correlation#
- scipy.spatial.distance.correlation(u, v, w=None, centered=True)[source]#
- Compute the correlation distance between two 1-D arrays. - 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 of floats
- Input array. - Deprecated since version 1.15.0: Complex u is deprecated and will raise an error in SciPy 1.17.0 
- v(N,) array_like of floats
- Input array. - Deprecated since version 1.15.0: Complex v is deprecated and will raise an error in SciPy 1.17.0 
- 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 
- centeredbool, optional
- If True, u and v will be centered. Default is True. 
 
- Returns:
- correlationdouble
- The correlation distance between 1-D array u and v. 
 
 - Examples - Find the correlation between two arrays. - >>> from scipy.spatial.distance import correlation >>> correlation([1, 0, 1], [1, 1, 0]) 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]) 1.1 - If centering is not needed, the correlation can be calculated as: - >>> correlation([1, 0, 1], [1, 1, 0], centered=False) 0.5