parameters: (data, targetClass = None, Y = None)
return_statement: return data.std(I)
docstring: returns a vector where component i is the standard deviation of feature i
      INPUT:
      data - a dataset
      targetClass - class for which to take the mean (optional)
      Y - alternative label vector (optional)
docstring_summary: returns a vector where component i is the standard deviation of feature i
      INPUT:
      data - a dataset
      targetClass - class for which to take the mean (optional)
      Y - alternative label vector (optional)
docstring_tokens: returns a vector where component i is the standard deviation of feature i INPUT : data - a dataset targetClass - class for which to take the mean ( optional ) Y - alternative label vector ( optional )
function: def featureStd(data, targetClass = None, Y = None) :
      '''returns a vector where component i is the standard deviation of feature i
      INPUT:
      data - a dataset
      targetClass - class for which to take the mean (optional)
      Y - alternative label vector (optional)
      '''
      
      if targetClass == None :
          I = range(len(data))
      elif Y == None :
          I = numpy.nonzero(numpy.equal(data.labels.Y, targetClass))[0]
      else :
          I = numpy.nonzero(numpy.equal(Y, targetClass))[0]
  
      if len(I) == 0 :
          return numpy.zeros(data.numFeatures, numpy.float_)
  
      return data.std(I)
function_tokens: def featureStd ( data , targetClass = None , Y = None ) : if targetClass == None : I = range ( len ( data ) ) elif Y == None : I = numpy . nonzero ( numpy . equal ( data . labels . Y , targetClass ) ) [ 0 ] else : I = numpy . nonzero ( numpy . equal ( Y , targetClass ) ) [ 0 ] if len ( I ) == 0 : return numpy . zeros ( data . numFeatures , numpy . float_ ) return data . std ( I )