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@@ -288,7 +288,6 @@ class ChanLunClassifier:
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# 使用ChanLun获取bi_list
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klc_list = self.chan.get_klc_list(dataframe)
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bi_list = self.chan.cal_bi_list(klc_list)
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seg_list = self.chan.get_seg_list(bi_list)
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# 筛选方向为UP的bi的起始klc
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feature_data = []
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labels = []
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@@ -312,7 +311,6 @@ class ChanLunClassifier:
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# 保存第一个样本的特征名称,用于CSV列名
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if len(feature_keys) == 0:
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feature_keys = list(features.keys())
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# 将特征转换为模型可用的格式
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feature_vec = []
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for key, value in features.items():
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@@ -335,7 +333,8 @@ class ChanLunClassifier:
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feature_data.append(feature_vec)
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labels.append(label)
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for index, key in enumerate(feature_keys):
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print(index, key, feature_data[0][index])
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# 如果需要保存到CSV
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if save_csv:
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# 创建DataFrame保存特征数据
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