januari_drva

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']
]

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)

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

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

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 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__":


    att1=input()
    att2=input()
    att3=input()
    att4=input()
    planttype=input()
    testCase=[att1,att2,att3,att4,planttype]
    mn1=[] #kreiranje prvo mnozestvo
    mn2=[] #kreiranje vtoro mnozestvo
    vkupno=len(trainingData) #vkupno redovi
    for i in range(0,vkupno/2): #polnenje na prvo trening mnozhestvo
        mn1.append(trainingData[i])
    for i in range(vkupno/2,vkupno): # polnenje na vtoro trening mnozhestvo
        mn2.append(trainingData[i])
    drvo1=buildtree(mn1) #kreiranje prvo drvo od prvo trening mnozhestvo
    drvo2=buildtree(mn2) #kreiranje vtoro drvo od vtoro trening mnozhestvo
    kl1=classify(testCase,drvo1) #prva klasifikacija
    kl2=classify(testCase,drvo2) #vtora klasifikacija
    if (kl1.keys()[0]==kl2.keys()[0]): #proverka dali se isti
        print kl1.keys()[0]
    if(kl1.values()[0]>kl2.values()[0]):
        print kl1.keys()[0]
        print 'KONTRADIKCIJA'
    if(kl2.values()[0]>kl1.values()[0]):
        print kl1.keys()[0]
        print 'KONTRADIKCIJA'