intuit w2

) {scanf ("%d", A[i]); } memset (Sum1,0,sizeof(SUM1)); memset (sum2,0,sizeof(sum2)); intT1 =0+ s2 =0; for(inti =1; i) { if(A[i] = =1) T1++; if(A[i] = =2) T2++; Sum1[i]=T1; Sum2[i]=T2; } intCNT =0; intW1 =0; intW2 =0; BOOLFlag; for(ints=1; s) {W1=0; W2=0; Flag=false; if(Sum1[n] s) Break; intpos =1; intLast =0; while(Pos N) {inttemp =Get_next (S, POS, n); if(temp = =-1) {flag=true; Break; } if(A[temp] = =1) {W1++; Last=1;

Konjac Pull half a day finally determine oneself is the language died early >_The following is the more ugly code tatIf the current bit is the maximum number of >num, then when the number is num, the number of the back can be arbitrarily taken, and each case will contribute an extra number (that is, the current bit of this), so the total number of +=10^ (i-1)1 var2Pre,orz,h,sum:Array[0.. -] ofInt64;3ANS,ANS1:Array[0..9] ofInt64;4 I,j,k,n,m,mid:longint;5 L,r,tot,tot1,l1,r1,x,w1,

approximate sum{x in V}{}{p Prime (x)}=1, p Prime (x) >=0 p Prime (candidates) =10^{-5} {P Prime (ask~candidates)}=10^{-8}b) obtains the language model (deriving Language model) I assigns probabilities to a set of Word sequences w_{1}w_{2}...w_{n} (Assign Probability to a sequencew_{1}w_{2}...w_{n}) Ii. Apply the chain rule (apply chain rule): 1. P (w1w2...wn) = P (w1| S)? P (w2| S,W1)? P (w3| S,W1,W2) ...

Specifies whether the rows of x and y should be all in the output file. Sort Whether the column specified by is to be sorted. Suffixes Specifies the suffix of the same column name, except by. Incomparables Specifies which cells in by are not merged. Example:w1:name SCHOOL class Englisha S1 10 85b S2 5 50a S1 4 90A S1 11 90c S1 1 12w2:name SCHOOL CLASS MATHS englisha S3 5 Span class= "Hljs-number" >80 88b S2 5 81c S1 1

def classifynb (Vec2classify, P0vec, P1vec, PClass1):P1 = SUM (vec2classify * P1vec) + log (PCLASS1)P0 = SUM (vec2classify * P0vec) + log (1.0-PCLASS1)If p1 > P0:Return 1Elsereturn 0Attention:P1vect = log (p1num/p1denom)P0vect = log (p0num/p0denom)>>> p0vArray ([0.04166667, 0.04166667, 0.04166667, 0., 0.,..0.04166667, 0. , 0.04166667, 0. , 0.04166667,0.04166667, 0.125])>>> p1vArray ([0., 0., 0., 0.05263158, 0.05263158,..0., 0.15789474, 0. , 0.05263158, 0. ,0., 0. ])P (W0,W1,

Sharedimagecache] storeimage:image recalculatefromimage:yes imagedata:data forKey:URL.absoluteString Todisk:yes];#endifsize = Image.size;}}Filter out the size of the image, big picture is too big to waste traffic, the user experience is not goodif (Size.Height > 2048 | | size.height return Cgsizezero;}Else{return size;}}+ (Cgsize) Downloadpngimagesizewithrequest: (nsmutableurlrequest*) Request{[Request setvalue:@ "bytes=16-23" forhttpheaderfield:@ "Range"];nsdata* data = [nsurlconnection sendsy

0 0 0]; Case 2 Output (i,:) =[0 1 0 0]; Case 3 Output (i,:) =[0 0 1 0]; Case 4 Output (i,:) =[0 0 0 1]; endend% randomly extracted 1500 samples for training samples, 500 samples for pre-measured samples traincharacter=input (n (1:1600),:); Trainoutput=output (n (1:1600),:); Testcharacter=input (n (1601:2000),:); Testoutput=output (n (1601:2000),:);% normalization of the characteristics of the training [traininput,inputps]= Mapminmax (Traincharacter '); Initialization of a perce

(Tot,0,sizeof(tot)); -memset (W2,0,sizeof(W2)); -memset (W5,0,sizeof(W5)); + for(inti =1; I ) { -scanf"%lld", a); + whileA5==0) w5[i]++, a/=5; A whileA2==0) w2[i]++, a/=2; atTot[i] = tot[i-1] +W2[i]; - } -f[0][0][0] =0; - intRET =0; - for(inti =1; I ) { - for(intK =1; K ) { in

Declaration of the Merge function: Merge (x, y, by = intersect (names (x), names (y)), by.x = by, By.y = by, all = FALSE, all.x = all, All.y = all, sor t = TRUE, suffixes = C (". X", ". Y"), incomparables = NULL, ...) Description of the merge function parameter: X, y: Two data frames for merging BY,BY.X,BY.Y: Specifies which rows are to be combined with the data frame, with the default value being the column with the same column name. ALL,ALL.X,ALL.Y: Specifies whether the rows of x and y should

o'clock, the linear model Y = * X is better fitting with the sample data.So when the number of items X = 6 o'clock, we can roughly estimate the total price y = 20 * 6 = 120Multivariate regression:A linear regression greater than an independent variable is called multivariate regression.The above example is just a self-variable, which is easier to handle, but if there are many independent variables, it is assumed that the arguments are M, [X1,X2,X3,X4.....XM].At this point we assume that the reg

;}intlbintx) {returnx (-X);}voidAddintXintV) {if(!x)return; for(intI=X;I1ll*x*v;}Long LongQues (intx) {Long LongRe=0; for(intI=x;i>0; i-=lb (i)) re+= (x+1) *c1[i]-c2[i];returnRe;}intMain () {N=read (), M=read (), P1=read (), P2=read (); a[0]=a[n+1]=n+1; s[top=1]=0; for(intI=1; i+1; i++) {if(i while(A[s[top]] for(intI=1; iintL=read (), R=read (); ans[i]+= (r-l) *p1;contribution generated by//adjacent two pointsB[i]=qwe (l,r,l-1I-1); B[i+m]=qwe (L,r,r,i,1); } sort (b+1B+1+2*M,CMP); for(intI=1; iif

. Therefore, the probability or frequency of the occurrence of the word and the word can reflect the credibility of the word better.The main statistical models are: N-ary Grammar model (N-gram), Hidden Markov model (Hidden Markov models, HMM)1.2.1n-gram model thoughtThe model is based on the assumption that the occurrence of the nth word is only related to the first N-1 word, but not to any other word, and the probability of the whole sentence is the product of the probability of each word appea

is at best marginal. For example, for workload estimates and defect forecasts, simpler data mining can achieve the same, or even better, results than the more sophisticated. 1,2 Landscape excavation Algorithm mining is "jump to see", the researcher threw the algorithm on the data, and then see what the result is. The second way is to "see and then jump", mining the data to find the possible reasoning space, and then with the learning device leap. This is the "landscape" of the data. Figure

. Starts with a set of words (start with some vocabulary):ν= {The, a, doctorate, candidate, professors, grill, cook, ask, ...}II. Get a training sample with the vocabulary set V-off (get a training sample of V):Grill Doctorate candidate.Cook professors.Ask professors.......Iii. hypothesis (assumption): The training sample is characterized by some hidden distribution p (training sample is drawn from some underlying distribution p)Iv. Objective (GOAL): Learning a probability distribution P prime a

auto-generated Method Stub while(true) { synchronized(obj) {if(Ticketnum > 0) {System.out.println (Thread.CurrentThread (). GetName ()+ "number sold" + ticketnum--); } } } }} Public classTextWindow2extendsthread{/** * @paramargs*/ Public Static voidMain (string[] args) {Windos2 W1=NewWindos2 (); Windos2 W2=NewWindos2 (); Windos2 W3=NewWindos2 (); W1.setname ("Ticket No. 1th");

points array object THREE composed of Vector3 objects. spline = function (points) {this. points = points; // set the parameter points to the points attribute var c = [], v3 = {x: 0, y: 0, z: 0} of the current spline object }, point, intPoint, weight, w2, w3, pa, pb, pc, pd; /*************************************** * ***** the following are the functions provided by the Spline object. **************************************** // * // The initFromArray

(The effect is not very good, for reference only)First: Create a new class win32native, introducing the Win32 external function.The code is as follows:public class Win32native{[System.Runtime.InteropServices.DllImport ("user32.dll", EntryPoint = "SetParent")]public extern static IntPtr SetParent (IntPtr childptr, IntPtr parentptr);}Second: Create a new two form:Window1.xamlWindow2.xamlThird: Add references in Window1.xaml.csUsing System.Windows.Interop;IV: Put a Button1 in the Window1 formThe ev

However, it is the same as a silent calibration process.1#include 2#include 3#include 4#include 5#include string>6 using namespacestd;7 intN;8 BOOLBalintW) {9 intw1,w2,d1,d2;Ten BOOLb1=1, b2=1; Onescanf"%d%d%d%d",w1,d1,w2,D2); A if(!W1) b1=Bal (W1); - if(!W2) b2=Bal (w2); -w=w1+

Problem descriptionThe numbers that contain only the factors 2, 3, and 5 are called ugly Numbers (Ugly number). For example, 6, 8 are ugly numbers, but 14 is not, because it contains factor 7. We used to think of 1 as the first ugly number. Find the nth ugly number in order from small to large.Algorithm analysisThe number of ugly numbers in each location is the result of an ugly multiply by 2 or 3 or 5, see the following example:The 1th ugly number is 1,The 2nd ugly number is 1 * 2 = 2, 1th ugly

'NO' otherwise.Sample Input10 2 0 40 3 0 11 1 1 12 4 4 21 6 3 2Sample OutputYESA clever use of the topic of clever answers. It's a little hard to think. is a good topic.1#include 2#include 3#include 4#include 5#include 6#include string>7#include 8#include 9#include Ten#include Set> One#include A#include -#include -#include the#include - using namespacestd; - Const intinf=0x5fffffff; - Const Doubleexp=1e-8; + Const intMod=100000007; - Const intms=10005; + A BOOLSolveintW) at { - intw1,