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# Copyright (c) 2015, Max Zwiessele
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[docs]class StochasticStorage(object):
'''
This is a container for holding the stochastic parameters,
such as subset indices or step length and so on.
self.d has to be a list of lists:
[dimension indices, nan indices for those dimensions]
so that the minibatches can be used as efficiently as possible.
'''
def __init__(self, model):
"""
Initialize this stochastic container using the given model
"""
[docs] def do_stochastics(self):
"""
Update the internal state to the next batch of the stochastic
descent algorithm.
"""
pass
[docs] def reset(self):
"""
Reset the state of this stochastics generator.
"""
[docs]class SparseGPMissing(StochasticStorage):
def __init__(self, model, batchsize=1):
"""
Here we want to loop over all dimensions everytime.
Thus, we can just make sure the loop goes over self.d every
time. We will try to get batches which look the same together
which speeds up calculations significantly.
"""
import numpy as np
self.Y = model.Y_normalized
bdict = {}
#For N > 1000 array2string default crops
opt = np.get_printoptions()
np.set_printoptions(threshold=np.inf)
for d in range(self.Y.shape[1]):
inan = np.isnan(self.Y)[:, d]
arr_str = np.array2string(inan, np.inf, 0, True, '', formatter={'bool':lambda x: '1' if x else '0'})
try:
bdict[arr_str][0].append(d)
except:
bdict[arr_str] = [[d], ~inan]
np.set_printoptions(**opt)
self.d = bdict.values()
[docs]class SparseGPStochastics(StochasticStorage):
"""
For the sparse gp we need to store the dimension we are in,
and the indices corresponding to those
"""
def __init__(self, model, batchsize=1, missing_data=True):
self.batchsize = batchsize
self.output_dim = model.Y.shape[1]
self.Y = model.Y_normalized
self.missing_data = missing_data
self.reset()
self.do_stochastics()
[docs] def do_stochastics(self):
import numpy as np
if self.batchsize == 1:
self.current_dim = (self.current_dim+1)%self.output_dim
self.d = [[[self.current_dim], np.isnan(self.Y[:, self.current_dim]) if self.missing_data else None]]
else:
self.d = np.random.choice(self.output_dim, size=self.batchsize, replace=False)
bdict = {}
if self.missing_data:
opt = np.get_printoptions()
np.set_printoptions(threshold=np.inf)
for d in self.d:
inan = np.isnan(self.Y[:, d])
arr_str = np.array2string(inan,np.inf, 0,True, '',formatter={'bool':lambda x: '1' if x else '0'})
try:
bdict[arr_str][0].append(d)
except:
bdict[arr_str] = [[d], ~inan]
np.set_printoptions(**opt)
self.d = bdict.values()
else:
self.d = [[self.d, None]]
[docs] def reset(self):
self.current_dim = -1
self.d = None