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I want to create a function that randomly selects items from a training set using the given bin probabilities. I divide the defined indices into 11 boxes and assign a specific probability to each of them.
bin_probs = [0.5, 0.3, 0.15, 0.04, 0.0025, 0.0025, 0.001, 0.001, 0.001, 0.001, 0.001]X_train = list(range(2000000))train_probs = bin_probs * int(len(X_train) / len(bin_probs)) # extend probabilities across bin elementstrain_probs.extend([0.001]*(len(X_train) - len(train_probs))) # a small fix to match number of elementstrain_probs = train_probs/np.sum(train_probs) # normalizeindices = np.random.choice(range(len(X_train)), replace=False, size=50000, p=train_probs)out_images = X_train[indices.astype(int)] # this is where I get the error
The following error appears:
TypeError: only integer scalar arrays can be converted to a scalar index with 1D NumPy indices array
This is strange to me because I've already examined the array of indices I've prepared. It's one-dimensional, integer, and scalar.
What am I overlooking?
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