correlation(u, v, w=None, centered=True)

The correlation distance between u and v, is defined as

$$1 - \frac{(u - \bar{u}) \cdot (v - \bar{v})} {{\|(u - \bar{u})\|}_2 {\|(v - \bar{v})\|}_2}$$

where $\bar{u}$ is the mean of the elements of u and $x \cdot y$ is the dot product of $x$ and $y$ .

Parameters

u : (N,) array_like

Input array.

v : (N,) array_like

Input array.

w : (N,) array_like, optional

The weights for each value in u and v. Default is None, which gives each value a weight of 1.0

centered : bool, optional

If True, u and v will be centered. Default is True.

Returns

correlation : double

The correlation distance between 1-D array u and v.

Compute the correlation distance between two 1-D arrays.

Examples

See :

Local connectivity graph

Hover to see nodes names; edges to Self not shown, Caped at 50 nodes.

Using a canvas is more power efficient and can get hundred of nodes ; but does not allow hyperlinks; , arrows or text (beyond on hover)

SVG is more flexible but power hungry; and does not scale well to 50 + nodes.

All aboves nodes referred to, (or are referred from) current nodes; Edges from Self to other have been omitted (or all nodes would be connected to the central node "self" which is not useful). Nodes are colored by the library they belong to, and scaled with the number of references pointing them

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GitHub : /scipy/spatial/distance.py#585
type: <class 'function'>
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