下面是使用Python计算余弦距离的示例代码: importmathdefcosine_distance(vector1,vector2):sum_product=sum([vector1[i]*vector2[i]foriinrange(len(vector1))])sum_squared1=sum([vector1[i]**2foriinrange(len(vector1))])sum_squared2=sum([vector2[i]**2foriinrange(len(vector2))])return1-su...
然后,我们可以使用以下代码计算余弦距离: importnumpyasnpdefcosine_distance(vec_a,vec_b):dot_product=np.dot(vec_a,vec_b)norm_a=np.linalg.norm(vec_a)norm_b=np.linalg.norm(vec_b)cosine_similarity=dot_product/(norm_a*norm_b)return1-cosine_similarity# 示例vector_a=np.array([1,2,3])vect...
matrix1).sum(axis=1))matrix1_norm=matrix1_norm[:,np.newaxis]matrix2_norm=np.sqrt(np.multiply(matrix2,matrix2).sum(axis=1))matrix2_norm=matrix2_norm[:,np.newaxis]cosine_distance=np.divide(matrix1_matrix2,np.dot(matrix1_norm,matrix2_norm...
print('Cosine_distance', d / (count+1)) #余弦取值范围为[-1,1] 0以上正相关,0一下负相关,0时两向量垂直 return d / (count+1) # 曼哈顿距离 参考:https://blog.csdn.net/hy592070616/article/details/121569933?spm=1001.2014.3001.5501 def Manhattan_distance(self,vector1,vector2): count = 0 f...
def CosineDistance(x, y): import numpy as np x = np.array(x) y = np.array(y) return np.dot(x,y)/(np.linalg.norm(x)*np.linalg.norm(y)) 还可用矩阵乘法表示,@ https://blog.csdn.net/qq_21997625/article/details/85001493矩阵乘法 input1 = torch.Tensor(([1],[2],[3])) input2...
(x,y):ifx.ndim==1:x=x[np.newaxis]num=np.sum(x*y,axis=1)den=np.sum(x**2,axis=1)**0.5den=den*np.sum(y**2,axis=1)**0.5return1-num/dendeftest_cosine():x=np.array([1e-7,1e-7])dA=coss(x,yA)dB=coss(x,yB).reshape(s.shape)plotDist(x,dA,dB,'cosine_distance',save...
distance.cosine([1,2],[2,1]) #0.19999999999999996 7 汉明距离 两个等长字符串s1与s2之间的汉明距离定义为将其中一个变为另外一个所需要作的最小替换次数 例如字符串“11010”与“10011”之间的汉明距离为2。 对向量按元素进行比较,并对差异的数量进行平均 ...
cal_distance(x, y, mod="euc"): if mod == "cos": distance = cosine_distances(...
defCosine_distance_1(vector1,vector2):# 点与点的夹角余弦距离returnnp.dot(vector1,vector2)/(np.linalg.norm(vector1)*(np.linalg.norm(vector2)))defCosine_distance_2(vector1,vector2):# 点与点的夹角余弦距离returnpdist(np.vstack((vector1,vector2)),'cosine') ...
闵可夫斯基距离(Minkowski Distance) 欧氏距离(Euclidean Distance) 曼哈顿距离(Manhattan Distance) 切比雪夫距离(Chebyshev Distance) 夹角余弦(Cosine) 汉明距离(Hamming distance) 杰卡德相似系数(Jaccard similarity coefficient) 编辑距离(Edit Distance) 标准化欧氏距离 (Standardized Euclidean distance ) ...