[knn] queries cannot be provided directly, use the [knn] body parameter instead
时间: 2023-12-02 18:03:39 浏览: 54
这个错误是因为 Elasticsearch 7.0 版本开始,KNN 查询不再支持在查询语句中直接提供查询条件,而是需要使用查询体参数 [knn]。因此,如果您在查询语句中直接提供了 KNN 查询条件,就会出现这个错误。
以下是一个使用查询体参数 [knn] 来执行 KNN 查询的示例:
```
POST my_index/_search
{
"query": {
"bool": {
"must": [
{
"match": {
"title": "book"
}
},
{
"knn": {
"my_vector_field": {
"vector": [1, 2, 3],
"k": 10
}
}
}
]
}
}
}
```
在上面的示例中,我们使用了查询体参数 [knn] 来执行 KNN 查询,并且将其与 match 查询结合使用。需要注意的是,如果您使用的是 Elasticsearch 7.0 及以上版本,就需要使用查询体参数 [knn] 来执行 KNN 查询,而不能再在查询语句中直接提供查询条件。
相关问题
使用KNN算法时报错ValueError: The 'VI' parameter is required for the mahalanobis metric when Y is passed.
这个错误是由于在使用KNN算法时,使用了马氏距离(mahalanobis)作为距离度量,并且传递了Y参数(即标签),但是未提供必需的VI参数(即协方差矩阵的逆矩阵)。在使用mahalanobis作为距离度量时,必须提供VI参数。
解决此问题的方法是,在调用KNeighborsClassifier或KNeighborsRegressor时,将metric参数设置为"euclidean"或"manhattan",而不是"mahalanobis"。如果您确实需要使用mahalanobis距离,请确保提供VI参数。
例如,如果您想使用mahalanobis距离,请在调用KNeighborsClassifier时,添加以下参数:
```
knn = KNeighborsClassifier(n_neighbors=k, metric='mahalanobis', metric_params={'V': np.cov(X_train)})
```
其中,np.cov(X_train)是训练数据的协方差矩阵。
Implement the knn_classifier function with Python,
Here's an implementation of the k-nearest neighbors (KNN) classifier in Python:
```
import numpy as np
def knn_classifier(X_train, y_train, X_test, k):
"""
K-nearest neighbors classifier
Parameters:
X_train (numpy.ndarray): Training data features
y_train (numpy.ndarray): Training data labels
X_test (numpy.ndarray): Test data features
k (int): Number of nearest neighbors to consider
Returns:
y_pred (numpy.ndarray): Predicted labels for test data
"""
# Calculate distances between test data and training data
dists = np.sqrt(np.sum((X_train - X_test[:, np.newaxis])**2, axis=2))
# Get indices of k nearest neighbors for each test data point
knn_indices = np.argsort(dists, axis=1)[:, :k]
# Get labels of k nearest neighbors
knn_labels = y_train[knn_indices]
# Predict labels based on majority vote
y_pred = np.apply_along_axis(lambda x: np.bincount(x).argmax(), axis=1, arr=knn_labels)
return y_pred
```
This function takes in the training data features (`X_train`) and labels (`y_train`), test data features (`X_test`), and the number of nearest neighbors to consider (`k`). It first calculates the distances between each test data point and each training data point using the Euclidean distance metric. It then finds the indices of the k nearest neighbors for each test data point, and gets the corresponding labels. Finally, it predicts the label of each test data point based on the majority vote of its k nearest neighbors.
Note that this implementation assumes that the input data is in the form of numpy arrays. If your data is in a different format, you may need to modify the function accordingly. Also, this implementation uses the `np.apply_along_axis()` function to apply a function to each row of a 2D array. This can be slower than using a loop, but is more concise and often easier to read. If performance is a concern, you may want to consider using a loop instead.