多目标滤波性能评价的新一致性指标

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本文档标题为《A Consistent Metric for Performance Evaluation of Multi-Object Filters》,着重于多目标滤波器性能评估领域的一项关键工作。在传统的多目标滤波理论中,尽管误差或遗漏距离(miss-distance)的概念在控制和滤波问题中扮演着基础角色,但现有的评价指标并未提供一个令人满意的定义,特别是在多目标滤波的复杂环境中。 多目标系统的研究起源于20世纪70年代早期,主要由航空、雷达、声纳、导航以及空中交通管理等应用驱动。在这些应用中,精确的目标跟踪是至关重要的,然而,如何量化和比较多个目标跟踪的性能,尤其是在存在杂波干扰的情况下,是一个长期存在的挑战。当前的多目标性能评估指标存在一些不一致性和局限性,它们可能无法准确反映滤波器在处理复杂场景下的实际效能。 论文作者Dominic Schuhmacher、Ba-Tuong Vo和Ba-Ngu Vo指出,衡量多目标滤波器的性能时,比如在误漏概率、目标状态估计精度、联合检测与跟踪的效率等方面,现有的评估方法可能无法提供一致和直观的理解。他们认识到,缺乏一个既数学上严谨又直观的指标体系,可能导致对滤波器性能的误解和优化方向的偏离。 为了克服这些问题,本文提出了一种新的多目标遗漏距离评估指标。这种新指标旨在解决现有方法的不足,通过引入 Wasserstein 距离(一种衡量随机集之间距离的统计学概念)或其他适当的数学工具,为多目标系统的性能评价提供了一个更为一致且实用的框架。论文详细阐述了新指标的数学原理,以及它如何改进了对多目标滤波器在真实应用场景中的表现的评估。 该文的工作对于改善多目标跟踪算法的设计、优化和比较具有重要意义,尤其是在现代信息技术如物联网和智能监控中,对多目标动态环境下的高效跟踪性能有很高的实际价值。因此,研究者和工程师们可以借助这篇论文中的理论和方法,提升多目标滤波器的性能评估标准,从而推动相关技术的发展。
2023-06-09 上传

ValueError Traceback (most recent call last) Input In [35], in <cell line: 2>() 1 scores, values = [], [] 2 for education in education_list: ----> 3 score, y = predict(data, education) 4 scores.append(score) 5 values.append(y) Input In [32], in predict(data, education) 13 # model 训练 14 model = LinearRegression() ---> 15 model.fit(x, y) 16 # model 预测 17 X = [[i] for i in range(11)] File D:\big data\lib\site-packages\sklearn\linear_model\_base.py:662, in LinearRegression.fit(self, X, y, sample_weight) 658 n_jobs_ = self.n_jobs 660 accept_sparse = False if self.positive else ["csr", "csc", "coo"] --> 662 X, y = self._validate_data( 663 X, y, accept_sparse=accept_sparse, y_numeric=True, multi_output=True 664 ) 666 if sample_weight is not None: 667 sample_weight = _check_sample_weight(sample_weight, X, dtype=X.dtype) File D:\big data\lib\site-packages\sklearn\base.py:581, in BaseEstimator._validate_data(self, X, y, reset, validate_separately, **check_params) 579 y = check_array(y, **check_y_params) 580 else: --> 581 X, y = check_X_y(X, y, **check_params) 582 out = X, y 584 if not no_val_X and check_params.get("ensure_2d", True): File D:\big data\lib\site-packages\sklearn\utils\validation.py:964, in check_X_y(X, y, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric, estimator) 961 if y is None: 962 raise ValueError("y cannot be None") --> 964 X = check_array( 965 X, 966 accept_sparse=accept_sparse, 967 accept_large_sparse=accept_large_sparse, 968 dtype=dtype, 969 order=order, 970 copy=copy, 971 force_all_finite=force_all_finite, 972 ensure_2d=ensure_2d, 973 allow_nd=allow_nd, 974 ensure_min_samples=ensure_min_samples, 975 ensure_min_features=ensure_min_features, 976 estimator=estimator, 977 ) 979 y = _check_y(y, multi_output=multi_output, y_numeric=y_numeric) 981 check_consistent_length(X, y) File D:\big data\lib\site-packages\sklearn\utils\validation.py:746, in check_array(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator) 744 array = array.astype(dtype, casting="unsafe", copy=False) 745 else: --> 746 array = np.asarray(array, order=order, dtype=dtype) 747 except ComplexWarning as complex_warning: 748 raise ValueError( 749 "Complex data not supported\n{}\n".format(array) 750 ) from complex_warning ValueError: could not convert string to float: '若干'

2023-06-07 上传

import pandas as pd from sklearn.linear_model import LinearRegression # 读取数据表 data = pd.read_excel('D://数据1.xlsx', sheet_name='4') # 将数据表分为X和y两部分,其中X为前三列数据,y为最后一列数据 X = data.iloc[:, :4] y = data.iloc[-1, :] # 拟合线性回归模型 model = LinearRegression() model.fit(X, y) # 预测每一列的预测值 y_pred = model.predict(X) # 输出每一列的预测值 print(y_pred)出现Traceback (most recent call last): File "D:\anaconda\lib\site-packages\IPython\core\interactiveshell.py", line 3460, in run_code exec(code_obj, self.user_global_ns, self.user_ns) File "<ipython-input-2-1c2c07b8ba7f>", line 1, in <module> runfile('D:\\Users\\Admin\\PycharmProjects\\pythonProject2\\线性预测8.py', wdir='D:\\Users\\Admin\\PycharmProjects\\pythonProject2') File "D:\PyCharm 2023.1.1\plugins\python\helpers\pydev\_pydev_bundle\pydev_umd.py", line 198, in runfile pydev_imports.execfile(filename, global_vars, local_vars) # execute the script File "D:\PyCharm 2023.1.1\plugins\python\helpers\pydev\_pydev_imps\_pydev_execfile.py", line 18, in execfile exec(compile(contents+"\n", file, 'exec'), glob, loc) File "D:\Users\Admin\PycharmProjects\pythonProject2\线性预测8.py", line 13, in <module> model.fit(X, y) File "D:\anaconda\lib\site-packages\sklearn\linear_model\_base.py", line 648, in fit X, y = self._validate_data( File "D:\anaconda\lib\site-packages\sklearn\base.py", line 565, in _validate_data X, y = check_X_y(X, y, **check_params) File "D:\anaconda\lib\site-packages\sklearn\utils\validation.py", line 1124, in check_X_y check_consistent_length(X, y) File "D:\anaconda\lib\site-packages\sklearn\utils\validation.py", line 397, in check_consistent_length raise ValueError( ValueError: Found input variables with inconsistent numbers of samples: [1258, 4]错误

2023-05-30 上传

# Look through unique values in each categorical column categorical_cols = train_df.select_dtypes(include="object").columns.tolist() for col in categorical_cols: print(f"{col}", f"Number of unique entries: {len(train_df[col].unique().tolist())},") print(train_df[col].unique().tolist()) def plot_bar_chart(df, columns, grid_rows, grid_cols, x_label='', y_label='', title='', whole_numbers_only=False, count_labels=True, as_percentage=True): num_plots = len(columns) grid_size = grid_rows * grid_cols num_rows = math.ceil(num_plots / grid_cols) if num_plots == 1: fig, axes = plt.subplots(1, 1, figsize=(12, 8)) axes = [axes] # Wrap the single axes in a list for consistent handling else: fig, axes = plt.subplots(num_rows, grid_cols, figsize=(12, 8)) axes = axes.ravel() # Flatten the axes array to iterate over it for i, column in enumerate(columns): df_column = df[column] if whole_numbers_only: df_column = df_column[df_column % 1 == 0] ax = axes[i] y = [num for (s, num) in df_column.value_counts().items()] x = [s for (s, num) in df_column.value_counts().items()] ax.bar(x, y, color='blue', alpha=0.5) try: ax.set_xticks(range(x[-1], x[0] + 1)) except: pass ax.set_xlabel(x_label) ax.set_ylabel(y_label) ax.set_title(title + ' - ' + column) if count_labels: df_col = df_column.value_counts(normalize=True).mul(100).round(1).astype(str) + '%' for idx, (year, value) in enumerate(df_column.value_counts().items()): if as_percentage == False: ax.annotate(f'{value}\n', xy=(year, value), ha='center', va='center') else: ax.annotate(f'{df_col[year]}\n', xy=(year, value), ha='center', va='center', size=8) if num_plots < grid_size: for j in range(num_plots, grid_size): fig.delaxes(axes[j]) # Remove empty subplots if present plt.tight_layout() plt.show()

2023-06-11 上传