Python 对多维数据使用knn时出错

我是机器学习的初学者，我试图将多维数据分为两类。每个数据点的浮点值为40x6。首先，我已经阅读了我的csv文件。在此文件中，快照编号表示数据点

以下是python中的代码：

import pandas as pd
  1 import numpy as np
  2 import matplotlib.pyplot as plot
  3
  4 from sklearn.neighbors import KNeighborsClassifier
  5
  6 # Read csv data into pandas data frame
  7 data_frame = pd.read_csv('data.csv')
  8
  9 extract_columns = ['LinearAccX', 'LinearAccY', 'LinearAccZ', 'Roll', 'pitch', 'compass']
 10
 11 # Number of sample in one shot
 12 samples_per_shot = 40
 13
 14 # Calculate number of shots in dataframe
 15 count_of_shots = len(data_frame.index)/samples_per_shot
 16
 17 # Initialize Empty data frame
 18 training_index = range(count_of_shots)
 19 training_data_list = []
 20
 21 # flag for backward compatibility
 22 make_old_data_compatible_with_new = 0
 23
 24 if make_old_data_compatible_with_new:
 25     # Convert 40 shot data to 25 shot data
 26     # New logic takes 25 samples/shot
 27     # old logic takes 40 samples/shot
 28     start_shot_sample_index = 9
 29     end_shot_sample_index = 34
 30 else:
 31     # Start index from 1 and continue till lets say 40
 32     start_shot_sample_index = 1
 33     end_shot_sample_index = samples_per_shot
 34
 35 # Extract each shot into pandas series
 36 for shot in range(count_of_shots):
 37      # Extract current shot
 38      current_shot_data = data_frame[data_frame['shot_no']==(shot+1)]
 39
 40      # Select only the following column
 41      selected_columns_from_shot = current_shot_data[extract_columns]
 42
 43      # Select columns from selected rows
 44      # Find start and end row indexes
 45      current_shot_data_start_index = shot * samples_per_shot + start_shot_sample_index
 46      current_shot_data_end_index = shot * samples_per_shot + end_shot_sample_index
 47      selected_rows_from_shot = selected_columns_from_shot.ix[current_shot_data_start_index:curren    t_shot_data_end_index]
 48
 49      # Append to list of lists
 50      # Convert selected short into multi-dimensional array
 51  

    training_data_list.append([selected_columns_from_shot[extract_columns[index]].values.tolist(    ) for index in range(len(extract_columns))])
  8
  7 # Append each sliced shot into training data
  6 training_data = pd.DataFrame(training_data_list, columns=extract_columns)
  5 training_features = [1 for i in range(count_of_shots)]
  4 knn = KNeighborsClassifier(n_neighbors=3)
  3 knn.fit(training_data, training_features)

training_data_list.append([selected_columns_from_shot[extract_columns[index]].values.tolist(    ) for index in range(len(extract_columns))])

运行上述代码后，我得到一个错误

ValueError:使用序列设置数组元素

排队

knn.fit(training_data, training_features)

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你能确认你的训练数据的形状是[n_样本，n_特征]并且训练特征的长度与训练特征的长度相同吗？你能详细说明数据的形状[n_样本，n_特征]吗。在这种情况下，training_功能和training data的长度为16。请确认training_data不是1d列表。