# Pearson correlation coefficient principle, and Java implementation

Source: Internet
Author: User

Reprint please indicate source: http://blog.csdn.net/u010670689/article/details/418951051. Principle:

The four formulas listed above are equivalent, where e is the mathematical expectation, CoV represents the covariance, and N indicates the number of variables to be evaluated.

Mathematical expectation, covariance interpretation article link: http://blog.csdn.net/u010670689/article/details/41896399

The value of the correlation coefficient is between –1 and +1, which is –1≤r≤+1. The properties are as follows:

• When r>0, two variables are positively correlated, and when r<0, they are negatively correlated.
• When |r|=1, it means that the two variables are completely linearly related, that is, a function relationship.
• When r=0, it indicates the relationship between the two variables of the wireless correlation.
• When 0<|r|<1, there is a certain degree of linear correlation between the two variables. The closer the |r| is to 1, the closer the linear relationship between the two variables is, and the closer the |r| is to 0, the weaker the linear correlation between the two variables.
• Generally can be divided into three levels: |r|<0.4 for low-grade linear correlation, 0.4≤|r|<0.7 is significant correlation; 0.7≤|r|<1 is highly linear dependent.

2.java implementation: Using Formula Two to achieve

`Package Youling.studio.pearson;import Java.util.arraylist;import Java.util.hashmap;import java.util.Iterator;  Import Java.util.list;import java.util.map;import org.apache.log4j.logger;/** * * */public class Similarity {static    Logger Logger = Logger.getlogger (Similarity.class.getName ());    map<string, double> rating_map = new hashmap<string, double> ();     list<double> rating_map_list = new arraylist<double> ();        /** * @param args */public static void main (string[] args) {Similarity similarity1 = new similarity ();        Similarity1.rating_map_list.add (20d);        Similarity1.rating_map_list.add (7d);        Similarity1.rating_map_list.add (26d);        Similarity similarity2 = new similarity ();        Similarity2.rating_map_list.add (7d);        Similarity2.rating_map_list.add (3d);        Similarity2.rating_map_list.add (6d); Logger.info ("" + Similarity1.getsimilarity_bydim (similarity2)); More than 0.8, belonging to highly correlated SimilaritY similarity3 = new similarity ();        Similarity3.rating_map_list.add (12d);        Similarity3.rating_map_list.add (4d);        Similarity3.rating_map_list.add (8d);        Similarity similarity4 = new similarity ();        Similarity4.rating_map_list.add (3d);        Similarity4.rating_map_list.add (1d);        Similarity4.rating_map_list.add (2d); Logger.info ("" + Similarity3.getsimilarity_bydim (similarity4)); The result is that the 1.0 ratio is actually the front and back is a multiple relationship} public Double Getsimilarity_bydim (similarity u) {if (This.rating_map_lis       T.size ()!=u.rating_map_list.size ()) {return null; } Double sim = 0d; The last Pearson correlation coefficient double common_items_len = this.rating_map_list.size (); Number of operands double this_sum = 0d; The first correlation number and double u_sum = 0d; The second correlation number and double this_sum_sq = 0d; The sum of squares of the first correlation number and double u_sum_sq = 0d; The second correlation number squared and double p_sum = 0d; Sum of two correlation number products and for (int i = 0;i<this.rating_map_list.size (); i++) {Double This_grade = THIS.RAting_map_list.get (i);   Double U_grade = U.rating_map_list.get (i);   Scoring sum//squared and//product and this_sum + = This_grade;   U_sum + = U_grade;   THIS_SUM_SQ + = Math.pow (This_grade, 2);   U_SUM_SQ + = Math.pow (U_grade, 2);       P_sum + = This_grade*u_grade;        } logger.info ("Common_items_len:" +common_items_len);        Logger.info ("P_sum:" +p_sum);        Logger.info ("This_sum:" +this_sum);        Logger.info ("U_sum:" +u_sum);        Double num = common_items_len * p_sum-this_sum * u_sum; Double den = math.sqrt ((Common_items_len * THIS_SUM_SQ-MATH.POW (This_sum, 2)) * (Common_items_len * U_SUM_SQ-MATH.POW (        U_sum, 2));        Logger.info ("+ num +": "+ den); Sim = (Den = = 0)?           1:num/den;   Return SIM;   }    }`

3. Scope of application:

When the standard deviation of two variables is not zero, the correlation coefficients are defined, and the Pearson correlation coefficient applies To:

(1), two variables are linear relations, are continuous data.

(2), two variables are generally normal, or nearly normal single-peak distribution.

The observed values of (3) and two variables are paired, and each pair of observations is independent of each other.

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Pearson correlation coefficient principle, and Java implementation

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