我有维度R的numpy数组150x3和维度D的另一个numpy数组150x4。
我正在尝试np.dot(R.T, D)，但我得到了
ValueError：操作数无法与形状（3,150）（150,4）一起广播
但是当我np.dot(D.T, R)时，我没有收到任何错误。
np.dot(R.T, D)有什么问题？
R = [[ 9.61020742e-02 3.46156874e-01 5.57741052e-01]
[ 7.89559849e-03 1.94729924e-01 7.97374478e-01]
[ 9.86036469e-03 3.58806741e-01 6.31332895e-01]
[ 2.48034126e-03 8.04021220e-01 1.93498439e-01]
[ 8.83193916e-02 5.48842033e-01 3.62838576e-01]
[ 3.71353736e-01 1.17560018e-01 5.11086246e-01]
[ 1.06980365e-02 4.27750286e-01 5.61551678e-01]
[ 3.86811475e-02 6.15241737e-01 3.46077116e-01]
[ 8.72297668e-04 6.71562777e-01 3.27564925e-01]
[ 6.89735774e-03 8.56750517e-01 1.36352125e-01]
[ 3.56313831e-01 2.78079828e-01 3.65606341e-01]
[ 7.57943813e-03 9.18418851e-01 7.40017112e-02]
[ 5.41821292e-03 7.30246525e-01 2.64335263e-01]
[ 1.47647182e-03 6.71706805e-01 3.26816723e-01]
[ 8.04498616e-01 3.75237147e-03 1.91749012e-01]
[ 8.97990546e-01 5.24969629e-03 9.67597575e-02]
[ 2.19970730e-01 4.20443727e-03 7.75824833e-01]
[ 6.09849253e-02 7.81150046e-02 8.60900070e-01]
[ 5.82902465e-01 7.96470608e-02 3.37450474e-01]
[ 1.90567056e-01 3.44574089e-01 4.64858855e-01]
[ 1.18054009e-01 5.35847701e-01 3.46098290e-01]
[ 8.64050519e-02 6.25795101e-02 8.51015438e-01]
[ 3.78483444e-02 2.02516101e-01 7.59635554e-01]
[ 8.59779741e-03 1.45064881e-02 9.76895714e-01]
[ 9.99346444e-04 9.94183378e-01 4.81727604e-03]
[ 9.40391340e-03 4.85716808e-01 5.04879279e-01]
[ 2.13243738e-02 8.65263690e-02 8.92149257e-01]
[ 1.15701417e-01 4.32636874e-01 4.51661709e-01]
[ 8.86018157e-02 1.84982960e-01 7.26415224e-01]
[ 3.01781162e-03 9.01584406e-01 9.53977822e-02]
[ 4.56990100e-03 7.91352466e-01 2.04077633e-01]
[ 3.45927029e-02 4.87892600e-03 9.60528371e-01]
[ 1.60632883e-01 8.27044274e-01 1.23228432e-02]
[ 8.35100215e-01 9.16902815e-02 7.32095037e-02]
[ 1.07230994e-02 4.73656742e-01 5.15620159e-01]
[ 1.87299922e-02 4.60373499e-02 9.35232658e-01]
[ 2.00924000e-01 2.51558825e-02 7.73920117e-01]
[ 2.70681415e-02 9.19211663e-01 5.37201953e-02]
[ 1.59735470e-03 5.41196014e-01 4.57206631e-01]
[ 5.95274793e-02 4.87221419e-01 4.53251102e-01]
[ 4.30642592e-02 5.31354413e-02 9.03800300e-01]
[ 4.82644394e-05 6.88366845e-03 9.93068067e-01]
[ 2.68874993e-03 7.18010358e-01 2.79300892e-01]
[ 6.64131338e-03 3.02540302e-03 9.90333284e-01]
[ 7.16077254e-02 7.62597372e-01 1.65794903e-01]
[ 3.26066793e-03 5.55729613e-02 9.41166371e-01]
[ 8.29860613e-02 8.51236805e-01 6.57771335e-02]
[ 4.92325113e-03 7.02327028e-01 2.92749721e-01]
[ 2.68651482e-01 4.07439949e-01 3.23908569e-01]
[ 3.48779651e-02 3.09743232e-01 6.55378803e-01]
[ 4.10371575e-02 3.25115421e-02 9.26451300e-01]
[ 4.23526092e-03 1.57896741e-02 9.79975065e-01]
[ 1.42010306e-02 3.56402209e-02 9.50158749e-01]
[ 1.89270683e-05 1.92636219e-02 9.80717451e-01]
[ 8.41129311e-04 5.24018083e-03 9.93918690e-01]
[ 1.69895846e-04 8.43802871e-01 1.56027233e-01]
[ 3.85831004e-03 3.59151277e-02 9.60226562e-01]
[ 2.66002749e-05 2.11684374e-01 7.88289025e-01]
[ 7.94266474e-03 1.78764870e-01 8.13292465e-01]
[ 2.86578904e-05 2.34398695e-02 9.76531473e-01]
[ 6.82866492e-06 1.83193984e-01 8.16799188e-01]
[ 3.43681400e-04 5.48735560e-03 9.94168963e-01]
[ 2.78629059e-04 2.56309299e-01 7.43412072e-01]
[ 1.12432440e-03 5.03094267e-01 4.95781409e-01]
[ 1.84562605e-04 2.85767672e-03 9.96957761e-01]
[ 8.00369161e-03 6.38415085e-03 9.85612158e-01]
[ 3.04872236e-04 2.93281239e-01 7.06413889e-01]
[ 2.44072328e-04 9.43368816e-01 5.63871119e-02]
[ 1.96883860e-05 9.11261180e-04 9.99069050e-01]
[ 1.98351546e-04 3.20962243e-01 6.78839406e-01]
[ 2.68683180e-04 1.02490410e-02 9.89482276e-01]
[ 6.35566443e-04 6.62457274e-03 9.92739861e-01]
[ 2.52439050e-04 6.96820959e-02 9.30065465e-01]
[ 2.43286411e-04 9.69657837e-01 3.00988762e-02]
[ 3.35900677e-03 3.47740518e-02 9.61866941e-01]
[ 4.11624011e-03 7.18683241e-03 9.88696927e-01]
[ 5.05652417e-03 4.91264721e-02 9.45817004e-01]
[ 1.38735573e-03 3.77829350e-03 9.94834351e-01]
[ 4.77430724e-04 3.62754653e-02 9.63247104e-01]
[ 5.06295723e-04 6.35474249e-02 9.35946279e-01]
[ 9.80128411e-05 1.72085050e-01 8.27816937e-01]
[ 1.46161671e-04 3.57717082e-01 6.42136756e-01]
[ 4.33049391e-04 5.20091202e-02 9.47557830e-01]
[ 1.78321609e-04 4.12044951e-01 5.87776727e-01]
[ 1.46347921e-04 5.31652774e-01 4.68200878e-01]
[ 2.46735826e-03 3.67683627e-02 9.60764279e-01]
[ 6.61311324e-03 1.54005833e-02 9.77986303e-01]
[ 1.77071930e-04 1.46768447e-02 9.85146083e-01]
[ 6.83094720e-04 3.13743642e-01 6.85573264e-01]
[ 5.09993051e-05 4.09115493e-02 9.59037451e-01]
[ 2.85804634e-05 9.48028881e-01 5.19425385e-02]
[ 1.87256703e-03 3.68745243e-01 6.29382190e-01]
[ 3.18003668e-04 8.67846435e-02 9.12897353e-01]
[ 2.15156635e-05 9.92444712e-02 9.00734013e-01]
[ 2.01886324e-04 2.67326809e-01 7.32471305e-01]
[ 6.17534155e-04 8.27995308e-01 1.71387158e-01]
[ 6.02903061e-04 3.23796846e-01 6.75600251e-01]
[ 2.29637394e-03 8.97763923e-02 9.07927234e-01]
[ 1.56780115e-05 1.27044886e-03 9.98713873e-01]
[ 3.76740795e-04 1.12972313e-01 8.86650946e-01]
[ 2.87005949e-05 2.22682411e-04 9.99748617e-01]
[ 1.51773249e-05 5.95561038e-03 9.94029212e-01]
[ 6.30006944e-04 1.06470667e-03 9.98305286e-01]
[ 4.17533071e-04 4.37824954e-01 5.61757513e-01]
[ 7.78017325e-05 1.24821334e-03 9.98673985e-01]
[ 6.86924196e-03 5.69614667e-02 9.36169291e-01]
[ 1.79387456e-06 4.14092395e-02 9.58588967e-01]
[ 4.19616727e-03 7.79044623e-01 2.16759210e-01]
[ 2.61103942e-04 1.25771751e-01 8.73967146e-01]
[ 7.08940723e-04 1.96467049e-05 9.99271413e-01]
[ 2.44579204e-04 1.79856898e-04 9.99575564e-01]
[ 6.42095442e-05 1.90401210e-03 9.98031778e-01]
[ 1.51746207e-04 1.13628244e-04 9.99734626e-01]
[ 1.53192339e-06 2.61257119e-04 9.99737211e-01]
[ 2.97672241e-07 5.89714434e-07 9.99999113e-01]
[ 2.31629596e-05 6.05785563e-06 9.99970779e-01]
[ 1.15482703e-03 1.41118116e-01 8.57727057e-01]
[ 1.65303872e-01 1.95290038e-01 6.39406091e-01]
[ 3.87539407e-04 2.76367328e-03 9.96848787e-01]
[ 3.64304533e-05 2.12756841e-01 7.87206729e-01]
[ 1.32951469e-04 2.07806473e-05 9.99846268e-01]
[ 4.71164398e-06 5.41626002e-04 9.99453662e-01]
[ 7.37701536e-03 2.26568147e-01 7.66054837e-01]
[ 5.19394987e-05 5.08165056e-04 9.99439895e-01]
[ 9.41246091e-04 3.90044573e-03 9.95158308e-01]
[ 1.70565028e-02 5.24482561e-01 4.58460936e-01]
[ 5.79928507e-05 4.85233058e-04 9.99456774e-01]
[ 1.71363177e-04 4.43037286e-03 9.95398264e-01]
[ 3.65223499e-05 9.98234443e-04 9.98965243e-01]
[ 1.03256791e-02 7.82752824e-01 2.06921497e-01]
[ 3.39500386e-03 3.19231731e-02 9.64681823e-01]
[ 4.42402046e-01 1.33305750e-01 4.24292203e-01]
[ 1.50282347e-05 1.46067493e-04 9.99838904e-01]
[ 7.75031897e-04 6.09241229e-01 3.89983739e-01]
[ 2.82386491e-06 9.99311738e-01 6.85438538e-04]
[ 4.49243709e-04 7.25519763e-06 9.99543501e-01]
[ 4.52262007e-05 5.28929786e-05 9.99901881e-01]
[ 1.35577576e-03 2.85828013e-01 7.12816211e-01]
[ 1.15450174e-04 2.87523077e-03 9.97009319e-01]
[ 2.33931095e-04 3.96689006e-05 9.99726400e-01]
[ 1.88516051e-05 2.20935448e-06 9.99978939e-01]
[ 1.95262213e-05 5.10008372e-08 9.99980423e-01]
[ 1.51773249e-05 5.95561038e-03 9.94029212e-01]
[ 1.81083155e-04 2.23844268e-04 9.99595073e-01]
[ 2.70194754e-05 1.79050294e-06 9.99971190e-01]
[ 1.07794371e-05 2.41601142e-07 9.99988979e-01]
[ 9.80962323e-06 8.73547012e-05 9.99902836e-01]
[ 1.12317424e-04 2.11402370e-04 9.99676280e-01]
[ 5.84789388e-05 9.18351123e-05 9.99849686e-01]
[ 1.84899708e-04 7.34680565e-02 9.26347044e-01]]
和
D = [[ 5.1 3.5 1.4 0.2]
[ 4.9 3. 1.4 0.2]
[ 4.7 3.2 1.3 0.2]
[ 4.6 3.1 1.5 0.2]
[ 5. 3.6 1.4 0.2]
[ 5.4 3.9 1.7 0.4]
[ 4.6 3.4 1.4 0.3]
[ 5. 3.4 1.5 0.2]
[ 4.4 2.9 1.4 0.2]
[ 4.9 3.1 1.5 0.1]
[ 5.4 3.7 1.5 0.2]
[ 4.8 3.4 1.6 0.2]
[ 4.8 3. 1.4 0.1]
[ 4.3 3. 1.1 0.1]
[ 5.8 4. 1.2 0.2]
[ 5.7 4.4 1.5 0.4]
[ 5.4 3.9 1.3 0.4]
[ 5.1 3.5 1.4 0.3]
[ 5.7 3.8 1.7 0.3]
[ 5.1 3.8 1.5 0.3]
[ 5.4 3.4 1.7 0.2]
[ 5.1 3.7 1.5 0.4]
[ 4.6 3.6 1. 0.2]
[ 5.1 3.3 1.7 0.5]
[ 4.8 3.4 1.9 0.2]
[ 5. 3. 1.6 0.2]
[ 5. 3.4 1.6 0.4]
[ 5.2 3.5 1.5 0.2]
[ 5.2 3.4 1.4 0.2]
[ 4.7 3.2 1.6 0.2]
[ 4.8 3.1 1.6 0.2]
[ 5.4 3.4 1.5 0.4]
[ 5.2 4.1 1.5 0.1]
[ 5.5 4.2 1.4 0.2]
[ 4.9 3.1 1.5 0.2]
[ 5. 3.2 1.2 0.2]
[ 5.5 3.5 1.3 0.2]
[ 4.9 3.6 1.4 0.1]
[ 4.4 3. 1.3 0.2]
[ 5.1 3.4 1.5 0.2]
[ 5. 3.5 1.3 0.3]
[ 4.5 2.3 1.3 0.3]
[ 4.4 3.2 1.3 0.2]
[ 5. 3.5 1.6 0.6]
[ 5.1 3.8 1.9 0.4]
[ 4.8 3. 1.4 0.3]
[ 5.1 3.8 1.6 0.2]
[ 4.6 3.2 1.4 0.2]
[ 5.3 3.7 1.5 0.2]
[ 5. 3.3 1.4 0.2]
[ 7. 3.2 4.7 1.4]
[ 6.4 3.2 4.5 1.5]
[ 6.9 3.1 4.9 1.5]
[ 5.5 2.3 4. 1.3]
[ 6.5 2.8 4.6 1.5]
[ 5.7 2.8 4.5 1.3]
[ 6.3 3.3 4.7 1.6]
[ 4.9 2.4 3.3 1. ]
[ 6.6 2.9 4.6 1.3]
[ 5.2 2.7 3.9 1.4]
[ 5. 2. 3.5 1. ]
[ 5.9 3. 4.2 1.5]
[ 6. 2.2 4. 1. ]
[ 6.1 2.9 4.7 1.4]
[ 5.6 2.9 3.6 1.3]
[ 6.7 3.1 4.4 1.4]
[ 5.6 3. 4.5 1.5]
[ 5.8 2.7 4.1 1. ]
[ 6.2 2.2 4.5 1.5]
[ 5.6 2.5 3.9 1.1]
[ 5.9 3.2 4.8 1.8]
[ 6.1 2.8 4. 1.3]
[ 6.3 2.5 4.9 1.5]
[ 6.1 2.8 4.7 1.2]
[ 6.4 2.9 4.3 1.3]
[ 6.6 3. 4.4 1.4]
[ 6.8 2.8 4.8 1.4]
[ 6.7 3. 5. 1.7]
[ 6. 2.9 4.5 1.5]
[ 5.7 2.6 3.5 1. ]
[ 5.5 2.4 3.8 1.1]
[ 5.5 2.4 3.7 1. ]
[ 5.8 2.7 3.9 1.2]
[ 6. 2.7 5.1 1.6]
[ 5.4 3. 4.5 1.5]
[ 6. 3.4 4.5 1.6]
[ 6.7 3.1 4.7 1.5]
[ 6.3 2.3 4.4 1.3]
[ 5.6 3. 4.1 1.3]
[ 5.5 2.5 4. 1.3]
[ 5.5 2.6 4.4 1.2]
[ 6.1 3. 4.6 1.4]
[ 5.8 2.6 4. 1.2]
[ 5. 2.3 3.3 1. ]
[ 5.6 2.7 4.2 1.3]
[ 5.7 3. 4.2 1.2]
[ 5.7 2.9 4.2 1.3]
[ 6.2 2.9 4.3 1.3]
[ 5.1 2.5 3. 1.1]
[ 5.7 2.8 4.1 1.3]
[ 6.3 3.3 6. 2.5]
[ 5.8 2.7 5.1 1.9]
[ 7.1 3. 5.9 2.1]
[ 6.3 2.9 5.6 1.8]
[ 6.5 3. 5.8 2.2]
[ 7.6 3. 6.6 2.1]
[ 4.9 2.5 4.5 1.7]
[ 7.3 2.9 6.3 1.8]
[ 6.7 2.5 5.8 1.8]
[ 7.2 3.6 6.1 2.5]
[ 6.5 3.2 5.1 2. ]
[ 6.4 2.7 5.3 1.9]
[ 6.8 3. 5.5 2.1]
[ 5.7 2.5 5. 2. ]
[ 5.8 2.8 5.1 2.4]
[ 6.4 3.2 5.3 2.3]
[ 6.5 3. 5.5 1.8]
[ 7.7 3.8 6.7 2.2]
[ 7.7 2.6 6.9 2.3]
[ 6. 2.2 5. 1.5]
[ 6.9 3.2 5.7 2.3]
[ 5.6 2.8 4.9 2. ]
[ 7.7 2.8 6.7 2. ]
[ 6.3 2.7 4.9 1.8]
[ 6.7 3.3 5.7 2.1]
[ 7.2 3.2 6. 1.8]
[ 6.2 2.8 4.8 1.8]
[ 6.1 3. 4.9 1.8]
[ 6.4 2.8 5.6 2.1]
[ 7.2 3. 5.8 1.6]
[ 7.4 2.8 6.1 1.9]
[ 7.9 3.8 6.4 2. ]
[ 6.4 2.8 5.6 2.2]
[ 6.3 2.8 5.1 1.5]
[ 6.1 2.6 5.6 1.4]
[ 7.7 3. 6.1 2.3]
[ 6.3 3.4 5.6 2.4]
[ 6.4 3.1 5.5 1.8]
[ 6. 3. 4.8 1.8]
[ 6.9 3.1 5.4 2.1]
[ 6.7 3.1 5.6 2.4]
[ 6.9 3.1 5.1 2.3]
[ 5.8 2.7 5.1 1.9]
[ 6.8 3.2 5.9 2.3]
[ 6.7 3.3 5.7 2.5]
[ 6.7 3. 5.2 2.3]
[ 6.3 2.5 5. 1.9]
[ 6.5 3. 5.2 2. ]
[ 6.2 3.4 5.4 2.3]
[ 5.9 3. 5.1 1.8]]
答案 0 :(得分：0)
>>> r = np.random.random((150, 3))
>>> d = np.random.random((150, 4))
>>>
>>> np.dot(r.T, d)
array([[ 42.50248324, 36.47470278, 37.01957774, 36.7750468 ],
[ 38.44103843, 32.94992495, 33.91911815, 35.04781215],
[ 44.35562949, 40.94601697, 40.07220766, 40.87044229]])
>>> np.dot(r.T, d).shape
(3, 4)
>>>
>>> np.dot(r.T, d) == np.dot(d.T, r).T
array([[ True, True, True, True],
[ True, True, True, True],
[ True, True, True, True]], dtype=bool)
>>>
>>> np.dot(r, d)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
ValueError: matrices are not aligned
>>> np.version.version
'1.7.1'
在numpy 1.8.0b2上的工作原理相同。唯一的区别是ValueError消息的具体措辞。