numpy. matmul (x1, x2, /, out=None, *, casting='same_kind', order='K', dtype=None, subok=True [, signature, axes, axis]) = <ufunc 'matmul'> # Matrix product of two arrays. Parameters :

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 ...

numpy.dot# numpy. dot (a, b, out = None) # Dot product of two arrays. Specifically, If both a and b are 1-D arrays, it is inner product of vectors (without complex conjugation). If both a and b are 2-D arrays, it is matrix multiplication, but using matmul or a @ b is preferred.

Returns a matrix from an array-like object, or from a string of data. A matrix is a specialized 2-D array that retains its 2-D nature through operations. It has certain special operators, such as * (matrix multiplication) and ** (matrix power).

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: >>>

Compute the sign and (natural) logarithm of the determinant of an array. trace (a[, offset, axis1, axis2, dtype, out]) Return the sum along diagonals of the array. linalg.trace (x, /, *[, offset, dtype]) Returns the sum along the specified diagonals of a matrix (or a stack of matrices) x.

numpy.linalg.multi_dot# linalg. multi_dot ( arrays , * , out = None ) [source] # Compute the dot product of two or more arrays in a single function call, while automatically selecting the fastest evaluation order.

You can pass Python lists of lists to create a 2-D array (or “matrix”) to represent them in NumPy. >>> data = np . array ([[ 1 , 2 ], [ 3 , 4 ], [ 5 , 6 ]]) >>> data array([[1, 2], [3, 4], [5, 6]])

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.)

numpy. vdot (a, b, /) # Return the dot product of two vectors. The vdot function handles complex numbers differently than dot : if the first argument is complex, it is replaced by its complex conjugate in the dot product calculation.