nnetsauce开源程序是使用随机和准随机（神经）网络（目前为Python和R）进行统计/机器学习

2025/12/14 8:45:14 来源：https://blog.csdn.net/struggle2025/article/details/148699628 浏览: 次关键词：nnetsauce开源程序是使用随机和准随机（神经）网络（目前为Python和R）进行统计/机器学习

一、软件介绍

文末提供程序和源码下载

二、Installing (for Python and R)安装（适用于 Python 和 R）

Python 蟒

1st method: by using pip at the command line for the stable version
第 1 种方法：在稳定版的命令行中使用 pip

pip install nnetsauce

2nd method: using conda (Linux and macOS only for now)
第二种方法：使用 conda （目前仅限 Linux 和 macOS）

conda install -c conda-forge nnetsauce

3rd method: from Github, for the development version
第 3 种方法：来自 Github，用于开发版本

pip install git+https://github.com/Techtonique/nnetsauce.git

git clone https://github.com/Techtonique/nnetsauce.git
cd nnetsauce
make install

R

From GitHub 来自 GitHub

remotes::install_github("Techtonique/nnetsauce_r") # the repo is in this organization

From R-universe 来自 R-universe

install.packages('nnetsauce', repos = c('https://techtonique.r-universe.dev',
'https://cloud.r-project.org'))

General rule for using the package in R: object accesses with .'s are replaced by $'s. R Examples can be found in the package, once installed, by typing (in R console):
在 R 中使用包的一般规则：带有 . ' 的对象访问将替换为 $ 's。安装后，可以通过键入（在 R 控制台中）在包中找到 R 示例：

?nnetsauce::MultitaskClassifier

三、Quick start 快速开始

There are multiple examples here on GitHub, plus notebooks (including R Markdown notebooks).
GitHub 上有多个示例，以及笔记本（包括 R Markdown 笔记本）。

You can also read these blog posts.
您还可以阅读这些博客文章。

Lazy Deep (quasi-randomized neural) networks example
Lazy Deep （准随机神经网络）网络示例

!pip install nnetsauce --upgrade

import os
import nnetsauce as ns
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split
from time import timedata = load_breast_cancer()
X = data.data
y= data.target
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = .2, random_state = 123)clf = ns.LazyDeepClassifier(n_layers=3, verbose=0, ignore_warnings=True)
start = time()
models, predictions = clf.fit(X_train, X_test, y_train, y_test)
print(f"\n\n Elapsed: {time()-start} seconds \n")model_dictionary = clf.provide_models(X_train, X_test, y_train, y_test)display(models)