Порівняти коміти
2 commits
316c910c3a
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67bc7efd5c
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67bc7efd5c | ||
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6cd6df4b89 |
1 змінених файлів з 5 додано та 3 видалено
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@ -242,6 +242,7 @@ def stats_NFDBP(R, N, t_i):
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for n in range(N):
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for n in range(N):
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print("Mean H_{} : {} (variance {})".format(n, mean(H[n]), variance(H[n])))
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print("Mean H_{} : {} (variance {})".format(n, mean(H[n]), variance(H[n])))
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# TODO variance for T_k doesn't see right
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print("Mean T_{} : {} (variance {})".format(k, mean(Sum_T), variance(Sum_T)))
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print("Mean T_{} : {} (variance {})".format(k, mean(Sum_T), variance(Sum_T)))
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# Loi math
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# Loi math
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for u in range(N):
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for u in range(N):
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@ -285,8 +286,9 @@ def stats_NFDBP(R, N, t_i):
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P
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P
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)
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)
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)
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)
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ax.legend(loc="upper left", title="Legend")
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ax.legend(loc="upper right", title="Legend")
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# TODO fix the graph below
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# Mathematical P(Ti=k) plot. It shows the Ti(t_i) law with the probability of each number of items.
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# Mathematical P(Ti=k) plot. It shows the Ti(t_i) law with the probability of each number of items.
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print(len(Tk[t_i]))
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print(len(Tk[t_i]))
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bx = fig.add_subplot(222)
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bx = fig.add_subplot(222)
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@ -310,7 +312,7 @@ def stats_NFDBP(R, N, t_i):
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bx.set_title(
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bx.set_title(
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"T{} histogram for {} items (Number of items in each bin)".format(t_i, P)
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"T{} histogram for {} items (Number of items in each bin)".format(t_i, P)
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)
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)
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bx.legend(loc="upper left", title="Legend")
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bx.legend(loc="upper right", title="Legend")
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# Loi mathematique
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# Loi mathematique
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print(T_maths)
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print(T_maths)
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@ -333,7 +335,7 @@ def stats_NFDBP(R, N, t_i):
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cx.set_ylabel("P(T{}=i)".format(t_i))
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cx.set_ylabel("P(T{}=i)".format(t_i))
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cx.set_xlabel("Bins i=(1-{})".format(N))
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cx.set_xlabel("Bins i=(1-{})".format(N))
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cx.set_title("Theoretical T{} values in %".format(t_i))
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cx.set_title("Theoretical T{} values in %".format(t_i))
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cx.legend(loc="upper left", title="Legend")
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cx.legend(loc="upper right", title="Legend")
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plt.show()
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plt.show()
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