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# This code is part of the Biopython distribution and governed by its
# license. Please see the LICENSE file that should have been included
# as part of this package.
#
"""Provide classes for dealing with Training Neural Networks."""
# standard modules
import random
class TrainingExample(object):
"""Hold inputs and outputs of a training example."""
def __init__(self, inputs, outputs, name=""):
self.name = name
self.inputs = inputs
self.outputs = outputs
class ExampleManager(object):
"""Manage a grouping of Training Examples.
This is meant to make it easy to split a bunch of training examples
into three types of data:
o Training Data -- These are the data used to do the actual training
of the network.
o Validation Data -- These data are used to validate the network
while training. They provide an independent method to evaluate how
the network is doing, and make sure the network gets trained independent
of noise in the training data set.
o Testing Data -- The data which are used to verify how well a network
works. They should not be used at all in the training process, so they
provide a completely independent method of testing how well a network
performs.
"""
def __init__(self, training_percent=.4, validation_percent=.4):
"""Initialize the manager with the training examples.
Arguments:
o training_percent - The percentage of the training examples that
should be used for training the network.
o validation_percent - Percent of training examples for validating
a network during training.
Attributes:
o train_examples - A randomly chosen set of examples for training
purposes.
o valdiation_examples - Randomly chosesn set of examples for
use in validation of a network during training.
o test_examples - Examples for training purposes.
"""
assert training_percent + validation_percent <= 1.0, \
"Training and validation percentages more than 100 percent"
self.train_examples = []
self.validation_examples = []
self.test_examples = []
self.training_percent = training_percent
self.validation_percent = validation_percent
def add_examples(self, training_examples):
"""Add a set of training examples to the manager.
Arguments:
o training_examples - A list of TrainingExamples to manage.
"""
placement_rand = random.Random()
# assign exact example randomly to the example types
for example in training_examples:
chance_num = placement_rand.random()
# assign with the specified percentage
if chance_num <= self.training_percent:
self.train_examples.append(example)
elif chance_num <= (self.training_percent +
self.validation_percent):
self.validation_examples.append(example)
else:
self.test_examples.append(example)
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