What is the process for creating a mask suitable for multinomial logistic regression with shapes x (n, m) and y (n,)?

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The process for creating a mask suitable for multinomial logistic regression with shapes x (n, m) and y (n) involves the following steps:

1. Identify the number of classes that are present in the y vector.

2. Create an empty mask of shape (n, c), where c represents the number of classes.

3. Iterate through the y vector and set the corresponding row of the mask to 1 for each class label.

4. Use the mask as the target variable in the multinomial logistic regression model.

Here is an example implementation in Python:

import numpy as np

# example input data
n = 100
m = 3
c = 4
x = np.random.rand(n, m)
y = np.random.randint(c, size=n)

# create mask
mask = np.zeros((n, c))
mask[np.arange(n), y] = 1

# fit multinomial logistic regression model
from sklearn.linear_model import LogisticRegression
model = LogisticRegression(multi_class='multinomial')
model.fit(x, mask)