Расчёт сходства методом Пирсона

import tensorflow as tf
 
normRatings = tf.constant([[0.2, 1.2, 0.2, 0, -0.8, -0.8], [-0.33, -0.33, -0.33, -1.33, 0.67, 1.67]])
numerator = tf.matmul(normRatings, tf.transpose(normRatings))
 
ones = tf.where(tf.not_equal(normRatings, 0), tf.ones(normRatings.shape), tf.zeros(normRatings.shape))
sqr = tf.multiply(normRatings, normRatings)
 
numeratorData = numerator.numpy()
 
for i in range(normRatings.shape[0]):
    for j in range(i, normRatings.shape[0]):
        maskI = ones[i:i+1, :]
        maskJ = ones[j:j+1, :]
        ANDmask = tf.multiply(maskI, maskJ)
 
        sqrI = tf.multiply(ANDmask, sqr[i:i+1, :])
        sqrJ = tf.multiply(ANDmask, sqr[j:j+1, :])
        sqrtI = tf.sqrt(tf.reduce_sum(sqrI))
        sqrtJ = tf.sqrt(tf.reduce_sum(sqrJ))
        numeratorData[i][j] = numeratorData[i][j] / (sqrtI.numpy() * sqrtJ.numpy())
        if i != j:
            numeratorData[j][i] = numeratorData[j][i] / (sqrtI.numpy() * sqrtJ.numpy())
 
print(numeratorData)

import tensorflow as tf
 
normRatings = tf.constant([[0.2, 1.2, 0.2, 0, -0.8, -0.8], [-0.33, -0.33, -0.33, -1.33, 0.67, 1.67])
 
squared_normRatings = tf.square(normRatings)
numerator = tf.matmul(normRatings, tf.transpose(normRatings))
ones = tf.where(tf.not_equal(normRatings, 0), tf.ones_like(normRatings), tf.zeros_like(normRatings))
squared_ones = tf.square(ones)
sqrt_sum_squares = tf.sqrt(tf.reduce_sum(tf.multiply(squared_normRatings, squared_ones), axis=1))
denominator = tf.einsum('i,j->ij', sqrt_sum_squares, sqrt_sum_squares)
numeratorData = tf.where(tf.not_equal(denominator, 0), numerator / denominator, tf.zeros_like(numerator))
 
print(numeratorData)

import tensorflow as tf
 
normRatings = tf.constant([[0.2, 1.2, 0.2, 0, -0.8, -0.8], [-0.33, -0.33, -0.33, -1.33, 0.67, 1.67])
 
squared_normRatings = tf.square(normRatings)
numerator = tf.matmul(normRatings, normRatings, transpose_b=True)
mask = tf.where(tf.not_equal(normRatings, 0), 1.0, 0.0)
denominator = tf.matmul(tf.sqrt(tf.reduce_sum(tf.multiply(squared_normRatings, mask), axis=1, keepdims=True), transpose_b=True))
numeratorData = numerator / denominator / denominator
 
print(numeratorData)