Which either means that my code is stupid or scipy is extremely well made.
SciPy produces the exact same result in blink of the eye. Nasledujci kd na vpoet euklidovskej vzdialenosti medzi dvoma bodmi sa vrti &lsqb &lsqb 9.&rsqb &rsqb : zo scipy import priestorov d1 &lsqb &lsqb 11, 3&rsqb &rsqb d2 &lsqb &lsqb 2, 3&rsqb. My python code takes like 5 minutes to complete on 3000 vertices, while searing my CPU. (XA, XB, metric'euclidean', p2, VNone, VINone, wNone) source Computes distance between each pair of the two collections of inputs. Is my approach too simpleminded and naive and scipy uses some hardcore advanced mathematics, which i am not aware of? Or can it be many times faster just because the same algorithm is written in C and then called by scipy from python? This is what scipy implements, and it far from easy for an average user. However, it is in fact only necessary to store 100 x 256 x 256 x 20 values, which is well below a gigabyte. See Notes for common calling conventions. If you wanted to compute the CDIST of the images in the sequence, the forward + backward pass of this operation would not fit on your GPU. #(_("Calculating distance %d of %d (SciPy not installed => using SLOW AF fallback method)"%(i,len(verts))), True) for j in range( i, len( verts)):ĭ = D = self. (XA, XB, metric'euclidean', args, kwargs) source Computes distance between each pair of the two collections of inputs. empty(( len( verts), len( verts)), dtype = np.
# Pairwise distance between verts #Use SciPy, otherwise use fallback try: If VI is not None, VI will be used as the inverse covariance matrix. The Mahalanobis distance between two points u and v is ( u v) ( 1 / V) ( u v) T where ( 1 / V) (the VI variable) is the inverse covariance. Return (( B - A) ** 2 +( B - A) ** 2 +( B - A) ** 2) **( 1.0 / 2) Y pdist (X, 'mahalanobis', VINone) Computes the Mahalanobis distance between the points.
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