asd

from math import log


def unique_counts(rows):
    """Create counts of possible results (the last column of
    each row is the result)

    :param rows: dataset
    :type rows: list
    :return: dictionary of possible classes as keys and count
             as values
    :rtype: dict
    """
    results = {}
    for row in rows:
        # The result is the last column
        r = row[len(row) - 1]
        if r not in results:
            results[r] = 0
        results[r] += 1
    return results


def gini_impurity(rows):
    """Probability that a randomly placed item will
    be in the wrong category

    :param rows: dataset
    :type rows: list
    :return: Gini impurity
    :rtype: float
    """
    total = len(rows)
    counts = unique_counts(rows)
    imp = 0
    for k1 in counts:
        p1 = float(counts[k1]) / total
        for k2 in counts:
            if k1 == k2:
                continue
            p2 = float(counts[k2]) / total
            imp += p1 * p2
    return imp


def entropy(rows):
    """Entropy is the sum of p(x)log(p(x)) across all
    the different possible results

    :param rows: dataset
    :type rows: list
    :return: entropy value
    :rtype: float
    """
    log2 = lambda x: log(x) / log(2)
    results = unique_counts(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


class DecisionNode:
    def __init__(self, col=-1, value=None, results=None, tb=None, fb=None):
        """
        :param col: index of the column (attribute) of the training set that
                    is represented with this instance i.e. this node
        :type col: int
        :param value: the value of the node according to which the partition
                      in the tree is made
        :param results: results for the current branch, value (not None)
                        only in leaves where decision is made.
        :type results: dict
        :param tb: branch that divides from the current node when value is
                   equal to value
        :type tb: DecisionNode
        :param fb: branch that divides from the current node when value is
                   different from value
        :type fb: DecisionNode
        """
        self.col = col
        self.value = value
        self.results = results
        self.tb = tb
        self.fb = fb


def compare_numerical(row, column, value):
    """Compare row value of the desired column with particular
    numerical value

    :param row: particular row in the set
    :type row: list
    :param column: index of the column (attribute) of the train set
    :type column: int
    :param value: the value of the node according to which the
                  partition in the tree is made
    :type value: int or float
    :return: True if the row >= value, else False
    :rtype: bool
    """
    return row[column] >= value


def compare_nominal(row, column, value):
    """Compare row value of the desired column with particular
    nominal value

    :param row: particular row in the set
    :type row: list
    :param column: index of the column (attribute) of the train set
    :type column: int
    :param value: the value of the node according to which the
                  partition in the tree is made
    :type value: str
    :return: True if the row == value, else False
    :rtype: bool
    """
    return row[column] == value


def divide_set(rows, column, value):
    """Divides a set on a specific column. Can handle numeric
    or nominal values.

    :param rows: the train set
    :type rows: list(list)
    :param column: index of the column (attribute) of the train set
    :type column: int
    :param value: the value of the node according to which the
                  partition in the tree for particular branch is made
    :type value: int or float or string
    :return: divided subsets
    :rtype: list, list
    """
    # Make a function that tells us if a row is in
    # the first group (true) or the second group (false)
    if isinstance(value, int) or isinstance(value, float):
        # if the value for comparison is of type int or float
        split_function = compare_numerical
    else:
        # if the value for comparison is of other type (string)
        split_function = compare_nominal

    # Divide the rows into two sets and return them
    # for each row that split_function returns True
    set1 = [row for row in rows if
            split_function(row, column, value)]
    # for each row that split_function returns False
    set2 = [row for row in rows if
            not split_function(row, column, value)]
    return set1, set2


def build_tree(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_criteria = None
    best_sets = None

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

    # Create the subbranches
    if best_gain > 0:
        true_branch = build_tree(best_sets[0], scoref)
        false_branch = build_tree(best_sets[1], scoref)
        return DecisionNode(col=best_criteria[0], value=best_criteria[1],
                            tb=true_branch, fb=false_branch)
    else:
        return DecisionNode(results=unique_counts(rows))


def print_tree(tree, indent='', level=0):
    # Is this a leaf node?
    if tree.results:
        print(str(tree.results))
    else:
        # Print the criteria
        print(str(tree.col) + ':' + str(tree.value) + '? ' + 'Level= (' + str(level) + ')')
        # Print the branches
        print(indent + 'T-> ', end='')
        print_tree(tree.tb, indent + '  ', level + 1)
        print(indent + 'F-> ', end='')
        print_tree(tree.fb, indent + '  ', level + 1)


def classify(observation, tree):
    if tree.results:
        return tree.results
    else:
        value = observation[tree.col]
        if isinstance(value, int) or isinstance(value, float):
            compare = compare_numerical
        else:
            compare = compare_nominal

        if compare(observation, tree.col, tree.value):
            branch = tree.tb
        else:
            branch = tree.fb

        return classify(observation, branch)


trainingData = [
    [6.3, 2.9, 5.6, 1.8, 'I. virginica'],
    [6.5, 3.0, 5.8, 2.2, 'I. virginica'],
    [7.6, 3.0, 6.6, 2.1, 'I. virginica'],
    [4.9, 2.5, 4.5, 1.7, 'I. virginica'],
    [7.3, 2.9, 6.3, 1.8, 'I. virginica'],
    [6.7, 2.5, 5.8, 1.8, 'I. virginica'],
    [7.2, 3.6, 6.1, 2.5, 'I. virginica'],
    [6.5, 3.2, 5.1, 2.0, 'I. virginica'],
    [6.4, 2.7, 5.3, 1.9, 'I. virginica'],
    [6.8, 3.0, 5.5, 2.1, 'I. virginica'],
    [5.7, 2.5, 5.0, 2.0, 'I. virginica'],
    [5.8, 2.8, 5.1, 2.4, 'I. virginica'],
    [6.4, 3.2, 5.3, 2.3, 'I. virginica'],
    [6.5, 3.0, 5.5, 1.8, 'I. virginica'],
    [7.7, 3.8, 6.7, 2.2, 'I. virginica'],
    [7.7, 2.6, 6.9, 2.3, 'I. virginica'],
    [6.0, 2.2, 5.0, 1.5, 'I. virginica'],
    [6.9, 3.2, 5.7, 2.3, 'I. virginica'],
    [5.6, 2.8, 4.9, 2.0, 'I. virginica'],
    [7.7, 2.8, 6.7, 2.0, 'I. virginica'],
    [6.3, 2.7, 4.9, 1.8, 'I. virginica'],
    [6.7, 3.3, 5.7, 2.1, 'I. virginica'],
    [7.2, 3.2, 6.0, 1.8, 'I. virginica'],
    [6.2, 2.8, 4.8, 1.8, 'I. virginica'],
    [6.1, 3.0, 4.9, 1.8, 'I. virginica'],
    [6.4, 2.8, 5.6, 2.1, 'I. virginica'],
    [7.2, 3.0, 5.8, 1.6, 'I. virginica'],
    [7.4, 2.8, 6.1, 1.9, 'I. virginica'],
    [7.9, 3.8, 6.4, 2.0, 'I. virginica'],
    [6.4, 2.8, 5.6, 2.2, 'I. virginica'],
    [6.3, 2.8, 5.1, 1.5, 'I. virginica'],
    [6.1, 2.6, 5.6, 1.4, 'I. virginica'],
    [7.7, 3.0, 6.1, 2.3, 'I. virginica'],
    [6.3, 3.4, 5.6, 2.4, 'I. virginica'],
    [5.1, 3.5, 1.4, 0.2, 'I. setosa'],
    [4.9, 3.0, 1.4, 0.2, 'I. setosa'],
    [4.7, 3.2, 1.3, 0.2, 'I. setosa'],
    [4.6, 3.1, 1.5, 0.2, 'I. setosa'],
    [5.0, 3.6, 1.4, 0.2, 'I. setosa'],
    [5.4, 3.9, 1.7, 0.4, 'I. setosa'],
    [4.6, 3.4, 1.4, 0.3, 'I. setosa'],
    [5.0, 3.4, 1.5, 0.2, 'I. setosa'],
    [4.4, 2.9, 1.4, 0.2, 'I. setosa'],
    [4.9, 3.1, 1.5, 0.1, 'I. setosa'],
    [5.4, 3.7, 1.5, 0.2, 'I. setosa'],
    [4.8, 3.4, 1.6, 0.2, 'I. setosa'],
    [4.8, 3.0, 1.4, 0.1, 'I. setosa'],
    [4.3, 3.0, 1.1, 0.1, 'I. setosa'],
    [5.8, 4.0, 1.2, 0.2, 'I. setosa'],
    [5.7, 4.4, 1.5, 0.4, 'I. setosa'],
    [5.4, 3.9, 1.3, 0.4, 'I. setosa'],
    [5.1, 3.5, 1.4, 0.3, 'I. setosa'],
    [5.7, 3.8, 1.7, 0.3, 'I. setosa'],
    [5.1, 3.8, 1.5, 0.3, 'I. setosa'],
    [5.4, 3.4, 1.7, 0.2, 'I. setosa'],
    [5.1, 3.7, 1.5, 0.4, 'I. setosa'],
    [4.6, 3.6, 1.0, 0.2, 'I. setosa'],
    [5.1, 3.3, 1.7, 0.5, 'I. setosa'],
    [4.8, 3.4, 1.9, 0.2, 'I. setosa'],
    [5.0, 3.0, 1.6, 0.2, 'I. setosa'],
    [5.0, 3.4, 1.6, 0.4, 'I. setosa'],
    [5.2, 3.5, 1.5, 0.2, 'I. setosa'],
    [5.2, 3.4, 1.4, 0.2, 'I. setosa'],
    [5.5, 2.3, 4.0, 1.3, 'I. versicolor'],
    [6.5, 2.8, 4.6, 1.5, 'I. versicolor'],
    [5.7, 2.8, 4.5, 1.3, 'I. versicolor'],
    [6.3, 3.3, 4.7, 1.6, 'I. versicolor'],
    [4.9, 2.4, 3.3, 1.0, 'I. versicolor'],
    [6.6, 2.9, 4.6, 1.3, 'I. versicolor'],
    [5.2, 2.7, 3.9, 1.4, 'I. versicolor'],
    [5.0, 2.0, 3.5, 1.0, 'I. versicolor'],
    [5.9, 3.0, 4.2, 1.5, 'I. versicolor'],
    [6.0, 2.2, 4.0, 1.0, 'I. versicolor'],
    [6.1, 2.9, 4.7, 1.4, 'I. versicolor'],
    [5.6, 2.9, 3.6, 1.3, 'I. versicolor'],
    [6.7, 3.1, 4.4, 1.4, 'I. versicolor'],
    [5.6, 3.0, 4.5, 1.5, 'I. versicolor'],
    [5.8, 2.7, 4.1, 1.0, 'I. versicolor'],
    [6.2, 2.2, 4.5, 1.5, 'I. versicolor'],
    [5.6, 2.5, 3.9, 1.1, 'I. versicolor'],
    [5.9, 3.2, 4.8, 1.8, 'I. versicolor'],
    [6.1, 2.8, 4.0, 1.3, 'I. versicolor'],
    [6.3, 2.5, 4.9, 1.5, 'I. versicolor'],
    [6.1, 2.8, 4.7, 1.2, 'I. versicolor'],
    [6.4, 2.9, 4.3, 1.3, 'I. versicolor'],
    [6.6, 3.0, 4.4, 1.4, 'I. versicolor'],
    [6.8, 2.8, 4.8, 1.4, 'I. versicolor'],
    [6.7, 3.0, 5.0, 1.7, 'I. versicolor'],
    [6.0, 2.9, 4.5, 1.5, 'I. versicolor'],
    [5.7, 2.6, 3.5, 1.0, 'I. versicolor'],
    [5.5, 2.4, 3.8, 1.1, 'I. versicolor'],
    [5.5, 2.4, 3.7, 1.0, 'I. versicolor'],
    [5.8, 2.7, 3.9, 1.2, 'I. versicolor'],
    [6.0, 2.7, 5.1, 1.6, 'I. versicolor'],
    [5.4, 3.0, 4.5, 1.5, 'I. versicolor'],
    [6.0, 3.4, 4.5, 1.6, 'I. versicolor'],
    [6.7, 3.1, 4.7, 1.5, 'I. versicolor'],
    [6.3, 2.3, 4.4, 1.3, 'I. versicolor'],
    [5.6, 3.0, 4.1, 1.3, 'I. versicolor'],
    [5.5, 2.5, 4.0, 1.3, 'I. versicolor'],
    [5.5, 2.6, 4.4, 1.2, 'I. versicolor'],
    [6.1, 3.0, 4.6, 1.4, 'I. versicolor'],
    [5.8, 2.6, 4.0, 1.2, 'I. versicolor'],
    [5.0, 2.3, 3.3, 1.0, 'I. versicolor'],
    [5.6, 2.7, 4.2, 1.3, 'I. versicolor'],
    [5.7, 3.0, 4.2, 1.2, 'I. versicolor'],
    [5.7, 2.9, 4.2, 1.3, 'I. versicolor'],
    [6.2, 2.9, 4.3, 1.3, 'I. versicolor'],
    [5.1, 2.5, 3.0, 1.1, 'I. versicolor'],
    [5.7, 2.8, 4.1, 1.3, 'I. versicolor'],
    [6.4, 3.1, 5.5, 1.8, 'I. virginica'],
    [6.0, 3.0, 4.8, 1.8, 'I. virginica'],
    [6.9, 3.1, 5.4, 2.1, 'I. virginica'],
    [6.7, 3.1, 5.6, 2.4, 'I. virginica'],
    [6.9, 3.1, 5.1, 2.3, 'I. virginica'],
    [5.8, 2.7, 5.1, 1.9, 'I. virginica'],
    [6.8, 3.2, 5.9, 2.3, 'I. virginica'],
    [6.7, 3.3, 5.7, 2.5, 'I. virginica'],
    [6.7, 3.0, 5.2, 2.3, 'I. virginica'],
    [6.3, 2.5, 5.0, 1.9, 'I. virginica'],
    [6.5, 3.0, 5.2, 2.0, 'I. virginica'],
    [6.2, 3.4, 5.4, 2.3, 'I. virginica'],
    [4.7, 3.2, 1.6, 0.2, 'I. setosa'],
    [4.8, 3.1, 1.6, 0.2, 'I. setosa'],
    [5.4, 3.4, 1.5, 0.4, 'I. setosa'],
    [5.2, 4.1, 1.5, 0.1, 'I. setosa'],
    [5.5, 4.2, 1.4, 0.2, 'I. setosa'],
    [4.9, 3.1, 1.5, 0.2, 'I. setosa'],
    [5.0, 3.2, 1.2, 0.2, 'I. setosa'],
    [5.5, 3.5, 1.3, 0.2, 'I. setosa'],
    [4.9, 3.6, 1.4, 0.1, 'I. setosa'],
    [4.4, 3.0, 1.3, 0.2, 'I. setosa'],
    [5.1, 3.4, 1.5, 0.2, 'I. setosa'],
    [5.0, 3.5, 1.3, 0.3, 'I. setosa'],
    [4.5, 2.3, 1.3, 0.3, 'I. setosa'],
    [4.4, 3.2, 1.3, 0.2, 'I. setosa'],
    [5.0, 3.5, 1.6, 0.6, 'I. setosa'],
    [5.1, 3.8, 1.9, 0.4, 'I. setosa'],
    [4.8, 3.0, 1.4, 0.3, 'I. setosa'],
    [5.1, 3.8, 1.6, 0.2, 'I. setosa'],
    [5.9, 3.0, 5.1, 1.8, 'I. virginica']
]

if __name__ == "__main__":
    att1 = float(input())
    att2 = float(input())
    att3 = float(input())
    att4 = float(input())
    planttype = input()
    testCase = [att1, att2, att3, att4, planttype]

    half = int(len(trainingData)/2)
    data1 = trainingData[:half]
    data2 = trainingData[half:]

    tree1 = build_tree(data1)
    tree2 = build_tree(data2)

    for key in classify(testCase, tree1).keys():
        res1 = key
    for key in classify(testCase, tree2).keys():
        res2 = key

    print_tree(tree1)
    print_tree(tree2)

    if res1 == res2:
        print(res1)
    else:
        print("KONTRADIKCIJA")