market = Faker()
market.genData(150, 10, 6, 500)
blp = BLP(market.X1, market.X2, market.Z, market.M, market.S)
blp.prepareSample()
a = np.random.rand(market.X2.shape[1])
bounds = [(-3, 3) for x in a]
# Keep initial delta vector
initial_delta = blp.initial_delta.copy()
# Test non-accelerated convergence
print("Starting non-accelerated...")
blp.delta_method = 0
start4 = time()
res4 = blp.solve(a, method="Nelder-Mead", delta_method="picard")
end4 = time()
# Test non-accelerated convergence
np.copyto(blp.initial_delta, initial_delta)
print("Starting BFGS non-accelerated...")
blp.delta_method = 0
start1 = time()
res1 = blp.solve(a, method="BFGS", delta_method="picard", bounds=bounds)
end1 = time()
# Test Anderson-accelerated convergence
np.copyto(blp.initial_delta, initial_delta)
print("Starting BFGS Anderson accelerated...")
blp.delta_method = 1
blp.anderson = np.empty((6, blp.initial_delta.shape[0]))
if __name__ == "__main__":
market = Faker()
market.genData(150, 10, 6, 500)
a = np.random.rand(market.X2.shape[1])
bounds = [(-3, 3) for x in a]
# Test serial unique convergence
blp = BLP(market.X1, market.X2, market.Z, market.M, market.S)
blp.prepareSample()
population = blp.population.copy()
population_size = blp.population_size # Keep these for easier comparison
print("Starting picard serial...")
start1 = time()
res1 = blp.solve(a, method="Nelder-Mead", delta_method="picard")
end1 = time()
blp = None
# Test serial multiple convergence
blp2 = BLP(market.X1, market.X2, market.Z, market.M, market.S, par_cut=2)
blp2.population = population
blp2.status = 1
blp2.population_size = population_size
print("Starting picard split...")
start2 = time()
res2 = blp2.solve(a, method="Nelder-Mead", delta_method="picard")
end2 = time()
blp2 = None
# Test parallel convergence
self.beta = beta
self.sigma = sigma
self.demand_shock = demand_shock
return
if __name__ == "__main__":
market = Faker()
market.genData(50, 15, 6, 500)
blp = BLP(market.X1, market.X2, market.Z, market.M, market.S)
blp.prepareSample()
a = np.random.rand(market.X2.shape[1])
ls = np.bincount(market.M, weights=market.S)
s = ls[market.M]
res = blp.solve(a, method="Nelder-Mead")
print("The resulting message is : %s" % res.message)
print("The flag is: %s" % res.success)
print("Found theta1 is %s " % res.theta1)
print("Real theta1 was %s %s" % (market.alpha, market.beta))
print("Found theta2 is %s" % (res.theta2))
print("Real theta2 was %s" % market.sigma)
print("Starting theta2 was %s" % a)
bounds = tuple([(-3, 3) for j in a])
res = blp.solve(a, method="BFGS", bounds=bounds)
print("The resulting message is : %s" % res.message)
print("The flag is: %s" % res.success)
print("Found theta1 is %s " % res.theta1)
