def __init__(self, data, interval_type=ClassIntervalType.ROOT): f = [] for d in data: f.append(float(d)) data = f DataSet.__init__(self, data) self.interval_type = interval_type if self.interval_type != ClassIntervalType.THREESIGMA: self.class_interval = self.calc_class_interval(interval_type, self.min, self.max, self.n); self.construct_bins(self.min, self.max, self.class_interval, False); else: sigma_span = 6 min = self.mean - self.stdev * (sigma_span / 2) max = self.mean + self.stdev * (sigma_span / 2) self.class_interval = self.calc_class_interval(ClassIntervalType.THREESIGMA, min, max, sigma_span) self.construct_bins(min, max, self.class_interval, True) self.fill_bins() self.sort_bins() total = 0 for bin in self.bins: total = total + bin.count() self.bin_contents_count = total
def __init__(self, inp, target):
"""Initialize an empty supervised dataset.
Pass `inp` and `target` to specify the dimensions of the input and
target vectors."""
DataSet.__init__(self)
if isscalar(inp):
# add input and target fields and link them
self.addField('input', inp)
self.addField('target', target)
else:
self.setField('input', inp)
self.setField('target', target)
self.linkFields(['input', 'target'])
# reset the index marker
self.index = 0
# the input and target dimensions
self.indim = self.getDimension('input')
self.outdim = self.getDimension('target')
def __init__(self, inp, target):
"""Initialize an empty supervised dataset.
Pass `inp` and `target` to specify the dimensions of the input and
target vectors."""
DataSet.__init__(self)
if isscalar(inp):
# add input and target fields and link them
self.addField('input', inp)
self.addField('target', target)
else:
self.setField('input', inp)
self.setField('target', target)
self.linkFields(['input', 'target'])
# reset the index marker
self.index = 0
# the input and target dimensions
self.indim = self.getDimension('input')
self.outdim = self.getDimension('target')
def __init__(self, statedim, actiondim):
""" initialize the reinforcement dataset, add the 3 fields state, action and
reward, and create an index marker. This class is basically a wrapper function
that renames the fields of SupervisedDataSet into the more common reinforcement
learning names. Instead of 'episodes' though, we deal with 'sequences' here. """
DataSet.__init__(self)
# add 3 fields: input, target, importance
self.addField('state', statedim)
self.addField('action', actiondim)
self.addField('reward', 1)
# link these 3 fields
self.linkFields(['state', 'action', 'reward'])
# reset the index marker
self.index = 0
# add field that stores the beginning of a new episode
self.addField('sequence_index', 1)
self.append('sequence_index', 0)
self.currentSeq = 0
self.statedim = statedim
self.actiondim = actiondim
# the input and target dimensions (for compatibility)
self.indim = self.statedim
self.outdim = self.actiondim
def __init__(self, statedim, actiondim):
""" initialize the reinforcement dataset, add the 3 fields state, action and
reward, and create an index marker. This class is basically a wrapper function
that renames the fields of SupervisedDataSet into the more common reinforcement
learning names. Instead of 'episodes' though, we deal with 'sequences' here. """
DataSet.__init__(self)
# add 3 fields: input, target, importance
self.addField('state', statedim)
self.addField('action', actiondim)
self.addField('reward', 1)
# link these 3 fields
self.linkFields(['state', 'action', 'reward'])
# reset the index marker
self.index = 0
# add field that stores the beginning of a new episode
self.addField('sequence_index', 1)
self.append('sequence_index', 0)
self.currentSeq = 0
self.statedim = statedim
self.actiondim = actiondim
# the input and target dimensions (for compatibility)
self.indim = self.statedim
self.outdim = self.actiondim
