def predict_prices(dates, prices, x):
dates = np.reshape(dates,(len(dates),1))
#Ocuapos un SVR para hacer regresion y obtene la prediccion del siguiente valor
svr_lin = SVR(kernel='linear', C=1e3)
svr_poly = SVR(kernel='poly', C=1e3)
svr_rbf =SVR(kernel='rbf', C=1e3, gamma=0.1)
svr_lin.fit(dates,prices)
svr_poly.fit(dates, prices)
svr_rbf.fit(dates,prices)
plt.scatter(dates,color='black', label='Data')
plt.plot(dates, svr_rbf.predict(dates),color='red', label='RBF model')
plt.plot(dates, svr_poly, color='blue', label = 'Polynomial model')
plt.plot(dates, svr_lin, color='green', label = 'Linear model')
print('OK')
plt.xlabel('Date')
plt.ylabel('Prices')
plt.title('Support Vector Regression')
plt.legend()
plt.show()
#Regresamos la preddicon del dia siguiente
return svr_rbf.predictx(x)[0],svr_poly.predict(x)[0], svr_lin.predict(x)[0]
