numpy.multiply# numpy. multiply (x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True [, signature]) = <ufunc 'multiply'> # Multiply arguments element-wise. Parameters: x1, x2 array_like. Input arrays to be multiplied. If x1.shape!= x2.shape, they must be broadcastable to a common shape (which becomes the ...

The matmul function implements the semantics of the @ operator introduced in Python 3.5 following PEP 465. It uses an optimized BLAS library when possible (see numpy.linalg). Examples. For 2-D arrays it is the matrix product: >>>

If either a or b is 0-D (scalar), it is equivalent to multiply and using numpy.multiply(a, b) or a * b is preferred. If a is an N-D array and b is a 1-D array, it is a sum product over the last axis of a and b .

For example, if you have a 256x256x3 array of RGB values, and you want to scale each color in the image by a different value, you can multiply the image by a one-dimensional array with 3 values. Lining up the sizes of the trailing axes of these arrays according to the broadcast rules, shows that they are compatible:

>>> np. prod (a, axis = 1) array([ 2., 12.]) >>> np. prod (a, axis = 0) array([3., 8.]) Or select specific elements to include: >>> np . prod ([ 1. , np . nan , 3. ], where = [ True , False , True ]) 3.0

>>> data = np. array ([[1, 2], [5, 3], [4, 6]]) >>> data array([[1, 2], [5, 3], [4, 6]]) >>> data. max (axis = 0) array([5, 6]) >>> data. max (axis = 1) array([2, 5, 6]) Once you’ve created your matrices, you can add and multiply them using arithmetic operators if you …

Return the cross product of two (arrays of) vectors. The cross product of a and b in \(R^3\) is a vector perpendicular to both a and b. If a and b are arrays of vectors, the vectors are defined by the last axis of a and b by default, and these axes can have dimensions 2 or 3.

NumPy performs operations element-by-element, so multiplying 2D arrays with * is not a matrix multiplication – it’s an element-by-element multiplication. (The @ operator, available since Python 3.5, can be used for conventional matrix multiplication.)

The Einstein summation convention can be used to compute many multi-dimensional, linear algebraic array operations. einsum provides a succinct way of representing these. A non-exhaustive list of these operations, which can be computed by einsum, is shown below along with examples: Trace of an array, numpy.trace. Return a diagonal, numpy.diag.

Given two tensors, a and b, and an array_like object containing two array_like objects, (a_axes, b_axes), sum the products of a’s and b’s elements (components) over the axes specified by a_axes and b_axes.