pydecisions  A Python Library of management decision making techniques
Project description
Python Library: pydecisions
Copyright (c) 2018 Balamurali M Author: Balamurali M Gmail: balamurali9m@gmail.com License: MIT
This library includes of some of the techniques employed in making high level management decisions. The below information is also present (in pdf format) at: https://drive.google.com/open?id=1qLL2v7fFoImvxwiXPlJQn60jw_pE35
INSTALLING pip install pydecisions
IMPORT STATEMENT import pydecisions as pyd
A COMPLETE EXAMPLE: import pydecisions as pyd a = pyd.evm(100,0.5,0.4,45) print(a.results())
The following examples illustrate how to use this library. EXAMPLES

Earned Value Management Example: a = pyd.evm(100,0.5,0.4,45) print(a.results()) (where arg1  Budget at Completion, arg2  work planned to be completed at that point against the total work planned, arg3  actual work completed at that point against the total work planned, arg4  Actual Cost incurred till that point)

Financial functions (a) Net Present Value Example: a = pyd.fin() print(a.npv(.3,[100,50,30,20,10])) (where arg1  rate, arg2  yearly cash flows)
(b) Future Value Example: a = pyd.fin() print(a.fv(0.10, 9, 300, 400)) (where arg1  rate, arg2  nos of years, arg3  payment, arg4  present value)
(c) Present Value Example: a = pyd.fin() print(a.pv(0.05, 10, 100, 30000)) (where arg1  rate, arg2  no of years, arg3payment, arg4  future value)
(d) Internal Rate of Return Example: a = pyd.fin() print(a.irr([100,30,90,75,20])) (where arg1  cash flows yearly)

Simple Linear Regression Example: a = pyd.slr() print(a.results([1,2,3,4],[1.5,2.5,3.3,4.2],3)) (where arg1  training X, arg2  training Y and arg3  test X)

Statistical tests (a) Ttest (mean of one group of scores) Example: a = pyd.statstest() print(a.tt1([20,44,50,70,30],45)) (where arg1  sample observations, arg2  population mean)
(b) Ttest (means of two independent samples of scores) Example: a = pyd.statstest() print(a.ttind([50,40,90,30,40], [60,40,20,10,70])) (where arg1  sample 1 observations, arg2  sample 2 observations)
(c) Ttest (2 related samples of data). Example: a = pyd.statstest() print(a.ttrel([55,20,23,12,12], [22,48,11,17,12])) (where arg1  sample 1 observations, arg2  sample 2 observations)

Decision Analysis and Resolution Example: a = pyd.dar() print(a.results([8,9],[7,6]))
(where arg1  criteria scores for Alternative 1 and arg2  criteria scores for Alternative 2) 
Markov Chain You need to import numpy in this example i.e. import pydecisions as pyd import numpy as n Example: a = pyd.mc() matrx = np.matrix([[0.7, 0.3], [0.6, 0.4]]) I = np.matrix([[0.5, 0.5]])
print(a.results(matrx,I,3)) (where matrx  the transition matrix, I  the current state matrix, the third argument (3 in the above example) is for the number of iterations)) 
Bayes Rule Example: (For calculating P(AB)) a = pyd.bayes() print(a.results(0.6,0.4,0.2)) (where arg1  P(A), arg2  P(B), arg3  P(B/A))

Linear Programming Example: Minimize: cost = 2x[0] + 5x[1], Subject to: 2x[0] + 3x[1] <= 7, 2x[0] + 1x[1] <= 5 x[1] >= 4 (where: infinity <= x[0] <= infinity)
a = pyd.lp() c = [2, 5] A = [[2, 3], [2, 1]] b = [7, 5] lp_x0b = (None, None) lp_x1b = (4, None) print(a.results(c,A,b,lp_x0b,lp_x1b))

Decision Trees : Regression Example: a = pyd.DTr() x = [[1, 2], [2, 2], [3, 3], [4, 5], [7, 4]] y = [3,4,5,8,11] z = [[3,2]] a.results(x,y,z) (where arg1  training x, arg2  training y and arg3  test x) Tree Image will be generated in the folder.

Decision Trees : Classification Example: a = pyd.DTc() x = [ [20, 15, 2],
[60, 25, 4],
[70, 35, 6],
[80, 40, 8],
[90, 45, 10]]
y = ['c0', 'c1', 'c1', 'c0', 'c1'] z = [[60, 30, 5]] a.results(x,y,z) (where arg1  training x, arg2  training y and arg3  test x) Tree Image will be generated in the folder.
Some of the are completely written from scratch and some functions are built on the top of the existing standard library functions.
Dependencies  numpy, scipy, sklearn and graphviz libraries
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