Hw10 solutions

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The important differences between Gillespie's '''first reaction''' and '''direct''' methods:
The important differences between Gillespie's '''first reaction''' and '''direct''' methods:
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In the '''direct method,''' we picked one random number (uniformly on (0,1)) to generate the ''time'' of the next reaction (with exponential distribution parametrized by <tt> a = sum(a_i) </tt>):
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In the '''direct method,''' we consider the time and identity of the next reaction independently.  First, we picked one random number (uniformly on (0,1)) to generate the ''time'' of the next reaction (with exponential distribution parametrized by <tt> a = sum(a_i) </tt>):
     # how long until the next reaction
     # how long until the next reaction
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Where <tt>a_sum</tt> is the "right edge" of the current bin you're considering.  
Where <tt>a_sum</tt> is the "right edge" of the current bin you're considering.  
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In the '''first reaction method''', one picks a random number (uniform on 0,1) and uses it to generate a a time (exponentially distributed with parameter a_i) for each reaction, and selects the reaction with the smallest time to fire at that time. Tau's for all reactions are recalculated at every step, never saved.
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In the '''first reaction method''', one picks a random number (uniform on 0,1) and uses it to generate a a time (exponentially distributed with parameter a_i) for each reaction, and selects the reaction with the smallest time to fire at that time. Tau's for all reactions are recalculated at every step, never saved. Note that the behavior of the '''first''' and '''direct''' methods is equivalent. 
     mintau = t_max
     mintau = t_max
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             mintau = tau # reset the min
             mintau = tau # reset the min
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This method of sorting seemed fastest to us, but maybe storing the tau's in a dictionary and sorting the values would have been faster, like this:
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This method of sorting seemed fastest to us, but here's another way of tackling the problem:
     tau_i = {}
     tau_i = {}

Revision as of 13:18, 8 December 2006

Solution in code form

The important differences between Gillespie's first reaction and direct methods:

In the direct method, we consider the time and identity of the next reaction independently. First, we picked one random number (uniformly on (0,1)) to generate the time of the next reaction (with exponential distribution parametrized by a = sum(a_i) ):

   # how long until the next reaction
   r1 = random.random()
   tau = (1.0/(a+eps))*math.log(1.0/r1)

Then we picked a second random number uniformly on (0,a) and identified which reaction's "bin" it fell into (where the width of each reactions's bin is equal to its current propensity).

   r2a = random.random()*a
   a_sum = 0
   for i in a_i:
       if r2a < (a_i[i]+a_sum):
           mu = i
           break
       a_sum += a_i[i]

Where a_sum is the "right edge" of the current bin you're considering.

In the first reaction method, one picks a random number (uniform on 0,1) and uses it to generate a a time (exponentially distributed with parameter a_i) for each reaction, and selects the reaction with the smallest time to fire at that time. Tau's for all reactions are recalculated at every step, never saved. Note that the behavior of the first and direct methods is equivalent.

   mintau = t_max
   # which reaction will happen first?
   # caluculate each reaction's time based on a different random number
   for rxn in a_i.keys():
       ai = a_i[rxn]
       ri = random.random()
       taui = (1.0/(ai+eps)) * math.log(1.0/ri)
       if taui < mintau: # "sort as you go"
           mu = rxn     # the putative first rxn 
           tau = taui   # the putative first time
           mintau = tau # reset the min

This method of sorting seemed fastest to us, but here's another way of tackling the problem:

   tau_i = {}
   # which reaction will happen first?
   # caluculate each reaction's time based on a different random number
   for rxn in a_i.keys():
       ai = a_i[rxn]
       ri = random.random()
       taui = (1.0/(ai+eps)) * math.log(1.0/ri)
       tau_i[taui] = rxn
   tau = min(tau_i.keys())
   mu = tau_i[tau]
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