sklearn.utils.sparsefuncs.mean_variance_axis
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sklearn.utils.sparsefuncs.mean_variance_axis(X, axis, weights=None, return_sum_weights=False)
[source] -
Compute mean and variance along an axis on a CSR or CSC matrix.
- Parameters
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Xsparse matrix of shape (n_samples, n_features)
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Input data. It can be of CSR or CSC format.
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axis{0, 1}
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Axis along which the axis should be computed.
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weightsndarray of shape (n_samples,) or (n_features,), default=None
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if axis is set to 0 shape is (n_samples,) or if axis is set to 1 shape is (n_features,). If it is set to None, then samples are equally weighted.
New in version 0.24.
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return_sum_weightsbool, default=False
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If True, returns the sum of weights seen for each feature if
axis=0
or each sample ifaxis=1
.New in version 0.24.
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- Returns
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meansndarray of shape (n_features,), dtype=floating
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Feature-wise means.
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variancesndarray of shape (n_features,), dtype=floating
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Feature-wise variances.
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sum_weightsndarray of shape (n_features,), dtype=floating
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Returned if
return_sum_weights
isTrue
.
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© 2007–2020 The scikit-learn developers
Licensed under the 3-clause BSD License.
https://scikit-learn.org/0.24/modules/generated/sklearn.utils.sparsefuncs.mean_variance_axis.html