sklearn.utils.extmath.weighted_mode — scikit-learn 0.20.4 documentation (original) (raw)
sklearn.utils.extmath. weighted_mode(a, w, axis=0)[source]¶
Returns an array of the weighted modal (most common) value in a
If there is more than one such value, only the first is returned. The bin-count for the modal bins is also returned.
This is an extension of the algorithm in scipy.stats.mode.
| Parameters: | a : array_like n-dimensional array of which to find mode(s). w : array_like n-dimensional array of weights for each value axis : int, optional Axis along which to operate. Default is 0, i.e. the first axis. |
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| Returns: | vals : ndarray Array of modal values. score : ndarray Array of weighted counts for each mode. |
Examples
from sklearn.utils.extmath import weighted_mode x = [4, 1, 4, 2, 4, 2] weights = [1, 1, 1, 1, 1, 1] weighted_mode(x, weights) (array([4.]), array([3.]))
The value 4 appears three times: with uniform weights, the result is simply the mode of the distribution.
weights = [1, 3, 0.5, 1.5, 1, 2] # deweight the 4's weighted_mode(x, weights) (array([2.]), array([3.5]))
The value 2 has the highest score: it appears twice with weights of 1.5 and 2: the sum of these is 3.