Пример #1
0
def classify_dataset_test():
    #create dataset
    filename = "Dataset/iris.data"
    dataset = DT.Dataset(filename, _delimiter=',')
    Tree = DT.DecisionTree(dataset)

    #load exemples
    exemple1 = np.array([5.4, 3.9, 1.3, 0.4]).astype('S15')
    exemple2 = np.array([6.3, 2.5, 4.9, 1.5]).astype('S15')
    exemple3 = np.array([
        6.5,
        3.0,
        5.5,
        1.8,
    ]).astype('S15')

    #classify exemples
    class1 = Tree.classify(exemple1)
    class2 = Tree.classify(exemple2)
    class3 = Tree.classify(exemple3)

    #verify classification
    eq_(class1, b'Iris-setosa')
    eq_(class2, b'Iris-versicolor')
    eq_(class3, b'Iris-virginica')
Пример #2
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def read_dataset_test():
    """construct numpy arrays from dataset"""
    filename = "/home/jorge/Documents/2-Programming/AI/DecisionTree/Dataset/players.txt"
    data = np.loadtxt(filename, dtype="S4", delimiter="\t")
    dataset = DT.Dataset(filename, _delimiter="\t")
    #TODO make sure that the arguments are valid so that the dataset can be correcly loaded
    eq_(3, dataset.NbAttr)
    eq_(np.array(['No', 'Yes']), dataset.classes)
Пример #3
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def decisionTreeLearning_test():
    """"Function to test the general decision tree learning function."""

    #create dataset
    filename = "/home/jorge/Documents/2-Programming/AI/DecisionTree/Dataset/restaurant.txt"
    dataset = DT.Dataset(filename, _delimiter='\t')

    Tree = DT.DecisionTree(dataset)

    #show first branch of decisition tree shown in page 702
    # print(Tree.root)
    # print(Tree.leaf)

    #return tree for classify function next
    return Tree
Пример #4
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def gain_test():
    """"Function to test given dataset and their gain. The dataset to test from
    is the one shown in the book in page 700. Only Pat, Type  and WillWait columns"""

    #create dataset
    filename = "/home/jorge/Documents/2-Programming/AI/DecisionTree/Dataset/restaurant.txt"
    dataset = DT.Dataset(filename, _delimiter='\t')

    Tree = DT.DecisionTree(dataset)

    #test entropy function. Because the probs are equal for each class, then 1.0
    eq_(1.0, Tree.entropy(Tree.dataset[:, -1]))

    #test two attribues, calculations got from book page 704
    eq_(0.541, Tree.gain(4))
    eq_(0.0, Tree.gain(1))

    print(Tree.getImportance())
Пример #5
0
def main(argv):

    exemple = ""

    #load dataset
    try:
        filename = argv[0]
        dataset = DT.Dataset(filename, _delimiter=',')
    except getopt.GetoptError:
        print('dt.py -i <dataset dir>')
        sys.exit(2)

    #create and train decision tree
    Tree = DT.DecisionTree(dataset)

    #load exemple
    try:
        exemple = np.array(argv[1].split(",")).astype('S15')
    except:
        print("error loading exemple")

    # classify
    eclass = Tree.classify(exemple)
    print(eclass)