Dell Precision Tower 3620 快速入门指南
2021-09-29 09:02:09 1.14MB T3620 DELL
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混淆矩阵 衡量一个分类器性能的更好的办法是混淆矩阵。它基于的思想是:计算类别A被分类为类别B的次数。例如在查看分类器将图片5分类成图片3时,我们会看混淆矩阵的第5行以及第3列。 为了计算一个混淆矩阵,我们首先需要有一组预测值,之后再可以将它们与标注值(label)进行对比。我们也可以在测试集上做预测,但是最好是先不要动测试集(测试集仅需要在最后的阶段使用,在我们有了一个准备上线的分类器后,最后再用测试集测试性能)。接下来,我们可以使用cross_val_predict() 方法: from sklearn.model_selection import cross_val_predict y_t
2021-09-27 11:36:49 196KB al ALL c
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该工具箱定义了一个新的 mp 类,允许通过与 GNU 多精度算术库和 MPFR 库的 mex 接口库在 Matlab 中创建多个精度对象。
2021-08-03 09:33:21 920KB 开源软件
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HP Precisionscan Pro 3.1 似乎不支持所有的HP扫描仪
2021-07-17 11:18:26 59.1MB HP Precision scan 3.1
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TI Precision Labs - DACs
2021-06-29 17:34:39 56.22MB DAC
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libtommath:LibTomMath是一个完全用C编写的免费开源可移植数字理论多精度整数库
2021-06-22 22:23:13 315KB c math mpi multi-precision
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给定一个混淆矩阵作为输入,该函数计算感兴趣的主要统计数据(包括宏 AVG 和 microAVG): 'name' 'classes' 'macroAVG' 'microAVG' 精度// / xo 特异性 // / / xo 灵敏度 / / / xo 准确度 / / / xo F1-score // / / xo
2021-06-07 20:30:56 2KB matlab
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ADI DAC选型指南
2021-06-02 09:01:49 711KB ADI DAC 选型指南
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In this paper, we focus on the problem of motion tracking in unknown environments using visual and inertial sensors.We term this estimation task visual-inertial odometry (VIO), in analogy to the well-known visual-odometry problem. We present a detailed study of EKF-based VIO algorithms, by comparing both their theoretical properties and empirical performance. We show that an EKF formulation where the state vector comprises a sliding window of poses (the MSCKF algorithm) attains better accuracy, consistency, and computational efficiency than the SLAM formulation of the EKF, in which the state vector contains the current pose and the features seen by the camera. Moreover, we prove that both types of EKF approaches are inconsistent, due to the way in which Jacobians are computed. Specifically, we show that the observability properties of the EKF’s linearized system models do not match those of the underlying system, which causes the filters to underestimate the uncertainty in the state estimates. Based on our analysis, we propose a novel, real-time EKF-based VIO algorithm, which achieves consistent estimation by (i) ensuring the correct observability properties of its linearized system model, and (ii) performing online estimation of the camera-to-IMU calibration parameters. This algorithm, which we term MSCKF 2.0, is shown to achieve accuracy and consistency higher than even an iterative, sliding-window fixed-lag smoother, in both Monte-Carlo simulations and real-world testing. I
2021-05-28 16:20:21 735KB VIO limingyang
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High Precision Electronic Scale Design Based on MCU; High Precision Electronic Scale Design Based on MCU
2021-05-23 23:54:04 226KB MCU
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