Updated March 28, 2023
What is numpy.dot()?
Numpy.dot() function Is it a tool that is responsible for returning the dot equivalent product for two different areas that had been entered by the user. In the case of a one-dimensional array, the function returns the inner product with respect to the adjudicating vectors. On the contrary, for two-dimensional arrays, the function returns the value which is equal to the resultant output returned on the multiplication of two arrays. Assuming the coder is not aware of the dimensions of the array (in case the address entered by the user) the output is equivalent to the sum-product derivative of the 2nd last axis of an array ‘b1’ and last access of the first array ‘a1’.
Syntax and Parameters
The following sentence is used for the functional operation of Numpy.dot() in a python programming language:
numpy.dot(a, b, out=None)The function is responsible for returning the dot-product of arrays while keeping the following projections in perspective:
- For a 1-D array, the function returns the inner product with respect to the adjudicating vectors
- In the case of a 2-D array, the function returns the value which is equal to the resultant output returned on the multiplication of two arrays.
- In case, the arrays are dimensionless or 0-D (i.e., scalar entities) the resultant output is the sum-product of the last axis
- In the case of N-D arrays, where the users are not aware of the dimensions of the array, the output is equivalent to the sum-product derivative of the 2nd last axis of an array ‘b1’ and last access of the first array ‘a1’.
The format of syntax in such condition would be:
Numpy.dot(a1, b1)[i1, j1, k1, m1] = sum(a1[i1,j1,:] * b1[k1,:,m1)Understanding the parameter:
| Parameters | a : array_like: First array of argument which has been entered by the user
b : array_like: The second array or argument which has been entered by the user. out: ndarray, optional: Output which would be derived after the execution of the function. The parameter ‘kind’ for the implementation in this function has to be exactly the same in the areas which have been entered by the user. Particularly speaking, the parameter ‘type’ has to be correct and must be of the C-contagious type. Moreover, it must be noted that the data type which is returned from the output must be the same as the dtype parameter entered for the function numpy.dot(a1, b1). This is an indicator feature that is relevant to the performance of the function. Thus it has to be understood that under circumstances weather conditions have not been met there is a chance of an exception being raised, rather than the function trying to given output which is flexible in terms of the data which is entered and the parameters. |
| Returns | output: ndarray: The parameter is responsible for returning the dot product for the Arrays (a1 and b1 here) which have been entered by the user. In case the areas entered are scalar values for a single dimension then the return value is scalar. On the contrary in the air is our 2D or above the resultant output is an array. |
| Raises | ValueError: In case the indicative last-dimension of the first array entered by the user (here a1) is not equal to the indicative second the last dimension of the secondary entered by the user (here b1) a value error in generated by the system. |
Examples of Using Numpy.dot()
Below are the examples:
Example #1
Code:
# Program which illustrates using NuPy.dot() in python language
import numpy as n1
# Code written for Scalar Values entered by the user
output1 = n1.dot(50, 40)
print("The output generated for dot-product of the provided values (scalar) is : ", output1)
# Code for 1D array
v_a1 = 20 + 30j
v_b1 = 40 + 50j
output2 = n1.dot(v_a1, v_b1)
print("The output generated for dot-product of the provided values (single dimensional values) is : ", output2)Output:
Example #2
Code:
# Program which illustrates using NuPy.dot() in python language
# Code for 2-D array
import numpy as n1
v_a1 = n1.array([[10, 40], [50, 60]])
v_b1 = n1.array([[20, 40], [50, 20]])
ans1 = n1.dot(v_a1, v_b1)
print("The output generated for dot-product of the provided values (two dimensional array A and B) is: \n", ans1 )
ans2 = n1.dot(v_b1, v_a1)
print("The output generated for dot-product of the provided values (two dimensional array B and A) is: \n", ans2 )Output:
How does the Function work for One Dimensional Array?
For the code provided with 1-D arrays being:
v_a1 = 20 + 30j
v_b1 = 40 + 50jPerforming computational analysis on the arrays to give the dot product
- = 20 (40 + 50j) + 30j (40 – 50j)
- = 80 + 100j + 120j – 150
- Answer is = -70 + 220j
The function similarly works with arrays provided to give a resultant output which is a factor of the two vector values provided and thus the sum-product for array and array B will have different outputs when compared to array B and array A, due to changes in their indices and cross positional multiplication of individual elements.
Conclusion
The NumPy.dot() function is a very essential tool in the numpy class it allows for small single line driven code for cross functional multi-liner programs which increases the turn-around time and decreases the verbosity for the function, serving as a very essential tool for large data driven coding.
Recommended Articles
This is a guide to numpy.dot(). Here we discuss the syntax, parameter, examples to implement and how does the function work in a one-dimensional array. You can also go through our other related articles to learn more –
