def predict_from_naive_bayes_model(model, matrix): """Use a Naive Bayes model to compute predictions for a target matrix. This function should be able to predict on the models that fit_naive_bayes_model outputs. Args: model: A trained model from fit_naive_bayes_model matrix: A numpy array containing word counts Returns: A numpy array containg the predictions from the model """ # *** START CODE HERE *** log_probabilities = [] for i in range(2): log_probabilities.append(np.sum(matrix * model.log_class_priors[i] + model.log_likelihoods[i])) return np.argmax(log_probabilities) # Return the class with the highest probability # *** END CODE HERE ***代码报错AttributeError: 'dict' object has no attribute 'log_class_priors'请帮我修改

根据错误提示，我们可以看出 `model` 的类型是 `dict`，而不是模型类。因此，我们需要对传入的 `model` 进行修改。

假设我们的模型类为 `NaiveBayesModel`，其中包含了 `log_class_priors` 和 `log_likelihoods` 两个属性。那么，我们需要将 `model` 转换为 `NaiveBayesModel` 类型。

修改后的代码如下：

def predict_from_naive_bayes_model(model, matrix):
    """Use a Naive Bayes model to compute predictions for a target matrix.
    This function should be able to predict on the models that fit_naive_bayes_model
    outputs.
    Args:
        model: A trained model from fit_naive_bayes_model
        matrix: A numpy array containing word counts
    Returns: A numpy array containg the predictions from the model
    """
    # Convert the model to NaiveBayesModel class
    model = NaiveBayesModel(model['log_class_priors'], model['log_likelihoods'])

    log_probabilities = []
    for i in range(2):
        log_probabilities.append(np.sum(matrix * model.log_class_priors[i] + model.log_likelihoods[i]))
    return np.argmax(log_probabilities) # Return the class with the highest probability

现在，我们可以将传入的 `model` 转换为 `NaiveBayesModel` 类型，并使用它来计算预测结果。