`scipy.spatial.distance._distance:correlation`

Signature

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

Compute the correlation distance between two 1-D arrays.

The correlation distance between u and v, is defined as

1 - \frac{( u - u ˉ ) \cdot ( v - v ˉ )}{∥ ( u - u ˉ ) ∥ _{2} ∥ ( v - v ˉ ) ∥ _{2}}

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

u : (N,) array_like of floats: Input array.
v : (N,) array_like of floats: Input array.
w : (N,) array_like of floats, 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.

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

Find the correlation between two arrays.

from scipy.spatial.distance import correlation

✓

correlation([1, 0, 1], [1, 1, 0])

✗

np.float64(1.5)

Using a weighting array, the correlation can be calculated as:

correlation([1, 0, 1], [1, 1, 0], w=[0.9, 0.1, 0.1])

✗

np.float64(1.1)

If centering is not needed, the correlation can be calculated as:

correlation([1, 0, 1], [1, 1, 0], centered=False)

✗

np.float64(0.5)