def test_train_predict_using_yuv(self): xs = MomentRandomForestTrainTestModel.get_xs_from_results(self.features) ys = MomentRandomForestTrainTestModel.get_ys_from_results(self.features) xys = MomentRandomForestTrainTestModel.get_xys_from_results(self.features) model = MomentRandomForestTrainTestModel({'norm_type':'normalize', 'random_state':0}) model.train(xys) result = model.evaluate(xs, ys) self.assertAlmostEquals(result['RMSE'], 0.51128487038576109, places=4)
def test_train_predict_using_yuv(self): xs = MomentRandomForestTrainTestModel.get_xs_from_results( self.features) ys = MomentRandomForestTrainTestModel.get_ys_from_results( self.features) xys = MomentRandomForestTrainTestModel.get_xys_from_results( self.features) model = MomentRandomForestTrainTestModel({ 'norm_type': 'normalize', 'random_state': 0 }) model.train(xys) result = model.evaluate(xs, ys) self.assertAlmostEquals(result['RMSE'], 0.51128487038576109, places=4)
def test_train_predict(self): xs = MomentRandomForestTrainTestModel.get_xs_from_results(self.features) ys = MomentRandomForestTrainTestModel.get_ys_from_results(self.features) xys = MomentRandomForestTrainTestModel.get_xys_from_results(self.features) # using dis_y only del xs['dis_u'] del xs['dis_v'] del xys['dis_u'] del xys['dis_v'] model = MomentRandomForestTrainTestModel({'norm_type':'normalize', 'random_state':0}) model.train(xys) result = model.evaluate(xs, ys) self.assertAlmostEquals(result['RMSE'], 0.17634739353518517, places=4)
def test_train_predict(self): xs = MomentRandomForestTrainTestModel.get_xs_from_results( self.features) ys = MomentRandomForestTrainTestModel.get_ys_from_results( self.features) xys = MomentRandomForestTrainTestModel.get_xys_from_results( self.features) # using dis_y only del xs['dis_u'] del xs['dis_v'] del xys['dis_u'] del xys['dis_v'] model = MomentRandomForestTrainTestModel({ 'norm_type': 'normalize', 'random_state': 0 }) model.train(xys) result = model.evaluate(xs, ys) self.assertAlmostEquals(result['RMSE'], 0.17634739353518517, places=4)