#! /usr/bin/python

import numpy
import pickle

import sys
import os
sys.path.append(os.path.join(os.path.abspath(os.path.dirname(__file__)),os.pardir))
import helperFunctions

import activeLearningWlinGPprototype

class Classifier(activeLearningWlinGPprototype.ClassifierPrototype):


    def __init__(self, sigmaN = 0.00178, loNoise = True, configFile=None):

        activeLearningWlinGPprototype.ClassifierPrototype.__init__(self, sigmaN=sigmaN, configFile=configFile)
        self.loNoise = helperFunctions.getConfig(configFile, 'activeLearning', 'loNoise', loNoise, 'bool', True)


    # x.shape = (number of samples, feat dim)
    def calcAlScores(self, x):

        loNoise = (self.yUni == -1).any() and self.loNoise
        sortedScores = numpy.sort(self.infer(x, loNoise), axis=1)
        alScores = numpy.abs(sortedScores[:,-1] - sortedScores[:,-2])

        # since we actually want to select min(scores) instead of max(scores), we have to turn the scores around
        return numpy.add(numpy.subtract(alScores, numpy.max(alScores))*(-1.0), numpy.finfo(numpy.float32).eps)