Commit 14de2bc7 by Steven Cordwell

### superficial changes to support two patches in firemdp example

parent b437721c
 # -*- coding: utf-8 -*- """Optimal fire management for a single population of a threatened species ======================================================================= """Optimal fire management for a threatened species ================================================ This PyMDPtoolbox example is based on a paper [Possingham1997]_ preseneted by Hugh Possingham and Geoff Tuck at the 1997 MODSIM conference. This version only considers a single population, rather than the two populations considered in the original paper. The paper is freely available to read from the link provided, so minimal details are given here. Hugh Possingham and Geoff Tuck at the 1997 MODSIM conference. The paper is freely available to read from the link provided, so minimal details are given here. .. [Possingham1997] Possingham H & Tuck G, 1997, ‘Application of stochastic dynamic programming to optimal fire management of a spatially structured ... ... @@ -56,7 +55,7 @@ FIRE_CLASSES = 13 # The number of states STATES = POPULATION_CLASSES * FIRE_CLASSES # The number of actions ACTIONS = 2 ACTIONS = 4 def convertStateToIndex(population, fire): """Convert state parameters to transition probability matrix index. ... ... @@ -165,6 +164,10 @@ def getTransitionProbabilities(s, x, F, a): elif a == 1: # When the patch is burned set the years since fire to 0. F = 0 elif a == 2: pass elif a == 3: pass # Population transitions if x == 0: # Demographic model probabilities ... ... @@ -182,6 +185,10 @@ def getTransitionProbabilities(s, x, F, a): if a == 1: x_1 -= 1 x_2 -= 1 elif a == 2: pass elif a == 3: pass # Demographic model probabilities new_state = convertStateToIndex(x_1, F) prob[new_state] = 1 - (1 - s) * (1 - r) # abundance stays the same ... ... @@ -202,6 +209,10 @@ def getTransitionProbabilities(s, x, F, a): # Ensure that the abundance class doesn't go to -1 if x_3 > 0: x_3 -= 1 elif a == 2: pass elif a == 3: pass # Demographic model probabilities new_state = convertStateToIndex(x_1, F) prob[new_state] = s # abundance stays the same ... ...
Markdown is supported
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment