Python Normalizer Beispiele

Beispiel #1

Datei: bank_marketing_script.py Projekt: Matheuslm10/T2IA

from datasets.data_normalizer import DataNormalizer as Normalizer
from datasets.evaluate_classifier import EvaluateClassifier

if __name__ == '__main__':

    data = Normalizer('./bank-marketing.data').ready_data

    x = data[:, 0:len(data[0]) - 1]
    y = data[:, len(data[0]) - 1]
    y.shape = (len(x), )

    EvaluateClassifier(x, y)

Beispiel #2

from datasets.data_normalizer import DataNormalizer as Normalizer
from datasets.evaluate_classifier import EvaluateClassifier

if __name__ == '__main__':

    data = Normalizer('./car-evaluation.data').ready_data

    x = data[:, 0:len(data[0]) - 1]
    y = data[:, len(data[0]) - 1]
    y.shape = (len(x), )

    EvaluateClassifier(x, y)

Beispiel #3

Datei: wine_quality_script.py Projekt: Matheuslm10/T2IA

from datasets.data_normalizer import DataNormalizer as Normalizer
from datasets.evaluate_classifier import EvaluateClassifier


if __name__ == '__main__':

    data = Normalizer('./wine-quality.data').ready_data

    x = data[:, 0:len(data[0])-1]
    y = data[:, len(data[0])-1]
    y.shape = (len(x),)

    EvaluateClassifier(x, y)

Beispiel #4

Datei: adult_script.py Projekt: Matheuslm10/T2IA

from datasets.data_normalizer import DataNormalizer as Normalizer
from datasets.evaluate_classifier import EvaluateClassifier

if __name__ == '__main__':

    data = Normalizer('./adult.data').ready_data

    x = data[:, 0:len(data[0]) - 1]
    y = data[:, len(data[0]) - 1]
    y.shape = (len(x), )

    EvaluateClassifier(x, y)

Beispiel #5

from datasets.data_normalizer import DataNormalizer as Normalizer
from datasets.evaluate_classifier import EvaluateClassifier

if __name__ == '__main__':

    data = Normalizer('./breast-cancer-wisconsin.data').ready_data

    x = data[:, 0:len(data[0]) - 1]
    y = data[:, len(data[0]) - 1]
    y.shape = (len(x), )

    EvaluateClassifier(x, y)

Beispiel #6

from datasets.data_normalizer import DataNormalizer as Normalizer
from datasets.evaluate_classifier import EvaluateClassifier


if __name__ == '__main__':

    data = Normalizer('./heart-disease.data').ready_data

    x = data[:, 0:len(data[0])-1]
    y = data[:, len(data[0])-1]
    y.shape = (len(x),)

    EvaluateClassifier(x, y)

Beispiel #7

Datei: tic_tac_toe_script.py Projekt: Matheuslm10/T2IA

from datasets.data_normalizer import DataNormalizer as Normalizer
from datasets.evaluate_classifier import EvaluateClassifier

if __name__ == '__main__':

    data = Normalizer('./tic-tac-toe.data').ready_data

    x = data[:, 0:len(data[0]) - 1]
    y = data[:, len(data[0]) - 1]
    y.shape = (len(x), )

    EvaluateClassifier(x, y)

Beispiel #8

Datei: wine_script.py Projekt: Matheuslm10/T2IA

from datasets.data_normalizer import DataNormalizer as Normalizer
from datasets.evaluate_classifier import EvaluateClassifier

if __name__ == '__main__':

    data = Normalizer('./wine.data').ready_data

    x = data[:, 1:]
    y = data[:, 0]
    y.shape = (len(x), )

    EvaluateClassifier(x, y)

Beispiel #9

Datei: iris_script.py Projekt: Matheuslm10/T2IA

from datasets.data_normalizer import DataNormalizer as Normalizer
from datasets.evaluate_classifier import EvaluateClassifier

if __name__ == '__main__':

    data = Normalizer('./iris.data').ready_data

    x = data[:, 0:len(data[0]) - 1]
    y = data[:, len(data[0]) - 1]
    y.shape = (len(x), )

    EvaluateClassifier(x, y)