An example multiplication with arrays shaped like yours succeeds: In [1]: import numpy In [2]: numpy.dot(numpy.ones([97, 2]), numpy.ones([2, 1])).shape Out[2]: (97, 1) For more complex models, this will not be the case # and model.predict() can be useful. Why does this fail? np.matmul(b, a) # displays the following error: # ValueError: shapes (4,3) and (2,4) not aligned: 3 (dim 1) != 2 (dim 0) NumPy’s dot function. The two vectors are not of the same length") but this Value Error: ValueError: shapes (5,) and (3,) not aligned: 5 (dim 0) != 3 (dim 0) This is what I have so far: You should be able to find the mean and variance of each of your arrays. # Here is how to use it. This scratches a long-standing itch of mine, which is that np.dot's "matrices not aligned" message never explains which of the two arguments I forgot to transpose somewhere deep inside an algorithm. You can use it to extract values or assign values! Then check the contents to ensure the values make sense especially for unexpected values. This patch makes it report the mismatching pair of dimensions. Please check the dtype and shape of your arrays created from the database query. It might be even prettier to report the full shape of both inputs, but I think this is a big enough improvement for now. We try to show where the problems come from by some easy examples and explain typical fixes. In reply to this post by Happyman-2 I understand ,sometimes, it is normal that number of equations are less or more than number of unknowns that means non square matrix appearance. 15 comments ... (vectors, self.W) File "ops.pyx", line 299, in thinc.neural.ops.NumpyOps.batch_dot ValueError: shapes (4,0) and (300,128) not aligned: 0 … - numpy/numpy Then I don't get the output that I want ("Error! In this section we collect some frequent errors typically found in beginner’s numpy code. If the shapes are wrong for numpy.dot, you get a different exception: ValueError: matrices are not aligned. In previous posts, we already explored how Numpy array takes slicing of pairs (such as x[range(x.shape[0]), y]), however, Numpy can also take another array as slicing.Assume x is an index array of shape (N, T), each element index You may sometimes see NumPy’s dot function in places where you would expect a matmul. If you still get this error, please post a minimal example of the problem. Your errors suggest that you are not getting what expect from the database query. First a simple example, we … The calculation for a linear model is a trivial # linear numpy calculation. Re: ValueError: matrices are not aligned!!! Trick 5: Use Array as Slicing index. So far everything works just fine,except when I use two files with vectors of different lengths. ... (A, b) ValueError: matrices are not aligned. The fundamental package for scientific computing with Python. If we try to perform some operation where the shapes of the operands do not match, NumPy still tries to do some computation if possible. The method applied to resolve the issue is called broadcasting and shown in the following pictures. It turns out that the results of dot and matmul are the same if the matrices are two dimensional.