def sort(args) -> list:
"""Performs a sort based on the given args.
Args is of the format (dist, auc, n0, n1) and is one tuple/list.
Throws an error if the array did not sort correctly.
Returns the results."""
dist, auc, n0, n1 = args
results = list()
data, D0, D1 = continuousScale(n0, n1)
comp = Comparator(data, level=0, rand=True)
sep = genSep(dist, auc)
comp.genRand(n0, n1, sep, dist)
for arr, stats in treeMergeSort(data, comp, [(D0, D1), dist, auc], n=2):
stats.extend([len(comp), comp.genSeps(), comp.pc[-1]])
comp.resetPC()
results.append(stats)
if arr != sorted(arr, key=lambda x: comp.getLatentScore(x)[0]):
print(arr)
print(sorted(arr, key=lambda x: comp.getLatentScore(x)[0]))
raise AssertionError("did not sort")
return results
update,
frames=np.arange(0, frames),
interval=100)
anim.save("rocs.gif", writer=PillowWriter(fps=10))
pbar.close()
else:
import matplotlib.pyplot as plt
from apng import APNG
from DylSort import treeMergeSort
from DylComp import Comparator
from DylData import continuousScale
from DylMath import genROC, avROC
seed = 15
data, D0, D1 = continuousScale(128, 128)
comp = Comparator(data, level=0, rand=True, seed=seed)
comp.genRand(len(D0), len(D1), 7.72, 'exponential')
np.random.seed(seed)
im = APNG()
fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2)
fig.suptitle("Pass number, MSE true, MSE empirical")
x = np.linspace(0, 1, num=200)
y = x**(1 / 7.72)
ax1.set_aspect('equal', 'box')
ax2.set_aspect('equal', 'box')
elo = simulation_ELO_targetAUC(True)
merge = treeMergeSort(data,
comp,
statParams=[(D0, D1)],
combGroups=False)
plt.tight_layout()
for i in trange(8):