numpy.bmat() in Python

Python | Numpy numpy.matrix.H()

Last Updated : 08 Apr, 2019

With the help of Numpy numpy.matrix.H() method, we can make a conjugate Transpose of any complex matrix either having dimension one or more than more.

Syntax : numpy.matrix.H() Return : Return conjugate transpose of every complex matrix

Example #1 : In this example we can see that with the help of matrix.H() method, we are able to transform any type of complex matrix.

Python3 1==

# import the important module in python
import numpy as np
        
# make a matrix with numpy
gfg = np.matrix([1-2j, 3-4j])
        
# applying matrix.H() method
geeks = gfg.getH()
  
print(geeks)

Output:

[[ 1.+2.j]
 [ 3.+4.j]]

Example #2 :

Python3 1==

# import the important module in python
import numpy as np
        
# make a matrix with numpy
gfg = np.matrix([[1-5j, 2 + 5j, 3-3j], [4 + 6j, 5-8j, 6-2j], [7 + 6j, 8-6j, 9 + 1.j]])
        
# applying matrix.H() method
geeks = gfg.getH()
  
print(geeks)

Output:

[[ 1.+5.j  4.-6.j  7.-6.j]
 [ 2.-5.j  5.+8.j  8.+6.j]
 [ 3.+3.j  6.+2.j  9.-1.j]]

numpy.bmat() in Python

J

Jitender_1998

Improve

Article Tags :

Practice Tags :

python

Similar Reads

Python | Numpy numpy.matrix.A()

With the help of Numpy numpy.matrix.A() method, we can get the same matrix as self. It means through this method we can get the identical matrix. Syntax : numpy.matrix.A() Return : Return self matrix Example #1 : In this example we can see that with the help of matrix.A() method, we are able to get

Python | Numpy numpy.matrix.T()

With the help of Numpy numpy.matrix.T() method, we can make a Transpose of any matrix either having dimension one or more than more. Syntax : numpy.matrix.T() Return : Return transpose of every matrix Example #1 : In this example we can see that with the help of matrix.T() method, we are able to tra

numpy.matrix() in Python

This class returns a matrix from a string of data or array-like object. Matrix obtained is a specialised 2D array. Syntax : numpy.matrix(data, dtype = None) : Parameters : data : data needs to be array-like or string dtype : Data type of returned array. Returns : data interpreted as a matrix Python

numpy.asmatrix() in Python

numpy.asmatrix(data, dtype = None) Returns a matrix by interpreting the input as a matrix. Parameters : data : array-like input data dtype : Data type of returned array Returns : Interprets the input as a matrix Python # Python Programming illustrating # numpy.asmatrix import numpy as geek # array-l

numpy.bmat() in Python

numpy.bmat(obj, l_dict = None, g_dict = None) : Return specialised 2-D matrix from nested objects that can be string-like or array-like. Parameters : object : array-like or string l_dict : (dict, optional) replaces local operands, A dictionary that replaces local operands in current frame g_dict : (

Python | Numpy numpy.ndarray.__imul__()

With the help of numpy.ndarray.__imul__() method, we can multiply a particular value that is provided as a parameter in the ndarray.__imul__() method. Value will be multiplied to every element in a numpy array. Syntax: ndarray.__imul__($self, value, /)Return: self*=value Example #1 : In this example

Python | Numpy matrix.sum()

With the help of matrix.sum() method, we are able to find the sum of values in a matrix by using the same method. Syntax : matrix.sum() Return : Return sum of values in a matrix Example #1 : In this example we are able to find the sum of values in a matrix by using matrix.sum() method. Python3 1=1 #

numpy.multiply() in Python

The numpy.multiply() is a numpy function in Python which is used to find element-wise multiplication of two arrays or scalar (single value). It returns the product of two input array element by element.Syntax:numpy.multiply(arr1, arr2, out=None, where=True, casting='same_kind', order='K', dtype=None

Python | Numpy matrix.take()

With the help of Numpy matrix.take() method, we can select the elements from a given matrix by passing the parameter as index value of that element. It will return a matrix having one dimension. Remember it will work for one axis at a time. Syntax : matrix.take(index, axis) Return : Return matrix of

numpy.hypot() in Python

This mathematical function helps user to calculate hypotenuse for the right angled triangle, given its side and perpendicular. Result is equivalent to Equivalent to sqrt(x1**2 + x2**2), element-wise. Syntax : numpy.exp2(arr1, arr2[, out]) = ufunc 'hypot') : Parameters : arr1, arr2 : [array_like] Leg