patrick - 5 months ago 10x

Python Question

I am trying to calculate which points in my data set (in the shape of a numpy array called "matrix") are closest to a vector (array called "vector") in ndimensional space. Then, I want to extract these same vectors from a data set which is identical to "matrix" but includes additional labels (="matrix_with_labels").

`vector=([1,2,3,...])`

matrix=[[1,2,3,...], [2,4,6,...], ...]]

matrix_with_labels=[[a,1,2,3,...], [b,2,4,6,...], ...]]

Thus, I compute the distances between the vector and each item in the matrix:

`dist=scipy.spatial.distance.cdist(matrix,vector,'euclidean')`

Then I sort these distances to identify the closest neighbors:

`sorted_index=np.argsort(dist, axis=0)`

Then I try to sort the "matrix_with_labels" by "sorted_index", using

`numpy.take`

`result= matrix_with_labels.take(sorted_index, 0)`

The outcome looks just fine until I try to process it further - it seems to have changed shape:

`print result.shape`

(20, 1, 11)

When I look at the shape of the initial "matrix_with_labels", however:

`matrix_with_labels.shape`

(20, 11)

The documentation on take says:

subarray : ndarray

The returned array has the same type as a.

What am I doing wrong? Any help is appreciated!

Answer

If you're starting with a `(20, 11)`

shape, I think the only way to get a `(20, 1, 11)`

shape is if `x`

has shape `(1, 11)`

.

Try `result = matrix_with_labels.take(x.reshape(-1), 0)`

.

Source (Stackoverflow)

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