Python implementation for hopfield neural network

This is the second stage that dictates the selection of relevant feature vectors to train the predictive classifier. The relevant features are obtained from stock data and sentimental data. The objective of this stage is to find out the frequent trend patterns based on close price of a day with increased recall capacity, which is helpful for the time-series data. Here, a novel Hopfield Neural Networks using technical indicator, Exponentially Weighted Moving Average (EWMA) is proposed.