#! /usr/bin/python

import numpy

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

class ClassifierPrototype:

  def __init__(self, sigmaN = 0.00178, configFile=None, probSampleRuns=1000):

    self.sigmaN = helperFunctions.getConfig(configFile, 'activeLearning', 'sigmaN', sigmaN, 'float', True)
    self.probSampleRuns = helperFunctions.getConfig(configFile, 'activeLearning', 'probSampleRuns', probSampleRuns, 'float', True)

    self.K = []
    self.alpha = []
    self.X = []
    self.y = []
    self.yBin = []
    self.yUni = []


  def checkModel(self):

    if not numpy.all(numpy.isfinite(self.K)):
      raise Exception('not numpy.all(numpy.isfinite(self.K))')

    if not numpy.all(numpy.isfinite(self.alpha)):
      raise Exception('not numpy.all(numpy.isfinite(self.alpha))')

    if not numpy.all(numpy.isfinite(self.X)):
      raise Exception('not numpy.all(numpy.isfinite(self.X))')

    if not numpy.all(numpy.isfinite(self.y)):
      raise Exception('not numpy.all(numpy.isfinite(self.y))')


  def train2(self, X, y, sigmaN=None):

    self.train(X[0,:],y[0,:],sigmaN)
    for idx in range(1,X.shape[0]):
      self.update(X[idx,:],y[idx,:])


  # X.shape = (number of samples, feat dim), y.shape = (number of samples, 1)
  def train(self, X, y, sigmaN=None):

    if sigmaN is not None:
        self.sigmaN = sigmaN

    self.X = X
    self.y = y

    self.yUni = numpy.asmatrix(numpy.unique(numpy.asarray(self.y)))

    tmpVec = numpy.asmatrix(numpy.empty([self.y.shape[0],1]))
    self.yBin = numpy.asmatrix(numpy.empty([self.y.shape[0],self.yUni.shape[1]]))
    for cls in range(self.yUni.shape[1]):
      mask = (self.y == self.yUni[0,cls])
      tmpVec[mask == True] = 1
      tmpVec[mask == False] = -1
      self.yBin[:,cls] = tmpVec

    self.K = numpy.dot(X,X.T)
    self.alpha = numpy.linalg.solve(numpy.add(self.K, numpy.identity(self.X.shape[0], dtype=numpy.float)*self.sigmaN), self.yBin)

    self.checkModel()


  # x.shape = (1, feat dim), y.shape = (1, 1)
  def update(self, x, y):

    # update for known classes
    binY = numpy.asmatrix(numpy.ones((1,self.alpha.shape[1])))*(-1)

    if self.alpha.shape[1] > 1:
      binY[0,numpy.asarray(numpy.squeeze(numpy.asarray(y==self.yUni)))] = 1
    elif y==self.yUni:
        binY[0,0] = 1

    # get new kernel columns
    k = numpy.dot(self.X,x.T)
    selfK = numpy.sum(numpy.multiply(x,x), axis=1)

    # get score of new sample
    infY = self.infer(x, k=k)

    # update alpha
    term1 = 1.0 / (self.sigmaN + self.calcSigmaF(x, k, selfK).item(0))

    term2 = numpy.asmatrix(numpy.ones((self.X.shape[0] + 1,x.shape[0])), dtype=numpy.float)*(-1.0)
    term2[0:self.X.shape[0],:] = numpy.linalg.solve(numpy.add(self.K, numpy.identity(self.X.shape[0], dtype=numpy.float)*self.sigmaN), k)

    term3 = infY - binY

    self.alpha = numpy.add(numpy.append(self.alpha, numpy.zeros((1,self.alpha.shape[1])), axis=0), numpy.dot(numpy.dot(term1,term2),term3))

    # update K
    self.K = numpy.append(numpy.append(self.K, k, axis=1), numpy.append(k.T, selfK, axis=1), axis=0)

    # update samples
    self.X = numpy.append(self.X, x, axis=0)
    self.y = numpy.append(self.y, y, axis=0)

    # update binary labels for knwon class
    if (self.yUni == y).any():
      mask = (y == self.yUni)
      tmpVec = numpy.empty([self.yBin.shape[1], 1])
      tmpVec[mask.T == True] = 1
      tmpVec[mask.T == False] = -1
      self.yBin = numpy.append(self.yBin, tmpVec.T, axis=0)

    # create labels and alpha for new class
    else:

      # create bigger matrices
      tmpyBin = numpy.asmatrix(numpy.empty([self.yBin.shape[0] + 1, self.yBin.shape[1] + 1]))
      tmpAlpha = numpy.asmatrix(numpy.empty([self.alpha.shape[0], self.alpha.shape[1] + 1]))

      # index of new class
      tmpIdx = -1

      # check all knwon classes
      for cls in range(self.yUni.shape[1]):

        # just copy all classes with lower label
        if y > self.yUni[0,cls]:
          tmpyBin[0:-1,cls] = self.yBin[:,cls]
          tmpyBin[tmpyBin.shape[0] - 1,cls] = -1
          tmpAlpha[:,cls] = self.alpha[:,cls]
          tmpIdx = cls

        # copy classes with higher label to shiftet position
        else: # y < self.yUni[cls]
          tmpyBin[0:-1,cls + 1] = self.yBin[:,cls]
          tmpyBin[tmpyBin.shape[0] - 1,cls + 1] = -1
          tmpAlpha[:,cls + 1] = self.alpha[:,cls]

      # add new binary label vector for new class
      tmpyBin[0:-1,tmpIdx + 1] = -1
      tmpyBin[tmpyBin.shape[0] - 1,tmpIdx + 1] = 1

      # set new binary matrix and append new label
      self.yBin = tmpyBin
      self.yUni = numpy.sort(numpy.append(self.yUni, y, axis=1))

      # train new alpha for new class
      tmpAlpha[:,tmpIdx + 1] = numpy.linalg.solve(numpy.add(self.K, numpy.identity(self.X.shape[0], dtype=numpy.float)*self.sigmaN), self.yBin[:,tmpIdx + 1])
      self.alpha = tmpAlpha

    self.checkModel()


  # X.shape = (number of samples, feat dim), loNoise = {0,1}
  def infer(self, x, loNoise=False, k=None):

    if k is None:
      k = numpy.dot(self.X,x.T)

    loNoise = loNoise and (self.yUni == -1).any()

    pred = numpy.asmatrix(numpy.dot(k.T,self.alpha[:,int(loNoise):]))

    if not numpy.all(numpy.isfinite(pred)):
      raise Exception('not numpy.all(numpy.isfinite(pred))')

    return pred


  # X.shape = (number of samples, feat dim)
  def test(self, x, loNoise=False):

    loNoise = loNoise and (self.yUni == -1).any()

    return self.yUni[0,numpy.argmax(self.infer(x, loNoise), axis=1) + int(loNoise)]


  # X.shape = (number of samples, feat dim)
  def calcSigmaF(self, x, k=None, selfK=None):

    if k is None:
        k = numpy.dot(self.X,x.T)

    if selfK is None:
        selfK = numpy.sum(numpy.multiply(x,x), axis=1)

    sigmaF = numpy.subtract(selfK, numpy.sum(numpy.multiply(numpy.linalg.solve(numpy.add(self.K, numpy.identity(self.X.shape[0], dtype=numpy.float)*self.sigmaN), k),k).T, axis=1))

    if not numpy.all(numpy.isfinite(sigmaF)):
      raise Exception('not numpy.all(numpy.isfinite(sigmaF))')

    return sigmaF


  # X.shape = (number of samples, feat dim)
  def calcProbs(self, x, mu=None, sigmaF=None, nmb=None):

    # set number of sampling iterations
    if nmb is None:
        nmb = self.probSampleRuns

    # get mu and sigma
    if mu is None:
      mu = self.infer(x)
    if sigmaF is None:
      sigmaF = self.calcSigmaF(x)

    # prepare
    probs = numpy.asmatrix(numpy.zeros(mu.shape))

    for idx in range(nmb):

      draws = numpy.asmatrix(numpy.random.randn(mu.shape[0],mu.shape[1]))
      draws = numpy.add(numpy.multiply(draws, numpy.repeat(sigmaF, draws.shape[1], axis=1)), mu)

      maxIdx = numpy.argmax(draws, axis=1)

      idxs = (range(len(maxIdx)),numpy.squeeze(maxIdx))
      probs[idxs] = probs[idxs] + 1

    # convert absolute to relative amount
    return probs/float(nmb)


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

    return None


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

    alScores = self.calcAlScores(x)

    if not numpy.all(numpy.isfinite(alScores)):
      raise Exception('not numpy.all(numpy.isfinite(alScores))')

    if alScores.shape[0] != x.shape[0] or alScores.shape[1] != 1:
      raise Exception('alScores.shape[0] != x.shape[0] or alScores.shape[1] != 1')

    return alScores


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

    return numpy.argmax(self.getAlScores(x), axis=0).item(0)