NLP-in-Disease-Diagnosis-master
所属分类：其他
开发工具：Python
文件大小：4201KB
下载次数：1
上传日期：2019-12-29 20:53:13
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一颗豆子呀
说明： 贝叶斯网络(Bayesian Network)实质上就是一种基于概率的不确定性推理网络。它是用来表示变量集合连接概率的图形模型，提供了一种表示因果信息的方法。当时主要用于处理人工智能中的不确定性信息。随后它逐步成为了处理不确定性信息技术的主流，并且在计算机智能科学、工业控制、医疗诊断等领域的许多智能化系统中得到了重要的应用。
(Bayesian network is essentially a kind of uncertainty reasoning network based on probability. It is a graphic model used to represent the connection probability of variable sets, and provides a method to represent causal information. At that time, it was mainly used to deal with uncertain information in artificial intelligence. Subsequently, it has gradually become the mainstream of information technology to deal with uncertainty, and has been applied in many intelligent systems in computer intelligent science, industrial control, medical diagnosis and other fields.)
文件列表:
Adverbs.json (350, 2017-08-02)
Match.py (1770, 2017-08-02)
alg (0, 2017-08-02)
alg\basic (0, 2017-08-02)
alg\basic\str_util.py (2893, 2017-08-02)
alg\ml (0, 2017-08-02)
alg\ml\ac_match (0, 2017-08-02)
alg\ml\ac_match\AhoCorasick.py (2469, 2017-08-02)
alg\ml\ac_match\Trie.py (3301, 2017-08-02)
alg\ml\pgmpy (0, 2017-08-02)
alg\ml\pgmpy\Contributing.md (5066, 2017-08-02)
alg\ml\pgmpy\LICENSE (1077, 2017-08-02)
alg\ml\pgmpy\continous_integration (0, 2017-08-02)
alg\ml\pgmpy\continous_integration\install.sh (1026, 2017-08-02)
alg\ml\pgmpy\dist (0, 2017-08-02)
alg\ml\pgmpy\dist\pgmpy-0.1.3-py2.6.egg (566778, 2017-08-02)
alg\ml\pgmpy\dist\pgmpy-0.1.3-py3.3.egg (717210, 2017-08-02)
alg\ml\pgmpy\dist\pgmpy-0.1.3-py3.4.egg (711601, 2017-08-02)
alg\ml\pgmpy\dist\pgmpy-0.1.3-py3.5.egg (710331, 2017-08-02)
alg\ml\pgmpy\docs (0, 2017-08-02)
alg\ml\pgmpy\docs\Installation.rst (0, 2017-08-02)
alg\ml\pgmpy\docs\Makefile (8069, 2017-08-02)
alg\ml\pgmpy\docs\conf.py (9834, 2017-08-02)
alg\ml\pgmpy\docs\contributing.rst (5124, 2017-08-02)
alg\ml\pgmpy\docs\estimators.rst (1099, 2017-08-02)
alg\ml\pgmpy\docs\factors.rst (961, 2017-08-02)
alg\ml\pgmpy\docs\index.rst (2482, 2017-08-02)
alg\ml\pgmpy\docs\inference.rst (590, 2017-08-02)
alg\ml\pgmpy\docs\installation.rst (0, 2017-08-02)
alg\ml\pgmpy\docs\logo.png (231179, 2017-08-02)
alg\ml\pgmpy\docs\models.rst (792, 2017-08-02)
alg\ml\pgmpy\docs\readwrite.rst (436, 2017-08-02)
alg\ml\pgmpy\docs\sampling.rst (440, 2017-08-02)
alg\ml\pgmpy\examples (0, 2017-08-02)
alg\ml\pgmpy\examples\Creating a Bayesian Network.ipynb (6521, 2017-08-02)
alg\ml\pgmpy\examples\Extending pgmpy.ipynb (5224, 2017-08-02)
alg\ml\pgmpy\examples\Inference in Bayesian Networks.ipynb (5353, 2017-08-02)
alg\ml\pgmpy\examples\Learning from data.ipynb (13393, 2017-08-02)
... ...
pgmpy
=====
[](https://travis-ci.org/pgmpy/pgmpy)
[](https://www.appveyor.com/)
[](https://coveralls.io/r/pgmpy/pgmpy?branch=dev)
[](https://landscape.io/github/pgmpy/pgmpy/dev)
[](https://gitter.im/pgmpy/pgmpy?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)
pgmpy is a python library for working with Probabilistic Graphical Models.
Documentation and list of algorithms supported is at our official site http://pgmpy.org/
Examples on using pgmpy: https://github.com/pgmpy/pgmpy/tree/dev/examples
Basic tutorial on Probabilistic Graphical models using pgmpy: https://github.com/pgmpy/pgmpy_notebook
Our mailing list is at https://groups.google.com/forum/#!forum/pgmpy .
We have our community chat at [gitter](https://gitter.im/pgmpy/pgmpy).
Dependencies
=============
pgmpy has following non optional dependencies:
- Python 2.7 or Python 3
- NetworkX 1.11
- Scipy 0.18.0
- Numpy 1.11.1
- Pandas 0.18.1
Download
=========
Currently pgmpy is not hosted on pypi or conda.
You can either clone the git repo with:
```
git clone https://github.com/pgmpy/pgmpy
```
or download a zip from: https://github.com/pgmpy/pgmpy/archive/dev.zip
Installation
=============
To install the dependencies switch to the pgmpy directory using:
```
$ cd /path/to/pgmpy
```
In the directory run either of the following:
Using pip
```
$ pip install -r requirements.txt # or requirements-dev.txt if you want to run unittests
```
or conda
```
$ conda install --file requirements.txt # or requirements-dev.txt
```
Then install using:
```bash
sudo python setup.py install
```
If you face any problems during installation let us know, via issues, mail or at our gitter channel.
Development
============
Code
----
You can check the latest sources from our github repository
use the command:
git clone https://github.com/pgmpy/pgmpy.git
Contributing
------------
Issues can be reported at our [issues section](https://github.com/pgmpy/pgmpy/issues) or via mail, or gitter.
We will try our best to solve the issue at the earliest.
Before opening a pull request , have look at our [contributing guide](
https://github.com/pgmpy/pgmpy/blob/dev/Contributing.md)
Contributing guide contains some points that will make our life's easier in reviewing and merging your PR.
If you face any problems in pull request, feel free to ask them at mail or gitter.
If you have any new features, please have a discussion on the issue tracker or the mailing
list before working on it.
Testing
-------
After installation, you can launch the test form pgmpy
source directory (you will need to have the ``nose`` package installed):
```bash
$ nosetests -v
```
to see the coverage of existing code use following command
```
$ nosetests --with-coverage --cover-package=pgmpy
```
Documentation and usage
=======================
Everything is at:
http://pgmpy.org/
You can also build the documentation in your local system. We use sphinx to help us building documentation from our code.
```
$ cd /path/to/pgmpy/docs
$ make html
```
Then the docs will be in _build/html
Example:
========
Here is a small snippet of pgmpy API
```python
>>> from pgmpy.models import BayesianModel
>>> from pgmpy.factors.discrete import TabularCPD
>>> student = BayesianModel()
>>> # instantiates a new Bayesian Model called 'student'
>>> student.add_nodes_from(['diff', 'intel', 'grade'])
>>> # adds nodes labelled 'diff', 'intel', 'grade' to student
>>> student.add_edges_from([('diff', 'grade'), ('intel', 'grade')])
>>> # adds directed edges from 'diff' to 'grade' and 'intel' to 'grade'
>>> """
... diff cpd:
...
... +-------+--------+
... |diff: | |
... +-------+--------+
... |easy | 0.2 |
... +-------+--------+
... |hard | 0.8 |
... +-------+--------+
... """
>>> diff_cpd = TabularCPD('diff', 2, [[0.2], [0.8]])
>>> """
... intel cpd:
...
... +-------+--------+
... |intel: | |
... +-------+--------+
... |dumb | 0.5 |
... +-------+--------+
... |avg | 0.3 |
... +-------+--------+
... |smart | 0.2 |
... +-------+--------+
... """
>>> intel_cpd = TabularCPD('intel', 3, [[0.5], [0.3], [0.2]])
>>> """
... grade cpd:
...
... +------+-----------------------+---------------------+
... |diff: | easy | hard |
... +------+------+------+---------+------+------+-------+
... |intel:| dumb | avg | smart | dumb | avg | smart |
... +------+------+------+---------+------+------+-------+
... |gradeA| 0.1 | 0.1 | 0.1 | 0.1 | 0.1 | 0.1 |
... +------+------+------+---------+------+------+-------+
... |gradeB| 0.1 | 0.1 | 0.1 | 0.1 | 0.1 | 0.1 |
... +------+------+------+---------+------+------+-------+
... |gradeC| 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 |
... +------+------+------+---------+------+------+-------+
... """
>>> grade_cpd = TabularCPD('grade', 3,
[[0.1,0.1,0.1,0.1,0.1,0.1],
[0.1,0.1,0.1,0.1,0.1,0.1],
[0.8,0.8,0.8,0.8,0.8,0.8]],
evidence=['intel', 'diff'],
evidence_card=[3, 2])
>>> student.add_cpds(diff_cpd, intel_cpd, grade_cpd)
>>> # Finding active trail
>>> student.active_trail_nodes('diff')
{'diff', 'grade'}
>>> # Finding active trail with observation
>>> student.active_trail_nodes('diff', observed='grade')
{'diff', 'intel'}
```
License
=======
pgmpy is released under MIT License. You can read about our lisence at [here](https://github.com/pgmpy/pgmpy/blob/dev/LICENSE)
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