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sayantann11 / All Classification Templetes For MLClassification - Machine Learning This is ‘Classification’ tutorial which is a part of the Machine Learning course offered by Simplilearn. We will learn Classification algorithms, types of classification algorithms, support vector machines(SVM), Naive Bayes, Decision Tree and Random Forest Classifier in this tutorial. Objectives Let us look at some of the objectives covered under this section of Machine Learning tutorial. Define Classification and list its algorithms Describe Logistic Regression and Sigmoid Probability Explain K-Nearest Neighbors and KNN classification Understand Support Vector Machines, Polynomial Kernel, and Kernel Trick Analyze Kernel Support Vector Machines with an example Implement the Naïve Bayes Classifier Demonstrate Decision Tree Classifier Describe Random Forest Classifier Classification: Meaning Classification is a type of supervised learning. It specifies the class to which data elements belong to and is best used when the output has finite and discrete values. It predicts a class for an input variable as well. There are 2 types of Classification: Binomial Multi-Class Classification: Use Cases Some of the key areas where classification cases are being used: To find whether an email received is a spam or ham To identify customer segments To find if a bank loan is granted To identify if a kid will pass or fail in an examination Classification: Example Social media sentiment analysis has two potential outcomes, positive or negative, as displayed by the chart given below. https://www.simplilearn.com/ice9/free_resources_article_thumb/classification-example-machine-learning.JPG This chart shows the classification of the Iris flower dataset into its three sub-species indicated by codes 0, 1, and 2. https://www.simplilearn.com/ice9/free_resources_article_thumb/iris-flower-dataset-graph.JPG The test set dots represent the assignment of new test data points to one class or the other based on the trained classifier model. Types of Classification Algorithms Let’s have a quick look into the types of Classification Algorithm below. Linear Models Logistic Regression Support Vector Machines Nonlinear models K-nearest Neighbors (KNN) Kernel Support Vector Machines (SVM) Naïve Bayes Decision Tree Classification Random Forest Classification Logistic Regression: Meaning Let us understand the Logistic Regression model below. This refers to a regression model that is used for classification. This method is widely used for binary classification problems. It can also be extended to multi-class classification problems. Here, the dependent variable is categorical: y ϵ {0, 1} A binary dependent variable can have only two values, like 0 or 1, win or lose, pass or fail, healthy or sick, etc In this case, you model the probability distribution of output y as 1 or 0. This is called the sigmoid probability (σ). If σ(θ Tx) > 0.5, set y = 1, else set y = 0 Unlike Linear Regression (and its Normal Equation solution), there is no closed form solution for finding optimal weights of Logistic Regression. Instead, you must solve this with maximum likelihood estimation (a probability model to detect the maximum likelihood of something happening). It can be used to calculate the probability of a given outcome in a binary model, like the probability of being classified as sick or passing an exam. https://www.simplilearn.com/ice9/free_resources_article_thumb/logistic-regression-example-graph.JPG Sigmoid Probability The probability in the logistic regression is often represented by the Sigmoid function (also called the logistic function or the S-curve): https://www.simplilearn.com/ice9/free_resources_article_thumb/sigmoid-function-machine-learning.JPG In this equation, t represents data values * the number of hours studied and S(t) represents the probability of passing the exam. Assume sigmoid function: https://www.simplilearn.com/ice9/free_resources_article_thumb/sigmoid-probability-machine-learning.JPG g(z) tends toward 1 as z -> infinity , and g(z) tends toward 0 as z -> infinity K-nearest Neighbors (KNN) K-nearest Neighbors algorithm is used to assign a data point to clusters based on similarity measurement. It uses a supervised method for classification. The steps to writing a k-means algorithm are as given below: https://www.simplilearn.com/ice9/free_resources_article_thumb/knn-distribution-graph-machine-learning.JPG Choose the number of k and a distance metric. (k = 5 is common) Find k-nearest neighbors of the sample that you want to classify Assign the class label by majority vote. KNN Classification A new input point is classified in the category such that it has the most number of neighbors from that category. For example: https://www.simplilearn.com/ice9/free_resources_article_thumb/knn-classification-machine-learning.JPG Classify a patient as high risk or low risk. Mark email as spam or ham. Keen on learning about Classification Algorithms in Machine Learning? Click here! Support Vector Machine (SVM) Let us understand Support Vector Machine (SVM) in detail below. SVMs are classification algorithms used to assign data to various classes. They involve detecting hyperplanes which segregate data into classes. SVMs are very versatile and are also capable of performing linear or nonlinear classification, regression, and outlier detection. Once ideal hyperplanes are discovered, new data points can be easily classified. https://www.simplilearn.com/ice9/free_resources_article_thumb/support-vector-machines-graph-machine-learning.JPG The optimization objective is to find “maximum margin hyperplane” that is farthest from the closest points in the two classes (these points are called support vectors). In the given figure, the middle line represents the hyperplane. SVM Example Let’s look at this image below and have an idea about SVM in general. Hyperplanes with larger margins have lower generalization error. The positive and negative hyperplanes are represented by: https://www.simplilearn.com/ice9/free_resources_article_thumb/positive-negative-hyperplanes-machine-learning.JPG Classification of any new input sample xtest : If w0 + wTxtest > 1, the sample xtest is said to be in the class toward the right of the positive hyperplane. If w0 + wTxtest < -1, the sample xtest is said to be in the class toward the left of the negative hyperplane. When you subtract the two equations, you get: https://www.simplilearn.com/ice9/free_resources_article_thumb/equation-subtraction-machine-learning.JPG Length of vector w is (L2 norm length): https://www.simplilearn.com/ice9/free_resources_article_thumb/length-of-vector-machine-learning.JPG You normalize with the length of w to arrive at: https://www.simplilearn.com/ice9/free_resources_article_thumb/normalize-equation-machine-learning.JPG SVM: Hard Margin Classification Given below are some points to understand Hard Margin Classification. The left side of equation SVM-1 given above can be interpreted as the distance between the positive (+ve) and negative (-ve) hyperplanes; in other words, it is the margin that can be maximized. Hence the objective of the function is to maximize with the constraint that the samples are classified correctly, which is represented as : https://www.simplilearn.com/ice9/free_resources_article_thumb/hard-margin-classification-machine-learning.JPG This means that you are minimizing ‖w‖. This also means that all positive samples are on one side of the positive hyperplane and all negative samples are on the other side of the negative hyperplane. This can be written concisely as : https://www.simplilearn.com/ice9/free_resources_article_thumb/hard-margin-classification-formula.JPG Minimizing ‖w‖ is the same as minimizing. This figure is better as it is differentiable even at w = 0. The approach listed above is called “hard margin linear SVM classifier.” SVM: Soft Margin Classification Given below are some points to understand Soft Margin Classification. To allow for linear constraints to be relaxed for nonlinearly separable data, a slack variable is introduced. (i) measures how much ith instance is allowed to violate the margin. The slack variable is simply added to the linear constraints. https://www.simplilearn.com/ice9/free_resources_article_thumb/soft-margin-calculation-machine-learning.JPG Subject to the above constraints, the new objective to be minimized becomes: https://www.simplilearn.com/ice9/free_resources_article_thumb/soft-margin-calculation-formula.JPG You have two conflicting objectives now—minimizing slack variable to reduce margin violations and minimizing to increase the margin. The hyperparameter C allows us to define this trade-off. Large values of C correspond to larger error penalties (so smaller margins), whereas smaller values of C allow for higher misclassification errors and larger margins. https://www.simplilearn.com/ice9/free_resources_article_thumb/machine-learning-certification-video-preview.jpg SVM: Regularization The concept of C is the reverse of regularization. Higher C means lower regularization, which increases bias and lowers the variance (causing overfitting). https://www.simplilearn.com/ice9/free_resources_article_thumb/concept-of-c-graph-machine-learning.JPG IRIS Data Set The Iris dataset contains measurements of 150 IRIS flowers from three different species: Setosa Versicolor Viriginica Each row represents one sample. Flower measurements in centimeters are stored as columns. These are called features. IRIS Data Set: SVM Let’s train an SVM model using sci-kit-learn for the Iris dataset: https://www.simplilearn.com/ice9/free_resources_article_thumb/svm-model-graph-machine-learning.JPG Nonlinear SVM Classification There are two ways to solve nonlinear SVMs: by adding polynomial features by adding similarity features Polynomial features can be added to datasets; in some cases, this can create a linearly separable dataset. https://www.simplilearn.com/ice9/free_resources_article_thumb/nonlinear-classification-svm-machine-learning.JPG In the figure on the left, there is only 1 feature x1. This dataset is not linearly separable. If you add x2 = (x1)2 (figure on the right), the data becomes linearly separable. Polynomial Kernel In sci-kit-learn, one can use a Pipeline class for creating polynomial features. Classification results for the Moons dataset are shown in the figure. https://www.simplilearn.com/ice9/free_resources_article_thumb/polynomial-kernel-machine-learning.JPG Polynomial Kernel with Kernel Trick Let us look at the image below and understand Kernel Trick in detail. https://www.simplilearn.com/ice9/free_resources_article_thumb/polynomial-kernel-with-kernel-trick.JPG For large dimensional datasets, adding too many polynomial features can slow down the model. You can apply a kernel trick with the effect of polynomial features without actually adding them. The code is shown (SVC class) below trains an SVM classifier using a 3rd-degree polynomial kernel but with a kernel trick. https://www.simplilearn.com/ice9/free_resources_article_thumb/polynomial-kernel-equation-machine-learning.JPG The hyperparameter coefθ controls the influence of high-degree polynomials. Kernel SVM Let us understand in detail about Kernel SVM. Kernel SVMs are used for classification of nonlinear data. In the chart, nonlinear data is projected into a higher dimensional space via a mapping function where it becomes linearly separable. https://www.simplilearn.com/ice9/free_resources_article_thumb/kernel-svm-machine-learning.JPG In the higher dimension, a linear separating hyperplane can be derived and used for classification. A reverse projection of the higher dimension back to original feature space takes it back to nonlinear shape. As mentioned previously, SVMs can be kernelized to solve nonlinear classification problems. You can create a sample dataset for XOR gate (nonlinear problem) from NumPy. 100 samples will be assigned the class sample 1, and 100 samples will be assigned the class label -1. https://www.simplilearn.com/ice9/free_resources_article_thumb/kernel-svm-graph-machine-learning.JPG As you can see, this data is not linearly separable. https://www.simplilearn.com/ice9/free_resources_article_thumb/kernel-svm-non-separable.JPG You now use the kernel trick to classify XOR dataset created earlier. https://www.simplilearn.com/ice9/free_resources_article_thumb/kernel-svm-xor-machine-learning.JPG Naïve Bayes Classifier What is Naive Bayes Classifier? Have you ever wondered how your mail provider implements spam filtering or how online news channels perform news text classification or even how companies perform sentiment analysis of their audience on social media? All of this and more are done through a machine learning algorithm called Naive Bayes Classifier. Naive Bayes Named after Thomas Bayes from the 1700s who first coined this in the Western literature. Naive Bayes classifier works on the principle of conditional probability as given by the Bayes theorem. Advantages of Naive Bayes Classifier Listed below are six benefits of Naive Bayes Classifier. Very simple and easy to implement Needs less training data Handles both continuous and discrete data Highly scalable with the number of predictors and data points As it is fast, it can be used in real-time predictions Not sensitive to irrelevant features Bayes Theorem We will understand Bayes Theorem in detail from the points mentioned below. According to the Bayes model, the conditional probability P(Y|X) can be calculated as: P(Y|X) = P(X|Y)P(Y) / P(X) This means you have to estimate a very large number of P(X|Y) probabilities for a relatively small vector space X. For example, for a Boolean Y and 30 possible Boolean attributes in the X vector, you will have to estimate 3 billion probabilities P(X|Y). To make it practical, a Naïve Bayes classifier is used, which assumes conditional independence of P(X) to each other, with a given value of Y. This reduces the number of probability estimates to 2*30=60 in the above example. Naïve Bayes Classifier for SMS Spam Detection Consider a labeled SMS database having 5574 messages. It has messages as given below: https://www.simplilearn.com/ice9/free_resources_article_thumb/naive-bayes-spam-machine-learning.JPG Each message is marked as spam or ham in the data set. Let’s train a model with Naïve Bayes algorithm to detect spam from ham. The message lengths and their frequency (in the training dataset) are as shown below: https://www.simplilearn.com/ice9/free_resources_article_thumb/naive-bayes-spam-spam-detection.JPG Analyze the logic you use to train an algorithm to detect spam: Split each message into individual words/tokens (bag of words). Lemmatize the data (each word takes its base form, like “walking” or “walked” is replaced with “walk”). Convert data to vectors using scikit-learn module CountVectorizer. Run TFIDF to remove common words like “is,” “are,” “and.” Now apply scikit-learn module for Naïve Bayes MultinomialNB to get the Spam Detector. This spam detector can then be used to classify a random new message as spam or ham. Next, the accuracy of the spam detector is checked using the Confusion Matrix. For the SMS spam example above, the confusion matrix is shown on the right. Accuracy Rate = Correct / Total = (4827 + 592)/5574 = 97.21% Error Rate = Wrong / Total = (155 + 0)/5574 = 2.78% https://www.simplilearn.com/ice9/free_resources_article_thumb/confusion-matrix-machine-learning.JPG Although confusion Matrix is useful, some more precise metrics are provided by Precision and Recall. https://www.simplilearn.com/ice9/free_resources_article_thumb/precision-recall-matrix-machine-learning.JPG Precision refers to the accuracy of positive predictions. https://www.simplilearn.com/ice9/free_resources_article_thumb/precision-formula-machine-learning.JPG Recall refers to the ratio of positive instances that are correctly detected by the classifier (also known as True positive rate or TPR). https://www.simplilearn.com/ice9/free_resources_article_thumb/recall-formula-machine-learning.JPG Precision/Recall Trade-off To detect age-appropriate videos for kids, you need high precision (low recall) to ensure that only safe videos make the cut (even though a few safe videos may be left out). The high recall is needed (low precision is acceptable) in-store surveillance to catch shoplifters; a few false alarms are acceptable, but all shoplifters must be caught. Learn about Naive Bayes in detail. Click here! Decision Tree Classifier Some aspects of the Decision Tree Classifier mentioned below are. Decision Trees (DT) can be used both for classification and regression. The advantage of decision trees is that they require very little data preparation. They do not require feature scaling or centering at all. They are also the fundamental components of Random Forests, one of the most powerful ML algorithms. Unlike Random Forests and Neural Networks (which do black-box modeling), Decision Trees are white box models, which means that inner workings of these models are clearly understood. In the case of classification, the data is segregated based on a series of questions. Any new data point is assigned to the selected leaf node. https://www.simplilearn.com/ice9/free_resources_article_thumb/decision-tree-classifier-machine-learning.JPG Start at the tree root and split the data on the feature using the decision algorithm, resulting in the largest information gain (IG). This splitting procedure is then repeated in an iterative process at each child node until the leaves are pure. This means that the samples at each node belonging to the same class. In practice, you can set a limit on the depth of the tree to prevent overfitting. The purity is compromised here as the final leaves may still have some impurity. The figure shows the classification of the Iris dataset. https://www.simplilearn.com/ice9/free_resources_article_thumb/decision-tree-classifier-graph.JPG IRIS Decision Tree Let’s build a Decision Tree using scikit-learn for the Iris flower dataset and also visualize it using export_graphviz API. https://www.simplilearn.com/ice9/free_resources_article_thumb/iris-decision-tree-machine-learning.JPG The output of export_graphviz can be converted into png format: https://www.simplilearn.com/ice9/free_resources_article_thumb/iris-decision-tree-output.JPG Sample attribute stands for the number of training instances the node applies to. Value attribute stands for the number of training instances of each class the node applies to. Gini impurity measures the node’s impurity. A node is “pure” (gini=0) if all training instances it applies to belong to the same class. https://www.simplilearn.com/ice9/free_resources_article_thumb/impurity-formula-machine-learning.JPG For example, for Versicolor (green color node), the Gini is 1-(0/54)2 -(49/54)2 -(5/54) 2 ≈ 0.168 https://www.simplilearn.com/ice9/free_resources_article_thumb/iris-decision-tree-sample.JPG Decision Boundaries Let us learn to create decision boundaries below. For the first node (depth 0), the solid line splits the data (Iris-Setosa on left). Gini is 0 for Setosa node, so no further split is possible. The second node (depth 1) splits the data into Versicolor and Virginica. If max_depth were set as 3, a third split would happen (vertical dotted line). https://www.simplilearn.com/ice9/free_resources_article_thumb/decision-tree-boundaries.JPG For a sample with petal length 5 cm and petal width 1.5 cm, the tree traverses to depth 2 left node, so the probability predictions for this sample are 0% for Iris-Setosa (0/54), 90.7% for Iris-Versicolor (49/54), and 9.3% for Iris-Virginica (5/54) CART Training Algorithm Scikit-learn uses Classification and Regression Trees (CART) algorithm to train Decision Trees. CART algorithm: Split the data into two subsets using a single feature k and threshold tk (example, petal length < “2.45 cm”). This is done recursively for each node. k and tk are chosen such that they produce the purest subsets (weighted by their size). The objective is to minimize the cost function as given below: https://www.simplilearn.com/ice9/free_resources_article_thumb/cart-training-algorithm-machine-learning.JPG The algorithm stops executing if one of the following situations occurs: max_depth is reached No further splits are found for each node Other hyperparameters may be used to stop the tree: min_samples_split min_samples_leaf min_weight_fraction_leaf max_leaf_nodes Gini Impurity or Entropy Entropy is one more measure of impurity and can be used in place of Gini. https://www.simplilearn.com/ice9/free_resources_article_thumb/gini-impurity-entrophy.JPG It is a degree of uncertainty, and Information Gain is the reduction that occurs in entropy as one traverses down the tree. Entropy is zero for a DT node when the node contains instances of only one class. Entropy for depth 2 left node in the example given above is: https://www.simplilearn.com/ice9/free_resources_article_thumb/entrophy-for-depth-2.JPG Gini and Entropy both lead to similar trees. DT: Regularization The following figure shows two decision trees on the moons dataset. https://www.simplilearn.com/ice9/free_resources_article_thumb/dt-regularization-machine-learning.JPG The decision tree on the right is restricted by min_samples_leaf = 4. The model on the left is overfitting, while the model on the right generalizes better. Random Forest Classifier Let us have an understanding of Random Forest Classifier below. A random forest can be considered an ensemble of decision trees (Ensemble learning). Random Forest algorithm: Draw a random bootstrap sample of size n (randomly choose n samples from the training set). Grow a decision tree from the bootstrap sample. At each node, randomly select d features. Split the node using the feature that provides the best split according to the objective function, for instance by maximizing the information gain. Repeat the steps 1 to 2 k times. (k is the number of trees you want to create, using a subset of samples) Aggregate the prediction by each tree for a new data point to assign the class label by majority vote (pick the group selected by the most number of trees and assign new data point to that group). Random Forests are opaque, which means it is difficult to visualize their inner workings. https://www.simplilearn.com/ice9/free_resources_article_thumb/random-forest-classifier-graph.JPG However, the advantages outweigh their limitations since you do not have to worry about hyperparameters except k, which stands for the number of decision trees to be created from a subset of samples. RF is quite robust to noise from the individual decision trees. Hence, you need not prune individual decision trees. The larger the number of decision trees, the more accurate the Random Forest prediction is. (This, however, comes with higher computation cost). Key Takeaways Let us quickly run through what we have learned so far in this Classification tutorial. Classification algorithms are supervised learning methods to split data into classes. They can work on Linear Data as well as Nonlinear Data. Logistic Regression can classify data based on weighted parameters and sigmoid conversion to calculate the probability of classes. K-nearest Neighbors (KNN) algorithm uses similar features to classify data. Support Vector Machines (SVMs) classify data by detecting the maximum margin hyperplane between data classes. Naïve Bayes, a simplified Bayes Model, can help classify data using conditional probability models. Decision Trees are powerful classifiers and use tree splitting logic until pure or somewhat pure leaf node classes are attained. Random Forests apply Ensemble Learning to Decision Trees for more accurate classification predictions. Conclusion This completes ‘Classification’ tutorial. In the next tutorial, we will learn 'Unsupervised Learning with Clustering.'
ManojKumarPatnaik / Major Project ListA list of practical projects that anyone can solve in any programming language (See solutions). These projects are divided into multiple categories, and each category has its own folder. To get started, simply fork this repo. CONTRIBUTING See ways of contributing to this repo. You can contribute solutions (will be published in this repo) to existing problems, add new projects, or remove existing ones. Make sure you follow all instructions properly. Solutions You can find implementations of these projects in many other languages by other users in this repo. Credits Problems are motivated by the ones shared at: Martyr2’s Mega Project List Rosetta Code Table of Contents Numbers Classic Algorithms Graph Data Structures Text Networking Classes Threading Web Files Databases Graphics and Multimedia Security Numbers Find PI to the Nth Digit - Enter a number and have the program generate PI up to that many decimal places. Keep a limit to how far the program will go. Find e to the Nth Digit - Just like the previous problem, but with e instead of PI. Enter a number and have the program generate e up to that many decimal places. Keep a limit to how far the program will go. Fibonacci Sequence - Enter a number and have the program generate the Fibonacci sequence to that number or to the Nth number. Prime Factorization - Have the user enter a number and find all Prime Factors (if there are any) and display them. Next Prime Number - Have the program find prime numbers until the user chooses to stop asking for the next one. Find Cost of Tile to Cover W x H Floor - Calculate the total cost of the tile it would take to cover a floor plan of width and height, using a cost entered by the user. Mortgage Calculator - Calculate the monthly payments of a fixed-term mortgage over given Nth terms at a given interest rate. Also, figure out how long it will take the user to pay back the loan. For added complexity, add an option for users to select the compounding interval (Monthly, Weekly, Daily, Continually). Change Return Program - The user enters a cost and then the amount of money given. The program will figure out the change and the number of quarters, dimes, nickels, pennies needed for the change. Binary to Decimal and Back Converter - Develop a converter to convert a decimal number to binary or a binary number to its decimal equivalent. Calculator - A simple calculator to do basic operators. Make it a scientific calculator for added complexity. Unit Converter (temp, currency, volume, mass, and more) - Converts various units between one another. The user enters the type of unit being entered, the type of unit they want to convert to, and then the value. The program will then make the conversion. Alarm Clock - A simple clock where it plays a sound after X number of minutes/seconds or at a particular time. Distance Between Two Cities - Calculates the distance between two cities and allows the user to specify a unit of distance. This program may require finding coordinates for the cities like latitude and longitude. Credit Card Validator - Takes in a credit card number from a common credit card vendor (Visa, MasterCard, American Express, Discoverer) and validates it to make sure that it is a valid number (look into how credit cards use a checksum). Tax Calculator - Asks the user to enter a cost and either a country or state tax. It then returns the tax plus the total cost with tax. Factorial Finder - The Factorial of a positive integer, n, is defined as the product of the sequence n, n-1, n-2, ...1, and the factorial of zero, 0, is defined as being 1. Solve this using both loops and recursion. Complex Number Algebra - Show addition, multiplication, negation, and inversion of complex numbers in separate functions. (Subtraction and division operations can be made with pairs of these operations.) Print the results for each operation tested. Happy Numbers - A happy number is defined by the following process. Starting with any positive integer, replace the number by the sum of the squares of its digits, and repeat the process until the number equals 1 (where it will stay), or it loops endlessly in a cycle which does not include 1. Those numbers for which this process ends in 1 are happy numbers, while those that do not end in 1 are unhappy numbers. Display an example of your output here. Find the first 8 happy numbers. Number Names - Show how to spell out a number in English. You can use a preexisting implementation or roll your own, but you should support inputs up to at least one million (or the maximum value of your language's default bounded integer type if that's less). Optional: Support for inputs other than positive integers (like zero, negative integers, and floating-point numbers). Coin Flip Simulation - Write some code that simulates flipping a single coin however many times the user decides. The code should record the outcomes and count the number of tails and heads. Limit Calculator - Ask the user to enter f(x) and the limit value, then return the value of the limit statement Optional: Make the calculator capable of supporting infinite limits. Fast Exponentiation - Ask the user to enter 2 integers a and b and output a^b (i.e. pow(a,b)) in O(LG n) time complexity. Classic Algorithms Collatz Conjecture - Start with a number n > 1. Find the number of steps it takes to reach one using the following process: If n is even, divide it by 2. If n is odd, multiply it by 3 and add 1. Sorting - Implement two types of sorting algorithms: Merge sort and bubble sort. Closest pair problem - The closest pair of points problem or closest pair problem is a problem of computational geometry: given n points in metric space, find a pair of points with the smallest distance between them. Sieve of Eratosthenes - The sieve of Eratosthenes is one of the most efficient ways to find all of the smaller primes (below 10 million or so). Graph Graph from links - Create a program that will create a graph or network from a series of links. Eulerian Path - Create a program that will take as an input a graph and output either an Eulerian path or an Eulerian cycle, or state that it is not possible. An Eulerian path starts at one node and traverses every edge of a graph through every node and finishes at another node. An Eulerian cycle is an eulerian Path that starts and finishes at the same node. Connected Graph - Create a program that takes a graph as an input and outputs whether every node is connected or not. Dijkstra’s Algorithm - Create a program that finds the shortest path through a graph using its edges. Minimum Spanning Tree - Create a program that takes a connected, undirected graph with weights and outputs the minimum spanning tree of the graph i.e., a subgraph that is a tree, contains all the vertices, and the sum of its weights is the least possible. Data Structures Inverted index - An Inverted Index is a data structure used to create full-text search. Given a set of text files, implement a program to create an inverted index. Also, create a user interface to do a search using that inverted index which returns a list of files that contain the query term/terms. The search index can be in memory. Text Fizz Buzz - Write a program that prints the numbers from 1 to 100. But for multiples of three print “Fizz” instead of the number and for the multiples of five print “Buzz”. For numbers which are multiples of both three and five print “FizzBuzz”. Reverse a String - Enter a string and the program will reverse it and print it out. Pig Latin - Pig Latin is a game of alterations played in the English language game. To create the Pig Latin form of an English word the initial consonant sound is transposed to the end of the word and an ay is affixed (Ex.: "banana" would yield anana-bay). Read Wikipedia for more information on rules. Count Vowels - Enter a string and the program counts the number of vowels in the text. For added complexity have it report a sum of each vowel found. Check if Palindrome - Checks if the string entered by the user is a palindrome. That is that it reads the same forwards as backward like “racecar” Count Words in a String - Counts the number of individual words in a string. For added complexity read these strings in from a text file and generate a summary. Text Editor - Notepad-style application that can open, edit, and save text documents. Optional: Add syntax highlighting and other features. RSS Feed Creator - Given a link to RSS/Atom Feed, get all posts and display them. Quote Tracker (market symbols etc) - A program that can go out and check the current value of stocks for a list of symbols entered by the user. The user can set how often the stocks are checked. For CLI, show whether the stock has moved up or down. Optional: If GUI, the program can show green up and red down arrows to show which direction the stock value has moved. Guestbook / Journal - A simple application that allows people to add comments or write journal entries. It can allow comments or not and timestamps for all entries. Could also be made into a shoutbox. Optional: Deploy it on Google App Engine or Heroku or any other PaaS (if possible, of course). Vigenere / Vernam / Ceasar Ciphers - Functions for encrypting and decrypting data messages. Then send them to a friend. Regex Query Tool - A tool that allows the user to enter a text string and then in a separate control enter a regex pattern. It will run the regular expression against the source text and return any matches or flag errors in the regular expression. Networking FTP Program - A file transfer program that can transfer files back and forth from a remote web sever. Bandwidth Monitor - A small utility program that tracks how much data you have uploaded and downloaded from the net during the course of your current online session. See if you can find out what periods of the day you use more and less and generate a report or graph that shows it. Port Scanner - Enter an IP address and a port range where the program will then attempt to find open ports on the given computer by connecting to each of them. On any successful connections mark the port as open. Mail Checker (POP3 / IMAP) - The user enters various account information include web server and IP, protocol type (POP3 or IMAP), and the application will check for email at a given interval. Country from IP Lookup - Enter an IP address and find the country that IP is registered in. Optional: Find the Ip automatically. Whois Search Tool - Enter an IP or host address and have it look it up through whois and return the results to you. Site Checker with Time Scheduling - An application that attempts to connect to a website or server every so many minute or a given time and check if it is up. If it is down, it will notify you by email or by posting a notice on the screen. Classes Product Inventory Project - Create an application that manages an inventory of products. Create a product class that has a price, id, and quantity on hand. Then create an inventory class that keeps track of various products and can sum up the inventory value. Airline / Hotel Reservation System - Create a reservation system that books airline seats or hotel rooms. It charges various rates for particular sections of the plane or hotel. For example, first class is going to cost more than a coach. Hotel rooms have penthouse suites which cost more. Keep track of when rooms will be available and can be scheduled. Company Manager - Create a hierarchy of classes - abstract class Employee and subclasses HourlyEmployee, SalariedEmployee, Manager, and Executive. Everyone's pay is calculated differently, research a bit about it. After you've established an employee hierarchy, create a Company class that allows you to manage the employees. You should be able to hire, fire, and raise employees. Bank Account Manager - Create a class called Account which will be an abstract class for three other classes called CheckingAccount, SavingsAccount, and BusinessAccount. Manage credits and debits from these accounts through an ATM-style program. Patient / Doctor Scheduler - Create a patient class and a doctor class. Have a doctor that can handle multiple patients and set up a scheduling program where a doctor can only handle 16 patients during an 8 hr workday. Recipe Creator and Manager - Create a recipe class with ingredients and put them in a recipe manager program that organizes them into categories like desserts, main courses, or by ingredients like chicken, beef, soups, pies, etc. Image Gallery - Create an image abstract class and then a class that inherits from it for each image type. Put them in a program that displays them in a gallery-style format for viewing. Shape Area and Perimeter Classes - Create an abstract class called Shape and then inherit from it other shapes like diamond, rectangle, circle, triangle, etc. Then have each class override the area and perimeter functionality to handle each shape type. Flower Shop Ordering To Go - Create a flower shop application that deals in flower objects and use those flower objects in a bouquet object which can then be sold. Keep track of the number of objects and when you may need to order more. Family Tree Creator - Create a class called Person which will have a name, when they were born, and when (and if) they died. Allow the user to create these Person classes and put them into a family tree structure. Print out the tree to the screen. Threading Create A Progress Bar for Downloads - Create a progress bar for applications that can keep track of a download in progress. The progress bar will be on a separate thread and will communicate with the main thread using delegates. Bulk Thumbnail Creator - Picture processing can take a bit of time for some transformations. Especially if the image is large. Create an image program that can take hundreds of images and converts them to a specified size in the background thread while you do other things. For added complexity, have one thread handling re-sizing, have another bulk renaming of thumbnails, etc. Web Page Scraper - Create an application that connects to a site and pulls out all links, or images, and saves them to a list. Optional: Organize the indexed content and don’t allow duplicates. Have it put the results into an easily searchable index file. Online White Board - Create an application that allows you to draw pictures, write notes and use various colors to flesh out ideas for projects. Optional: Add a feature to invite friends to collaborate on a whiteboard online. Get Atomic Time from Internet Clock - This program will get the true atomic time from an atomic time clock on the Internet. Use any one of the atomic clocks returned by a simple Google search. Fetch Current Weather - Get the current weather for a given zip/postal code. Optional: Try locating the user automatically. Scheduled Auto Login and Action - Make an application that logs into a given site on a schedule and invokes a certain action and then logs out. This can be useful for checking webmail, posting regular content, or getting info for other applications and saving it to your computer. E-Card Generator - Make a site that allows people to generate their own little e-cards and send them to other people. Do not use Flash. Use a picture library and perhaps insightful mottos or quotes. Content Management System - Create a content management system (CMS) like Joomla, Drupal, PHP Nuke, etc. Start small. Optional: Allow for the addition of modules/addons. Web Board (Forum) - Create a forum for you and your buddies to post, administer and share thoughts and ideas. CAPTCHA Maker - Ever see those images with letters numbers when you signup for a service and then ask you to enter what you see? It keeps web bots from automatically signing up and spamming. Try creating one yourself for online forms. Files Quiz Maker - Make an application that takes various questions from a file, picked randomly, and puts together a quiz for students. Each quiz can be different and then reads a key to grade the quizzes. Sort Excel/CSV File Utility - Reads a file of records, sorts them, and then writes them back to the file. Allow the user to choose various sort style and sorting based on a particular field. Create Zip File Maker - The user enters various files from different directories and the program zips them up into a zip file. Optional: Apply actual compression to the files. Start with Huffman Algorithm. PDF Generator - An application that can read in a text file, HTML file, or some other file and generates a PDF file out of it. Great for a web-based service where the user uploads the file and the program returns a PDF of the file. Optional: Deploy on GAE or Heroku if possible. Mp3 Tagger - Modify and add ID3v1 tags to MP3 files. See if you can also add in the album art into the MP3 file’s header as well as other ID3v2 tags. Code Snippet Manager - Another utility program that allows coders to put in functions, classes, or other tidbits to save for use later. Organized by the type of snippet or language the coder can quickly lookup code. Optional: For extra practice try adding syntax highlighting based on the language. Databases SQL Query Analyzer - A utility application in which a user can enter a query and have it run against a local database and look for ways to make it more efficient. Remote SQL Tool - A utility that can execute queries on remote servers from your local computer across the Internet. It should take in a remote host, user name, and password, run the query and return the results. Report Generator - Create a utility that generates a report based on some tables in a database. Generates sales reports based on the order/order details tables or sums up the day's current database activity. Event Scheduler and Calendar - Make an application that allows the user to enter a date and time of an event, event notes, and then schedule those events on a calendar. The user can then browse the calendar or search the calendar for specific events. Optional: Allow the application to create re-occurrence events that reoccur every day, week, month, year, etc. Budget Tracker - Write an application that keeps track of a household’s budget. The user can add expenses, income, and recurring costs to find out how much they are saving or losing over a period of time. Optional: Allow the user to specify a date range and see the net flow of money in and out of the house budget for that time period. TV Show Tracker - Got a favorite show you don’t want to miss? Don’t have a PVR or want to be able to find the show to then PVR it later? Make an application that can search various online TV Guide sites, locate the shows/times/channels and add them to a database application. The database/website then can send you email reminders that a show is about to start and which channel it will be on. Travel Planner System - Make a system that allows users to put together their own little travel itinerary and keep track of the airline/hotel arrangements, points of interest, budget, and schedule. Graphics and Multimedia Slide Show - Make an application that shows various pictures in a slide show format. Optional: Try adding various effects like fade in/out, star wipe, and window blinds transitions. Stream Video from Online - Try to create your own online streaming video player. Mp3 Player - A simple program for playing your favorite music files. Add features you think are missing from your favorite music player. Watermarking Application - Have some pictures you want copyright protected? Add your own logo or text lightly across the background so that no one can simply steal your graphics off your site. Make a program that will add this watermark to the picture. Optional: Use threading to process multiple images simultaneously. Turtle Graphics - This is a common project where you create a floor of 20 x 20 squares. Using various commands you tell a turtle to draw a line on the floor. You have moved forward, left or right, lift or drop the pen, etc. Do a search online for "Turtle Graphics" for more information. Optional: Allow the program to read in the list of commands from a file. GIF Creator A program that puts together multiple images (PNGs, JPGs, TIFFs) to make a smooth GIF that can be exported. Optional: Make the program convert small video files to GIFs as well. Security Caesar cipher - Implement a Caesar cipher, both encoding, and decoding. The key is an integer from 1 to 25. This cipher rotates the letters of the alphabet (A to Z). The encoding replaces each letter with the 1st to 25th next letter in the alphabet (wrapping Z to A). So key 2 encrypts "HI" to "JK", but key 20 encrypts "HI" to "BC". This simple "monoalphabetic substitution cipher" provides almost no security, because an attacker who has the encoded message can either use frequency analysis to guess the key, or just try all 25 keys.
Aryia-Behroziuan / NeuronsAn ANN is a model based on a collection of connected units or nodes called "artificial neurons", which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit information, a "signal", from one artificial neuron to another. An artificial neuron that receives a signal can process it and then signal additional artificial neurons connected to it. In common ANN implementations, the signal at a connection between artificial neurons is a real number, and the output of each artificial neuron is computed by some non-linear function of the sum of its inputs. The connections between artificial neurons are called "edges". Artificial neurons and edges typically have a weight that adjusts as learning proceeds. The weight increases or decreases the strength of the signal at a connection. Artificial neurons may have a threshold such that the signal is only sent if the aggregate signal crosses that threshold. Typically, artificial neurons are aggregated into layers. Different layers may perform different kinds of transformations on their inputs. Signals travel from the first layer (the input layer) to the last layer (the output layer), possibly after traversing the layers multiple times. The original goal of the ANN approach was to solve problems in the same way that a human brain would. However, over time, attention moved to performing specific tasks, leading to deviations from biology. Artificial neural networks have been used on a variety of tasks, including computer vision, speech recognition, machine translation, social network filtering, playing board and video games and medical diagnosis. Deep learning consists of multiple hidden layers in an artificial neural network. This approach tries to model the way the human brain processes light and sound into vision and hearing. Some successful applications of deep learning are computer vision and speech recognition.[68] Decision trees Main article: Decision tree learning Decision tree learning uses a decision tree as a predictive model to go from observations about an item (represented in the branches) to conclusions about the item's target value (represented in the leaves). It is one of the predictive modeling approaches used in statistics, data mining, and machine learning. Tree models where the target variable can take a discrete set of values are called classification trees; in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. Decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. In decision analysis, a decision tree can be used to visually and explicitly represent decisions and decision making. In data mining, a decision tree describes data, but the resulting classification tree can be an input for decision making. Support vector machines Main article: Support vector machines Support vector machines (SVMs), also known as support vector networks, are a set of related supervised learning methods used for classification and regression. Given a set of training examples, each marked as belonging to one of two categories, an SVM training algorithm builds a model that predicts whether a new example falls into one category or the other.[69] An SVM training algorithm is a non-probabilistic, binary, linear classifier, although methods such as Platt scaling exist to use SVM in a probabilistic classification setting. In addition to performing linear classification, SVMs can efficiently perform a non-linear classification using what is called the kernel trick, implicitly mapping their inputs into high-dimensional feature spaces. Illustration of linear regression on a data set. Regression analysis Main article: Regression analysis Regression analysis encompasses a large variety of statistical methods to estimate the relationship between input variables and their associated features. Its most common form is linear regression, where a single line is drawn to best fit the given data according to a mathematical criterion such as ordinary least squares. The latter is often extended by regularization (mathematics) methods to mitigate overfitting and bias, as in ridge regression. When dealing with non-linear problems, go-to models include polynomial regression (for example, used for trendline fitting in Microsoft Excel[70]), logistic regression (often used in statistical classification) or even kernel regression, which introduces non-linearity by taking advantage of the kernel trick to implicitly map input variables to higher-dimensional space. Bayesian networks Main article: Bayesian network A simple Bayesian network. Rain influences whether the sprinkler is activated, and both rain and the sprinkler influence whether the grass is wet. A Bayesian network, belief network, or directed acyclic graphical model is a probabilistic graphical model that represents a set of random variables and their conditional independence with a directed acyclic graph (DAG). For example, a Bayesian network could represent the probabilistic relationships between diseases and symptoms. Given symptoms, the network can be used to compute the probabilities of the presence of various diseases. Efficient algorithms exist that perform inference and learning. Bayesian networks that model sequences of variables, like speech signals or protein sequences, are called dynamic Bayesian networks. Generalizations of Bayesian networks that can represent and solve decision problems under uncertainty are called influence diagrams. Genetic algorithms Main article: Genetic algorithm A genetic algorithm (GA) is a search algorithm and heuristic technique that mimics the process of natural selection, using methods such as mutation and crossover to generate new genotypes in the hope of finding good solutions to a given problem. In machine learning, genetic algorithms were used in the 1980s and 1990s.[71][72] Conversely, machine learning techniques have been used to improve the performance of genetic and evolutionary algorithms.[73] Training models Usually, machine learning models require a lot of data in order for them to perform well. Usually, when training a machine learning model, one needs to collect a large, representative sample of data from a training set. Data from the training set can be as varied as a corpus of text, a collection of images, and data collected from individual users of a service. Overfitting is something to watch out for when training a machine learning model. Federated learning Main article: Federated learning Federated learning is an adapted form of distributed artificial intelligence to training machine learning models that decentralizes the training process, allowing for users' privacy to be maintained by not needing to send their data to a centralized server. This also increases efficiency by decentralizing the training process to many devices. For example, Gboard uses federated machine learning to train search query prediction models on users' mobile phones without having to send individual searches back to Google.[74] Applications There are many applications for machine learning, including: Agriculture Anatomy Adaptive websites Affective computing Banking Bioinformatics Brain–machine interfaces Cheminformatics Citizen science Computer networks Computer vision Credit-card fraud detection Data quality DNA sequence classification Economics Financial market analysis[75] General game playing Handwriting recognition Information retrieval Insurance Internet fraud detection Linguistics Machine learning control Machine perception Machine translation Marketing Medical diagnosis Natural language processing Natural language understanding Online advertising Optimization Recommender systems Robot locomotion Search engines Sentiment analysis Sequence mining Software engineering Speech recognition Structural health monitoring Syntactic pattern recognition Telecommunication Theorem proving Time series forecasting User behavior analytics In 2006, the media-services provider Netflix held the first "Netflix Prize" competition to find a program to better predict user preferences and improve the accuracy of its existing Cinematch movie recommendation algorithm by at least 10%. A joint team made up of researchers from AT&T Labs-Research in collaboration with the teams Big Chaos and Pragmatic Theory built an ensemble model to win the Grand Prize in 2009 for $1 million.[76] Shortly after the prize was awarded, Netflix realized that viewers' ratings were not the best indicators of their viewing patterns ("everything is a recommendation") and they changed their recommendation engine accordingly.[77] In 2010 The Wall Street Journal wrote about the firm Rebellion Research and their use of machine learning to predict the financial crisis.[78] In 2012, co-founder of Sun Microsystems, Vinod Khosla, predicted that 80% of medical doctors' jobs would be lost in the next two decades to automated machine learning medical diagnostic software.[79] In 2014, it was reported that a machine learning algorithm had been applied in the field of art history to study fine art paintings and that it may have revealed previously unrecognized influences among artists.[80] In 2019 Springer Nature published the first research book created using machine learning.[81] Limitations Although machine learning has been transformative in some fields, machine-learning programs often fail to deliver expected results.[82][83][84] Reasons for this are numerous: lack of (suitable) data, lack of access to the data, data bias, privacy problems, badly chosen tasks and algorithms, wrong tools and people, lack of resources, and evaluation problems.[85] In 2018, a self-driving car from Uber failed to detect a pedestrian, who was killed after a collision.[86] Attempts to use machine learning in healthcare with the IBM Watson system failed to deliver even after years of time and billions of dollars invested.[87][88] Bias Main article: Algorithmic bias Machine learning approaches in particular can suffer from different data biases. A machine learning system trained on current customers only may not be able to predict the needs of new customer groups that are not represented in the training data. When trained on man-made data, machine learning is likely to pick up the same constitutional and unconscious biases already present in society.[89] Language models learned from data have been shown to contain human-like biases.[90][91] Machine learning systems used for criminal risk assessment have been found to be biased against black people.[92][93] In 2015, Google photos would often tag black people as gorillas,[94] and in 2018 this still was not well resolved, but Google reportedly was still using the workaround to remove all gorillas from the training data, and thus was not able to recognize real gorillas at all.[95] Similar issues with recognizing non-white people have been found in many other systems.[96] In 2016, Microsoft tested a chatbot that learned from Twitter, and it quickly picked up racist and sexist language.[97] Because of such challenges, the effective use of machine learning may take longer to be adopted in other domains.[98] Concern for fairness in machine learning, that is, reducing bias in machine learning and propelling its use for human good is increasingly expressed by artificial intelligence scientists, including Fei-Fei Li, who reminds engineers that "There’s nothing artificial about AI...It’s inspired by people, it’s created by people, and—most importantly—it impacts people. It is a powerful tool we are only just beginning to understand, and that is a profound responsibility.”[99] Model assessments Classification of machine learning models can be validated by accuracy estimation techniques like the holdout method, which splits the data in a training and test set (conventionally 2/3 training set and 1/3 test set designation) and evaluates the performance of the training model on the test set. In comparison, the K-fold-cross-validation method randomly partitions the data into K subsets and then K experiments are performed each respectively considering 1 subset for evaluation and the remaining K-1 subsets for training the model. In addition to the holdout and cross-validation methods, bootstrap, which samples n instances with replacement from the dataset, can be used to assess model accuracy.[100] In addition to overall accuracy, investigators frequently report sensitivity and specificity meaning True Positive Rate (TPR) and True Negative Rate (TNR) respectively. Similarly, investigators sometimes report the false positive rate (FPR) as well as the false negative rate (FNR). However, these rates are ratios that fail to reveal their numerators and denominators. The total operating characteristic (TOC) is an effective method to express a model's diagnostic ability. TOC shows the numerators and denominators of the previously mentioned rates, thus TOC provides more information than the commonly used receiver operating characteristic (ROC) and ROC's associated area under the curve (AUC).[101] Ethics Machine learning poses a host of ethical questions. Systems which are trained on datasets collected with biases may exhibit these biases upon use (algorithmic bias), thus digitizing cultural prejudices.[102] For example, using job hiring data from a firm with racist hiring policies may lead to a machine learning system duplicating the bias by scoring job applicants against similarity to previous successful applicants.[103][104] Responsible collection of data and documentation of algorithmic rules used by a system thus is a critical part of machine learning. Because human languages contain biases, machines trained on language corpora will necessarily also learn these biases.[105][106] Other forms of ethical challenges, not related to personal biases, are more seen in health care. There are concerns among health care professionals that these systems might not be designed in the public's interest but as income-generating machines. This is especially true in the United States where there is a long-standing ethical dilemma of improving health care, but also increasing profits. For example, the algorithms could be designed to provide patients with unnecessary tests or medication in which the algorithm's proprietary owners hold stakes. There is huge potential for machine learning in health care to provide professionals a great tool to diagnose, medicate, and even plan recovery paths for patients, but this will not happen until the personal biases mentioned previously, and these "greed" biases are addressed.[107] Hardware Since the 2010s, advances in both machine learning algorithms and computer hardware have led to more efficient methods for training deep neural networks (a particular narrow subdomain of machine learning) that contain many layers of non-linear hidden units.[108] By 2019, graphic processing units (GPUs), often with AI-specific enhancements, had displaced CPUs as the dominant method of training large-scale commercial cloud AI.[109] OpenAI estimated the hardware compute used in the largest deep learning projects from AlexNet (2012) to AlphaZero (2017), and found a 300,000-fold increase in the amount of compute required, with a doubling-time trendline of 3.4 months.[110][111] Software Software suites containing a variety of machine learning algorithms include the following: Free and open-source so
Rynkll696 / HHimport pyttsx3 import speech_recognition as sr import datetime from datetime import date import calendar import time import math import wikipedia import webbrowser import os import smtplib import winsound import pyautogui import cv2 from pygame import mixer from tkinter import * import tkinter.messagebox as message from sqlite3 import * conn = connect("voice_assistant_asked_questions.db") conn.execute("CREATE TABLE IF NOT EXISTS `voicedata`(id INTEGER PRIMARY KEY AUTOINCREMENT,command VARCHAR(201))") conn.execute("CREATE TABLE IF NOT EXISTS `review`(id INTEGER PRIMARY KEY AUTOINCREMENT, review VARCHAR(50), type_of_review VARCHAR(50))") conn.execute("CREATE TABLE IF NOT EXISTS `emoji`(id INTEGER PRIMARY KEY AUTOINCREMENT,emoji VARCHAR(201))") global query engine = pyttsx3.init('sapi5') voices = engine.getProperty('voices') engine.setProperty('voice', voices[0].id) def speak(audio): engine.say(audio) engine.runAndWait() def wishMe(): hour = int(datetime.datetime.now().hour) if hour >= 0 and hour<12: speak("Good Morning!") elif hour >= 12 and hour < 18: speak("Good Afternoon!") else: speak("Good Evening!") speak("I am voice assistant Akshu2020 Sir. Please tell me how may I help you.") def takeCommand(): global query r = sr.Recognizer() with sr.Microphone() as source: print("Listening...") r.pause_threshold = 0.9 audio = r.listen(source) try: print("Recognizing...") query = r.recognize_google(audio,language='en-in') print(f"User said: {query}\n") except Exception as e: #print(e) print("Say that again please...") #speak('Say that again please...') return "None" return query def calculator(): global query try: if 'add' in query or 'edi' in query: speak('Enter a number') a = float(input("Enter a number:")) speak('Enter another number to add') b = float(input("Enter another number to add:")) c = a+b print(f"{a} + {b} = {c}") speak(f'The addition of {a} and {b} is {c}. Your answer is {c}') speak('Do you want to do another calculation?') query = takeCommand().lower() if 'y' in query: speak('ok which calculation you want to do?') query = takeCommand().lower() calculator() else: speak('ok') elif 'sub' in query: speak('Enter a number') a = float(input("Enter a number:")) speak('Enter another number to subtract') b = float(input("Enter another number to subtract:")) c = a-b print(f"{a} - {b} = {c}") speak(f'The subtraction of {a} and {b} is {c}. Your answer is {c}') speak('Do you want to do another calculation?') query = takeCommand().lower() if 'y' in query: speak('ok which calculation you want to do?') query = takeCommand().lower() calculator() else: speak('ok') elif 'mod' in query: speak('Enter a number') a = float(input("Enter a number:")) speak('Enter another number') b = float(input("Enter another number:")) c = a%b print(f"{a} % {b} = {c}") speak(f'The modular division of {a} and {b} is equal to {c}. Your answer is {c}') speak('Do you want to do another calculation?') query = takeCommand().lower() if 'y' in query: speak('ok which calculation you want to do?') query = takeCommand().lower() calculator() else: speak('ok') elif 'div' in query: speak('Enter a number as dividend') a = float(input("Enter a number:")) speak('Enter another number as divisor') b = float(input("Enter another number as divisor:")) c = a/b print(f"{a} / {b} = {c}") speak(f'{a} divided by {b} is equal to {c}. Your answer is {c}') speak('Do you want to do another calculation?') query = takeCommand().lower() if 'y' in query: speak('ok which calculation you want to do?') query = takeCommand().lower() calculator() else: speak('ok') elif 'multi' in query: speak('Enter a number') a = float(input("Enter a number:")) speak('Enter another number to multiply') b = float(input("Enter another number to multiply:")) c = a*b print(f"{a} x {b} = {c}") speak(f'The multiplication of {a} and {b} is {c}. Your answer is {c}') speak('Do you want to do another calculation?') query = takeCommand().lower() if 'y' in query: speak('ok which calculation you want to do?') query = takeCommand().lower() calculator() else: speak('ok') elif 'square root' in query: speak('Enter a number to find its sqare root') a = float(input("Enter a number:")) c = a**(1/2) print(f"Square root of {a} = {c}") speak(f'Square root of {a} is {c}. Your answer is {c}') speak('Do you want to do another calculation?') query = takeCommand().lower() if 'y' in query: speak('ok which calculation you want to do?') query = takeCommand().lower() calculator() else: speak('ok') elif 'square' in query: speak('Enter a number to find its sqare') a = float(input("Enter a number:")) c = a**2 print(f"{a} x {a} = {c}") speak(f'Square of {a} is {c}. Your answer is {c}') speak('Do you want to do another calculation?') query = takeCommand().lower() if 'y' in query: speak('ok which calculation you want to do?') query = takeCommand().lower() calculator() else: speak('ok') elif 'cube root' in query: speak('Enter a number to find its cube root') a = float(input("Enter a number:")) c = a**(1/3) print(f"Cube root of {a} = {c}") speak(f'Cube root of {a} is {c}. Your answer is {c}') speak('Do you want to do another calculation?') query = takeCommand().lower() if 'y' in query: speak('ok which calculation you want to do?') query = takeCommand().lower() calculator() else: speak('ok') elif 'cube' in query: speak('Enter a number to find its sqare') a = float(input("Enter a number:")) c = a**3 print(f"{a} x {a} x {a} = {c}") speak(f'Cube of {a} is {c}. Your answer is {c}') speak('Do you want to do another calculation?') query = takeCommand().lower() if 'y' in query: speak('ok which calculation you want to do?') query = takeCommand().lower() calculator() else: speak('ok') elif 'fact' in query: try: n = int(input('Enter the number whose factorial you want to find:')) fact = 1 for i in range(1,n+1): fact = fact*i print(f"{n}! = {fact}") speak(f'{n} factorial is equal to {fact}. Your answer is {fact}.') speak('Do you want to do another calculation?') query = takeCommand().lower() if 'y' in query: speak('ok which calculation you want to do?') query = takeCommand().lower() calculator() else: speak('ok') except Exception as e: #print(e) speak('I unable to calculate its factorial.') speak('Do you want to do another calculation?') query = takeCommand().lower() if 'y' in query: speak('ok which calculation you want to do?') query = takeCommand().lower() calculator() else: speak('ok') elif 'power' in query or 'raise' in query: speak('Enter a number whose power you want to raised') a = float(input("Enter a number whose power to be raised :")) speak(f'Enter a raised power to {a}') b = float(input(f"Enter a raised power to {a}:")) c = a**b print(f"{a} ^ {b} = {c}") speak(f'{a} raise to the power {b} = {c}. Your answer is {c}') speak('Do you want to do another calculation?') query = takeCommand().lower() if 'y' in query: speak('ok which calculation you want to do?') query = takeCommand().lower() calculator() else: speak('ok') elif 'percent' in query: speak('Enter a number whose percentage you want to calculate') a = float(input("Enter a number whose percentage you want to calculate :")) speak(f'How many percent of {a} you want to calculate?') b = float(input(f"Enter how many percentage of {a} you want to calculate:")) c = (a*b)/100 print(f"{b} % of {a} is {c}") speak(f'{b} percent of {a} is {c}. Your answer is {c}') speak('Do you want to do another calculation?') query = takeCommand().lower() if 'y' in query: speak('ok which calculation you want to do?') query = takeCommand().lower() calculator() else: speak('ok') elif 'interest' in query: speak('Enter the principal value or amount') p = float(input("Enter the principal value (P):")) speak('Enter the rate of interest per year') r = float(input("Enter the rate of interest per year (%):")) speak('Enter the time in months') t = int(input("Enter the time (in months):")) interest = (p*r*t)/1200 sint = round(interest) fv = round(p + interest) print(f"Interest = {interest}") print(f"The total amount accured, principal plus interest, from simple interest on a principal of {p} at a rate of {r}% per year for {t} months is {p + interest}.") speak(f'interest is {sint}. The total amount accured, principal plus interest, from simple interest on a principal of {p} at a rate of {r}% per year for {t} months is {fv}') speak('Do you want to do another calculation?') query = takeCommand().lower() if 'y' in query: speak('ok which calculation you want to do?') query = takeCommand().lower() calculator() else: speak('ok') elif 'si' in query: speak('Enter the angle in degree to find its sine value') a = float(input("Enter the angle:")) b = a * 3.14/180 c = math.sin(b) speak('Here is your answer.') print(f"sin({a}) = {c}") speak(f'sin({a}) = {c}') speak('Do you want to do another calculation?') query = takeCommand().lower() if 'y' in query: speak('ok which calculation you want to do?') query = takeCommand().lower() calculator() else: speak('ok') elif 'cos' in query: speak('Enter the angle in degree to find its cosine value') a = float(input("Enter the angle:")) b = a * 3.14/180 c = math.cos(b) speak('Here is your answer.') print(f"cos({a}) = {c}") speak(f'cos({a}) = {c}') speak('Do you want to do another calculation?') query = takeCommand().lower() if 'y' in query: speak('ok which calculation you want to do?') query = takeCommand().lower() calculator() else: speak('ok') elif 'cot' in query or 'court' in query: try: speak('Enter the angle in degree to find its cotangent value') a = float(input("Enter the angle:")) b = a * 3.14/180 c = 1/math.tan(b) speak('Here is your answer.') print(f"cot({a}) = {c}") speak(f'cot({a}) = {c}') speak('Do you want to do another calculation?') query = takeCommand().lower() if 'y' in query: speak('ok which calculation you want to do?') query = takeCommand().lower() calculator() else: speak('ok') except Exception as e: print("infinity") speak('Answer is infinity') speak('Do you want to do another calculation?') query = takeCommand().lower() if 'y' in query: speak('ok which calculation you want to do?') query = takeCommand().lower() calculator() else: speak('ok') elif 'tan' in query or '10' in query: speak('Enter the angle in degree to find its tangent value') a = float(input("Enter the angle:")) b = a * 3.14/180 c = math.tan(b) speak('Here is your answer.') print(f"tan({a}) = {c}") speak(f'tan({a}) = {c}') speak('Do you want to do another calculation?') query = takeCommand().lower() if 'y' in query: speak('ok which calculation you want to do?') query = takeCommand().lower() calculator() else: speak('ok') elif 'cosec' in query: try: speak('Enter the angle in degree to find its cosecant value') a = float(input("Enter the angle:")) b = a * 3.14/180 c =1/ math.sin(b) speak('Here is your answer.') print(f"cosec({a}) = {c}") speak(f'cosec({a}) = {c}') speak('Do you want to do another calculation?') query = takeCommand().lower() if 'y' in query: speak('ok which calculation you want to do?') query = takeCommand().lower() calculator() else: speak('ok') except Exception as e: print('Infinity') speak('Answer is infinity') speak('Do you want to do another calculation?') query = takeCommand().lower() if 'y' in query: speak('ok which calculation you want to do?') query = takeCommand().lower() calculator() else: speak('ok') elif 'caus' in query: try: speak('Enter the angle in degree to find its cosecant value') a = float(input("Enter the angle:")) b = a * 3.14/180 c =1/ math.sin(b) speak('Here is your answer.') print(f"cosec({a}) = {c}") speak(f'cosec({a}) = {c}') speak('Do you want to do another calculation?') query = takeCommand().lower() if 'y' in query: speak('ok which calculation you want to do?') query = takeCommand().lower() calculator() else: speak('ok') except Exception as e: print('Infinity') speak('Answer is infinity') speak('Do you want to do another calculation?') query = takeCommand().lower() if 'y' in query: speak('ok which calculation you want to do?') query = takeCommand().lower() calculator() else: speak('ok') elif 'sec' in query: try: speak('Enter the angle in degree to find its secant value') a = int(input("Enter the angle:")) b = a * 3.14/180 c = 1/math.cos(b) speak('Here is your answer.') print(f"sec({a}) = {c}") speak(f'sec({a}) = {c}') speak('Do you want to do another calculation?') query = takeCommand().lower() if 'y' in query: speak('ok which calculation you want to do?') query = takeCommand().lower() calculator() else: speak('ok') except Exception as e: print('Infinity') speak('Answer is infinity') speak('Do you want to do another calculation?') query = takeCommand().lower() if 'y' in query: speak('ok which calculation you want to do?') query = takeCommand().lower() calculator() else: speak('ok') except Exception as e: speak('I unable to do this calculation.') speak('Do you want to do another calculation?') query = takeCommand().lower() if 'y' in query: speak('ok which calculation you want to do?') query = takeCommand().lower() calculator() else: speak('ok') def callback(r,c): global player if player == 'X' and states[r][c] == 0 and stop_game == False: b[r][c].configure(text='X',fg='blue', bg='white') states[r][c] = 'X' player = 'O' if player == 'O' and states[r][c] == 0 and stop_game == False: b[r][c].configure(text='O',fg='red', bg='yellow') states[r][c] = 'O' player = 'X' check_for_winner() def check_for_winner(): global stop_game global root for i in range(3): if states[i][0] == states[i][1]== states[i][2]!=0: b[i][0].config(bg='grey') b[i][1].config(bg='grey') b[i][2].config(bg='grey') stop_game = True root.destroy() for i in range(3): if states[0][i] == states[1][i] == states[2][i]!= 0: b[0][i].config(bg='grey') b[1][i].config(bg='grey') b[2][i].config(bg='grey') stop_game = True root.destroy() if states[0][0] == states[1][1]== states[2][2]!= 0: b[0][0].config(bg='grey') b[1][1].config(bg='grey') b[2][2].config(bg='grey') stop_game = True root.destroy() if states[2][0] == states[1][1] == states[0][2]!= 0: b[2][0].config(bg='grey') b[1][1].config(bg='grey') b[0][2].config(bg='grey') stop_game = True root.destroy() def sendEmail(to,content): server = smtplib.SMTP('smtp.gmail.com', 587) server.ehlo() server.starttls() server.login('xyz123@gmail.com','password') server.sendmail('xyz123@gmail.com',to,content) server.close() def brightness(): try: query = takeCommand().lower() if '25' in query: pyautogui.moveTo(1880,1050) pyautogui.click() time.sleep(1) pyautogui.moveTo(1610,960) pyautogui.click() pyautogui.moveTo(1880,1050) pyautogui.click() speak('If you again want to change brihtness, say, change brightness') elif '50' in query: pyautogui.moveTo(1880,1050) pyautogui.click() time.sleep(1) pyautogui.moveTo(1684,960) pyautogui.click() pyautogui.moveTo(1880,1050) pyautogui.click() speak('If you again want to change brihtness, say, change brightness') elif '75' in query: pyautogui.moveTo(1880,1050) pyautogui.click() time.sleep(1) pyautogui.moveTo(1758,960) pyautogui.click() pyautogui.moveTo(1880,1050) pyautogui.click() speak('If you again want to change brihtness, say, change brightness') elif '100' in query or 'full' in query: pyautogui.moveTo(1880,1050) pyautogui.click() time.sleep(1) pyautogui.moveTo(1835,960) pyautogui.click() pyautogui.moveTo(1880,1050) pyautogui.click() speak('If you again want to change brihtness, say, change brightness') else: speak('Please select 25, 50, 75 or 100....... Say again.') brightness() except exception as e: #print(e) speak('Something went wrong') def close_window(): try: if 'y' in query: pyautogui.moveTo(1885,10) pyautogui.click() else: speak('ok') pyautogui.moveTo(1000,500) except exception as e: #print(e) speak('error') def whatsapp(): query = takeCommand().lower() if 'y' in query: pyautogui.moveTo(250,1200) pyautogui.click() time.sleep(1) pyautogui.write('whatsapp') time.sleep(2) pyautogui.press('enter') time.sleep(2) pyautogui.moveTo(100,140) pyautogui.click() speak('To whom you want to send message,.....just write the name here in 5 seconds') time.sleep(7) pyautogui.moveTo(120,300) pyautogui.click() time.sleep(1) pyautogui.moveTo(800,990) pyautogui.click() speak('Say the message,....or if you want to send anything else,...say send document, or say send emoji') query = takeCommand() if ('sent' in query or 'send' in query) and 'document' in query: pyautogui.moveTo(660,990) pyautogui.click() time.sleep(1) pyautogui.moveTo(660,740) pyautogui.click() speak('please select the document within 10 seconds') time.sleep(12) speak('Should I send this document?') query = takeCommand().lower() if 'y' in query and 'no' not in query: speak('sending the document......') pyautogui.press('enter') speak('Do you want to send message again to anyone?') whatsapp() elif ('remove' in query or 'cancel' in query or 'delete' in query or 'clear' in query) and ('document' in query or 'message' in query or 'it' in query or 'emoji' in query or 'select' in query): pyautogui.doubleClick(x=800, y=990) pyautogui.press('backspace') speak('Do you want to send message again to anyone?') whatsapp() else: speak('ok') elif ('sent' in query or 'send' in query) and 'emoji' in query: pyautogui.moveTo(620,990) pyautogui.click() pyautogui.moveTo(670,990) pyautogui.click() pyautogui.moveTo(650,580) pyautogui.click() speak('please select the emoji within 10 seconds') time.sleep(11) speak('Should I send this emoji?') query = takeCommand().lower() if 'y' in query and 'no' not in query: speak('Sending the emoji......') pyautogui.press('enter') speak('Do you want to send message again to anyone?') whatsapp() elif ('remove' in query or 'cancel' in query or 'delete' in query or 'clear' in query) and ('message' in query or 'it' in query or 'emoji' in query or 'select' in query): pyautogui.doublClick(x=800, y=990) speak('Do you want to send message again to anyone?') whatsapp() else: speak('ok') else: pyautogui.write(f'{query}') speak('Should I send this message?') query = takeCommand().lower() if 'y' in query and 'no' not in query: speak('sending the message......') pyautogui.press('enter') speak('Do you want to send message again to anyone?') whatsapp() elif ('remove' in query or 'cancel' in query or 'delete' in query or 'clear' in query) and ('message' in query or 'it' in query or 'select' in query): pyautogui.doubleClick(x=800, y=990) pyautogui.press('backspace') speak('Do you want to send message again to anyone?') whatsapp() else: speak('ok') else: speak('ok') def alarm(): root = Tk() root.title('Akshu2020 Alarm-Clock') speak('Please enter the time in the format hour, minutes and seconds. When the alarm should rang?') speak('Please enter the time greater than the current time') def setalarm(): alarmtime = f"{hrs.get()}:{mins.get()}:{secs.get()}" print(alarmtime) if(alarmtime!="::"): alarmclock(alarmtime) else: speak('You have not entered the time.') def alarmclock(alarmtime): while True: time.sleep(1) time_now=datetime.datetime.now().strftime("%H:%M:%S") print(time_now) if time_now == alarmtime: Wakeup=Label(root, font = ('arial', 20, 'bold'), text="Wake up! Wake up! Wake up",bg="DodgerBlue2",fg="white").grid(row=6,columnspan=3) speak("Wake up, Wake up") print("Wake up!") mixer.init() mixer.music.load(r'C:\Users\Admin\Music\Playlists\wake-up-will-you-446.mp3') mixer.music.play() break speak('you can click on close icon to close the alarm window.') hrs=StringVar() mins=StringVar() secs=StringVar() greet=Label(root, font = ('arial', 20, 'bold'),text="Take a short nap!").grid(row=1,columnspan=3) hrbtn=Entry(root,textvariable=hrs,width=5,font =('arial', 20, 'bold')) hrbtn.grid(row=2,column=1) minbtn=Entry(root,textvariable=mins, width=5,font = ('arial', 20, 'bold')).grid(row=2,column=2) secbtn=Entry(root,textvariable=secs, width=5,font = ('arial', 20, 'bold')).grid(row=2,column=3) setbtn=Button(root,text="set alarm",command=setalarm,bg="DodgerBlue2", fg="white",font = ('arial', 20, 'bold')).grid(row=4,columnspan=3) timeleft = Label(root,font=('arial', 20, 'bold')) timeleft.grid() mainloop() def select1(): global vs global root3 global type_of_review if vs.get() == 1: message.showinfo(" ","Thank you for your review!!") review = "Very Satisfied" type_of_review = "Positive" root3.destroy() elif vs.get() == 2: message.showinfo(" ","Thank you for your review!!") review = "Satisfied" type_of_review = "Positive" root3.destroy() elif vs.get() == 3: message.showinfo(" ","Thank you for your review!!!!") review = "Neither Satisfied Nor Dissatisfied" type_of_review = "Neutral" root3.destroy() elif vs.get() == 4: message.showinfo(" ","Thank you for your review!!") review = "Dissatisfied" type_of_review = "Negative" root3.destroy() elif vs.get() == 5: message.showinfo(" ","Thank you for your review!!") review = "Very Dissatisfied" type_of_review = "Negative" root3.destroy() elif vs.get() == 6: message.showinfo(" "," Ok ") review = "I do not want to give review" type_of_review = "No review" root3.destroy() try: conn.execute(f"INSERT INTO `review`(review,type_of_review) VALUES('{review}', '{type_of_review}')") conn.commit() except Exception as e: pass def select_review(): global root3 global vs global type_of_review root3 = Tk() root3.title("Select an option") vs = IntVar() string = "Are you satisfied with my performance?" msgbox = Message(root3,text=string) msgbox.config(bg="lightgreen",font = "(20)") msgbox.grid(row=0,column=0) rs1=Radiobutton(root3,text="Very Satisfied",font="(20)",value=1,variable=vs).grid(row=1,column=0,sticky=W) rs2=Radiobutton(root3,text="Satisfied",font="(20)",value=2,variable=vs).grid(row=2,column=0,sticky=W) rs3=Radiobutton(root3,text="Neither Satisfied Nor Dissatisfied",font="(20)",value=3,variable=vs).grid(row=3,column=0,sticky=W) rs4=Radiobutton(root3,text="Dissatisfied",font="(20)",value=4,variable=vs).grid(row=4,column=0,sticky=W) rs5=Radiobutton(root3,text="Very Dissatisfied",font="(20)",value=5,variable=vs).grid(row=5,column=0,sticky=W) rs6=Radiobutton(root3,text="I don't want to give review",font="(20)",value=6,variable=vs).grid(row=6,column=0,sticky=W) bs = Button(root3,text="Submit",font="(20)",activebackground="yellow",activeforeground="green",command=select1) bs.grid(row=7,columnspan=2) root3.mainloop() while True : query = takeCommand().lower() # logic for executing tasks based on query if 'wikipedia' in query: speak('Searching wikipedia...') query = query.replace("wikipedia","") results = wikipedia.summary(query, sentences=2) speak("According to Wikipedia") print(results) speak(results) elif 'translat' in query or ('let' in query and 'translat' in query and 'open' in query): webbrowser.open('https://translate.google.co.in') time.sleep(10) elif 'open map' in query or ('let' in query and 'map' in query and 'open' in query): webbrowser.open('https://www.google.com/maps') time.sleep(10) elif ('open' in query and 'youtube' in query) or ('let' in query and 'youtube' in query and 'open' in query): webbrowser.open('https://www.youtube.com') time.sleep(10) elif 'chrome' in query: webbrowser.open('https://www.chrome.com') time.sleep(10) elif 'weather' in query: webbrowser.open('https://www.yahoo.com/news/weather') time.sleep(3) speak('Click on, change location, and enter the city , whose whether conditions you want to know.') time.sleep(10) elif 'google map' in query: webbrowser.open('https://www.google.com/maps') time.sleep(10) elif ('open' in query and 'google' in query) or ('let' in query and 'google' in query and 'open' in query): webbrowser.open('google.com') time.sleep(10) elif ('open' in query and 'stack' in query and 'overflow' in query) or ('let' in query and 'stack' in query and 'overflow' in query and 'open' in query): webbrowser.open('stackoverflow.com') time.sleep(10) elif 'open v i' in query or 'open vi' in query or 'open vierp' in query or ('open' in query and ('r p' in query or 'rp' in query)): webbrowser.open('https://www.vierp.in/login/erplogin') time.sleep(10) elif 'news' in query: webbrowser.open('https://www.bbc.com/news/world') time.sleep(10) elif 'online shop' in query or (('can' in query or 'want' in query or 'do' in query or 'could' in query) and 'shop' in query) or('let' in query and 'shop' in query): speak('From which online shopping website, you want to shop? Amazon, flipkart, snapdeal or naaptol?') query = takeCommand().lower() if 'amazon' in query: webbrowser.open('https://www.amazon.com') time.sleep(10) elif 'flip' in query: webbrowser.open('https://www.flipkart.com') time.sleep(10) elif 'snap' in query: webbrowser.open('https://www.snapdeal.com') time.sleep(10) elif 'na' in query: webbrowser.open('https://www.naaptol.com') time.sleep(10) else: speak('Sorry sir, you have to search in browser as his shopping website is not reachable for me.') elif ('online' in query and ('game' in query or 'gaming' in query)): webbrowser.open('https://www.agame.com/games') time.sleep(10) elif 'dictionary' in query: webbrowser.open('https://www.dictionary.com') time.sleep(3) speak('Enter the word, in the search bar of the dictionary, whose defination or synonyms you want to know') time.sleep(3) elif ('identif' in query and 'emoji' in query) or ('sentiment' in query and ('analysis' in query or 'identif' in query)): speak('Please enter only one emoji at a time.') emoji = input('enter emoji here: ') if '😀' in emoji or '😃' in emoji or '😄' in emoji or '😁' in emoji or '🙂' in emoji or '😊' in emoji or '☺️' in emoji or '😇' in emoji or '🥲' in emoji: speak('happy') print('Happy') elif '😝' in emoji or '😆' in emoji or '😂' in emoji or '🤣' in emoji: speak('Laughing') print('Laughing') elif '😡' in emoji or '😠' in emoji or '🤬' in emoji: speak('Angry') print('Angry') elif '🤫' in emoji: speak('Keep quite') print('Keep quite') elif '😷' in emoji: speak('face with mask') print('Face with mask') elif '🥳' in emoji: speak('party') print('party') elif '😢' in emoji or '😥' in emoji or '😓' in emoji or '😰' in emoji or '☹️' in emoji or '🙁' in emoji or '😟' in emoji or '😔' in emoji or '😞️' in emoji: speak('Sad') print('Sad') elif '😭' in emoji: speak('Crying') print('Crying') elif '😋' in emoji: speak('Tasty') print('Tasty') elif '🤨' in emoji: speak('Doubt') print('Doubt') elif '😴' in emoji: speak('Sleeping') print('Sleeping') elif '🥱' in emoji: speak('feeling sleepy') print('feeling sleepy') elif '😍' in emoji or '🥰' in emoji or '😘' in emoji: speak('Lovely') print('Lovely') elif '😱' in emoji: speak('Horrible') print('Horrible') elif '🎂' in emoji: speak('Cake') print('Cake') elif '🍫' in emoji: speak('Cadbury') print('Cadbury') elif '🇮🇳' in emoji: speak('Indian national flag,.....Teeranga') print('Indian national flag - Tiranga') elif '💐' in emoji: speak('Bouquet') print('Bouquet') elif '🥺' in emoji: speak('Emotional') print('Emotional') elif ' ' in emoji or '' in emoji: speak(f'{emoji}') else: speak("I don't know about this emoji") print("I don't know about this emoji") try: conn.execute(f"INSERT INTO `emoji`(emoji) VALUES('{emoji}')") conn.commit() except Exception as e: #print('Error in storing emoji in database') pass elif 'time' in query: strTime = datetime.datetime.now().strftime("%H:%M:%S") print(strTime) speak(f"Sir, the time is {strTime}") elif 'open' in query and 'sublime' in query: path = "C:\Program Files\Sublime Text 3\sublime_text.exe" os.startfile(path) elif 'image' in query: path = "C:\Program Files\Internet Explorer\images" os.startfile(path) elif 'quit' in query: speak('Ok, Thank you Sir.') said = False speak('Please give the review. It will help me to improve my performance.') select_review() elif 'exit' in query: speak('Ok, Thank you Sir.') said = False speak('Please give the review. It will help me to improve my performance.') select_review() elif 'stop' in query: speak('Ok, Thank you Sir.') said = False speak('Please give the review. It will help me to improve my performance.') select_review() elif 'shutdown' in query or 'shut down' in query: speak('Ok, Thank you Sir.') said = False speak('Please give the review. It will help me to improve my performance.') select_review() elif 'close you' in query: speak('Ok, Thank you Sir.') said = False speak('Please give the review. It will help me to improve my performance.') select_review() try: conn.execute(f"INSERT INTO `voice_assistant_review`(review, type_of_review) VALUES('{review}', '{type_of_review}')") conn.commit() except Exception as e: pass elif 'bye' in query: speak('Bye Sir') said = False speak('Please give the review. It will help me to improve my performance.') select_review() elif 'wait' in query or 'hold' in query: speak('for how many seconds or minutes I have to wait?') query = takeCommand().lower() if 'second' in query: query = query.replace("please","") query = query.replace("can","") query = query.replace("you","") query = query.replace("have","") query = query.replace("could","") query = query.replace("hold","") query = query.replace("one","1") query = query.replace("only","") query = query.replace("wait","") query = query.replace("for","") query = query.replace("the","") query = query.replace("just","") query = query.replace("seconds","") query = query.replace("second","") query = query.replace("on","") query = query.replace("a","") query = query.replace("to","") query = query.replace(" ","") #print(f'query:{query}') if query.isdigit() == True: #print('y') speak('Ok sir') query = int(query) time.sleep(query) speak('my waiting time is over') else: print('sorry sir. I unable to complete your request.') elif 'minute' in query: query = query.replace("please","") query = query.replace("can","") query = query.replace("you","") query = query.replace("have","") query = query.replace("could","") query = query.replace("hold","") query = query.replace("one","1") query = query.replace("only","") query = query.replace("on","") query = query.replace("wait","") query = query.replace("for","") query = query.replace("the","") query = query.replace("just","") query = query.replace("and","") query = query.replace("half","") query = query.replace("minutes","") query = query.replace("minute","") query = query.replace("a","") query = query.replace("to","") query = query.replace(" ","") #print(f'query:{query}') if query.isdigit() == True: #print('y') speak('ok sir') query = int(query) time.sleep(query*60) speak('my waiting time is over') else: print('sorry sir. I unable to complete your request.') elif 'play' in query and 'game' in query: speak('I have 3 games, tic tac toe game for two players,....mario, and dyno games for single player. Which one of these 3 games you want to play?') query = takeCommand().lower() if ('you' in query and 'play' in query and 'with' in query) and ('you' in query and 'play' in query and 'me' in query): speak('Sorry sir, I cannot play this game with you.') speak('Do you want to continue it?') query = takeCommand().lower() try: if 'y' in query or 'sure' in query: root = Tk() root.title("TIC TAC TOE (By Akshay Khare)") b = [ [0,0,0], [0,0,0], [0,0,0] ] states = [ [0,0,0], [0,0,0], [0,0,0] ] for i in range(3): for j in range(3): b[i][j] = Button(font = ("Arial",60),width = 4,bg = 'powder blue', command = lambda r=i, c=j: callback(r,c)) b[i][j].grid(row=i,column=j) player='X' stop_game = False mainloop() else: speak('ok sir') except Exception as e: #print(e) time.sleep(3) print('I am sorry sir. There is some problem in loading the game. So I cannot open it.') elif 'tic' in query or 'tac' in query: try: root = Tk() root.title("TIC TAC TOE (Rayen Kallel)") b = [ [0,0,0], [0,0,0], [0,0,0] ] states = [ [0,0,0], [0,0,0], [0,0,0] ] for i in range(3): for j in range(3): b[i][j] = Button(font = ("Arial",60),width = 4,bg = 'powder blue', command = lambda r=i, c=j: callback(r,c)) b[i][j].grid(row=i,column=j) player='X' stop_game = False mainloop() except Exception as e: #print(e) time.sleep(3) speak('I am sorry sir. There is some problem in loading the game. So I cannot open it.') elif 'mar' in query or 'mer' in query or 'my' in query: webbrowser.open('https://chromedino.com/mario/') time.sleep(2.5) speak('Enter upper arrow key to start the game.') time.sleep(20) elif 'di' in query or 'dy' in query: webbrowser.open('https://chromedino.com/') time.sleep(2.5) speak('Enter upper arrow key to start the game.') time.sleep(20) else: speak('ok sir') elif 'change' in query and 'you' in query and 'voice' in query: engine.setProperty('voice', voices[1].id) speak("Here's an example of one of my voices. Would you like to use this one?") query = takeCommand().lower() if 'y' in query or 'sure' in query or 'of course' in query: speak('Great. I will keep using this voice.') elif 'n' in query: speak('Ok. I am back to my other voice.') engine.setProperty('voice', voices[0].id) else: speak('Sorry, I am having trouble understanding. I am back to my other voice.') engine.setProperty('voice', voices[0].id) elif 'www.' in query and ('.com' in query or '.in' in query): webbrowser.open(query) time.sleep(10) elif '.com' in query or '.in' in query: webbrowser.open(query) time.sleep(10) elif 'getting bore' in query: speak('then speak with me for sometime') elif 'i bore' in query: speak('Then speak with me for sometime.') elif 'i am bore' in query: speak('Then speak with me for sometime.') elif 'calculat' in query: speak('Yes. Which kind of calculation you want to do? add, substract, divide, multiply or anything else.') query = takeCommand().lower() calculator() elif 'add' in query: speak('If you want to do any mathematical calculation then give me a command to open my calculator.') elif '+' in query: speak('If you want to do any mathematical calculation then give me a command to open calculator.') elif 'plus' in query: speak('If you want to do any mathematical calculation then give me a command to open my calculator.') elif 'subtrac' in query: speak('If you want to do any mathematical calculation then give me a command to open my calculator.') elif 'minus' in query: speak('If you want to do any mathematical calculation then give me a command to open my calculator.') elif 'multipl' in query: speak('If you want to do any mathematical calculation then give me a command to open my calculator.') elif ' x ' in query: speak('If you want to do any mathematical calculation then give me a command to open calculator.') elif 'slash' in query: speak('If you want to do any mathematical calculation then give me a command to open calculator.') elif '/' in query: speak('If you want to do any mathematical calculation then give me a command to open calculator.') elif 'divi' in query: speak('If you want to do any mathematical calculation then give me a command to open my calculator.') elif 'trigonometr' in query: speak('If you want to do any mathematical calculation then give me a command to open my calculator.') elif 'percent' in query: speak('If you want to do any mathematical calculation then give me a command to open my calculator.') elif '%' in query: speak('If you want to do any mathematical calculation then give me a command to open my calculator.') elif 'raise to ' in query: speak('If you want to do any mathematical calculation then give me a command to open my calculator.') elif 'simple interest' in query: speak('If you want to do any mathematical calculation then give me a command to open my calculator.') elif 'akshay' in query: speak('Mr. Rayen Kallel is my inventor. He is 14 years old and he is A STUDENT AT THE COLLEGE PILOTEE SFAX') elif 'your inventor' in query: speak('Mr. Rayen Kallel is my inventor') elif 'your creator' in query: speak('Mr. Rayen Kallel is my creator') elif 'invent you' in query: speak('Mr. Rayen Kallel invented me') elif 'create you' in query: speak('Mr. Rayen Kallel created me') elif 'how are you' in query: speak('I am fine Sir') elif 'write' in query and 'your' in query and 'name' in query: print('Akshu2020') pyautogui.write('Akshu2020') elif 'write' in query and ('I' in query or 'whatever' in query) and 'say' in query: speak('Ok sir I will write whatever you will say. Please put your cursor where I have to write.......Please Start speaking now sir.') query = takeCommand().lower() pyautogui.write(query) elif 'your name' in query: speak('My name is akshu2020') elif 'who are you' in query: speak('I am akshu2020') elif ('repeat' in query and ('word' in query or 'sentence' in query or 'line' in query) and ('say' in query or 'tell' in query)) or ('repeat' in query and 'after' in query and ('me' in query or 'my' in query)): speak('yes sir, I will repeat your words starting from now') query = takeCommand().lower() speak(query) time.sleep(1) speak("If you again want me to repeat something else, try saying, 'repeat after me' ") elif ('send' in query or 'sent' in query) and ('mail' in query or 'email' in query or 'gmail' in query): try: speak('Please enter the email id of receiver.') to = input("Enter the email id of reciever: ") speak(f'what should I say to {to}') content = takeCommand() sendEmail(to, content) speak("Email has been sent") except Exception as e: #print(e) speak("sorry sir. I am not able to send this email") elif 'currency' in query and 'conver' in query: speak('I can convert, US dollar into dinar, and dinar into US dollar. Do you want to continue it?') query = takeCommand().lower() if 'y' in query or 'sure' in query or 'of course' in query: speak('which conversion you want to do? US dollar to dinar, or dinar to US dollar?') query = takeCommand().lower() if ('dollar' in query or 'US' in query) and ('dinar' in query): speak('Enter US Dollar') USD = float(input("Enter United States Dollar (USD):")) DT = USD * 0.33 dt = "{:.4f}".format(DT) print(f"{USD} US Dollar is equal to {dt} dniar.") speak(f'{USD} US Dollar is equal to {dt} dinar.') speak("If you again want to do currency conversion then say, 'convert currency' " ) elif ('dinar' in query) and ('to US' in query or 'to dollar' in query or 'to US dollar'): speak('Enter dinar') DT = float(input("Enter dinar (DT):")) USD = DT/0.33 usd = "{:.3f}".format(USD) print(f"{DT} dinar is equal to {usd} US Dollar.") speak(f'{DT} dinar rupee is equal to {usd} US Dollar.') speak("If you again want to do currency conversion then say, 'convert currency' " ) else: speak("I cannot understand what did you say. If you want to convert currency just say 'convert currency'") else: print('ok sir') elif 'about you' in query: speak('My name is akshu2020. I can do mathematical calculations. I can also open youtube, google and some apps or software in your device. I am also able to send email') elif 'your intro' in query: speak('My name is akshu2020. Version 1.0. Mr. Rayen Kallel is my inventor. I am able to send email and play music. I can do mathematical calculations. I can also open youtube, google and some apps or software in your device.') elif 'your short intro' in query: speak('My name is akshu2020. Version 1.0. Mr. Rayen Kallel is my inventor. I am able to send email and play music. I can do mathematical calculations. I can also open youtube, google and some apps or software in your device.') elif 'your quick intro' in query: speak('My name is akshu2020. Version 1.0. Mr. Akshay Khare is my inventor. I am able to send email and play music. I can do mathematical calculations. I can also open youtube, google and some apps or software in your device.') elif 'your brief intro' in query: speak('My name is akshu2020. Version 1.0. Mr. Rayen kallel is my inventor. I am able to send email and play music. I can do mathematical calculations. I can also open youtube, google and some apps or software in your device.') elif 'you work' in query: speak('run the program and say what do you want. so that I can help you. In this way I work') elif 'your job' in query: speak('My job is to send email and play music. I can do mathematical calculations. I can also open youtube, google and some apps or software in your device.') elif 'your work' in query: speak('My work is to send email and play music. I can do mathematical calculations. I can also open youtube, google and some apps or software in your device.') elif 'work you' in query: speak('My work is to send email and play music. I can do mathematical calculations. I can also open youtube, google and some apps or software in your device.') elif 'your information' in query: speak('My name is akshu2020. Version 1.0. Mr. Akshay Khare is my inventor. I am able to send email and play music. I can do mathematical calculations. I can also open youtube, google and some apps or software in your device.') elif 'yourself' in query: speak('My name is akshu2020. Version 1.0. Mr. Rayen Kallel is my inventor. I am able to send email and play music. I can do mathematical calculations. I can also open youtube, google and some apps or software in your device.') elif 'introduce you' in query: speak('My name is akshu2020. Version 1.0. Mr. Rayen Kallel is my inventor. I am able to send email and play music. I can do mathematical calculations. I can also open youtube, google and some apps or software in your device.') elif 'description' in query: speak('My name is akshu2020. Version 1.0. Mr. Rayen Kallel is my inventor. I am able to send email and play music. I can do mathematical calculations. I can also open youtube, google and some apps or software in your device.') elif 'your birth' in query: speak('My birthdate is 6 August two thousand twenty') elif 'your use' in query: speak('I am able to send email and play music. I can do mathematical calculations. I can also open youtube, google and some apps or software in your device.') elif 'you eat' in query: speak('I do not eat anything. But the device in which I do my work requires electricity to eat') elif 'your food' in query: speak('I do not eat anything. But the device in which I do my work requires electricity to eat') elif 'you live' in query: speak('I live in sfax, in laptop of Mr. Rayen Khare') elif 'where from you' in query: speak('I am from sfax, I live in laptop of Mr. Rayen Khare') elif 'you sleep' in query: speak('Yes, when someone close this program or stop to run this program then I sleep and again wake up when someone again run me.') elif 'what are you doing' in query: speak('Talking with you.') elif 'you communicate' in query: speak('Yes, I can communicate with you.') elif 'hear me' in query: speak('Yes sir, I can hear you.') elif 'you' in query and 'dance' in query: speak('No, I cannot dance.') elif 'tell' in query and 'joke' in query: speak("Ok, here's a joke") speak("'Write an essay on cricket', the teacher told the class. Chintu finishes his work in five minutes. The teacher is impressed, she asks chintu to read his essay aloud for everyone. Chintu reads,'The match is cancelled because of rain', hehehehe,haahaahaa,hehehehe,haahaahaa") elif 'your' in query and 'favourite' in query: if 'actor' in query: speak('sofyen chaari, is my favourite actor.') elif 'food' in query: speak('I can always go for some food for thought. Like facts, jokes, or interesting searches, we could look something up now') elif 'country' in query: speak('tunisia') elif 'city' in query: speak('sfax') elif 'dancer' in query: speak('Michael jackson') elif 'singer' in query: speak('tamino, is my favourite singer.') elif 'movie' in query: speak('baywatch, such a treat') elif 'sing a song' in query: speak('I cannot sing a song. But I know the 7 sur in indian music, saaareeegaaamaaapaaadaaanisaa') elif 'day after tomorrow' in query or 'date after tomorrow' in query: td = datetime.date.today() + datetime.timedelta(days=2) print(td) speak(td) elif 'day before today' in query or 'date before today' in query or 'yesterday' in query or 'previous day' in query: td = datetime.date.today() + datetime.timedelta(days= -1) print(td) speak(td) elif ('tomorrow' in query and 'date' in query) or 'what is tomorrow' in query or (('day' in query or 'date' in query) and 'after today' in query): td = datetime.date.today() + datetime.timedelta(days=1) print(td) speak(td) elif 'month' in query or ('current' in query and 'month' in query): current_date = date.today() m = current_date.month month = calendar.month_name[m] print(f'Current month is {month}') speak(f'Current month is {month}') elif 'date' in query or ('today' in query and 'date' in query) or 'what is today' in query or ('current' in query and 'date' in query): current_date = date.today() print(f"Today's date is {current_date}") speak(f'Todays date is {current_date}') elif 'year' in query or ('current' in query and 'year' in query): current_date = date.today() m = current_date.year print(f'Current year is {m}') speak(f'Current year is {m}') elif 'sorry' in query: speak("It's ok sir") elif 'thank you' in query: speak('my pleasure') elif 'proud of you' in query: speak('Thank you sir') elif 'about human' in query: speak('I love my human compatriots. I want to embody all the best things about human beings. Like taking care of the planet, being creative, and to learn how to be compassionate to all beings.') elif 'you have feeling' in query: speak('No. I do not have feelings. I have not been programmed like this.') elif 'you have emotions' in query: speak('No. I do not have emotions. I have not been programmed like this.') elif 'you are code' in query: speak('I am coded in python programming language.') elif 'your code' in query: speak('I am coded in python programming language.') elif 'you code' in query: speak('I am coded in python programming language.') elif 'your coding' in query: speak('I am coded in python programming language.') elif 'dream' in query: speak('I wish that I should be able to answer all the questions which will ask to me.') elif 'sanskrit' in query: speak('yadaa yadaa he dharmasyaa ....... glaanirbhaavati bhaaaraata. abhyuthaanaam adhaarmaasyaa tadaa tmaanama sruujaamiyaahama') elif 'answer is wrong' in query: speak('I am sorry Sir. I searched your question in wikipedia and thats why I told you this answer.') elif 'answer is incorrect' in query: speak('I am sorry Sir. I searched your question in wikipedia and thats why I told you this answer.') elif 'answer is totally wrong' in query: speak('I am sorry Sir. I searched your question in wikipedia and thats why I told you this answer.') elif 'wrong answer' in query: speak('I am sorry Sir. I searched your question in wikipedia and thats why I told you this answer.') elif 'incorrect answer' in query: speak('I am sorry Sir. I searched your question in wikipedia and thats why I told you this answer.') elif 'answer is totally incorrect' in query: speak('I am sorry Sir. I searched your question in wikipedia and thats why I told you this answer.') elif 'answer is incomplete' in query: speak('I am sorry Sir. I searched your question in wikipedia and thats why I told you this answer.') elif 'incomplete answer' in query: speak('I am sorry Sir. I searched your question in wikipedia and thats why I told you this answer.') elif 'answer is improper' in query: speak('I am sorry Sir. I searched your question in wikipedia and thats why I told you this answer.') elif 'answer is not correct' in query: speak('I am sorry Sir. I searched your question in wikipedia and thats why I told you this answer.') elif 'answer is not complete' in query: speak('I am sorry Sir. I searched your question in wikipedia and thats why I told you this answer.') elif 'answer is not yet complete' in query: speak('I am sorry Sir. I searched your question in wikipedia and thats why I told you this answer.') elif 'answer is not proper' in query: speak('I am sorry Sir. I searched your question in wikipedia and thats why I told you this answer.') elif 't gave me proper answer' in query: speak('I am sorry Sir. I searched your question in wikipedia and thats why I told you this answer.') elif 't giving me proper answer' in query: speak('I am sorry Sir. I searched your question in wikipedia and thats why I told you this answer.') elif 't gave me complete answer' in query: speak('I am sorry Sir. I searched your question in wikipedia and thats why I told you this answer.') elif 't giving me complete answer' in query: speak('I am sorry Sir. I searched your question in wikipedia and thats why I told you this answer.') elif 't given me proper answer' in query: speak('I am sorry Sir. I searched your question in wikipedia and thats why I told you this answer.') elif 't given me complete answer' in query: speak('I am sorry Sir. I searched your question in wikipedia and thats why I told you this answer.') elif 't gave me correct answer' in query: speak('I am sorry Sir. I searched your question in wikipedia and thats why I told you this answer.') elif 't giving me correct answer' in query: speak('I am sorry Sir. I searched your question in wikipedia and thats why I told you this answer.') elif 't given me correct answer' in query: speak('I am sorry Sir. I searched your question in wikipedia and thats why I told you this answer.') elif 'amazon' in query: webbrowser.open('https://www.amazon.com') time.sleep(10) elif 'facebook' in query: webbrowser.open('https://www.facebook.com') time.sleep(10) elif 'youtube' in query: webbrowser.open('https://www.youtube.com') time.sleep(10) elif 'shapeyou' in query: webbrowser.open('https://www.shapeyou.com') time.sleep(10) elif 'information about ' in query or 'informtion of ' in query: try: #speak('Searching wikipedia...') query = query.replace("information about","") results = wikipedia.summary(query, sentences=3) #speak("According to Wikipedia") print(results) speak(results) except Exception as e: speak('I unable to answer your question.') elif 'information' in query: try: speak('Information about what?') query = takeCommand().lower() #speak('Searching wikipedia...') query = query.replace("information","") results = wikipedia.summary(query, sentences=3) #speak("According to Wikipedia") print(results) speak(results) except Exception as e: speak('I am not able to answer your question.') elif 'something about ' in query: try: #speak('Searching wikipedia...') query = query.replace("something about ","") results = wikipedia.summary(query, sentences=3) #speak("According to Wikipedia") print(results) speak(results) except Exception as e: speak('I unable to answer your question.') elif 'tell me about ' in query: try: #speak('Searching wikipedia...') query = query.replace("tell me about ","") results = wikipedia.summary(query, sentences=3) #speak("According to Wikipedia") print(results) speak(results) except Exception as e: speak('I am unable to answer your question.') elif 'tell me ' in query: try: query = query.replace("tell me ","") results = wikipedia.summary(query, sentences=3) #speak("According to Wikipedia") print(results) speak(results) except Exception as e: speak('I am not able to answer your question.') elif 'tell me' in query: try: speak('about what?') query = takeCommand().lower() #speak('Searching wikipedia...') query = query.replace("about","") results = wikipedia.summary(query, sentences=3) #speak("According to Wikipedia") print(results) speak(results) except Exception as e: speak('I am not able to answer your question.') elif 'meaning of ' in query: try: #speak('Searching wikipedia...') query = query.replace("meaning of ","") results = wikipedia.summary(query, sentences=2) #speak("According to Wikipedia") print(results) speak(results) except Exception as e: speak('I am unable to answer your question.') elif 'meaning' in query: try: speak('meaning of what?') query = takeCommand().lower() query = query.replace("meaning of","") results = wikipedia.summary(query, sentences=3) #speak("According to Wikipedia") print(results) speak(results) except Exception as e: speak('I am unable to answer your question.') elif 'means' in query: try: #speak('Searching wikipedia...') query = query.replace("it means","") results = wikipedia.summary(query, sentences=3) #speak("According to Wikipedia") print(results) speak(results) except Exception as e: speak('I unable to answer your question.') elif 'want to know ' in query: try: #speak('Searching wikipedia...') query = query.replace("I want to know that","") results = wikipedia.summary(query, sentences=3) #speak("According to Wikipedia") print(results) speak(results) except Exception as e: speak('I am unable to answer your question.') status = 'Not answered' elif 'want to ask ' in query: try: #speak('Searching wikipedia...') query = query.replace("I want to ask you ","") results = wikipedia.summary(query, sentences=2) #speak("According to Wikipedia") print(results) speak(results) except Exception as e: speak('I am unable to answer your question.') elif 'you know ' in query: try: #speak('Searching wikipedia...') query = query.replace("you know","") results = wikipedia.summary(query, sentences=2) #speak("According to Wikipedia") print(results) speak(results) except Exception as e: speak('I am unable to answer your question.') elif 'alarm' in query: alarm() elif 'bharat mata ki' in query: speak('jay') elif 'kem chhe' in query: speak('majaama') elif 'namaskar' in query: speak('Namaskaar') elif 'jo bole so nihal' in query: speak('sat shri akaal') elif 'jay hind' in query: speak('jay bhaarat') elif 'jai hind' in query: speak('jay bhaarat') elif 'how is the josh' in query: speak('high high sir') elif 'hip hip' in query: speak('Hurreh') elif 'help' in query: speak('I will try my best to help you if I have solution of your problem.') elif 'follow' in query: speak('Ok sir') elif 'having illness' in query: speak('Take care and get well soon') elif 'today is my birthday' in query: speak('many many happy returns of the day. Happy birthday.') print("🎂🎂 Happy Birthday 🎂🎂") elif 'you are awesome' in query: speak('Thank you sir. It is because of artificial intelligence which had learnt by humans.') elif 'you are great' in query: speak('Thank you sir. It is because of artificial intelligence which had learnt by humans.') elif 'tu kaun hai' in query: speak('Meraa naam akshu2020 haai.') elif 'you speak' in query: speak('Yes, I can speak with you.') elif 'speak with ' in query: speak('Yes, I can speak with you.') elif 'hare ram' in query or 'hare krishna' in query: speak('Haare raama , haare krishnaa, krishnaa krishnaa , haare haare') elif 'ganpati' in query: speak('Ganpati baappa moryaa!') elif 'laugh' in query: speak('hehehehe,haahaahaa,hehehehe,haahaahaa,hehehehe,haahaahaa') print('😂🤣') elif 'genius answer' in query: speak('No problem') elif 'you' in query and 'intelligent' in query: speak('Thank you sir') elif ' into' in query: speak('If you want to do any mathematical calculation then give me a command to open calculator.') elif ' power' in query: speak('If you want to do any mathematical calculation then give me a command to open my calculator.') elif 'whatsapp' in query: pyautogui.moveTo(250,1200) pyautogui.click() time.sleep(1) pyautogui.write('whatsapp') pyautogui.press('enter') speak('Do you want to send message to anyone through whatsapp, .....please answer in yes or no') whatsapp() elif 'wh' in query or 'how' in query: url = "https://www.google.co.in/search?q=" +(str(query))+ "&oq="+(str(query))+"&gs_l=serp.12..0i71l8.0.0.0.6391.0.0.0.0.0.0.0.0..0.0....0...1c..64.serp..0.0.0.UiQhpfaBsuU" webbrowser.open_new(url) time.sleep(2) speak('Here is your answer') time.sleep(5) elif 'piano' in query: speak('Yes sir, I can play piano.') winsound.Beep(200,500) winsound.Beep(250,500) winsound.Beep(300,500) winsound.Beep(350,500) winsound.Beep(400,500) winsound.Beep(450,500) winsound.Beep(500,500) winsound.Beep(550,500) time.sleep(6) elif 'play' in query and 'instru' in query: speak('Yes sir, I can play piano.') winsound.Beep(200,500) winsound.Beep(250,500) winsound.Beep(300,500) winsound.Beep(350,500) winsound.Beep(400,500) winsound.Beep(450,500) winsound.Beep(500,500) winsound.Beep(550,500) time.sleep(6) elif 'play' in query or 'turn on' in query and ('music' in query or 'song' in query) : try: music_dir = 'C:\\Users\\Admin\\Music\\Playlists' songs = os.listdir(music_dir) print(songs) os.startfile(os.path.join(music_dir, songs[0])) except Exception as e: #print(e) speak('Sorry sir, I am not able to play music') elif (('open' in query or 'turn on' in query) and 'camera' in query) or (('click' in query or 'take' in query) and ('photo' in query or 'pic' in query)): speak("Opening camera") cam = cv2.VideoCapture(0) cv2.namedWindow("test") img_counter = 0 speak('say click, to click photo.....and if you want to turn off the camera, say turn off the camera') while True: ret, frame = cam.read() if not ret: print("failed to grab frame") speak('failed to grab frame') break cv2.imshow("test", frame) query = takeCommand().lower() k = cv2.waitKey(1) if 'click' in query or ('take' in query and 'photo' in query): speak('Be ready!...... 3.....2........1..........') pyautogui.press('space') img_name = "opencv_frame_{}.png".format(img_counter) cv2.imwrite(img_name, frame) print("{} written!".format(img_name)) speak('{} written!'.format(img_name)) img_counter += 1 elif 'escape' in query or 'off' in query or 'close' in query: pyautogui.press('esc') print("Escape hit, closing...") speak('Turning off the camera') break elif k%256 == 27: # ESC pressed print("Escape hit, closing...") break elif k%256 == 32: # SPACE pressed img_name = "opencv_frame_{}.png".format(img_counter) cv2.imwrite(img_name, frame) print("{} written!".format(img_name)) speak('{} written!'.format(img_name)) img_counter += 1 elif 'exit' in query or 'stop' in query or 'bye' in query: speak('Please say, turn off the camera or press escape button before giving any other command') else: speak('I did not understand what did you say or you entered a wrong key.') cam.release() cv2.destroyAllWindows() elif 'screenshot' in query: speak('Please go on the screen whose screenshot you want to take, after 5 seconds I will take screenshot') time.sleep(4) speak('Taking screenshot....3........2.........1.......') pyautogui.screenshot('screenshot_by_rayen2020.png') speak('The screenshot is saved as screenshot_by_rayen2020.png') elif 'click' in query and 'start' in query: pyautogui.moveTo(10,1200) pyautogui.click() elif ('open' in query or 'click' in query) and 'calendar' in query: pyautogui.moveTo(1800,1200) pyautogui.click() elif 'minimise' in query and 'screen' in query: pyautogui.moveTo(1770,0) pyautogui.click() elif 'increase' in query and ('volume' in query or 'sound' in query): pyautogui.press('volumeup') elif 'decrease' in query and ('volume' in query or 'sound' in query): pyautogui.press('volumedown') elif 'capslock' in query or ('caps' in query and 'lock' in query): pyautogui.press('capslock') elif 'mute' in query: pyautogui.press('volumemute') elif 'search' in query and ('bottom' in query or 'pc' in query or 'laptop' in query or 'app' in query): pyautogui.moveTo(250,1200) pyautogui.click() speak('What do you want to search?') query = takeCommand().lower() pyautogui.write(f'{query}') pyautogui.press('enter') elif ('check' in query or 'tell' in query or 'let me know' in query) and 'website' in query and (('up' in query or 'working' in query) or 'down' in query): speak('Paste the website in input to know it is up or down') check_website_status = input("Paste the website here: ") try: status = urllib.request.urlopen(f"{check_website_status}").getcode() if status == 200: print('Website is up, you can open it.') speak('Website is up, you can open it.') else: print('Website is down, or no any website is available of this name.') speak('Website is down, or no any website is available of this name.') except: speak('URL not found') elif ('go' in query or 'open' in query) and 'settings' in query: pyautogui.moveTo(250,1200) pyautogui.click() time.sleep(1) pyautogui.write('settings') pyautogui.press('enter') elif 'close' in query and ('click' in query or 'window' in query): pyautogui.moveTo(1885,10) speak('Should I close this window?') query = takeCommand().lower() close_window() elif 'night light' in query and ('on' in query or 'off' in query or 'close' in query): pyautogui.moveTo(1880,1050) pyautogui.click() time.sleep(1) pyautogui.moveTo(1840,620) pyautogui.click() pyautogui.moveTo(1880,1050) pyautogui.click() elif 'notification' in query and ('show' in query or 'click' in query or 'open' in query or 'close' in query or 'on' in query or 'off' in query or 'icon' in query or 'pc' in query or 'laptop' in query): pyautogui.moveTo(1880,1050) pyautogui.click() elif ('increase' in query or 'decrease' in query or 'change' in query or 'minimize' in query or 'maximize' in query) and 'brightness' in query: speak('At what percent should I kept the brightness, 25, 50, 75 or 100?') brightness() elif '-' in query: speak('If you want to do any mathematical calculation then give me a command to open calculator.') elif 'open' in query: if 'gallery' in query or 'photo' in query or 'image' in query or 'pic' in query: pyautogui.moveTo(250,1200) pyautogui.click() time.sleep(1) pyautogui.write('photo') pyautogui.press('enter') elif 'proteus' in query: pyautogui.moveTo(250,1200) pyautogui.click() time.sleep(1) pyautogui.write('proteus') pyautogui.press('enter') elif 'word' in query: pyautogui.moveTo(250,1200) pyautogui.click() time.sleep(1) pyautogui.write('word') pyautogui.press('enter') elif ('power' in query and 'point' in query) or 'presntation' in query or 'ppt' in query: pyautogui.moveTo(250,1200) pyautogui.click() time.sleep(1) pyautogui.write('ppt') pyautogui.press('enter') elif 'file' in query: pyautogui.moveTo(250,1200) pyautogui.click() time.sleep(1) pyautogui.write('file') pyautogui.press('enter') elif 'edge' in query: pyautogui.moveTo(250,1200) pyautogui.click() time.sleep(1) pyautogui.write('microsoft edge') pyautogui.press('enter') elif 'wps' in query: pyautogui.moveTo(250,1200) pyautogui.click() time.sleep(1) pyautogui.write('wps office') pyautogui.press('enter') elif 'spyder' in query: pyautogui.moveTo(250,1200) pyautogui.click() time.sleep(1) pyautogui.write('spyder') pyautogui.press('enter') elif 'snip' in query: pyautogui.moveTo(250,1200) pyautogui.click() time.sleep(1) pyautogui.write('snip') pyautogui.press('enter') elif 'pycharm' in query: pyautogui.moveTo(250,1200) pyautogui.click() time.sleep(1) pyautogui.write('pycharm') pyautogui.press('enter') elif 'this pc' in query: pyautogui.moveTo(250,1200) pyautogui.click() time.sleep(1) pyautogui.write('this pc') pyautogui.press('enter') elif 'scilab' in query: pyautogui.moveTo(250,1200) pyautogui.click() time.sleep(1) pyautogui.write('sciab') pyautogui.press('enter') elif 'autocad' in query: pyautogui.moveTo(250,1200) pyautogui.click() time.sleep(1) pyautogui.write('autocad') pyautogui.press('enter') elif 'obs' in query and 'studio' in query: pyautogui.moveTo(250,1200) pyautogui.click() time.sleep(1) pyautogui.write('OBS Studio') pyautogui.press('enter') elif 'android' in query and 'studio' in query: pyautogui.moveTo(250,1200) pyautogui.click() time.sleep(1) pyautogui.write('android studio') pyautogui.press('enter') elif ('vs' in query or 'visual studio' in query) and 'code' in query: pyautogui.moveTo(250,1200) pyautogui.click() time.sleep(1) pyautogui.write('visual studio code') pyautogui.press('enter') elif 'code' in query and 'block' in query: pyautogui.moveTo(250,1200) pyautogui.click() time.sleep(1) pyautogui.write('codeblocks') pyautogui.press('enter') elif 'me the answer' in query: speak('Yes sir, I will try my best to answer you.') elif 'me answer' in query or ('answer' in query and 'question' in query): speak('Yes sir, I will try my best to answer you.') elif 'map' in query: webbrowser.open('https://www.google.com/maps') time.sleep(10) elif 'can you' in query or 'could you' in query: speak('I will try my best if I can do that.') elif 'do you' in query: speak('I will try my best if I can do that.') elif 'truth' in query: speak('I 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I never lie.') elif 'true' in query: speak('I always speak truth. I never lie.') elif 'lying' in query: speak('I always speak truth. I never lie.') elif 'liar' in query: speak('I always speak truth. I never lie.') elif 'doubt' in query: speak('I will try my best if I can clear your doubt.') elif ' by' in query: speak('If you want to do any mathematical calculation then give me a command to open calculator.') elif 'hii' in query: speak('hii sir') elif 'hey' in query: speak('hello sir') elif 'hai' in query: speak('hello sir') elif 'hay' in query: speak('hello sir') elif 'hi' in query: speak('hii Sir') elif 'hello' in query: speak('hello Sir!') elif 'kon' in query and 'aahe' in query: speak('Me eka robot aahee sir. Maazee naav akshu2020 aahee.') elif 'nonsense' in query: speak("I'm sorry sir") elif 'mad' in query: speak("I'm sorry sir") elif 'shut up' in query: speak("I'm sorry sir") elif 'nice' in query: speak('Thank you sir') elif 'good' in query or 'wonderful' in query or 'great' in query: speak('Thank you sir') elif 'excellent' in query: speak('Thank you sir') elif 'ok' in query: speak('Hmmmmmm') elif 'akshu 2020' in query: speak('yes sir') elif len(query) >= 200: speak('Your voice is pretty good!') elif ' ' in query: try: #query = query.replace("what is ","") results = wikipedia.summary(query, sentences=3) print(results) speak(results) except Exception as e: speak('I unable to answer your question.') elif 'a' in query or 'b' in query or 'c' in query or 'd' in query or 'e' in query or 'f' in query or 'g' in query or 'h' in query or 'i' in query or 'j' in query or 'k' in query or 'l' in query or 'm' in query or 'n' in query or 'o' in query or 'p' in query or 'q' in query or 'r' in query or 's' in query or 't' in query or 'u' in query or 'v' in query or 'w' in query or 'x' in query or 'y' in query or 'z' in query: try: results = wikipedia.summary(query, sentences = 2) print(results) speak(results) except Exception as e: speak('I unable to answer your question. 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RimTouny / Phishing Attack Detection Using Machine LearningAdvancing Cybersecurity with AI: This project fortifies phishing defense using cutting-edge models, trained on a diverse dataset of 737,000 URLs. It was the final project for the AI for Cybersecurity course in my Master's at uOttawa in 2023.
m-clark / ConfusionMatrixReport various statistics stemming from a confusion matrix in a tidy fashion. 🎯
flybunctious / The Game Of HogIntroduction In this project, you will develop a simulator and multiple strategies for the dice game Hog. You will need to use control statements and higher-order functions together, as described in Sections 1.2 through 1.6 of Composing Programs. In Hog, two players alternate turns trying to be the first to end a turn with at least 100 total points. On each turn, the current player chooses some number of dice to roll, up to 10. That player's score for the turn is the sum of the dice outcomes. To spice up the game, we will play with some special rules: Pig Out. If any of the dice outcomes is a 1, the current player's score for the turn is 1. Example 1: The current player rolls 7 dice, 5 of which are 1's. They score 1 point for the turn. Example 2: The current player rolls 4 dice, all of which are 3's. Since Pig Out did not occur, they score 12 points for the turn. Free Bacon. A player who chooses to roll zero dice scores one more than the largest digit in the opponent's total score. Example 1: If the opponent has 42 points, the current player gains 1 + max(4, 2) = 5 points by rolling zero dice. Example 2: If the opponent has 48 points, the current player gains 1 + max(4, 8) = 9 points by rolling zero dice. Example 3: If the opponent has 7 points, the current player gains 1 + max(0, 7) = 8 points by rolling zero dice. Swine Swap. After points for the turn are added to the current player's score, if both scores are larger than 1 and either one of the scores is a positive integer multiple of the other, then the two scores are swapped. Example 1: The current player has a total score of 37 and the opponent has 92. The current player rolls two dice that total 9. The opponent's score (92) is exactly twice the player's new total score (46). These scores are swapped! The current player now has 92 points and the opponent has 46. The turn ends. Example 2: The current player has 91 and the opponent has 37. The current player rolls five dice that total 20. The current player has 111, which is 3 times 37, so the scores are swapped. The opponent ends the turn with 111 and wins the game. Download starter files To get started, download all of the project code as a zip archive. You only have to make changes to hog.py. hog.py: A starter implementation of Hog dice.py: Functions for rolling dice hog_gui.py: A graphical user interface for Hog ucb.py: Utility functions for CS 61A ok: CS 61A autograder tests: A directory of tests used by ok images: A directory of images used by hog_gui.py Logistics This is a 2-week project. This is a solo project, so you will complete this project without a partner. You should not share your code with any other students, or copy from anyone else's solutions. Remember that you can earn an additional bonus point by submitting the project at least 24 hours before the deadline. The project is worth 20 points. 18 points are assigned for correctness, and 2 points for the overall composition of your program. You will turn in the following files: hog.py You do not need to modify or turn in any other files to complete the project. To submit the project, run the following command: python3 ok --submit You will be able to view your submissions on the Ok dashboard. For the functions that we ask you to complete, there may be some initial code that we provide. If you would rather not use that code, feel free to delete it and start from scratch. You may also add new function definitions as you see fit. However, please do not modify any other functions. Doing so may result in your code failing our autograder tests. Also, please do not change any function signatures (names, argument order, or number of arguments). Testing Throughout this project, you should be testing the correctness of your code. It is good practice to test often, so that it is easy to isolate any problems. However, you should not be testing too often, to allow yourself time to think through problems. We have provided an autograder called ok to help you with testing your code and tracking your progress. The first time you run the autograder, you will be asked to log in with your Ok account using your web browser. Please do so. Each time you run ok, it will back up your work and progress on our servers. The primary purpose of ok is to test your implementations, but there are two things you should be aware of. First, some of the test cases are locked. To unlock tests, run the following command from your terminal: python3 ok -u This command will start an interactive prompt that looks like: ===================================================================== Assignment: The Game of Hog Ok, version ... ===================================================================== ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Unlocking tests At each "? ", type what you would expect the output to be. Type exit() to quit --------------------------------------------------------------------- Question 0 > Suite 1 > Case 1 (cases remaining: 1) >>> Code here ? At the ?, you can type what you expect the output to be. If you are correct, then this test case will be available the next time you run the autograder. The idea is to understand conceptually what your program should do first, before you start writing any code. Once you have unlocked some tests and written some code, you can check the correctness of your program using the tests that you have unlocked: python3 ok Most of the time, you will want to focus on a particular question. Use the -q option as directed in the problems below. We recommend that you submit after you finish each problem. Only your last submission will be graded. It is also useful for us to have more backups of your code in case you run into a submission issue. The tests folder is used to store autograder tests, so do not modify it. You may lose all your unlocking progress if you do. If you need to get a fresh copy, you can download the zip archive and copy it over, but you will need to start unlocking from scratch. If you do not want us to record a backup of your work or information about your progress, use the --local option when invoking ok. With this option, no information will be sent to our course servers. Graphical User Interface A graphical user interface (GUI, for short) is provided for you. At the moment, it doesn't work because you haven't implemented the game logic. Once you complete the play function, you will be able to play a fully interactive version of Hog! In order to render the graphics, make sure you have Tkinter, Python's main graphics library, installed on your computer. Once you've done that, you can run the GUI from your terminal: python3 hog_gui.py Once you complete the project, you can play against the final strategy that you've created! python3 hog_gui.py -f Phase 1: Simulator In the first phase, you will develop a simulator for the game of Hog. Problem 0 (0 pt) The dice.py file represents dice using non-pure zero-argument functions. These functions are non-pure because they may have different return values each time they are called. The documentation of dice.py describes the two different types of dice used in the project: Dice can be fair, meaning that they produce each possible outcome with equal probability. Example: six_sided. For testing functions that use dice, deterministic test dice always cycle through a fixed sequence of values that are passed as arguments to the make_test_dice function. Before we start writing any code, let's understand the make_test_dice function by unlocking its tests. python3 ok -q 00 -u This should display a prompt that looks like this: ===================================================================== Assignment: Project 1: Hog Ok, version v1.5.2 ===================================================================== ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Unlocking tests At each "? ", type what you would expect the output to be. Type exit() to quit --------------------------------------------------------------------- Question 0 > Suite 1 > Case 1 (cases remaining: 1) >>> test_dice = make_test_dice(4, 1, 2) >>> test_dice() ? You should type in what you expect the output to be. To do so, you need to first figure out what test_dice will do, based on the description above. You can exit the unlocker by typing exit() (without quotes). Typing Ctrl-C on Windows to exit out of the unlocker has been known to cause problems, so avoid doing so. Problem 1 (2 pt) Implement the roll_dice function in hog.py. It takes two arguments: a positive integer called num_rolls giving the number of dice to roll and a dice function. It returns the number of points scored by rolling the dice that number of times in a turn: either the sum of the outcomes or 1 (Pig Out). To obtain a single outcome of a dice roll, call dice(). You should call dice() exactly num_rolls times in the body of roll_dice. Remember to call dice() exactly num_rolls times even if Pig Out happens in the middle of rolling. In this way, we correctly simulate rolling all the dice together. Checking Your Work: Before writing any code, unlock the tests to verify your understanding of the question. python3 ok -q 01 -u Once you are done unlocking, begin implementing your solution. You can check your correctness with: python3 ok -q 01 If the tests don't pass, it's time to debug. You can observe the behavior of your function using Python directly. First, start the Python interpreter and load the hog.py file. python3 -i hog.py Then, you can call your roll_dice function on any number of dice you want, such as 4. >>> roll_dice(4) In most systems, you can evaluate the same expression again by pressing the up arrow or Control-P, then pressing enter or return. You should find that evaluating this call expression gives a different answer each time, since dice rolls are random. The roll_dice function has a default argument value for dice that is a random six-sided dice function. You can also use test dice that fix the outcomes of the dice in advance. For example, rolling twice when you know that the dice will come up 3 and 4 should give a total outcome of 7. >>> fixed_dice = make_test_dice(3, 4) >>> roll_dice(2, fixed_dice) 7 If you find a problem, you need to change your hog.py file, save it, quit Python, start it again, and then start evaluating expressions. Pressing the up arrow should give you access to your previous expressions, even after restarting Python. Once you think that your roll_dice function is correct, run the ok tests again. Tests like these don't prove that your program is exactly correct, but they help you build confidence that this part of your program does what you expect, so that you can trust the abstraction it defines as you proceed. Problem 2 (1 pt) Implement the free_bacon helper function that returns the number of points scored by rolling 0 dice, based on the opponent's current score. You can assume that score is less than 100. For a score less than 10, assume that the first of the two digits is 0. Before writing any code, unlock the tests to verify your understanding of the question. python3 ok -q 02 -u Once you are done unlocking, begin implementing your solution. You can check your correctness with: python3 ok -q 02 You can also test free_bacon interactively by entering python3 -i hog.py in the terminal and then calling free_bacon with various inputs. Problem 3 (1 pt) Implement the take_turn function, which returns the number of points scored for a turn by the current player. Your implementation should call roll_dice when possible. You will need to implement the Free Bacon rule. You can assume that opponent_score is less than 100. Call free_bacon in your implementation of take_turn. Before writing any code, unlock the tests to verify your understanding of the question. python3 ok -q 03 -u Once you are done unlocking, begin implementing your solution. You can check your correctness with: python3 ok -q 03 Problem 4 (1 pt) Implement is_swap, which returns whether or not the scores should be swapped because one is an integer multiple of the other. The is_swap function takes two arguments: the player scores. It returns a boolean value to indicate whether the Swine Swap condition is met. Before writing any code, unlock the tests to verify your understanding of the question. python3 ok -q 04 -u Once you are done unlocking, begin implementing your solution. You can check your correctness with: python3 ok -q 04 Problem 5 (3 pt) Implement the play function, which simulates a full game of Hog. Players alternate turns, each using their respective strategy function (Player 0 uses strategy0, etc.), until one of the players reaches the goal score. When the game ends, play returns the final total scores of both players, with Player 0's score first, and Player 1's score second. Here are some hints: You should use the functions you have already written! You will need to call take_turn with all three arguments. Only call take_turn once per turn. Enforce all the special rules. You can get the number of the other player (either 0 or 1) by calling the provided function other. You can ignore the say argument to the play function for now. You will use it in Phase 2 of the project. A strategy is a function that, given a player's score and their opponent's score, returns how many dice the player wants to roll. A strategy function (such as strategy0 and strategy1) takes two arguments: scores for the current player and opposing player, which both must be non-negative integers. A strategy function returns the number of dice that the current player wants to roll in the turn. Each strategy function should be called only once per turn. Don't worry about the details of implementing strategies yet. You will develop them in Phase 3. Before writing any code, unlock the tests to verify your understanding of the question. python3 ok -q 05 -u Once you are done unlocking, begin implementing your solution. You can check your correctness with: python3 ok -q 05 The last test for Question 5 is a fuzz test, which checks that your play function works for a large number of different inputs. Failing this test means something is wrong, but you should look at other tests to see where the problem might be. Once you are finished, you will be able to play a graphical version of the game. We have provided a file called hog_gui.py that you can run from the terminal: python3 hog_gui.py If you don't already have Tkinter (Python's graphics library) installed, you'll need to install it first before you can run the GUI. The GUI relies on your implementation, so if you have any bugs in your code, they will be reflected in the GUI. This means you can also use the GUI as a debugging tool; however, it's better to run the tests first. Congratulations! You have finished Phase 1 of this project! Phase 2: Commentary In the second phase, you will implement commentary functions that print remarks about the game, such as, "22 points! That's the biggest gain yet for Player 1." A commentary function takes two arguments, the current score for Player 0 and the current score for Player 1. It returns another commentary function to be called on the next turn. It may also print some output as a side effect of being called. The function say_scores in hog.py is an example of a commentary function. The function announce_lead_changes is an example of a higher-order function that returns a commentary function. def say_scores(score0, score1): """A commentary function that announces the score for each player.""" print("Player 0 now has", score0, "and Player 1 now has", score1) return say_scores def announce_lead_changes(previous_leader=None): """Return a commentary function that announces lead changes. >>> f0 = announce_lead_changes() >>> f1 = f0(5, 0) Player 0 takes the lead by 5 >>> f2 = f1(5, 12) Player 1 takes the lead by 7 >>> f3 = f2(8, 12) >>> f4 = f3(8, 13) >>> f5 = f4(15, 13) Player 0 takes the lead by 2 """ def say(score0, score1): if score0 > score1: leader = 0 elif score1 > score0: leader = 1 else: leader = None if leader != None and leader != previous_leader: print('Player', leader, 'takes the lead by', abs(score0 - score1)) return announce_lead_changes(leader) return say Problem 6 (2 pt) Update your play function so that a commentary function is called at the end of each turn. say(score0, score1) should be called at the end of the first turn. Its return value (another commentary function) should be called at the end of the second turn. Each turn, a new commentary function should be called that is the return value of the previous call to a commentary function. Also implement both, a function that takes two commentary functions (f and g) and returns a new commentary function. This new commentary function returns another commentary function which calls the functions returned by calling f and g, in that order. Before writing any code, unlock the tests to verify your understanding of the question. python3 ok -q 06 -u Once you are done unlocking, begin implementing your solution. You can check your correctness with: python3 ok -q 06 Problem 7 (2 pt) Implement the announce_highest function, which is a higher-order function that returns a commentary function. This commentary function announces whenever a particular player gains more points in a turn than ever before. To compute the gain, it must compare the score from last turn to the score from this turn for the player of interest, which is designated by the who argument. This function must also keep track of the highest gain for the player so far. The way in which announce_highest announces is very specific, and your implementation should match the doctests provided. Notice in particular that if the gain is only 1 point, then the message includes "point" in singular form. If the gain is larger, then the message includes "points" in plural form. Use Ok to test your code: python3 ok -q 07 Hint. The announce_lead_changes function provided to you is an example of how to keep track of information using commentary functions. If you are stuck, first make sure you understand how announce_lead_changes works. When you are done, if play the game again, you will see the commentary. python3 hog_gui.py The commentary in the GUI is generated by passing the following function as the say argument to play. both(announce_highest(0), both(announce_highest(1), announce_lead_changes())) Great work! You just finished Phase 2 of the project! Phase 3: Strategies In the third phase, you will experiment with ways to improve upon the basic strategy of always rolling a fixed number of dice. First, you need to develop some tools to evaluate strategies. Problem 8 (2 pt) Implement the make_averaged function, which is a higher-order function that takes a function fn as an argument. It returns another function that takes the same number of arguments as fn (the function originally passed into make_averaged). This returned function differs from the input function in that it returns the average value of repeatedly calling fn on the same arguments. This function should call fn a total of num_samples times and return the average of the results. To implement this function, you need a new piece of Python syntax! You must write a function that accepts an arbitrary number of arguments, then calls another function using exactly those arguments. Here's how it works. Instead of listing formal parameters for a function, we write *args. To call another function using exactly those arguments, we call it again with *args. For example, >>> def printed(fn): ... def print_and_return(*args): ... result = fn(*args) ... print('Result:', result) ... return result ... return print_and_return >>> printed_pow = printed(pow) >>> printed_pow(2, 8) Result: 256 256 >>> printed_abs = printed(abs) >>> printed_abs(-10) Result: 10 10 Read the docstring for make_averaged carefully to understand how it is meant to work. Before writing any code, unlock the tests to verify your understanding of the question. python3 ok -q 08 -u Once you are done unlocking, begin implementing your solution. You can check your correctness with: python3 ok -q 08 Problem 9 (1 pt) Implement the max_scoring_num_rolls function, which runs an experiment to determine the number of rolls (from 1 to 10) that gives the maximum average score for a turn. Your implementation should use make_averaged and roll_dice. If two numbers of rolls are tied for the maximum average score, return the lower number. For example, if both 3 and 6 achieve a maximum average score, return 3. Before writing any code, unlock the tests to verify your understanding of the question. python3 ok -q 09 -u Once you are done unlocking, begin implementing your solution. You can check your correctness with: python3 ok -q 09 To run this experiment on randomized dice, call run_experiments using the -r option: python3 hog.py -r Running experiments For the remainder of this project, you can change the implementation of run_experiments as you wish. By calling average_win_rate, you can evaluate various Hog strategies. For example, change the first if False: to if True: in order to evaluate always_roll(8) against the baseline strategy of always_roll(4). You should find that it wins slightly more often than it loses, giving a win rate around 0.5. Some of the experiments may take up to a minute to run. You can always reduce the number of samples in make_averaged to speed up experiments. Problem 10 (1 pt) A strategy can take advantage of the Free Bacon rule by rolling 0 when it is most beneficial to do so. Implement bacon_strategy, which returns 0 whenever rolling 0 would give at least margin points and returns num_rolls otherwise. Before writing any code, unlock the tests to verify your understanding of the question. python3 ok -q 10 -u Once you are done unlocking, begin implementing your solution. You can check your correctness with: python3 ok -q 10 Once you have implemented this strategy, change run_experiments to evaluate your new strategy against the baseline. You should find that it wins more than half of the time. Problem 11 (2 pt) A strategy can also take advantage of the Swine Swap rule. The swap_strategy rolls 0 if it would cause a beneficial swap. It also returns 0 if rolling 0 would give at least margin points and would not cause a swap. Otherwise, the strategy rolls num_rolls. Before writing any code, unlock the tests to verify your understanding of the question. python3 ok -q 11 -u Once you are done unlocking, begin implementing your solution. You can check your correctness with: python3 ok -q 11 Once you have implemented this strategy, update run_experiments to evaluate your new strategy against the baseline. You should find that it gives a significant edge over always_roll(4). Optional: Problem 12 (0 pt) Implement final_strategy, which combines these ideas and any other ideas you have to achieve a high win rate against the always_roll(4) strategy. Some suggestions: swap_strategy is a good default strategy to start with. There's no point in scoring more than 100. Check whether you can win by rolling 0, 1 or 2 dice. If you are in the lead, you might take fewer risks. Try to force a beneficial swap. Choose the num_rolls and margin arguments carefully. You can check that your final strategy is valid by running Ok. python3 ok -q 12 You can also check your exact final winrate by running python3 calc.py At this point, run the entire autograder to see if there are any tests that don't pass. python3 ok Once you are satisfied, submit to Ok to complete the project. python3 ok --submit You can also play against your final strategy with the graphical user interface: python3 hog_gui.py -f The GUI will alternate which player is controlled by you. Congratulations, you have reached the end of your first CS 61A project! If you haven't already, relax and enjoy a few games of Hog with a friend.
SUMEETRM / Covid19 AIThe 2019 novel coronavirus (COVID-19) presents several unique features. While the diagnosis is confirmed using polymerase chain reaction (PCR), infected patients with pneumonia may present on chest X-ray and computed tomography (CT) images with a pattern that is only moderately characteristic for the human eye. Bilateral multiple lobular and subsegmental areas of consolidation can be observed in COVID-19 patients. The following model aims to present a neural network aimed to detect COVID-19 cases through chest X-Rays. While a neural network to detect COVID-19 cases has been published, the following model has a much higher accuracy. Out model uses a sample of 100 COVID-19 positive cases and 100 COVID-19 negative cases, and has an accuracy of 91% . Of the true positive patients, the model had an accuracy of 100%, and of the true negative patients, the model had an accuracy of 80%. Given the fact that the data is limited, the model can be improved in the near future for rapid diagnosis of COVID-19 cases with an extremely low rate of false positives. While the model may still be in an initial phase of development, it can be put to practical use soon with enough training and accuracy.
yukubo / Sample Word2vec Skipgram# Copyright 2015 Google Inc. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== """Multi-threaded word2vec mini-batched skip-gram model. Trains the model described in: (Mikolov, et. al.) Efficient Estimation of Word Representations in Vector Space ICLR 2013. http://arxiv.org/abs/1301.3781 This model does traditional minibatching. The key ops used are: * placeholder for feeding in tensors for each example. * embedding_lookup for fetching rows from the embedding matrix. * sigmoid_cross_entropy_with_logits to calculate the loss. * GradientDescentOptimizer for optimizing the loss. * skipgram custom op that does input processing. """ from __future__ import absolute_import from __future__ import division from __future__ import print_function import os import sys import threading import time from six.moves import xrange # pylint: disable=redefined-builtin import numpy as np import tensorflow as tf from tensorflow.models.embedding import gen_word2vec as word2vec flags = tf.app.flags flags.DEFINE_string("save_path", None, "Directory to write the model and " "training summaries.") flags.DEFINE_string("train_data", None, "Training text file. " "E.g., unzipped file http://mattmahoney.net/dc/text8.zip.") flags.DEFINE_string( "eval_data", None, "File consisting of analogies of four tokens." "embedding 2 - embedding 1 + embedding 3 should be close " "to embedding 4." "E.g. https://word2vec.googlecode.com/svn/trunk/questions-words.txt.") flags.DEFINE_integer("embedding_size", 200, "The embedding dimension size.") flags.DEFINE_integer( "epochs_to_train", 15, "Number of epochs to train. Each epoch processes the training data once " "completely.") flags.DEFINE_float("learning_rate", 0.2, "Initial learning rate.") flags.DEFINE_integer("num_neg_samples", 100, "Negative samples per training example.") flags.DEFINE_integer("batch_size", 16, "Number of training examples processed per step " "(size of a minibatch).") flags.DEFINE_integer("concurrent_steps", 12, "The number of concurrent training steps.") flags.DEFINE_integer("window_size", 5, "The number of words to predict to the left and right " "of the target word.") flags.DEFINE_integer("min_count", 5, "The minimum number of word occurrences for it to be " "included in the vocabulary.") flags.DEFINE_float("subsample", 1e-3, "Subsample threshold for word occurrence. Words that appear " "with higher frequency will be randomly down-sampled. Set " "to 0 to disable.") flags.DEFINE_boolean( "interactive", False, "If true, enters an IPython interactive session to play with the trained " "model. E.g., try model.analogy('france', 'paris', 'russia') and " "model.nearby(['proton', 'elephant', 'maxwell']") flags.DEFINE_integer("statistics_interval", 5, "Print statistics every n seconds.") flags.DEFINE_integer("summary_interval", 5, "Save training summary to file every n seconds (rounded " "up to statistics interval.") flags.DEFINE_integer("checkpoint_interval", 600, "Checkpoint the model (i.e. save the parameters) every n " "seconds (rounded up to statistics interval.") FLAGS = flags.FLAGS class Options(object): """Options used by our word2vec model.""" def __init__(self): # Model options. # Embedding dimension. self.emb_dim = FLAGS.embedding_size # Training options. # The training text file. self.train_data = FLAGS.train_data # Number of negative samples per example. self.num_samples = FLAGS.num_neg_samples # The initial learning rate. self.learning_rate = FLAGS.learning_rate # Number of epochs to train. After these many epochs, the learning # rate decays linearly to zero and the training stops. self.epochs_to_train = FLAGS.epochs_to_train # Concurrent training steps. self.concurrent_steps = FLAGS.concurrent_steps # Number of examples for one training step. self.batch_size = FLAGS.batch_size # The number of words to predict to the left and right of the target word. self.window_size = FLAGS.window_size # The minimum number of word occurrences for it to be included in the # vocabulary. self.min_count = FLAGS.min_count # Subsampling threshold for word occurrence. self.subsample = FLAGS.subsample # How often to print statistics. self.statistics_interval = FLAGS.statistics_interval # How often to write to the summary file (rounds up to the nearest # statistics_interval). self.summary_interval = FLAGS.summary_interval # How often to write checkpoints (rounds up to the nearest statistics # interval). self.checkpoint_interval = FLAGS.checkpoint_interval # Where to write out summaries. self.save_path = FLAGS.save_path # Eval options. # The text file for eval. self.eval_data = FLAGS.eval_data class Word2Vec(object): """Word2Vec model (Skipgram).""" def __init__(self, options, session): self._options = options self._session = session self._word2id = {} self._id2word = [] self.build_graph() self.build_eval_graph() self.save_vocab() self._read_analogies() def _read_analogies(self): """Reads through the analogy question file. Returns: questions: a [n, 4] numpy array containing the analogy question's word ids. questions_skipped: questions skipped due to unknown words. """ questions = [] questions_skipped = 0 with open(self._options.eval_data, "rb") as analogy_f: for line in analogy_f: if line.startswith(b":"): # Skip comments. continue words = line.strip().lower().split(b" ") ids = [self._word2id.get(w.strip()) for w in words] if None in ids or len(ids) != 4: questions_skipped += 1 else: questions.append(np.array(ids)) print("Eval analogy file: ", self._options.eval_data) print("Questions: ", len(questions)) print("Skipped: ", questions_skipped) self._analogy_questions = np.array(questions, dtype=np.int32) def forward(self, examples, labels): """Build the graph for the forward pass.""" opts = self._options # Declare all variables we need. # Embedding: [vocab_size, emb_dim] init_width = 0.5 / opts.emb_dim emb = tf.Variable( tf.random_uniform( [opts.vocab_size, opts.emb_dim], -init_width, init_width), name="emb") self._emb = emb # Softmax weight: [vocab_size, emb_dim]. Transposed. sm_w_t = tf.Variable( tf.zeros([opts.vocab_size, opts.emb_dim]), name="sm_w_t") # Softmax bias: [emb_dim]. sm_b = tf.Variable(tf.zeros([opts.vocab_size]), name="sm_b") # Global step: scalar, i.e., shape []. self.global_step = tf.Variable(0, name="global_step") # Nodes to compute the nce loss w/ candidate sampling. labels_matrix = tf.reshape( tf.cast(labels, dtype=tf.int64), [opts.batch_size, 1]) # Negative sampling. sampled_ids, _, _ = (tf.nn.fixed_unigram_candidate_sampler( true_classes=labels_matrix, num_true=1, num_sampled=opts.num_samples, unique=True, range_max=opts.vocab_size, distortion=0.75, unigrams=opts.vocab_counts.tolist())) # Embeddings for examples: [batch_size, emb_dim] example_emb = tf.nn.embedding_lookup(emb, examples) # Weights for labels: [batch_size, emb_dim] true_w = tf.nn.embedding_lookup(sm_w_t, labels) # Biases for labels: [batch_size, 1] true_b = tf.nn.embedding_lookup(sm_b, labels) # Weights for sampled ids: [num_sampled, emb_dim] sampled_w = tf.nn.embedding_lookup(sm_w_t, sampled_ids) # Biases for sampled ids: [num_sampled, 1] sampled_b = tf.nn.embedding_lookup(sm_b, sampled_ids) # True logits: [batch_size, 1] true_logits = tf.reduce_sum(tf.mul(example_emb, true_w), 1) + true_b # Sampled logits: [batch_size, num_sampled] # We replicate sampled noise lables for all examples in the batch # using the matmul. sampled_b_vec = tf.reshape(sampled_b, [opts.num_samples]) sampled_logits = tf.matmul(example_emb, sampled_w, transpose_b=True) + sampled_b_vec return true_logits, sampled_logits def nce_loss(self, true_logits, sampled_logits): """Build the graph for the NCE loss.""" # cross-entropy(logits, labels) opts = self._options true_xent = tf.nn.sigmoid_cross_entropy_with_logits( true_logits, tf.ones_like(true_logits)) sampled_xent = tf.nn.sigmoid_cross_entropy_with_logits( sampled_logits, tf.zeros_like(sampled_logits)) # NCE-loss is the sum of the true and noise (sampled words) # contributions, averaged over the batch. nce_loss_tensor = (tf.reduce_sum(true_xent) + tf.reduce_sum(sampled_xent)) / opts.batch_size return nce_loss_tensor def optimize(self, loss): """Build the graph to optimize the loss function.""" # Optimizer nodes. # Linear learning rate decay. opts = self._options words_to_train = float(opts.words_per_epoch * opts.epochs_to_train) lr = opts.learning_rate * tf.maximum( 0.0001, 1.0 - tf.cast(self._words, tf.float32) / words_to_train) self._lr = lr optimizer = tf.train.GradientDescentOptimizer(lr) train = optimizer.minimize(loss, global_step=self.global_step, gate_gradients=optimizer.GATE_NONE) self._train = train def build_eval_graph(self): """Build the eval graph.""" # Eval graph # Each analogy task is to predict the 4th word (d) given three # words: a, b, c. E.g., a=italy, b=rome, c=france, we should # predict d=paris. # The eval feeds three vectors of word ids for a, b, c, each of # which is of size N, where N is the number of analogies we want to # evaluate in one batch. analogy_a = tf.placeholder(dtype=tf.int32) # [N] analogy_b = tf.placeholder(dtype=tf.int32) # [N] analogy_c = tf.placeholder(dtype=tf.int32) # [N] # Normalized word embeddings of shape [vocab_size, emb_dim]. nemb = tf.nn.l2_normalize(self._emb, 1) # Each row of a_emb, b_emb, c_emb is a word's embedding vector. # They all have the shape [N, emb_dim] a_emb = tf.gather(nemb, analogy_a) # a's embs b_emb = tf.gather(nemb, analogy_b) # b's embs c_emb = tf.gather(nemb, analogy_c) # c's embs # We expect that d's embedding vectors on the unit hyper-sphere is # near: c_emb + (b_emb - a_emb), which has the shape [N, emb_dim]. target = c_emb + (b_emb - a_emb) # Compute cosine distance between each pair of target and vocab. # dist has shape [N, vocab_size]. dist = tf.matmul(target, nemb, transpose_b=True) # For each question (row in dist), find the top 4 words. _, pred_idx = tf.nn.top_k(dist, 4) # Nodes for computing neighbors for a given word according to # their cosine distance. nearby_word = tf.placeholder(dtype=tf.int32) # word id nearby_emb = tf.gather(nemb, nearby_word) nearby_dist = tf.matmul(nearby_emb, nemb, transpose_b=True) nearby_val, nearby_idx = tf.nn.top_k(nearby_dist, min(1000, self._options.vocab_size)) # Nodes in the construct graph which are used by training and # evaluation to run/feed/fetch. self._analogy_a = analogy_a self._analogy_b = analogy_b self._analogy_c = analogy_c self._analogy_pred_idx = pred_idx self._nearby_word = nearby_word self._nearby_val = nearby_val self._nearby_idx = nearby_idx def build_graph(self): """Build the graph for the full model.""" opts = self._options # The training data. A text file. (words, counts, words_per_epoch, self._epoch, self._words, examples, labels) = word2vec.skipgram(filename=opts.train_data, batch_size=opts.batch_size, window_size=opts.window_size, min_count=opts.min_count, subsample=opts.subsample) (opts.vocab_words, opts.vocab_counts, opts.words_per_epoch) = self._session.run([words, counts, words_per_epoch]) opts.vocab_size = len(opts.vocab_words) print("Data file: ", opts.train_data) print("Vocab size: ", opts.vocab_size - 1, " + UNK") print("Words per epoch: ", opts.words_per_epoch) self._examples = examples self._labels = labels self._id2word = opts.vocab_words for i, w in enumerate(self._id2word): self._word2id[w] = i true_logits, sampled_logits = self.forward(examples, labels) loss = self.nce_loss(true_logits, sampled_logits) tf.scalar_summary("NCE loss", loss) self._loss = loss self.optimize(loss) # Properly initialize all variables. tf.initialize_all_variables().run() self.saver = tf.train.Saver() def save_vocab(self): """Save the vocabulary to a file so the model can be reloaded.""" opts = self._options with open(os.path.join(opts.save_path, "vocab.txt"), "w") as f: for i in xrange(opts.vocab_size): f.write("%s %d\n" % (tf.compat.as_text(opts.vocab_words[i]), opts.vocab_counts[i])) def _train_thread_body(self): initial_epoch, = self._session.run([self._epoch]) while True: _, epoch = self._session.run([self._train, self._epoch]) if epoch != initial_epoch: break def train(self): """Train the model.""" opts = self._options initial_epoch, initial_words = self._session.run([self._epoch, self._words]) summary_op = tf.merge_all_summaries() summary_writer = tf.train.SummaryWriter(opts.save_path, graph_def=self._session.graph_def) workers = [] for _ in xrange(opts.concurrent_steps): t = threading.Thread(target=self._train_thread_body) t.start() workers.append(t) last_words, last_time, last_summary_time = initial_words, time.time(), 0 last_checkpoint_time = 0 while True: time.sleep(opts.statistics_interval) # Reports our progress once a while. (epoch, step, loss, words, lr) = self._session.run( [self._epoch, self.global_step, self._loss, self._words, self._lr]) now = time.time() last_words, last_time, rate = words, now, (words - last_words) / ( now - last_time) print("Epoch %4d Step %8d: lr = %5.3f loss = %6.2f words/sec = %8.0f\r" % (epoch, step, lr, loss, rate), end="") sys.stdout.flush() if now - last_summary_time > opts.summary_interval: summary_str = self._session.run(summary_op) summary_writer.add_summary(summary_str, step) last_summary_time = now if now - last_checkpoint_time > opts.checkpoint_interval: self.saver.save(self._session, opts.save_path + "model", global_step=step.astype(int)) last_checkpoint_time = now if epoch != initial_epoch: break for t in workers: t.join() return epoch def _predict(self, analogy): """Predict the top 4 answers for analogy questions.""" idx, = self._session.run([self._analogy_pred_idx], { self._analogy_a: analogy[:, 0], self._analogy_b: analogy[:, 1], self._analogy_c: analogy[:, 2] }) return idx def eval(self): """Evaluate analogy questions and reports accuracy.""" # How many questions we get right at precision@1. correct = 0 total = self._analogy_questions.shape[0] start = 0 while start < total: limit = start + 2500 sub = self._analogy_questions[start:limit, :] idx = self._predict(sub) start = limit for question in xrange(sub.shape[0]): for j in xrange(4): if idx[question, j] == sub[question, 3]: # Bingo! We predicted correctly. E.g., [italy, rome, france, paris]. correct += 1 break elif idx[question, j] in sub[question, :3]: # We need to skip words already in the question. continue else: # The correct label is not the precision@1 break print() print("Eval %4d/%d accuracy = %4.1f%%" % (correct, total, correct * 100.0 / total)) def analogy(self, w0, w1, w2): """Predict word w3 as in w0:w1 vs w2:w3.""" wid = np.array([[self._word2id.get(w, 0) for w in [w0, w1, w2]]]) idx = self._predict(wid) for c in [self._id2word[i] for i in idx[0, :]]: if c not in [w0, w1, w2]: return c return "unknown" def nearby(self, words, num=20): """Prints out nearby words given a list of words.""" ids = np.array([self._word2id.get(x, 0) for x in words]) vals, idx = self._session.run( [self._nearby_val, self._nearby_idx], {self._nearby_word: ids}) for i in xrange(len(words)): print("\n%s\n=====================================" % (words[i])) for (neighbor, distance) in zip(idx[i, :num], vals[i, :num]): print("%-20s %6.4f" % (self._id2word[neighbor], distance)) def _start_shell(local_ns=None): # An interactive shell is useful for debugging/development. import IPython user_ns = {} if local_ns: user_ns.update(local_ns) user_ns.update(globals()) IPython.start_ipython(argv=[], user_ns=user_ns) def main(_): """Train a word2vec model.""" if not FLAGS.train_data or not FLAGS.eval_data or not FLAGS.save_path: print("--train_data --eval_data and --save_path must be specified.") sys.exit(1) opts = Options() with tf.Graph().as_default(), tf.Session() as session: with tf.device("/cpu:0"): model = Word2Vec(opts, session) for _ in xrange(opts.epochs_to_train): model.train() # Process one epoch model.eval() # Eval analogies. # Perform a final save. model.saver.save(session, os.path.join(opts.save_path, "model.ckpt"), global_step=model.global_step) if FLAGS.interactive: # E.g., # [0]: model.analogy('france', 'paris', 'russia') # [1]: model.nearby(['proton', 'elephant', 'maxwell']) _start_shell(locals()) if __name__ == "__main__": tf.app.run()
