xiezhi-ai


Namexiezhi-ai JSON
Version 0.0.0 PyPI version JSON
download
home_page
SummaryAnomaly detection for one-dimensional data
upload_time2023-10-21 21:28:27
maintainer
docs_urlNone
authorZhilin Wang
requires_python
licenseMIT
keywords python anomaly detection one-dimentional data
VCS
bugtrack_url
requirements No requirements were recorded.
Travis-CI No Travis.
coveralls test coverage No coveralls.
            ### xiezhi: The First One-dimensional Anomaly Detection Tool

### Install

pip install xiezhi-ai

### Usage
The inputs include data, beta, and alpha.

data: The current version only supports the detection of one-dimensional data, so the data should be a list. 

beta and alpha are set between 0 and 1 and beta is smaller than alpha, if there are few anomalies, beta and alpha can be set close to 1; 
otherwize, it should be set close to 0.5. If the number of anomalies are unknown, then both of beta and alpha should be close to 0.5. 

Below is the example:

```python
import xiezhi as xz

data=[1,2,3,4,5,6,7,9,10,20] # here 20 is the anomaly
benign_data=xz(data,0.7,0.9) # xiezhi will return the benign data
```

            

Raw data

            {
    "_id": null,
    "home_page": "",
    "name": "xiezhi-ai",
    "maintainer": "",
    "docs_url": null,
    "requires_python": "",
    "maintainer_email": "",
    "keywords": "python,anomaly detection,one-dimentional data",
    "author": "Zhilin Wang",
    "author_email": "wang5327@purdue.edu",
    "download_url": "https://files.pythonhosted.org/packages/5a/50/52cb57125b44b2f133565b7311b82fa5369cdd27b32cd05a9b9596301cd4/xiezhi-ai-0.0.0.tar.gz",
    "platform": null,
    "description": "### xiezhi: The First One-dimensional Anomaly Detection Tool\n\n### Install\n\npip install xiezhi-ai\n\n### Usage\nThe inputs include data, beta, and alpha.\n\ndata: The current version only supports the detection of one-dimensional data, so the data should be a list. \n\nbeta and alpha are set between 0 and 1 and beta is smaller than alpha, if there are few anomalies, beta and alpha can be set close to 1; \notherwize, it should be set close to 0.5. If the number of anomalies are unknown, then both of beta and alpha should be close to 0.5. \n\nBelow is the example:\n\n```python\nimport xiezhi as xz\n\ndata=[1,2,3,4,5,6,7,9,10,20] # here 20 is the anomaly\nbenign_data=xz(data,0.7,0.9) # xiezhi will return the benign data\n```\n",
    "bugtrack_url": null,
    "license": "MIT",
    "summary": "Anomaly detection for one-dimensional data",
    "version": "0.0.0",
    "project_urls": null,
    "split_keywords": [
        "python",
        "anomaly detection",
        "one-dimentional data"
    ],
    "urls": [
        {
            "comment_text": "",
            "digests": {
                "blake2b_256": "93ba948c1f78786f63e9431ecf5339b6cac9f60fc42d8612eed9a8832ea05c12",
                "md5": "5a21b209657e7287fec45ece5f897f90",
                "sha256": "e8d091b9ecbcaed5a8816332ce6996d7952cdda9d180cdb03afeb3d0ef8751c1"
            },
            "downloads": -1,
            "filename": "xiezhi_ai-0.0.0-py3-none-any.whl",
            "has_sig": false,
            "md5_digest": "5a21b209657e7287fec45ece5f897f90",
            "packagetype": "bdist_wheel",
            "python_version": "py3",
            "requires_python": null,
            "size": 2166,
            "upload_time": "2023-10-21T21:28:25",
            "upload_time_iso_8601": "2023-10-21T21:28:25.586565Z",
            "url": "https://files.pythonhosted.org/packages/93/ba/948c1f78786f63e9431ecf5339b6cac9f60fc42d8612eed9a8832ea05c12/xiezhi_ai-0.0.0-py3-none-any.whl",
            "yanked": false,
            "yanked_reason": null
        },
        {
            "comment_text": "",
            "digests": {
                "blake2b_256": "5a5052cb57125b44b2f133565b7311b82fa5369cdd27b32cd05a9b9596301cd4",
                "md5": "b5ea198c675491935c61579c85df40fa",
                "sha256": "eea4f274e22e890591c65f915849755d2cd246302e3a967d679a407db3c66262"
            },
            "downloads": -1,
            "filename": "xiezhi-ai-0.0.0.tar.gz",
            "has_sig": false,
            "md5_digest": "b5ea198c675491935c61579c85df40fa",
            "packagetype": "sdist",
            "python_version": "source",
            "requires_python": null,
            "size": 1957,
            "upload_time": "2023-10-21T21:28:27",
            "upload_time_iso_8601": "2023-10-21T21:28:27.718350Z",
            "url": "https://files.pythonhosted.org/packages/5a/50/52cb57125b44b2f133565b7311b82fa5369cdd27b32cd05a9b9596301cd4/xiezhi-ai-0.0.0.tar.gz",
            "yanked": false,
            "yanked_reason": null
        }
    ],
    "upload_time": "2023-10-21 21:28:27",
    "github": false,
    "gitlab": false,
    "bitbucket": false,
    "codeberg": false,
    "lcname": "xiezhi-ai"
}
        
Elapsed time: 1.72988s