semvec/pypi-packages huggingface dataset:
rpa-openapi 1.0.1
This is the SDK for AlibabaCloudRPA OpenAPI.
Requires Python: >=3.5

# 阿里云RPA OpenAPI SDK



## 查询客户端信息
```python
from rpa_openapi.RPAClient import RPAClient
from rpa_openapi.V20200430.QueryClientViewsRequest import QueryClientViewsRequest
from rpa_openapi.V20200430.ResponseModels import QueryClientViewsResponse

AccessKeyId = "你的AccessKeyId"
AccessSecret = "你的AccessSecret"

rpa_client = RPAClient(AccessKeyId, AccessSecret)
query_client_views_request = QueryClientViewsRequest()
query_client_views_request.current_page = 1
query_client_views_request.page_size = 1
query_client_views_response = rpa_client.do_action(query_client_views_request)
print(query_client_views_response.json())
query_client_views_response_data = QueryClientViewsResponse(**query_client_views_response.json())
# requestId
print("requestId: ", query_client_views_response_data.requestId)
# success
print("success: ", query_client_views_response_data.success)
# code
print("code: ", query_client_views_response_data.code)
# msg
print("msg: ", query_client_views_response_data.msg)
# msgCode
print("msgCode: ", query_client_views_response_data.msgCode)
# uuid
print("uuid: ", query_client_views_response_data.data[0].uuid)
# groupId
print("groupId: ", query_client_views_response_data.data[0].groupId)
# clientType
print("clientType: ", query_client_views_response_data.data[0].clientType)
# name
print("name: ", query_client_views_response_data.data[0].name)
# ip
print("ip: ", query_client_views_response_data.data[0].ip)
# macAddress
print("macAddress: ", query_client_views_response_data.data[0].macAddress)
# status
print("status: ", query_client_views_response_data.data[0].status)
# dispatchMode
print("dispatchMode: ", query_client_views_response_data.data[0].dispatchMode)
# remark
print("remark: ", query_client_views_response_data.data[0].remark)
# connectTime
print("connectTime: ", query_client_views_response_data.data[0].connectTime)
# disconnectTime
print("disconnectTime: ", query_client_views_response_data.data[0].disconnectTime)
# userId
print("userId: ", query_client_views_response_data.data[0].userId)
# userName
print("userName: ", query_client_views_response_data.data[0].userName)
# appId
print("appId: ", query_client_views_response_data.data[0].appId)
# appName
print("appName: ", query_client_views_response_data.data[0].appName)
# taskId
print("taskId: ", query_client_views_response_data.data[0].taskId)
# connectStatus
print("connectStatus: ", query_client_views_response_data.data[0].connectStatus)
# currentPage
print("currentPage: ", query_client_views_response_data.pager.currentPage)
# totalPage
print("totalPage: ", query_client_views_response_data.pager.currentPage)
# pageSize
print("pageSize: ", query_client_views_response_data.pager.pageSize)
# total
print("total: ", query_client_views_response_data.pager.total)
# limit
print("limit: ", query_client_views_response_data.pager.limit)
# offset
print("offset: ", query_client_views_response_data.pager.offset)
# instanceId
print("instanceId: ", query_client_views_response_data.instanceId)
```
helloworld3663 0.0.1
Say hello!

# Hello World

This is an example project demonstrating how to publish a python module to PyPI


## Installation

Run the following to install:

'''python
pip install helloworld3663
'''


## Usage

'''python
from helloworld3663 import say_hello

# Generate "Hello, World!"
say_hello()

# Generate "Hello, Everyone"
say_hello("Everyone")
'''

# Developing Hello World

To install helloworld3663, along with the tools you need to develop and run tests, run the following in your virtualenv:

'''bash
$ pip install -e .[dev]
'''
sparqly 0.1.1
SPARQL semantic translator and ORM for Python.
Requires Python: >=3.7

# sparqly
SPARQL semantic translator and object-mapper for Python (wip).

## Example
We define a query object as an instance of `sparqly.Item`, with the predicates
listed as attributes following the idiom of `pretty_name = "actual name"`. For
example, to query Pre-Raphaelite aritsts on WikiData, the schema we define could
look like

```py
from sparqly import Item

class Artist(Item):
  movement      = "wdt:P135"
  instance_of   = "wdt:P31"
```

We can then generate the query text for a selection with
```py
from sparqly import query

q = query()
q.select(Artist).where(
  movement = "wd:Q184814", # pre-raphaelite
  instance_of = "wd:Q5"    # human
)

print(q.all())
```
Which will print to the console:
```
SELECT ?Artist WHERE {
	?Artist wdt:P135 wd:Q184814 ;
	wdt:P31 wd:Q5 .
}
```

## Changelog
Version 0.1.0:
- Object-Relational-Mapping (ORM)
- `SELECT`
- `WHERE`

## Planned Features
- Ability to attach service
- `CONSTRUCT`/`ASK`/`DESCRIBE`
- Filters like `ORDER`, `LIMIT`, `GROUP`
- More semantic support
- Additional methods for constructing queries depending on circumstance and complexity
machinepy 2.0.1
Machine learning library
Requires Python: >=3.6

![machinePy_logo](https://machinepy.xyz/wp-content/uploads/2020/09/big_logo4.png)
# machinePy 2.0

Welcome to machinePy, the worlds most user-friendly machine learning neural network library!<br>
To learn how to use it, visit **[machinePy](https://machinepy.xyz)**, where you'll find everything you need to start including detailed examples.<br><br>
#### Quick rundown<br>
```
foo = initnet(network_structure)
```
Use this function to declare your neural network structure and assign random weights and biases. It takes an array of integers. The first element determines the amount of inputs and the last element determines the amount of outputs. Every element in between represents a hidden layer with the integer value as the amount of neurons in that particular hidden layer.  
```
foo = forward(inputs, weights)
```
This is the forward propagation function. It takes two arguments, an array of inputs, and an array of weights, which is the output of `initnet`.
```
foo = train(inputs, weights, targets, learning_rate)
```
This is the implementation of the infamous backpropagation. It takes four arguments, an array of inputs, an array of weights, again in the form of `initnet`, an array of target outputs, and an optional learning rate value, otherwise assigned 0.1.<br><br>
#### Example
![machinepy_example](https://machinepy.xyz/wp-content/uploads/2020/09/XNOR-Gate-2.png)
```
import machinepy as mp

inputs = [[0,0],[0,1],[1,0],[1,1]]
outputs = [[1],[0],[0],[1]]
weights = mp.initnet([2,3,1])

epochs = 10000

i = 0
j = 0 
while(i < len(inputs)*epochs):
	weights = mp.train(inputs[j],weights,outputs[j],0.1)
		if(j==3):
			j = 0
		else:
			j+=1
		i+=1

i = 0
while i < len(inputs):
	foo = mp.forward(inputs[i],weights)
	print(foo)
	i+=1
```