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Optimization deals with selecting the simplest option among a number of possible choices that are feasible or do not violate constraints. Python is used to optimize parameters in a model to best fit data, increase profitability of a possible engineering style, or meet another form of objective which will be described mathematically with variables and equations.
pyOpt is a Python-based package for formulating and solving nonlinear constrained optimization problems in an efficient, reusable and portable manner. Python programming uses object-oriented concepts, such as class inheritance and operator overloading, to maintain a distinct separation between the problem formulation and the optimization approach used to solve the problem.
All optimisation downside solvers inherit from the Optimizer abstract category. The category attributes include the solver name (name), an optimizer kind symbol (category), and dictionaries that contain the solver setup parameters (options) and message output settings (informs). The class provides ways to check and alter default solver parameters (getOption, setOption), as well as a method that runs the solver for a given optimisation problem (solve).
A number of constrained optimization solvers are designed to solve the general nonlinear optimization problem.
To solve an optimization problem with pyOpt an optimizer must be initialized. The initialization of one or more optimizers is independent of the initialization of any number of optimization problems. To initialize SLSQP, which is an open-source, sequential least squares programming algorithm that comes as part of the pyOpt package, use:
>>> slsqp = pyOpt.SLSQP()
This initializes an instance of SLSQP with the default options. The setOption method can be used to change any optimizer specific option, for example the internal output flag of SLSQP:
>>> slsqp.setOption('IPRINT', -1)
Now Schittkowski’s constrained problem can be solved using SLSQP and for example, pyOpt’s automatic finite difference for the gradients:
>>> [fstr, xstr, inform] = slsqp(opt_prob,sens_type='FD')
By default, the solution information of an optimizer is also stored in the specific optimization problem. To output solution to the screen one can use:
>>> print opt_prob.solution(0)
The problem is taken from the set of nonlinear programming examples by Hock and Schittkowski and it is defined as
min − x1x2x3
subjected to x1 + 2x2 + 2x3 − 72 ≤ 0
− x1 − 2x2 − 2x3 ≤ 0
0 ≤ x1 ≤ 42
0 ≤ x2 ≤ 42
0 ≤ x3 ≤ 42
The optimum of this problem is at (x1∗ , x2∗ , x3* ) = (24, 12, 12), with an objective function value of f ∗ = −3456, and constraint values g (x∗ ) = (0, −72).
# Standard Python modules
import os, sys, time
# Extension modules
#from pyOpt import *
from pyOpt import Optimization
from pyOpt import PSQP
from pyOpt import SLSQP
from pyOpt import CONMIN
from pyOpt import COBYLA
from pyOpt import SOLVOPT
from pyOpt import KSOPT
from pyOpt import NSGA2
from pyOpt import ALGENCAN
from pyOpt import FILTERSD
f = -x*x*x
g = [0.0]*2
g = x + 2.*x + 2.*x - 72.0
g = -x - 2.*x - 2.*x
fail = 0
return f,g, fail
# Instantiate Optimization Problem
opt_prob = Optimization('Hock and Schittkowski Constrained Problem',objfunc)
# Instantiate Optimizer (PSQP) & Solve Problem
psqp = PSQP()
# Instantiate Optimizer (SLSQP) & Solve Problem
slsqp = SLSQP()
# Instantiate Optimizer (CONMIN) & Solve Problem
conmin = CONMIN()
# Instantiate Optimizer (COBYLA) & Solve Problem
cobyla = COBYLA()
# Instantiate Optimizer (SOLVOPT) & Solve Problem
solvopt = SOLVOPT()
# Instantiate Optimizer (KSOPT) & Solve Problem
ksopt = KSOPT()
# Instantiate Optimizer (NSGA2) & Solve Problem
nsga2 = NSGA2()
# Instantiate Optimizer (ALGENCAN) & Solve Problem
algencan = ALGENCAN()
# Instantiate Optimizer (FILTERSD) & Solve Problem
filtersd = FILTERSD()
Solving non-linear global optimization problems could be tedious task sometimes. If the problem is not that complex then general purpose solvers could work. However, as the complexity of problem increases, general purpose global optimizers start to take time. That is when need to create your problem specific fast and direct global optimizer’s need arises.
We have an specialized team with PHD holders and coders to design and develop customized global optimizers. If you need help with one, please feel free to send your queries to us.
We first understand the problem and data by visualizing it. After that we create a solution to your needs.
Please do read to understand what a solver is and how it works - If you want to create your own simple solver. This is not exactly how every solver works, however, this will give you a pretty solid idea of what is a solver and how it is supposed to work.
A chatbot is an artificial intelligence powered piece of software in a device, application, web site or alternative networks that try to complete consumer’s needs and then assist them to perform a selected task. Now a days almost every company has a chatbot deployed to interact with the users.
Chatbots are often used in many departments, businesses and every environment. They are artificial narrow intelligence (ANI). Chatbots only do a restricted quantity of task i.e. as per their design. However, these Chatbots make our lives easier and convenient. The trend of Chatbots is growing rapidly between businesses and entrepreneurs, and are willing to bring chatbots to their sites. You might also produce it yourself using Python.
There are broadly two variants of chatbots: Rule-Based and Self learning.
The field of study that focuses on the interactions between human language and computers is called Natural Language Processing. NLP is a way for computers to analyze, understand, and derive meaning from human language in a smart and useful way. However, if you are new to NLP, you can read Natural Language Processing in Python.
NLTK (Natural Language Toolkit) is a leading platform for building Python programs to work with human language data. It provides easy-to-use lexical resources such as WordNet, along with a suite of text processing libraries.
Importing necessary libraries
import numpy as np
import string # to process standard python strings
Copy the content in text file named ‘chatbot.txt’, read in the text file and convert the entire file content into a list of sentences and a list of words for further pre-processing.
f=open('chatbot.txt','r',errors = 'ignore')
raw=raw.lower()# converts to lowercase
nltk.download('punkt') # first-time use only
nltk.download('wordnet') # first-time use only
sent_tokens = nltk.sent_tokenize(raw)# converts to list of sentences
word_tokens = nltk.word_tokenize(raw)# converts to list of words
We shall now define a function called LemTokens which will take as input the tokens and return normalized tokens.
lemmer = nltk.stem.WordNetLemmatizer()
#WordNet is a semantically-oriented dictionary of English included in NLTK.
return [lemmer.lemmatize(token) for token in tokens]
remove_punct_dict = dict((ord(punct), None) for punct in string.punctuation)
Define a function for greeting by bot i.e. if user’s input is greeting, the bot shall return a greeting response.
GREETING_INPUTS = ("hello", "hi", "greetings", "sup", "what's up","hey",)
GREETING_RESPONSES = ["hi", "hey", "*nods*", "hi there", "hello", "I am glad! You are talking to me"]
for word in sentence.split():
if word.lower() in GREETING_INPUTS:
To generate a response from our bot for input queries, the concept of document similarity is used. Therefore, we start by importing necessary modules.
From scikit learn library, import the TFidf vector to convert a collection of raw documents to a matrix of TF-IDF features
from sklearn.feature_extraction.text import TfidfVectorizer
Also, import cosine similarity module from scikit learn library
from sklearn.metrics.pairwise import cosine_similarity
This will be used to find the similarity between words entered by the user and therefore the words within the corpus. This can be the simplest possible implementation of a chatbot.
Define a function response that searches the user’s vocalization for one or more known keywords and returns one of several possible responses. If it doesn’t find the input matching any of the keywords, it returns a response: “I’m sorry! I don’t understand you”
TfidfVec = TfidfVectorizer(tokenizer=LemNormalize, stop_words='english')
tfidf = TfidfVec.fit_transform(sent_tokens)
vals = cosine_similarity(tfidf[-1], tfidf)
flat = vals.flatten()
req_tfidf = flat[-2]
robo_response=robo_response+"I am sorry! I don't understand you"
else: robo_response = robo_response+sent_tokens[idx]
I have tried to explain in simple steps how you can build your own chatbot using NLTK and of course it’s not an intelligent one.
I hope you guys have enjoyed reading.
A virtual assistant (VA) is a professional who provides administrative and support gigs. These can range from invoicing clients on a regular basis to managing a one-time project. Thanks to the wonders of email, instant messaging, conference calls, and video chat, your virtual assistant works remotely—he or she could be on the other side of the country, or even in another country—which means you don’t have to worry about finding additional office space.
Working from home, on your set schedule, from your own comfort, and earn more is not always as glamorous as it might seem. Becoming a Virtual Assistant it requires time and patience, along with some smart work.
Assuming that you are clear with the basics of Virtual Assistant profession and know about the resources to find a Virtual Assistant services, let’s move on to how you can be more effective working as a Virtual Assistant.
Being an top professional Virtual Assistant is not rocket science and could be mastered with a little effort and patience. Given the high competition for these jobs, you need to stand out from your competition to land higher paying services.
Before you go on your virtual assistant journey, It is mandatory to make sure that you possess all the basic skills required. An ideal and successful virtual assistant would be on par with the basic skill-set required.
So do you think you have a good hold on the basic skills? Let’s go through a few basic skills expected by each client. Develope these skills and get hired today:
Comfortable with using Windows / Mac.
More than 45 words per minute.
Know how to use Internet and emailing.
Know the basics of social media marketing.
Have a good time and resource management skills.
Have a good understanding of newsletter creation.
Since 2007, virtual assistance has become a very in-demand field, and is seeing steady growth each year. With the numbers in mind, and the growing rate of business done online, it is no surprise that several virtual assistance companies have been born. These companies hire men and women from all across the world to work remotely and offer their clients the online support they need.
This is very good news for the brand new virtual assistant, because it means there are options for you to get a steady stream of work, steady paychecks, and professional support and training in the field. You can become a strong assistant very quickly, and then decide if you’d like to move up in the company or branch out on your own. Just be sure never to sign a non-compete agreement if you plan to eventually go it alone.
Learn about the basic ideas for the following term:
Processing Online Orders
A right tool-set is a power in the hands of VA to build a legacy out of nothing. Make sure you have got nothing but the best tools before you jump in the market to play a solid role of virtual assistant.
A phone (cell or landline)
A good computer/laptop that works perfectly.
At least two up to date browsers.
A good email client.
Skype or other chat software.
A good speed Internet connection.
A perfect reminder device which tells or update you about your upcoming challenges.
These are just the basic tools required, and most of the people would mostly satisfy the criteria.
No matter you work locally or remotely, nothing is going to work for you if you neglect the importance of a fixed schedule. Although virtual assistant allows you flexibility from your home office, that doesn’t mean you wake up suddenly at the wake of the night and start working.
Make a habit of working during the same hours of the day, every day. You don’t need a 9-5 schedule, but a schedule is a must to get the most out of your work. Make a schedule as per the times when you are at your best with least distractions and day in day out, work during the same hours.
If having a plan is good, having a plan B is a must. So, for every plan that you make, have an alternative plan ready to execute. Things might not always work out the way you planned them to, so having a backup plan will help you in the long run.
Build a strategy of how many hours you’re willing to work,
Where you’re going to work,
What about your taxes,
You should continue making a plan, and you will end up with more questions to be answered even before you accept your first service.
Deliver more: When was the last time you over delivered and your customer couldn’t stop singing your songs? For customers to sing your song you need to provide that rare high-quality service. So it’s always good to deliver more, surprise your customers and keep them happy.
Don’t always do everything to earn more and at least sometimes do for the love of it. I love making people happy and go the extra mile to deliver more than anyone expects. Go out of your way to leave a legacy in whatever you do. Make people remember you for what you’ve done rather than as a man who’s always bugging about the next paycheck.
Optimization deals with selecting the simplest option among a number of possible choices that are feasible or do not violate constraints. Python is used to optimize parameters in a...Read More
A chatbot is an artificial intelligence powered piece of software in a device, application, web site or alternative networks that try to complete consumer’s needs and then assist t...Read More
A virtual assistant (VA) is a professional who provides administrative and support gigs. These can range from invoicing clients on a regular basis to managing a one-time project. T...Read More
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