def main():
#valList = []
start_time = time.clock()
output = dict()
output[0] = []
for shock in range(5):
paramL['curr_theta'] = paramL['theta_list'][shock]
valnew = TasmanianSG.TasmanianSparseGrid()
valnew = interpol.sparse_grid(paramL)
valold = TasmanianSG.TasmanianSparseGrid()
valold = valnew
output[0].append(valold)
valnew.write("valnew_1." + str(paramL['numstart']) +
".s{}".format(shock) +
".txt") #write file to disk for restart
for i in range(paramL['numstart'] + 1, paramL['numits'] + 1):
output[i] = list()
for shock in range(5):
paramL['curr_theta'] = paramL['theta_list'][shock]
valnew = TasmanianSG.TasmanianSparseGrid()
valnew = interpol_iter.sparse_grid_iter(paramL,
list(output[i - 1]))
valold = TasmanianSG.TasmanianSparseGrid()
valold = valnew
output[i].append(valold)
valnew.write("valnew_1." + str(i) + ".s{}".format(shock) + ".txt")
avg_err = post.ls_error(paramL)
end_time = time.clock() - start_time
print('END')
print(end_time)
return output
def main():
start_time = time.clock()
paramL['curr_theta'] = paramL['theta_list'][rank]
valnew = TasmanianSG.TasmanianSparseGrid()
valnew = interpol.sparse_grid(paramL)
valold = TasmanianSG.TasmanianSparseGrid()
valold = valnew
valnew.write("valnew_1." + str(paramL['numstart']) + ".s{}".format(rank) +
".txt") #write file to disk for restart
comm.barrier()
for i in range(paramL['numstart'] + 1, paramL['numits'] + 1):
print('Size: ', size, ' rank ', rank)
paramL['curr_theta'] = paramL['theta_list'][rank]
valnew = TasmanianSG.TasmanianSparseGrid()
output = get_iteration_list(i - 1)
valnew = interpol_iter.sparse_grid_iter(paramL, list(output))
valold = TasmanianSG.TasmanianSparseGrid()
valold = valnew
valnew.write("valnew_1." + str(i) + ".s{}".format(rank) + ".txt")
comm.barrier()
end_time = time.clock() - start_time
print('END')
if rank == 0:
avg_err = post.ls_error(paramL)
print(end_time)
def run_all(n_agents):
valnew=TasmanianSG.TasmanianSparseGrid()
if (numstart==0):
valnew=interpol.sparse_grid(n_agents, iDepth)
valnew.write("valnew_1." + str(numstart) + ".txt") #write file to disk for restart
# value function during iteration
else:
valnew.read("valnew_1." + str(numstart) + ".txt") #write file to disk for restart
valold=TasmanianSG.TasmanianSparseGrid()
valold=valnew
for i in range(numstart, numits):
valnew=TasmanianSG.TasmanianSparseGrid()
valnew=interpol_iter.sparse_grid_iter(n_agents, iDepth, valold)
valold=TasmanianSG.TasmanianSparseGrid()
valold=valnew
valnew.write("valnew_1." + str(i+1) + ".txt")
#======================================================================
print "==============================================================="
print " "
print " Computation of a growth model of dimension ", n_agents ," finished after ", numits, " steps"
print " "
print "==============================================================="
#======================================================================
# compute errors
avg_err, max_err =post.ls_error(n_agents, numstart, numits, No_samples)
#======================================================================
print "==============================================================="
print " "
print " Errors are computed -- see errors.txt"
print " "
print "==============================================================="
#======================================================================
return avg_err, max_err
#valnew=interpol_iter.sparse_grid_iter(n_agents, iDepth, valold)
valnew = interpol_iter_adap.ad_grid_iter(n_agents, iDepth, storeValOld)
# valold=TasmanianSG.TasmanianSparseGrid()
# valold=valnew
# valnew.write("valnew_1." + str(i+1) + ".txt")
storeVal.append(valnew)
storeValOld = []
for jj in range(5):
valold = TasmanianSG.TasmanianSparseGrid()
valold = storeVal[jj]
storeValOld.append(valold)
#======================================================================
print "==============================================================="
print " "
print " Computation of a growth model of dimension ", n_agents, " finished after ", numits, " steps"
print " "
print "==============================================================="
#======================================================================
# compute errors
avg_err = post.ls_error(n_agents, numstart, numits, No_samples)
#======================================================================
print "==============================================================="
print " "
print " Errors are computed -- see errors.txt"
print " "
print "==============================================================="
#======================================================================
def run_all(n_agents):
valnew=TasmanianSG.TasmanianSparseGrid()
if (numstart==0):
gridlist = []
for tT in range(ntheta):
valnew=TasmanianSG.TasmanianSparseGrid()
valnew=interpol.sparse_grid(n_agents, iDepth, theta[tT])
gridlist.append(valnew)
valnew.write("valnew_1." + str(numstart) + "theta_" + str(tT) + ".txt") #write file to disk for restart
# value function during iteration
else:
gridlist = []
for tT in range(ntheta):
valnew.read("valnew_1." + str(numstart) + "theta_" + str(tT) + ".txt") #write file to disk for restart
gridlist.append(valnew)
#valold=TasmanianSG.TasmanianSparseGrid()
#valold=valnew
# avals_list = []
for i in range(numstart, numits):
print " ================================================= "
print " Iteration", i
print " ================================================= "
for tT in range(ntheta):
thet = theta[tT]
valnew = TasmanianSG.TasmanianSparseGrid()
valnew = interpol_iter.sparse_grid_iter(n_agents, iDepth, gridlist, thet)
valnew.write("valnew_1." + str(i+1) + "theta_" + str(tT) + ".txt")
gridlist[tT].copyGrid(valnew)
# valnew0=TasmanianSG.TasmanianSparseGrid()
# valnew1=TasmanianSG.TasmanianSparseGrid()
# valnew2=TasmanianSG.TasmanianSparseGrid()
# valnew3=TasmanianSG.TasmanianSparseGrid()
# valnew4=TasmanianSG.TasmanianSparseGrid()
#
# valnew0=interpol_iter.sparse_grid_iter(n_agents, iDepth, valold, theta[0])
# valnew1=interpol_iter.sparse_grid_iter(n_agents, iDepth, valold, theta[1])
# valnew2=interpol_iter.sparse_grid_iter(n_agents, iDepth, valold, theta[2])
# valnew3=interpol_iter.sparse_grid_iter(n_agents, iDepth, valold, theta[3])
# valnew4=interpol_iter.sparse_grid_iter(n_agents, iDepth, valold, theta[4])
# evaluate all grids at the same points
# chosen arbitrarily to be the points of the third grid where theta=1
# eval_points = valnew2.getPoints()
#
# aVals0 = valnew0.evaluateBatch(eval_points)[:,0]
# aVals1 = valnew1.evaluateBatch(eval_points)[:,0]
# aVals2 = valnew2.evaluateBatch(eval_points)[:,0]
# aVals3 = valnew3.evaluateBatch(eval_points)[:,0]
# aVals4 = valnew4.evaluateBatch(eval_points)[:,0]
# print aVals0, aVals4
# aVals_new = 0.2 *(aVals0 + aVals1 + aVals2 + aVals3 + aVals4)
# aVals_new = np.reshape(aVals_new, (eval_points.shape[0], 1))
#f=open("aVals_new.txt", 'a')
#np.savetxt(f, aVals_new, fmt='% 2.16f')
#f.close()
# print "==================================================================="
# print " print avals shape here :"
# print aVals_new.shape
#
# valold=TasmanianSG.TasmanianSparseGrid()
# valold.copyGrid(valnew2)
# valold.loadNeededPoints(aVals_new)
#valold=valnew
# valold.write("valnew_1." + str(i+1) + "theta_" + str(tT) + ".txt")
#======================================================================
print "==============================================================="
print " "
print " Computation of a growth model of dimension ", n_agents ," finished after ", numits, " steps"
print " "
print "==============================================================="
#======================================================================
# compute errors
post.ls_error(n_agents, numstart, numits, No_samples)
#======================================================================
print "==============================================================="
print " "
print " Errors are computed -- see errors_theta_NUM.txt"
print " "
print " Groupwork of Max, Clint and Ben at OSM Lab 2017"
print "==============================================================="
for i in range(paramL['numstart'], paramL['numits']):
for state in range(5):
paramL['curr_theta'] = paramL['theta_list'][state]
valnew=TasmanianSG.TasmanianSparseGrid()
valnew=interpol_iter.sparse_grid_iter(paramL, valListold[state])
valList[state] = valnew
for state in range(5):
valold=TasmanianSG.TasmanianSparseGrid()
valold=valList[state]
valListold[state] = valold
#======================================================================
print( "===============================================================")
print( " " )
print( " Computation of a growth model of dimension ", paramL['n_agents'] ," finished after ", paramL['numits'], " steps")
print( " " )
print( "===============================================================")
#======================================================================
# compute errors
avg_err=post.ls_error(paramL)
#======================================================================
print( "===============================================================")
print( " ")
print( " Errors are computed -- see errors.txt")
print( " ")
print( "===============================================================")
#======================================================================
