NumPy Matrix Library

NumPy contains a matrix library, i.e. numpy.matlib which is used to configure matrices instead of ndarray objects.

numpy.matlib.empty() function

This function is used to return a new matrix with the uninitialized entries. The syntax to use this function is given below.

It accepts the following parameter.

1. shape: It is the tuple defining the shape of the matrix.
2. dtype: It is the data type of the matrix.
3. order: It is the insertion order of the matrix, i.e. C or F.

Consider the following example.

Example

Output:

[[6.90262230e-310 6.90262230e-310 6.90262304e-310]
 [6.90262304e-310 6.90261674e-310 6.90261552e-310]
 [6.90261326e-310 6.90262311e-310 3.95252517e-322]]

numpy.matlib.zeros() function

This function is used to create the matrix where the entries are initialized to zero.

Consider the following example.

Example

Output:

[[0. 0. 0.]
 [0. 0. 0.]
 [0. 0. 0.]
 [0. 0. 0.]]

numpy.matlib.ones() function

This function returns a matrix with all the elements initialized to 1.

Consider the following example.

Example

Output:

[[1. 1.]
 [1. 1.]]

numpy.matlib.eye() function

This function returns a matrix with the diagonal elements initialized to 1 and zero elsewhere. The syntax to use this function is given below.

It accepts the following parameters.

1. n: It represents the number of rows in the resulting matrix.
2. m: It represents the number of columns, defaults to n.
3. k: It is the index of diagonal.
4. dtype: It is the data type of the output

Consider the following example.

Example

Output:

[[1 0 0]
 [0 1 0]
 [0 0 1]]

numpy.matlib.identity() function

This function is used to return an identity matrix of the given size. An identity matrix is the one with diagonal elements initializes to 1 and all other elements to zero.

Consider the following example.

Example

Output:

[[1 0 0 0 0]
 [0 1 0 0 0]
 [0 0 1 0 0]
 [0 0 0 1 0]
 [0 0 0 0 1]]

numpy.matlib.rand() function

This function is used to generate a matrix where all the entries are initialized with random values.

Consider the following example.

Example

Output:

[[0.86201511 0.86980769 0.06704884]
 [0.80531086 0.53814098 0.84394673]
 [0.85653048 0.8146121  0.35744405]]