# TimeComplex
This package was created to evaluate functions for time complexity. The function runs an algorithm over and over with randomized input sizes to judge the time complexity.
PYPI:
<https://pypi.org/project/SpaceTimeComplex/>
## Example
```python
def looper2(n):
for x in range(n):
print(x)
def testone(n):
for x in range(len(n)):
for y in range(len(n)):
for z in range(len(n)):
two = y
one = x
three = z
def looper(today,stringer):
for x in range(today):
print(x)
for y in stringer:
print(y)
def logfunc(n):
for x in range(0,len(n[0]),20):
print(x)
def binary_search(array,target):
low = 0
high = len(array) - 1
while low <= high:
mid = (low + high) // 2 # integer division
element = array[mid]
if element == target:
return mid
elif element < target:
low = mid + 1
else:
high = mid - 1
return -1
real = SpaceTimeComplex.RealTime() # Create the class
#x.realTimeComplex(stmt="looper(10)",value=10)
testSet = real.generateTestSet(size2=100) #generate a test set
testSet1 = [[4,"stnr=gwege"], [12,"sagsdgg"], [3,"esfsfsseafesfsefsef"], [45,"stnrefgseege"], [17,"sagwetjtwfwe"], [34,"esfsfssem"],[41,"stn"], [53,"sakhhksdgg"], [24,"esjfjkkfsefsef"], [70,"stnwete"], [7,"sagwefwewsdfsdffwe"] ]
# format of array. 2d array with each test set inside. You can make your own or just generate one with generateTestSet()
logFunction, SlopeConst = real.complexGuess(testone,testSet) #guess the complexity of a function. Returns the guess and a plot
ratio = real.bestWorst(testone,testSet)
```
## Example of the non-log function graphed using the derived power. $O(n^{2.8})$
This is in respects to time (ms). When accounting for just the growth of input, the input will grow in a polynomial fashion.
## Math behind the calculations
A simplistic understanding how the time complexity is extracted stems from the log power rule.
Power Rule:
$log(n^k) = k \cdot log(n)$
After apply a log to both n and y variables, a linear regression is applied.
$y = time, n = input size, b = constant$
$log(y) = log(n^k) + b$
apply the power rule
$log(y) = k \cdot log(n) + b$
Everything can be ignored except for k. k determines the slope of line and tells us the time complexity.
$time complexity = N^k$
## Jupyter Lab
The package can also be used in jupyter notebooks.
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"description": "# TimeComplex\r\n\r\nThis package was created to evaluate functions for time complexity. The function runs an algorithm over and over with randomized input sizes to judge the time complexity.\r\nPYPI:\r\n<https://pypi.org/project/SpaceTimeComplex/>\r\n\r\n## Example\r\n\r\n```python\r\n\r\ndef looper2(n):\r\n for x in range(n):\r\n print(x)\r\n\r\ndef testone(n):\r\n for x in range(len(n)):\r\n for y in range(len(n)):\r\n for z in range(len(n)):\r\n two = y\r\n one = x\r\n three = z\r\n\r\ndef looper(today,stringer):\r\n for x in range(today):\r\n print(x)\r\n \r\n for y in stringer:\r\n print(y)\r\n\r\n\r\n\r\ndef logfunc(n):\r\n for x in range(0,len(n[0]),20):\r\n print(x) \r\n\r\n\r\ndef binary_search(array,target):\r\n low = 0\r\n high = len(array) - 1\r\n while low <= high:\r\n mid = (low + high) // 2 # integer division\r\n element = array[mid]\r\n if element == target:\r\n return mid\r\n elif element < target:\r\n low = mid + 1\r\n else:\r\n high = mid - 1\r\n return -1\r\n\r\n\r\nreal = SpaceTimeComplex.RealTime() # Create the class\r\n#x.realTimeComplex(stmt=\"looper(10)\",value=10)\r\n\r\ntestSet = real.generateTestSet(size2=100) #generate a test set\r\n\r\ntestSet1 = [[4,\"stnr=gwege\"], [12,\"sagsdgg\"], [3,\"esfsfsseafesfsefsef\"], [45,\"stnrefgseege\"], [17,\"sagwetjtwfwe\"], [34,\"esfsfssem\"],[41,\"stn\"], [53,\"sakhhksdgg\"], [24,\"esjfjkkfsefsef\"], [70,\"stnwete\"], [7,\"sagwefwewsdfsdffwe\"] ] \r\n# format of array. 2d array with each test set inside. You can make your own or just generate one with generateTestSet()\r\n\r\nlogFunction, SlopeConst = real.complexGuess(testone,testSet) #guess the complexity of a function. Returns the guess and a plot\r\n\r\nratio = real.bestWorst(testone,testSet)\r\n\r\n```\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n## Example of the non-log function graphed using the derived power. $O(n^{2.8})$\r\n\r\n\r\n\r\nThis is in respects to time (ms). When accounting for just the growth of input, the input will grow in a polynomial fashion.\r\n\r\n## Math behind the calculations\r\n\r\nA simplistic understanding how the time complexity is extracted stems from the log power rule.\r\n\r\nPower Rule:\r\n\r\n$log(n^k) = k \\cdot log(n)$\r\n\r\nAfter apply a log to both n and y variables, a linear regression is applied.\r\n\r\n$y = time, n = input size, b = constant$\r\n\r\n$log(y) = log(n^k) + b$\r\n\r\napply the power rule\r\n\r\n$log(y) = k \\cdot log(n) + b$\r\n\r\nEverything can be ignored except for k. k determines the slope of line and tells us the time complexity.\r\n\r\n$time complexity = N^k$\r\n\r\n## Jupyter Lab\r\n\r\nThe package can also be used in jupyter notebooks.\r\n",
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