Дрва на одлучување - Колоквиум 2017

data = [[242.0, 23.2, 25.4, 30.0, 38.4, 13.4, 1],
        [290.0, 24.0, 26.3, 31.2, 40.0, 13.8, 1],
        [340.0, 23.9, 26.5, 31.1, 39.8, 15.1, 1],
        [363.0, 26.3, 29.0, 33.5, 38.0, 13.3, 1],
        [430.0, 26.5, 29.0, 34.0, 36.6, 15.1, 1],
        [450.0, 26.8, 29.7, 34.7, 39.2, 14.2, 1],
        [500.0, 26.8, 29.7, 34.5, 41.1, 15.3, 1],
        [390.0, 27.6, 30.0, 35.0, 36.2, 13.4, 1],
        [450.0, 27.6, 30.0, 35.1, 39.9, 13.8, 1],
        [500.0, 28.5, 30.7, 36.2, 39.3, 13.7, 1],
        [475.0, 28.4, 31.0, 36.2, 39.4, 14.1, 1],
        [500.0, 28.7, 31.0, 36.2, 39.7, 13.3, 1],
        [500.0, 29.1, 31.5, 36.4, 37.8, 12.0, 1],
        [500.0, 29.5, 32.0, 37.3, 37.3, 13.6, 1],
        [600.0, 29.4, 32.0, 37.2, 40.2, 13.9, 1],
        [600.0, 29.4, 32.0, 37.2, 41.5, 15.0, 1],
        [700.0, 30.4, 33.0, 38.3, 38.8, 13.8, 1],
        [700.0, 30.4, 33.0, 38.5, 38.8, 13.5, 1],
        [610.0, 30.9, 33.5, 38.6, 40.5, 13.3, 1],
        [650.0, 31.0, 33.5, 38.7, 37.4, 14.8, 1],
        [575.0, 31.3, 34.0, 39.5, 38.3, 14.1, 1],
        [685.0, 31.4, 34.0, 39.2, 40.8, 13.7, 1],
        [620.0, 31.5, 34.5, 39.7, 39.1, 13.3, 1],
        [680.0, 31.8, 35.0, 40.6, 38.1, 15.1, 1],
        [700.0, 31.9, 35.0, 40.5, 40.1, 13.8, 1],
        [725.0, 31.8, 35.0, 40.9, 40.0, 14.8, 1],
        [720.0, 32.0, 35.0, 40.6, 40.3, 15.0, 1],
        [714.0, 32.7, 36.0, 41.5, 39.8, 14.1, 1],
        [850.0, 32.8, 36.0, 41.6, 40.6, 14.9, 1],
        [1000.0, 33.5, 37.0, 42.6, 44.5, 15.5, 1],
        [920.0, 35.0, 38.5, 44.1, 40.9, 14.3, 1],
        [955.0, 35.0, 38.5, 44.0, 41.1, 14.3, 1],
        [925.0, 36.2, 39.5, 45.3, 41.4, 14.9, 1],
        [975.0, 37.4, 41.0, 45.9, 40.6, 14.7, 1],
        [950.0, 38.0, 41.0, 46.5, 37.9, 13.7, 1],
        [270.0, 23.6, 26.0, 28.7, 29.2, 14.8, 2],
        [270.0, 24.1, 26.5, 29.3, 27.8, 14.5, 2],
        [306.0, 25.6, 28.0, 30.8, 28.5, 15.2, 2],
        [540.0, 28.5, 31.0, 34.0, 31.6, 19.3, 2],
        [800.0, 33.7, 36.4, 39.6, 29.7, 16.6, 2],
        [1000.0, 37.3, 40.0, 43.5, 28.4, 15.0, 2],
        [40.0, 12.9, 14.1, 16.2, 25.6, 14.0, 3],
        [69.0, 16.5, 18.2, 20.3, 26.1, 13.9, 3],
        [78.0, 17.5, 18.8, 21.2, 26.3, 13.7, 3],
        [87.0, 18.2, 19.8, 22.2, 25.3, 14.3, 3],
        [120.0, 18.6, 20.0, 22.2, 28.0, 16.1, 3],
        [0.0, 19.0, 20.5, 22.8, 28.4, 14.7, 3],
        [110.0, 19.1, 20.8, 23.1, 26.7, 14.7, 3],
        [120.0, 19.4, 21.0, 23.7, 25.8, 13.9, 3],
        [150.0, 20.4, 22.0, 24.7, 23.5, 15.2, 3],
        [145.0, 20.5, 22.0, 24.3, 27.3, 14.6, 3],
        [160.0, 20.5, 22.5, 25.3, 27.8, 15.1, 3],
        [140.0, 21.0, 22.5, 25.0, 26.2, 13.3, 3],
        [160.0, 21.1, 22.5, 25.0, 25.6, 15.2, 3],
        [169.0, 22.0, 24.0, 27.2, 27.7, 14.1, 3],
        [161.0, 22.0, 23.4, 26.7, 25.9, 13.6, 3],
        [200.0, 22.1, 23.5, 26.8, 27.6, 15.4, 3],
        [180.0, 23.6, 25.2, 27.9, 25.4, 14.0, 3],
        [290.0, 24.0, 26.0, 29.2, 30.4, 15.4, 3],
        [272.0, 25.0, 27.0, 30.6, 28.0, 15.6, 3],
        [390.0, 29.5, 31.7, 35.0, 27.1, 15.3, 3],
        [55.0, 13.5, 14.7, 16.5, 41.5, 14.1, 4],
        [60.0, 14.3, 15.5, 17.4, 37.8, 13.3, 4],
        [90.0, 16.3, 17.7, 19.8, 37.4, 13.5, 4],
        [120.0, 17.5, 19.0, 21.3, 39.4, 13.7, 4],
        [150.0, 18.4, 20.0, 22.4, 39.7, 14.7, 4],
        [140.0, 19.0, 20.7, 23.2, 36.8, 14.2, 4],
        [170.0, 19.0, 20.7, 23.2, 40.5, 14.7, 4],
        [145.0, 19.8, 21.5, 24.1, 40.4, 13.1, 4],
        [200.0, 21.2, 23.0, 25.8, 40.1, 14.2, 4],
        [273.0, 23.0, 25.0, 28.0, 39.6, 14.8, 4],
        [300.0, 24.0, 26.0, 29.0, 39.2, 14.6, 4],
        [6.7, 9.3, 9.8, 10.8, 16.1, 9.7, 5],
        [7.5, 10.0, 10.5, 11.6, 17.0, 10.0, 5],
        [7.0, 10.1, 10.6, 11.6, 14.9, 9.9, 5],
        [9.7, 10.4, 11.0, 12.0, 18.3, 11.5, 5],
        [9.8, 10.7, 11.2, 12.4, 16.8, 10.3, 5],
        [8.7, 10.8, 11.3, 12.6, 15.7, 10.2, 5],
        [10.0, 11.3, 11.8, 13.1, 16.9, 9.8, 5],
        [9.9, 11.3, 11.8, 13.1, 16.9, 8.9, 5],
        [9.8, 11.4, 12.0, 13.2, 16.7, 8.7, 5],
        [12.2, 11.5, 12.2, 13.4, 15.6, 10.4, 5],
        [13.4, 11.7, 12.4, 13.5, 18.0, 9.4, 5],
        [12.2, 12.1, 13.0, 13.8, 16.5, 9.1, 5],
        [19.7, 13.2, 14.3, 15.2, 18.9, 13.6, 5],
        [19.9, 13.8, 15.0, 16.2, 18.1, 11.6, 5],
        [200.0, 30.0, 32.3, 34.8, 16.0, 9.7, 6],
        [300.0, 31.7, 34.0, 37.8, 15.1, 11.0, 6],
        [300.0, 32.7, 35.0, 38.8, 15.3, 11.3, 6],
        [300.0, 34.8, 37.3, 39.8, 15.8, 10.1, 6],
        [430.0, 35.5, 38.0, 40.5, 18.0, 11.3, 6],
        [345.0, 36.0, 38.5, 41.0, 15.6, 9.7, 6],
        [456.0, 40.0, 42.5, 45.5, 16.0, 9.5, 6],
        [510.0, 40.0, 42.5, 45.5, 15.0, 9.8, 6],
        [540.0, 40.1, 43.0, 45.8, 17.0, 11.2, 6],
        [500.0, 42.0, 45.0, 48.0, 14.5, 10.2, 6],
        [567.0, 43.2, 46.0, 48.7, 16.0, 10.0, 6],
        [770.0, 44.8, 48.0, 51.2, 15.0, 10.5, 6],
        [950.0, 48.3, 51.7, 55.1, 16.2, 11.2, 6],
        [1250.0, 52.0, 56.0, 59.7, 17.9, 11.7, 6],
        [1600.0, 56.0, 60.0, 64.0, 15.0, 9.6, 6],
        [1550.0, 56.0, 60.0, 64.0, 15.0, 9.6, 6],
        [1650.0, 59.0, 63.4, 68.0, 15.9, 11.0, 6],
        [5.9, 7.5, 8.4, 8.8, 24.0, 16.0, 7],
        [32.0, 12.5, 13.7, 14.7, 24.0, 13.6, 7],
        [40.0, 13.8, 15.0, 16.0, 23.9, 15.2, 7],
        [51.5, 15.0, 16.2, 17.2, 26.7, 15.3, 7],
        [70.0, 15.7, 17.4, 18.5, 24.8, 15.9, 7],
        [100.0, 16.2, 18.0, 19.2, 27.2, 17.3, 7],
        [78.0, 16.8, 18.7, 19.4, 26.8, 16.1, 7],
        [80.0, 17.2, 19.0, 20.2, 27.9, 15.1, 7],
        [85.0, 17.8, 19.6, 20.8, 24.7, 14.6, 7],
        [85.0, 18.2, 20.0, 21.0, 24.2, 13.2, 7],
        [110.0, 19.0, 21.0, 22.5, 25.3, 15.8, 7],
        [115.0, 19.0, 21.0, 22.5, 26.3, 14.7, 7],
        [125.0, 19.0, 21.0, 22.5, 25.3, 16.3, 7],
        [130.0, 19.3, 21.3, 22.8, 28.0, 15.5, 7],
        [120.0, 20.0, 22.0, 23.5, 26.0, 14.5, 7],
        [120.0, 20.0, 22.0, 23.5, 24.0, 15.0, 7],
        [130.0, 20.0, 22.0, 23.5, 26.0, 15.0, 7],
        [135.0, 20.0, 22.0, 23.5, 25.0, 15.0, 7],
        [110.0, 20.0, 22.0, 23.5, 23.5, 17.0, 7],
        [130.0, 20.5, 22.5, 24.0, 24.4, 15.1, 7],
        [150.0, 20.5, 22.5, 24.0, 28.3, 15.1, 7],
        [145.0, 20.7, 22.7, 24.2, 24.6, 15.0, 7],
        [150.0, 21.0, 23.0, 24.5, 21.3, 14.8, 7],
        [170.0, 21.5, 23.5, 25.0, 25.1, 14.9, 7],
        [225.0, 22.0, 24.0, 25.5, 28.6, 14.6, 7],
        [145.0, 22.0, 24.0, 25.5, 25.0, 15.0, 7],
        [188.0, 22.6, 24.6, 26.2, 25.7, 15.9, 7],
        [180.0, 23.0, 25.0, 26.5, 24.3, 13.9, 7],
        [197.0, 23.5, 25.6, 27.0, 24.3, 15.7, 7],
        [218.0, 25.0, 26.5, 28.0, 25.6, 14.8, 7],
        [300.0, 25.2, 27.3, 28.7, 29.0, 17.9, 7],
        [260.0, 25.4, 27.5, 28.9, 24.8, 15.0, 7],
        [265.0, 25.4, 27.5, 28.9, 24.4, 15.0, 7],
        [250.0, 25.4, 27.5, 28.9, 25.2, 15.8, 7],
        [250.0, 25.9, 28.0, 29.4, 26.6, 14.3, 7],
        [300.0, 26.9, 28.7, 30.1, 25.2, 15.4, 7],
        [320.0, 27.8, 30.0, 31.6, 24.1, 15.1, 7],
        [514.0, 30.5, 32.8, 34.0, 29.5, 17.7, 7],
        [556.0, 32.0, 34.5, 36.5, 28.1, 17.5, 7],
        [840.0, 32.5, 35.0, 37.3, 30.8, 20.9, 7],
        [685.0, 34.0, 36.5, 39.0, 27.9, 17.6, 7],
        [700.0, 34.0, 36.0, 38.3, 27.7, 17.6, 7],
        [700.0, 34.5, 37.0, 39.4, 27.5, 15.9, 7],
        [690.0, 34.6, 37.0, 39.3, 26.9, 16.2, 7],
        [900.0, 36.5, 39.0, 41.4, 26.9, 18.1, 7],
        [650.0, 36.5, 39.0, 41.4, 26.9, 14.5, 7],
        [820.0, 36.6, 39.0, 41.3, 30.1, 17.8, 7],
        [850.0, 36.9, 40.0, 42.3, 28.2, 16.8, 7],
        [900.0, 37.0, 40.0, 42.5, 27.6, 17.0, 7],
        [1015.0, 37.0, 40.0, 42.4, 29.2, 17.6, 7],
        [820.0, 37.1, 40.0, 42.5, 26.2, 15.6, 7],
        [1100.0, 39.0, 42.0, 44.6, 28.7, 15.4, 7],
        [1000.0, 39.8, 43.0, 45.2, 26.4, 16.1, 7],
        [1100.0, 40.1, 43.0, 45.5, 27.5, 16.3, 7],
        [1000.0, 40.2, 43.5, 46.0, 27.4, 17.7, 7],
        [1000.0, 41.1, 44.0, 46.6, 26.8, 16.3, 7]]

class decisionnode:
    def __init__(self, col=-1, value=None, results=None, tb=None, fb=None):
        self.col = col
        self.value = value
        self.results = results
        self.tb = tb
        self.fb = fb


def sporedi_broj(row, column, value):
    return row[column] >= value


def sporedi_string(row, column, value):
    return row[column] == value


# Divides a set on a specific column. Can handle numeric
# or nominal values
def divideset(rows, column, value):
    # Make a function that tells us if a row is in
    # the first group (true) or the second group (false)
    split_function = None
    if isinstance(value, int) or isinstance(value, float):  # ako vrednosta so koja sporeduvame e od tip int ili float
        # split_function=lambda row:row[column]>=value # togas vrati funkcija cij argument e row i vrakja vrednost true ili false
        split_function = sporedi_broj
    else:
        # split_function=lambda row:row[column]==value # ako vrednosta so koja sporeduvame e od drug tip (string)
        split_function = sporedi_string

    # Divide the rows into two sets and return them
    set_false = []
    set_true = []
    for row in rows:
        if split_function(row, column, value):
            set_true.append(row)
        else:
            set_false.append(row)
    set1 = [row for row in rows if
            split_function(row, column, value)]  # za sekoj row od rows za koj split_function vrakja true
    set2 = [row for row in rows if
            not split_function(row, column, value)]  # za sekoj row od rows za koj split_function vrakja false
    # return (set1, set2)
    return (set_true, set_false)

# Create counts of possible results (the last column of
# each row is the result)
def uniquecounts(rows):
    results = {}
    for row in rows:
        # The result is the last column
        r = row[-1]
        results.setdefault(r, 0)
        results[r] += 1

    return results

# Probability that a randomly placed item will
# be in the wrong category

def log2(x):
    from math import log
    l2 = log(x) / log(2)
    return l2


# Entropy is the sum of p(x)log(p(x)) across all
# the different possible results
def entropy(rows):
    results = uniquecounts(rows)
    # Now calculate the entropy
    ent = 0.0
    for r in results.keys():
        p = float(results[r]) / len(rows)
        ent = ent - p * log2(p)
    return ent


def buildtree(rows, scoref=entropy):
    if len(rows) == 0: return decisionnode()
    current_score = scoref(rows)

    # Set up some variables to track the best criteria
    best_gain = 0.0
    best_column = -1
    best_value = None
    best_subsetf = None
    best_subsett = None

    column_count = len(rows[0]) - 1
    for col in range(column_count):
        # Generate the list of different values in
        # this column
        column_values = set()
        for row in rows:
            column_values.add(row[col])
        # Now try dividing the rows up for each value
        # in this column
        for value in column_values:
            (set1, set2) = divideset(rows, col, value)

            # Information gain
            p = float(len(set1)) / len(rows)
            gain = current_score - p * scoref(set1) - (1 - p) * scoref(set2)
            if gain > best_gain and len(set1) > 0 and len(set2) > 0:
                best_gain = gain
                best_column = col
                best_value = value
                best_subsett = set1
                best_subsetf = set2
                # best_criteria = (col, value)
                # best_sets = (set1, set2)

    # Create the subbranches
    if best_gain > 0:
        trueBranch = buildtree(best_subsett, scoref)
        falseBranch = buildtree(best_subsetf, scoref)
        return decisionnode(col=best_column, value=best_value,
                            tb=trueBranch, fb=falseBranch)
    else:
        return decisionnode(results=uniquecounts(rows))




def printtree(tree, indent=''):
    # Is this a leaf node?
    if tree.results != None:
        print(indent + str(sorted(tree.results.items())))
    else:
        # Print the criteria
        print(indent + str(tree.col) + ':' + str(tree.value) + '? ')
        # Print the branches
        print(indent + 'T->')
        printtree(tree.tb, indent + '  ')
        print(indent + 'F->')
        printtree(tree.fb, indent + '  ')



def classify(observation, tree):
    if tree.results != None:
        return tree.results
    else:
        vrednost = observation[tree.col]
        branch = None

        if isinstance(vrednost, int) or isinstance(vrednost, float):
            if vrednost >= tree.value:
                branch = tree.tb
            else:
                branch = tree.fb
        else:
            if vrednost == tree.value:
                branch = tree.tb
            else:
                branch = tree.fb

        return classify(observation, branch)


if __name__ == "__main__":
    index = input()
    trainingData = []
    br = 0
    klasa=1
    for d in data:
        if br < 5 and d[6] == klasa:
            trainingData.append(d)
            br = br + 1
        if br == 5:
            klasa = klasa + 1
            br = 0
    t = buildtree(trainingData)
    klasa = classify(data[int(index)], t)
    print(klasa)