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%matplotlib inline
import matplotlib.pyplot as plt
import time
import math
def random(n):
    
    lastXn = int(time.clock())+7897857
    a = lastXn*8
    b = a + 98756465
    m = 900217+int(time.clock())
    rList = []
    for i in range(n):
        
        Xn = float((a*lastXn + b)% m)
        lastXn = Xn
        random = float(Xn/m)
        rList.append(random)
    return rList

def genVAD(n, x, px):
    
    dAcum = distAcum (x,px)
    rList = random(n)
    xList = []
    for i in rList:
        for j in range(len(dAcum)):
            if(i <= dAcum[j]):
                xList.append(x[j])
                break
    print "Generated numbers: "
    print xList
    plt.hist(xList)
      
def distAcum(x, px):
    
    Px = []
    suma = 0
    for i in range(len(x)):
        Px.append(suma + px[i])
        suma = suma + px[i]
    return Px

def genBernoulli(p):
    # p = success probability
    x = [0,1]
    px = [(1-p),p]
    return x,px

def genBinomial(ni,p):
    # ni = number of attempts
    # p = success probability for each attempt
    x = []
    px = []
    for i in range(ni):
        x.append(i)
    for i in range(ni):
        pi = (math.factorial(ni)/(math.factorial(x[i])*math.factorial(ni-x[i])))*(p**x[i])*((1-p)**(ni-x[i]))
        px.append(pi)
        print px
    
    return x,px

def genHiperGeometrica(N, nm, d):
    # N = Total number of the population
    # n = Size of the extracted sample
    # d = Number of elements in N that belong to the class we are looking for.
    # x = Number of elements in n that belong to d
    x = []
    px = []
    c = 0
    while(c<=d):
        x.append(c)
        c = c+1
    for i in x:
        if(i>nm):
            pi = 0
        else:
            pi = (float(math.factorial(d)/(math.factorial(i)*math.factorial(d-i)))*float(math.factorial(N-d)
                /(math.factorial(nm-i)*math.factorial(N-d-nm+i))))/float(math.factorial(N)
                                                                         /(math.factorial(nm)*math.factorial(N-nm)))

        px.append(pi)
    return x,px
    
def main():
    #Distribucion de Bernoulli
    '''
    p = float(raw_input('Please input the p value: ')) #p value should be between 0 and 1
    x, px = genBernoulli(p)
    n = int(raw_input('Please input the amount of random numbers to generate: '))
    genVAD(n,x,px)
    '''
    #Distribucion Binomial
    '''
    p = float(raw_input('Please input the p value: ')) #p value should be between 0 and 1
    ni = int(raw_input('Please input the n value: '))
    x, px = genBinomial(ni,p)
    n = int(raw_input('Please input the amount of random numbers to generate: '))
    genVAD(n,x,px)
    '''
    #Distrubcion Hipergeometrica
    N = int(raw_input('Input the total number of population elements N:'))
    nm = int(raw_input('Input the number of sample elements: '))
    d = int(raw_input('Input the number of elements that belong to the wanted class: '))
    x, px = genHiperGeometrica(N, nm, d)
    n = int(raw_input('Please input the amount of random numbers to generate: '))
    genVAD(n,x,px) 
    
       
main()


    

    




Input the total number of population elements N:100
Input the number of sample elements: 10
Input the number of elements that belong to the wanted class: 65
Please input the amount of random numbers to generate: 100
Generated numbers: 
[5, 8, 7, 7, 7, 7, 4, 6, 8, 8, 7, 7, 7, 7, 7, 8, 7, 8, 7, 5, 10, 4, 8, 7, 8, 5, 8, 6, 7, 6, 7, 5, 7, 3, 8, 8, 5, 8, 8, 7, 7, 6, 4, 8, 7, 4, 6, 7, 5, 6, 4, 5, 7, 4, 8, 6, 5, 7, 10, 4, 6, 9, 6, 4, 5, 7, 7, 6, 6, 5, 9, 6, 6, 4, 6, 7, 9, 5, 6, 5, 5, 9, 8, 9, 9, 5, 7, 4, 7, 9, 6, 9, 8, 5, 5, 6, 6, 5, 8, 6]






    

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