semvec/pypi-packages huggingface dataset:
tinysklearn 0.0.3
A small machine learning package, which can help people learn ML more easier

# tinysklearn
仿制了部分sklearn接口，结构简单，容易理解，非常适合机器学习入门
- [项目地址](https://github.com/WhatAboutMyStar/MachineLearningAlgorithm)
## 使用方法
- 安装
```
pip install tinysklearn
```
- 使用 <br>
使用方法和sklearn是一致的(至少实现了fit,predict,transform,score这四种方法)
```
from tinysklearn.tinysklearn import LinearRegression
from tinysklearn.datasets import load_boston
from tinysklearn.preprocessing import StandardScaler
from tinysklearn.neighbors import KNeighborsClassifier
from tinysklearn.model_selection import train_test_split
from tinysklearn.decomposition import PCA
from tinysklearn.metrics import mean_absolute_error

#读取数据
boston = load_boston()
x = boston.data
y = boston.target

#分割训练集测试集
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=666666)

#构建模型,训练
lr = LinearRegression()
lr.fit(x_train, x_test)

#预测
lr.predict(x_test)

#评估
lr.score(x_test, y_test)
```
Advanced-Neural-Network 1.0.2
Advanced use of neural networks

## ANN

A module to simplify the establishment of neural networks


## Installation

Run the following to install:
```python
pip install Advanced-Neural-Network
```


## Usage
```python
from ANN import *

####Initialise network####
my_network = neural_network()

####Set INPUT_LAYER####
#Add Input neurons
my_network.Add_Input_Neuron("speed_neuron","Input Cell")
my_network.Add_Input_Neuron("pos_neuron","Input Cell")

####Set HIDDEN_LAYER####
#Add Hidden neurons
my_network.Add_Hidden_Neuron("neuron1_layer1","Hidden Cell","Sigmoid")
my_network.Add_Hidden_Neuron("neuron1_layer2","Hidden Cell","Linear",alpha=1)
my_network.Add_Hidden_Neuron("neuron2_layer2","Hidden Cell","Sigmoid",biais=0.7)

####Set OUTPUT_LAYER####
my_network.Add_Output_Neuron("output1","Output Cell","Sigmoid")

####Set Bridge####
bridge_list = [
    ["speed_neuron","neuron1_layer1"],#Bridge from speed_neuron to neuron1_layer1
    ["pos_neuron","neuron1_layer1"],
    ["pos_neuron","neuron2_layer2"],
    ["neuron1_layer1","neuron1_layer2"],
    ["neuron1_layer1","neuron2_layer2"],
    ["neuron1_layer2","output1"],
    ["neuron2_layer2","output1"]
]
my_network.Add_Bridge(bridge_list)

#####TRAIN NEURONAL NETWORK#####
#return 1 when speed_neuron and pos_neuron is one
inputs = [
    [0,0],
    [0,1],
    [1,1],
    [1,0],
    [1,1]
    ]
expected = [
    [0],
    [0],
    [1],
    [0],
    [1]
    ]
#set learning_rate
learning_rate = 0.01
#set number of epoch
nb_epoch = 2000

#start training
my_network.train(inputs,expected,learning_rate,nb_epoch,display=True)

#predict output value
print(my_network.predict([0,1]))
print(my_network.predict([1,1]))
```


```bash
$ pip install -e .[dev]
```