I am trying to get two samples with unique elements in each sample. That is, the strings on the "first" vector cannot be in the "second" vector. Unfortunately,
I have a large dataset dataset with 20 columns, and the columns "ltv", that contains the classification high, low and medium and "ticket" that contains values.
I am using a bidirectional GRU autoencoder to obtain a single vector that represents in a compressed form the input time series, in order to cluster them. These
I have a large list of elements (in this example I'll assume it's filled with numbers). For example: l = [1,2,3,4,5,6,7,8,9,10] Now I want to take 2 samples fr
I would like to sample a dataframe base in python. This sample has to be stratified by specific variables. I tried sklearn.cross_validation but the problem is t