使用ML.NET实现NBA得分预测
使用ML.NET实现NBA得分预测
导读：ML.NET系列文章
ML.NET已经发布了v0.2版本，新增了聚类训练器，执行性能进一步增强。本文将介绍一种特殊的回归——泊松回归，并以NBA比赛得分预测的案例来演练。
泊松回归 Poisson regression
前面的文章已提过，回归是用来预测连续值的，泊松回归是其中一种，其特殊在仅用于预测正整数，通常为计数类的数值。泊松分布是离散分布，所以特征值和标签值应为相同（或接近相同）时间间隔下的独立随机事件。
那么什么场景是符合计数，可以适用泊松回归呢？举几个例子，比如共享单车的调度，每一处地域中心，每隔1小时都要统计借车和还车数，根据这个统计我们就可以预测下一个小时此处地域需要调配多少车辆才能满足需要。再比如，公司每个月都有离职员工，那么人力资源部门就可以对月人员流失数进行计数，然后通过泊松回归来预测下个月的流失情况，以便提早采取措施做好招聘计划。
是不是有一点感觉了，本次我们用大家喜欢的NBA比赛得分来进行演练，因为比赛得分正好也是一种计数，也符合连续相同时间间隔（比赛时长的大体相近），比赛结果具有不确定性，所以也是泊松回归大显身手的地方，为了易于理解，我将示范预测的是主场球队的得分。
NBA比赛数据
本案例数据来源Kaggle.com，内容是NBA Team Game Stats from 2014 to 2018，这份数据集收集了最近4年的NBA比赛，格式类似如下：
"","Team","Game","Date","Home","Opponent","WINorLOSS","TeamPoints","OpponentPoints","FieldGoals","FieldGoalsAttempted","FieldGoals.","X3PointShots","X3PointShotsAttempted","X3PointShots.","FreeThrows","FreeThrowsAttempted","FreeThrows.","OffRebounds","TotalRebounds","Assists","Steals","Blocks","Turnovers","TotalFouls","Opp.FieldGoals","Opp.FieldGoalsAttempted","Opp.FieldGoals.","Opp.3PointShots","Opp.3PointShotsAttempted","Opp.3PointShots.","Opp.FreeThrows","Opp.FreeThrowsAttempted","Opp.FreeThrows.","Opp.OffRebounds","Opp.TotalRebounds","Opp.Assists","Opp.Steals","Opp.Blocks","Opp.Turnovers","Opp.TotalFouls"
"1","ATL","1",2014-10-29,"Away","TOR","L","102","109","40","80",".500","13","22",".591","9","17",".529","10","42","26","6","8","17","24","37","90",".411","8","26",".308","27","33",".818","16","48","26","13","9","9","22"
"2","ATL","2",2014-11-01,"Home","IND","W","102","92","35","69",".507","7","20",".350","25","33",".758","3","37","26","10","6","12","20","31","81",".383","12","32",".375","18","21",".857","11","44","25","5","5","18","26"
"3","ATL","3",2014-11-05,"Away","SAS","L","92","94","38","92",".413","8","25",".320","8","11",".727","10","37","26","14","5","13","25","31","69",".449","5","17",".294","27","38",".711","11","50","25","7","9","19","15"
"4","ATL","4",2014-11-07,"Away","CHO","L","119","122","43","93",".462","13","33",".394","20","26",".769","7","38","28","8","3","19","33","48","97",".495","6","21",".286","20","27",".741","11","51","31","6","7","19","30"
"5","ATL","5",2014-11-08,"Home","NYK","W","103","96","33","81",".407","9","22",".409","28","36",".778","12","41","18","10","5","8","17","40","84",".476","8","21",".381","8","11",".727","13","44","26","2","6","15","29"
"6","ATL","6",2014-11-10,"Away","NYK","W","91","85","27","71",".380","10","27",".370","27","28",".964","9","38","20","7","3","15","16","36","83",".434","6","26",".231","7","12",".583","11","40","23","4","2","15","26"
"7","ATL","7",2014-11-12,"Home","UTA","W","100","97","39","76",".513","9","20",".450","13","18",".722","13","46","23","8","4","18","12","43","86",".500","5","23",".217","6","12",".500","8","30","28","12","8","11","17"
"8","ATL","8",2014-11-14,"Home","MIA","W","114","103","42","75",".560","11","28",".393","19","23",".826","3","36","33","10","5","13","20","35","74",".473","10","21",".476","23","25",".920","5","32","27","10","3","14","20"
各字段如下：
比赛基本信息:主场Team，比赛场次序号Game，比赛日期Date，主队Home，客队Opponent，主队胜负Win or Loss。
比赛主客队技术数据：Team Points，Field Goals，Field Goals Attempted，Field Goals Percentage，3 Point Shots，3 Point Shots Attempted，3 Point Shots Percentage，Free Throws，Free Throws Attempted，Free Throws Percentage，Offensive Rebounds，Total Rebounds，Assists，Steals，Blocks，Turnovers，Total Fouls。
这些指标反映了主客队投篮出手次数、命中数、命中率，三分球的出手次数、命中数、命中率，罚球的出手次数、命中数、命中率，助攻，抢断，犯规等，这些都是我们在看NBA时常见的统计。
由于只有这一份数据，为了分别用于训练、评估和预测，我将数据集按7:2:1的比例进行分割。
代码片段分解
定义原始数据结构、预测数据结构,TeamPoints是主队得分，是本次示例要预测的目标，因此定义为标签字段。
public class Match
{
[Column(ordinal: "0")]
public string Id;
[Column(ordinal: "1")]
public string Team;
[Column(ordinal: "2")]
public string Game;
[Column(ordinal: "3")]
public string Date;
[Column(ordinal: "4")]
public string Home;
[Column(ordinal: "5")]
public string Opponent;
[Column(ordinal: "6")]
public string WINorLOSS;
[Column(ordinal: "7", name: "Label")]
public float TeamPoints;
[Column(ordinal: "8")]
public float OpponentPoints;
[Column(ordinal: "9")]
public float FieldGoals;
[Column(ordinal: "10")]
public float FieldGoalsAttempted;
[Column(ordinal: "11")]
public float FieldGoals_;
[Column(ordinal: "12")]
public float X3PointShots;
[Column(ordinal: "13")]
public float X3PointShotsAttempted;
[Column(ordinal: "14")]
public float X3PointShots_;
[Column(ordinal: "15")]
public float FreeThrows;
[Column(ordinal: "16")]
public float FreeThrowsAttempted;
[Column(ordinal: "17")]
public float FreeThrows_;
[Column(ordinal: "18")]
public float OffRebounds;
[Column(ordinal: "19")]
public float TotalRebounds;
[Column(ordinal: "20")]
public float Assists;
[Column(ordinal: "21")]
public float Steals;
[Column(ordinal: "22")]
public float Blocks;
[Column(ordinal: "23")]
public float Turnovers;
[Column(ordinal: "24")]
public float TotalFouls;
[Column(ordinal: "25")]
public float Opp_FieldGoals;
[Column(ordinal: "26")]
public float Opp_FieldGoalsAttempted;
[Column(ordinal: "27")]
public float Opp_FieldGoals_;
[Column(ordinal: "28")]
public float Opp_3PointShots;
[Column(ordinal: "29")]
public float Opp_3PointShotsAttempted;
[Column(ordinal: "30")]
public float Opp_3PointShots_;
[Column(ordinal: "31")]
public float Opp_FreeThrows;
[Column(ordinal: "32")]
public float Opp_FreeThrowsAttempted;
[Column(ordinal: "33")]
public float Opp_FreeThrows_;
[Column(ordinal: "34")]
public float Opp_OffRebounds;
[Column(ordinal: "35")]
public float Opp_TotalRebounds;
[Column(ordinal: "36")]
public float Opp_Assists;
[Column(ordinal: "37")]
public float Opp_Steals;
[Column(ordinal: "38")]
public float Opp_Blocks;
[Column(ordinal: "39")]
public float Opp_Turnovers;
[Column(ordinal: "40")]
public float Opp_TotalFouls;
}
public class MatchPrediction
{
[ColumnName("Score")]
public float TeamPoints;
}
加载数据部分
const string DATA_PATH = "data/nba.games.stats.csv";
static ICollection<Match> LoadData()
{
var matches = new List<Match>();
using (var sr = new StreamReader(File.OpenRead(DATA_PATH)))
{
sr.ReadLine();
while (!sr.EndOfStream)
{
var line = sr.ReadLine();
var values = line.Split(",");
var match = new Match
{
Id = values[0].Trim('"'),
Team = values[1].Trim('"'),
Game = values[2].Trim('"'),
Date = values[3].Trim('"'),
Home = values[4].Trim('"'),
Opponent = values[5].Trim('"'),
WINorLOSS = values[6].Trim('"'),
TeamPoints = Convert.ToSingle(values[7].Trim('"')),
OpponentPoints = Convert.ToSingle(values[8].Trim('"')),
FieldGoals = Convert.ToSingle(values[9].Trim('"')),
FieldGoalsAttempted = Convert.ToSingle(values[10].Trim('"')),
FieldGoals_ = Convert.ToSingle(values[11].Trim('"')),
X3PointShots = Convert.ToSingle(values[12].Trim('"')),
X3PointShotsAttempted = Convert.ToSingle(values[13].Trim('"')),
X3PointShots_ = Convert.ToSingle(values[14].Trim('"')),
FreeThrows = Convert.ToSingle(values[15].Trim('"')),
FreeThrowsAttempted = Convert.ToSingle(values[16].Trim('"')),
FreeThrows_ = Convert.ToSingle(values[17].Trim('"')),
OffRebounds = Convert.ToSingle(values[18].Trim('"')),
TotalRebounds = Convert.ToSingle(values[19].Trim('"')),
Assists = Convert.ToSingle(values[20].Trim('"')),
Steals = Convert.ToSingle(values[21].Trim('"')),
Blocks = Convert.ToSingle(values[22].Trim('"')),
Turnovers = Convert.ToSingle(values[23].Trim('"')),
TotalFouls = Convert.ToSingle(values[24].Trim('"')),
Opp_FieldGoals = Convert.ToSingle(values[25].Trim('"')),
Opp_FieldGoalsAttempted = Convert.ToSingle(values[26].Trim('"')),
Opp_FieldGoals_ = Convert.ToSingle(values[27].Trim('"')),
Opp_3PointShots = Convert.ToSingle(values[28].Trim('"')),
Opp_3PointShotsAttempted = Convert.ToSingle(values[29].Trim('"')),
Opp_3PointShots_ = Convert.ToSingle(values[30].Trim('"')),
Opp_FreeThrows = Convert.ToSingle(values[31].Trim('"')),
Opp_FreeThrowsAttempted = Convert.ToSingle(values[32].Trim('"')),
Opp_FreeThrows_ = Convert.ToSingle(values[33].Trim('"')),
Opp_OffRebounds = Convert.ToSingle(values[34].Trim('"')),
Opp_TotalRebounds = Convert.ToSingle(values[35].Trim('"')),
Opp_Assists = Convert.ToSingle(values[36].Trim('"')),
Opp_Steals = Convert.ToSingle(values[37].Trim('"')),
Opp_Blocks = Convert.ToSingle(values[38].Trim('"')),
Opp_Turnovers = Convert.ToSingle(values[39].Trim('"')),
Opp_TotalFouls = Convert.ToSingle(values[40].Trim('"'))
};
matches.Add(match);
}
}
return matches;
}
训练、评估、预测部分
static PredictionModel<Match, MatchPrediction> Train(IEnumerable<Match> trainData)
{
var pipeline = new LearningPipeline();
pipeline.Add(CollectionDataSource.Create(trainData));
pipeline.Add(new ColumnDropper() { Column = new[] { "Id" } });
pipeline.Add(new CategoricalOneHotVectorizer("Team", "Game", "Date", "Home", "Opponent", "WINorLOSS"));
pipeline.Add(new ColumnConcatenator("Features", "Team", "Game", "Date", "Home", "Opponent", "WINorLOSS", "OpponentPoints", "FieldGoals", "FieldGoalsAttempted", "FieldGoals_", "X3PointShots", "X3PointShotsAttempted", "X3PointShots_", "FreeThrows", "FreeThrowsAttempted", "FreeThrows_", "OffRebounds", "TotalRebounds", "Assists", "Steals", "Blocks", "Turnovers", "TotalFouls", "Opp_FieldGoals", "Opp_FieldGoalsAttempted", "Opp_FieldGoals_", "Opp_3PointShots", "Opp_3PointShotsAttempted", "Opp_3PointShots_", "Opp_FreeThrows", "Opp_FreeThrowsAttempted", "Opp_FreeThrows_", "Opp_OffRebounds", "Opp_TotalRebounds", "Opp_Assists", "Opp_Steals", "Opp_Blocks", "Opp_Turnovers", "Opp_TotalFouls"));
pipeline.Add(new PoissonRegressor());
var model = pipeline.Train<Match, MatchPrediction>();
return model;
}
static void Evaluate(PredictionModel<Match, MatchPrediction> model, IEnumerable<Match> evaluateData)
{
var evaluator = new RegressionEvaluator();
var metric = evaluator.Evaluate(model, CollectionDataSource.Create(evaluateData));
Console.WriteLine("LossFn: {0}", metric.LossFn);
Console.WriteLine("RSquared: {0}", metric.RSquared);
Console.WriteLine("Rms: {0}", metric.Rms);
}
static void Predict(PredictionModel<Match, MatchPrediction> model, IEnumerable<Match> predictData)
{
var predicts = model.Predict(predictData);
var results = predictData.Zip(predicts, (d, p) => (d, p));
foreach (var result in results)
{
Console.WriteLine("Date: {0}, Team: {1} Opponent: {2}, Score: {3}-{4}, Predict Home Score: {5}",
result.d.Date, result.d.Team, result.d.Opponent, result.d.TeamPoints, result.d.OpponentPoints, result.p.TeamPoints);
}
}
最后是Main调用部分
static void Main(string[] args)
{
var data = LoadData();
var trainCount = Convert.ToInt32(data.Count * 0.7);
var evaluateCount = Convert.ToInt32(data.Count * 0.2);
var trainData = data.Take(trainCount);
var evaluateData = data.Skip(trainCount).Take(evaluateCount);
var predictData = data.Skip(trainCount + evaluateCount);
var model = Train(trainData);
Evaluate(model, evaluateData);
Predict(model, predictData);
}
执行结果
结尾
可以看到，最近的NBA比赛主队预测得分与真实结果对比，正确率已相当可观了，由于特征值都是比赛技术数据，用在以后的比赛时，可根据比赛进行的实时情况不断更新，便可越来越接近结果。
对球迷来说这可是一件神器呀。想想2018世界杯也马上要开始了，保罗、阿喀琉斯什么的都弱爆了，相信小伙伴们也要尝试一下ML.NET的套路了吧，记得拿到历年完整的数据哟！
完整代码如下：
using Microsoft.ML;
using Microsoft.ML.Data;
using Microsoft.ML.Models;
using Microsoft.ML.Runtime.Api;
using Microsoft.ML.Runtime.Learners;
using Microsoft.ML.Trainers;
using Microsoft.ML.Transforms;
using System;
using System.Collections.Generic;
using System.IO;
using System.Linq;
namespace NBAPrediction
{
class Program
{
const string DATA_PATH = "data/nba.games.stats.csv";
static ICollection<Match> LoadData()
{
var matches = new List<Match>();
using (var sr = new StreamReader(File.OpenRead(DATA_PATH)))
{
sr.ReadLine();
while (!sr.EndOfStream)
{
var line = sr.ReadLine();
var values = line.Split(",");
var match = new Match
{
Id = values[0].Trim('"'),
Team = values[1].Trim('"'),
Game = values[2].Trim('"'),
Date = values[3].Trim('"'),
Home = values[4].Trim('"'),
Opponent = values[5].Trim('"'),
WINorLOSS = values[6].Trim('"'),
TeamPoints = Convert.ToSingle(values[7].Trim('"')),
OpponentPoints = Convert.ToSingle(values[8].Trim('"')),
FieldGoals = Convert.ToSingle(values[9].Trim('"')),
FieldGoalsAttempted = Convert.ToSingle(values[10].Trim('"')),
FieldGoals_ = Convert.ToSingle(values[11].Trim('"')),
X3PointShots = Convert.ToSingle(values[12].Trim('"')),
X3PointShotsAttempted = Convert.ToSingle(values[13].Trim('"')),
X3PointShots_ = Convert.ToSingle(values[14].Trim('"')),
FreeThrows = Convert.ToSingle(values[15].Trim('"')),
FreeThrowsAttempted = Convert.ToSingle(values[16].Trim('"')),
FreeThrows_ = Convert.ToSingle(values[17].Trim('"')),
OffRebounds = Convert.ToSingle(values[18].Trim('"')),
TotalRebounds = Convert.ToSingle(values[19].Trim('"')),
Assists = Convert.ToSingle(values[20].Trim('"')),
Steals = Convert.ToSingle(values[21].Trim('"')),
Blocks = Convert.ToSingle(values[22].Trim('"')),
Turnovers = Convert.ToSingle(values[23].Trim('"')),
TotalFouls = Convert.ToSingle(values[24].Trim('"')),
Opp_FieldGoals = Convert.ToSingle(values[25].Trim('"')),
Opp_FieldGoalsAttempted = Convert.ToSingle(values[26].Trim('"')),
Opp_FieldGoals_ = Convert.ToSingle(values[27].Trim('"')),
Opp_3PointShots = Convert.ToSingle(values[28].Trim('"')),
Opp_3PointShotsAttempted = Convert.ToSingle(values[29].Trim('"')),
Opp_3PointShots_ = Convert.ToSingle(values[30].Trim('"')),
Opp_FreeThrows = Convert.ToSingle(values[31].Trim('"')),
Opp_FreeThrowsAttempted = Convert.ToSingle(values[32].Trim('"')),
Opp_FreeThrows_ = Convert.ToSingle(values[33].Trim('"')),
Opp_OffRebounds = Convert.ToSingle(values[34].Trim('"')),
Opp_TotalRebounds = Convert.ToSingle(values[35].Trim('"')),
Opp_Assists = Convert.ToSingle(values[36].Trim('"')),
Opp_Steals = Convert.ToSingle(values[37].Trim('"')),
Opp_Blocks = Convert.ToSingle(values[38].Trim('"')),
Opp_Turnovers = Convert.ToSingle(values[39].Trim('"')),
Opp_TotalFouls = Convert.ToSingle(values[40].Trim('"'))
};
matches.Add(match);
}
}
return matches;
}
static PredictionModel<Match, MatchPrediction> Train(IEnumerable<Match> trainData)
{
var pipeline = new LearningPipeline();
pipeline.Add(CollectionDataSource.Create(trainData));
pipeline.Add(new ColumnDropper() { Column = new[] { "Id" } });
pipeline.Add(new CategoricalOneHotVectorizer("Team", "Game", "Date", "Home", "Opponent", "WINorLOSS"));
pipeline.Add(new ColumnConcatenator("Features", "Team", "Game", "Date", "Home", "Opponent", "WINorLOSS", "OpponentPoints", "FieldGoals", "FieldGoalsAttempted", "FieldGoals_", "X3PointShots", "X3PointShotsAttempted", "X3PointShots_", "FreeThrows", "FreeThrowsAttempted", "FreeThrows_", "OffRebounds", "TotalRebounds", "Assists", "Steals", "Blocks", "Turnovers", "TotalFouls", "Opp_FieldGoals", "Opp_FieldGoalsAttempted", "Opp_FieldGoals_", "Opp_3PointShots", "Opp_3PointShotsAttempted", "Opp_3PointShots_", "Opp_FreeThrows", "Opp_FreeThrowsAttempted", "Opp_FreeThrows_", "Opp_OffRebounds", "Opp_TotalRebounds", "Opp_Assists", "Opp_Steals", "Opp_Blocks", "Opp_Turnovers", "Opp_TotalFouls"));
pipeline.Add(new PoissonRegressor());
var model = pipeline.Train<Match, MatchPrediction>();
return model;
}
static void Evaluate(PredictionModel<Match, MatchPrediction> model, IEnumerable<Match> evaluateData)
{
var evaluator = new RegressionEvaluator();
var metric = evaluator.Evaluate(model, CollectionDataSource.Create(evaluateData));
Console.WriteLine("LossFn: {0}", metric.LossFn);
Console.WriteLine("RSquared: {0}", metric.RSquared);
Console.WriteLine("Rms: {0}", metric.Rms);
}
static void Predict(PredictionModel<Match, MatchPrediction> model, IEnumerable<Match> predictData)
{
var predicts = model.Predict(predictData);
var results = predictData.Zip(predicts, (d, p) => (d, p));
foreach (var result in results)
{
Console.WriteLine("Date: {0}, Team: {1} Opponent: {2}, Score: {3}-{4}, Predict Home Score: {5}",
result.d.Date, result.d.Team, result.d.Opponent, result.d.TeamPoints, result.d.OpponentPoints, result.p.TeamPoints);
}
}
static void Main(string[] args)
{
var data = LoadData();
var trainCount = Convert.ToInt32(data.Count * 0.7);
var evaluateCount = Convert.ToInt32(data.Count * 0.2);
var trainData = data.Take(trainCount);
var evaluateData = data.Skip(trainCount).Take(evaluateCount);
var predictData = data.Skip(trainCount + evaluateCount);
var model = Train(trainData);
Evaluate(model, evaluateData);
Predict(model, predictData);
}
}
public class Match
{
[Column(ordinal: "0")]
public string Id;
[Column(ordinal: "1")]
public string Team;
[Column(ordinal: "2")]
public string Game;
[Column(ordinal: "3")]
public string Date;
[Column(ordinal: "4")]
public string Home;
[Column(ordinal: "5")]
public string Opponent;
[Column(ordinal: "6")]
public string WINorLOSS;
[Column(ordinal: "7", name: "Label")]
public float TeamPoints;
[Column(ordinal: "8")]
public float OpponentPoints;
[Column(ordinal: "9")]
public float FieldGoals;
[Column(ordinal: "10")]
public float FieldGoalsAttempted;
[Column(ordinal: "11")]
public float FieldGoals_;
[Column(ordinal: "12")]
public float X3PointShots;
[Column(ordinal: "13")]
public float X3PointShotsAttempted;
[Column(ordinal: "14")]
public float X3PointShots_;
[Column(ordinal: "15")]
public float FreeThrows;
[Column(ordinal: "16")]
public float FreeThrowsAttempted;
[Column(ordinal: "17")]
public float FreeThrows_;
[Column(ordinal: "18")]
public float OffRebounds;
[Column(ordinal: "19")]
public float TotalRebounds;
[Column(ordinal: "20")]
public float Assists;
[Column(ordinal: "21")]
public float Steals;
[Column(ordinal: "22")]
public float Blocks;
[Column(ordinal: "23")]
public float Turnovers;
[Column(ordinal: "24")]
public float TotalFouls;
[Column(ordinal: "25")]
public float Opp_FieldGoals;
[Column(ordinal: "26")]
public float Opp_FieldGoalsAttempted;
[Column(ordinal: "27")]
public float Opp_FieldGoals_;
[Column(ordinal: "28")]
public float Opp_3PointShots;
[Column(ordinal: "29")]
public float Opp_3PointShotsAttempted;
[Column(ordinal: "30")]
public float Opp_3PointShots_;
[Column(ordinal: "31")]
public float Opp_FreeThrows;
[Column(ordinal: "32")]
public float Opp_FreeThrowsAttempted;
[Column(ordinal: "33")]
public float Opp_FreeThrows_;
[Column(ordinal: "34")]
public float Opp_OffRebounds;
[Column(ordinal: "35")]
public float Opp_TotalRebounds;
[Column(ordinal: "36")]
public float Opp_Assists;
[Column(ordinal: "37")]
public float Opp_Steals;
[Column(ordinal: "38")]
public float Opp_Blocks;
[Column(ordinal: "39")]
public float Opp_Turnovers;
[Column(ordinal: "40")]
public float Opp_TotalFouls;
}
public class MatchPrediction
{
[ColumnName("Score")]
public float TeamPoints;
}
}
使用ML.NET实现NBA得分预测的更多相关文章
- 使用ML.NET实现白葡萄酒品质预测
导读:ML.NET系列文章 本文将基于ML.NET v0.2预览版,介绍机器学习中的分类和回归两个重要概念,并实现白葡萄酒品质预测. 本系列前面的文章也提到了一些,经典的机器学习最主要的特点就是模拟, ...
- ML.NET
ML.NET http://www.cnblogs.com/BeanHsiang/category/1218714.html 随笔分类 - 使用ML.NET实现NBA得分预测 摘要: 本文将介绍一种特 ...
- ML.NET指南
ML.NET是一个免费的.开源和跨平台的机器学习框架,使您能够构建定制的机器学习解决方案,并将它们集成到您的. net应用程序.本指南提供了许多关于与ML.NET合作资源. 关于ML.NET的更多信息 ...
- 100+诡异的数据集，20万Eclipse Bug、死囚遗言等
摘要:近日,Robert Seaton整理了100多个最有趣的数据集,其中包括Jeopardy真题,死囚的最后一句话,20万个Eclipse Bug,足球比赛相关,柏拉图式的爱情,太阳系以外的行星,1 ...
- 人工智能大数据,公开的海量数据集下载,ImageNet数据集下载,数据挖掘机器学习数据集下载
人工智能大数据,公开的海量数据集下载,ImageNet数据集下载,数据挖掘机器学习数据集下载 ImageNet挑战赛中超越人类的计算机视觉系统微软亚洲研究院视觉计算组基于深度卷积神经网络(CNN)的计 ...
- R语言 多元线性回归分析
#线性模型中有关函数#基本函数 a<-lm(模型公式,数据源) #anova(a)计算方差分析表#coef(a)提取模型系数#devinace(a)计算残差平方和#formula(a)提取模型公 ...
- 深度学习与计算机视觉系列(3)_线性SVM与SoftMax分类器
作者: 寒小阳 &&龙心尘 时间:2015年11月. 出处: http://blog.csdn.net/han_xiaoyang/article/details/49949535 ht ...
- 论文阅读笔记（七）YOLO
You Only Look Once: Unified, Real-Time Object Detection Joseph Redmon, CVPR, 2016 1. 之前的目标检测工作将分类器用作 ...
- 项目实战-使用PySpark处理文本多分类问题
原文链接:https://cloud.tencent.com/developer/article/1096712 在大神创作的基础上,学习了一些新知识,并加以注释. TARGET:将旧金山犯罪记录(S ...
随机推荐
- Django：django-cors-headers 报错no module named "corsheaders"
django跨域使用 pip install django-cors-headers 然后在settings文件中加上参数设置 # app配置 INSTALLED_APPS = [ 'django.c ...
- 网络对抗技术 20165220 Exp3 免杀原理与实践
实验任务 1 正确使用msf编码器,msfvenom生成如jar之类的其他文件,veil-evasion,自己利用shellcode编程等免杀工具或技巧: 2 通过组合应用各种技术实现恶意代码免杀(1 ...
- Ext使用中问题总结
隐藏 Ext.form.DateField 的触发(trigger)元素使其内容不能修改并使其所有的文本框(text field)显示格式为Y-m-d items : [{ xtype : ' dat ...
- 如何删除github上的某个文件夹
在github上只能删除仓库,却无法删除文件夹或文件, 所以只能通过命令来解决 首先进入你的master文件夹下, Git Bash Here ,打开命令窗口 $ git –help 帮助命令 $ g ...
- 【转】Zookeeper 安装和配置
转自:http://coolxing.iteye.com/blog/1871009 Zookeeper的安装和配置十分简单, 既可以配置成单机模式, 也可以配置成集群模式. 下面将分别进行介绍. 单机 ...
- 解决audio控制播放音量
在写手机端项目时,可能会遇到使用audio播放音乐,那么怎样控制audio默认播放的音量呢?下面时解决办法 volume 属于是控制audio 播放音乐的音量,其范围0-1,1表示音量最大 getVi ...
- 使用gulp打包普通项目
前言: 在使用gulp打包工具之前,我做的H5项目在浏览器中的缓存是很严重的,若改了一点css,加了一句js代码,不手动清除浏览器缓存是看不到效果的.老总也在项目演示当中遇到这些问题,一查找原因却是缓 ...
- MySQL8主从配置
最近在看MySQL的主从配置,罗列一下过程. 一.环境介绍 我使用的是两个MySQL8.0.13Windows版,Master和Slave安装的在一个机器上,Master库的端口为3306,Slave ...
- manacher算法，求回文串
用来求字符串最长回文串或者回文串的总数量 #include<map> #include<queue> #include<stack> #include<cma ...
- Round A - Kick Start 2019
a.链接:https://codingcompetitions.withgoogle.com/kickstart/round/0000000000050e01/00000000000698d6 题意: ...