pos = np.where(Y == 1) #vector with index of the Y = 1
plt.scatter(X[pos, 0], X[pos, 1], marker='*', c='y')
pos = np.where(Y == 0) #vector with index of the Y = 1
plt.scatter(X[pos, 0], X[pos, 1], marker='$F$', c='r')
def draw_decision_boundary(theta, X, Y, poly):
x0_min, x0_max = X[:,0].min(), X[:,0].max()
x1_min, x1_max = X[:,1].min(), X[:,1].max()
xx1, xx2 = np.meshgrid(np.linspace(x0_min, x0_max), np.linspace(x1_min, x1_max))
sigm = g(poly.fit_transform(np.c_[ xx1.ravel(), xx2.ravel()]).dot(theta))
sigm = sigm.reshape(xx1.shape)
plt.contour(xx1, xx2, sigm, [0.5], linewidths = 1, colors = 'g')
def draw(theta, X, Y, poly):
plt.figure()
draw_data(X, Y)
draw_decision_boundary(theta, X, Y, poly)
plt.show()
data = Data_Management.load_csv(sys.argv[1]) #sys.argv[1])
X, Y, m, n = Data_Management.get_parameters_from_training_examples(data)
poly, X_poly = polinomial_features(X, int(sys.argv[2]))
theta = np.zeros([1, np.shape(X_poly)[1]], dtype=float)
theta = tnc(func=J, x0=theta, fprime=gradient, args=(X_poly,Y))[0]
draw(theta, X, Y, poly)
from ML_UtilsModule import Data_Management
X, y = Data_Management.load_csv("pokemon.csv") #no vale
#print(X[:, 0])