service_batch_generator.py

#-*- coding: utf-8 -*-
import numpy as np

class ServiceBatchGenerator(object):
    """
        Implementation of a random service chain generator

        Attributes:
            state[batchSize, maxServiceLength] -- Generated random service chains
            serviceLength[batchSize] -- Generated array contining services length
    """
    def __init__(self, batchSize, minServiceLength, maxServiceLength, numDescriptors):
        """
        Args:
            batchSize(int) -- Number of service chains to be generated
            minServiceLength(int) -- Minimum service length
            maxServiceLength(int) -- Maximum service length
            numDescriptors(int) -- Number of unique descriptors
        """
        self.batchSize = batchSize
        self.minServiceLength = minServiceLength
        self.maxServiceLength = maxServiceLength
        self.numDescriptors = numDescriptors

        self.serviceLength = np.zeros(self.batchSize,  dtype='int32')
        self.state = np.zeros((self.batchSize, self.maxServiceLength),  dtype='int32')

    def getNewState(self):
        """ Generate new batch of service chain """

        # Clean attributes
        self.serviceLength = np.zeros(self.batchSize,  dtype='int32')
        self.state = np.zeros((self.batchSize, self.maxServiceLength),  dtype='int32')

        # Compute random services
        for batch in range(self.batchSize):
            self.serviceLength[batch] = np.random.randint(self.minServiceLength, self.maxServiceLength+1, dtype='int32')
            for i in range(self.serviceLength[batch]):
                pktID = np.random.randint(0, self.numDescriptors,  dtype='int32')
                self.state[batch][i] = pktID


if __name__ == "__main__":

    # Define generator
    batch_size = 5
    minServiceLength = 2
    maxServiceLength = 6
    numDescriptors = 8

    env = ServiceBatchGenerator(batch_size, minServiceLength, maxServiceLength, numDescriptors)
    env.getNewState()