Euclidean and cosine distance
Python
Two measures of distance in numpy
1| import numpy as np 2| 3| VEC_1 = [-0.25560104, 0.06393334, -0.43760861, 0.35258494, -0.06174621] 4| VEC_2 = [0.16257878, -0.88344182, 1.14405499, 0.33765161, 1.206262] 5| 6| def euclidean_distance(vec1, vec2): 7| """ 8| Compute the Euclidean distance between two vectors. 9| :param vec1: Vector represented as a numpy array 10| :param vec2: Vector represented as a numpy array 11| :return: The Euclidean distance between the vectors 12| """ 13| v1 = np.array(VEC_1) 14| v2 = np.array(VEC_2) 15| diff = v1-v2 16| sq = np.dot(diff.T, diff) 17| return np.sqrt(sq) 18| 19| 20| 21| def cosine_distance(vec1, vec2): 22| 23| """ 24| Compute the cosine distance between two vectors. 25| :param vec1: Vector represented as a numpy array 26| :param vec2: Vector represented as a numpy array 27| :return: The cosine distance between the vectors 28| """ 29| 30| v1 = np.array(VEC_1) 31| v2 = np.array(VEC_2) 32| dot = np.dot(v1,v2) 33| norm1= np.linalg.norm(v1) 34| norm2 = np.linalg.norm(v2) 35| return dot/(norm1 * norm2)
133
121
116
109