約 2,105,329 件
https://w.atwiki.jp/ce00582/pages/4349.html
class ex42{ double mis[][]=new double[100][51]; double mos[][]=new double[100][51]; double f2by[][]=new double[200][100]; double alpha[]=new double[100]; double beta[]=new double[100]; double gamma[]=new double[100]; double theta[]=new double[100]; double ftutime1[]=new double[200]; int byear,cho,car,age; double z1,z2,z3,z4,z5,z6,z7; double v; void makedata(String file){ ex16 sub1=new ex16(); sub1.makedata(file); f2by=sub1.f2by; data18 d18=new data18(); d18.makedata(); alpha=d18.alpha; beta=d18.beta; gamma=d18.gamma; data152 d152=new data152(); d152.makedata(); theta=d152.ftheta; cho=1900; for(byear=1990; byear 2035; byear++){ for(age=15;age 65;age++){ for(car=0;car 50;car++){ mis[age][car]=0; mos[age][car]=0; } } for(age=16;age 65;age++){ z1= (1 - gamma[age - 1])* f2by[byear-cho][age-1]; z2=f2by[byear-cho][age] - z1; if(z2 0)z2 = 0; z3 =(1 - theta[age]) * z2; z4 = theta[age] * z2; mis[age][1] = z3; z5 = 0; for(car=1;car 50;car++){ z5 = z5 + mos[age-1][car]; } if(z5 == 0)z5 = 1; v=(double)z4/z5; if(v 1)v = 1; for(car=2;car 50;car++){ z6 =(1 - gamma[age - 1]) *mis[age-1][car-1]; mis[age][car]=z6+v * mos[age-1][car-1]; } for(car=1;car 50;car++){ z7=(gamma[age - 1] - alpha[age - 1] - beta[age-1]) * mis[age-1][car]; mos[age][car] = z7+(1 - alpha[age - 1] - v) * mos[age-1][car]; } } z1=0; z2=0; for(car=1;car 25;car++){ z1 = z1 + mis[64][car] + mos[64][car]; z2 = z2 +car*(mis[64][car] + mos[64][car]); } ftutime1[byear-cho] =z2/z1; } } }
https://w.atwiki.jp/ce00582/pages/525.html
Function seekx(n As Single, x, b) As Single Dim m As Single Dim opx As Single Dim h As Single Dim sx As Single sx = 0 h = 0 For m = 1 To 21 If x(m, n) 10 ^ (-5) Then h = h + 1 If x(m, n) 10 ^ (-5) Then opx = m Next If h = 1 Then sx = b(opx) / x(opx, n) seekx = sx End Function Function seekpibot(target As Single, x, b) As Single Dim m As Single Dim under As Single Dim c(1 To 21) As Single Dim px As Single For m = 1 To 21 under = x(m, target) c(m) = 999 If under 10 ^ (-5) Then c(m) = b(m) / under Next px = 999 minc = 999 For m = 1 To 21 If c(m) minc Then px = m If c(m) minc Then minc = c(m) Next seekpibot = px End Function Function seektarget(x) As Single Dim n As Single Dim tx As Single tx = 999 For n = 1 To 41 If x(0, n) 0 Then tx = n Next seektarget = tx End Function Private Sub Command1_Click() Dim m As Single Dim n As Single Dim x(0 To 21, 1 To 41) As Single Dim b(0 To 21) As Single Dim target As Single Dim pibot As Single Dim z As Single Dim theta As Single theta = 1.2 For n = 1 To 10 x(0, n) = 11 - n x(0, 10 + n) = 11 - n Next For m = 1 To 20 x(m, m) = 1 Next For m = 1 To 20 x(m, m + 20) = 1 Next For n = 1 To 10 x(21, n) = 1 Next For n = 11 To 20 x(21, n) = theta Next x(21, 41) = 1 b(0) = 0 For n = 1 To 20 b(n) = 1 Next b(21) = theta * 10 target = seektarget(x) Do Until target 100 pibot = seekpibot(target, x, b) For m = 0 To 21 z = x(m, target) / x(pibot, target) If m = pibot Then z = 0 For n = 1 To 41 x(m, n) = x(m, n) - z * x(pibot, n) Next b(m) = b(m) - z * b(pibot) Next Debug.Print target, pibot, b(0) target = seektarget(x) Loop For n = 1 To 20 Debug.Print n, seekx(n, x, b) Next End Sub
https://w.atwiki.jp/gtav/pages/517.html
逃走車両 - 大強盗ミッション（Getaway Vehicle - The Big Score） 逃走車両 - 大強盗ミッション（Getaway Vehicle - The Big Score）概要 ミッション攻略 動画 概要 大強盗の準備ミッション。 派手の逃走用車両を用意する。 そのへんの市民から奪うだけでもよい。 ミッション攻略 ４人乗れる車両を用意し、指定された場所へ運ぶとミッション完了。 座席が4つあってもドアが4枚無いと駄目なので注意。 使える車両に乗ってレスターに電話すれば場所を教えてくれる。 ここで用意した逃走用車両で☆5の手配度から逃げることになる。性能の良い車種をカスタムしておくのがベスト。防弾タイヤも必要。 動画
https://w.atwiki.jp/ce00582/pages/5406.html
class ex31{ double mis[][]=new double[100][51]; double mos[][]=new double[100][51]; double m2by[][]=new double[200][100]; double alpha[]=new double[100]; double beta[]=new double[100]; double gamma[]=new double[100]; double theta[]=new double[100]; double mtime1[]=new double[200]; int byear,cho,car,age; double z1,z2,z3,z4,z5,z6,z7; double v; void makedata(String file){ ex16 sub1=new ex16(); sub1.makedata(file); m2by=sub1.m2by; data151 sub2=new data151(); sub2.makedata(); alpha=sub2.alpha; beta=sub2.beta; gamma=sub2.gamma; data152 sub3=new data152(); sub3.makedata(); theta=sub3.mtheta; cho=1900; for(byear=1990; byear 2035; byear++){ for(age=15;age 65;age++){ for(car=0;car 50;car++){ mis[age][car]=0; mos[age][car]=0; } } for(age=16;age 65;age++){ z1= (1 - gamma[age - 1])* m2by[byear-cho][age-1]; z2=m2by[byear-cho][age] - z1; if(z2 0)z2 = 0; z3 =(1 - theta[age]) * z2; z4 = theta[age] * z2; mis[age][1] = z3; z5 = 0; for(car=1;car 50;car++){ z5 = z5 + mos[age-1][car]; } if(z5 == 0)z5 = 1; v=(double)z4/z5; if(v 1)v = 1; for(car=2;car 50;car++){ z6 =(1 - gamma[age - 1]) *mis[age-1][car-1]; mis[age][car]=z6+v * mos[age-1][car-1]; } for(car=1;car 50;car++){ z7=(gamma[age - 1] - alpha[age - 1] - beta[age-1]) * mis[age-1][car]; mos[age][car] = z7+(1 - alpha[age - 1] - v) * mos[age-1][car]; } } z1=0; z2=0; for(car=25;car 50;car++){ z1 = z1 + mis[64][car] + mos[64][car]; z2=z2+car*(mis[64][car] + mos[64][car]); } mtime1[byear-cho] = z2/z1; } } }
https://w.atwiki.jp/ce00582/pages/4161.html
class ex24{ int byear,cho,car,stage,age; double z1,z2,z3,z4,z5,z6,z7; double v; double fnew2[]=new double[200]; double mis[][]=new double[100][51]; double mos[][]=new double[100][51]; double f2by[][]=new double[200][100]; double alpha[]=new double[100]; double beta[]=new double[100]; double gamma[]=new double[100]; double theta[]=new double[100]; double minx[][]=new double[100][100]; double mout[][]=new double[100][100]; void makedata(String file){ ex20 sub19=new ex20(); sub19.makedata(); minx=sub19.mint; ex22 sub21=new ex22(); sub21.makedata(); mout=sub21.mint; ex16 sub16=new ex16(); sub16.makedata(file); f2by=sub16.f2by; data18 d151=new data18(); d151.makedata(); alpha=d151.alpha; beta=d151.beta; gamma=d151.gamma; data152 d152=new data152(); d152.makedata(); theta=d152.ftheta; cho=1900; for(byear=1950; byear 1990; byear++){ cho=1900; for(age=15;age 65;age++){ for(car=0;car 50;car++){ mis[age][car]=0; mos[age][car]=0; } } stage =2008-byear; for(car=1;car 50;car++){ mis[stage][car] = minx[stage][car]; mos[stage][car] = mout[stage][car]; } for(age=stage+1;age 65;age++){ z1= (1 - gamma[age - 1])* f2by[byear-cho][age-1]; z2=f2by[byear-cho][age] - z1; if(z2 0)z2 = 0; z3 =(1 - theta[age]) * z2; z4 = theta[age] * z2; mis[age][1] = z3; z5 = 0; for(car=1;car 50;car++){ z5 = z5 + mos[age-1][car]; } if(z5 == 0)z5 = 1; v=(double)z4/z5; if(v 1)v = 1; for(car=2;car 50;car++){ z6 =(1 - gamma[age - 1]) *mis[age-1][car-1]; mis[age][car]=z6+v * mos[age-1][car-1]; } for(car=1;car 50;car++){ z7=(gamma[age - 1] - alpha[age - 1] - beta[age-1]) * mis[age-1][car]; mos[age][car] = z7+(1 - alpha[age - 1] - v) * mos[age-1][car]; } } z1=0; for(car=25;car 50;car++){ z1 = z1 + mis[64][car] + mos[64][car]; } fnew2[byear-cho] = z1; } } }
https://w.atwiki.jp/gtavi_gta6/pages/517.html
逃走車両 - 大強盗ミッション（Getaway Vehicle - The Big Score） 逃走車両 - 大強盗ミッション（Getaway Vehicle - The Big Score）概要 ミッション攻略 動画 概要 大強盗の準備ミッション。 派手の逃走用車両を用意する。 そのへんの市民から奪うだけでもよい。 ミッション攻略 ４人乗れる車両を用意し、指定された場所へ運ぶとミッション完了。 座席が4つあってもドアが4枚無いと駄目なので注意。 使える車両に乗ってレスターに電話すれば場所を教えてくれる。 ここで用意した逃走用車両で☆5の手配度から逃げることになる。性能の良い車種をカスタムしておくのがベスト。防弾タイヤも必要。 動画
https://w.atwiki.jp/buckethead/pages/24.html
ソロ スタジオアルバム Solo Studio Album Bucketheadland 2 No. Title Length 1 Welcome 0 13 2 Slaughter Zone Entrance 0 11 3 The Cobra s Hood 2 58 4 Transportation Options 0 51 5 Machete Mirage 3 01 6 Slaughter Buddies Outside the Revenge Wedge 0 21 7 We Cannot Guarantee Bodily Harm 0 17 8 John Merrick_- Elephant Man Bones Explosion 4 53 9 Taxidermy Tots 0 23 10 Bloody Rainbow Spiraling Sherbert Scoop 2 55 11 Can You Get Past Albert? 0 27 12 Vladimir Pockets Incredible Bloated Slunk Show 3 10 13 The Ballad of the Inside-Out Face 1 05 14 The Battery Cage Brawls (Cage Announcer The Ghost of_Abraham Lincoln; Winner Has to Eat His Way Out) 2 18 15 Ferris Wheel Apology 0 08 16 Can You Help Me? 1 02 17 Grimm s Sponsorship 0 14 18 Realistic Coop Replica 0 42 19 Frozen Brains Tell No Tales 5 33 20 Rooster Landing (1st Movement) / Lime Time (2nd Movement) 2 35 21 Two Pints 0 27 22 Health Safety Advisory 2 14 23 Digger s Den 3 13 24 One-Way Ticket to Grab Bag Alley 0 46 25 Fun for You 1 03 26 Carpal Tunnel Tomb Torker 3 35 27 Today s Schedule 0 08 28 The Corpse Plower 3 20 29 Unemployment Blues 2 05 30 Slaughter Zone Exit 8 16
https://w.atwiki.jp/todo314/pages/142.html
Towards Context-Aware Search by Learning A Very Large Variable Length Hidden Markov Model from Search Logs Huanhuan Cao, Daxin Jiang, Jian Pei, Enhong Chen, Hang Li MSRAとUniversity of Science and Technology of China WWW 2009 概要 たった今調べたクエリからURLを正しくレコメンドするのは無理 例 ホントは車のレビューサイトを見たい 検索クエリ Ford new cars → Toyota new cars 個々のクエリに着目するとautohome.comは出てこない クエリ全体を見ると分かる→context-aware!! 汎用的なフレームワークを作りたい！！ document re-ranking query suggestion URL recommendation variable length Hidden Markov Model(vlHMM)によるクエリコンテキストモデルを提案 事後確率 $$ P(s_t | q_t, O_{1 \cdots t-1}) $$ s_t ユーザーの思考、検索意向 q_t 今のクエリ O_{1…t-1} q_tのコンテキスト（今までのクエリ+クリック） さらに予測 $$ P(s_{t+1} | q_t, O_{1 \cdots t-1}) $$ HMMの学習 セッションが多すぎる パラメータが爆発 (⌒⌒) ii!i!i ﾄﾞｶｰﾝ ﾉ~~~＼ ,,,,,,,／´･ω･` ＼,,,,,,,,,, 解決法 パラメータ初期化法 Map-Reduce 3. vlHMM 1-HMMs s_tはs_1,…,s_{t-2}とは独立 今回のに適用するにはやばめ vlHMM s_1,…,s_N_s 隠れ状態の集合 q_1,…,q_N_q クエリの集合 u_1,…,u_N_u URLの集合 T_max 状態列の最大長 遷移確率分布 $$ \Delta = \{ P(s_i | S_i) \} $$ 初期状態分布 $$ \Psi = \{ P(s_i) \} $$ 放出確率 $$ \Lambda = \{ P(q,U | S_j) \} $$ T_{j-1}ステップ後に状態$$ s_{j,T_j} $$からクエリqを出してUをクリックする $$ P(q,U | S_j) \equiv P(q,U | s_{j,T_j}) $$ 最後の状態だけにしちゃう $$ P(q,U | s_{j,T_j}) \equiv P(q | s_{j,T_j})\prod_{u \in U}P(u | s_{j,T_j}) $$ 条件付き独立にしちゃう ☆結局考えるべきは$$ (\Lambda_q, \Lambda_u) \equiv (\{P(q | s_i)\}, \{P(u | s_i)\}) $$ Q. 何をtrainするのか？？？ A. $$ \Theta = (\Psi, \Delta, \Lambda_q, \Lambda_u) $$ セッションを30分ごとに区切る $$ \chi = \{ O_1, \cdots, O_N \} $$ 訓練データ $$ O_n = \langle (q_{n,1}, u_{n,1}), \cdots, (q_{n,T_n}, u_{n,T_n}) \rangle $$ 最大尤度なΘを求めた ↑訓練データを最もよく表現する $$ \Theta^* = \arg \max_{\Theta} \ln P(\chi | \Theta) = \arg \max_{\Theta} \sum_{n}\ln P(O_n | \Theta) $$ あとは色々頑張る 最終的にEM(Expectation Maximization) algorithmに落ち着く 4. Training EMアルゴリズムを適用したいけどやばお 隠れ状態の数 よく分からん 二部グラフ（？）作ってクラスタリング クラスタ(Q,U)が隠れ状態 ログ大杉 MapReduce!!! そもそもユニークなクエリ・URL大杉 パラメータ数10^30 うま～く減らすし、理論的に抑えるよ！ 5. Application O q_1, …, q_t, U_1, …, U_tの列 Γ_O 候補状態 P(S_m | O, Θ) 事後確率を推論できる で？っていう P(s_t | O, Θ)もP(s_{t+1} | O, Θ)も↑から計算できる Context-Aware actionsの数々 Document re-ranking $$ P(u | O) = \sum_{s_t \in S_t}P(u | s_t)\cdot P(s_t | O) $$の順にre-rank $$ S_t = \{s_t | P(s_t | O) \neq 0\} $$ U q_tに対して検索エンジンが返すURLリスト Query suggestion $$ P(q | O) = \sum_{s_{t+1} \in S_{t+1}}P(q | s_{t+1})\cdot P(s_{t+1} | O) $$ $$ S_{t+1} = \{ s_{t+1} | P(s_{t+1} | O) \neq 0 \} $$ $$ q \in Q_{t+1} = \{ q | s_{t+1} \in S_{t+1}, P(q | s_{t+1}) \neq 0 \} $$ URL recommendation $$ P(u | O) = \sum_{s_{t+1} \in S_{t+1}} P(u | s_{t+1})\cdot P(s_{t+1} | O) $$ ↑と大体同じ オンラインでの応用 知らないクエリ or URLが来たら？ 速度は？ 6. 実験 データセット 1.8B queries 151M unique queries 2.6B clicks 114M unique URLs 840M sessions 前処理 あんまり適当だと消しすぎる 出現回数5未満は消す（ジャンクだ！！！ 48%消えた 結局 ユニーククエリ・URLは相当減った クエリ、クリック、セッションの数は半分程度 Efficiency 隠れ状態（クラスタ）数 10^6位 パラメータ #P(s_i), #P(q|s_i), #P(u|s_i), #P(s_i|S_j) 時間、パラメータ数は訓練データの量に比例するよ！！ Effectiveness 応用としてあげたのと比較 まとめ 超でかいデータにスケールするようなフレームワークを考え動かしたのが全て Hidden Markov Model WWW context-aware search kk 2013-12-10 01 08 02 (Tue)
https://w.atwiki.jp/ce00582/pages/2958.html
import java.awt.*; import java.awt.geom.*; import java.awt.event.*; import javax.swing.*; import java.awt.image.BufferedImage; import java.io.File; import javax.imageio.ImageIO; import java.io.IOException; import java.io.*; public class pro extends JFrame{ double c[][]=new double[6][6]; double y1[][]=new double[6][6]; double y2[][]=new double[6][6]; int s1,s2; public static void main(String[] args){ pro test = new pro(); test.addWindowListener(new WindowAdapter(){ public void windowClosing(WindowEvent e){System.exit(0);} }); test.setBounds( 0, 0, 700, 700); test.setVisible(true); } pro(){ try{ BufferedReader br = new BufferedReader(new FileReader("tax.txt")); String str; String x[]=new String[5]; for(int i=0; i 25; i++){ str = br.readLine(); x=str.split(","); s1=Integer.valueOf(x[0]); s2=Integer.valueOf(x[1]); c[s1][s2]=Double.valueOf(x[2]); y1[s1][s2]=Double.valueOf(x[3]); y2[s1][s2]=Double.valueOf(x[4]); } br.close(); } catch(IOException e){System.out.println("入出力エラーです。");} } public void paint(Graphics g){ double x,y,z; int px,py; int px1,px2,py1,py2; Graphics2D g2 = (Graphics2D)g; BufferedImage readImage = null; if (readImage == null){ readImage = new BufferedImage(getWidth(), getHeight(), BufferedImage.TYPE_INT_BGR); } Graphics2D off = readImage.createGraphics(); off.setRenderingHint(RenderingHints.KEY_ANTIALIASING, RenderingHints.VALUE_ANTIALIAS_ON); BasicStroke wideStroke = new BasicStroke(2.0f); off.setStroke(wideStroke); off.setPaint(Color.red); x=0; y=0; z=500; px=pointx(x,y,z); py=pointy(x,y,z); off.drawLine(100,600,100+px,600-py); x=500; y=0; z=0; px=pointx(x,y,z); py=pointy(x,y,z); off.drawLine(100,600,100+px,600-py); x=0; y=500; z=0; px=pointx(x,y,z); py=pointy(x,y,z); off.drawLine(100,600,100+px,600-py); off.setColor(Color.blue); for (s2=1;s2 6;s2++){ for (s1=1;s1 5;s1++){ x=1000*y1[s1][s2]; y=1000*y2[s1][s2]; z=1000*c[s1][s2]; px1=pointx(x,y,z); py1=pointy(x,y,z); x=1000*y1[s1+1][s2]; y=1000*y2[s1+1][s2]; z=1000*c[s1+1][s2]; px2=pointx(x,y,z); py2=pointy(x,y,z); off.drawLine(100+px1,600-py1,100+px2,600-py2); } } for (s1=1;s1 6;s1++){ for (s2=1;s2 5;s2++){ x=1000*y1[s1][s2]; y=1000*y2[s1][s2]; z=1000*c[s1][s2]; px1=pointx(x,y,z); py1=pointy(x,y,z); x=1000*y1[s1][s2+1]; y=1000*y2[s1][s2+1]; z=1000*c[s1][s2+1]; px2=pointx(x,y,z); py2=pointy(x,y,z); off.drawLine(100+px1,600-py1,100+px2,600-py2); } } off.drawString("Clear",100,100); if (readImage != null){g2.drawImage(readImage,0,0, this);} try { boolean result = ImageIO.write(readImage, "jpg", new File("sam.jpg")); } catch (Exception e) { e.printStackTrace(); } } public static int pointy(double x,double y,double z){ int p2; double theta,phi; theta=-0.222222*Math.PI; phi=0.333333*Math.PI; p2=(int)(-Math.cos(theta)*Math.cos(phi)*x-Math.sin(theta)*Math.cos(phi)*y+Math.sin(phi)*z); return p2; } public static int pointx(double x,double y,double z){ int p1; double theta; theta=-0.222222*Math.PI; p1=(int)(-Math.sin(theta)*x+Math.cos(theta)*y); return p1; } }
https://w.atwiki.jp/aesthetica/pages/129.html
Kenneth Clark, The Gothic Revival (1928) Catalogue of the Drawings by Leonardo da Vinci in the Collection of HM King at Windsor Castle (1935 2 vols) One Hundred Details from Pictures in the National Gallery, London, 1938. More Details from Pictures in the National Gallery, London, 1941. Leonardo da Vinci An Account of his development as an Artist (1939, rev. ed. 1952) Florentine Painting The Fifteenth Century (1945) Piero della Francesca (1951) Landscape into Art (1949), adapted from his Slade Lectures Moments of Vision (1954), the Romanes Lecture for 1954. Oxford Clarendon Press. The Nude a study in ideal form (1956) A.W. Mellon Lectures in the Fine Arts, delivered in 1953. Looking at Pictures (1960) Ruskin Today (1964) (edited and annotated by) Rembrandt and the Italian Renaissance (1966) The Drawings by Leonardo da Vinci in the Collection of HM Queen at Windsor Castle (1968/9 with Carlo Pedretti 3 vols) Civilisation A Personal View (1969), book version of the television series Blake and Visionary Art (1973) The Romantic Rebellion (1973), book version of the television series Another Part of the Wood (1974) (autobiography) Animals and Men (1977) The Other Half (1977) (autobiography) What is a Masterpiece? (1979) Feminine Beauty (1980) ************************************************************