def predict(self, X: np.ndarray)->np.ndarray: ''' Predict label probability for the input X Parameters: X is the data input. X is one dimensional or two dimensional numpy array. Return: predicted label probability, which is a one dimensional numpy array. ''' X = self.add_feature(X) # (n, m) -> (n, m+1) #### write your code below #### return #### write your code above ####。请帮我完成需要填写的代码
时间: 2024-02-10 14:17:16 浏览: 33
好的,根据您提供的代码,以下是需要填写的代码:
```python
# compute predicted label probability
prob = self.sigmoid(X)
return prob
```
以上代码会先调用 `add_feature` 方法为输入数据添加全为1的一列,然后将处理后的数据作为自变量,带入 `sigmoid` 函数中,得到模型预测的标签概率。
希望对您有帮助!
相关问题
解释下面这段代码,并说明哪些参数是可调整的:def adjust_predicts(score, label, threshold, pred=None, calc_latency=False): """ Calculate adjusted predict labels using given `score`, `threshold` (or given `pred`) and `label`. Args: score (np.ndarray): The anomaly score label (np.ndarray): The ground-truth label threshold (float): The threshold of anomaly score. A point is labeled as "anomaly" if its score is lower than the threshold. pred (np.ndarray or None): if not None, adjust `pred` and ignore `score` and `threshold`, calc_latency (bool): Returns: np.ndarray: predict labels Method from OmniAnomaly (https://github.com/NetManAIOps/OmniAnomaly) """ if label is None: predict = score > threshold return predict, None if pred is None: if len(score) != len(label): raise ValueError("score and label must have the same length") predict = score > threshold else: predict = pred actual = label > 0.1 anomaly_state = False anomaly_count = 0 latency = 0 for i in range(len(predict)): if any(actual[max(i, 0) : i + 1]) and predict[i] and not anomaly_state: anomaly_state = True anomaly_count += 1 for j in range(i, 0, -1): if not actual[j]: break else: if not predict[j]: predict[j] = True latency += 1 elif not actual[i]: anomaly_state = False if anomaly_state: predict[i] = True if calc_latency: return predict, latency / (anomaly_count + 1e-4) else: return
该函数用于调整预测标签,基于给定的分数 `score`、阈值 `threshold`(或给定的预测值 `pred`)和真实标签 `label`。 如果一个点的分数低于阈值,则将其标记为“异常”点。
参数 `score`、`label` 和 `threshold` 都是可调整的。
参数说明:
- `score`:np.ndarray 类型,表示每个时间步的异常分数;
- `label`:np.ndarray 类型,表示每个时间步的真实标签;
- `threshold`:float 类型,表示异常分数的阈值。如果一个点的分数低于阈值,则将其标记为“异常”点;
- `pred`:np.ndarray 类型或者 None,表示是否给定预测值,如果给定,会忽略 `score` 和 `threshold`;
- `calc_latency`:bool 类型,表示是否计算延迟。
该函数首先判断是否有真实标签 `label`,如果没有,则直接通过阈值 `threshold` 对异常分数 `score` 进行预测,并返回预测标签和延迟。如果有真实标签,则通过阈值 `threshold` 或预测值 `pred` 对异常分数 `score` 进行预测,并根据实际情况对预测标签进行调整,最终返回调整后的标签和延迟。
其中 `actual` 表示真实标签中大于 0.1 的标签,`anomaly_state` 表示当前是否处于异常状态,`anomaly_count` 表示异常的数量,`latency` 表示延迟的数量。
该函数中可调整的参数是 `score`、`label` 和 `threshold`。
AttributeError: 'numpy.ndarray' object has no attribute 'predict'
This error message indicates that you are trying to call the `predict` method on a NumPy array object, which does not have that method.
The `predict` method is usually part of a machine learning model object, such as a scikit-learn estimator, that has been trained on data.
To fix this error, you should check that you are calling the `predict` method on the correct object. If you are trying to make predictions with a machine learning model, make sure that you have properly trained the model and that you are calling the `predict` method on the model object, not on the input data.
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