In this post, we’ll discuss the reasons for choosing Python for AI. Python is a standout amongst the most well-known programming language of ongoing occasions. Python, made by Guido van Rossum in 1991, is an open-source, broadly useful programming language. Python is also called a dynamic programming language which supports object-arranged, basic, useful and procedural improvement standards.
Python is well known in machine picking up programming. Python is the principal programming languages that got the help of machine learning by means of an assortment of libraries and devices. Scikit and TensorFlow are two prominent machine learning libraries accessible to Python engineers.
Python is viewed as in first place in the rundown of all AI advancement languages because of the simplicity. The syntaxes of python are very simple and can be effectively learned. In this way, numerous AI algorithms can be effectively implemented in it. Python takes short advancement time in contrast with different Programming languages like Java, C++ or Ruby. There are a lot of libraries in python, which make our requirement less demanding. For instance: Numpy is a library for python that encourages us to comprehend many logical/scientific calculations. Additionally, we have Pybrain, which is for utilizing machine learning in Python.
Python is broadly utilized for Artificial Intelligence, with bundles for various applications including General AI, Machine Learning, Natural Language Processing, and Neural Networks.
Important Features of Python
Python is an abnormal state, translated, intelligent and object-situated scripting dialect. Python is intended to be highly readable. Whereas Python involves simple syntax and less coding among other languages.
Simple-to-learn
− Python has a couple of catchphrases, straightforward structure, and a characterized grammar.
Simple to-peruse
− Python code is all the more unmistakably characterized and obvious to the eyes.
Simple to keep up
− Python’s source code is very simple to keep up compared to other languages.
Can be utilized for the expansive scope of programming − Python can be utilized for a wide scope of programming errands like little shell content to big business web applications. This is another reason Python is appropriate for AI tasks.
A wide standard library
− Python’s heft of the library is entirely compact and cross-stage good on UNIX, Windows, and Macintosh.
Intelligent Mode
− Python has bolster for an intuitive mode which permits intelligent testing and investigating of pieces of code.
Convenient
− Python can also run on a different hardware platform and has a similar interface on all platforms.
Extendable
− We can add low-level modules to the Python translator. These low-level modules empower the developers to add to or modify their devices to be more proficient.
GUI Programming
− Python gives support to GUI applications that can be created and ported to numerous, windows frameworks, framework calls, and libraries, for example, Macintosh, Windows MFC, and the X Window arrangement of Unix.
Versatile
− Python gives a better structure and as well as support for substantial projects than shell scripting.
Building Artificial Intelligence with Python
Whether it is a small scale business or large scale business, Python gives numerous advantages to everybody. The use of Python enables designers to play out different exercises at one time. Python takes some time to build AI. The measure of the time required relies upon your inspiration, abilities, the dimension of programming knowledge, and so on.
So as to fabricate AI with Python, you should need some base knowledge of this programming language. And the first important thing is to install Python. For this, you can install Anaconda. Which includes the required packages for sci-kit-learn, machine learning, Matplotlib, NumPy and iPython Notebook.
When discussing Python, I’ve just referenced logical libraries. These libraries are very useful while building AI applications. For suppose, you utilize NumPy, which integrates tools for Fourier transforms, C or C++ code, irregular number capacities, and different capacities, NumPy is the standout amongst the most valuable packages for logical computing.
Pandas(open source library) is another crucial tool, which provides analytics data structures and clients simple to-utilize information structures and diagnostic devices for Python language. Matplotlib is another administration with a 2D plotting lib(library) that makes quality figures for distribution. The best preferences of the matplotlib library are the accessibility of Python contents, web application servers, and 6 graphical clients interface kits. Scikit-learn open source tool is used as a productive data analysis tool. This tool is only used for commercial purpose. It’s the most well known broadly useful Machine learning/AI library.
While you start working with open source scikit-learn tool, then only you can reach a higher level of programming AI utilizing Python. You should also read about decision trees, logistic regression, continuous numeric prediction, etc. On the off chance that you need to become familiar with Python with AI, need to read about a profound learning structure Python library Theano and Caffee.
Example logic programming AI With Python
Actually, we can find the unknown values by using logic programming in a very effective way. The following Python code will help you match a mathematical expression. Consider importing the following packages first:
from kanren import run, var, fact from kanren.assoccomm import eq_assoccomm as eq from kanren.assoccomm import commutative, associative
We need to define the mathematical operations which we are going to use:
add = 'add' mul = 'mul'
Both addition and multiplication are communicative processes. Hence, we need to specify it and this can be done as follows:
fact(commutative, mul) fact(commutative, add) fact(associative, mul) fact(associative, add)
It is compulsory to define variables; this can be done as follows:
a, b = var('a'), var('b')
We need to match the expression with the original pattern. We have the following original pattern, which is basically (5+a)*b:
Original_pattern = (mul, (add, 5, a), b)
We have the following two expressions to match the original pattern:
exp1 = (mul, 2, (add, 3, 1)) exp2 = (add,5,(mul,8,1))
Output can be printed with the following command:
print(run(0, (a,b), eq(original_pattern, exp1))) print(run(0, (a,b), eq(original_pattern, exp2)))
After running this code, we will get the following output:
((3,2)) ()
The first output represents the values for a and b. The first expression matched the original pattern and returned the values for a and b but the second expression did not match the original pattern hence nothing has been returned.
Checking for Prime Numbers (AI with Python)
With the help of logic programming, we can find the prime numbers from a list of numbers and can also generate prime numbers. The Python code given below will find the prime number from a list of numbers and will also generate the first 10 prime numbers.
Let us first consider importing the following packages:
from kanren import isvar, run, membero from kanren.core import success, fail, goaleval, condeseq, eq, var from sympy.ntheory.generate import prime, isprime import itertools as it
Now, we will define a function called prime_check which will check the prime numbers based on the given numbers as data.
def prime_check(x): if isvar(x): return condeseq([(eq,x,p)] for p in map(prime, it.count(1))) else: return success if isprime(x) else fail
Now, we need to declare a variable which will be used:
x = var() print((set(run(0,x,(membero,x,(12,14,15,19,20,21,22,23,29,30,41,44,52,62,65,85)), (prime_check,x))))) print((run(10,x,prime_check(x))))
The output of the above code will be as follows:
{19, 23, 29, 41}
(2, 3, 5, 7, 11, 13, 17, 19, 23, 29)
Solving Puzzles
Logic programming can be used to solve many problems like 8-puzzles, Zebra puzzle, Sudoku, N-queen, etc. Here we are taking an example of a variant of Zebra puzzle which is as follows −
There are five houses. The English man lives in the red house. The Swede has a dog. The Dane drinks tea. The green house is immediately to the left of the white house. They drink coffee in the green house. The man who smokes Pall Mall has birds. In the yellow house they smoke Dunhill. In the middle house they drink milk. The Norwegian lives in the first house. The man who smokes Blend lives in the house next to the house with cats. In a house next to the house where they have a horse, they smoke Dunhill. The man who smokes Blue Master drinks beer. The German smokes Prince. The Norwegian lives next to the blue house. They drink water in a house next to the house where they smoke Blend. We are solving it for the question who owns zebra with the help of Python.
Let us import the necessary packages:
from kanren import * from kanren.core import lall import time
Now, we need to define two functions − left() and next() to check whose house is left or next to who’s house:
def left(q, p, list): return membero((q,p), zip(list, list[1:])) def next(q, p, list): return conde([left(q, p, list)], [left(p, q, list)])
Now, we will declare a variable house as follows:
houses = var()
We need to define the rules with the help of lall package as follows.
There are 5 houses:
rules_zebraproblem = lall(
(eq, (var(), var(), var(), var(), var()), houses),
(membero,('Englishman', var(), var(), var(), 'red'), houses),
(membero,('Swede', var(), var(), 'dog', var()), houses),
(membero,('Dane', var(), 'tea', var(), var()), houses),
(left,(var(), var(), var(), var(), 'green'),
(var(), var(), var(), var(), 'white'), houses),
(membero,(var(), var(), 'coffee', var(), 'green'), houses),
(membero,(var(), 'Pall Mall', var(), 'birds', var()), houses),
(membero,(var(), 'Dunhill', var(), var(), 'yellow'), houses),
(eq,(var(), var(), (var(), var(), 'milk', var(), var()), var(), var()), houses),
(eq,(('Norwegian', var(), var(), var(), var()), var(), var(), var(), var()), houses),
(next,(var(), 'Blend', var(), var(), var()),
(var(), var(), var(), 'cats', var()), houses),
(next,(var(), 'Dunhill', var(), var(), var()),
(var(), var(), var(), 'horse', var()), houses),
(membero,(var(), 'Blue Master', 'beer', var(), var()), houses),
(membero,('German', 'Prince', var(), var(), var()), houses),
(next,('Norwegian', var(), var(), var(), var()),
(var(), var(), var(), var(), 'blue'), houses),
(next,(var(), 'Blend', var(), var(), var()),
(var(), var(), 'water', var(), var()), houses),
(membero,(var(), var(), var(), 'zebra', var()), houses)
)
Now, run the solver with the preceding constraints:
solutions = run(0, houses, rules_zebraproblem)
With the help of the following code, we can extract the output from the solver:
output_zebra = [house for house in solutions[0] if 'zebra' in house][0][0]
The following code will help print the solution:
print ('\n'+ output_zebra + 'owns zebra.')
The output of the above code would be as follows:
German owns zebra.
There are some Python with AI libraries:pyDatalog, AIMA, EasyAi, SimpleAI and so forth. And also their libraries for machine learning are PyBrain, MDP, scikit, PyML. You need to look at NLTK in case you’re looking for regular dialect and content handling libraries.
As we can see the significance of Python language for Artificial Intelligence is self-evident. And any AI project will get beneficiaries by utilizing Python. But Artificial intelligence needs a lot more to the output using Python for AI projects is very effective.
Conclusion:
Artificial Intelligence is getting advanced day by day. Also, Python is making the advancement procedure of AI-based task significantly simpler. Because of the shorter line of codes, developers/designers can think that it’s simple to compose codes in Python. With the presence of different inbuilt libraries, AI-based projects are getting into advanced mode.