Note:
----
Don't expect the test to work every
time. There is a lot fine-tuning to
do in each run and it is possible that
this automatic run will fail due to this
lack of tuning. The system will provide
more information on errors if they occur.
"""
if __name__ == "__main__":
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
do in each run and it is possible that
this automatic run will fail due to this
lack of tuning. The system will provide
more information on errors if they occur.
"""
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...")
self.X1 = np.hstack((P.reshape(-1, 1), X[:, (nonlinear_chars):]))
self.X2 = np.hstack((P.reshape(-1, 1), X[:, :nonlinear_chars]))
self.Z = Z
self.M = M
self.S = S
self.alpha = alpha
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)
