from .LossFunction import LossFunction

import numpy

class LogitLoss(LossFunction):
  """ The class to implement the logit loss function for the boosting framework."""

  def loss(self, targets, scores):
    """The function computes the logit loss values using prediction scores and targets.

    Keyword parameters:

      targets (float <#samples, #outputs>): The target values that should be reached.

      scores (float <#samples, #outputs>): The scores provided by the classifier.

    Returns
      (float <#samples, #outputs>): The loss values for the samples, which is always >= 0
    """
    e = numpy.exp(-(targets * scores))
    return numpy.log(1. + e)


  def loss_gradient(self, targets, scores):
    """The function computes the gradient of the logit loss function using prediction scores and targets.

    Keyword parameters:

      targets (float <#samples, #outputs>): The target values that should be reached.

      scores (float <#samples, #outputs>): The scores provided by the classifier.

    Returns
      loss (float <#samples, #outputs>): The gradient of the loss based on the given scores and targets.
    """
    e = numpy.exp(-(targets * scores))
    denom = 1./(1. + e)
    return -targets * e * denom