How can I multiply more than 3 vectors at once in NumPy

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I'm looking for a vectorised way to multiply more than 3 vectors in NumPy.

As an example,

X = np.array([1,2,3])
Y = np.array([4,5,6])
Z = np.array([7,8,9])


Multiply([X,Y,Z])

would produce as an output

np.array([28, 80, 162])

The vectors I want to multiply need not to be defined separately as I did above. The could be, for example, the rows (or columns) of a matrix, and in that case I would like to multiply all the rows (or columns) of such a matrix.

Helps appreciated :)

You can use the reduce method of the ufunc:

>>> np.multiply.reduce((X, Y, Z))                                                                                                                                                                                                                        
array([ 28,  80, 162])

What's going on here is that the ufunc np.multiply, which looks and acts like function, is technically an instance of the class numpy.ufunc; all ufuncs have four special methods, one of them being .reduce(), which does what you're looking for in this case and produces a 1d result from multiple same-length 1d arrays.

The default axis is 0; if you want to work along the other axis, just specify that:

>>> np.multiply.reduce((X, Y, Z), axis=1)                                                                                                                                                                                                                
array([  6, 120, 504])

numpy.linalg.multi_dot — NumPy v1.19 Manual, Compute the dot product of two or more arrays in a single function call, while Depending on the shapes of the matrices, this can speed up the multiplication a lot. If the first argument is 1-D it is treated as a row vector. NumPy Basic Exercises, Practice and Solution: Write a NumPy program to multiply the values of two given vectors.

You can use numpy.prod, which uses multiply.reduce under the hood.


>>> np.prod([X, Y, Z], 0)
array([ 28,  80, 162])

>>> np.prod([X, Y, Z], 1)
array([  6, 120, 504])

numpy.multiply — NumPy v1.19 Manual, outndarray, None, or tuple of ndarray and None, optional 3)) >>> x2 = np. arange(3.0) >>> np.multiply(x1, x2) array([[ 0., 1., 4.], [ 0., 4., 10.], [ 0. numpy.multiply() function is used when we want to compute the multiplication of two array. It returns the product of arr1 and arr2, element-wise. It returns the product of arr1 and arr2, element-wise.

Or very simply use the usual * notation:

In [180]: X * Y * Z
Out[180]: array([ 28,  80, 162])

In general, you can use as many arrays as you need:

In [181]: X * Y * Z * X * Y * Z
Out[181]: array([  784,  6400, 26244])

numpy.dot — NumPy v1.19 Manual, If both a and b are 1-D arrays, it is inner product of vectors (without complex If either a or b is 0-D (scalar), it is equivalent to multiply and using b = np.arange( 3*4*5*6)[::-1].reshape((5,4,6,3)) >>> np.dot(a, b)[2,3,2,1,2,2]� Transpose does not change anything. It is still the same 1-dimensional array. To overcome this problem (although it is not a problem per se because numpy will broadcast this vector in case of vector-matrix related operations), the 1-dimensional vector can be changed to a 2-dimensional vector using any of the following two methods: 1.

numpy.matmul — NumPy v1.19 Manual, This is a scalar only when both x1, x2 are 1-d vectors. Raises If either argument is N-D, N > 2, it is treated as a stack of matrices residing in the last two indexes and broadcast accordingly. After matrix multiplication the prepended 1 is removed. np.matmul([1,2], 3) Traceback (most recent call last): . Numpy can also be used as an efficient multi-dimensional container of data. for more information visit numpy documentation. Matrix Multiplication in Python. in this tutorial, we will see two segments to solve matrix. nested loop; using Numpy array

Numerical & Scientific Computing with Python: Matrix Arithmetics in , Introduction with examples into Matrix-Arithmetics with the NumPy Module. Matrix addition; Matrix subtraction; Matrix multiplication; Scalar product; Cross product; and x = np.array([1,5,2]) >>> y = np.array([7,4,1]) >>> x + y array([8, 9, 3]) >>> x * y The addition of two vectors, in our example (see picture) x and y, may be� I’m looking to replace list comprehensions like the following with something more efficient and I was wondering if numpy can be used. [obj.matrix_world @ v.co for v in obj.data.vertices] I can do this, which is significantly faster, but still lacks the matrix multiplication, that needs to happen for each coordinate. coords = np.empty((len(obj.data.vertices), 3), 'f') obj.data.vertices

20+ examples for NumPy matrix multiplication, NumPy's array() method is used to represent vectors, matrices, and We'll use NumPy's matmul() method for most of our matrix multiplication operations. Let's define a 3�3 matrix and multiply it with a vector of length 3. However, if one dimension of a matrix is missing, NumPy would broadcast it to match� Eg. [1,2,3,4] Matrix: A matrix (plural matrices) is a 2-dimensional arrangement of numbers or a collection of vectors. Ex: [[1,2,3], [4,5,6], [7,8,9]] Dot Product: A dot product is a mathematical operation between 2 equal-length vectors. It is equal to the sum of the products of the corresponding elements of the vectors.

Comments
  • Can you concatenate them using np.stack and then call np.prod along the new axis?
  • I was kind of assuming the question concerned a variadic arg, i.e. multiply_arrays(*arrays), or multiply_arrays(arrays) (sequence arg). If not, this would definitely be simpler