Howto 06 - (Data) Buffer

Ver. 1.0.0 (2021-10-26)

This module demonstrates how to use classes Buffer and BufferElement.

Prerequisites

Please install the following packages to run this examples properly:

• MLPro

• NumPy

• Matplotlib

Example Code

## -------------------------------------------------------------------------------------------------
## -- Project : MLPro - A Synoptic Framework for Standardized Machine Learning Tasks
## -- Package : mlpro
## -- Module  : Howto 06 - (Data) Buffer
## -------------------------------------------------------------------------------------------------
## -- History :
## -- yyyy-mm-dd  Ver.      Auth.    Description
## -- 2021-10-26  1.0.0     SY       Creation/Release
## -------------------------------------------------------------------------------------------------

"""
Ver. 1.0.0 (2021-10-26)

This module demonstrates how to use classes Buffer and BufferElement.
"""


from mlpro.bf.data import *
import random

# 1. Instantiate a buffer with random sampling
buffer              = BufferRnd(p_size=100)

# 2.1. Generate random values and store them to the Buffer
num_cycles          = 150

for i in range(num_cycles):
    
    #2.2. Store the values and their names in a BufferElement
    buffer_element  =  BufferElement({"reward":random.uniform(-10,10),
                                      "actions":[random.uniform(0,1),random.uniform(0,1)]})
    
    #2.3. Example: add value element in the developed BufferElement
    buffer_element.add_value_element(dict(accuracy=random.uniform(0,1)))
    
    #2.4. Add the BufferElement into the Buffer
    buffer.add_element(buffer_element)
    print('Cycle : %.i'%int(i+1))
    
    #2.5. Checking whether buffer is full or not
    if not buffer.is_full():
        print('Buffer is not full yet, keep collecting data!\n')
    else:
        print('Buffer is full, ready to use!')
        
        #2.6. Get all data from the Buffer
        all_data = buffer.get_all()
        _actions            = all_data["actions"]
        _reward             = all_data["reward"]
        _accuracy           = all_data["accuracy"]
        
        #2.7. Get sample data from the Buffer, you define your sampling strategy by
        # redifining method _gen_sample_ind(self, p_num:int)
        sample_data = buffer.get_sample(p_num=10)
        print('Get sample!\n')
        _actions_sample    = sample_data["actions"]
        _reward_sample     = sample_data["reward"]
        _accuracy_sample   = sample_data["accuracy"]

# 3. To clear your buffer        
if buffer is not None:
    buffer.clear()
    print('Buffer is cleared!')

Results

Descriptions, plots, images, screenshots of expected results.