What is the best approach to calculate pairwise mutual information using numpy?

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One approach to calculate pairwise mutual information using numpy is as follows:

- Load your data as a numpy array.
- Define a function to calculate mutual information between two variables.
- Use nested loops, one for each pair of variables, to calculate the mutual information between each pair of variables.
- Create a matrix or dataframe to store the mutual information values for each pair of variables.

Here's an example code snippet:

```
import numpy as np
from sklearn.metrics import mutual_info_score
# load data as a numpy array
data = np.loadtxt("data.csv", delimiter=",")
# define function to calculate mutual information
def calc_mutual_info(x, y):
return mutual_info_score(x, y)
# initialize matrix to store mutual info values
n_vars = data.shape[1]
mi_matrix = np.zeros((n_vars, n_vars))
# calculate mutual information between each pair of variables
for i in range(n_vars):
for j in range(i+1, n_vars):
mi = calc_mutual_info(data[:,i], data[:,j])
mi_matrix[i,j] = mi
mi_matrix[j,i] = mi
# display results
print(mi_matrix)
```

Note that we use `sklearn.metrics.mutual_info_score`

to calculate the mutual information between two variables. We also use a nested loop to calculate the mutual information between each pair of variables, assuming that the data is stored as a matrix where each column represents a variable. Finally, we store the results in a matrix where each element represents the mutual information between two variables.

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Asked: ** 2022-11-23 11:00:00 +0000 **

Seen: **12 times**

Last updated: **Nov 09 '22**

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