67 lines
1.9 KiB
Python
67 lines
1.9 KiB
Python
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from numpy import array_split,arange
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def cv(n, k, randomise=False, sequential=False):
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"""
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Generates k (training, validation) index pairs.
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Each pair is a partition of arange(n), where validation is an iterable
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of length ~n/k.
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If randomise is true, a copy of index is shuffled before partitioning,
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otherwise its order is preserved in training and validation.
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Randomise overrides the sequential argument. If randomise is true,
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sequential is False
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If sequential is true the index is partioned in continous blocks,
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otherwise interleaved ordering is used.
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"""
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index = xrange(N)
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if randomise:
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from random import shuffle
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index = list(index)
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shuffle(index)
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sequential = False
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if sequential:
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for validation in array_split(index, K):
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training = [i for i in index if i not in validation]
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yield training, validation
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else:
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for k in xrange(K):
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training = [i for i in index if i % K != k]
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validation = [i for i in index if i % K == k]
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yield training, validation
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def shuffle_diag(shape, K, randomise=False, sequential=False):
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"""
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Generates k (training, validation) index pairs.
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"""
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m, n = shape
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if K>m or K>n:
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msg = "You may not use more subsets than max(n_rows, n_cols)"
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raise ValueError, msg
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mon = max(m, n)
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#index = xrange(n)
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index = [i for i in range(m*n) if i % m == 0]
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print index
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if randomise:
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from random import shuffle
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index = list(index)
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shuffle(index)
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sequential = False
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if sequential:
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start_inds = array_split(index, K)
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else:
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for k in xrange(K):
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start_inds = [index[i] for i in xrange(n) if i % K == k]
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print start_inds
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for start in start_inds:
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ind = arange(start, n*m, mon+1)
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yield ind
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