class_docstr: *** RMSProp *** 
  
  Computes adaptive learning rates for each parameter. Has an EWMA of squared gradients.
code: def __init__(
      self, starting_parameters, starting_variance, learning_rate, ewma
  ):
      self.parameters = starting_parameters
      self.variance = starting_variance
      self.learning_rate = learning_rate
      self.ewma = ewma
      self.epsilon = np.power(10.0, -8)
      self.t = 1
  
file_path: pyflux/pyflux/inference/stoch_optim.py
imports: import numpy
label_desc: Use image node_id 1 to create a new RMSProp object from inherited base classes: object with example: obj = RMSProp(starting_parameters, starting_variance, learning_rate, ewma)