def _multinomial_loss(w, X, Y, alpha, sample_weight):
    n_classes = Y.shape[1]
    n_features = X.shape[1]
    fit_intercept = w.size == (n_classes * (n_features + 1))
    w = w.reshape(n_classes, -1)
    sample_weight = sample_weight[:, np.newaxis]
    if fit_intercept:
        intercept = w[:, -1]
        w = w[:, :-1]
    else:
        intercept = 0
    p = safe_sparse_dot(X, w.T)
    p += intercept
    p -= logsumexp(p, axis=1)[:, np.newaxis]
    loss = -(sample_weight * Y * p).sum()
    loss += 0.5 * alpha * squared_norm(w)
    p = np.exp(p, p)
    return loss, p, w
Deletes a certificate from the specified account.
dataset/ETHPy150Open scikit-learn/scikit-learn/sklearn/linear_model/logistic.py/_multinomial_loss--crossover with--dataset/ETHPy150Open Azure/azure-sdk-for-python/azure-batch/azure/batch/operations/certificate_operations.py/CertificateOperations.delete