Normalized Cross-Correlation in Python
Nice Question. There is no direct way but you can "normalize" the input vectors before using np.correlate like this and reasonable values will be returned within a range of [-1,1]:
Here i define the correlation as generally defined in signal processing textbooks.
c'_{ab}[k] = sum_n a[n] conj(b[n+k])CODE: If a and b are the vectors:
a = (a - np.mean(a)) / (np.std(a) * len(a))b = (b - np.mean(b)) / (np.std(b))c = np.correlate(a, b, 'full')References:
https://docs.scipy.org/doc/numpy/reference/generated/numpy.correlate.html
https://en.wikipedia.org/wiki/Cross-correlation
a = np.dot(abs(var1),abs(var2),'full')
b = np.correlate(var1,var2,'full')
c = b/a
This is my idea: but it will normalize it 0-1