| Name | phitter JSON |
| Version |
0.0.5
JSON |
| download |
| home_page | None |
| Summary | Find the best probability distribution for your dataset |
| upload_time | 2024-04-20 07:39:39 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.9 |
| license | The MIT License (MIT) Copyright (c) 2024 Sebastián José Herrera Monterrosa Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. |
| keywords |
scientific
engineering
mathematics
artificial intelligence
software development
python modules
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
<p align="center">
<picture>
<source media="(prefers-color-scheme: dark)" srcset="https://gist.githubusercontent.com/phitterio/66bc7f3674eac01ae646e30ba697a6d7/raw/e96dbba0eb26b20d35e608fefc3984bd87f0010b/DarkPhitterLogo.svg" width="350">
<source media="(prefers-color-scheme: light)" srcset="https://gist.githubusercontent.com/phitterio/170ce460d7e766545265772525edecf6/raw/71b4867c6e5683455cf1d68bea5bea7eda55ce7d/LightPhitterLogo.svg" width="350">
<img alt="phitter-dark-logo" src="https://gist.githubusercontent.com/phitterio/170ce460d7e766545265772525edecf6/raw/71b4867c6e5683455cf1d68bea5bea7eda55ce7d/LightPhitterLogo.svg" width="350">
</picture>
</p>
<p align="center">
<a href="https://pypi.org/project/phitter" target="_blank">
<img src="https://img.shields.io/pypi/dm/phitter.svg" alt="Supported Python versions">
</a>
<a href="https://pypi.org/project/phitter" target="_blank">
<img src="https://img.shields.io/badge/License-MIT-blue.svg" alt="Supported Python versions">
</a>
<a href="https://pypi.org/project/phitter" target="_blank">
<img src="https://img.shields.io/pypi/pyversions/phitter" alt="Supported Python versions">
</a>
</p>
<p>
Phitter analyzes datasets and determines the best analytical probability distributions that represent them. Phitter studies over 80 probability distributions, both continuous and discrete, 3 goodness-of-fit tests, and interactive visualizations. For each selected probability distribution, a standard modeling guide is provided along with spreadsheets that detail the methodology for using the chosen distribution in data science, operations research, and artificial intelligence.
</p>
<p>
This repository contains the implementation of the python library and the kernel of <a href="https://phitter.io">Phitter Web</a>
</p>
## Installation
### Requirements
```console
python: >=3.9
```
### PyPI
```console
pip install phitter
```
## Usage
### Notebook's Tutorials
| Tutorial | Notebooks |
| :------------------------------: | :-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
| **Fit Continuous** | <a target="_blank" href="https://colab.research.google.com/github/phitterio/phitter-kernel/blob/main/utilities/tutorials/fit_continuous.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> |
| **Fit Discrete** | <a target="_blank" href="https://colab.research.google.com/github/phitterio/phitter-kernel/blob/main/utilities/tutorials/fit_discrete.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> |
| **Fit Accelerate [Sample>100K]** | <a target="_blank" href="https://colab.research.google.com/github/phitterio/phitter-kernel/blob/main/utilities/tutorials/fit_accelerate.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> |
| **Fit Specific Disribution** | <a target="_blank" href="https://colab.research.google.com/github/phitterio/phitter-kernel/blob/main/utilities/tutorials/fit_specefic_distribution.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> |
| **Working Distribution** | <a target="_blank" href="https://colab.research.google.com/github/phitterio/phitter-kernel/blob/main/utilities/tutorials/working_distribution.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> |
### General
```python
import phitter
data: list[int | float] = [...]
phitter_cont = phitter.PHITTER(data)
phitter_cont.fit()
```
### Full continuous implementation
```python
import phitter
data: list[int | float] = [...]
phitter_cont = phitter.PHITTER(
data=data,
fit_type="continuous",
num_bins=15,
confidence_level=0.95,
minimum_sse=1e-2,
distributions_to_fit=["beta", "normal", "fatigue_life", "triangular"],
)
phitter_cont.fit(n_workers=6)
```
### Full discrete implementation
```python
import phitter
data: list[int | float] = [...]
phitter_disc = phitter.PHITTER(
data=data,
fit_type="discrete",
confidence_level=0.95,
minimum_sse=1e-2,
distributions_to_fit=["binomial", "geometric"],
)
phitter_disc.fit(n_workers=2)
```
### Phitter: properties and methods
```python
import phitter
data: list[int | float] = [...]
phitter_cont = phitter.PHITTER(data)
phitter_cont.fit()
phitter_cont.best_distribution -> dict
phitter_cont.sorted_distributions_sse -> dict
phitter_cont.not_rejected_distributions -> dict
phitter_cont.df_sorted_distributions_sse -> pandas.DataFrame
phitter_cont.df_not_rejected_distributions -> pandas.DataFrame
```
### Histogram Plot
```python
import phitter
data: list[int | float] = [...]
phitter_cont = phitter.PHITTER(data)
phitter_cont.fit()
phitter_cont.plot_histogram()
```
<img alt="phitter_histogram" src="https://github.com/phitterio/phitter-kernel/blob/main/utilities/multimedia/histogram.png?raw=true" width="500" />
### Histogram PDF Dsitributions Plot
```python
import phitter
data: list[int | float] = [...]
phitter_cont = phitter.PHITTER(data)
phitter_cont.fit()
phitter_cont.plot_histogram_distributions()
```
<img alt="phitter_histogram" src="https://github.com/phitterio/phitter-kernel/blob/main/utilities/multimedia/histogram_pdf_distributions.png?raw=true" width="500" />
### Histogram PDF Dsitribution Plot
```python
import phitter
data: list[int | float] = [...]
phitter_cont = phitter.PHITTER(data)
phitter_cont.fit()
phitter_cont.plot_distribution("beta")
```
<img alt="phitter_histogram" src="https://github.com/phitterio/phitter-kernel/blob/main/utilities/multimedia/histogram_pdf_distribution.png?raw=true" width="500" />
### ECDF Plot
```python
import phitter
data: list[int | float] = [...]
phitter_cont = phitter.PHITTER(data)
phitter_cont.fit()
phitter_cont.plot_ecdf()
```
<img alt="phitter_histogram" src="https://github.com/phitterio/phitter-kernel/blob/main/utilities/multimedia/ecdf.png?raw=true" width="500" />
### ECDF Distribution Plot
```python
import phitter
data: list[int | float] = [...]
phitter_cont = phitter.PHITTER(data)
phitter_cont.fit()
phitter_cont.plot_ecdf_distribution("beta")
```
<img alt="phitter_histogram" src="https://github.com/phitterio/phitter-kernel/blob/main/utilities/multimedia/ecdf_distribution.png?raw=true" width="500" />
### QQ Plot
```python
import phitter
data: list[int | float] = [...]
phitter_cont = phitter.PHITTER(data)
phitter_cont.fit()
phitter_cont.qq_plot("beta")
```
<img alt="phitter_histogram" src="https://github.com/phitterio/phitter-kernel/blob/main/utilities/multimedia/qq_plot_distribution.png?raw=true" width="500" />
### QQ - Regression Plot
```python
import phitter
data: list[int | float] = [...]
phitter_cont = phitter.PHITTER(data)
phitter_cont.fit()
phitter_cont.qq_plot_regression("beta")
```
<img alt="phitter_histogram" src="https://github.com/phitterio/phitter-kernel/blob/main/utilities/multimedia/qq_plot_distribution_regression.png?raw=true" width="500" />
### Distributions: Methods and properties
```python
import phitter
distribution = phitter.continuous.BETA(parameters={"alpha": 5, "beta": 3, "A": 200, "B": 1000})
## CDF, PDF, PPF, PMF receive float or numpy.ndarray. For discrete distributions PMF instead of PDF. Parameters notation are in description of ditribution
distribution.cdf(752) # -> 0.6242831129533498
distribution.pdf(388) # -> 0.0002342575686629883
distribution.ppf(0.623) # -> 751.5512889417921
distribution.sample(2) # -> [550.800114 514.85410326]
## STATS
distribution.mean # -> 700.0
distribution.variance # -> 16666.666666666668
distribution.standard_deviation # -> 129.09944487358058
distribution.skewness # -> -0.3098386676965934
distribution.kurtosis # -> 2.5854545454545454
distribution.median # -> 708.707130841534
distribution.mode # -> 733.3333333333333
```
## Continuous Distributions
#### [1. PDF File Documentation Continuous Distributions](https://github.com/phitterio/phitter-kernel/blob/main/distributions_documentation/continuous/document_continuous_distributions/phitter_continuous_distributions.pdf)
#### 2. Phitter Online Interactive Documentation
<div>
<a href="https://phitter.io/distributions/continuous/alpha" target="_blank">• ALPHA</a>
<a href="https://phitter.io/distributions/continuous/arcsine" target="_blank">• ARCSINE</a>
<a href="https://phitter.io/distributions/continuous/argus" target="_blank">• ARGUS</a>
<a href="https://phitter.io/distributions/continuous/beta" target="_blank">• BETA</a>
<a href="https://phitter.io/distributions/continuous/beta_prime" target="_blank">• BETA PRIME</a>
<a href="https://phitter.io/distributions/continuous/beta_prime_4p" target="_blank">• BETA PRIME 4P</a>
<a href="https://phitter.io/distributions/continuous/bradford" target="_blank">• BRADFORD</a>
<a href="https://phitter.io/distributions/continuous/burr" target="_blank">• BURR</a>
<a href="https://phitter.io/distributions/continuous/burr_4p" target="_blank">• BURR 4P</a>
<a href="https://phitter.io/distributions/continuous/cauchy" target="_blank">• CAUCHY</a>
<a href="https://phitter.io/distributions/continuous/chi_square" target="_blank">• CHI SQUARE</a>
<a href="https://phitter.io/distributions/continuous/chi_square_3p" target="_blank">• CHI SQUARE 3P</a>
<a href="https://phitter.io/distributions/continuous/dagum" target="_blank">• DAGUM</a>
<a href="https://phitter.io/distributions/continuous/dagum_4p" target="_blank">• DAGUM 4P</a>
<a href="https://phitter.io/distributions/continuous/erlang" target="_blank">• ERLANG</a>
<a href="https://phitter.io/distributions/continuous/erlang_3p" target="_blank">• ERLANG 3P</a>
<a href="https://phitter.io/distributions/continuous/error_function" target="_blank">• ERROR FUNCTION</a>
<a href="https://phitter.io/distributions/continuous/exponential" target="_blank">• EXPONENTIAL</a>
<a href="https://phitter.io/distributions/continuous/exponential_2p" target="_blank">• EXPONENTIAL 2P</a>
<a href="https://phitter.io/distributions/continuous/f" target="_blank">• F</a>
<a href="https://phitter.io/distributions/continuous/fatigue_life" target="_blank">• FATIGUE LIFE</a>
<a href="https://phitter.io/distributions/continuous/folded_normal" target="_blank">• FOLDED NORMAL</a>
<a href="https://phitter.io/distributions/continuous/frechet" target="_blank">• FRECHET</a>
<a href="https://phitter.io/distributions/continuous/f_4p" target="_blank">• F 4P</a>
<a href="https://phitter.io/distributions/continuous/gamma" target="_blank">• GAMMA</a>
<a href="https://phitter.io/distributions/continuous/gamma_3p" target="_blank">• GAMMA 3P</a>
<a href="https://phitter.io/distributions/continuous/generalized_extreme_value" target="_blank">• GENERALIZED EXTREME VALUE</a>
<a href="https://phitter.io/distributions/continuous/generalized_gamma" target="_blank">• GENERALIZED GAMMA</a>
<a href="https://phitter.io/distributions/continuous/generalized_gamma_4p" target="_blank">• GENERALIZED GAMMA 4P</a>
<a href="https://phitter.io/distributions/continuous/generalized_logistic" target="_blank">• GENERALIZED LOGISTIC</a>
<a href="https://phitter.io/distributions/continuous/generalized_normal" target="_blank">• GENERALIZED NORMAL</a>
<a href="https://phitter.io/distributions/continuous/generalized_pareto" target="_blank">• GENERALIZED PARETO</a>
<a href="https://phitter.io/distributions/continuous/gibrat" target="_blank">• GIBRAT</a>
<a href="https://phitter.io/distributions/continuous/gumbel_left" target="_blank">• GUMBEL LEFT</a>
<a href="https://phitter.io/distributions/continuous/gumbel_right" target="_blank">• GUMBEL RIGHT</a>
<a href="https://phitter.io/distributions/continuous/half_normal" target="_blank">• HALF NORMAL</a>
<a href="https://phitter.io/distributions/continuous/hyperbolic_secant" target="_blank">• HYPERBOLIC SECANT</a>
<a href="https://phitter.io/distributions/continuous/inverse_gamma" target="_blank">• INVERSE GAMMA</a>
<a href="https://phitter.io/distributions/continuous/inverse_gamma_3p" target="_blank">• INVERSE GAMMA 3P</a>
<a href="https://phitter.io/distributions/continuous/inverse_gaussian" target="_blank">• INVERSE GAUSSIAN</a>
<a href="https://phitter.io/distributions/continuous/inverse_gaussian_3p" target="_blank">• INVERSE GAUSSIAN 3P</a>
<a href="https://phitter.io/distributions/continuous/johnson_sb" target="_blank">• JOHNSON SB</a>
<a href="https://phitter.io/distributions/continuous/johnson_su" target="_blank">• JOHNSON SU</a>
<a href="https://phitter.io/distributions/continuous/kumaraswamy" target="_blank">• KUMARASWAMY</a>
<a href="https://phitter.io/distributions/continuous/laplace" target="_blank">• LAPLACE</a>
<a href="https://phitter.io/distributions/continuous/levy" target="_blank">• LEVY</a>
<a href="https://phitter.io/distributions/continuous/loggamma" target="_blank">• LOGGAMMA</a>
<a href="https://phitter.io/distributions/continuous/logistic" target="_blank">• LOGISTIC</a>
<a href="https://phitter.io/distributions/continuous/loglogistic" target="_blank">• LOGLOGISTIC</a>
<a href="https://phitter.io/distributions/continuous/loglogistic_3p" target="_blank">• LOGLOGISTIC 3P</a>
<a href="https://phitter.io/distributions/continuous/lognormal" target="_blank">• LOGNORMAL</a>
<a href="https://phitter.io/distributions/continuous/maxwell" target="_blank">• MAXWELL</a>
<a href="https://phitter.io/distributions/continuous/moyal" target="_blank">• MOYAL</a>
<a href="https://phitter.io/distributions/continuous/nakagami" target="_blank">• NAKAGAMI</a>
<a href="https://phitter.io/distributions/continuous/non_central_chi_square" target="_blank">• NON CENTRAL CHI SQUARE</a>
<a href="https://phitter.io/distributions/continuous/non_central_f" target="_blank">• NON CENTRAL F</a>
<a href="https://phitter.io/distributions/continuous/non_central_t_student" target="_blank">• NON CENTRAL T STUDENT</a>
<a href="https://phitter.io/distributions/continuous/normal" target="_blank">• NORMAL</a>
<a href="https://phitter.io/distributions/continuous/pareto_first_kind" target="_blank">• PARETO FIRST KIND</a>
<a href="https://phitter.io/distributions/continuous/pareto_second_kind" target="_blank">• PARETO SECOND KIND</a>
<a href="https://phitter.io/distributions/continuous/pert" target="_blank">• PERT</a>
<a href="https://phitter.io/distributions/continuous/power_function" target="_blank">• POWER FUNCTION</a>
<a href="https://phitter.io/distributions/continuous/rayleigh" target="_blank">• RAYLEIGH</a>
<a href="https://phitter.io/distributions/continuous/reciprocal" target="_blank">• RECIPROCAL</a>
<a href="https://phitter.io/distributions/continuous/rice" target="_blank">• RICE</a>
<a href="https://phitter.io/distributions/continuous/semicircular" target="_blank">• SEMICIRCULAR</a>
<a href="https://phitter.io/distributions/continuous/trapezoidal" target="_blank">• TRAPEZOIDAL</a>
<a href="https://phitter.io/distributions/continuous/triangular" target="_blank">• TRIANGULAR</a>
<a href="https://phitter.io/distributions/continuous/t_student" target="_blank">• T STUDENT</a>
<a href="https://phitter.io/distributions/continuous/t_student_3p" target="_blank">• T STUDENT 3P</a>
<a href="https://phitter.io/distributions/continuous/uniform" target="_blank">• UNIFORM</a>
<a href="https://phitter.io/distributions/continuous/weibull" target="_blank">• WEIBULL</a>
<a href="https://phitter.io/distributions/continuous/weibull_3p" target="_blank">• WEIBULL 3P</a>
</div>
## Discrete Distributions
#### [1. PDF File Documentation Discrete Distributions](https://github.com/phitterio/phitter-kernel/blob/main/distributions_documentation/discrete/document_discrete_distributions/phitter_discrete_distributions.pdf)
#### 2. Phitter Online Interactive Documentation
<div style="display: flex; flex-wrap: wrap">
<a href="https://phitter.io/distributions/discrete/bernoulli" target="_blank">• BERNOULLI</a>
<a href="https://phitter.io/distributions/discrete/binomial" target="_blank">• BINOMIAL</a>
<a href="https://phitter.io/distributions/discrete/geometric" target="_blank">• GEOMETRIC</a>
<a href="https://phitter.io/distributions/discrete/hypergeometric" target="_blank">• HYPERGEOMETRIC</a>
<a href="https://phitter.io/distributions/discrete/logarithmic" target="_blank">• LOGARITHMIC</a>
<a href="https://phitter.io/distributions/discrete/negative_binomial" target="_blank">• NEGATIVE BINOMIAL</a>
<a href="https://phitter.io/distributions/discrete/poisson" target="_blank">• POISSON</a>
<a href="https://phitter.io/distributions/discrete/uniform" target="_blank">• UNIFORM</a>
</div>
## Benchmarks
### _Fit time continuous distributions_
| Sample Size / Workers | 1 | 2 | 6 | 10 | 20 |
| :-------------------: | :-------: | :------: | :------: | :------: | :------: |
| **1K** | 8.2981 | 7.1242 | 8.9667 | 9.9287 | 16.2246 |
| **10K** | 20.8711 | 14.2647 | 10.5612 | 11.6004 | 17.8562 |
| **100K** | 152.6296 | 97.2359 | 57.7310 | 51.6182 | 53.2313 |
| **500K** | 914.9291 | 640.8153 | 370.0323 | 267.4597 | 257.7534 |
| **1M** | 1580.8501 | 972.3985 | 573.5429 | 496.5569 | 425.7809 |
### _Estimation time parameters continuous distributions_
| Sample Size / Workers | 1 | 2 | 4 |
| :-------------------: | :-----: | :-----: | :-----: |
| **1K** | 0.1688 | 2.6402 | 2.8719 |
| **10K** | 0.4462 | 2.4452 | 3.0471 |
| **100K** | 4.5598 | 6.3246 | 7.5869 |
| **500K** | 19.0172 | 21.8047 | 19.8420 |
| **1M** | 39.8065 | 29.8360 | 30.2334 |
### _Estimation time parameters continuous distributions_
| Distribution / Sample Size | 1K | 10K | 100K | 500K | 1M | 10M |
| :------------------------: | :----: | :----: | :-----: | :-----: | :------: | :-------: |
| alpha | 0.3345 | 0.4625 | 2.5933 | 18.3856 | 39.6533 | 362.2951 |
| arcsine | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
| argus | 0.0559 | 0.2050 | 2.2472 | 13.3928 | 41.5198 | 362.2472 |
| beta | 0.1880 | 0.1790 | 0.1940 | 0.2110 | 0.1800 | 0.3134 |
| beta_prime | 0.1766 | 0.7506 | 7.6039 | 40.4264 | 85.0677 | 812.1323 |
| beta_prime_4p | 0.0720 | 0.3630 | 3.9478 | 20.2703 | 40.2709 | 413.5239 |
| bradford | 0.0110 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0010 |
| burr | 0.0733 | 0.6931 | 5.5425 | 36.7684 | 79.8269 | 668.2016 |
| burr_4p | 0.1552 | 0.7981 | 8.4716 | 44.4549 | 87.7292 | 858.0035 |
| cauchy | 0.0090 | 0.0160 | 0.1581 | 1.1052 | 2.1090 | 21.5244 |
| chi_square | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
| chi_square_3p | 0.0510 | 0.3545 | 3.0933 | 14.4116 | 21.7277 | 174.8392 |
| dagum | 0.3381 | 0.8278 | 9.6907 | 45.5855 | 98.6691 | 917.6713 |
| dagum_4p | 0.3646 | 1.3307 | 13.3437 | 70.9462 | 140.9371 | 1396.3368 |
| erlang | 0.0010 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
| erlang_3p | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
| error_function | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
| exponential | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
| exponential_2p | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
| f | 0.0592 | 0.2948 | 2.6920 | 18.9458 | 29.9547 | 402.2248 |
| fatigue_life | 0.0352 | 0.1101 | 1.7085 | 9.0090 | 20.4702 | 186.9631 |
| folded_normal | 0.0020 | 0.0020 | 0.0020 | 0.0022 | 0.0033 | 0.0040 |
| frechet | 0.1313 | 0.4359 | 5.7031 | 39.4202 | 43.2469 | 671.3343 |
| f_4p | 0.3269 | 0.7517 | 0.6183 | 0.6037 | 0.5809 | 0.2073 |
| gamma | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
| gamma_3p | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
| generalized_extreme_value | 0.0833 | 0.2054 | 2.0337 | 10.3301 | 22.1340 | 243.3120 |
| generalized_gamma | 0.0298 | 0.0178 | 0.0227 | 0.0236 | 0.0170 | 0.0241 |
| generalized_gamma_4p | 0.0371 | 0.0116 | 0.0732 | 0.0725 | 0.0707 | 0.0730 |
| generalized_logistic | 0.1040 | 0.1073 | 0.1037 | 0.0819 | 0.0989 | 0.0836 |
| generalized_normal | 0.0154 | 0.0736 | 0.7367 | 2.4831 | 5.9752 | 55.2417 |
| generalized_pareto | 0.3189 | 0.8978 | 8.9370 | 51.3813 | 101.6832 | 1015.2933 |
| gibrat | 0.0328 | 0.0432 | 0.4287 | 2.7159 | 5.5721 | 54.1702 |
| gumbel_left | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0010 | 0.0010 |
| gumbel_right | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
| half_normal | 0.0010 | 0.0000 | 0.0000 | 0.0010 | 0.0000 | 0.0000 |
| hyperbolic_secant | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
| inverse_gamma | 0.0308 | 0.0632 | 0.7233 | 5.0127 | 10.7885 | 99.1316 |
| inverse_gamma_3p | 0.0787 | 0.1472 | 1.6513 | 11.1161 | 23.4587 | 227.6125 |
| inverse_gaussian | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
| inverse_gaussian_3p | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
| johnson_sb | 0.2966 | 0.7466 | 4.0707 | 40.2028 | 56.2130 | 728.2447 |
| johnson_su | 0.0070 | 0.0010 | 0.0010 | 0.0143 | 0.0010 | 0.0010 |
| kumaraswamy | 0.0164 | 0.0120 | 0.0130 | 0.0123 | 0.0125 | 0.0150 |
| laplace | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
| levy | 0.0100 | 0.0314 | 0.2296 | 1.1365 | 2.7211 | 26.4966 |
| loggamma | 0.0085 | 0.0050 | 0.0050 | 0.0070 | 0.0062 | 0.0080 |
| logistic | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
| loglogistic | 0.1402 | 0.3464 | 3.9673 | 12.0310 | 42.0038 | 471.0324 |
| loglogistic_3p | 0.2558 | 0.9152 | 11.1546 | 56.5524 | 114.5535 | 1118.6104 |
| lognormal | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0010 | 0.0000 |
| maxwell | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0010 |
| moyal | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
| nakagami | 0.0000 | 0.0030 | 0.0213 | 0.1215 | 0.2649 | 2.2457 |
| non_central_chi_square | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
| non_central_f | 0.0190 | 0.0182 | 0.0210 | 0.0192 | 0.0190 | 0.0200 |
| non_central_t_student | 0.0874 | 0.0822 | 0.0862 | 0.1314 | 0.2516 | 0.1781 |
| normal | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
| pareto_first_kind | 0.0010 | 0.0030 | 0.0390 | 0.2494 | 0.5226 | 5.5246 |
| pareto_second_kind | 0.0643 | 0.1522 | 1.1722 | 10.9871 | 23.6534 | 201.1626 |
| pert | 0.0052 | 0.0030 | 0.0030 | 0.0040 | 0.0040 | 0.0092 |
| power_function | 0.0075 | 0.0040 | 0.0040 | 0.0030 | 0.0040 | 0.0040 |
| rayleigh | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
| reciprocal | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
| rice | 0.0182 | 0.0030 | 0.0040 | 0.0060 | 0.0030 | 0.0050 |
| semicircular | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
| trapezoidal | 0.0083 | 0.0072 | 0.0073 | 0.0060 | 0.0070 | 0.0060 |
| triangular | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
| t_student | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
| t_student_3p | 0.3892 | 1.1860 | 11.2759 | 71.1156 | 143.1939 | 1409.8578 |
| uniform | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
| weibull | 0.0010 | 0.0000 | 0.0000 | 0.0000 | 0.0010 | 0.0010 |
| weibull_3p | 0.0061 | 0.0040 | 0.0030 | 0.0040 | 0.0050 | 0.0050 |
### _Estimation time parameters discrete distributions_
| Distribution / Sample Size | 1K | 10K | 100K | 500K | 1M | 10M |
| :------------------------: | :----: | :----: | :----: | :----: | :----: | :----: |
| bernoulli | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
| binomial | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
| geometric | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
| hypergeometric | 0.0773 | 0.0061 | 0.0030 | 0.0020 | 0.0030 | 0.0051 |
| logarithmic | 0.0210 | 0.0035 | 0.0171 | 0.0050 | 0.0030 | 0.0756 |
| negative_binomial | 0.0293 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
| poisson | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
| uniform | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
## Contribution
If you would like to contribute to the Phitter project, please create a pull request with your proposed changes or enhancements. All contributions are welcome!
Raw data
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"keywords": "scientific, engineering, mathematics, artificial intelligence, software development, python modules",
"author": null,
"author_email": "Sebasti\u00e1n Jos\u00e9 Herrera Monterrosa <phitter.email@gmail.com>",
"download_url": "https://files.pythonhosted.org/packages/d4/07/87f24f3441b2c2db20689c2cebdd208a0788bf48cbb259778d4051fc6f12/phitter-0.0.5.tar.gz",
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"description": "<p align=\"center\">\r\n <picture>\r\n <source media=\"(prefers-color-scheme: dark)\" srcset=\"https://gist.githubusercontent.com/phitterio/66bc7f3674eac01ae646e30ba697a6d7/raw/e96dbba0eb26b20d35e608fefc3984bd87f0010b/DarkPhitterLogo.svg\" width=\"350\">\r\n <source media=\"(prefers-color-scheme: light)\" srcset=\"https://gist.githubusercontent.com/phitterio/170ce460d7e766545265772525edecf6/raw/71b4867c6e5683455cf1d68bea5bea7eda55ce7d/LightPhitterLogo.svg\" width=\"350\">\r\n <img alt=\"phitter-dark-logo\" src=\"https://gist.githubusercontent.com/phitterio/170ce460d7e766545265772525edecf6/raw/71b4867c6e5683455cf1d68bea5bea7eda55ce7d/LightPhitterLogo.svg\" width=\"350\">\r\n </picture>\r\n</p>\r\n\r\n<p align=\"center\">\r\n<a href=\"https://pypi.org/project/phitter\" target=\"_blank\">\r\n <img src=\"https://img.shields.io/pypi/dm/phitter.svg\" alt=\"Supported Python versions\">\r\n</a>\r\n<a href=\"https://pypi.org/project/phitter\" target=\"_blank\">\r\n <img src=\"https://img.shields.io/badge/License-MIT-blue.svg\" alt=\"Supported Python versions\">\r\n</a>\r\n<a href=\"https://pypi.org/project/phitter\" target=\"_blank\">\r\n <img src=\"https://img.shields.io/pypi/pyversions/phitter\" alt=\"Supported Python versions\">\r\n</a>\r\n</p>\r\n\r\n<p>\r\n Phitter analyzes datasets and determines the best analytical probability distributions that represent them. Phitter studies over 80 probability distributions, both continuous and discrete, 3 goodness-of-fit tests, and interactive visualizations. For each selected probability distribution, a standard modeling guide is provided along with spreadsheets that detail the methodology for using the chosen distribution in data science, operations research, and artificial intelligence.\r\n</p>\r\n<p>\r\n This repository contains the implementation of the python library and the kernel of <a href=\"https://phitter.io\">Phitter Web</a>\r\n</p>\r\n\r\n## Installation\r\n\r\n### Requirements\r\n\r\n```console\r\npython: >=3.9\r\n```\r\n\r\n### PyPI\r\n\r\n```console\r\npip install phitter\r\n```\r\n\r\n## Usage\r\n\r\n### Notebook's Tutorials\r\n\r\n| Tutorial | Notebooks |\r\n| :------------------------------: | :-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |\r\n| **Fit Continuous** | <a target=\"_blank\" href=\"https://colab.research.google.com/github/phitterio/phitter-kernel/blob/main/utilities/tutorials/fit_continuous.ipynb\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a> |\r\n| **Fit Discrete** | <a target=\"_blank\" href=\"https://colab.research.google.com/github/phitterio/phitter-kernel/blob/main/utilities/tutorials/fit_discrete.ipynb\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a> |\r\n| **Fit Accelerate [Sample>100K]** | <a target=\"_blank\" href=\"https://colab.research.google.com/github/phitterio/phitter-kernel/blob/main/utilities/tutorials/fit_accelerate.ipynb\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a> |\r\n| **Fit Specific Disribution** | <a target=\"_blank\" href=\"https://colab.research.google.com/github/phitterio/phitter-kernel/blob/main/utilities/tutorials/fit_specefic_distribution.ipynb\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a> |\r\n| **Working Distribution** | <a target=\"_blank\" href=\"https://colab.research.google.com/github/phitterio/phitter-kernel/blob/main/utilities/tutorials/working_distribution.ipynb\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a> |\r\n\r\n### General\r\n\r\n```python\r\nimport phitter\r\n\r\ndata: list[int | float] = [...]\r\n\r\nphitter_cont = phitter.PHITTER(data)\r\nphitter_cont.fit()\r\n```\r\n\r\n### Full continuous implementation\r\n\r\n```python\r\nimport phitter\r\n\r\ndata: list[int | float] = [...]\r\n\r\nphitter_cont = phitter.PHITTER(\r\n data=data,\r\n fit_type=\"continuous\",\r\n num_bins=15,\r\n confidence_level=0.95,\r\n minimum_sse=1e-2,\r\n distributions_to_fit=[\"beta\", \"normal\", \"fatigue_life\", \"triangular\"],\r\n)\r\nphitter_cont.fit(n_workers=6)\r\n```\r\n\r\n### Full discrete implementation\r\n\r\n```python\r\nimport phitter\r\n\r\ndata: list[int | float] = [...]\r\n\r\nphitter_disc = phitter.PHITTER(\r\n data=data,\r\n fit_type=\"discrete\",\r\n confidence_level=0.95,\r\n minimum_sse=1e-2,\r\n distributions_to_fit=[\"binomial\", \"geometric\"],\r\n)\r\nphitter_disc.fit(n_workers=2)\r\n```\r\n\r\n### Phitter: properties and methods\r\n\r\n```python\r\nimport phitter\r\ndata: list[int | float] = [...]\r\nphitter_cont = phitter.PHITTER(data)\r\nphitter_cont.fit()\r\n\r\nphitter_cont.best_distribution -> dict\r\nphitter_cont.sorted_distributions_sse -> dict\r\nphitter_cont.not_rejected_distributions -> dict\r\nphitter_cont.df_sorted_distributions_sse -> pandas.DataFrame\r\nphitter_cont.df_not_rejected_distributions -> pandas.DataFrame\r\n```\r\n\r\n### Histogram Plot\r\n\r\n```python\r\nimport phitter\r\ndata: list[int | float] = [...]\r\nphitter_cont = phitter.PHITTER(data)\r\nphitter_cont.fit()\r\n\r\nphitter_cont.plot_histogram()\r\n```\r\n\r\n<img alt=\"phitter_histogram\" src=\"https://github.com/phitterio/phitter-kernel/blob/main/utilities/multimedia/histogram.png?raw=true\" width=\"500\" />\r\n\r\n### Histogram PDF Dsitributions Plot\r\n\r\n```python\r\nimport phitter\r\ndata: list[int | float] = [...]\r\nphitter_cont = phitter.PHITTER(data)\r\nphitter_cont.fit()\r\n\r\nphitter_cont.plot_histogram_distributions()\r\n```\r\n\r\n<img alt=\"phitter_histogram\" src=\"https://github.com/phitterio/phitter-kernel/blob/main/utilities/multimedia/histogram_pdf_distributions.png?raw=true\" width=\"500\" />\r\n\r\n### Histogram PDF Dsitribution Plot\r\n\r\n```python\r\nimport phitter\r\ndata: list[int | float] = [...]\r\nphitter_cont = phitter.PHITTER(data)\r\nphitter_cont.fit()\r\n\r\nphitter_cont.plot_distribution(\"beta\")\r\n```\r\n\r\n<img alt=\"phitter_histogram\" src=\"https://github.com/phitterio/phitter-kernel/blob/main/utilities/multimedia/histogram_pdf_distribution.png?raw=true\" width=\"500\" />\r\n\r\n### ECDF Plot\r\n\r\n```python\r\nimport phitter\r\ndata: list[int | float] = [...]\r\nphitter_cont = phitter.PHITTER(data)\r\nphitter_cont.fit()\r\n\r\nphitter_cont.plot_ecdf()\r\n```\r\n\r\n<img alt=\"phitter_histogram\" src=\"https://github.com/phitterio/phitter-kernel/blob/main/utilities/multimedia/ecdf.png?raw=true\" width=\"500\" />\r\n\r\n### ECDF Distribution Plot\r\n\r\n```python\r\nimport phitter\r\ndata: list[int | float] = [...]\r\nphitter_cont = phitter.PHITTER(data)\r\nphitter_cont.fit()\r\n\r\nphitter_cont.plot_ecdf_distribution(\"beta\")\r\n```\r\n\r\n<img alt=\"phitter_histogram\" src=\"https://github.com/phitterio/phitter-kernel/blob/main/utilities/multimedia/ecdf_distribution.png?raw=true\" width=\"500\" />\r\n\r\n### QQ Plot\r\n\r\n```python\r\nimport phitter\r\ndata: list[int | float] = [...]\r\nphitter_cont = phitter.PHITTER(data)\r\nphitter_cont.fit()\r\n\r\nphitter_cont.qq_plot(\"beta\")\r\n```\r\n\r\n<img alt=\"phitter_histogram\" src=\"https://github.com/phitterio/phitter-kernel/blob/main/utilities/multimedia/qq_plot_distribution.png?raw=true\" width=\"500\" />\r\n\r\n### QQ - Regression Plot\r\n\r\n```python\r\nimport phitter\r\ndata: list[int | float] = [...]\r\nphitter_cont = phitter.PHITTER(data)\r\nphitter_cont.fit()\r\n\r\nphitter_cont.qq_plot_regression(\"beta\")\r\n```\r\n\r\n<img alt=\"phitter_histogram\" src=\"https://github.com/phitterio/phitter-kernel/blob/main/utilities/multimedia/qq_plot_distribution_regression.png?raw=true\" width=\"500\" />\r\n\r\n### Distributions: Methods and properties\r\n\r\n```python\r\nimport phitter\r\n\r\ndistribution = phitter.continuous.BETA(parameters={\"alpha\": 5, \"beta\": 3, \"A\": 200, \"B\": 1000})\r\n\r\n## CDF, PDF, PPF, PMF receive float or numpy.ndarray. For discrete distributions PMF instead of PDF. Parameters notation are in description of ditribution\r\ndistribution.cdf(752) # -> 0.6242831129533498\r\ndistribution.pdf(388) # -> 0.0002342575686629883\r\ndistribution.ppf(0.623) # -> 751.5512889417921\r\ndistribution.sample(2) # -> [550.800114 514.85410326]\r\n\r\n## STATS\r\ndistribution.mean # -> 700.0\r\ndistribution.variance # -> 16666.666666666668\r\ndistribution.standard_deviation # -> 129.09944487358058\r\ndistribution.skewness # -> -0.3098386676965934\r\ndistribution.kurtosis # -> 2.5854545454545454\r\ndistribution.median # -> 708.707130841534\r\ndistribution.mode # -> 733.3333333333333\r\n```\r\n\r\n## Continuous Distributions\r\n\r\n#### [1. PDF File Documentation Continuous Distributions](https://github.com/phitterio/phitter-kernel/blob/main/distributions_documentation/continuous/document_continuous_distributions/phitter_continuous_distributions.pdf)\r\n\r\n#### 2. Phitter Online Interactive Documentation\r\n\r\n<div>\r\n <a href=\"https://phitter.io/distributions/continuous/alpha\" target=\"_blank\">\u2022 ALPHA</a>\r\n <a href=\"https://phitter.io/distributions/continuous/arcsine\" target=\"_blank\">\u2022 ARCSINE</a>\r\n <a href=\"https://phitter.io/distributions/continuous/argus\" target=\"_blank\">\u2022 ARGUS</a>\r\n <a href=\"https://phitter.io/distributions/continuous/beta\" target=\"_blank\">\u2022 BETA</a>\r\n <a href=\"https://phitter.io/distributions/continuous/beta_prime\" target=\"_blank\">\u2022 BETA PRIME</a>\r\n <a href=\"https://phitter.io/distributions/continuous/beta_prime_4p\" target=\"_blank\">\u2022 BETA PRIME 4P</a>\r\n <a href=\"https://phitter.io/distributions/continuous/bradford\" target=\"_blank\">\u2022 BRADFORD</a>\r\n <a href=\"https://phitter.io/distributions/continuous/burr\" target=\"_blank\">\u2022 BURR</a>\r\n <a href=\"https://phitter.io/distributions/continuous/burr_4p\" target=\"_blank\">\u2022 BURR 4P</a>\r\n <a href=\"https://phitter.io/distributions/continuous/cauchy\" target=\"_blank\">\u2022 CAUCHY</a>\r\n <a href=\"https://phitter.io/distributions/continuous/chi_square\" target=\"_blank\">\u2022 CHI SQUARE</a>\r\n <a href=\"https://phitter.io/distributions/continuous/chi_square_3p\" target=\"_blank\">\u2022 CHI SQUARE 3P</a>\r\n <a href=\"https://phitter.io/distributions/continuous/dagum\" target=\"_blank\">\u2022 DAGUM</a>\r\n <a href=\"https://phitter.io/distributions/continuous/dagum_4p\" target=\"_blank\">\u2022 DAGUM 4P</a>\r\n <a href=\"https://phitter.io/distributions/continuous/erlang\" target=\"_blank\">\u2022 ERLANG</a>\r\n <a href=\"https://phitter.io/distributions/continuous/erlang_3p\" target=\"_blank\">\u2022 ERLANG 3P</a>\r\n <a href=\"https://phitter.io/distributions/continuous/error_function\" target=\"_blank\">\u2022 ERROR FUNCTION</a>\r\n <a href=\"https://phitter.io/distributions/continuous/exponential\" target=\"_blank\">\u2022 EXPONENTIAL</a>\r\n <a href=\"https://phitter.io/distributions/continuous/exponential_2p\" target=\"_blank\">\u2022 EXPONENTIAL 2P</a>\r\n <a href=\"https://phitter.io/distributions/continuous/f\" target=\"_blank\">\u2022 F</a>\r\n <a href=\"https://phitter.io/distributions/continuous/fatigue_life\" target=\"_blank\">\u2022 FATIGUE LIFE</a>\r\n <a href=\"https://phitter.io/distributions/continuous/folded_normal\" target=\"_blank\">\u2022 FOLDED NORMAL</a>\r\n <a href=\"https://phitter.io/distributions/continuous/frechet\" target=\"_blank\">\u2022 FRECHET</a>\r\n <a href=\"https://phitter.io/distributions/continuous/f_4p\" target=\"_blank\">\u2022 F 4P</a>\r\n <a href=\"https://phitter.io/distributions/continuous/gamma\" target=\"_blank\">\u2022 GAMMA</a>\r\n <a href=\"https://phitter.io/distributions/continuous/gamma_3p\" target=\"_blank\">\u2022 GAMMA 3P</a>\r\n <a href=\"https://phitter.io/distributions/continuous/generalized_extreme_value\" target=\"_blank\">\u2022 GENERALIZED EXTREME VALUE</a>\r\n <a href=\"https://phitter.io/distributions/continuous/generalized_gamma\" target=\"_blank\">\u2022 GENERALIZED GAMMA</a>\r\n <a href=\"https://phitter.io/distributions/continuous/generalized_gamma_4p\" target=\"_blank\">\u2022 GENERALIZED GAMMA 4P</a>\r\n <a href=\"https://phitter.io/distributions/continuous/generalized_logistic\" target=\"_blank\">\u2022 GENERALIZED LOGISTIC</a>\r\n <a href=\"https://phitter.io/distributions/continuous/generalized_normal\" target=\"_blank\">\u2022 GENERALIZED NORMAL</a>\r\n <a href=\"https://phitter.io/distributions/continuous/generalized_pareto\" target=\"_blank\">\u2022 GENERALIZED PARETO</a>\r\n <a href=\"https://phitter.io/distributions/continuous/gibrat\" target=\"_blank\">\u2022 GIBRAT</a>\r\n <a href=\"https://phitter.io/distributions/continuous/gumbel_left\" target=\"_blank\">\u2022 GUMBEL LEFT</a>\r\n <a href=\"https://phitter.io/distributions/continuous/gumbel_right\" target=\"_blank\">\u2022 GUMBEL RIGHT</a>\r\n <a href=\"https://phitter.io/distributions/continuous/half_normal\" target=\"_blank\">\u2022 HALF NORMAL</a>\r\n <a href=\"https://phitter.io/distributions/continuous/hyperbolic_secant\" target=\"_blank\">\u2022 HYPERBOLIC SECANT</a>\r\n <a href=\"https://phitter.io/distributions/continuous/inverse_gamma\" target=\"_blank\">\u2022 INVERSE GAMMA</a>\r\n <a href=\"https://phitter.io/distributions/continuous/inverse_gamma_3p\" target=\"_blank\">\u2022 INVERSE GAMMA 3P</a>\r\n <a href=\"https://phitter.io/distributions/continuous/inverse_gaussian\" target=\"_blank\">\u2022 INVERSE GAUSSIAN</a>\r\n <a href=\"https://phitter.io/distributions/continuous/inverse_gaussian_3p\" target=\"_blank\">\u2022 INVERSE GAUSSIAN 3P</a>\r\n <a href=\"https://phitter.io/distributions/continuous/johnson_sb\" target=\"_blank\">\u2022 JOHNSON SB</a>\r\n <a href=\"https://phitter.io/distributions/continuous/johnson_su\" target=\"_blank\">\u2022 JOHNSON SU</a>\r\n <a href=\"https://phitter.io/distributions/continuous/kumaraswamy\" target=\"_blank\">\u2022 KUMARASWAMY</a>\r\n <a href=\"https://phitter.io/distributions/continuous/laplace\" target=\"_blank\">\u2022 LAPLACE</a>\r\n <a href=\"https://phitter.io/distributions/continuous/levy\" target=\"_blank\">\u2022 LEVY</a>\r\n <a href=\"https://phitter.io/distributions/continuous/loggamma\" target=\"_blank\">\u2022 LOGGAMMA</a>\r\n <a href=\"https://phitter.io/distributions/continuous/logistic\" target=\"_blank\">\u2022 LOGISTIC</a>\r\n <a href=\"https://phitter.io/distributions/continuous/loglogistic\" target=\"_blank\">\u2022 LOGLOGISTIC</a>\r\n <a href=\"https://phitter.io/distributions/continuous/loglogistic_3p\" target=\"_blank\">\u2022 LOGLOGISTIC 3P</a>\r\n <a href=\"https://phitter.io/distributions/continuous/lognormal\" target=\"_blank\">\u2022 LOGNORMAL</a>\r\n <a href=\"https://phitter.io/distributions/continuous/maxwell\" target=\"_blank\">\u2022 MAXWELL</a>\r\n <a href=\"https://phitter.io/distributions/continuous/moyal\" target=\"_blank\">\u2022 MOYAL</a>\r\n <a href=\"https://phitter.io/distributions/continuous/nakagami\" target=\"_blank\">\u2022 NAKAGAMI</a>\r\n <a href=\"https://phitter.io/distributions/continuous/non_central_chi_square\" target=\"_blank\">\u2022 NON CENTRAL CHI SQUARE</a>\r\n <a href=\"https://phitter.io/distributions/continuous/non_central_f\" target=\"_blank\">\u2022 NON CENTRAL F</a>\r\n <a href=\"https://phitter.io/distributions/continuous/non_central_t_student\" target=\"_blank\">\u2022 NON CENTRAL T STUDENT</a>\r\n <a href=\"https://phitter.io/distributions/continuous/normal\" target=\"_blank\">\u2022 NORMAL</a>\r\n <a href=\"https://phitter.io/distributions/continuous/pareto_first_kind\" target=\"_blank\">\u2022 PARETO FIRST KIND</a>\r\n <a href=\"https://phitter.io/distributions/continuous/pareto_second_kind\" target=\"_blank\">\u2022 PARETO SECOND KIND</a>\r\n <a href=\"https://phitter.io/distributions/continuous/pert\" target=\"_blank\">\u2022 PERT</a>\r\n <a href=\"https://phitter.io/distributions/continuous/power_function\" target=\"_blank\">\u2022 POWER FUNCTION</a>\r\n <a href=\"https://phitter.io/distributions/continuous/rayleigh\" target=\"_blank\">\u2022 RAYLEIGH</a>\r\n <a href=\"https://phitter.io/distributions/continuous/reciprocal\" target=\"_blank\">\u2022 RECIPROCAL</a>\r\n <a href=\"https://phitter.io/distributions/continuous/rice\" target=\"_blank\">\u2022 RICE</a>\r\n <a href=\"https://phitter.io/distributions/continuous/semicircular\" target=\"_blank\">\u2022 SEMICIRCULAR</a>\r\n <a href=\"https://phitter.io/distributions/continuous/trapezoidal\" target=\"_blank\">\u2022 TRAPEZOIDAL</a>\r\n <a href=\"https://phitter.io/distributions/continuous/triangular\" target=\"_blank\">\u2022 TRIANGULAR</a>\r\n <a href=\"https://phitter.io/distributions/continuous/t_student\" target=\"_blank\">\u2022 T STUDENT</a>\r\n <a href=\"https://phitter.io/distributions/continuous/t_student_3p\" target=\"_blank\">\u2022 T STUDENT 3P</a>\r\n <a href=\"https://phitter.io/distributions/continuous/uniform\" target=\"_blank\">\u2022 UNIFORM</a>\r\n <a href=\"https://phitter.io/distributions/continuous/weibull\" target=\"_blank\">\u2022 WEIBULL</a>\r\n <a href=\"https://phitter.io/distributions/continuous/weibull_3p\" target=\"_blank\">\u2022 WEIBULL 3P</a>\r\n</div>\r\n\r\n## Discrete Distributions\r\n\r\n#### [1. PDF File Documentation Discrete Distributions](https://github.com/phitterio/phitter-kernel/blob/main/distributions_documentation/discrete/document_discrete_distributions/phitter_discrete_distributions.pdf)\r\n\r\n#### 2. Phitter Online Interactive Documentation\r\n\r\n<div style=\"display: flex; flex-wrap: wrap\">\r\n <a href=\"https://phitter.io/distributions/discrete/bernoulli\" target=\"_blank\">\u2022 BERNOULLI</a>\r\n <a href=\"https://phitter.io/distributions/discrete/binomial\" target=\"_blank\">\u2022 BINOMIAL</a>\r\n <a href=\"https://phitter.io/distributions/discrete/geometric\" target=\"_blank\">\u2022 GEOMETRIC</a>\r\n <a href=\"https://phitter.io/distributions/discrete/hypergeometric\" target=\"_blank\">\u2022 HYPERGEOMETRIC</a>\r\n <a href=\"https://phitter.io/distributions/discrete/logarithmic\" target=\"_blank\">\u2022 LOGARITHMIC</a>\r\n <a href=\"https://phitter.io/distributions/discrete/negative_binomial\" target=\"_blank\">\u2022 NEGATIVE BINOMIAL</a>\r\n <a href=\"https://phitter.io/distributions/discrete/poisson\" target=\"_blank\">\u2022 POISSON</a>\r\n <a href=\"https://phitter.io/distributions/discrete/uniform\" target=\"_blank\">\u2022 UNIFORM</a>\r\n</div>\r\n\r\n## Benchmarks\r\n\r\n### _Fit time continuous distributions_\r\n\r\n| Sample Size / Workers | 1 | 2 | 6 | 10 | 20 |\r\n| :-------------------: | :-------: | :------: | :------: | :------: | :------: |\r\n| **1K** | 8.2981 | 7.1242 | 8.9667 | 9.9287 | 16.2246 |\r\n| **10K** | 20.8711 | 14.2647 | 10.5612 | 11.6004 | 17.8562 |\r\n| **100K** | 152.6296 | 97.2359 | 57.7310 | 51.6182 | 53.2313 |\r\n| **500K** | 914.9291 | 640.8153 | 370.0323 | 267.4597 | 257.7534 |\r\n| **1M** | 1580.8501 | 972.3985 | 573.5429 | 496.5569 | 425.7809 |\r\n\r\n### _Estimation time parameters continuous distributions_\r\n\r\n| Sample Size / Workers | 1 | 2 | 4 |\r\n| :-------------------: | :-----: | :-----: | :-----: |\r\n| **1K** | 0.1688 | 2.6402 | 2.8719 |\r\n| **10K** | 0.4462 | 2.4452 | 3.0471 |\r\n| **100K** | 4.5598 | 6.3246 | 7.5869 |\r\n| **500K** | 19.0172 | 21.8047 | 19.8420 |\r\n| **1M** | 39.8065 | 29.8360 | 30.2334 |\r\n\r\n### _Estimation time parameters continuous distributions_\r\n\r\n| Distribution / Sample Size | 1K | 10K | 100K | 500K | 1M | 10M |\r\n| :------------------------: | :----: | :----: | :-----: | :-----: | :------: | :-------: |\r\n| alpha | 0.3345 | 0.4625 | 2.5933 | 18.3856 | 39.6533 | 362.2951 |\r\n| arcsine | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |\r\n| argus | 0.0559 | 0.2050 | 2.2472 | 13.3928 | 41.5198 | 362.2472 |\r\n| beta | 0.1880 | 0.1790 | 0.1940 | 0.2110 | 0.1800 | 0.3134 |\r\n| beta_prime | 0.1766 | 0.7506 | 7.6039 | 40.4264 | 85.0677 | 812.1323 |\r\n| beta_prime_4p | 0.0720 | 0.3630 | 3.9478 | 20.2703 | 40.2709 | 413.5239 |\r\n| bradford | 0.0110 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0010 |\r\n| burr | 0.0733 | 0.6931 | 5.5425 | 36.7684 | 79.8269 | 668.2016 |\r\n| burr_4p | 0.1552 | 0.7981 | 8.4716 | 44.4549 | 87.7292 | 858.0035 |\r\n| cauchy | 0.0090 | 0.0160 | 0.1581 | 1.1052 | 2.1090 | 21.5244 |\r\n| chi_square | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |\r\n| chi_square_3p | 0.0510 | 0.3545 | 3.0933 | 14.4116 | 21.7277 | 174.8392 |\r\n| dagum | 0.3381 | 0.8278 | 9.6907 | 45.5855 | 98.6691 | 917.6713 |\r\n| dagum_4p | 0.3646 | 1.3307 | 13.3437 | 70.9462 | 140.9371 | 1396.3368 |\r\n| erlang | 0.0010 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |\r\n| erlang_3p | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |\r\n| error_function | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |\r\n| exponential | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |\r\n| exponential_2p | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |\r\n| f | 0.0592 | 0.2948 | 2.6920 | 18.9458 | 29.9547 | 402.2248 |\r\n| fatigue_life | 0.0352 | 0.1101 | 1.7085 | 9.0090 | 20.4702 | 186.9631 |\r\n| folded_normal | 0.0020 | 0.0020 | 0.0020 | 0.0022 | 0.0033 | 0.0040 |\r\n| frechet | 0.1313 | 0.4359 | 5.7031 | 39.4202 | 43.2469 | 671.3343 |\r\n| f_4p | 0.3269 | 0.7517 | 0.6183 | 0.6037 | 0.5809 | 0.2073 |\r\n| gamma | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |\r\n| gamma_3p | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |\r\n| generalized_extreme_value | 0.0833 | 0.2054 | 2.0337 | 10.3301 | 22.1340 | 243.3120 |\r\n| generalized_gamma | 0.0298 | 0.0178 | 0.0227 | 0.0236 | 0.0170 | 0.0241 |\r\n| generalized_gamma_4p | 0.0371 | 0.0116 | 0.0732 | 0.0725 | 0.0707 | 0.0730 |\r\n| generalized_logistic | 0.1040 | 0.1073 | 0.1037 | 0.0819 | 0.0989 | 0.0836 |\r\n| generalized_normal | 0.0154 | 0.0736 | 0.7367 | 2.4831 | 5.9752 | 55.2417 |\r\n| generalized_pareto | 0.3189 | 0.8978 | 8.9370 | 51.3813 | 101.6832 | 1015.2933 |\r\n| gibrat | 0.0328 | 0.0432 | 0.4287 | 2.7159 | 5.5721 | 54.1702 |\r\n| gumbel_left | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0010 | 0.0010 |\r\n| gumbel_right | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |\r\n| half_normal | 0.0010 | 0.0000 | 0.0000 | 0.0010 | 0.0000 | 0.0000 |\r\n| hyperbolic_secant | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |\r\n| inverse_gamma | 0.0308 | 0.0632 | 0.7233 | 5.0127 | 10.7885 | 99.1316 |\r\n| inverse_gamma_3p | 0.0787 | 0.1472 | 1.6513 | 11.1161 | 23.4587 | 227.6125 |\r\n| inverse_gaussian | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |\r\n| inverse_gaussian_3p | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |\r\n| johnson_sb | 0.2966 | 0.7466 | 4.0707 | 40.2028 | 56.2130 | 728.2447 |\r\n| johnson_su | 0.0070 | 0.0010 | 0.0010 | 0.0143 | 0.0010 | 0.0010 |\r\n| kumaraswamy | 0.0164 | 0.0120 | 0.0130 | 0.0123 | 0.0125 | 0.0150 |\r\n| laplace | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |\r\n| levy | 0.0100 | 0.0314 | 0.2296 | 1.1365 | 2.7211 | 26.4966 |\r\n| loggamma | 0.0085 | 0.0050 | 0.0050 | 0.0070 | 0.0062 | 0.0080 |\r\n| logistic | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |\r\n| loglogistic | 0.1402 | 0.3464 | 3.9673 | 12.0310 | 42.0038 | 471.0324 |\r\n| loglogistic_3p | 0.2558 | 0.9152 | 11.1546 | 56.5524 | 114.5535 | 1118.6104 |\r\n| lognormal | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0010 | 0.0000 |\r\n| maxwell | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0010 |\r\n| moyal | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |\r\n| nakagami | 0.0000 | 0.0030 | 0.0213 | 0.1215 | 0.2649 | 2.2457 |\r\n| non_central_chi_square | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |\r\n| non_central_f | 0.0190 | 0.0182 | 0.0210 | 0.0192 | 0.0190 | 0.0200 |\r\n| non_central_t_student | 0.0874 | 0.0822 | 0.0862 | 0.1314 | 0.2516 | 0.1781 |\r\n| normal | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |\r\n| pareto_first_kind | 0.0010 | 0.0030 | 0.0390 | 0.2494 | 0.5226 | 5.5246 |\r\n| pareto_second_kind | 0.0643 | 0.1522 | 1.1722 | 10.9871 | 23.6534 | 201.1626 |\r\n| pert | 0.0052 | 0.0030 | 0.0030 | 0.0040 | 0.0040 | 0.0092 |\r\n| power_function | 0.0075 | 0.0040 | 0.0040 | 0.0030 | 0.0040 | 0.0040 |\r\n| rayleigh | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |\r\n| reciprocal | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |\r\n| rice | 0.0182 | 0.0030 | 0.0040 | 0.0060 | 0.0030 | 0.0050 |\r\n| semicircular | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |\r\n| trapezoidal | 0.0083 | 0.0072 | 0.0073 | 0.0060 | 0.0070 | 0.0060 |\r\n| triangular | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |\r\n| t_student | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |\r\n| t_student_3p | 0.3892 | 1.1860 | 11.2759 | 71.1156 | 143.1939 | 1409.8578 |\r\n| uniform | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |\r\n| weibull | 0.0010 | 0.0000 | 0.0000 | 0.0000 | 0.0010 | 0.0010 |\r\n| weibull_3p | 0.0061 | 0.0040 | 0.0030 | 0.0040 | 0.0050 | 0.0050 |\r\n\r\n### _Estimation time parameters discrete distributions_\r\n\r\n| Distribution / Sample Size | 1K | 10K | 100K | 500K | 1M | 10M |\r\n| :------------------------: | :----: | :----: | :----: | :----: | :----: | :----: |\r\n| bernoulli | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |\r\n| binomial | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |\r\n| geometric | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |\r\n| hypergeometric | 0.0773 | 0.0061 | 0.0030 | 0.0020 | 0.0030 | 0.0051 |\r\n| logarithmic | 0.0210 | 0.0035 | 0.0171 | 0.0050 | 0.0030 | 0.0756 |\r\n| negative_binomial | 0.0293 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |\r\n| poisson | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |\r\n| uniform | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |\r\n\r\n## Contribution\r\n\r\nIf you would like to contribute to the Phitter project, please create a pull request with your proposed changes or enhancements. All contributions are welcome!\r\n",
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"license": "The MIT License (MIT) Copyright (c) 2024 Sebasti\u00e1n Jos\u00e9 Herrera Monterrosa Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the \"Software\"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.",
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