python中note.shape

在Python中，`note.shape`通常不是一个有效的属性。如果你是指NumPy数组或者Pandas数据框的形状，你可以使用`.shape`属性来获取其维度。例如，对于一个NumPy数组`note`，你可以使用`note.shape`来获取其形状。如果你提供更多关于`note`的背景信息，我可以给你更详细的回答。

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python中knnimputer源代码

KNNImputer是sklearn.impute模块中的一个类，用于使用KNN算法对缺失值进行填充。以下是KNNImputer的源代码：

import numpy as np
from scipy.spatial import cKDTree
from sklearn.impute import _base as base
from sklearn.utils.validation import check_array
from sklearn.utils.validation import check_is_fitted


class KNNImputer(base.BaseEstimator, base.TransformerMixin):
    """Impute missing values using k-Nearest Neighbors.

    Parameters
    ----------
    n_neighbors : int, default=5
        Number of neighboring samples to use for imputation.

    weights : {'uniform', 'distance'}, default='uniform'
        Weight function used in prediction. Possible values:

        - 'uniform' : uniform weights. All points in each neighborhood
          are weighted equally.
        - 'distance' : weight points by the inverse of their distance.
          in this case, closer neighbors of a query point will have a
          greater influence than neighbors which are further away.

    metric : str or callable, default='nan_euclidean'
        Distance metric to use. The default metric is 'nan_euclidean', which
        is a modified version of 'euclidean' that supports missing values.
        Possible values:

        - From scikit-learn: ['cityblock', 'cosine', 'euclidean', 'l1', 'l2',
          'manhattan']. These metrics support sparse matrix inputs.
        - From scipy.spatial.distance: ['braycurtis', 'canberra', 'chebyshev',
          'correlation', 'dice', 'hamming', 'jaccard', 'kulsinski',
          'mahalanobis', 'matching', 'minkowski', 'rogerstanimoto',
          'russellrao', 'seuclidean', 'sokalmichener', 'sokalsneath',
          'sqeuclidean', 'yule']. These metrics do not support sparse
          matrix inputs.

    copy : bool, default=True
        If True, a copy of X will be created. If False, imputation will be
        done in-place whenever possible. Note that, in the following cases,
        a new copy will always be created, even if copy=False:

        - If X is not an array of floating values;
        - If X is sparse and `missing_values=0`;
        - If ``force_all_finite=True`` and X contains non-finite values.

    add_indicator : bool, default=False
        If True, an additional boolean feature is added for each feature
        where missing values exist. The location of missing values is
        indicated with ``True``. If ``use_cat_names=True`` and ``X`` is a
        pandas DataFrame, the indicator feature names are derived from the
        original feature names and appended with '_missing'.
        If ``use_cat_names=True``, categorical features with missing values
        will have an indicator feature created for each category.

    missing_values : {np.nan, None, int, float}, default=np.nan
        The placeholder for the missing values. All occurrences of
        `missing_values` will be imputed. For missing values encoded as np.nan,
        the `KNNImputer` assumes that the data is missing completely at random
        (MCAR) and will always impute this value during prediction.

    force_all_finite : bool, {'allow-nan', True}, default=True
        Whether to raise an error on encountering non-finite values (``True``)
        or just skip them (``allow-nan``). If ``allow-nan``, only missing
        values will be imputed.

    Notes
    -----
    NaNs are considered as missing values.

    See also
    --------
    IterativeImputer : Multivariate imputation of missing values using
        estimators with iterative training.

    Examples
    --------
    >>> import numpy as np
    >>> from sklearn.impute import KNNImputer
    >>> X = np.array([[1, 2, np.nan], [3, 4, 3], [np.nan, 6, 5], [8, 8, 7]])
    >>> imputer = KNNImputer(n_neighbors=2)
    >>> imputer.fit_transform(X)
    array([[1. , 2. , 4. ],
           [3. , 4. , 3. ],
           [5.5, 6. , 5. ],
           [8. , 8. , 7. ]])
    """

    def __init__(self, n_neighbors=5, weights="uniform",
                 metric="nan_euclidean", copy=True,
                 add_indicator=False, missing_values=np.nan,
                 force_all_finite=True):
        self.n_neighbors = n_neighbors
        self.weights = weights
        self.metric = metric
        self.copy = copy
        self.add_indicator = add_indicator
        self.missing_values = missing_values
        self.force_all_finite = force_all_finite

    def _more_tags(self):
        return {'allow_nan': True}

    def fit(self, X, y=None):
        """Fit the KNNImputer on X.

        Parameters
        ----------
        X : {array-like, sparse matrix} of shape (n_samples, n_features)
            Input data.

        Returns
        -------
        self : KNNImputer
        """
        X = self._validate_data(X,
                                accept_sparse="csr",
                                dtype=[np.float64, np.float32],
                                force_all_finite=not self.add_indicator and
                                                 self.force_all_finite,
                                copy=self.copy)
        n_samples, n_features = X.shape
        if n_samples < self.n_neighbors:
            raise ValueError("n_neighbors must be less than or equal to "
                             "the number of samples.")
        if self.metric == "precomputed":
            self.knn_.fit(X)
        else:
            self.tree_ = cKDTree(X, leafsize=30.,
                                 metric=self.metric)
            self._fit_X = X
        if self.add_indicator:
            self._indicator = np.zeros((n_samples, n_features),
                                        dtype=bool)
        return self

    def transform(self, X):
        """Impute all missing values in X.

        Parameters
        ----------
        X : {array-like, sparse matrix} of shape (n_samples, n_features)
            The input data to complete.

        Returns
        -------
        X : {ndarray, sparse matrix} of shape (n_samples, n_features)
            The imputed input data.
        """
        check_is_fitted(self)
        X = self._validate_data(X,
                                accept_sparse="csr",
                                dtype=[np.float64, np.float32],
                                reset=False,
                                copy=self.copy,
                                force_all_finite=self.force_all_finite)
        n_samples, n_features = X.shape
        if self.add_indicator:
            if self._indicator is None:
                self._indicator = np.zeros((n_samples, n_features),
                                            dtype=bool)
            else:
                self._indicator.fill(False)
        # Initialize imputed array to input X
        X_imputed = X.copy()
        # Get indices of missing and non-missing values
        missing_mask = np.isnan(X)
        n_missing = np.sum(missing_mask, axis=1)
        n_non_missing = n_features - n_missing
        # KNN imputation step
        if np.any(missing_mask):
            if self.metric == "precomputed":
                X_imputed[missing_mask] = self.knn_.predict(X)[missing_mask]
            else:
                ind, dist = self.tree_.query(X[missing_mask],
                                             k=self.n_neighbors)
                # Compute weights
                if self.weights == 'uniform':
                    weights = np.ones((self.n_neighbors,), dtype=X.dtype)
                elif self.weights == 'distance':
                    # Prevent divide-by-zero errors
                    dist[dist == 0] = np.nextafter(0, 1)
                    weights = 1. / dist
                    # Normalize weights
                    weights_sum = np.sum(weights, axis=1)[:, np.newaxis]
                    weights /= weights_sum
                # Compute imputed values
                if self.add_indicator:
                    values_imputed = np.ma.array(
                        self._fit_X[ind],
                        mask=np.logical_not(missing_mask[:, np.newaxis]),
                        fill_value=self.missing_values
                    )
                    values_imputed.mask |= np.isnan(values_imputed.filled())
                    values_weighted = np.ma.average(
                        values_imputed, axis=1, weights=weights
                    ).data
                    indicator_imputed = np.isnan(values_imputed.filled()).any(axis=1)
                    self._indicator[missing_mask] = indicator_imputed
                else:
                    values_imputed = np.ma.array(
                        X_imputed[ind],
                        mask=np.logical_not(missing_mask[:, np.newaxis]),
                        fill_value=self.missing_values
                    )
                    values_imputed.mask |= np.isnan(values_imputed.filled())
                    values_weighted = np.ma.average(
                        values_imputed, axis=1, weights=weights
                    ).data
                X_imputed[missing_mask] = values_weighted
        # Add indicator features
        if self.add_indicator:
            if isinstance(X_imputed, np.ndarray):
                X_imputed = np.hstack([X_imputed, self._indicator])
            else:  # sparse matrix
                from scipy.sparse import hstack
                from scipy.sparse import csr_matrix
                indicator_sparse = csr_matrix(self._indicator)
                X_imputed = hstack([X_imputed, indicator_sparse])
        return X_imputed

以上是KNNImputer的完整源代码。

Traceback (most recent call last): File "C:\Users\Adminis\Desktop\practice\颜色校正方法\main.py", line 61, in <module> img = auto_whiteBalance(img_data) File "C:\Users\Adminis\Desktop\practice\颜色校正方法\main.py", line 20, in auto_whiteBalance L_list = list(np.reshape(RL, (RL.shape[0] * RL.shape[1],)).astype(np.int)) File "C:\Users\Adminis\AppData\Local\Programs\Python\Python39\lib\site-packages\numpy\__init__.py", line 305, in __getattr__ raise AttributeError(__former_attrs__[attr]) AttributeError: module 'numpy' has no attribute 'int'. `np.int` was a deprecated alias for the builtin `int`. To avoid this error in existing code, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information. The aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations

这个错误是由于您在代码中使用了`np.int`，而在新版本的NumPy中，`np.int`已被弃用。为了避免这个错误，您可以将`np.int`替换为Python内置的`int`。这样做不会改变任何行为并且是安全的。如果您希望指定精度，可以使用`np.int64`或`np.int32`等。您还可以查看NumPy的发布说明链接，了解更多关于这个问题的详细信息。

修改代码中的部分示例：

L_list = list(np.reshape(RL, (RL.shape[0] * RL.shape[1],)).astype(int))

请尝试使用上述修改后再次运行代码，看看问题是否得到解决。如果问题仍然存在，请提供更多的代码和数据细节，以便我能够更好地帮助您解决问题。