util.py
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	'''
	Created on Apr 8, 2022

	@author: wsimon
	'''

	import torch

	def flipVectorValues(vector, vecType = 'bin'):
	'''turns 0 into 1 and 1 into 0'''
	if (vecType=='bin'):
	vectorFliped=(vector*(-1)+1)
	else:
	vectorFliped = vector * (-1)
	return(vectorFliped)

	def xor( vec_a, vec_b):
	''' xor between vec_a and vec_b'''
	vec_c = (torch.add(vec_a, vec_b) == 1).short() # xor
	return vec_c

	def xor_bipolar( vec_a, vec_b):
	''' xor between vec_a and vec_b'''
	#vec_c = (torch.sub(vec_a, vec_b) != 0).short() # 1
	vec_c = torch.sub(vec_a, vec_b)
	vec_c [vec_c!=0]= 1
	vec_c[vec_c == 0] = -1
	return vec_c

	def rotateVec(mat,shifts):
	n_rows, n_cols = mat.shape
	arange1 = torch.arange(n_cols,device=mat.device).view(( 1,n_cols)).repeat((n_rows,1))
	arange2 = (arange1.unsqueeze(0) - shifts.unsqueeze(2)) % n_cols
	return torch.gather(mat.unsqueeze(0).expand(shifts.shape[0],-1,-1),2,arange2)

	def normalizeAndDiscretizeData(data, min_val, max_val, numSegLevels):
	''' normalize train and test data using normalization values from train set
	also discretize values to specific number of levels given by numSegLevels'''

	#normalize and discretize train adn test data
	dataNorm = (data - min_val) / (max_val - min_val)
	dataDisc = torch.floor((numSegLevels - 1) * dataNorm)
	#check for outliers
	dataDisc[dataDisc >= numSegLevels] = numSegLevels - 1
	dataDisc[dataDisc < 0] = 0
	dataDisc[torch.isnan(dataDisc)] = 0
	#discr values to int
	return dataDisc.to(int)