			'''
			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)

hdtorch/util.py
a60320933691master

util.py
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