Upgrade to Pro
— share decks privately, control downloads, hide ads and more …
Speaker Deck
Features
Speaker Deck
PRO
Sign in
Sign up for free
Search
Search
階層クラスタリングにおける仮説検定
Search
saltcooky
May 23, 2020
Science
1.1k
0
Share
Embed
Copy iframe code
Copy JS code
Copy link
Start on current slide
階層クラスタリングにおける仮説検定
saltcooky
May 23, 2020
More Decks by saltcooky
See All by saltcooky
SpatialRDDパッケージによる空間回帰不連続デザイン
saltcooky12
0
270
動的トリートメント・レジームを推定するDynTxRegimeパッケージ
saltcooky12
0
290
FIBA W杯の日本代表って組み合わせ次第で2次ラウンド行けたんじゃね?をデータで検証
saltcooky12
0
360
Rで有名絵画を安全に買いたい
saltcooky12
0
410
データドリブンな仮説検証のためのSelective Inference
saltcooky12
1
1.5k
ストリートスナップデータに 統計的ネットワーク分析の適用を試みた
saltcooky12
0
890
Other Decks in Science
See All in Science
Wet Active Matter
rajeshrinet
0
130
HajimetenoLT vol.17
hashimoto_kei
1
250
機械学習 - ニューラルネットワーク入門
trycycle
PRO
0
1.1k
医療 LLM ベンチマークの現在地:多面的評価 と日本ローカライズ
analokmaus
1
610
Non-Gaussian, nonlinear causal discovery with hidden variables and application
sshimizu2006
0
160
[NLP2026 参加報告会] AI for Science まとめ / NLP2026
lychee1223
0
1.9k
AIを用いた PID制御で部屋 の温度制御をしてみた
nearme_tech
PRO
0
180
機械学習 - K近傍法 & 機械学習のお作法
trycycle
PRO
1
1.6k
1. CPC理論の展開と集合的知能モデル(JSAI2026 KS-27 集合的予測符号化と新たな知性の時代)
hayashiyus884
1
310
Testing the Longevity Bottleneck Hypothesis
chinson03
0
380
ssmonline #51 ヤマサキ春のサメ祭り 2026 / ssmjp Yamasaki Spring JAWS Festival 2026
naospon
1
110
機械学習 - 決定木からはじめる機械学習
trycycle
PRO
0
1.6k
Featured
See All Featured
No one is an island. Learnings from fostering a developers community.
thoeni
21
3.8k
Design of three-dimensional binary manipulators for pick-and-place task avoiding obstacles (IECON2024)
konakalab
0
500
Noah Learner - AI + Me: how we built a GSC Bulk Export data pipeline
techseoconnect
PRO
0
340
What Being in a Rock Band Can Teach Us About Real World SEO
427marketing
0
1.1k
Dealing with People You Can't Stand - Big Design 2015
cassininazir
367
27k
Distributed Sagas: A Protocol for Coordinating Microservices
caitiem20
333
23k
The Psychology of Web Performance [Beyond Tellerrand 2023]
tammyeverts
49
3.5k
Let's Do A Bunch of Simple Stuff to Make Websites Faster
chriscoyier
508
140k
The Mindset for Success: Future Career Progression
greggifford
PRO
0
430
ラッコキーワード サービス紹介資料
rakko
1
4.1M
[Rails World 2023 - Day 1 Closing Keynote] - The Magic of Rails
eileencodes
38
2.9k
Agile that works and the tools we love
rasmusluckow
331
22k
Transcript
֊ΫϥελϦϯάʹ͓͚ΔԾઆݕఆ !TBMUDPPLZ 5PLZP3 1
୭ !TBMUDPPLZ • 3ྺɿ͙Β͍͔ͳ • ۈઌɿݪ॓ʹ͋Δ*5ܥͷձࣾ • ࣄ༰ɿ3%తͳ෦ॺͰ ɹɹɹ3Λͬͨσʔλੳ͞Μ ৽ਓݚमͷ"*ߨ࠲ͷ४උ
• झຯɿϑΝογϣϯඒज़ؗ८Γ 2
༰ΛҰݴͰ ίϩφΟϧεͷܥ౷ੳʹར༻͞Ε͍ͯΔ ΫϥελϦϯάʹ͓͚Δ౷ܭతԾઆݕఆΛհ 3 5IFTQFDJFT4FWFSFBDVUFSFTQJSBUPSZTZOESPNFSFMBUFEDPSPOBWJSVTDMBTTJGZJOHO$P7BOEOBNJOHJU4"34$P7
ΫϥελϦϯάͷؾʹͳΔ͜ͱ σʔλ͕গ͠มΘ͚ͬͨͩͰΫϥελܗͷํ͕มԽ͢Δ ࠓಘΒΕ͍ͯΔσʔλͷΫϥελɺਅͷΫϥελ͔ʁ 4 ಘΒΕ͍ͯΔ σʔληοτ" ಘΒΕ͍ͯͳ͍ σʔληοτ# σʔλ͕ҰͭมԽͱ Ϋϥελͷܗ͕มԽ
͢ΔՄೳੑ͕͋Δ ಘΒΕ͍ͯΔΫϥελʹ ॴଐ͍ͯ͠Δσʔλ ຊʹಉ͡Ϋϥελ͔ʁ
ϒʔτετϥοϓ๏ʹΑΔݕఆ ͭͷର ͷҨࢠ ʹΑΓܗ͞ΕΔथܗਤ ܥ౷थ ௨Γ 5
ը૾Ҿ༻(Ұ෦վม) http://stat.sys.i.kyoto-u.ac.jp/titech/multiboot-j.html
ϒʔτετϥοϓ๏ʹΑΔݕఆ w /ݸͷઆ໌ม Ҩࢠྻ ͷσʔλ͔ΒॏෳΛڐ͠ɺ/ݸͷઆ໌มΛ ϦαϯϓϦϯάͯ͠ϒʔτετϥοϓඪຊΛੜ w ϦαϯϓϦϯάͨ͠σʔλΛ༻͍ͯथܗਤΛ࡞͢Δ w ͜ΕΛ#ճ܁Γฦ͢͜ͱͰಘΒΕͨ#ݸͷथܗਤಘΒΕ·͢
6 $ $ 5 ( ( 5 ( 5 ( ( ( ( $ ( 5 ( ( $ ( 5 $ $ 5 ( ( 5 ( 5 ( ( ( ( $ ( 5 ( ( $ ( 5 $ $ 5 ( ( 5 ( 5 ( ( ( ( $ ( 5 ( ( $ ( 5
7 ϒʔτετϥοϓ๏ʹΑΔथܗਤΛ/ݸ࡞͢Δ͜ͱͰɺ֤थܗਤ͕ಘΒ ΕΔ֬Λਪఆ͢Δ͜ͱ͕Ͱ͖Δɻ ը૾Ҿ༻(Ұ෦վม) http://stat.sys.i.kyoto-u.ac.jp/titech/multiboot-j.html ϒʔτετϥοϓ๏ʹΑΔݕఆ
ϒʔτετϥοϓ๏ʹΑΔݕఆ 8 4IJNPEBJSB)BTFHBXBݕఆ थܗਤͷ#4๏ʹΑΔݕఆͰɺݕग़ͷِӄੑ͕ଟ͘ͳͬͯ͠·͏ͨΊɺ ଟॏൺֱʹΑΔӨڹΛิਖ਼ͨ͠ݕఆ ը૾Ҿ༻(Ұ෦վม) http://stat.sys.i.kyoto-u.ac.jp/titech/multiboot-j.html
Ϛϧνεέʔϧϒʔτετϥοϓ "QQSPYJNBUFMZ6OCJBTFEݕఆ ϦαϯϓϦϯάΛ/͔Β༷ʑͳΛͱΔ/`ʹ͢ΔϚϧνεέʔϧϒʔτ ετϥοϓ๏ʹΑΔݕఆ 4)ݕఆͳͲΑΓෆภͳਪఆΛߦ͏͜ͱ͕Ͱ͖Δ 9 $ $ 5
( ( 5 ( 5 ( ( ( ( $ ( 5 ( ( $ ( 5 $ $ 5 5 $ $ 5 ( ( 5 ( 5 ( ( ( ( $ ( 5 ( ( $ ( 5 $ $ 5 5 $ $ 5 ( ( 5 ( 5 ( ( ( ( $ ( 5 ( ( $ ( 5 $ $ 5 5
Ϛϧνεέʔϧϒʔτετϥοϓ 10 ͳͥϚϧνεέʔϧʹ͢Δ͜ͱͰෆภͳਪఆΛߦ͏͜ͱ͕Ͱ͖Δͷ͔ زԿֶతʹߟ͑ΔͱϚϧνεέʔϧʹ͢Δ͜ͱͰɺԾઆͷۭؒͷܗঢ়Λ ௨ৗͷݕఆʹ͚ۙͮΔ͜ͱ͕Ͱ͖Δ ৄ͘͠ࢀߟจݙ UݕఆͳͲ ܥ౷थʹ͓͚Δݕఆ ը૾Ҿ༻ https://www.ism.ac.jp/editsec/toukei/pdf/50-1-033.pdf
3ͰͬͯΈΔ 11 ./*45ͷσʔλ͔Β ͷσʔλΛݸͣͭϥϯμϜʹऔಘ
3ͰͬͯΈΔ 12 3Ͱ"6ݕఆΛߦ͏ͨΊʹQWDMVTUύοέʔδΛར༻͢Δ ɹlibrary(pvclust) ɹlibrary(parallel) ɹcl <- makeCluster(detectCores()) #ฒྻԽͷ͓·͡ͳ͍ ɹ
ɹ# આ໌ม͕ߦɺର͕ྻͷঢ়ଶʹ͢ΔͨΊʹసஔ ɹmnist_df_t <- ɹɹɹɹmnist_df %>% ɹɹɹɹdplyr::select(-label) %>% ɹɹɹɹt() ɹcolnames(mnist_df_t) <- mnist_df$label ɹsa <- 9^seq(-1,1,length=13)ɹ# ϚϧνεέʔϧϒʔτετϥοϓͷεέʔϧྻΛੜ ɹmnist_boot <- pvclust(data = mnist_df_t, ɹ r = 1/sa, ɹ nboot = 2000, ɹ method.hclust = "ward.D2", ɹɹɹɹɹɹɹɹɹɹɹɹɹparallel = cl)
3ͰͬͯΈΔ 13 ݁ՌΛ֬ೝ ɹ> mnist_boot ɹCluster method: ward.D2 ɹDistance :
euclidean ɹEstimates on edges: ɹ si au bp se.si se.au se.bp v c pchi ɹ1 0.999 1.000 0.999 0.000 0.000 0.000 -3.175 0.156 0.000 ɹ2 0.979 0.990 0.971 0.001 0.001 0.001 -2.117 0.221 0.000 ɹ3 0.579 0.801 0.759 0.004 0.002 0.001 -0.775 0.073 0.000 ɹ4 0.392 0.718 0.655 0.003 0.002 0.001 -0.487 0.089 0.000 ɹ5 0.771 0.900 0.852 0.004 0.002 0.002 -1.151 0.105 0.000 ɹ6 0.112 0.634 0.455 0.003 0.002 0.001 -0.119 0.231 0.000 ɹ7 0.213 0.795 0.323 0.004 0.002 0.002 -0.182 0.641 0.000 ɹ8 0.532 0.770 0.755 0.004 0.002 0.001 -0.716 0.025 0.000 ɹ9 0.266 0.779 0.394 0.004 0.002 0.002 -0.250 0.520 0.000 ɹ10 0.915 0.969 0.878 0.003 0.001 0.002 -1.518 0.354 0.183 ɹ11 0.223 0.757 0.397 0.004 0.002 0.001 -0.211 0.471 0.000 ɹ12 0.229 0.704 0.475 0.003 0.002 0.001 -0.237 0.299 0.000 ɹ13 0.246 0.752 0.409 0.004 0.002 0.001 -0.235 0.464 0.017 ɹ14 0.229 0.691 0.492 0.003 0.002 0.001 -0.239 0.258 0.000
3ͰͬͯΈΔ 14 ݁ՌΛՄࢹԽͯ͠ΈΔ QΛ༗ҙͱ͢Δ ɹplot(mnist_boot, cex=0.5, cex.pv=0.5)ɹ# σϯυϩάϥϜͷϓϩοτ ɹpvrect(mnist_boot,
alpha = 0.9, pv = “au") # p-value >= 0.9 Λғ͏ ༗ҙ ͦͷଞ ༗ҙ ͦͷଞ
3ͰͬͯΈΔ 15 ݁ՌΛՄࢹԽͯ͠ΈΔ ɹplot(mnist_boot, cex=0.5, cex.pv=0.5)ɹ# σϯυϩάϥϜͷϓϩοτ ɹpvrect(mnist_boot, alpha =
0.9, pv = “au") # p-value >= 0.9 Λғ͏ ڑ͍͕ۙ ༗ҙͰͳ͍
؆୯ʹ·ͱΊ w֊ΫϥελϦϯάʹ͓͚Δϒʔτετϥοϓ๏Λ༻͍ͨ ԾઆݕఆΛհ wϦαϯϓϦϯάΛϚϧνεέʔϧ ༷ʑͳΛͱΔ ʹ͢Δ ͜ͱͰෆภͳݕఆΛߦ͏͜ͱ͕Ͱ͖Δ w3ͰQWDMVTUύοέʔδͷQWDMVTUؔΛ༻͍Δ͜ͱͰ࣮ ߦ͢Δ͜ͱͰ͖Δ 16
ࢀߟ ϒʔτετϥοϓ๏ʹΑΔΫϥελੳͷόϥπΩධՁ IUUQTXXXJTNBDKQFEJUTFDUPVLFJQEGQEG Ϛϧνεέʔϧϒʔτετϥοϓͷۙཧ ɹIUUQTXXXUFSSBQVCDPKQKPVSOBMTKKTTKQEGQEG ϚϧνεςοϓʹϚϧνεέʔϧɾϒʔτετϥοϓ๏Լฏӳण ɹIUUQTUBUTZTJLZPUPVBDKQUJUFDINVMUJCPPUKIUNM Ϛϧνεέʔϧϒʔτετϥοϓ๏ʹΑΔΫϥελϦϯάͷ༗ҙࠩݕఆ ɹIUUQTTBMUDPPLZIBUFOBCMPHDPNFOUSZNVMUJTDBMF@CPPUTUSBQ 17
&/% 18 &OKPZ