def main():
sampleSize = 500
distMatrices = []
for filename in inputs:
outfilename = replaceEnforced(filename, "../data/CCS/", "pairwise_earthmover_")
np.random.seed(1)
print "reading data: {}".format(filename)
df = pd.read_csv(filename)
indices = np.random.choice([0, 1], sampleSize).astype(bool)
df = df.iloc[indices, :]
print "computing pairwise distances"
wavelengths = map(extractWavelength, df.columns[3:])
metric = lambda y1, y2: earthmover1d(wavelengths, y1, y2)
D = smp.pairwise_distances(df.iloc[:, 3:], metric=metric)
distMatrices.append(D)
print "saving to {}".format(outfilename)
pd.DataFrame(D).to_csv(outfilename, index=None, header=None)
for i in range(len(inputs) - 1):
diff = distMatrices[i] - distMatrices[-1]
frobenius = np.linalg.norm(diff, ord="fro")
print "diff between {} and {}: {}".format(inputs[i], inputs[-1], frobenius)
def demo():
np.random.seed(1)
n = 1000.0
xmin = -2.0
xmax = 3.0
xs = np.linspace(xmin, xmax, n)
#ys = np.cos(4 * np.pi * xs) ** 2
#ys = np.random.lognormal(0, 1, len(xs)) * (xs > 0) * (xs < 1); ys /= np.sum(ys)
ys = uniform(xs, 0.3, 0.4)
#ys = uniform_randomEndpoints(xs)
centers = np.array([-1,0,1,2])
m1 = nearestMapping(xs, centers)
xs1, ys1 = discretize(xs, ys, m1)
#showDiscretization(xs, ys, xs1, ys1, m1)
m2 = proportionalMapping(xs, centers)
xs2, ys2 = discretize(xs, ys, m2)
#showDiscretization(xs, ys, xs2, ys2, m2)
for i in range(4096):
#testName = "nearest" if i % 2 == 0 else "proportional"
#testMapping = m1 if i % 2 == 0 else m2
#testName = "nearest"
#testMapping = m1
testName = "proportional"
testMapping = m2
p1 = uniform_randomEndpoints(xs, numIntervals = 6)
p2 = uniform_randomEndpoints(xs, numIntervals = 6)
#if i >= 4:
# p1 *= np.random.lognormal(0, 1, len(xs))
# p1 /= np.sum(p1)
# if i >= 6:
# p2 *= np.random.lognormal(0, 1, len(xs))
# p2 /= np.sum(p2)
distance_before = earthmover1d(xs, p1, p2)
xs_new, p1_new = discretize(xs, p1, testMapping)
xs_new2, p2_new = discretize(xs, p2, testMapping)
assert np.array_equal(xs_new, xs_new2)
distance_after = earthmover1d(xs_new, p1_new, p2_new)
print "{} -> {}".format(distance_before, distance_after)
title = "{}: original distance = {:.2f}, discretized distance = {:.2f}".format(testName, distance_before, distance_after)
d11_before = earthmover1d(xs, p1, p1)
d11_after = earthmover1d(xs_new, p1_new, p1_new)
assert d11_before == 0
assert d11_after == 0
epsilon = 1e-6
if distance_after - distance_before > epsilon:
title += "; counterexample"
fig, axes = plt.subplots(3, 2, sharex='col')
a1 = [axes[i][0] for i in range(3)]
a2 = [axes[i][1] for i in range(3)]
showDiscretization(xs, p1, xs_new, p1_new, testMapping, axes = a1)
showDiscretization(xs, p2, xs_new, p2_new, testMapping, axes = a2)
fig.suptitle(title)
break
plt.show()
