比较提交

...

2 提交

作者 SHA1 备注 提交日期
Paul ALNET
67bc7efd5c chore: add todos 2023-06-04 08:39:42 +02:00
Paul ALNET
6cd6df4b89 fix: better legend placement on graphs 2023-06-04 08:39:29 +02:00

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@ -242,6 +242,7 @@ def stats_NFDBP(R, N, t_i):
for n in range(N):
print("Mean H_{} : {} (variance {})".format(n, mean(H[n]), variance(H[n])))
# TODO variance for T_k doesn't see right
print("Mean T_{} : {} (variance {})".format(k, mean(Sum_T), variance(Sum_T)))
# Loi math
for u in range(N):
@ -285,8 +286,9 @@ def stats_NFDBP(R, N, t_i):
P
)
)
ax.legend(loc="upper left", title="Legend")
ax.legend(loc="upper right", title="Legend")
# TODO fix the graph below
# Mathematical P(Ti=k) plot. It shows the Ti(t_i) law with the probability of each number of items.
print(len(Tk[t_i]))
bx = fig.add_subplot(222)
@ -310,7 +312,7 @@ def stats_NFDBP(R, N, t_i):
bx.set_title(
"T{} histogram for {} items (Number of items in each bin)".format(t_i, P)
)
bx.legend(loc="upper left", title="Legend")
bx.legend(loc="upper right", title="Legend")
# Loi mathematique
print(T_maths)
@ -333,7 +335,7 @@ def stats_NFDBP(R, N, t_i):
cx.set_ylabel("P(T{}=i)".format(t_i))
cx.set_xlabel("Bins i=(1-{})".format(N))
cx.set_title("Theoretical T{} values in %".format(t_i))
cx.legend(loc="upper left", title="Legend")
cx.legend(loc="upper right", title="Legend")
plt.show()