correlation

问题 I have 684 nodes and its corresponding temperature data.I calculated the correlation between all nodes and then reshape correlation list in adjacency matrix for further calculation. But after introducing lag in correlation I am not able to reshape the list in adjacency matrix. I am able to reshape the list to adjacency matrix without lag but having problem when introducing the lag. I tried corr_k= [] for k in range(4): for i in range(len(lat)): for j in range(len(lon)): for m in range(len(lat

问题 In every paper i read about encryption they like to show the correlation coefficients of their encrypted image by showing 3 values: Horizontal correlation coefficient . vertical correlation coefficient. diagonal correlation coefficient . and they show these 3 values for encrypted image and also for plain image(lena). My question is how to do this in matlab ? and if there is no matlab function for it , what are the equation they are using to get those 3 values ? Table 2 Correlation

问题 I have a Pandas Dataframe like so: id cat1 cat2 cat3 num1 num2 1 0 WN 29 2003 98 2 1 TX 12 755 76 3 0 WY 11 845 32 4 1 IL 19 935 46 I want to find out the correlation between Cat1 and column cat3, num1 and num2 or between cat1 and num1 and num2 or between cat2 and cat1, cat3,num1,num2 When I use df.corr() it gives Correlation between all the columns in the dataframe, but I want to see Correlation between just these selective columns detailed above. How do I do that in Python pandas? A

问题 set.seed(8) df <- data.frame( A=sample(c(1:3), 10, replace=T), B=sample(c(1:3), 10, replace=T), C=sample(c(1:3), 10, replace=T), D=sample(c(1:3), 10, replace=T), E=sample(c(1:3), 10, replace=T), F=sample(c(1:3), 10, replace=T)) Would like to pass a subset of columns into a dplyr mutate() and make a row-wise calculation, for instance cor() to get correlation between column A-C and D-F, but cannot figure out how. Found SO inspiration here, here and here, but nevertheless failed to produce an

问题 set.seed(8) df <- data.frame( A=sample(c(1:3), 10, replace=T), B=sample(c(1:3), 10, replace=T), C=sample(c(1:3), 10, replace=T), D=sample(c(1:3), 10, replace=T), E=sample(c(1:3), 10, replace=T), F=sample(c(1:3), 10, replace=T)) Would like to pass a subset of columns into a dplyr mutate() and make a row-wise calculation, for instance cor() to get correlation between column A-C and D-F, but cannot figure out how. Found SO inspiration here, here and here, but nevertheless failed to produce an

问题 I have a dataframe df : df = pandas.DataFrame(pd.read_csv(loggerfile, header = 2)) values = df.as_matrix() df2 = pd.DataFrame.from_records(values, index = datetimeIdx, columns = Columns) EDIT: Now reading the data this way as suggested: df2 = pd.read_csv(loggerfile, header = None, skiprows = [0,1,2]) Sample: 0 1 2 3 4 5 6 7 8 \ 0 2014-03-19T12:44:32.695Z 1395233072695 703425 0 2 1 13 5 21 1 2014-03-19T12:44:32.727Z 1395233072727 703425 0 2 1 13 5 21 9 10 11 12 13 14 15 16 0 25 0 25 209 0 145

问题 I am new to coding and R. I was trying to visualize a correlation matrix using corrplot , but don't want to show all the correlation values. I wish to hide/cancel a chunk of selected columns and rows correlation values, so only an inverted 'L' of values are shown. As an example, see edited image of an example corrplot here: 回答1: Set those entries you want blank in the plot to NA in the correlation matrix (or a copy of it) and then set the argument na.label=" " in the call to corrplot. 回答2:

问题 does anybody have an idea how to normalize the scipy.ndimage.filters.correlate function to get : XCM = 1/N(xc(a-mu_a,b-mu_b)/(sig_a*sig_b)) What is N for the correlation? It usually is the # of datapoints / pixels for images. Which value shall I choose for scipy.ndimage.filters.correlate ? My images differ in size. I guess the scipy correlate function pads the small image into zeros? The size of the final matrix N = XCM.sizeX() * XCM.sizeY() ? Thanks, El 回答1: It looks to me like you're trying

问题 I´m building a correlation between two different matrices with rcorr() function in R: res <- rcorr(as.matrix(table1), as.matrix(table2),type="pearson") It seems to be working fine, however I want to avoid within table correlations - any suggestion? 回答1: Consider using R's base cor() for distinct correlations between two sets as Hmisc's rcorr() returns all possible combinations. Notice below the upper right quadrant of rcorr() (which repeats diagonally symmetrical on lower left) is the entire

问题 I've got four MySQL tables: users (id, name) polls (id, text) options (id, poll_id, text) responses (id, poll_id, option_id, user_id) Given a particular poll and a particular option, I'd like to generate a table that shows which options from other polls are most strongly correlated. Suppose this is our data set: TABLE users: +------+-------+ | id | name | +------+-------+ | 1 | Abe | | 2 | Bob | | 3 | Che | | 4 | Den | +------+-------+ TABLE polls: +------+-----------------------+ | id | text