numpy.argmin
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numpy.argmin(a, axis=None, out=None)
[source] -
Returns the indices of the minimum values along an axis.
- Parameters
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aarray_like
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Input array.
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axisint, optional
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By default, the index is into the flattened array, otherwise along the specified axis.
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outarray, optional
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If provided, the result will be inserted into this array. It should be of the appropriate shape and dtype.
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- Returns
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index_arrayndarray of ints
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Array of indices into the array. It has the same shape as
a.shape
with the dimension alongaxis
removed.
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See also
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amin
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The minimum value along a given axis.
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unravel_index
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Convert a flat index into an index tuple.
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take_along_axis
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Apply
np.expand_dims(index_array, axis)
from argmin to an array as if by calling min.
Notes
In case of multiple occurrences of the minimum values, the indices corresponding to the first occurrence are returned.
Examples
>>> a = np.arange(6).reshape(2,3) + 10 >>> a array([[10, 11, 12], [13, 14, 15]]) >>> np.argmin(a) 0 >>> np.argmin(a, axis=0) array([0, 0, 0]) >>> np.argmin(a, axis=1) array([0, 0])
Indices of the minimum elements of a N-dimensional array:
>>> ind = np.unravel_index(np.argmin(a, axis=None), a.shape) >>> ind (0, 0) >>> a[ind] 10
>>> b = np.arange(6) + 10 >>> b[4] = 10 >>> b array([10, 11, 12, 13, 10, 15]) >>> np.argmin(b) # Only the first occurrence is returned. 0
>>> x = np.array([[4,2,3], [1,0,3]]) >>> index_array = np.argmin(x, axis=-1) >>> # Same as np.min(x, axis=-1, keepdims=True) >>> np.take_along_axis(x, np.expand_dims(index_array, axis=-1), axis=-1) array([[2], [0]]) >>> # Same as np.max(x, axis=-1) >>> np.take_along_axis(x, np.expand_dims(index_array, axis=-1), axis=-1).squeeze(axis=-1) array([2, 0])
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https://numpy.org/doc/1.18/reference/generated/numpy.argmin.html