scientimate.similaritymeasure#
d = scientimate.similaritymeasure(x, y, CalcMethod='euclidean', dispout='no')
Description#
Measure similarity between two arrays
Inputs#
- x
First array, its similarity is measured against y array
- y
Second array, its similarity is measured against x array
- CalcMethod=’euclidean’
- Similarity calculation method‘euclidean’: Euclidean distance‘manhattan’: Manhattan distance‘minkowski’: Minkowski distance (power=3)‘cosine’: Cosine distance‘pearson’: Pearson’s correlation coefficient‘spearman’: spearman’s correlation coefficient‘norm’: Absolute difference of norm‘covariance’: Covariance‘inv_covariance’: Euclidean distance of inverse covariance‘histogram’: Mean of absolute difference of histogram‘t-test’: Two-sample t-test statistic
- dispout=’no’
Define to display outputs or not (‘yes’: display, ‘no’: not display)
Outputs#
- d
Arrays similarity measure
Examples#
import scientimate as sm
import numpy as np
x=[0,2,4,6]
y=[2,3,5,7]
d=sm.similaritymeasure(x,y,'euclidean','yes')
x=[1,2,3,4,5,6,7,8,9,10]
y=[1.1,1.98,3.3,4.2,4.8,5.95,7.5,7.7,8.99,10.5]
d=sm.similaritymeasure(x,y,'pearson','yes')
References#
Kianimajd, A., Ruano, M. G., Carvalho, P., Henriques, J., Rocha, T., Paredes, S., & Ruano, A. E. (2017). Comparison of different methods of measuring similarity in physiologic time series. IFAC-PapersOnLine, 50(1), 11005-11010.