Metadata-Version: 2.1
Name: pydecisions
Version: 0.2.4
Summary: pydecisions - A Python Library of management decision making techniques
Home-page: https://drive.google.com/open?id=1qLL2-v7fFoImvxwiXPlJQn60jw_p-E35
Author: Balamurali M
Author-email: balamurali9m@gmail.com
License: UNKNOWN
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=1qLL2-v7fFoImvxwiXPlJQn60jw_p-E35
        
        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
        
        1. 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)
        
        2. 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, arg3-payment, 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)
        
        3. 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)
        
        4. Statistical tests
        (a)  T-test (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) T-test (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) T-test (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)
        
        5. 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) 
        
        6. 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))
        
        7. Bayes Rule
        Example:
        (For calculating P(A|B))
        a = pyd.bayes()
        print(a.results(0.6,0.4,0.2))
        (where arg1 - P(A), arg2 - P(B), arg3 - P(B/A))
        
        8. Linear Programming
        Example:
        Minimize: cost = -2*x[0] + 5*x[1], Subject to: -2*x[0] + 3*x[1] <= 7, 2*x[0] + 1*x[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))
        
        9.  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.
        
        10. 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
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Information Technology
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: Topic :: Education
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Office/Business :: Financial
Classifier: Topic :: Software Development :: Libraries
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
