best bitcoin mining algorithm

matrix. Step 1: calculate similarity (I, j) The similarity calculation is different here. Only the user that has been evaluated by item_ I and item_j is used to calculate the similarity. In this step, obtain the N x n item-similarity matrix. Step 2: predict ????? Variants 1. Slope one Core Idea: replace weighted score with function f (x) = x + B, the free parameter B is then simply the average differencebetween the two items ratings. How: L find out the average score difference between (item_

';update finalgini set statetemp=state; end if;delete from aa where namee = @x;end a1;end if;if (@count>1) thenset @id = (select count(id) from bb); if(@id = 2) thenw:beginupdate bb left join weather on bb.id=weather.id set class = current_num where play='yes';set current_num = current_num+1;update bb left join weather on bb.id=weather.id set class = current_num where play='no';set current_num = current_num+1;if (current_table ='cc') thendelete from cc where id in (select id from bb);end if;set

One, unsupervised learning1. Clustering: It is a process of classifying and organizing data members with similar data concentrations in some aspects. Therefore, a cluster is a collection of some data instances. Clustering techniques are often called unsupervised learning.Second, K-means clustering1, K-means algorithm: is the discovery of a given dataset K cluster algorithm2. Steps:1), randomly selected K number of points as the initial cluster center

I. Frequent itemsets mining Why is there fp-growth?Reason: This has to start with the principle of the Apriori algorithm, Apriori will produce a large number of candidate itemsets (which is generated after the connection ), in pruning, you need to scan the entire database ( that is, the data given ), through pattern matching to check the candidate collection ( To find items that meet the minimum support le

Mining assocaition rule algrothm (algorithm for mining related rules)There are two key parameters in this algorithm, coverage (which indicates the correct number of cases to be predicted, and this parameter is used to filter which instances are greater than or equal to this worth the relevant rule)Accuracy (indicates t

/* The MO team algorithm is an off-line algorithm, used to solve the problem of known l,r ask you l,r inside the value of the order of the block, the following block through the front of the block left to move to the right, so there may be a situation can get O (1) or lower complexity of the order when divided by Good n (? The question test instructions: How many different numbers are asked in the range fro

modification in the manner of processing, the code does not need to change the big.= = There is no way, after all, not everyone will write code ... ）namespace fmanage{public partial class Analy:form {private system.windows.forms.checkbox[] Checkboxfactor S Private DataSet DS; Private int[] rowtables; Private int[] Flag; Private int[] dimention; Private int[] fee; private int p; Public Analy () {InitializeComponent (); This.p

Clustering (cluster)Partitioning Methods:K-meansSaquential LeaderModel Based MethodsDensity Based MethodsHierachical MethodsUnsupervised learning (unsupervised learning)No LabelsData drivenSpecial needs to be aware of:Arbitrary shape handles a wide variety of shapes noise and outlier noise, and the ability to process outliers---------------------------------------------------------------------K-means algorithmEvaluationThis note is mainly about the effect of spherical data is better, for other e

metric options, which is None by default.6.n_jobs is the number of threads that are computed in parallel, default is 1, and input-1 is set to the number of cores of the CPU.Function method:neighbors.KNeighborsClassifier.fit(X,y)Make predictions on a datasetneighbors.kNeighborsClassifier.predict(X)Output prediction Probability:neighbors.kNeighborsClassifier.predict_proba(X)Correct rate Scoreneighbors.KNeighborsClassifier.score(X, y, sample_weight=None)?#coding=gbk#KNN算法实现对电影类型的分类import numpy as

SVM, support vector machine. A classical algorithm in data mining, Bo Master learned a long time, to learn some things to share with you.SVM (Svm,support vector machine) is a learning system using linear function hypothesis space in high dimensional feature space, which is trained by a learning algorithm from the optimization theory. The

(): Fast approximate K-mean clustering algorithmSimplekmeans (): K-Mean clustering algorithmXmeans (): Improved K-mean method, can automatically determine the number of categoriesDBScan (): A density-based clustering method that continuously grows clusters based on the density surrounding the object. It can find any shape clustering from a spatial database containing noise. This method defines a cluster as a set of points for a set of "Density joins."5) Association RulesApriori (): Apriori is t

. Clusterlist.size (); i++) {List This. Clusterlist.get (i); System.out.println ("------------"); for(intj = 0; J ) {System.out.println (C.get (j). x+ "" + C.get (j). Y + "" +C.get (j). Point_type); } System.out.println (C.size ()); System.out.println ("------------"); } System.out.println ("Noise Point has" + This. Noiselist.size () + "X"); System.out.println ("------------"); for(inti = 0; I This. Noiselist.size (); i++) {System.out.println ( This. Noiselist.get (i). x + "" + Th

FP Tree constructionThe FP growth algorithm takes advantage of ingenious data structures, greatly reducing the cost of the Aproir mining algorithm, and he does not need to constantly generate candidate project queues and constantly scan the entire database for comparison. To achieve this effect, it uses a concise data structure called Frequent-pattern tree (frequ

Association rules are expressions such as a->b, and A and B are the two subkeys that intersect each other in the entire set.The main purpose of mining association rules is to find meaningful correlation relationships in data. Shopping basket analysis is the analysis of customer purchase behavior to discover the relationship between different products.Support degree, confidence level, promotion degreeSupport Degree (a->b) =| ab|/| s|Confidence level (A

1. Introduction What is the Appriori algorithm used for? The main thing is to solve a problem like this: if a customer buys a beer, does he still buy diapers? The core of the theory:A subset of frequent itemsets is still a frequent project set, and a superset of a non-frequent item set is a non-frequent project set. This theory has been applied as a classical data mining theory.Theorem (Appriori attribute 1

, we calculate the split information metric h (V): Outlook properties Property Outlook has 3 values, where Sunny has 5 samples, rainy has 5 samples, overcast has 4 samples, then 1 H(OUTLOOK) = - 5/14 * log2(5/14) - 5/14 * log2(5/14) - 4/14 * log2(4/14) = 1.577406282852345 Temperature property Attribute temperature has 3 values, in which hot has 4 samples, mild has 6 samples, cool has 4 samples, then 1 H(TEMPERATURE

are interrelated when C is known. In the case of C unknown, a, B is blocked (blocked), is independent. (For an inappropriate example, your parents and you, before you were born, they were independent, after you were born, they were connected by you, no longer independent of each other)In the alarm network diagram, the Theft (B) and the earthquake (E) will ring the alarm bell (A), resulting in a sink structure: e-->aWhen a is unknown, B and e are independent of each other, knowing that an earthq

Today, we investigate the parallel frequent pattern mining algorithm PFP growth and its command use under Mahout, simply record the test results for later reference: Environment: Jdk1.7 + Hadoop2.2.0 stand-alone pseudo cluster + Mahout0.6 (both versions 0.8 and 0.9 do not include this algorithm.) Mahout0.6 can have a bit of an accident with Hadoop2.2.0 Orz) Par

properties can be processed3, high computational complexity (such as the number of samples of known classification is n, then to each unknown classification point to calculate n distance)Problems with KNN algorithm:1, the determination of K value is a difficult problem.2, if the nearest K-known classification samples, the highest frequency of the type has multiple (the same frequency), how to choose the Unknown sample classification? At the moment, i

intFirst ; $ intID; -Segment (): from(0), End (0), First (0), ID (0) { } - -BooleanoperatorConst { A if(First! = B.first)returnFirst B.first; + returnEnd B.end; the } - }segment; $ the intN, c, q; the intm; the int*Flo; thesegment*seg; - int*Res; in int*counter; the theInlinevoidinit () { About Readinteger (n); the Readinteger (c); the Readinteger (q); theFlo =New int[(Const int) (n +1)]; +SEG =Newsegment[(Const int) (q +1)]; -re