# tft_beam.info_theory.calculate_partial_expected_mutual_information

``````tft_beam.info_theory.calculate_partial_expected_mutual_information(
n,
x_i,
y_j
)
``````

Calculates the partial expected mutual information (EMI) of two variables.

EMI reflects the MI expected by chance, and is used to compute adjusted mutual information. See www.wikipedia.org/wiki/Adjusted_mutual_information.

The EMI for two variables x and y, is the sum of the expected mutual info for each value of x with each value of y. This function computes the EMI for a single value of each variable (x_i, y_j) and is thus considered a partial EMI calculation.

Specifically: EMI(x, y) = sum_{n_ij = max(0, x_i + y_j - n) to min(x_i, y_j)} ( n_ij / n * log2((n * n_ij / (x_i * y_j)) * ((x_i! * y_j! * (n - x_i)! * (n - y_j)!) / (n! * n_ij! * (x_i - n_ij)! * (y_j - n_ij)! * (n - x_i - y_j + n_ij)!))) where n_ij is the joint count of x taking on value i and y taking on value j, x_i is the count for x taking on value i, y_j is the count for y taking on value j, and n represents total count.

#### Args:

• `n`: The sum of weights for all values.
• `x_i`: The sum of weights for the first variable taking on value i
• `y_j`: The sum of weights for the second variable taking on value j

#### Returns:

Calculated expected mutual information for x_i, y_j.