kontraDrva

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(object):
    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(value1, value2):
    return value1 >= value2


def sporedi_string(value1, value2):
    return value1 == value2


# In[43]:


def get_compare_func(value):
    if isinstance(value, int) or isinstance(value, float):
        comparer = sporedi_broj
    else:
        comparer = sporedi_string
    return comparer


def compare_values(v1, v2):
    sporedi = get_compare_func(v1)
    return sporedi(v1, v2)


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


def uniquecounts(rows):
    d = {}
    for r in rows:
        #         print(r[-1])
        d.setdefault(r[-1], 0)
        d[r[-1]] += 1
    return d


from math import log

log2 = lambda x: log(x) / log(2)


def entropy(rows):
    results = uniquecounts(rows)
    # Now calculate the entropy
    ent = 0.0
    n = len(rows)
    for label, cnt in results.items():
        #         print(r)
        p = float(cnt) / n
        #         print(label, cnt, p)
        ent -= p * log2(p)
    return ent


def info_gain(current_score, sets, scoref=entropy):
    m = sum([len(s) for s in sets])
    gain = current_score
    for s in sets:
        n = len(s)
        p = 1. * n / m
        gain -= p * scoref(s)
    return gain


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])
        #         print(column_values)
        column_values = set([row[col] for row in rows])
        #         print('Zemame vo predvid podelba po:', col, len(column_values), column_values)
        #         continue
        # Now try dividing the rows up for each value
        # in this column
        for value in column_values:
            sets = divideset(rows, col, value)

            # Information gain
            #             p = float(len(set1)) / len(rows)
            #             gain = current_score - p * scoref(set1) - (1 - p) * scoref(set2)
            gain = info_gain(current_score, sets, scoref)
            if gain > best_gain and len(sets) > 0 and len(sets[0]) > 0 and len(sets[1]) > 0:
                best_gain = gain
                best_column = col
                best_value = value
                best_subsett = sets[0]
                best_subsetf = sets[1]
                # best_criteria = (col, value)
                # best_sets = (set1, set2)
    #             print('Dividing dataset', col, value, gain, sets)
    # pronajden e korenot
    #     return
    # Create the subbranches
    if best_gain > 0:
        #         print(best_subsett)
        #         print(best_subsetf)
        #print(best_column, best_value, best_gain)
        #print('Starting true subbranch')
        trueBranch = buildtree(best_subsett, scoref)
        #print()
        #print('Starting false subbranch')
        falseBranch = buildtree(best_subsetf, scoref)
        #print()
        return decisionnode(col=best_column, value=best_value,
                            tb=trueBranch, fb=falseBranch)

    else:
        #print('Terminalen jazol')
        #print()
        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
        if len(tree.results) == 1:
            return list(tree.results.keys())[0], 1.0
        else:
            inv = sorted([(cnt, label) for label, cnt in tree.results.items()], reverse=True)
            label = inv[0][1]
            cnt = inv[0][0]
            total_count = sum(tree.results.values())
            return label, cnt / total_count
    #         return tree.results
    else:
        vrednost = observation[tree.col]
        if compare_values(vrednost, tree.value):
            branch = tree.tb
        else:
            branch = tree.fb
        return classify(observation, branch)


if __name__ == '__main__':

    arg1 = 3.3
    arg2 = 3.2
    arg3 = 3.3
    arg4 = 1.7
    cl = 'NA'

    tmp = [arg1, arg2, arg3, arg4, cl]
    m1 = []
    m2 = []
    leng = len(trainingData)
    for i in range(0, int(leng / 2)):
        m1.append(trainingData[i])
    for i in range (int(leng / 2), leng):
        m2.append(trainingData[i])

    t1 = buildtree(m1)
    t2 = buildtree(m2)

    k1 = classify(tmp, t1)
    k2 = classify(tmp, t2)
    print('k1', k1)
    print('k2', k2)

    if (k1 == k2):
        print("Ista klasa ", k1)
    else:
        print("Kontradikcija")