data.py

import numpy as np

class CyberData(object):

    def __init__(self, config):

        self.num_samples = config.num_samples
        self.num_features = config.num_features
        self.num_nodes = config.num_nodes
        mean = 100
        std = 20

        # create array for data
        self.X = np.zeros((self.num_samples, self.num_nodes, self.num_features, 1), dtype='float32')

        # Data for Auto_encoder
        self.X_Auto = np.zeros((self.num_samples, self.num_nodes, self.num_features, 1), dtype='float32')

        # generate user data from Gaussian distribution
        for n in range(self.num_samples):
            for i in range(self.num_nodes):
                self.X[n, i, :, 0] = np.random.normal(loc=mean, scale=std, size=self.num_features)

        self.y = np.zeros((self.num_samples, 1)).astype('float32')

    def next_batch(self, batch_size):
        """ get next batch of samples"""
        for i in range(0, self.num_samples, batch_size):
            yield self.X[i: i + batch_size, :, :], self.y[i: i + batch_size], self.X_Auto[i: i + batch_size, :, :]

    def randomize(self):
        """ Randomizes the order of data samples and their corresponding labels"""
        permutation = np.random.permutation(self.X.shape[0])
        self.X = self.X[permutation, :, :, :]
        self.X_Auto = self.X_Auto[permutation, :, :, :]
        self.y = self.y[permutation, :]


if __name__ == '__main__':
    data = CyberData()