EurekaMoments

ロボットや自動車の自律移動に関する知識や技術、プログラミング、ソフトウェア開発について勉強したことをメモするブログ

限られた卓球の試合データから追加データを自動計算してみた

スポーツを10倍楽しむ統計学 (DOJIN選書)

スポーツを10倍楽しむ統計学 (DOJIN選書)

  • 作者:鳥越 規央
  • 出版社/メーカー: 化学同人
  • 発売日: 2015/05/20
  • メディア: 単行本(ソフトカバー)

目次

  • 目次
  • 背景・目的
  • 分析の目標
  • 制約条件
  • サンプルデータの入手
  • 追加データの自動計算
  • 次の取り組み

背景・目的

最近では深層学習などを駆使したスポーツのデータ分析が非常に
盛り上がっていますが、自分も趣味である卓球をテーマに何か
データ分析をしてみたくなりました。
その第一歩として、今回は限られたデータから追加でデータを
自動計算させる事にトライしてみたので紹介します。

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Fundamentals of Classification by Supervised Learning ~2 dimensional input~

Table of Contents

  • Table of Contents
  • Introduction
  • Author
  • GitHub
  • Generating sample data
  • 2 classes classification
    • Logistic regression model on 2 dimension
    • Mean cross entropy error
    • Calculating parameter by Gradient method
  • 3 classes classification
    • Logistic regression model for 3 classes classification
    • Mean cross entropy error
    • Calculating parameter by Gradient method
  • Question

Introduction

I released the following article about 1 dimensional classification by supervised learning.
www.eureka-moments-blog.com
In this article, I wrote a memo about 2 dimensional classification into 2 or 3 classes. And then, I referred to the following book.

Pythonで動かして学ぶ!あたらしい機械学習の教科書 第2版 (AI & TECHNOLOGY)

Pythonで動かして学ぶ!あたらしい機械学習の教科書 第2版 (AI & TECHNOLOGY)

Author

researchmap.jp

GitHub

Sample codes and any other related files are released at the following GitHub repository.
github.com

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Fundamentals of Classification by Supervised Learning ~1 dimensional input~

Table of Contents

  • Table of Contents
  • Introduction
  • Author
  • GitHub
  • Input sample data: Weight of insect
  • Target sample data: Sex of insect
  • Sample data generation
  • Policy to solve problem
  • Classification with probability
  • Maximum likelihood estimation
  • Logistic Regression
  • Cross entropy error
  • Calculating parameter by Gradient method
  • Summary of classification sequence
    • 1. Creating "Logistic regression model"
    • 2. Defining "Likelihood"
    • 3. Defining "Mean cross entropy error"
    • 4. Calculating "Partial derivative" of
    • 5. Calculating parameters by Gradient method

Introduction

This is my studying log about machine learning, supervised classification. I referred to a following book.

Pythonで動かして学ぶ!あたらしい機械学習の教科書 第2版 (AI & TECHNOLOGY)

Pythonで動かして学ぶ!あたらしい機械学習の教科書 第2版 (AI & TECHNOLOGY)


I extracted some important points and some related sample python codes and wrote them as memo. In particular, this article focuses on 2 class classification with 1 dimensional input.

Author

researchmap.jp

GitHub

Sample codes and any other related files are released at the following GitHub repository.
github.com

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SLAM Beginner's Program ~Input/Output~

Table of Contents

  • Table of Contents
  • Introduction
  • Reference
  • External sensor
  • Internal sensor
  • Robot pose
  • Map
    • Metrical map
    • Topological map
    • Feature map
    • Occupancy grid map
      • Expressing change over time
      • Disadvantage of Occupancy grid map

Introduction

I studied SLAM refer to the following book. I'm introducing my studying log in dividing into multiple articles. In this article, I wrote an system configuration of SLAM sample program.

Reference

SLAM入門: ロボットの自己位置推定と地図構築の技術

SLAM入門: ロボットの自己位置推定と地図構築の技術

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SLAM Beginner's Program ~Fundamental knowledge~

Table of contents

  • Table of contents
  • Introduction
  • Reference
  • Executable form
  • Type
  • Diagram of 2D SLAM
  • Accumulated error problem
  • Data association
  • Loop closure
  • Batch processing
  • Sequential processing

Introduction

I studied SLAM refer to the following book. I'm introducing my studying log in dividing into multiple articles. In this article, I wrote some fundamental knowledge of SLAM as memo.

Reference

SLAM入門: ロボットの自己位置推定と地図構築の技術

SLAM入門: ロボットの自己位置推定と地図構築の技術

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How to decide required performance of Obstacle detection function

Table of content

  • Table of content
  • Introduction
  • Reference
  • Finding boundary of obstacle
    • Taking a detour
  • Detecting an obstacle in a short term
    • Case of running out into road
    • How to calculate a braking distance
      • Free running distance
      • Braking distance
      • Reference about brake
    • How to decide reaction performance
      • Distance for halving collision accident
    • Calculating relative velocity
    • Predicting obstacle behavior
    • Judging the object is obstacle or not

Introduction

Obstacle detection system needs multiple functions to detect an obstacle and escape a collision. When we develop the system, it is very important to decide that the system should have what kind of function and how performance the functions should satisfy.
In this article, I'm introducing how to consider functions and performance the obstacle detection system should satisfy.

Reference

I read the following book as a reference. This book has good exlanations about techniques of perception with LiDAR, RADAR and Camera.

トランジスタ技術 2019年 03月号

トランジスタ技術 2019年 03月号

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What is a thing a software test professional take care?

Table of content

  • Table of content
  • Introduction
  • Test has already started as of requirement definition
  • How should we decide to write a test code or not?
  • How can we reduce test cases effectively?
  • What we should do to improve our test

Introduction

I read the following article for studying "What should we take care in testing software?" and "How can we do a good test?".

employment.en-japan.com

The content of this article is an interview with 2 engineers, Hiroyuki Sato and Shunsuke Tanioka who are working at SHIFT Inc.. This company's businesses are software quality assurance and testing.
I am writing about a thing what I studied by reading this interview.

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