from random import random
from math import sqrt

def product(it,key=lambda x: x):
	p = 1
	for n in map(key,it):
		p*=n
	return n

class Vector(list):
	def __add__(self,other):
		if len(self) != len(other):
			raise Exception("Can't add vectors of different size: %d %d" % (len(self), len(other)))
		return Vector([ self[i]+other[i] for i in range(len(self)) ])

	def __sub__(self,other):
		if len(self) != len(other):
			raise Exception("Can't subtract vectors of different size")
		return Vector([ self[i]-other[i] for i in range(len(self)) ])

	def __mul__(self,factor):
		return Vector([ self[i]*factor for i in range(len(self)) ])

	def __div__(self,factor):
		return self.__mul__(1.0/factor)

	def __rmul__(self,factor):
		return self.__mul__(factor)

	def __rdiv__(self,factor):
		raise NotImplemented

	def __iadd__(self,other):
		return self.__add__(other)

	def __isub__(self,other):
		return self.__sub__(other)

	def __neg__(self):
		return self.__mul__(-1.0)

	def __setitem__(self,k,v):
		raise Exception("Not available") # immutable

	def distance(self, other):
		if len(self) != len(other):
			raise Exception("Can't calculate distance between vectors of different size")
		return sqrt(sum([ (self[i]-other[i])**2 for i in range(len(self)) ]))

class Particle:
	def __init__(self,position,velocity):
		self.gsize = len(position)
		self.position = Vector(position)
		self.bestposition = Vector(position[:])
		self.velocity = Vector(velocity)

class ParticleCreator:
	def __init__(self,searchspace):
		self.searchspace = searchspace

	def create(self):
		return Particle([ (random()*(r[1]-r[0]) + r[0]) for r in self.searchspace ],
			[0 for i in range(len(self.searchspace))])

class Fitness:
	targets = [Vector([0,0])]

	def fitness(self,position):
		return min([ position.distance(target) for target in self.targets ])

class Swarm:
	def __init__(self, n, creator, fitness, inertiaWeight, cog1, cog2):
		self.particles = map(lambda x: creator.create(), range(n))
		self.inertiaWeight = inertiaWeight
		self.cog1 = cog1
		self.cog2 = cog2
		self.fitness = fitness

	def bestOfSwarm(self):
		return min( map(lambda p: p.bestposition, self.particles), key=lambda position: self.fitness.fitness(position) )

	def __bestNeighbor(self,particle, distance=20):
		#distance = max(min( self.particles, key=lambda p: particle.position.distance(p.position) ),distance)
		#return min( map(lambda p: p.bestposition, filter(lambda p: particle.position.distance(p.position) <= distance, self.particles) ), key=lambda position: self.fitness.fitness(position))
		i = self.particles.index(particle)
		l = (i+1) % len(self.particles)
		r = (i-1) % len(self.particles)
		return min( map(lambda p: p.bestposition, [self.particles[l], self.particles[r], particle]), key=lambda position: self.fitness.fitness(position) )


	def __updatePosition(self):
		for p in self.particles:
			new_position = p.position + p.velocity
			if self.fitness.fitness(new_position) < self.fitness.fitness(p.position):
				p.bestposition = new_position
			p.position = new_position

	def __updateVelocity(self):
		bestOfSwarm = self.bestOfSwarm()
		for p in self.particles:
			#bestNeighbor = self.__bestNeighbor(p)
			m = p.velocity*self.inertiaWeight + (p.bestposition-p.position)*(self.cog1*random()) + (bestOfSwarm-p.position)*(self.cog2*random())
			p.velocity = m

	def step(self):
		self.__updateVelocity()
		self.__updatePosition()

	def stepN(self,n):
		for i in range(n):
			self.step()

	def runUntilSwarmSize(self,sizeMax=1.0):
		iterations = 0
		while self.__swarmSize() > sizeMax:
			self.step()
			iterations += 1
		return iterations


	def __centerOfSwarm(self):
		return reduce(lambda a,b: a+b, [ p.bestposition for p in self.particles ], Vector([0,0]) )/len(self.particles)

	def __swarmSize(self):
		center = self.__centerOfSwarm()
		return max([ p.bestposition.distance(center) for p in self.particles ])

if __name__ == '__main__':
	swarm = Swarm( 20, ParticleCreator([(-2000.0,2000.0),(-2000.0,2000.0)]), Fitness(), 1, 0.4, 0.2 )

	iterations = 10000
	swarm.stepN(iterations)

	print "%d iterations" % iterations

	print swarm.bestOfSwarm()