From af148ed971de3352a361ff53b6bff6db92929158 Mon Sep 17 00:00:00 2001
From: duanmu <774052669@qq.com>
Date: Thu, 16 Mar 2023 21:39:36 +0800
Subject: [PATCH] =?UTF-8?q?=E8=B0=83=E4=BA=86=E4=B8=80=E4=B8=AA=E8=BF=98?=
 =?UTF-8?q?=E4=B8=8D=E9=94=99=E7=9A=84=E5=8F=82=E6=95=B0?=
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---
 classifer.py | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/classifer.py b/classifer.py
index b0de65c..8764e5a 100755
--- a/classifer.py
+++ b/classifer.py
@@ -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]