求矩阵中向量两两间的欧氏距离(python实现)
程序员文章站
2022-06-15 17:15:29
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假设有两个三维向量集,用矩阵表示:
要求A,B两个集合中的元素两两间欧氏距离。
先求出ABT:
然后对A和BT分别求其中每个向量的模平方,并扩展为2*3矩阵:
然后:
将上面这个矩阵一开平方,就得到了A,B向量集两两间的欧式距离了。
代码:
def EuclideanDistances(A, B):
BT = B.transpose()
# vecProd = A * BT
vecProd = np.dot(A,BT)
# print(vecProd)
SqA = A**2
# print(SqA)
sumSqA = np.matrix(np.sum(SqA, axis=1))
sumSqAEx = np.tile(sumSqA.transpose(), (1, vecProd.shape[1]))
# print(sumSqAEx)
SqB = B**2
sumSqB = np.sum(SqB, axis=1)
sumSqBEx = np.tile(sumSqB, (vecProd.shape[0], 1))
SqED = sumSqBEx + sumSqAEx - 2*vecProd
SqED[SqED<0]=0.0
ED = np.sqrt(SqED)
return ED
例子:
import numpy as np
import scipy as sp
# 向量矩阵
nodematrix = np.mat([[0.18454707,0.07877431,0.03511417,-0.32287475,0.23515885,-0.18110873,0.048741672,0.06893173,-0.23685655,0.043925785,0.31994045,-0.24165289,0.13900638,0.056432284,0.11355302,-0.04609316,-0.052616715,0.23289913,0.16946493,0.32099992,0.1409426,0.14139928,0.062529296,0.005625876,-0.024155464,0.09008224,-0.19214138,0.1398599,-0.069819,-0.14604269,0.3511576,-0.15226561,-0.14551574,-0.10126025,0.05427547,-0.24835448,0.15672702,-0.011962634,0.08181411,-0.06230553,-0.13467935,-0.009406202,-0.26009077,-0.1196819,0.31967723,-0.1310049,-0.18763539,0.11704104,-0.20122299,0.15155968,-0.1440493,-0.15665102,0.04375443,-0.24735771,0.0982016,0.101773486,0.1453648,-0.1844227,-0.08450979,-0.22261475,-0.078076385,0.07616588,0.053922217,0.071635224],
[0.16241522,0.030028613,0.00501509,-0.40497243,0.35783088,-0.1463465,0.1162642,0.0025282209,-0.19606775,0.08517304,0.2558237,-0.14098686,-0.059887953,0.18139145,0.16566025,-0.14257768,-0.03772987,0.1619154,0.15329891,0.30993557,0.24604511,0.13277014,0.045256298,-0.024693,-0.10674798,0.058585282,-0.29165882,0.24461408,0.005139245,-0.16787156,0.30382633,-0.14275697,0.06372774,-0.23374648,-0.048392028,-0.36086082,0.13913788,-0.16261087,0.043675978,-0.101465054,-0.117435366,-0.085971706,-0.36121935,-0.1571226,0.15281583,-0.0034408702,-0.08296094,0.3360551,-0.18179558,-0.057699423,-0.16708843,0.02841481,-0.06829184,-0.16543542,0.019792272,-0.0121178785,0.16203862,-0.22075787,-0.27897227,-0.24739979,-0.0762489,0.12593369,0.065210156,0.14470446],
[0.15471825,-0.009560898,0.044638064,-0.3334582,0.2528265,-0.023827955,0.2105105,-0.040138807,-0.12214413,0.13366897,0.20146476,-0.029900132,-0.033629015,0.10772486,0.073309004,-0.18203571,-0.03172069,0.049771123,0.09460871,0.08535822,0.17931731,0.08702089,0.07659155,-0.07248056,-0.15958676,0.05345656,-0.24768034,0.18058199,-0.06615136,-0.24867545,0.14499323,-0.14823414,0.10333892,-0.2746339,-0.015144608,-0.30523992,0.099423714,-0.16731471,0.13232969,-0.056383006,-0.1103352,-0.13529888,-0.2588229,-0.11967849,0.111871056,0.08303513,-0.092684194,0.25987405,-0.10226027,-0.14604586,-0.11458803,-0.04001497,-0.009192579,-0.02120677,0.14012279,-0.061846178,0.074880384,-0.25790447,-0.21985511,-0.30482677,-0.01992341,0.08783396,0.046929568,0.20357957],
[0.14336285,0.025450729,0.030937783,-0.38646004,0.3831792,-0.12658559,0.11587829,-0.00094238063,-0.20711565,0.116168045,0.2619768,-0.16534837,-0.076349474,0.1910923,0.1761144,-0.117769174,-0.05778647,0.15578617,0.1770518,0.3299434,0.28326258,0.11662979,0.030391019,-0.024187434,-0.13119648,0.11671877,-0.29864267,0.2607055,-0.0014046635,-0.16056718,0.32242507,-0.15809427,0.07620385,-0.24686214,-0.053542577,-0.3769508,0.15533,-0.18925558,0.03750794,-0.10124978,-0.12879665,-0.06370331,-0.39504737,-0.18121615,0.13492465,-0.013443653,-0.08084006,0.36568502,-0.20093451,-0.041457146,-0.20096765,0.05318851,-0.076807216,-0.18296471,0.021850985,0.0072131567,0.17274004,-0.24819276,-0.31100342,-0.2147306,-0.08602295,0.1464944,0.059046265,0.11183362],
[0.26588073,0.14150523,-0.066580854,-0.32842296,0.20054933,-0.311242,0.027391246,0.220159,-0.3213798,-0.059942015,0.43377757,-0.4325063,0.3782408,-0.057497203,0.14789854,-0.046590663,-0.013819736,0.44173974,0.20694812,0.45672962,0.12681784,0.15629336,0.10186779,-0.01538804,0.14938115,0.00080065127,-0.21110442,0.0750086,-0.08364635,-0.21890001,0.48687533,-0.18377657,-0.35746732,0.00683676,0.21058857,-0.1659275,0.10746653,0.20458831,0.10176073,-0.08301095,-0.18864691,0.07533139,-0.25550327,-0.057406623,0.5441203,-0.29227442,-0.32799053,-0.018170757,-0.25572824,0.27149796,-0.09855748,-0.38477862,0.18221247,-0.38255888,0.1667156,0.22324125,0.20890681,-0.12635048,0.098535806,-0.32271296,-0.10179064,0.029363632,0.09413725,0.0853047],
[0.28390682,0.14821959,-0.07243146,-0.31380302,0.17729533,-0.3514834,0.015846489,0.25777772,-0.35790017,-0.07803212,0.49184337,-0.466249,0.45263693,-0.09243493,0.14506024,-0.03347256,-0.059323907,0.4768754,0.23787509,0.51546514,0.08862736,0.1546034,0.1132154,-0.015038503,0.1742082,0.023245092,-0.18185928,0.06533995,-0.109746985,-0.23858051,0.5158076,-0.21007662,-0.4443825,0.05093318,0.23896919,-0.15287971,0.11856414,0.24033959,0.11668239,-0.0883758,-0.21929263,0.08000682,-0.25499132,-0.010891287,0.6080768,-0.34543636,-0.35245344,-0.094734706,-0.26169282,0.35251272,-0.09735272,-0.41804987,0.20288697,-0.4721476,0.19991674,0.28229624,0.22733806,-0.14755258,0.16689868,-0.34101963,-0.10447109,0.016718479,0.09580001,0.026380999],
[0.30237454,0.14683928,-0.038298786,-0.29058316,0.09569749,-0.2887462,0.08512673,0.25653952,-0.3306917,-0.07338041,0.43983808,-0.39612237,0.4771336,-0.15741485,0.08673556,-0.06200445,-0.05399839,0.43036908,0.19016203,0.36112237,0.037544876,0.16374564,0.13751066,-0.031518582,0.17014316,0.0054007443,-0.1412699,0.030166686,-0.1515889,-0.2804208,0.41181204,-0.20286052,-0.42974722,0.033308703,0.26093522,-0.101090506,0.11384971,0.25500837,0.16215882,-0.07417901,-0.20966761,0.038042348,-0.16307639,0.018035095,0.5900221,-0.3077835,-0.3638132,-0.16962387,-0.1844693,0.3052745,-0.060528554,-0.48142967,0.267378,-0.38124034,0.2627384,0.26814613,0.13943395,-0.13735828,0.2260544,-0.39549655,-0.08738619,-0.027837485,0.07852614,0.073204376],
[0.15111448,0.028105294,0.018643193,-0.39076355,0.35179225,-0.10192434,0.13584058,-0.01791851,-0.1876253,0.12551734,0.26209727,-0.14577623,-0.050282184,0.16817413,0.14472039,-0.12881413,-0.0009551516,0.14232564,0.14243901,0.24533655,0.26847097,0.11434139,0.043083258,-0.035031863,-0.12093203,0.07047154,-0.3023595,0.22925626,-0.022292228,-0.18379278,0.2770088,-0.14995696,0.07333245,-0.27771124,-0.027451774,-0.34053907,0.11851374,-0.16989794,0.06365318,-0.0777267,-0.120107174,-0.08308494,-0.34980863,-0.17573133,0.1607972,0.018753562,-0.11188955,0.34290648,-0.18527506,-0.06975901,-0.15833554,-0.0013007335,-0.05372122,-0.12500457,0.055650927,-0.038529787,0.1553037,-0.23561591,-0.29254356,-0.2623043,-0.064346865,0.13105302,0.06940119,0.16754109],
[0.1726481,-0.012440336,0.05777382,-0.29918647,0.11327566,0.028526327,0.25259495,-0.046879847,-0.083430484,0.14164263,0.18082081,0.035763584,0.06169322,-0.011473012,-0.028997375,-0.209982,-0.01166824,0.012950984,0.06836558,-0.08352182,0.07329331,0.08865248,0.12769547,-0.092709474,-0.13191105,0.0008182716,-0.17990653,0.113506615,-0.11055532,-0.31831446,0.03438223,-0.16406292,0.04131899,-0.2529639,0.03221567,-0.20100339,0.056884043,-0.06371642,0.21594793,-0.030609736,-0.113626,-0.18997502,-0.119332895,-0.060368035,0.17932867,0.09511753,-0.13219997,0.0711628,-0.035274208,-0.14551517,-0.029601278,-0.19388005,0.07590197,0.07678794,0.24299903,-0.084034674,-0.0004984068,-0.26196072,-0.07771122,-0.4030828,0.031955462,0.027905114,0.04942222,0.27295566],
[0.14423797,-0.0432808,0.08190974,-0.3191024,0.1842165,0.04050622,0.28313497,-0.099449195,-0.07827723,0.18302734,0.16223152,0.07542272,-0.048375804,0.05273844,0.011945316,-0.22902112,-0.02473319,-0.040633097,0.08694341,-0.06543084,0.14732157,0.08990496,0.11173066,-0.07845281,-0.22100301,0.045767106,-0.22423856,0.16899069,-0.07569042,-0.30694303,0.040872153,-0.16286385,0.14818484,-0.31789201,-0.043595877,-0.28272822,0.091681845,-0.18193623,0.1951894,-0.05589307,-0.107812,-0.22844492,-0.17869087,-0.11350503,0.08892242,0.15473714,-0.04950841,0.19699448,-0.04127596,-0.23042417,-0.07641474,-0.06739464,0.01028838,0.0937099,0.20463021,-0.13939577,0.0041027023,-0.30471212,-0.19514567,-0.38047338,0.03354903,0.083094895,0.05826267,0.2721687],
[0.2793651,0.15185933,-0.13177046,-0.33180004,0.17327645,-0.32736593,0.05041652,0.2559635,-0.33712912,-0.09623723,0.43651047,-0.4513071,0.43436667,-0.07025617,0.15559532,-0.069526486,0.014860244,0.50843346,0.1856622,0.4444423,0.1378033,0.15556376,0.1007079,-0.041156724,0.20312013,-0.071682364,-0.24855424,0.068367764,-0.07386292,-0.2374091,0.4783143,-0.19213393,-0.37353987,0.00675678,0.22759077,-0.12006049,0.031968668,0.26030996,0.10890474,-0.09292061,-0.18150233,0.067264214,-0.25004223,-0.025943859,0.5547775,-0.31370807,-0.37688726,-0.033713557,-0.23954678,0.24213813,-0.073403485,-0.4356436,0.20409836,-0.37392157,0.18340923,0.21839713,0.19377775,-0.07935003,0.121938154,-0.37857115,-0.09527563,-0.001988189,0.08951302,0.116948694],
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[0.17685892,-0.049328245,0.069831386,-0.30092248,0.090350956,0.10929393,0.33366963,-0.09147194,-0.047566082,0.1854566,0.15903713,0.12734325,0.04860585,-0.031574704,-0.07594497,-0.26819673,-0.0031301803,-0.052297626,0.024744488,-0.21933036,0.06396181,0.07063064,0.14660215,-0.12421251,-0.20001669,-0.0018085735,-0.17249289,0.1152019,-0.14527637,-0.37708488,-0.053285465,-0.177324,0.09767333,-0.32052764,0.026820146,-0.22028546,0.024559041,-0.11973184,0.28401002,-0.0048154476,-0.116903506,-0.25389233,-0.08461089,-0.06103407,0.13181527,0.18670411,-0.121669315,0.083243914,0.0050915745,-0.25549623,-0.0007974085,-0.21008848,0.07717522,0.17593127,0.31061208,-0.15265298,-0.051967423,-0.31836212,-0.08692709,-0.46341407,0.0834318,0.033166405,0.058264393,0.32750255]])
#%%
def EuclideanDistances(A, B):
BT = B.transpose()
vecProd = np.dot(A,BT)
SqA = A**2
sumSqA = np.matrix(np.sum(SqA, axis=1))
sumSqAEx = np.tile(sumSqA.transpose(), (1, vecProd.shape[1]))
SqB = B**2
sumSqB = np.sum(SqB, axis=1)
sumSqBEx = np.tile(sumSqB, (vecProd.shape[0], 1))
SqED = sumSqBEx + sumSqAEx - 2*vecProd
SqED[SqED<0]=0.0
ED = np.sqrt(SqED)
return ED
Euclidean_dis=EuclideanDistances(np.array(nodematrix),np.array(nodematrix))
print (Euclidean_dis)
运行结果: