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48 lines
No EOL
1.4 KiB
Python
48 lines
No EOL
1.4 KiB
Python
'''
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Created on 9 Oct 2014
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@author: maxz
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'''
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class StochasticStorage(object):
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'''
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This is a container for holding the stochastic parameters,
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such as subset indices or step length and so on.
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'''
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def __init__(self, model):
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"""
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Initialize this stochastic container using the given model
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"""
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def do_stochastics(self):
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"""
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Update the internal state to the next batch of the stochastic
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descent algorithm.
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"""
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pass
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class SparseGPMissing(StochasticStorage):
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def __init__(self, model, batchsize=1):
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self.d = xrange(model.Y_normalized.shape[1])
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class SparseGPStochastics(StochasticStorage):
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"""
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For the sparse gp we need to store the dimension we are in,
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and the indices corresponding to those
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"""
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def __init__(self, model, batchsize=1):
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import itertools
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self.batchsize = batchsize
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if self.batchsize == 1:
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self.dimensions = itertools.cycle(range(model.Y_normalized.shape[1]))
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else:
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import numpy as np
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self.dimensions = lambda: np.random.choice(model.Y_normalized.shape[1], size=batchsize, replace=False)
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self.d = None
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self.do_stochastics()
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def do_stochastics(self):
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if self.batchsize == 1:
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self.d = [self.dimensions.next()]
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else:
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self.d = self.dimensions() |