<commit_before># Module:	ann
# Date:		18th March 2006
# Author:	<NAME> <<EMAIL>>

"""Artificial Neural Networking Library

...
"""

import math
from time import time, sleep

from circuits import listener, Event, Worker


class SignalEvent(Event):

	def __init__(self, source, level):
		Event.__init__(self, source=source, level=level)


class Node(Worker):

	def __init__(self, event=None):
		Worker.__init__(self, event)

		self._inputs = []

	def __repr__(self):
		return "<Node running=%s>" % self.isRunning()

	def _getInputs(self):
		return self._inputs

	def link(self, node):
		Worker.link(self, node)
		node._inputs.append(self)

	def fire(self, level=1.0):
		self.event.push(SignalEvent(self, level), "signal")

	def run(self):
		while self.isRunning():
			self.flush()
			sleep(0.01)

	inputs = property(_getInputs)


def new_node(*args, **kwargs):

	class NewNode(Node):
		pass
	return NewNode(*args, **kwargs)


class Connection(Node):

	def __init__(self, event=None, weight=1.0):
		Node.__init__(self)
		self._weight = weight

	def __repr__(self):
		return "<Connection weight=%0.2f>" % self._weight

	def _get_weight(self):
		try:
			return self._weight
		except:
			return None

	def _set_weight(self, weight):
		self._weight = weight

	@listener("signal")
	def onSIGNAL(self, source, level):
		self.fire(level * self._weight)

	weight = property(_get_weight, _set_weight)


def new_connection(*args, **kwargs):

	class NewConnection(Connection):
		pass
	return NewConnection(*args, **kwargs)


class _StepNeuron(object):

	def compute(self):
		if self._level >= self._threshold:
			self.fire(1.0)
		else:
			self.fire(0.0)


class _LinearNeuron(object):

	def compute(self):
		self.fire(self._level + self._threshold)


class _SigmoidNeuron(object):

	def compute(self):
		self.fire(
				1.0 / (
					1.0 + math.exp(-1 * (
						self._level + self._threshold)
						)
					)
				)


class Neuron(Node):

	def __init__(self, event=None, threshold=1.0, type="step"):
		Node.__init__(self)

		self._threshold = threshold
		self._level = 0.0
		self._ls = None

		if type == "step":
			self._type = "Step"
			base = _StepNeuron
		elif type == "linear":
			self._type = "Linear"
			base = _LinearNeuron
		elif type == "sigmoid":
			self._type = "Sigmoid"
			base = _SigmoidNeuron
		else:
			raise TypeError("Invalid type")

		self.__class__.__bases__ += (base,)

	def __repr__(self):
		return "<Neuron type=%s threshold=%0.2f level=%0.2f>" % (
				self._type, self._threshold, self._level)

	def _get_threshold(self):
		try:
			return self._threshold
		except:
			return None

	def _set_threshold(self, threshold):
		self._threshold = threshold

	@listener("signal")
	def onSIGNAL(self, source, level):
		self._ls = time()
		self._level += level

	def run(self):
		while self.isRunning():
			if self._ls is not None and (time() - self._ls > 0.01):
				self.compute()
				self._level = 0.0
				self._ls = None
			self.event.flush()
			sleep(0.01)

	threshold = property(_get_threshold, _set_threshold)

def new_neuron(*args, **kwargs):
	class NewNeuron(Neuron):
		pass
	return NewNeuron(*args, **kwargs)


class Output(Node):

	def __init__(self, event=None, f=lambda n: None):
		Node.__init__(self)<commit_msg>lib/ann: Fixed __init__ to properly call their base class<commit_after># Module:	ann
# Date:		18th March 2006
# Author:	<NAME> <<EMAIL>>

"""Artificial Neural Networking Library

...
"""

import math
from time import time, sleep

from circuits import listener, Event, Thread


class SignalEvent(Event): pass


class Node(Thread):

   def __init__(self, *args, **kwargs):
      super(Node, self).__init__(*args, **kwargs)

		self._inputs = []

	def __repr__(self):
		return "<Node running=%s>" % self.isRunning()

	def _getInputs(self):
		return self._inputs

	def link(self, node):
		Worker.link(self, node)
		node._inputs.append(self)

	def fire(self, level=1.0):
		self.event.push(SignalEvent(self, level), "signal")

	def run(self):
		while self.isRunning():
			self.flush()
			sleep(0.01)

	inputs = property(_getInputs)


def new_node(*args, **kwargs):

	class NewNode(Node):
		pass
	return NewNode(*args, **kwargs)


class Connection(Node):

   def __init__(self, *args, **kwargs):
		super(Connection, self).__init__(*args, **kwargs)

		self._weight = kwargs.get("weight", 1.0)

	def __repr__(self):
		return "<Connection weight=%0.2f>" % self._weight

	def _get_weight(self):
		try:
			return self._weight
		except:
			return None

	def _set_weight(self, weight):
		self._weight = weight

	@listener("signal")
	def onSIGNAL(self, source, level):
		self.fire(level * self._weight)

	weight = property(_get_weight, _set_weight)


def new_connection(*args, **kwargs):

	class NewConnection(Connection):
		pass
	return NewConnection(*args, **kwargs)


class _StepNeuron(object):

	def compute(self):
		if self._level >= self._threshold:
			self.fire(1.0)
		else:
			self.fire(0.0)


class _LinearNeuron(object):

	def compute(self):
		self.fire(self._level + self._threshold)


class _SigmoidNeuron(object):

	def compute(self):
		self.fire(
				1.0 / (
					1.0 + math.exp(-1 * (
						self._level + self._threshold)
						)
					)
				)


class Neuron(Node):

   def __init__(self, *args, **kwargs):
		super(Neuron, self).__init__(*args, **kwargs)

		self._threshold = threshold
		self._level = 0.0
		self._ls = None

		if type == "step":
			self._type = "Step"
			base = _StepNeuron
		elif type == "linear":
			self._type = "Linear"
			base = _LinearNeuron
		elif type == "sigmoid":
			self._type = "Sigmoid"
			base = _SigmoidNeuron
		else:
			raise TypeError("Invalid type")

		self.__class__.__bases__ += (base,)

	def __repr__(self):
		return "<Neuron type=%s threshold=%0.2f level=%0.2f>" % (
				self._type, self._threshold, self._level)

	def _get_threshold(self):
		try:
			return self._threshold
		except:
			return None

	def _set_threshold(self, threshold):
		self._threshold = threshold

	@listener("signal")
	def onSIGNAL(self, source, level):
		self._ls = time()
		self._level += level

	def run(self):
		while self.isRunning():
			if self._ls is not None and (time() - self._ls > 0.01):
				self.compute()
				self._level = 0.0
				self._ls = None
			self.event.flush()
			sleep(0.01)

	threshold = property(_get_threshold, _set_threshold)

def new_neuron(*args, **kwargs):
	class NewNeuron(Neuron):
		pass
	return NewNeuron(*args, **kwargs)


class Output(Node):

   def __init__(self, *args, **kwargs):
		super(Output, self).__init__(*args, **kwargs)
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{'dir': 'git-commits-cleaned', 'id': '5352572', 'n_tokens_mistral': 2602, 'n_tokens_neox': 2247, 'n_words': 547}