NanjingForestryUniversity/supermachine-wood
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调了一个还不错的参数

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duanmu 2023-03-16 21:39:36 +08:00
parent b37e064594
commit af148ed971
1 changed files with 5 additions and 5 deletions
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10
classifer.py
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@ -31,7 +31,7 @@ sys.path.append(os.getcwd())
from root_dir import ROOT_DIR
import utils
FEATURE_INDEX = [0,1,2]
FEATURE_INDEX = [1,2,3]
delete_columns = 10 # 已弃用
num_bins = 10
@ -60,10 +60,10 @@ class WoodClass(object):
self._single_pick = single_pick_mode
self.set_purity(self.pur)
self.change_pick_mode(single_pick_mode)
# self.model = LogisticRegression(C=1e5)
self.model = LogisticRegression(C=1e5)
self.left_correct = left_correct
# self.model = KNeighborsClassifier()
self.model = DecisionTreeClassifier()
# self.model = DecisionTreeClassifier()
else:
self.load(load_from)
self.isCorrect = False
@ -122,7 +122,7 @@ class WoodClass(object):
model_name = self.save(file_name)
return model_name
def fit(self, x, y, test_size=0.3):
def fit(self, x, y, test_size=0.7):
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=test_size, random_state=0)
self.model.fit(x_train, y_train)
y_pred = self.model.predict(x_test)
@ -317,7 +317,7 @@ class WoodClass(object):
(x[:, 0] > bins[second_hist_number]) & (x[:, 0] < bins[second_hist_number + 1])), :]
# hist, bins = np.histogram(x[:, 0], bins=5)
# hist, bins = np.histogram(x[:, 0], bins=9)
# sorted_indices = np.argsort(hist)
# hist_number = sorted_indices[-1]
# second_hist_number = sorted_indices[-2]