# https://www.udemy.com/data-science-supervised-machine-learning-in-python from knn import KNN from util import get_donut import matplotlib.pyplot as plt if __name__ == '__main__': X, Y = get_donut() # 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)
def donut(): X, Y = get_donut() 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
# 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_donut import matplotlib.pyplot as plt if __name__ == '__main__': X, Y = get_donut() # 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))
def donut(): X, Y = get_donut() Xtrain, Xtest, Ytrain, Ytest = train_test_split(X, Y, test_size=0.33) kernel = lambda X1, X2: rbf(X1, X2, gamma=1.) return Xtrain, Xtest, Ytrain, Ytest, kernel, 1e-3, 300