class reactor_modeler(object):
def __init__(self, sensorPins, hiddenLayerSize=10):
assert (type(sensorPins) == dict)
self.net = buildNetwork(len(sensorPins.keys), hiddenLayerSize, len(sensorPins), bias=true)
self.dataset = ImportanceDataSet(len(sensorPins.keys), len(sensorPins.keys))
def add_trained_datapoint(self, inputDataPoint, outputDataPoint):
self.dataset.addSample()
def fit(self, inp, y, sample_weight=None):
self.classes_, y = numpy.unique(y, return_inverse=True)
self.n_classes_ = len(self.classes_)
n_features = inp.shape[1]
random_state = check_random_state(self.random_state)
# We need to build an ImportanceDataSet from inp, y and sample_weight
dataset = ImportanceDataSet(n_features, self.n_classes_)
if sample_weight is None:
sample_weight = numpy.ones(len(y))
for x, label_pos, weight in izip(inp, y, sample_weight):
target = numpy.zeros(self.n_classes_)
target[label_pos] = 1
weight = weight * numpy.ones(self.n_classes_)
dataset.newSequence()
dataset.addSample(x, target, weight)
if self.hidden_neurons is None:
hidden_neurons = (n_features + self.n_classes_)/2
else:
hidden_neurons = self.hidden_neurons
self.network_ = buildNetwork(
n_features, hidden_neurons, self.n_classes_,
outclass=self._get_output_class()
)
# Set the initial parameters in a repeatable way
net_params = random_state.random_sample(self.network_.paramdim)
self.network_.params[:] = net_params
self.trainer_ = BackpropTrainer(
self.network_, dataset=dataset, learningrate=self.learning_rate,
lrdecay=self.lr_decay, momentum=self.momentum,
weightdecay=self.weight_decay
)
self.trainer_.trainUntilConvergence(
random_state, maxEpochs=self.max_epochs,
continueEpochs=self.continue_epochs,
validationProportion=self.validation_percent
)
return self
def __init__(self, sensorPins, hiddenLayerSize=10):
assert (type(sensorPins) == dict)
self.net = buildNetwork(len(sensorPins.keys), hiddenLayerSize, len(sensorPins), bias=true)
self.dataset = ImportanceDataSet(len(sensorPins.keys), len(sensorPins.keys))