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src/s/c/scikit-learn-0.14.1/sklearn/preprocessing/data.py   scikit-learn(Download)
from ..utils import atleast2d_or_csr
from ..utils import safe_asarray
from ..utils import warn_if_not_float
 
from ..utils.sparsefuncs import inplace_csr_row_normalize_l1
            raise ValueError("Can only scale sparse matrix on axis=0, "
                             " got axis=%d" % axis)
        warn_if_not_float(X, estimator='The scale function')
        if not sparse.isspmatrix_csr(X):
            X = X.tocsr()
    else:
        X = np.asarray(X)
        warn_if_not_float(X, estimator='The scale function')
        mean_, std_ = _mean_and_std(
            X, axis, with_mean=with_mean, with_std=with_std)
        """
        X = check_arrays(X, sparse_format="dense", copy=self.copy)[0]
        warn_if_not_float(X, estimator=self)
        feature_range = self.feature_range
        if feature_range[0] >= feature_range[1]:
        """
        X = check_arrays(X, copy=self.copy, sparse_format="csr")[0]
        if warn_if_not_float(X, estimator=self):
            X = X.astype(np.float)
        if sparse.issparse(X):