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import
numpy as np
import
matplotlib.pyplot as plt
# 生成数据
def
gen_data(x1, x2):
y
=
np.sin(x1)
*
1
/
2
+
np.cos(x2)
*
1
/
2
+
0.1
*
x1
return
y
def
load_data():
x1_train
=
np.linspace(
0
,
50
,
500
)
x2_train
=
np.linspace(
-
10
,
10
,
500
)
data_train
=
np.array([[x1, x2, gen_data(x1, x2)
+
np.random.random(
1
)
-
0.5
]
for
x1, x2
in
zip
(x1_train, x2_train)])
x1_test
=
np.linspace(
0
,
50
,
100
)
+
np.random.random(
100
)
*
0.5
x2_test
=
np.linspace(
-
10
,
10
,
100
)
+
0.02
*
np.random.random(
100
)
data_test
=
np.array([[x1, x2, gen_data(x1, x2)]
for
x1, x2
in
zip
(x1_test, x2_test)])
return
data_train, data_test
train, test
=
load_data()
# train的前两列是x，后一列是y，这里的y有随机噪声
x_train, y_train
=
train[:, :
2
], train[:,
2
]
x_test, y_test
=
test[:, :
2
], test[:,
2
]
# 同上，但这里的y没有噪声
# 回归部分
def
try_different_method(model, method):
model.fit(x_train, y_train)
score
=
model.score(x_test, y_test)
result
=
model.predict(x_test)
plt.figure()
plt.plot(np.arange(
len
(result)), y_test,
"go-"
, label
=
"True value"
)
plt.plot(np.arange(
len
(result)), result,
"ro-"
, label
=
"Predict value"
)
plt.title(f
"method:{method}---score:{score}"
)
plt.legend(loc
=
"best"
)
plt.show()
# 方法选择
# 1.决策树回归
from
sklearn
import
tree
model_decision_tree_regression
=
tree.DecisionTreeRegressor()
# 2.线性回归
from
sklearn.linear_model
import
LinearRegression
model_linear_regression
=
LinearRegression()
# 3.SVM回归
from
sklearn
import
svm
model_svm
=
svm.SVR()
# 4.kNN回归
from
sklearn
import
neighbors
model_k_neighbor
=
neighbors.KNeighborsRegressor()
# 5.随机森林回归
from
sklearn
import
ensemble
model_random_forest_regressor
=
ensemble.RandomForestRegressor(n_estimators
=
20
)
# 使用20个决策树
# 6.Adaboost回归
from
sklearn
import
ensemble
model_adaboost_regressor
=
ensemble.AdaBoostRegressor(n_estimators
=
50
)
# 这里使用50个决策树
# 7.GBRT回归
from
sklearn
import
ensemble
model_gradient_boosting_regressor
=
ensemble.GradientBoostingRegressor(n_estimators
=
100
)
# 这里使用100个决策树
# 8.Bagging回归
from
sklearn
import
ensemble
model_bagging_regressor
=
ensemble.BaggingRegressor()
# 9.ExtraTree极端随机数回归
from
sklearn.tree
import
ExtraTreeRegressor
model_extra_tree_regressor
=
ExtraTreeRegressor()
|
