将所有内容强制转换为n x d，然后使用np.where。

positive = [0.07973422, 0.02657807]
negative = [0.04651163, 0.02491694]  # both of these have the dimension d

x = np.array(
    [[0, 1], [1, 0], [1, 1]]
)  # dimension is n*d : note that the d is same as above
y = np.array([-1, 1, -1])  # dimension is n

d = len(positive)
n = len(x)

# Cast all to n x d

positive = np.array([positive]*n)
negative = np.array([negative]*n)

y = np.repeat(y, d).reshape(n, d)

# Determine whether to use pos or neg probabilities
pos_neg = np.where(y == 1, positive, negative)

# Determine whether to use prob or 1-prob
probs = np.where(x == 0, 1 - pos_neg, pos_neg)

# Take logs and then sum
log_probs = np.log(probs)

log_like = np.sum(log_probs, axis = 1)

print(log_like)