
Quick Contact
Python Tutorial
- What is Python?
- How to Install Python?
- Python Variables and Operators
- Python Loops
- Python Functions
- Python Files
- Python Errors and Exceptions
- Python Packages
- Python Classes and Objects
- Python Strings
- PostgreSQL Data Types
- Python Generators and Decorators
- Python Dictionary
- Python Date and Time
- Python List and Tuples
- Python Multithreading and Synchronization
- Python Modules
- What is Python bytecode?
- Python Regular Expressions
Python Panda Tutorial
- Python Pandas Tutorial
- Python Pandas Features
- Advantages and Disadvantages of Python Pandas
- Pandas Library In Python
- Pandas Series To Frame
- Python Dataframeaggregate and Assign
- Pandas Dataframe Describe
- Pandas Dataframe Mean
- Pandas Hist
- Pandas Dataframe Sum
- How to convert Pandas DataFrame to Numpy array
- Pandas Concatenation
Python Selenium
- Selenium Basics
- Selenium with Python Introduction and Installation
- Navigating links using get method Selenium Python
- Locating Single Elements in Selenium Python
- Locating Multiple elements in Selenium Python
Python Flask Tutorial
Python Django
- How to Install Django and Set Up a Virtual Environment in 6 Steps
- Django MTV Architecture
- Django Models
- Django Views
- Django Templates
- Django Template Language
- Django Project Layout
- Django Admin Interface
- Django Database
- Django URLs and URLConf
- Django Redirects
- Django Cookies and Cookies Handling
- Django Caching
- Types of Caching in Django
- Django Sessions
- Django Forms Handling & Validation
- Django Exceptions & Error-handling
- Django Forms Validation
- Django Redirects
- Django Admin Interface
- Django Bootstrap
- Ajax in Django
- Django Migrations and Database Connectivity
- Django Web Hosting and IDE
- Django Admin Customization
- What is CRUD?
- Django ORM
- Django Request-Response Cycle
- Django ORM
- Making a basic_api with DRF
- Django Logging
- Django Applications
- Difference between Flask vs Django
- Difference between Django vs PHP
Numpy
- Numpy Introduction
- NumPy– Environment Setup
- NumPy - Data Types
- NumPy–Functions
- NumPy Histogram
- numPy.where
- numpy.sort
- NumPyfloor
- Matrix in NumPy
- NumPy Arrays
- NumPy Array Functions
- Matrix Multiplication in NumPy
- NumPy Matrix Transpose
- NumPy Array Append
- NumPy empty array
- NumPy Linear Algebra
- numpy.diff()
- numpy.unique()
- numpy.dot()
- numpy.mean()
- Numpy.argsort()
- numpy.pad()
- NumPyvstack
- NumPy sum
- NumPy Normal Distribution
- NumPylogspace()
- NumPy correlation
- Why we learn and use Numpy?
Tensorflow
- Introduction To Tensorflow
- INTRODUCTION TO DEEP LEARNING
- EXPLAIN NEURAL NETWORK?
- CONVOLUTIONAL AND RECURRENT NEURAL NETWORK
- INTRODUCTION TO TENSORFLOW
- INSTALLATION OF TENSORFLOW
- TENSORBOARD VISUALIZATION
- Linear regression in tensorflow
- Word Embedding
- Difference between CNN And RNN
- Explain Keras
- Program elements in tensorflow
- Recurrent Neural Network
- Tensorflow Object Detection
- EXPLAIN MULTILAYER PERCEPTRON
- GRADIENT DESCENT OPTIMIZATION
Interview Questions & Answers
NumPy correlation
Numpy.correlation in Python is excited by the application of the correlate * () function. It is used in the Python coding language that Enables the cross-correlation ship between two unique One dimension of single dimension arrays containing a sequence of data set. This function is enabled to perform computation upon the correlation as it is generally defined in the process of signal enhancement and evaluation.
Syntax for application of numpy.correlate* ()
Following is the syntax which used to utilize the numpy.correlate*() while writing codes in the Python programming language:
numpy*.* correlate* (* a1, v1, mode=’valid’, * old* _* behavior* =* False)
Parameters for application of NumPy.correlate* ()
The following is the parameters used for the numpy.correlate() function written in the Python programming language:
Parameter | Description of Parameter |
---|---|
a1 single dimensioned array or sequence of array_like
They represent the input variable; all the single-dimensional array is that have been entered into the system by the user. They contain a sequence of data which can be random or having a pattern mode* ** ** ** ** *{* * ‘valid’, * * ‘same’, * * ‘full’* *},* * optional* ** ** * This refers to the conversation that is expected in the docstring. It is an optional variable that is used in the parameter while using the function. It must be noted that the parameter has its default value set as valid. Unlike the similar function of convolving, which is a default set at ‘full.’ old_behavior : bool* ** ** * In case the parameter turns out to be true, it utilizes the previously existing behaviour from the numeric, * * (correlation* * (a1,v1) =* *= * *correlation* * (v1,a1), and then further conjugate is not considered for utilisation on complex arrays. Increase the value set for the parameter is false; it then utilizes the pre-defined conventional signal processing. |
|
Returns | Out: ndarray, optional* ** *
The parameter enables the program to show the exact destination where the correlation executed resultant array has to be placed. This parameter is not an unnecessary addition and can be skipped while writing the command. |
Examples of NumPy correlation
An example displaying the used of NumPy.correlate() in Python to perform correlation:
Example #1
Code:
# Python program explaining the use of NumPy.correlate () function import numpy as geek1 # Python program explaining # numpy.correlate() function # importing numpy as geek import numpy as geek1 print("Input the value for the array that has to given") a1 = [20, 50, 70] v1 = [0, 10, 0.50] print("The Output with the application of the numpy's correlation function is: ") gfg1 = geek1.correlate(a1, v1, "same") print(gfg1)
Example #2
Code:
# Python program explaining the use of NumPy.correlate () function import numpy as geek2 # Python program explaining # numpy.correlate() function # importing numpy as geek2 import numpy as geek2 print("Input the value for the array that has to given: ") a2 = [200, 500, 700] v2 = [0, 100, 50] output = geek2.correlate(a2, v2) print("The Output with the application of the numpy's correlation function is: ") print(output)