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第577行： − 这是计算一个马尔科夫概率转移矩阵的Python源代码。输入tpm为一个满足行归一化条件的马尔科夫概率转移矩阵，返回的ei_all为其EI值。+ − # marginal distribution of y given x ~ Unifrom Dist+ − puy = tpm.sum(axis=0)+ − n = tpm.shape[0]+ − # replace 0 to a small positive number to avoid log error+ − eps = 1E-10+ − tpm_e = np.where(tpm==0, eps, tpm)+ − puy_e = np.where(tpm==0, eps, puy)+ − + − # calculate EI of specific x+ − ei_x = (np.log2(n * tpm_e / puy_e) / np.log2(log_base) * tpm).sum(axis=1)+ − + − # calculate total EI+ − ei_all = ei_x.mean()+ − return ei_all+ + + + + +
→计算EI的源代码
==计算EI的源代码==
==计算EI的源代码==
这是计算一个马尔科夫概率转移矩阵的Python源代码。输入tpm为一个满足行归一化条件的马尔科夫概率转移矩阵，返回的ei_all为其EI值，其余两项分别为确定性和简并性。
python:<syntaxhighlight lang="python3">
python:<syntaxhighlight lang="python3">
def tpm_ei(tpm, log_base = 2):
def tpm_ei(tpm, log_base = 2):
# marginal distribution of y given x ~ Unifrom Dist
puy = tpm.sum(axis=0)
n = tpm.shape[0]
# replace 0 to a small positive number to avoid log error
eps = 1E-10
tpm_e = np.where(tpm==0, eps, tpm)
puy_e = np.where(tpm==0, eps, puy)
# calculate EI of specific x
ei_x = (np.log2(n * tpm_e / puy_e) / np.log2(log_base) * tpm).sum(axis=1)
# calculate det and deg
det = np.log2(n) + (tpm * np.log2(tpm_e)).sum(axis=1).mean(axis=0)
deg = np.log2(n) + (tpm.mean(axis=0) * np.log2(tpm_e.mean(axis=0))).sum()
# calculate total EI
ei_all = ei_x.mean()
return ei_all,det/np.log2(log_base),deg/np.log2(log_base)
</syntaxhighlight>
</syntaxhighlight>