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    "info": {
        "author": "Balamurali M",
        "author_email": "balamurali9m@gmail.com",
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        "classifiers": [
            "Intended Audience :: Developers",
            "Intended Audience :: Education",
            "Intended Audience :: Information Technology",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: MIT License",
            "Operating System :: OS Independent",
            "Programming Language :: Python :: 3",
            "Topic :: Education",
            "Topic :: Office/Business :: Financial",
            "Topic :: Scientific/Engineering :: Artificial Intelligence",
            "Topic :: Scientific/Engineering :: Mathematics",
            "Topic :: Software Development :: Libraries",
            "Topic :: Software Development :: Libraries :: Python Modules"
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        "description": "Python Library: pydecisions\n\nCopyright (c) 2018 Balamurali M\nAuthor: Balamurali M\nGmail: balamurali9m@gmail.com\nLicense: MIT\n\nThis library includes of some of the techniques employed in making high level management decisions.\nThe below information is also present (in pdf format) at: https://drive.google.com/open?id=1qLL2-v7fFoImvxwiXPlJQn60jw_p-E35\n\nINSTALLING\npip install pydecisions\n\nIMPORT STATEMENT\nimport pydecisions as pyd\n\nA COMPLETE EXAMPLE:\nimport pydecisions as pyd\na = pyd.evm(100,0.5,0.4,45)\nprint(a.results())\n\nThe following examples illustrate how to use this library.\nEXAMPLES\n\n1. Earned Value Management\nExample:\na = pyd.evm(100,0.5,0.4,45)\nprint(a.results())\n(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)\n\n2. Financial functions\n(a) Net Present Value\nExample:\na = pyd.fin()\nprint(a.npv(.3,[-100,50,30,20,10]))\n(where arg1 - rate, arg2 - yearly cash flows)\n\n(b) Future Value\nExample:\na = pyd.fin()\nprint(a.fv(0.10, 9, 300, 400))\n(where arg1 - rate, arg2 - nos of years, arg3 - payment, arg4 - present value)\n\n(c) Present Value\nExample:\na = pyd.fin()\nprint(a.pv(0.05, 10, 100, 30000)) \n(where arg1 - rate, arg2 - no of years, arg3-payment, arg4 - future value)\n\n(d) Internal Rate of Return\nExample:\na = pyd.fin()\nprint(a.irr([-100,30,90,75,20]))\n(where arg1 - cash flows yearly)\n\n3. Simple Linear Regression\nExample:\na = pyd.slr()\nprint(a.results([1,2,3,4],[1.5,2.5,3.3,4.2],3))\n(where arg1 - training X, arg2 - training Y and arg3 - test X)\n\n4. Statistical tests\n(a)  T-test (mean of one group of scores)\nExample:\na = pyd.statstest()\nprint(a.tt1([20,44,50,70,30],45)) \n(where arg1 - sample observations, arg2 - population mean)\n\n(b) T-test (means of two independent samples of scores)\nExample:\na = pyd.statstest()\nprint(a.ttind([50,40,90,30,40], [60,40,20,10,70]))\n(where arg1 -  sample 1 observations, arg2 - sample 2 observations)\n\n(c) T-test (2 related samples of data).\nExample:\na = pyd.statstest()\nprint(a.ttrel([55,20,23,12,12], [22,48,11,17,12])) \n(where arg1 - sample 1 observations, arg2 - sample 2 observations)\n\n5. Decision Analysis and Resolution\nExample:\na = pyd.dar()\nprint(a.results([8,9],[7,6]))  \n(where arg1 - criteria scores for Alternative 1 and arg2 - criteria scores for Alternative 2) \n\n6. Markov Chain\nYou need to import numpy in this example\ni.e.\nimport pydecisions as pyd \nimport numpy as n\nExample:\na = pyd.mc()\nmatrx = np.matrix([[0.7, 0.3],\n                 [0.6, 0.4]]) \nI = np.matrix([[0.5, 0.5]])    \nprint(a.results(matrx,I,3))\n(where matrx - the transition matrix, I - the current state matrix, the third argument (3 in the above\nexample) is for the number of iterations))\n\n7. Bayes Rule\nExample:\n(For calculating P(A|B))\na = pyd.bayes()\nprint(a.results(0.6,0.4,0.2))\n(where arg1 - P(A), arg2 - P(B), arg3 - P(B/A))\n\n8. Linear Programming\nExample:\nMinimize: cost = -2*x[0] + 5*x[1], Subject to: -2*x[0] + 3*x[1] <= 7, 2*x[0] + 1*x[1] <= 5\nx[1] >= -4 (where: -infinity <= x[0] <= infinity)\n\na = pyd.lp()\nc = [-2, 5]\nA = [[-2, 3], [2, 1]]\nb = [7, 5]\nlp_x0b = (None, None)\nlp_x1b = (-4, None)\nprint(a.results(c,A,b,lp_x0b,lp_x1b))\n\n9.  Decision Trees : Regression\nExample:\na = pyd.DTr()\nx = [[1, 2], \n     [2, 2],\n     [3, 3],\n     [4, 5],\n     [7, 4]]\ny = [3,4,5,8,11]\nz = [[3,2]]\na.results(x,y,z)\n(where arg1 - training x, arg2 - training y and arg3 - test x)\nTree Image will be generated in the folder.\n\n10. Decision Trees : Classification\nExample:\na = pyd.DTc()\nx = [ [20, 15, 2],                                                              \n      [60, 25, 4],                                                              \n      [70, 35, 6],                                                              \n      [80, 40, 8],                                                              \n      [90, 45, 10]]                                                              \ny = ['c0', 'c1', 'c1', 'c0', 'c1'] \nz = [[60, 30, 5]]\na.results(x,y,z)\n(where arg1 - training x, arg2 - training y and arg3 - test x)\nTree Image will be generated in the folder.\n\nSome of the are completely written from scratch and some functions are built on the top of the existing standard library functions. \n\nDependencies - numpy, scipy, sklearn and graphviz libraries\n\n",
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