tweet-generator's Introduction

CS 1.2: Intro to Data Structures

Course Description

A project based course that looks under the hood at data structures and algorithms to see how they work. In addition to implementing these structures in an application; students will build them from scratch, analyze their complexity, and benchmark their performance to gain an understanding of their tradeoffs and when to use them in practice. Students will write scripts, functions, and library modules to use text processing tools like regular expressions, construct and sample probability distributions to create a Markov language model and gain insight into how grammar works and natural language processing techniques.

Repository Setup

⚠️ Important: Please follow these instructions exactly to set up your clone of this course repository.

Schedule

Course Dates: Monday, October 21 – Wednesday, December 11, 2019 (7.5 weeks)

Class Times: Monday & Wednesday at 1:30-3:20pm (section A) or 3:30–5:20pm (section B)

Class	Date	Topics
1	Mon, Oct 21	Strings & Random Numbers
2	Wed, Oct 23	Histogram Data Structures
3	Mon, Oct 28	Probability & Sampling
4	Wed, Oct 30	Flask Web App Development
5	Mon, Nov 4	Application Architecture
6	Wed, Nov 6	Generating Sentences
7	Mon, Nov 11	Arrays & Linked Lists
8	Wed, Nov 13	Linked List Algorithm Analysis
9	Mon, Nov 18	Hash Tables
10	Wed, Nov 20	Hash Table Algorithm Analysis
11	Mon, Nov 25	Project Lab Day
–	Wed, Nov 27	No Class (Thanksgiving Break)
12	Mon, Dec 2	Higher Order Markov Chains
13	Wed, Dec 4	Regular Expressions
14	Mon, Dec 9	Time to Tweet & Launch Day!
15	Wed, Dec 11	Activity To Be Determined

Prerequisites

Students must pass the following course and demonstrate mastery of its competencies:

CS 1.1: Programming Fundamentals

Learning Objectives

By the end of this course, students will be able to:

Create Python programs that read and write text files and manipulate strings
Build web apps with the Flask framework and deploy to the web
Construct and sample probability distributions based on observed word frequencies
Create Markov language models and use them to generate new sentences
Use unit tests that assert correct behavior of functions and classes
Implement core data structures including singly linked lists and hash tables
Analyze the complexity of iterative algorithms and data structures with visual loop counting

Project Tutorial

Students will complete the following guided project tutorial in this course:

Tweet Generator: Data Structures & Probability with Python

Evaluation

To pass this course, students must meet the following requirements:

Actively participate in class and abide by the attendance policy
Make up all classwork from all absences
Complete the required project tutorial
Pass the project according to the associated project rubric
Pass the summative assessment (final exam)

Attendance

Just like any job, attendance at Make School is required and a key component of your success. Attendance is being onsite from 9:30am to 5:30pm each day, attending all scheduled sessions including classes, huddles, coaching and school meetings, and working in the study labs when not in a scheduled session. Working onsite allows you to learn with your peers, have access to support from TAs, instructors and others, and is vital to your learning.

Attendance requirements for scheduled sessions are:

No more than two unexcused absences ("no-call-no-shows") per term in any scheduled session.
No more than four excused absences (communicated in advance) per term in any scheduled session.

Failure to meet these requirements will result in a Participation Improvement Plan (PIP). Failure to improve after the PIP is cause for not being allowed to continue at Make School.

Make School Policies

tweet-generator's People

Contributors

Watchers

tweet-generator's Issues

Code Review of Hashtables

I encourage you to add run time analysis of each method to push your understanding of hash tables.
Great job completing stretch challenges, especially adding the useful subscript-able methods. I recommend trying alternative collision resolution strategies such as linear probing to push your understanding of the how this data structure works!

Tweet Generator Final Project Submission

If you clean your source text from symbols and use regex effectively you will have much better accuracy when generating sentences.
frequency() method in listogram.py file supposed to return 0 if word not found but you are rasing value error which causing the test failing.
Also, there might be issue in your or add_count() method. so test_entries test is failing
One of your import files doesn't exist in the folder so I could not run your markov chain locally but seems like it is working on the web. This is the import line
from data_struct_algo.queues.circular_buffer import CircularBuffer
But great work on building nth order markov chain

Code Review of Tweet Generator Submission 2

All tests pass with no errors, great job!
Replace does not work. This is due to using == instead of assigning a new value to current.data. Additionally, you do not break out of the loop and return, so it will always get to the end of the linked list, see that current is None, and raise an error.
Correct for all annotations
Great job completing stretch challenges!

Tweet Generator Project Check in

Random dictionary words:

It would be better to use calculating the length of the dictionary instead of using a fixed number
If you add if statement in your main function to check the args in the terminal

Histogram:

I don't think you need a Node class to create Histogram class
Dont forget to add comments for complex lines of code or docstring for the function

Sample words by frequency:

I couldn’t run the file, make sure to include the
Add docstrings to your functions some of them seems too complicated to understand

Web application

Make sure you are using right syntax when you call an external function from other files

Markov chain

Add docstrings to your functions some of them seems too complicated to understand
Add comments to explain the complex lines e.g. line 29 in your Markov chain class
self._word_pointers = KeyDefaultDict(lambda x: self._new_word(x))
Don't forget to close the file in your read_file() function on line 40, don't forget to have return statement for your list of words

Recommend Projects

kevin-meyers / tweet-generator Goto Github PK