gistfile1.txt

class TimeSeriesData():
    def __init__(self,num_points,xmin,xmax):
        self.xmin=xmin
        self.xmax=xmax
        self.num_points=num_points
        self.resolution=(xmax-xmin)/num_points
        self.x_data=np.linspace(xmin,xmax,num_points)
        self.y_true=np.sin(self.x_data)
        
    def ret_true(self,x_series):
        return np.sin(x_series)
    
    def next_batch(self,batch_size,steps,return_batch_ts=False):
        #Random starting point for batch
        random_start=np.random.rand(batch_size,1)
        
        #Put the random point on the time series
        ts_start=random_start*(self.xmax-self.xmin-(steps*self.resolution)) #Trick to convert any number into a point in the time series
        
        
        batch_ts=ts_start+np.arange(0.0,steps+1) * self.resolution #steps +1 because we are predicting just one tme step ahead
        
        y_batch=np.sin(batch_ts)
        
        #Formatting for RNN
        if return_batch_ts:
            return y_batch[:,:-1].reshape(-1,steps,1) , y_batch[:,1:].reshape(-1,steps,1), batch_ts
        else:
            return y_batch[:,:-1].reshape(-1,steps,1) , y_batch[:,1:].reshape(-1,steps,1) #Returning the sequence shifted over one time step