擅长:python、mysql、java
<p>对这两个集合使用估计的pdf如何?你知道吗</p>
<pre><code>def get_most_likely_distribution_membership(value,d1,d2):
nparam_density1 = stats.kde.gaussian_kde(d1) # can use a different kernel
nparam_density2 = stats.kde.gaussian_kde(d2)
x = np.linspace(-20, 30, 200) # maybe pre-define a range
nparam_density1 = nparam_density1(x)
nparam_density2 = nparam_density2(x)
assert d1!=d2
if nparam_density1[np.where(abs(x-(value))==min(abs(x-(value))))].tolist() > nparam_density2[np.where(abs(x-(value))==min(abs(x-(value))))].tolist():
return 1
else:
return 2
</code></pre>
<p>本质上,我们是说,如果一个单一的值在一个分布中更可能,它可能来自那个分布。你知道吗</p>
<p>示例:</p>
<pre><code>import numpy as np
import scipy.stats as stats
import matplotlib.pyplot as plt
a = [5,4,8,3,6,4,7,2] # 1
b = [9,5,14,10,11,18,9] # 2
print(get_most_likely_distribution_membership(6,a,b))
print(get_most_likely_distribution_membership(10,a,b))
</code></pre>
<p>分别为1和2。你知道吗</p>