kmedian.py File Reference

model for solving the k-median problem. More...

Go to the source code of this file.

Functions
def	kmedian (I, J, c, k)
def	distance (x1, y1, x2, y2)
def	make_data (n, m, same=True)

Variables
int	n = 200
	m = n
	I
	J
	c
	x_pos
	y_pos
	same
int	k = 20
	model = kmedian(I,J,c,k)
int	EPS = 1
	x
	y
list	edges = [(i,j) for (i,j) in x if model.getVal(x[i,j]) > EPS]
list	facilities = [j for j in y if model.getVal(y[j]) > EPS]
	G = NX.Graph()
	other = set(j for j in J if j not in facilities)
	client = set(i for i in I if i not in facilities and i not in other)
dictionary	position = {}
	with_labels
	False
	node_color
	nodelist
	node_size

Detailed Description

model for solving the k-median problem.

Definition in file kmedian.py.

Function Documentation

def kmedian.distance	(		x1,
			y1,
			x2,
			y2
	)

return distance of two points

Definition at line 43 of file kmedian.py.

def kmedian.kmedian	(		I,
			J,
			c,
			k
	)

kmedian -- minimize total cost of servicing customers from k facilities
Parameters:
    - I: set of customers
    - J: set of potential facilities
    - c[i,j]: cost of servicing customer i from facility j
    - k: number of facilities to be used
Returns a model, ready to be solved.

Definition at line 13 of file kmedian.py.

def kmedian.make_data	(		n,
			m,
			same = True
	)

creates example data set

Definition at line 48 of file kmedian.py.