algorithmic-thinking
It's about graphs and networks
Science Score: 31.0%
This score indicates how likely this project is to be science-related based on various indicators:
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✓CITATION.cff file
Found CITATION.cff file -
✓codemeta.json file
Found codemeta.json file -
○.zenodo.json file
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○DOI references
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○Academic publication links
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○Academic email domains
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○Institutional organization owner
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○JOSS paper metadata
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○Scientific vocabulary similarity
Low similarity (6.3%) to scientific vocabulary
Repository
It's about graphs and networks
Basic Info
- Host: GitHub
- Owner: leomartinez2020
- Language: Python
- Default Branch: main
- Size: 1.6 MB
Statistics
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
- Releases: 0
Metadata Files
README.md
Algorithmic Thinking 1
Experienced Computer Scientists analyze and solve computational problems at a level of abstraction that is beyond that of any particular programming language. This two-part course builds on the principles that you learned in our Principles of Computing course and is designed to train students in the mathematical concepts and process of "Algorithmic Thinking", allowing them to build simpler, more efficient solutions to real-world computational problems.
In part 1 of this course, we will study the notion of algorithmic efficiency and consider its application to several problems from graph theory. As the central part of the course, students will implement several important graph algorithms in Python and then use these algorithms to analyze two large real-world data sets. The main focus of these tasks is to understand interaction between the algorithms and the structure of the data sets being analyzed by these algorithms.
Recommended Background - Students should be comfortable writing intermediate size (300+ line) programs in Python and have a basic understanding of searching, sorting, and recursion. Students should also have a solid math background that includes algebra, pre-calculus and a familiarity with the math concepts covered in "Principles of Computing".
Owner
- Name: Leonardo Martinez
- Login: leomartinez2020
- Kind: user
- Location: Barranquilla, Colombia
- Company: Independent
- Twitter: pythonista100
- Repositories: 13
- Profile: https://github.com/leomartinez2020
Technologies I use: HTML, CSS, JavaScript, AWS, Python and Shopify.
Citation (citation.py)
from grafos import in_degree_distribution
filename = 'alg_phys-cite.txt'
def create_graph_from_file(fname):
answer_graph = {}
with open(fname) as fhandler:
for line in fhandler:
add_line_to_graph(answer_graph, line)
return answer_graph
def add_line_to_graph(graph, line):
neighbors = line.split(' ')
node = int(neighbors[0])
graph[node] = set([])
for neighbor in neighbors[1 : -1]:
graph[node].add(int(neighbor))
def compute_indegree_dist():
g = create_graph_from_file(filename)
indist= in_degree_distribution(g)
print(indist)
#compute_indegree_dist()