Ejemplo n.º 1
0
# https://deeplearningcourses.com/c/data-science-supervised-machine-learning-in-python
# https://www.udemy.com/data-science-supervised-machine-learning-in-python
from knn import KNN
from util import get_xor
import matplotlib.pyplot as plt

if __name__ == '__main__':
    X, Y = get_xor()

    # display the data
    plt.scatter(X[:, 0], X[:, 1], s=100, c=Y, alpha=0.5)
    plt.show()

    # get the accuracy
    model = KNN(3)
    model.fit(X, Y)
    print "Accuracy:", model.score(X, Y)
Ejemplo n.º 2
0
# https://deeplearningcourses.com/c/data-science-supervised-machine-learning-in-python
# https://www.udemy.com/data-science-supervised-machine-learning-in-python
from __future__ import print_function, division
from builtins import range, input
# Note: you may need to update your version of future
# sudo pip install -U future


from knn import KNN
from util import get_xor
import matplotlib.pyplot as plt

if __name__ == '__main__':
    X, Y = get_xor()

    # display the data
    plt.scatter(X[:,0], X[:,1], s=100, c=Y, alpha=0.5)
    plt.show()

    # get the accuracy
    model = KNN(3)
    model.fit(X, Y)
    print("Accuracy:", model.score(X, Y))
Ejemplo n.º 3
0
from util import get_xor

data = get_xor()
def xor():
  X, Y = get_xor()
  Xtrain, Xtest, Ytrain, Ytest = train_test_split(X, Y, test_size=0.33)
  kernel = lambda X1, X2: rbf(X1, X2, gamma=5.)
  return Xtrain, Xtest, Ytrain, Ytest, kernel, 1e-2, 300
def xor():
  X, Y = get_xor()
  Xtrain, Xtest, Ytrain, Ytest = train_test_split(X, Y, test_size=0.33)
  kernel = lambda X1, X2: rbf(X1, X2, gamma=3.)
  return Xtrain, Xtest, Ytrain, Ytest, kernel, 1e-3, 500