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Change to https. (TheAlgorithms#7277)
* Change to https. * Revert the py_tf file.
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fractals/julia_sets.py

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https://en.wikipedia.org/wiki/File:Julia_z2%2B0,25.png
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- Other examples from https://en.wikipedia.org/wiki/Julia_set
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- An exponential map Julia set, ambiantly homeomorphic to the examples in
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http://www.math.univ-toulouse.fr/~cheritat/GalII/galery.html
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https://www.math.univ-toulouse.fr/~cheritat/GalII/galery.html
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and
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https://ddd.uab.cat/pub/pubmat/02141493v43n1/02141493v43n1p27.pdf
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fractals/sierpinski_triangle.py

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- $python sierpinski_triangle.py <int:depth_for_fractal>
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Credits: This code was written by editing the code from
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http://www.riannetrujillo.com/blog/python-fractal/
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https://www.riannetrujillo.com/blog/python-fractal/
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"""
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import sys

machine_learning/lstm/lstm_prediction.py

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"""
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Create a Long Short Term Memory (LSTM) network model
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An LSTM is a type of Recurrent Neural Network (RNN) as discussed at:
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* http://colah.github.io/posts/2015-08-Understanding-LSTMs
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* https://colah.github.io/posts/2015-08-Understanding-LSTMs
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* https://en.wikipedia.org/wiki/Long_short-term_memory
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"""
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import numpy as np

machine_learning/sequential_minimum_optimization.py

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Reference:
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https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/smo-book.pdf
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https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/tr-98-14.pdf
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http://web.cs.iastate.edu/~honavar/smo-svm.pdf
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https://web.cs.iastate.edu/~honavar/smo-svm.pdf
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"""
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from sklearn.preprocessing import StandardScaler
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CANCER_DATASET_URL = (
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"http://archive.ics.uci.edu/ml/machine-learning-databases/"
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"https://archive.ics.uci.edu/ml/machine-learning-databases/"
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"breast-cancer-wisconsin/wdbc.data"
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)
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maths/matrix_exponentiation.py

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"""
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Matrix Exponentiation is a technique to solve linear recurrences in logarithmic time.
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You read more about it here:
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http://zobayer.blogspot.com/2010/11/matrix-exponentiation.html
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https://zobayer.blogspot.com/2010/11/matrix-exponentiation.html
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https://www.hackerearth.com/practice/notes/matrix-exponentiation-1/
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"""
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maths/test_prime_check.py

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"""
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Minimalist file that allows pytest to find and run the Test unittest. For details, see:
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http://doc.pytest.org/en/latest/goodpractices.html#conventions-for-python-test-discovery
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https://doc.pytest.org/en/latest/goodpractices.html#conventions-for-python-test-discovery
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"""
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from .prime_check import Test

physics/n_body_simulation.py

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numerical divergences when a particle comes too close to another (and the force
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goes to infinity).
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(Description adapted from https://en.wikipedia.org/wiki/N-body_simulation )
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(See also http://www.shodor.org/refdesk/Resources/Algorithms/EulersMethod/ )
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(See also https://www.shodor.org/refdesk/Resources/Algorithms/EulersMethod/ )
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"""
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Example 1: figure-8 solution to the 3-body-problem
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This example can be seen as a test of the implementation: given the right
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initial conditions, the bodies should move in a figure-8.
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(initial conditions taken from http://www.artcompsci.org/vol_1/v1_web/node56.html)
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(initial conditions taken from https://www.artcompsci.org/vol_1/v1_web/node56.html)
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>>> body_system = example_1()
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>>> len(body_system)
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strings/frequency_finder.py

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import string
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# frequency taken from http://en.wikipedia.org/wiki/Letter_frequency
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# frequency taken from https://en.wikipedia.org/wiki/Letter_frequency
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english_letter_freq = {
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"E": 12.70,
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"T": 9.06,

web_programming/crawl_google_results.py

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if link.text == "Maps":
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webbrowser.open(link.get("href"))
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else:
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webbrowser.open(f"http://google.com{link.get('href')}")
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webbrowser.open(f"https://google.com{link.get('href')}")

web_programming/crawl_google_scholar_citation.py

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"year": 2018,
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"hl": "en",
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}
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print(get_citation("http://scholar.google.com/scholar_lookup", params=params))
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print(get_citation("https://scholar.google.com/scholar_lookup", params=params))

web_programming/current_weather.py

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import requests
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APPID = "" # <-- Put your OpenWeatherMap appid here!
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URL_BASE = "http://api.openweathermap.org/data/2.5/"
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URL_BASE = "https://api.openweathermap.org/data/2.5/"
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def current_weather(q: str = "Chicago", appid: str = APPID) -> dict:

web_programming/giphy.py

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Get a list of URLs of GIFs based on a given query..
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"""
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formatted_query = "+".join(query.split())
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url = f"http://api.giphy.com/v1/gifs/search?q={formatted_query}&api_key={api_key}"
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url = f"https://api.giphy.com/v1/gifs/search?q={formatted_query}&api_key={api_key}"
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gifs = requests.get(url).json()["data"]
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return [gif["url"] for gif in gifs]
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