semvec/pypi-packages huggingface dataset:
StockTrainer 0.0.7
Stock environment for training machine learning agents
Keywords: USEFULL,STOCKS,MACHINE LEARNING,AI,ARTIFICAL INTELLIGENCE

# StockTrainer: Stock Environment for Human
## StockTrainer: Stocks Made Easy

StockTrainer is high level API data generator for training python machine learning models on stock/cryptocurrency data and is capable of running with Keras, Tensorflow, sklearn, and many other machine learning APIs

Capabilities:

- Predict day to day stock prices
- Use multiple days to predict next stock price
- Predict succeeding stock prices over multiple days
- Train a reinforcement learning agent to simulate stock trades


Documentation available soon ;)

StockTrainer is compatible with: Python 3.6+

## Getting Started
The core of algorithm is the model, here is a simple LSTM model to based on 5 days of stock data to predict the next day

	import keras
	import numpy as np
	from keras.models import Sequential
	from keras.layers import Dropout ,BatchNormalization, LSTM, Dense 

  
	model = Sequential()
	#input shape 5 days of data 
	#each day has 6 data points (open, close, high , low volums, adj CLose)
	model.add(BatchNormalization(input_shape=(5, 6)))#batchnorm bc high values
    model.add(LSTM(512, return_sequences=True, activation='relu'))
    model.add(Dropout(0.2))
    model.add(Dense(512, activation='relu'))
	model.add(Dense(128, activation='relu'))	
    model.add(Dense(1, activation='relu'))

    model.compile(loss='mse', optimizer='adam')

Next import StockTrainer and create your environment
 
    from StockTrainer import Env
    environment = Env("Standard", "AAPL")

Time to collect your data to train!!!

	test_percent =.30
	shuffle =True
	start_date ='2003-01-01'
	end_date='now'
	agent_memory = 5
	seed = 42
	trainx,testx,trainy, testy = environment.train_test(
      test_percent= test_percent, shuffle = shuffle, start_date=start_date, 
      end_date=end_date, agent_memory=agent_memory, seed=seed)

Futher information on parameters in Documentation 


That's it now train and test your model
	
	#fit model
    model.fit(trainx, trainy, epochs=10, batch_size=128, verbose=2)
    model.save('model.h5')

    #evaluate model
    model.evaluate(testx,testy )
    #use model to predict
    model.predict(testx)

More examples on samples folder in github

## Installation

Using pip
	
	pip install StockTrainer

or download directly: [https://pypi.org/project/StockTrainer/](https://pypi.org/project/StockTrainer/ "install")
scipion-em-jjsoft 3.0.2
jjsoft ready to use in scipion.
Keywords: scipion cryoem imageprocessing scipion-3.0

=============
jjsoft plugin
=============

This plugin allows to use jjsoft within the Scipion framework. The binaries for the plugin must be obtained from 
https://sites.google.com/site/3demimageprocessing/

=====
Setup
=====

- **Install this plugin:**

.. code-block::

    scipion installp -p scipion-em-jjsoft

OR

  - through the plugin manager GUI by launching Scipion and following **Configuration** >> **Plugins**

Alternatively, in devel mode:

.. code-block::

    scipion installp -p local/path/to/scipion-em-jjsoft --devel
easy_import 0.4
easy_import.py

# easy_import
Python imports made easy! Import any module or package from anywhere - as though it was in your current working directory.

## github
[easy_import on github](https://github.com/NerdyTurkey/easy_import)

## Installation
Requires python 3.

```bash
pip install easy_import
```

## Usage
```python
"""
data_tools.py is a (fictious) module located....somwehere on your computer. 
But not in the current working directory.

Directly trying to import it will raise an ImportError since its not visible.

easy_import to the rescue!

It will search the entire storage (or a given directory) for the module and make it visible
for importing as though it was in the current working directory.
"""

from easy_import import EasyImport
with EasyImport("data_tools.py"):
    import data_tools

# etc

"""
Now we can use a function or class in data_tools.py
e.g. :
"""
smoothed_data = data_tools.smooth(data)

"""
Since searching the entire storage is s l o w, the locations of searched items
are cached to json files so the next time you run it, it will be super fast!

Check out example.py in the repository for a runnable example and more details.
"""

```

## Contributing
Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.


## License
[MIT](https://choosealicense.com/licenses/mit/)
coleta-dadosjusbr 0.4
Contém esquema de dados e funções para os coletores do DadosjusBr

# proto
Esquema de dados em Protobuf.

# Gerar cÃ³digo go e python

go install google.golang.org/protobuf/cmd/protoc-gen-go

// Gerar stub na raiz.
protoc --go_out=./ --go_opt=paths=source_relative coleta.proto

protoc --go_out=./ pacote.proto

protoc --go_out=pipeline/ --go_opt=paths=source_relative --proto_path=coleta --proto_path=pipeline pipeline/pipeline.proto