Читать книгу Algorithms For Dummies - John Paul Mueller, John Mueller Paul, Luca Massaron - Страница 85

Many of the same techniques you use with vectors also work with matrixes. To create a basic matrix, you simply use the array() function as you would with a vector, but you define additional dimensions. A dimension is a direction in the matrix. For example, a two-dimensional matrix contains rows (one direction) and columns (a second direction). The array call myMatrix = np.array([[1,2,3], [4,5,6], [7,8,9]]) produces a matrix containing three rows and three columns, like this:

[[1 2 3] [4 5 6] [7 8 9]]

Note how you embed three lists within a container list to create the two dimensions. To access a particular array element, you provide a row and column index value, such as myMatrix[0, 0] to access the first value of 1. You can find a full listing of vector and matrix array-creation functions at https://numpy.org/doc/stable/reference/routines.array-creation.html.

The NumPy package supports an actual matrix class. The matrix class supports special features that make it easier to perform matrix-specific tasks. You discover these features later in the chapter. For now, all you really need to know is how to create a matrix of the matrix data type. The easiest method is to make a call similar to the one you use for the array function, but using the mat function instead, such as myMatrix = np.mat([[1,2,3], [4,5,6], [7,8,9]]), which produces the following matrix:

[[1 2 3] [4 5 6] [7 8 9]]

To determine that this actually is a matrix, try print(type(myMatrix)), which outputs <class 'numpy.matrix'>. You can also convert an existing array to a matrix using the asmatrix() function. Use the asarray() function to convert a matrix object back to an array form.

The only problem with the matrix class is that it works on only two-dimensional matrixes. If you attempt to convert a three-dimensional matrix to the matrix class, you see an error message telling you that the shape is too large to be a matrix.