µ’[k] = E[(X - E[X])ᵏ] = ∫(x-µ[k])ᵏ∙f(x) dx

Return type:

float | None

get_parameters(continuous_measures)

Calculate proper parameters of the distribution from sample continuous_measures. The parameters are calculated by solving the equations of the measures expected for this distribution.The number of equations to consider is equal to the number of parameters.

Parameters:

continuous_measures (MEASUREMESTS) – attributes: mean, std, variance, skewness, kurtosis, median, mode, min, max, size, num_bins, data

Returns:

parameters

Return type:

{“gamma”: *, “sigma”: *}

property kurtosis: float

Parametric kurtosis

property mean: float

Parametric mean

property median: float

Parametric median

property mode: float

Parametric mode

property name

non_central_moments(k)

Parametric no central moments. µ[k] = E[Xᵏ] = ∫xᵏ∙f(x) dx

Return type:

float | None

property num_parameters: int

Number of parameters of the distribution

parameter_restrictions()

Check parameters restrictions

Return type:

bool

property parameters_example: dict[str, int | float]

pdf(x)

Probability density function

Return type:

float | ndarray

ppf(u)

Percent point function. Inverse of Cumulative distribution function. If CDF[x] = u => PPF[u] = x

Return type:

float | ndarray

sample(n, seed=None)

Sample of n elements of ditribution

Return type:

ndarray

property skewness: float

Parametric skewness

property standard_deviation: float

Parametric standard deviation

property variance: float

Parametric variance

phitter.continuous.continuous_distributions.reciprocal module

class phitter.continuous.continuous_distributions.reciprocal.Reciprocal(parameters=None, continuous_measures=None, init_parameters_examples=False)

Bases: object

Reciprocal distribution - Parameters Reciprocal Distribution: {“a”: *, “b”: *} - https://phitter.io/distributions/continuous/reciprocal

cdf(x)

Cumulative distribution function

Return type:

float | ndarray

central_moments(k)

Parametric central moments. µ’[k] = E[(X - E[X])ᵏ] = ∫(x-µ[k])ᵏ∙f(x) dx

Return type:

float | None

get_parameters(continuous_measures)

Calculate proper parameters of the distribution from sample continuous_measures. The parameters are calculated by formula.

Parameters:

continuous_measures (MEASUREMESTS) – attributes: mean, std, variance, skewness, kurtosis, median, mode, min, max, size, num_bins, data

Returns:

parameters

Return type:

{“a”: *, “b”: *}

property kurtosis: float

Parametric kurtosis

property mean: float

Parametric mean

property median: float

Parametric median

property mode: float

Parametric mode

property name

non_central_moments(k)

Parametric no central moments. µ[k] = E[Xᵏ] = ∫xᵏ∙f(x) dx

Return type:

float | None

property num_parameters: int

Number of parameters of the distribution

parameter_restrictions()

Check parameters restrictions

Return type:

bool

property parameters_example: dict[str, int | float]

pdf(x)

Probability density function

Return type:

float | ndarray

ppf(u)

Percent point function. Inverse of Cumulative distribution function. If CDF[x] = u => PPF[u] = x

Return type:

float | ndarray

sample(n, seed=None)

Sample of n elements of ditribution

Return type:

ndarray

property skewness: float

Parametric skewness

property standard_deviation: float

Parametric standard deviation

property variance: float

Parametric variance

phitter.continuous.continuous_distributions.rice module

class phitter.continuous.continuous_distributions.rice.Rice(parameters=None, continuous_measures=None, init_parameters_examples=False)

Bases: object

Rice distribution - Parameters Rice Distribution: {“v”: *, “sigma”: *} - https://phitter.io/distributions/continuous/rice

cdf(x)

Cumulative distribution function

Return type:

float | ndarray

central_moments(k)

Parametric central moments. µ’[k] = E[(X - E[X])ᵏ] = ∫(x-µ[k])ᵏ∙f(x) dx

Return type:

float | None

get_parameters(continuous_measures)

Calculate proper parameters of the distribution from sample continuous_measures. The parameters are calculated by solving the equations of the measures expected for this distribution.The number of equations to consider is equal to the number of parameters.

Parameters:

continuous_measures (MEASUREMESTS) – attributes: mean, std, variance, skewness, kurtosis, median, mode, min, max, size, num_bins, data

Returns:

parameters

Return type:

{“v”: *, “sigma”: *}

property kurtosis: float

Parametric kurtosis

property mean: float

Parametric mean

property median: float

Parametric median

property mode: float

Parametric mode

property name

non_central_moments(k)

Parametric no central moments. µ[k] = E[Xᵏ] = ∫xᵏ∙f(x) dx

Return type:

float | None

property num_parameters: int

Number of parameters of the distribution

parameter_restrictions()

Check parameters restrictions

Return type:

bool

property parameters_example: dict[str, int | float]

pdf(x)

Probability density function

Return type:

float | ndarray

ppf(u)

Percent point function. Inverse of Cumulative distribution function. If CDF[x] = u => PPF[u] = x

Return type:

float | ndarray

sample(n, seed=None)

Sample of n elements of ditribution

Return type:

ndarray

property skewness: float

Parametric skewness

property standard_deviation: float

Parametric standard deviation

property variance: float

Parametric variance

phitter.continuous.continuous_distributions.semicircular module

class phitter.continuous.continuous_distributions.semicircular.Semicircular(parameters=None, continuous_measures=None, init_parameters_examples=False)

Bases: object

Semicicrcular Distribution - https://phitter.io/distributions/continuous/semicircular

cdf(x)

Cumulative distribution function

Return type:

float | ndarray

central_moments(k)

Parametric central moments. µ’[k] = E[(X - E[X])ᵏ] = ∫(x-µ[k])ᵏ∙f(x) dx

Return type:

float | None

get_parameters(continuous_measures)

Calculate proper parameters of the distribution from sample continuous_measures. The parameters are calculated by formula.

Parameters:

continuous_measures (dict) – {“mu”: * , “variance”: * , “skewness”: * , “kurtosis”: * , “data”: * }

Returns:

parameters

Return type:

{“loc”: *, “R”: *}

property kurtosis: float

Parametric kurtosis

property mean: float

Parametric mean

property median: float

Parametric median

property mode: float

Parametric mode

property name

non_central_moments(k)

Parametric no central moments. µ[k] = E[Xᵏ] = ∫xᵏ∙f(x) dx

Return type:

float | None

property num_parameters: int

Number of parameters of the distribution

parameter_restrictions()

Check parameters restrictions

Return type:

bool

property parameters_example: dict[str, int | float]

pdf(x)

Probability density function

Return type:

float | ndarray

ppf(u)

Percent point function. Inverse of Cumulative distribution function. If CDF[x] = u => PPF[u] = x

Return type:

float | ndarray

sample(n, seed=None)

Sample of n elements of ditribution

Return type:

ndarray

property skewness: float

Parametric skewness

property standard_deviation: float

Parametric standard deviation

property variance: float

Parametric variance

phitter.continuous.continuous_distributions.t_student module

class phitter.continuous.continuous_distributions.t_student.TStudent(parameters=None, continuous_measures=None, init_parameters_examples=False)

Bases: object

T distribution - Parameters TStudent Distribution: {“df”: *} - https://phitter.io/distributions/continuous/t_student

cdf(x)

Cumulative distribution function

Return type:

float | ndarray

central_moments(k)

Parametric central moments. µ’[k] = E[(X - E[X])ᵏ] = ∫(x-µ[k])ᵏ∙f(x) dx

Return type:

float | None

get_parameters(continuous_measures)

Calculate proper parameters of the distribution from sample continuous_measures. The parameters are calculated by solving the equations of the measures expected for this distribution.The number of equations to consider is equal to the number of parameters.

Parameters:

continuous_measures (MEASUREMESTS) – attributes: mean, std, variance, skewness, kurtosis, median, mode, min, max, size, num_bins, data

Returns:

parameters

Return type:

{“df”: *}

property kurtosis: float

Parametric kurtosis

property mean: float

Parametric mean

property median: float

Parametric median

property mode: float

Parametric mode

property name

non_central_moments(k)

Parametric no central moments. µ[k] = E[Xᵏ] = ∫xᵏ∙f(x) dx

Return type:

float | None

property num_parameters: int

Number of parameters of the distribution

parameter_restrictions()

Check parameters restrictions

Return type:

bool

property parameters_example: dict[str, int | float]

pdf(x)

Probability density function

Return type:

float | ndarray

ppf(u)

Percent point function. Inverse of Cumulative distribution function. If CDF[x] = u => PPF[u] = x

Return type:

float | ndarray

sample(n, seed=None)

Sample of n elements of ditribution

Return type:

ndarray

property skewness: float

Parametric skewness

property standard_deviation: float

Parametric standard deviation

property variance: float

Parametric variance

phitter.continuous.continuous_distributions.t_student_3p module

class phitter.continuous.continuous_distributions.t_student_3p.TStudent3P(parameters=None, continuous_measures=None, init_parameters_examples=False)

Bases: object

T distribution - Parameters TStudent3P Distribution: {“df”: *, “loc”: *, “scale”: *} - https://phitter.io/distributions/continuous/t_student_3p

cdf(x)

Cumulative distribution function

Return type:

float | ndarray

central_moments(k)

Parametric central moments. µ’[k] = E[(X - E[X])ᵏ] = ∫(x-µ[k])ᵏ∙f(x) dx

Return type:

float | None

get_parameters(continuous_measures)

Calculate proper parameters of the distribution from sample continuous_measures. The parameters are calculated by solving the equations of the measures expected for this distribution.The number of equations to consider is equal to the number of parameters.

Parameters:

continuous_measures (MEASUREMESTS) – attributes: mean, std, variance, skewness, kurtosis, median, mode, min, max, size, num_bins, data

Returns:

parameters

Return type:

{“df”: *, “loc”: *, “scale”: *}

property kurtosis: float

Parametric kurtosis

property mean: float

Parametric mean

property median: float

Parametric median

property mode: float

Parametric mode

property name

non_central_moments(k)

Parametric no central moments. µ[k] = E[Xᵏ] = ∫xᵏ∙f(x) dx

Return type:

float | None

property num_parameters: int

Number of parameters of the distribution

parameter_restrictions()

Check parameters restrictions

Return type:

bool

property parameters_example: dict[str, int | float]

pdf(x)

Probability density function

Return type:

float | ndarray

ppf(u)

sample(n, seed=None)

Sample of n elements of ditribution

Return type:

ndarray

property skewness: float

Parametric skewness

property standard_deviation: float

Parametric standard deviation

property variance: float

Parametric variance

phitter.continuous.continuous_distributions.trapezoidal module

class phitter.continuous.continuous_distributions.trapezoidal.Trapezoidal(parameters=None, continuous_measures=None, init_parameters_examples=False)

Bases: object

Trapezoidal distribution - Parameters Trapezoidal Distribution: {“a”: *, “b”: *, “c”: *, “d”: *} - https://phitter.io/distributions/continuous/trapezoidal

cdf(x)

Cumulative distribution function

Return type:

float | ndarray

central_moments(k)

Parametric central moments. µ’[k] = E[(X - E[X])ᵏ] = ∫(x-µ[k])ᵏ∙f(x) dx

Return type:

float | None

get_parameters(continuous_measures)

Calculate proper parameters of the distribution from sample continuous_measures. The parameters are calculated by formula.

Parameters:

continuous_measures (MEASUREMESTS) – attributes: mean, std, variance, skewness, kurtosis, median, mode, min, max, size, num_bins, data

Returns:

parameters

Return type:

{“a”: *, “b”: *, “c”: *, “d”: *}

property kurtosis: float

Parametric kurtosis

property mean: float

Parametric mean

property median: float

Parametric median

property mode: float

Parametric mode

property name

non_central_moments(k)

Parametric no central moments. µ[k] = E[Xᵏ] = ∫xᵏ∙f(x) dx

Return type:

float | None

property num_parameters: int

Number of parameters of the distribution

parameter_restrictions()

Check parameters restrictions

Return type:

bool

property parameters_example: dict[str, int | float]

pdf(x)

Probability density function

Return type:

float | ndarray

ppf(u)

Percent point function. Inverse of Cumulative distribution function. If CDF[x] = u => PPF[u] = x

Return type:

float | ndarray

sample(n, seed=None)

Sample of n elements of ditribution

Return type:

ndarray

property skewness: float

Parametric skewness

property standard_deviation: float

Parametric standard deviation

property variance: float

Parametric variance

phitter.continuous.continuous_distributions.triangular module

class phitter.continuous.continuous_distributions.triangular.Triangular(parameters=None, continuous_measures=None, init_parameters_examples=False)

Bases: object

Triangular distribution - Parameters Triangular Distribution: {“a”: *, “b”: *, “c”: *} - https://phitter.io/distributions/continuous/triangular

cdf(x)

Cumulative distribution function

Return type:

float | ndarray

central_moments(k)

Parametric central moments. µ’[k] = E[(X - E[X])ᵏ] = ∫(x-µ[k])ᵏ∙f(x) dx

Return type:

float | None

get_parameters(continuous_measures)

Calculate proper parameters of the distribution from sample continuous_measures. The parameters are calculated by formula.

Parameters:

continuous_measures (MEASUREMESTS) – attributes: mean, std, variance, skewness, kurtosis, median, mode, min, max, size, num_bins, data

Returns:

parameters

Return type:

{“a”: *, “b”: *, “c”: *}

property kurtosis: float

Parametric kurtosis

property mean: float

Parametric mean

property median: float

Parametric median

property mode: float

Parametric mode

property name

non_central_moments(k)

Parametric no central moments. µ[k] = E[Xᵏ] = ∫xᵏ∙f(x) dx

Return type:

float | None

property num_parameters: int

Number of parameters of the distribution

parameter_restrictions()

Check parameters restrictions

Return type:

bool

property parameters_example: dict[str, int | float]

pdf(x)

Probability density function

Return type:

float | ndarray

ppf(u)

Percent point function. Inverse of Cumulative distribution function. If CDF[x] = u => PPF[u] = x

Return type:

float | ndarray

sample(n, seed=None)

Sample of n elements of ditribution

Return type:

ndarray

property skewness: float

Parametric skewness

property standard_deviation: float

Parametric standard deviation

property variance: float

Parametric variance

phitter.continuous.continuous_distributions.uniform module

class phitter.continuous.continuous_distributions.uniform.Uniform(parameters=None, continuous_measures=None, init_parameters_examples=False)

Bases: object

Uniform distribution - Parameters Uniform Distribution: {“a”: *, “b”: *} - https://phitter.io/distributions/continuous/uniform

cdf(x)

Cumulative distribution function

Return type:

float | ndarray

central_moments(k)

Parametric central moments. µ’[k] = E[(X - E[X])ᵏ] = ∫(x-µ[k])ᵏ∙f(x) dx

Return type:

float | None

get_parameters(continuous_measures)

Calculate proper parameters of the distribution from sample continuous_measures. The parameters are calculated by formula.

Parameters:

continuous_measures (MEASUREMESTS) – attributes: mean, std, variance, skewness, kurtosis, median, mode, min, max, size, num_bins, data

Returns:

parameters

Return type:

{“a”: *, “b”: *}

property kurtosis: float

Parametric kurtosis

property mean: float

Parametric mean

property median: float

Parametric median

property mode: float

Parametric mode

property name

non_central_moments(k)

Parametric no central moments. µ[k] = E[Xᵏ] = ∫xᵏ∙f(x) dx

Return type:

float | None

property num_parameters: int

Number of parameters of the distribution

parameter_restrictions()

Check parameters restrictions

Return type:

bool

property parameters_example: dict[str, int | float]

pdf(x)

Probability density function

Return type:

float | ndarray

ppf(u)

Percent point function. Inverse of Cumulative distribution function. If CDF[x] = u => PPF[u] = x

Return type:

float | ndarray

sample(n, seed=None)

Sample of n elements of ditribution

Return type:

ndarray

property skewness: float

Parametric skewness

property standard_deviation: float

Parametric standard deviation

property variance: float

Parametric variance

phitter.continuous.continuous_distributions.weibull module

class phitter.continuous.continuous_distributions.weibull.Weibull(parameters=None, continuous_measures=None, init_parameters_examples=False)

Bases: object

Weibull distribution - Parameters Weibull Distribution: {“alpha”: *, “beta”: *} - https://phitter.io/distributions/continuous/weibull

cdf(x)

Cumulative distribution function. Calculated with known formula.

Return type:

float | ndarray

central_moments(k)

Parametric central moments. µ’[k] = E[(X - E[X])ᵏ] = ∫(x-µ[k])ᵏ∙f(x) dx

Return type:

float | None

get_parameters(continuous_measures)

Calculate proper parameters of the distribution from sample continuous_measures. The parameters are calculated by formula.

Parameters:

continuous_measures (MEASUREMESTS) – attributes: mean, std, variance, skewness, kurtosis, median, mode, min, max, size, num_bins, data

Returns:

parameters

Return type:

{“alpha”: *, “beta”: *}

property kurtosis: float

Parametric kurtosis

property mean: float

Parametric mean

property median: float

Parametric median

property mode: float

Parametric mode

property name

non_central_moments(k)

Parametric no central moments. µ[k] = E[Xᵏ] = ∫xᵏ∙f(x) dx

Return type:

float | None

property num_parameters: int

Number of parameters of the distribution

parameter_restrictions()

Check parameters restrictions

Return type:

bool

property parameters_example: dict[str, int | float]

pdf(x)

Probability density function

Return type:

float | ndarray

ppf(u)

Percent point function. Inverse of Cumulative distribution function. If CDF[x] = u => PPF[u] = x

Return type:

float | ndarray

sample(n, seed=None)

Sample of n elements of ditribution

Return type:

ndarray

property skewness: float

Parametric skewness

property standard_deviation: float

Parametric standard deviation

property variance: float

Parametric variance

phitter.continuous.continuous_distributions.weibull_3p module

class phitter.continuous.continuous_distributions.weibull_3p.Weibull3P(parameters=None, continuous_measures=None, init_parameters_examples=False)

Bases: object

Weibull distribution - Parameters Weibull3P Distribution: {“alpha”: *, “loc”: *, “beta”: *} - https://phitter.io/distributions/continuous/weibull_3p

cdf(x)

Cumulative distribution function. Calculated with known formula.

Return type:

float | ndarray

central_moments(k)

Parametric central moments. µ’[k] = E[(X - E[X])ᵏ] = ∫(x-µ[k])ᵏ∙f(x) dx

Return type:

float | None

get_parameters(continuous_measures)

Calculate proper parameters of the distribution from sample continuous_measures. The parameters are calculated by formula.

Parameters:

continuous_measures (MEASUREMESTS) – attributes: mean, std, variance, skewness, kurtosis, median, mode, min, max, size, num_bins, data

Returns:

parameters

Return type:

{“alpha”: *, “loc”: *, “beta”: *}

property kurtosis: float

Parametric kurtosis

property mean: float

Parametric mean

property median: float

Parametric median

property mode: float

Parametric mode

property name

non_central_moments(k)

Parametric no central moments. µ[k] = E[Xᵏ] = ∫xᵏ∙f(x) dx

Return type:

float | None

property num_parameters: int

Number of parameters of the distribution

parameter_restrictions()

Check parameters restrictions

Return type:

bool

property parameters_example: dict[str, int | float]

pdf(x)

Probability density function

Return type:

float | ndarray

ppf(u)

Percent point function. Inverse of Cumulative distribution function. If CDF[x] = u => PPF[u] = x

Return type:

float | ndarray

sample(n, seed=None)

Sample of n elements of ditribution

Return type:

ndarray

property skewness: float

Parametric skewness

property standard_deviation: float

Parametric standard deviation

property variance: float

Parametric variance

Module contents