Python list notation, Numpy array notation: predictions[predictions < 1e-10] = 1e-10

Question

I am trying to find out operation applied on list. I have list/array name predictions and and executing following set of instruction.

predictions[predictions < 1e-10] = 1e-10

This code snippet is from a Udacity Machine Learning assignment that uses Numpy.

It was used in the following manner:

def logprob(predictions, labels):
    """Log-probability of the true labels in a predicted batch."""
    predictions[predictions < 1e-10] = 1e-10
    return np.sum(np.multiply(labels, -np.log(predictions))) / labels.shape[0]

As pointed out by @MosesKoledoye and various others, it is actually a Numpy array. (Numpy is a Python library)

What does this line do?

Answer 1

As pointed out by @MosesKoledoye, predictions is most likely a numpy array.

A boolean array would then be generated using predictions < 1e-10. At all indices where the boolean array set by the condition is True, the value will be changed to 1e-10, ie. 10^-10.

Example:

  >>> a = np.array([1,2,3,4,5]) #define array
  >>> a < 3 #define boolean array through condition
  array([ True,  True, False, False, False], dtype=bool)

  >>> a[a<3]  #select elements using boolean array
  array([1, 2])

  >>> a[a<3] = -1  #change value of elements which fit condition
  >>> a 
  array([-1, -1,  3,  4,  5])

The reason this might be done in the code could be to prevent division by zero or to prevent negative numbers messing up things by instead inserting a very small number.

Answer 2

All elements of the array, for which the condition (element < 1e-10) is true, are set to 1e-10. Practically you are setting a minimum value.