def test_interchange(self):
print 'Single Scan Interchange algorithm'
for N in map(int, [10, 1e2, 1e3, 1e4, 1e5, 1e6]):
pr = PPReader('population.csv', 'dummy.csv', N=N)
# Read dummy files into buffer
start_time = time.time()
#pr.init()
elapsed_time = time.time() - start_time
sys.stdout.write('init done: %f seconds taken.\t' % elapsed_time)
sys.stdout.flush()
# Actual test
start_time = time.time()
lin = Linear(dist_func, K=5)
lin.update(pr)
elapsed_time = time.time() - start_time
sampled = lin.get_sampled()
sys.stdout.write('%d: %f\n' % (N, elapsed_time))
sys.stdout.flush()
return loss
np.random.seed(1)
for psize in xrange(10, 110, 10):
#for psize in xrange(100, 1100, 100):
# Generate population
population = np.random.rand(psize, 2)
# Generate sampled data
K = 2
# First: we get samples using linear algorithm
lin = Linear(dist_func, K = K, r = 1)
lin.update(population)
lin_sample = lin.get_sampled()
# Second: we enumerate all the samples of size K, and find the one with the
# minimal loss.
min_loss = float('Inf')
bf_sample = None
for c in combinations(population, K):
loss = loss_of(c)
if loss < min_loss:
min_loss = loss
bf_sample = c
print 'Population size: %d' % psize
print 'Linear\tsize: %d, loss: %f' % (len(lin_sample), loss_of(lin_sample))
print 'BF\tSize: %d, loss: %f' % (len(bf_sample), loss_of(bf_sample))
print
fout.write('x,y,label\n')
# Generate population
pp = population1()
for x, y in pp:
fout.write('%f,%f,a\n' % (x, y))
x, y = zip(*pp)
plt.scatter(x, y, s=1, c='b')
# Generate sampled data
def dist_func(a, b):
d = pow(np.linalg.norm(a - b), 2)
return d
lin = Linear(dist_func, K = 50, r = 0.3)
lin.update(pp)
sampled = lin.get_sampled()
for x, y in sampled:
fout.write('%f,%f,b\n' % (x, y))
fout.close()
x, y = zip(*sampled)
plt.scatter(x, y, s=30, c='r')
plt.show()
