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‎Overflow/Beginners-Python-Examples-master/algorithms/README.html

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<!DOCTYPE html>
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<html xmlns="http://www.w3.org/1999/xhtml" lang="" xml:lang="">
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<head>
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<meta charset="utf-8" />
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<meta name="generator" content="pandoc" />
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<meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=yes" />
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<title>README</title>
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<style type="text/css">
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code{white-space: pre-wrap;}
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span.smallcaps{font-variant: small-caps;}
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span.underline{text-decoration: underline;}
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div.column{display: inline-block; vertical-align: top; width: 50%;}
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</style>
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</head>
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<body>
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<pre>
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<i>'algorithms'</i> Sub-directory contains all <strong>algorithms</strong>
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further separated in different particular sub directories
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</pre>
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<p>The programs have been <strong>re-checked</strong> and <strong>re-mastered</strong> by me! <br/> Although i’ve tried to keep it as orignal as possible. <br/> Keep Patience It’ll take time to go through every program!! <br/> <strong>Thanks</strong></p>
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</body>
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</html>

‎Overflow/Beginners-Python-Examples-master/algorithms/README.md

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<pre>
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<i>'algorithms'</i> Sub-directory contains all <strong>algorithms</strong>
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further separated in different particular sub directories
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</pre>
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The programs have been <strong>re-checked</strong> and <strong>re-mastered</strong> by me! <br/>
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Although i've tried to keep it as orignal as possible. <br/>
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Keep Patience It'll take time to go through every program!! <br/>
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<strong>Thanks</strong>

‎Overflow/Beginners-Python-Examples-master/algorithms/analysis/README.html

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<!DOCTYPE html>
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<html xmlns="http://www.w3.org/1999/xhtml" lang="" xml:lang="">
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<head>
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<meta charset="utf-8" />
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<meta name="generator" content="pandoc" />
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<meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=yes" />
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<title>README</title>
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<style type="text/css">
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code{white-space: pre-wrap;}
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span.smallcaps{font-variant: small-caps;}
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span.underline{text-decoration: underline;}
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div.column{display: inline-block; vertical-align: top; width: 50%;}
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</style>
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</head>
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<body>
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<pre>
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<i>'analysis'</i> Sub-directory contains all
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<strong>analysis related algorithms</strong>.
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<strong>Thanks</strong>
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</pre>
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</body>
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</html>

‎Overflow/Beginners-Python-Examples-master/algorithms/analysis/README.md

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<pre>
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<i>'analysis'</i> Sub-directory contains all
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<strong>analysis related algorithms</strong>.
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<strong>Thanks</strong>
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</pre>

‎Overflow/Beginners-Python-Examples-master/algorithms/analysis/count.py

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#!/usr/bin/python
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# -*- coding: utf-8 -*-
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# Simple algorithm to count
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# number of occurrences of (n) in (ar)
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# Sudo: Algorithm
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# each time (n) is found in (ar)
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# (count) varible in incremented (by 1)
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# I've put spaces to separate different
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# stages of algorithms for easy understanding
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# however isn't a good practise
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def count(ar, n):
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count = 0
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for element in ar:
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# More complex condition could be
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# => (not element != n)
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if element == n:
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count += 1
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return count
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# Testing
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# add your test cases in list below
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test_cases = [([1, 1, 2, 3, 5, 8, 13, 21, 1], 1), ("Captain America", "a")]
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for test_case in test_cases:
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print("TestCase: {}, {}".format(test_case[0], test_case[1]))
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print("Results: {}\n".format(count(test_case[0], test_case[1])))
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# You can add condition to check weather output is correct
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# or not

‎Overflow/Beginners-Python-Examples-master/algorithms/analysis/enum.py

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#!/usr/bin/python
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# -*- coding: utf-8 -*-
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# Enum function
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# yields a tuple of element and it's index
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def enum(ar):
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for index in range(len(ar)):
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yield((index, ar[index]))
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# Test
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case_1 = [19, 17, 20, 23, 27, 15]
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for tup in list(enum(case_1)):
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print(tup)
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# Enum function is a generator does not
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# return any value, instead generates
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# tuple as it encounters element of array
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# Tuples can be appended to list
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# and can be returned after iteration
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# However,
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# Generator is a good option

‎Overflow/Beginners-Python-Examples-master/algorithms/analysis/length.py

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#!/usr/bin/python
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# -*- coding: utf-8 -*-
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import math
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# Length of array(number of elements in array)
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# is simple algorithm
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# Iterates through array
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# at each iteration (count) variable
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# is incremented
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#
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# This solution is ideal for small array
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# But not that good for bigger array's
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# There are many ways to do same thing
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# but always go with solution that is
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# simple, and has least instructions
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# for computer to execute, making
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# process faster
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# is_ap parameter indicates weather array
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# is an arithmetic progression or not
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#
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# Similarly,
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# is_gp indicates weather array is an geometric progression or not
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# If array is either ap or gp then it's length can be
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# found by derivation of it's general term formula
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# e.g. ap's general term,
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# tn = a + (n - 1)d
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# thus, n = (tn - a) / d + 1
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def length(ar, is_ap = False, is_gp = False, big_data = False, data_outline = []):
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# Length of data if it is an arithmetic progression
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# using derived formula, n = (tn - a) / d + 1
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if is_ap:
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return ((ar[-1] - ar[0]) / (ar[1] - ar[0])) + 1
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# Length of data if it is an geometric progression
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# using derived formula, n = ((log base 10 an / a1) / log 10 r) + 1
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elif is_gp:
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# length is never a float
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return int(math.log10((ar[-1] / ar[0])) / math.log10((ar[1] / ar[0])) + 1)
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# Length of big data using data outline
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# data outline is selective elements to be counted from entire
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# data
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elif big_data:
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count = 0
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for element in data_outline:
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count += ar.count(element)
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return count
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# Sequential counting
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# of elements in array
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else:
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res = 0
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for item in ar:
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res += 1
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return res
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# Test
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# Geometric progression Test
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if length([1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024, 2048, 4096], None, is_gp = True) == 13:
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print("\nLength of geometric progression: " + str(13))
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print("--> geometric progression counting works!\n")
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else:
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print("Something's wrong with gp feature")
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# Arithmetic progression test
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if length([1, 4, 7, 10, 13, 16, 19, 22, 25, 28, 31, 34, 37, 40, 43], is_ap = True) == 15:
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print("Length of arithmetic progression: " + str(15))
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print("--> arithmetic progression counting works!\n")
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else:
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print("Something's wrong with ap feature")
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# Big data test
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# Passing only useful numbers in outline array
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if length([1, 1, 4, 5, 7, 1, 9, 5, 2, 4, 3, 5, 9], None, None, True, [1, 4, 5, 7, 9]) == 11:
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print("Length of arithmetic progression: " + str(11))
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print("--> big data counting works!\n")
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else:
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print("Something's wrong with ap feature")
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# Small data
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print("Length: " + str(length([1, 1, 2, 3, 5, 8, 13, 21, 34, 55])))
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print("Everything Works!\n")

‎Overflow/Beginners-Python-Examples-master/algorithms/analysis/max.py

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#!/usr/bin/python
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# -*- coding: utf-8 -*-
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# Finds the maximum number
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# in un-sorted data
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# Sudo Algo:
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# Iterate through data
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# each time a number(say k) greater than, previous
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# consideration(say p) is found, replace previous
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# consideration(p) with that greater number(k)
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# By this way,
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# At last we get the maximum number from data
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def max_(seq):
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max_n = seq[0]
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for item in seq[1:]:
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if item > max_n:
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max_n = item
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return max_n
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# Test
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# Add your tests too!
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tests = [[9017289, 782367, 736812903, 9367821, 71256716278, 676215, 2398, 0, 1],
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[19208, 9239, 4376, 738, 78, 51, 5, 6, 12, 78, 123, 65765, 1999999999],
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[1, 2, 4, 7, 9]]
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# checking our functions results
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# with python's built-in max() function
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for test_i in range(len(tests)):
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m = max_(tests[test_i])
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if m == max(tests[test_i]):
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print("Max number in array({}) -> ".format(test_i + 1) + str(m))
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else:
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print("Oops! Someting went wrong!")

‎Overflow/Beginners-Python-Examples-master/algorithms/analysis/mean.py

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#!/usr/bin/python
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# -*- coding: utf-8 -*-
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# f(x) = s(x) / l(x) ______(e1)
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# Functions below same principle as (e1)
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#
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# Here,
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# Mean value can be float
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# 'er is need to declare float values
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# Function to get mean of given args
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def mean_(*args):
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sum_ = 0.0
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length_ = 0.0
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for arg in args:
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sum_ += arg
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length_ += 1.0
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return sum_ / length_
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# Function to get mean of array
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def mean_ar(ar):
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return float(sum(ar))/float(len(ar))
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# Another feature can be start index
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# and end index of array
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# Test
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# First function
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if mean_(12, 445, 76, 23, 7, 9, 17, 19, 100) == 78.66666666666667:
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print("First Function Works!")
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# Second function
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if mean_ar([12, 445, 76, 23, 7, 9, 17, 19, 10]) == 68.66666666666667:
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print("Second Function Works!")
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‎Overflow/Beginners-Python-Examples-master/algorithms/analysis/median.py

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#!/usr/bin/python
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# -*- coding: utf-8 -*-
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# Median
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# Median is the middle value of data
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# Data of odd length has a mid value
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# but, data of even length has 2 mid values
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# so their median is mean of these 2 values
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# ranked parameter is to tell function
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# weather data is ranked(sorted) or not
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#
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def median(ar, ranked = False):
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if not ranked:
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data = sorted(ar[:])
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else:
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# Don't need an else block still
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# but to map program properly
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# i've added it
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data = ar[:]
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# Data with odd length
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if len(data) % 2 != 0:
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# f(x) = (l(x) + 1) / 2 th term is the median of data
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# but since computer starts counting from 0
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# and not from 1, there is no need to add 1
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# to length of data, otherwise results are
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# not accurate
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return data[len(data) / 2]
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# Data with even length
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# f(x) = [l(x) / 2 th term + (l(x) + 2) / 2th term] / 2
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# 2.0 is to declare that median can be a float
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# in case of even length data
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return (data[len(data) / 2 - 1] + data[(len(data) + 1) / 2]) / 2.0
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# Test
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odd = [123, 456, 789, 101112, 131415, 161718, 192021, 222324, 252627]
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even = [8, 7, 5, 2, 1, 3, 4, 6]
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if median(odd, ranked = True) == 131415 and median(even) == 4.5:
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# Print statements on separate lines look better
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print("Median of odd data: " + str(131415))
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print("Median of even data: " + str(4.5))
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print("Yeah, it works!")
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else:
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# If algo didn't work
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print("There's something wrong!")
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# This median is for un-distributed/un-grouped data
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# i.e. no frequencies
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# plain numbers in an array

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