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= nothingEnd Function End ClassFunction getmysqlconnection (byref tmpcon, byval tmpsvname, byval tmpdbname, byval tmpuid, byval tmppwd) Set tmpcon = Createobject ("ADODB. Connection ") On Error resume next'Mysql uses mongoado to achieve zookeeper.Tmpcon. Open "driver = {MySQL ODBC 5.1 Driver };"_ "Server =" tmpsvname ";"_ "Database =" tmpdbname ";"_ "Uid =" tmpuid "; Pwd =" tmppwd ";"'Zookeeper was wrongly formed when the final result was judgedIf err. Number = 0 thenGetmysqlconnection = t

Both the Apriori algorithm and the fptree algorithm are the Association Rule Mining Algorithms in Data Mining. They process the simplest single-dimension boolean association rules. Apriori algorithm The Apriori algorithm is the most influential algorithm used to mine frequent item sets of Boolean association rules. It is based on the fact that algorithms use a prior knowledge of the nature of frequent item sets. Apriori uses an iteration method called layer-by-layer search,K-Item Set for explora

T100T200T300T400T500T600T700T800T900 I1, I2, I5I2, I4I2, I3I1, I2, I4I1, I3I2, I3I1, I3I1, I2, I3, I5I1, I2, I3 The basic process of an algorithm is as follows: First, scan all transactions, get 1-item set C1, filter out the item set that does not meet the condition according to the sup

Smo_svm.pas: {Author: LiuWebsite: www.aivisoft.netE-mail: geo.cra@gmail.comThis code is translated from a p-code by J. Platt.This code shows a smo (sequential minimal optimization) SVM.Enjoy fast and efficient SVM!This code now provide 4 different kernels.}Unit smo_svm; Interface UsesWindows, sysutils, variants, classes, graphics, math, unitypes; TypeTvector = recordItems: tsingleextendedarray;Target: extended;End; Tvectorarray = array of tvector; Tkernels = (kllinear, klgsrbf, klexprbf, klpolyn

The contains () method and the Remove () method in collection.Boolean contains (Object O), which is used to determine whether an element is contained in the collection, if it contains, returns true, and does not contain a return of false. Combine the following code to see.Import java.util.*;PublicClasscollectiontest03{PublicStaticvoidMainString[] (args) {Collection c=NewArrayList ();Integer i1=NewInteger (10);C.Add (I1);TrueSystem.Out.println (C.contains (I1));Integer

enable you to win the game as soon as possible (this is the key, to be clear). The calculation method of the evaluation points of the leaf nodes in the Strategy tree (winning, losing or draw evaluation method) is given only in the topic, how to calculate the evaluation score of each non-leaf node in the strategy tree?The answer is to use the depth-first search for the entire strategy tree to post-sequence traversal, so that the evaluation of the leaf node in the policy tree, the evaluation of t

); in for(inti =1; I i) { -scanf" %d%d%d", a[i].x, AMP;A[I].Y, a[i].z); to for(intj =1; J j) { +COST[I][J] = cost[j][i] = (a[i].x-a[j].x) * (a[i].x-a[j].x) + (A[I].Y-A[J].Y) * (A[I].Y-A[J].Y) + (a[i].z-a[j].z) * (A [I].z-a[j].z); - } the } * edge s1, S2, S3; $ intans, res =0, x1, x2, y1, y2, len1, len2;//X1 Y1 is a triangular face with different sides of the ends len1 for the isosceles triangle of the waistPanax Notoginseng for(intI1 =1;

and TipsData Size Hint"Data Range"There is 1≤n≤30,1≤m≤12 for 30% of the data.For 50% of the data is 1≤n≤120,1≤m≤50, and 4 kinds of crawling cards, each card will not exceed the number ofOver 20.For 100% of the data is 1≤n≤350,1≤m≤120, and 4 kinds of crawling cards, each card number of cards will notMore than 40;0≤ai≤100,1≤i≤n;1≤bi≤4,1≤i≤m. Input Data Assurance N−1=σmI B1Assuming the remaining T1 Zhang 1,t2 Zhang 2,t3 Zhang 3,t4 Zhang 4, the maximum score is F[T1,T2,T3,T4]Then push forward, the

example above, the confidence indicates the percentage of users who purchased the milk and who bought the eggs. As with support, the confidence will also have an initial value (60% in the above example, indicating that 60% of the users who purchased the milk also purchased the eggs), if the final confidence level is less than the initial value, then milk and eggs can not become a frequent modeSupport and confidence can also be represented by specific data, not necessarily a percentageThe basic

First, let's look at a piece of code (using JDK 5), as follows: public class test { Public static void main (string [] ARGs ){ Integer I1 = 127; Integer I2 = 127; If (I1 = I2) System. Out. println ("Equal! "); Else System. Out. println ("not equal! "); } } The output result must be "Equal !". Now, change the I1 and I2 values from 127 to 128 to see what will happe

compilation error occurs.)(in jdk1.5 and above, B can be the basic data type, a cannot) the same way, other encapsulation classes and basic types are the same. The difference between equals and = = in Java= = compares the addresses of 2 objects, while equals compares the contents of 2 objects.In versions above jdk1.5, the base and wrapper classes can be converted automatically, similar to string-type objects and strings constants.Integer i1 = 123; Integer i