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#data section:

S = 30

D = 100



decisions=['a','c','d','e']

events = ['b','f','g','h']

tree = {

'a':('b','e'),

'b':(('c',0.7),('d',0.3)),

'c':('f',90-S),

'd':('g',90-S),

'e':('h',90),

'f':((800-D-S,.143),(-D-S,.857)),

'g':((800-D-S,.5),(-D-S,.5)),

'h':((800-D,.25),(-D,.75))}

tree_root='a'



from pymprog import *



beginModel("dectree")

verbose(True)



x = var(decisions, 'x', bounds = (None, None))

y = var(events, 'y', bounds = (None, None))



#print "x['a']<=None:", x['a'] <= None



def vnode(node):

   if node not in tree:

      return node #assume a number

   return (y[node] if node in events else x[node])



minimize(sum(x[i] for i in decisions),'forced')

decst = {}

for i in decisions:

   decst[i] = st([ 0 <= x[i] - vnode(j) 

     for j in tree[i]], 'dec_node[%s]'%i)



st([y[i] == sum(j[1]*vnode(j[0]) for j in tree[i])

    for i in events], 'evt_nodes')



solve()



print status()



for t in x: print x[t]

for t in y: print y[t]



print x[tree_root].primal



print "EVSI =", evaluate(y['b'] - x['e']+S)



def trace_dec(cnode):

   #print "tracing node", cnode

   if cnode in decisions:

      k = 0

      for j in tree[cnode]:

         #print "branch",j,decst[cnode][k].dual

         if decst[cnode][k].dual:

            print "%s -> %s"%(cnode,str(j))

         trace_dec(j)

         k += 1

   elif cnode in events:

      for j in tree[cnode]:

         trace_dec(j[0])



trace_dec(tree_root)