src/nupic/encoders/logarithm.py
def closenessScores(self, expValues, actValues, fractional=True):
    """
    See the function description in base.py
    """

    # Compute the percent error in log space
    if expValues[0] > 0:
      expValue = math.log10(expValues[0])
    else:
      expValue = self.minScaledValue

    if actValues  [0] > 0:
      actValue = math.log10(actValues[0])
    else:
      actValue = self.minScaledValue

    if fractional:
      err = abs(expValue - actValue)
      pctErr = err / (self.maxScaledValue - self.minScaledValue)
      pctErr = min(1.0, pctErr)
      closeness = 1.0 - pctErr
    else:
      err = abs(expValue - actValue)
      closeness = err

    #print "log::", "expValue:", expValues[0], "actValue:", actValues[0], \
    #      "closeness", closeness
    #import pdb; pdb.set_trace()
    return numpy.array([closeness])
def closenessScores(self, expValues, actValues, fractional=True):
    """
    See the function description in base.py
    """

    # Compute the percent error in log space
    if expValues[0] > 0:
      expValue = math.log10(expValues[0])
    else:
      expValue = self.minScaledValue

    if actValues  [0] > 0:
      actValue = math.log10(actValues[0])
    else:
      actValue = self.minScaledValue

    if fractional:
      err = abs(expValue - actValue)
      pctErr = err / (self.maxScaledValue - self.minScaledValue)
      pctErr = min(1.0, pctErr)
      closeness = 1.0 - pctErr
    else:
      err = abs(expValue - actValue)
      closeness = err

    #print "log::", "expValue:", expValues[0], "actValue:", actValues[0], \
    #      "closeness", closeness
    #import pdb; pdb.set_trace()
    return numpy.array([closeness])
def closenessScores ( self , expValues , actValues , fractional = True ) : # Compute the percent error in log space if expValues [ 0 ] > 0 : expValue = math . log10 ( expValues [ 0 ] ) else : expValue = self . minScaledValue if actValues [ 0 ] > 0 : actValue = math . log10 ( actValues [ 0 ] ) else : actValue = self . minScaledValue if fractional : err = abs ( expValue - actValue ) pctErr = err / ( self . maxScaledValue - self . minScaledValue ) pctErr = min ( 1.0 , pctErr ) closeness = 1.0 - pctErr else : err = abs ( expValue - actValue ) closeness = err #print "log::", "expValue:", expValues[0], "actValue:", actValues[0], \ #      "closeness", closeness #import pdb; pdb.set_trace() return numpy . array ( [ closeness ] )
See the function description in base.py
See the function description in base . py