An algorithm is a step-by-step procedure or a set of instructions used to solve a specific problem or accomplish a particular task. It serves as a blueprint for solving problems in a systematic and efficient manner. Algorithms are essential in computer science and programming as they provide a structured approach to problem-solving.
Algorithms can be found in various domains, such as sorting and searching data, graph traversal, mathematical computations, artificial intelligence, and more. They are designed to handle specific inputs and produce desired outputs through a series of well-defined steps.
Developing an algorithm involves several phases, from understanding the problem to reassembling the response. Let's break down the process into detailed steps:
Remember, developing an algorithm is an iterative process. It involves analyzing, designing, implementing, testing, and refining the solution until the desired outcome is achieved.
By following these detailed steps, you can effectively develop algorithms that solve problems accurately and efficiently. Continuous practice and refinement of algorithmic skills will lead to better problem-solving abilities in computer science and programming.
Here's an algorithmic approach to identifying and cleaning email addresses in a string, without using regular expressions, along with the steps and explanations:
Problem: Given a string containing email addresses with extraneous characters, write a Python function that identifies and cleans the email addresses before storing them in an array.
Understanding the Problem: We have a string that may contain email addresses along with other characters. We need to identify the email addresses and clean them by removing any extraneous characters. The cleaned email addresses should be stored in an array.
If we break down the task of extracting emails from a text string, then it might look something like this:
By breaking the problem and solution into steps, we discover that this complex problem is actually just a series of simpler problems all connected together. Using this list of simple probems as our guide, we can now write the parts of the algorithm.
def clean_email_addresses(string):
cleaned_addresses = []
words = string.split()
for word in words:
if "@" in word:
# Clean the email address
cleaned_address = ""
for char in word:
if char.isalpha() or char.isdigit() or char in ['.', '_', '-']:
cleaned_address += char
cleaned_addresses.append(cleaned_address)
return cleaned_addresses
text = "Please contact me at john.doe@example.com or jane_123@example.com"
result = clean_email_addresses(text)
print(result) # Output: ['john.doe@example.com', 'jane_123@example.com']
It is important that when you test the code, you give it many different test cases, or in this case different strings. For example, how about one that has different kinds of punctuation, or an invalid email address that looks valid. What happens if you feed the algorithm an empty string? A thorough approach to testing might include all of these test cases.
Optimization: In this example, the algorithm is relatively simple. However, you can optimize it further by considering additional checks, such as validating the email address structure (e.g., checking for the presence of a domain and a top-level domain) or removing any duplicates from the array.
Documentation and Maintenance: Provide clear comments and instructions within the code to explain the purpose, inputs, and outputs of the function. Regularly maintain and update the code and documentation as needed.
This algorithmic approach allows you to identify and clean email addresses in a string using string methods in Python. By following these steps, you can extract valid email addresses while disregarding any extraneous characters present in the string.
No terms have been published for this module.
Test your knowledge of this module by choosing options below. You can keep trying until you get the right answer.
Skip to the Next QuestionIn this sandbox, we are going to build on what we did last time — We explored some different things that one might use to measure how smart or intelligent a piece of writing is. In this sandbox, we are going to take this idea a step further and create an algorithm that gives us some kind of score indicating how “smart” a piece of text is.
You might want to use some of the ideas you explored last module, but also introduce some new ones. You will need to think critically about how much weight to give different things, like word variety, word length, and length of the writing sample.
When you are done, you should be able to enter a text sample, and it should output a score.
To test it, use the following three samples of text:
4th Grade Level:
The moon seems to change shape every night. These shapes are called moon phases. The cycle starts with the new moon, which we can't see because it's between the Earth and the sun. Then, as the moon moves, we see more of it lit up, first a crescent, then a half moon, and finally a full moon, which is all bright and round. After the full moon, it looks like the moon is shrinking, going back through the half moon and crescent until it's new again. This whole cycle takes about a month.
10th Grade Level:
The moon goes through different phases, which make it look like it's changing shape. This happens over a cycle called a lunar month, lasting about 29.5 days. It starts with the new moon, when the moon is lined up between Earth and the sun, showing us its shadowed side. As the moon moves, we see more of its sunlit side, moving through crescent to quarter, then to a full moon when it's completely lit up. After the full moon, the moon's visible light starts to decrease, moving back towards the new moon.
Ph.D. Level:
The lunar phases reflect a cyclical pattern of visibility and illumination, governed by the moon’s synodic period of roughly 29.53 days. Initially, during the new moon phase, the moon is positioned directly between Earth and the sun, displaying its unilluminated side to us. As it orbits, the sunlit portion we can see increases—this is called waxing. It progresses through crescent to gibbous until the full moon, where the entire face is visible. Then it begins to wane, or decrease in visibility, returning once more to the new moon phase, completing the cycle.
You can use the code from the Lincoln vs. Swift sandbox to help you get started. Using the three samples of writing above, try to come up with an algorithm that will “know” the difference between the different levels of writing.
For an extra challenge, find some additional text snippets to test out, and see how it works.
Take a screenshot of the results, and submit it to this discussion with an explanation of how you designed it to work.