Ch12 App4:SVM Classifier Programming Collective Intelligence

""" Chapter 12 Programming Collective Intelligence: Support Vector Machines.

   As the chapter has no exercises in it I have decided to create a practical
   application for each of the Algorithms and Methods described.
   Next up the Support Vector Machine Classifier.
   For this I have created a simple application that reads a dataset,containing
   details for passengers on the Titanic who either died or survived.
   The dataset is represented here by two lists:
      answers - a list of whether a passenger survived or died
      inputs  - a list of lists of passenger attributes *see end for attributes
   The list 'inputs' is scaled to values between 0.0 and 1.0 and an SVM is created
   using this scaled list and answers. The user then inputs data and is classified
   as having possibly died or possibly survived.
   The SVM classifies with an accuracy of 81.1448 %.
   This uses libsvm-2.91 so for later versions rewrite the code yourself :p
   I scaled the data as floats so there is always hope... :)
         --lolamontes69

"""

from svm import *
import os as os
from time import sleep

def am_I_survived():
    global inputs,answers
    scaledset=scale_data(inputs)
    param = svm_parameter(kernel_type = RBF, C = 0.25, gamma = 0.5) # optimized using grid.py
    prob = svm_problem(answers, scaledset)
    m = svm_model(prob,param)
    newrow=inputDialogue()
    scaleduser=scale_data([newrow])
    a=scaleduser[0]
    print "a =",a
    res=m.predict(a)
    os.system('clear')
    print "-"*44
    print "I can say with an accuracy of 81.1448 % that...."
    sleep(1)
    if res>0.5: print "\nHURRAH. You would have survived the sinking of the Titanic.\n"
    else: print "\nAhem.... if the sharks didn't get you first,\nand you weren't crushed to death in the mad scramble\nto reach the lifeboats, or\nopportunistically murdered by a jealous partner..."
    sleep(8)
    print "...you would have drowned."
    print "-"*44

def inputDialogue():
    while True:
        list1=[]
        os.system('clear')
        print "-"*44
        try:
            print "    WOULD I SURVIVE THE TITANIC SINKING?"
            print "        (please enter numbers only)"
            print "-"*44
            print "What class are you?"
            list1.append(int(raw_input('   1:Upper,2:Middle or 3:Lower? > ')))
            print "\nWhat gender are you?"
            list1.append(int(raw_input('   0:Male or 1:Female? > ')))
            print "\nWhat is your age?"
            list1.append(float(raw_input('   > ')))
            print "\nHow many spouses,brother or sisters are travelling with you?"
            list1.append(int(raw_input('   > ')))
            print "\nHow many parents or children are travelling with you?"
            list1.append(int(raw_input('   > ')))
            print "\nWhich port will you be embarking from?"
            list1.append(int(raw_input('   0:Unknown, 1:Cherbourg, 2:Queenstown or 3:Southampton? > ')))
            print "-"*44,"\n"
            return list1
        except:
            print "-"*44,"\n"
            print "Try inputting the data properly"
        print "\n","-"*44
        moar=str(raw_input('Enter q to QUIT or anything else to try to do it properly>'))
        if moar=='q': break

def scale_data(inputs):
    lohi=[(1,3),(0,1),(0,80),(0,8),(0,6),(0,3)] # lo/hi range of passenger values
    scaledrows=[]
    for a in range(len(inputs)):
        scaledrow=[]
        for b in range(len(inputs[a])):
            lo,hi=lohi[b]
            s=(inputs[a][b]-lo)/float(hi-lo)
            if s<0: s=0.0                       # if below passenger range set to 0
            elif s>1: s=1.0                     # if above passenger range set to 1
            scaledrow.append(s)
        scaledrows.append(scaledrow)
    return scaledrows

answers=[0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0]


inputs=[[3, 0, 22.0, 1, 0, 2], [1, 1, 38.0, 1, 0, 0], [3, 1, 26.0, 0, 0, 2], [1, 1, 35.0, 1, 0, 2], [3, 0, 35.0, 0, 0, 2], [3, 0, 0.0, 0, 0, 1], [1, 0, 54.0, 0, 0, 2], [3, 0, 2.0, 3, 1, 2], [3, 1, 27.0, 0, 2, 2], [2, 1, 14.0, 1, 0, 0], [3, 1, 4.0, 1, 1, 2], [1, 1, 58.0, 0, 0, 2], [3, 0, 20.0, 0, 0, 2], [3, 0, 39.0, 1, 5, 2], [3, 1, 14.0, 0, 0, 2], [2, 1, 55.0, 0, 0, 2], [3, 0, 2.0, 4, 1, 1], [2, 0, 0.0, 0, 0, 2], [3, 1, 31.0, 1, 0, 2], [3, 1, 0.0, 0, 0, 0], [2, 0, 35.0, 0, 0, 2], [2, 0, 34.0, 0, 0, 2], [3, 1, 15.0, 0, 0, 1], [1, 0, 28.0, 0, 0, 2], [3, 1, 8.0, 3, 1, 2], [3, 1, 38.0, 1, 5, 2], [3, 0, 0.0, 0, 0, 0], [1, 0, 19.0, 3, 2, 2], [3, 1, 0.0, 0, 0, 1], [3, 0, 0.0, 0, 0, 2], [1, 0, 40.0, 0, 0, 0], [1, 1, 0.0, 1, 0, 0], [3, 1, 0.0, 0, 0, 1], [2, 0, 66.0, 0, 0, 2], [1, 0, 28.0, 1, 0, 0], [1, 0, 42.0, 1, 0, 2], [3, 0, 0.0, 0, 0, 0], [3, 0, 21.0, 0, 0, 2], [3, 1, 18.0, 2, 0, 2], [3, 1, 14.0, 1, 0, 0], [3, 1, 40.0, 1, 0, 2], [2, 1, 27.0, 1, 0, 2], [3, 0, 0.0, 0, 0, 0], [2, 1, 3.0, 1, 2, 0], [3, 1, 19.0, 0, 0, 1], [3, 0, 0.0, 0, 0, 2], [3, 0, 0.0, 1, 0, 1], [3, 1, 0.0, 0, 0, 1], [3, 0, 0.0, 2, 0, 0], [3, 1, 18.0, 1, 0, 2], [3, 0, 7.0, 4, 1, 2], [3, 0, 21.0, 0, 0, 2], [1, 1, 49.0, 1, 0, 0], [2, 1, 29.0, 1, 0, 2], [1, 0, 65.0, 0, 1, 0], [1, 0, 0.0, 0, 0, 2], [2, 1, 21.0, 0, 0, 2], [3, 0, 28.5, 0, 0, 0], [2, 1, 5.0, 1, 2, 2], [3, 0, 11.0, 5, 2, 2], [3, 0, 22.0, 0, 0, 0], [1, 1, 38.0, 0, 0, 0], [1, 0, 45.0, 1, 0, 2], [3, 0, 4.0, 3, 2, 2], [1, 0, 0.0, 0, 0, 0], [3, 0, 0.0, 1, 1, 0], [2, 1, 29.0, 0, 0, 2], [3, 0, 19.0, 0, 0, 2], [3, 1, 17.0, 4, 2, 2], [3, 0, 26.0, 2, 0, 2], [2, 0, 32.0, 0, 0, 2], [3, 1, 16.0, 5, 2, 2], [2, 0, 21.0, 0, 0, 2], [3, 0, 26.0, 1, 0, 0], [3, 0, 32.0, 0, 0, 2], [3, 0, 25.0, 0, 0, 2], [3, 0, 0.0, 0, 0, 2], [3, 0, 0.0, 0, 0, 2], [2, 0, 0.82999999999999996, 0, 2, 2], [3, 1, 30.0, 0, 0, 2], [3, 0, 22.0, 0, 0, 2], [3, 0, 29.0, 0, 0, 2], [3, 1, 0.0, 0, 0, 1], [1, 0, 28.0, 0, 0, 2], [2, 1, 17.0, 0, 0, 2], [3, 1, 33.0, 3, 0, 2], [3, 0, 16.0, 1, 3, 2], [3, 0, 0.0, 0, 0, 2], [1, 1, 23.0, 3, 2, 2], [3, 0, 24.0, 0, 0, 2], [3, 0, 29.0, 0, 0, 2], [3, 0, 20.0, 0, 0, 2], [1, 0, 46.0, 1, 0, 2], [3, 0, 26.0, 1, 2, 2], [3, 0, 59.0, 0, 0, 2], [3, 0, 0.0, 0, 0, 2], [1, 0, 71.0, 0, 0, 0], [1, 0, 23.0, 0, 1, 0], [2, 1, 34.0, 0, 1, 2], [2, 0, 34.0, 1, 0, 2], [3, 1, 28.0, 0, 0, 2], [3, 0, 0.0, 0, 0, 2], [1, 0, 21.0, 0, 1, 2], [3, 0, 33.0, 0, 0, 2], [3, 0, 37.0, 2, 0, 2], [3, 0, 28.0, 0, 0, 2], [3, 1, 21.0, 0, 0, 2], [3, 0, 0.0, 0, 0, 2], [3, 0, 38.0, 0, 0, 2], [3, 1, 0.0, 1, 0, 1], [1, 0, 47.0, 0, 0, 2], [3, 1, 14.5, 1, 0, 0], [3, 0, 22.0, 0, 0, 2], [3, 1, 20.0, 1, 0, 2], [3, 1, 17.0, 0, 0, 0], [3, 0, 21.0, 0, 0, 2], [3, 0, 70.5, 0, 0, 1], [2, 0, 29.0, 1, 0, 2], [1, 0, 24.0, 0, 1, 0], [3, 1, 2.0, 4, 2, 2], [2, 0, 21.0, 2, 0, 2], [3, 0, 0.0, 0, 0, 2], [2, 0, 32.5, 1, 0, 0], [2, 1, 32.5, 0, 0, 2], [1, 0, 54.0, 0, 1, 2], [3, 0, 12.0, 1, 0, 0], [3, 0, 0.0, 0, 0, 1], [3, 0, 24.0, 0, 0, 2], [3, 1, 0.0, 1, 1, 0], [3, 0, 45.0, 0, 0, 2], [3, 0, 33.0, 0, 0, 0], [3, 0, 20.0, 0, 0, 2], [3, 1, 47.0, 1, 0, 2], [2, 1, 29.0, 1, 0, 2], [2, 0, 25.0, 0, 0, 2], [2, 0, 23.0, 0, 0, 0], [1, 1, 19.0, 0, 2, 2], [1, 0, 37.0, 1, 0, 2], [3, 0, 16.0, 0, 0, 2], [1, 0, 24.0, 0, 0, 0], [3, 1, 0.0, 0, 2, 0], [3, 1, 22.0, 0, 0, 2], [3, 1, 24.0, 1, 0, 2], [3, 0, 19.0, 0, 0, 1], [2, 0, 18.0, 0, 0, 2], [2, 0, 19.0, 1, 1, 2], [3, 0, 27.0, 0, 0, 2], [3, 1, 9.0, 2, 2, 2], [2, 0, 36.5, 0, 2, 2], [2, 0, 42.0, 0, 0, 2], [2, 0, 51.0, 0, 0, 2], [1, 1, 22.0, 1, 0, 2], [3, 0, 55.5, 0, 0, 2], [3, 0, 40.5, 0, 2, 2], [3, 0, 0.0, 0, 0, 2], [1, 0, 51.0, 0, 1, 0], [3, 1, 16.0, 0, 0, 1], [3, 0, 30.0, 0, 0, 2], [3, 0, 0.0, 0, 0, 2], [3, 0, 0.0, 8, 2, 2], [3, 0, 44.0, 0, 1, 2], [2, 1, 40.0, 0, 0, 2], [3, 0, 26.0, 0, 0, 2], [3, 0, 17.0, 0, 0, 2], [3, 0, 1.0, 4, 1, 2], [3, 0, 9.0, 0, 2, 2], [1, 1, 0.0, 0, 1, 2], [3, 1, 45.0, 1, 4, 2], [1, 0, 0.0, 0, 0, 2], [3, 0, 28.0, 0, 0, 2], [1, 0, 61.0, 0, 0, 2], [3, 0, 4.0, 4, 1, 1], [3, 1, 1.0, 1, 1, 2], [3, 0, 21.0, 0, 0, 2], [1, 0, 56.0, 0, 0, 0], [3, 0, 18.0, 1, 1, 2], [3, 0, 0.0, 3, 1, 2], [1, 1, 50.0, 0, 0, 0], [2, 0, 30.0, 0, 0, 2], [3, 0, 36.0, 0, 0, 2], [3, 1, 0.0, 8, 2, 2], [2, 0, 0.0, 0, 0, 0], [3, 0, 9.0, 4, 2, 2], [2, 0, 1.0, 2, 1, 2], [3, 1, 4.0, 0, 2, 2], [1, 0, 0.0, 0, 0, 2], [3, 1, 0.0, 1, 0, 1], [1, 0, 45.0, 0, 0, 2], [3, 0, 40.0, 1, 1, 1], [3, 0, 36.0, 0, 0, 2], [2, 1, 32.0, 0, 0, 2], [2, 0, 19.0, 0, 0, 2], [3, 1, 19.0, 1, 0, 2], [2, 0, 3.0, 1, 1, 2], [1, 1, 44.0, 0, 0, 0], [1, 1, 58.0, 0, 0, 0], [3, 0, 0.0, 0, 0, 1], [3, 0, 42.0, 0, 1, 2], [3, 1, 0.0, 0, 0, 1], [2, 1, 24.0, 0, 0, 2], [3, 0, 28.0, 0, 0, 2], [3, 0, 0.0, 8, 2, 2], [3, 0, 34.0, 0, 0, 2], [3, 0, 45.5, 0, 0, 0], [3, 0, 18.0, 0, 0, 2], [3, 1, 2.0, 0, 1, 2], [3, 0, 32.0, 1, 0, 2], [3, 0, 26.0, 0, 0, 0], [3, 1, 16.0, 0, 0, 1], [1, 0, 40.0, 0, 0, 0], [3, 0, 24.0, 0, 0, 2], [2, 1, 35.0, 0, 0, 2], [3, 0, 22.0, 0, 0, 2], [2, 0, 30.0, 0, 0, 2], [3, 0, 0.0, 1, 0, 1], [1, 1, 31.0, 1, 0, 0], [3, 1, 27.0, 0, 0, 2], [2, 0, 42.0, 1, 0, 2], [1, 1, 32.0, 0, 0, 0], 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0, 46.0, 0, 0, 0], [3, 0, 0.0, 0, 0, 1], [2, 0, 16.0, 0, 0, 2], [3, 1, 0.0, 8, 2, 2], [1, 0, 0.0, 0, 0, 0], [3, 0, 25.0, 0, 0, 2], [2, 0, 39.0, 0, 0, 2], [1, 1, 49.0, 0, 0, 2], [3, 1, 31.0, 0, 0, 2], [3, 0, 30.0, 0, 0, 0], [3, 1, 30.0, 1, 1, 2], [2, 0, 34.0, 0, 0, 2], [2, 1, 31.0, 1, 1, 2], [1, 0, 11.0, 1, 2, 2], [3, 0, 0.41999999999999998, 0, 1, 0], [3, 0, 27.0, 0, 0, 2], [3, 0, 31.0, 0, 0, 2], [1, 0, 39.0, 0, 0, 2], [3, 1, 18.0, 0, 0, 2], [2, 0, 39.0, 0, 0, 2], [1, 1, 33.0, 1, 0, 2], [3, 0, 26.0, 0, 0, 2], [3, 0, 39.0, 0, 0, 2], [2, 0, 35.0, 0, 0, 2], [3, 1, 6.0, 4, 2, 2], [3, 0, 30.5, 0, 0, 2], [1, 0, 0.0, 0, 0, 2], [3, 1, 23.0, 0, 0, 2], [2, 0, 31.0, 1, 1, 0], [3, 0, 43.0, 0, 0, 2], [3, 0, 10.0, 3, 2, 2], [1, 1, 52.0, 1, 1, 2], [3, 0, 27.0, 0, 0, 2], [1, 0, 38.0, 0, 0, 2], [3, 1, 27.0, 0, 1, 2], [3, 0, 2.0, 4, 1, 2], [3, 0, 0.0, 0, 0, 1], [3, 0, 0.0, 0, 0, 2], [2, 0, 1.0, 0, 2, 0], [3, 0, 0.0, 0, 0, 1], [1, 1, 62.0, 0, 0, 0], [3, 1, 15.0, 1, 0, 0], [2, 0, 0.82999999999999996, 1, 1, 2], [3, 0, 0.0, 0, 0, 0], [3, 0, 23.0, 0, 0, 2], [3, 0, 18.0, 0, 0, 2], [1, 1, 39.0, 1, 1, 0], [3, 0, 21.0, 0, 0, 2], [3, 0, 0.0, 0, 0, 2], [3, 0, 32.0, 0, 0, 2], [1, 0, 0.0, 0, 0, 0], [3, 0, 20.0, 0, 0, 2], [2, 0, 16.0, 0, 0, 2], [1, 1, 30.0, 0, 0, 0], [3, 0, 34.5, 0, 0, 0], [3, 0, 17.0, 0, 0, 2], [3, 0, 42.0, 0, 0, 2], [3, 0, 0.0, 8, 2, 2], [3, 0, 35.0, 0, 0, 0], [2, 0, 28.0, 0, 1, 2], [1, 1, 0.0, 1, 0, 0], [3, 0, 4.0, 4, 2, 2], [3, 0, 74.0, 0, 0, 2], [3, 1, 9.0, 1, 1, 0], [1, 1, 16.0, 0, 1, 2], [2, 1, 44.0, 1, 0, 2], [3, 1, 18.0, 0, 1, 2], [1, 1, 45.0, 1, 1, 2], [1, 0, 51.0, 0, 0, 2], [3, 1, 24.0, 0, 3, 0], [3, 0, 0.0, 0, 0, 0], [3, 0, 41.0, 2, 0, 2], [2, 0, 21.0, 1, 0, 2], [1, 1, 48.0, 0, 0, 2], [3, 1, 0.0, 8, 2, 2], [2, 0, 24.0, 0, 0, 2], [2, 1, 42.0, 0, 0, 2], [2, 1, 27.0, 1, 0, 0], [1, 0, 31.0, 0, 0, 2], [3, 0, 0.0, 0, 0, 2], [3, 0, 4.0, 1, 1, 2], [3, 0, 26.0, 0, 0, 2], [1, 1, 47.0, 1, 1, 2], [1, 0, 33.0, 0, 0, 2], [3, 0, 47.0, 0, 0, 2], [2, 1, 28.0, 1, 0, 0], [3, 1, 15.0, 0, 0, 0], [3, 0, 20.0, 0, 0, 2], [3, 0, 19.0, 0, 0, 2], [3, 0, 0.0, 0, 0, 2], [1, 1, 56.0, 0, 1, 0], [2, 1, 25.0, 0, 1, 2], [3, 0, 33.0, 0, 0, 2], [3, 1, 22.0, 0, 0, 2], [2, 0, 28.0, 0, 0, 2], [3, 0, 25.0, 0, 0, 2], [3, 1, 39.0, 0, 5, 1], [2, 0, 27.0, 0, 0, 2], [1, 1, 19.0, 0, 0, 2], [3, 1, 0.0, 1, 2, 2], [1, 0, 26.0, 0, 0, 0], [3, 0, 32.0, 0, 0, 1]]

if __name__ == "__main__":
    am_I_survived()

"""
 Passenger attributes
**********************
 pclass      Passenger Class            1 upper/2 middle/3 lower
 sex         Sex                        0 male/1 female
 age         Age                        float
 sibsp       Siblings/Spouses Aboard    float
 parch       Parents/Children Aboard    float
 embarked    Port of Embarkation        (0= Unknown; 1 = Cherbourg; 2 = Queenstown; 3 = Southampton)

"""