"""So below the AdaGrad class that shows how a way to implement it in python. The class get one layers node number and its input dimensions. A function get_steps takes in current gradients for weights and biases and returns the step that we need to update those weights and biases"""

import numpy as np

class AdaGrad():

    def __init__(self, input_dims, nodes):
  
      self.learning_rate = 0.01

      # G matrix for parameters
      self.G_weights = np.zeros((input_dims, nodes))

      # G matrix for biases
      self.G_biases = np.zeros(nodes)

    def get_steps(self, grad_weights, grad_biases):

      eps = 1e-8
      
      # updating G matrixes
      self.G_weights += np.multiply(grad_weights, grad_weights)
      self.G_biases += np.multiply(grad_biases, grad_biases)

      weights_step = np.multiply((self.learning_rate / np.sqrt(self.G_weights + eps)), grad_weights)
      biases_step = np.multiply((self.learning_rate / np.sqrt(self.G_biases + eps)), grad_biases)

      return weights_step, biases_step