drwxr xr x

Today Mayuyu to learn the R language , first to learn some basic operations.(1) C ()C () is used to create a vector, such as(2) Length ()Length () is used to get a vector of the lengths.(3) mode ()Gets the type of data in the vector, such as(4) Rbind () and Cbind ()Both of these functions combine multiple vectors into a single matrix, or combine multiple matrices into a single matrix, and Rbind () is merged by rows.and Cbind () is merged by column.(5)

Today goes on to the most complex time series in exponential smoothing: a predictive algorithm that has a time series of increasing or decreasing trends and having seasonal fluctuations is holt-winters and shared with everyone. This sequence can be decomposed into the horizontal trend part, the seasonal fluctuation part, therefore these two factors should have the corresponding parameter to control in the algorithm.The holt-winters algorithm provides alpha, beta, and gamma to correspond to the h

Cannot find-lgl error when using QT compilation under Ubuntu, use commandBecause the system lacks a link library, the following command can be used at the terminal to resolve the problem.sudo apt-get install Libqt4-dev or sudo apt-get install Libgl1-mesa-dev or Libgl1-mesa-dev or Libglu1-mesa-devcan solve the problemYou can apt-get install update firstI encountered a collection of errors for Linux:1:ps-aux | grep Corsegui Find the Corsegui processKill-s 9 5283 kills the Corsegui process2: Keyboa

1 inserting a column of new data into a data.frame specified column1.1 Inserting a column to a specified positionY)) data2 Insert to end of data1Data2Insert in first columnData22 Inserts a row of new data into a data.frame specified line2.1 Inserting a row to a specified positiondata1)) row) data2Insert to end of data1data2Insert to data1 first rowdata23 Setting column names for data frameColnames (DATA2) 4 Create an empty data frame, and make a column name (method a bit of a setback, there is n

Logical (0) is not equal to numeric (0). Both are not equal to null values, i.e. Is.null (logical (0)) and is.null (numeric (0)) return values are false. It is interesting to note that a zero-length value is sometimes not counted as a null value, but the definition of a null value is a parameter that is not assigned in the function, and is characterized by no value. How do I differentiate between null and NA? Very simple, the latter logical length is 1, while the former logical length is 0, mean

=-inf, upper = INF,Control = List (), Hessian = FALSE)Optimhess (Par, fn, gr = NULL, ..., control = list ())Parameters:Par: vector. Contains the initial value of the optimized object, the default null, and the general algorithm automatically generates default values.fn: Minimizes the function, uses a parameter vector as the first parameter of the function, and minimizes it based on it. Returns the result of a scalar.GR: A function that returns a gradient for the Bfgs/cg/l-bfgs-b method, or null

Root is a basic problem of numerical calculation, and it is usually solved by iterative algorithm, which includes fixed point iterative method, Newton-Pull enrichment Algorithm, secant method and dichotomy method. Fixed point iterative method The so-called fixed point refers to those points of x=f (x), and the so-called non-point iterative method refers to the original equation into x=f (x) Form, the next X value is the step f (x), so that we can always approach the x we need, that

execution state more than once, or it can be blocked multiple times. Figure 3_4 describes the three basic states of the process and their transformations.(1) Ready → Executes the process in the ready state, and when the process dispatcher assigns it a processor, the process is transformed from the ready state to the execution state.(2) execution → Ready in the execution of the process during its execution, because a time slice allocated to it has been exhausted and had to give up the processor,

distribution.How to adjust θ so that J (θ) obtains the minimum value there are many methods, this article will focus on the gradient descent method and the normal equation method.Gradient DescentAfter the linear regression model is selected, the model can be used for prediction only if the parameter θ is determined. However, Theta needs to make J (θ) the smallest. So the problem boils down to the problem of finding the minimum value.The gradient descent process is as follows:1. First assign a v

separation for each variableFor (i in 1:numvariables) {VariableiVariablenameVwVbSepPrint (Paste ("variable", VariableName, "vw=", Vw, "vb=", Vb, "separation=", Sep))}}# For example, to calculate the interval of 13 chemical concentrations per variable, we enter:Calcseparations (Wine[2:14],wine[1])# Therefore, the maximum interval for individual variables within a group (wine variety) is V2 (interval 233.0).# as we will discuss below, the purpose of linear discriminant analysis (LDA) is to look f

effectThe above does not talk about the test process, for the above example, the KNN first two parameters are used train, because the use of the same data set, so the result is the correct rate can reach 100%. In the case of more training sets, it can be randomly assigned to 7:3 or 8:2 in two parts, the former training the latter to do the test is good. There is no longer a detailed statement.In the case that the classification effect is not ideal, it is necessary to enrich the training set to

Suppose there is a file file1.xlsx, which reads as follows:There is a file file2.xlsx, which reads as follows:Now I want to start with column seventh and replace all file2 characters with file1, i.e. the 第七、八、九、十 column does not need to change because the characters of file1 and File2 are the same (3 and 4), and file1 and file2 characters are different from column 11th. My naming rules are starting from column 11th, File2 2 changed to 3,4 to 1,3 to 2,1 to 4;Here is the code implementation proces

follows the Poisson distribution, and the linear model fits the form:Many of the functions of the analytical standard Linear model LM () have a corresponding form in GLM ():#使用robust包中的癫痫数据Breslow to discuss the impact of epilepsy data on the incidence of Epilepsy Library (robust) data (Breslow.dat,package = "robust") names (Breslow.dat) # We are only concerned with the TRT treatment conditions, Age: ages, base epilepsy number base, response variable eight weeks after the onset of Sumy randomiz

OSL Regression Simple linear regression> fitGet Predictive regression formula: Weight=-87.52+3.45*heightPolynomial regressionFIT2Three quadratic linear regressionFIT3Multivariate linear regressionFITmultivariate linear regression with interacting itemsFITRegression judgmentFITFIT2NEWFIT2Detecting outlier values#离群点library (CAR) outliertest (FIT) rstudent unadjusted p-value bonferonni pnevada 3.542929 0.00095088 0.047544 # High leverage value hat.plot#强影响点cutoff Library (CAR) avplots (fit,ask=fal

ifid $i >/dev/null;then read-n1 -p "comfirmdeleteuser" $i "[y=comfirm,otherkey=cancel]:" sure case$ Sureiny| Y) echo ps-u $i -- no-heading|awk ' {print$1} ' |xargskill-9>/dev /null #kill userpid find/-user $i - execrm-rf{}\;>/dev/null #delete userfiles userdelnBSP, $i >/dev/null #delete user echo the user $i deleted " ;; *) echo echo "$i Deletenotsuccessful " nBSP;;; esac fidoneThis article is from the "remote to the South" blog, please be sure to keep this source http://slitobo.blog.51cto

factor is larger, if you want to further determine whether this factor is related, you need to use the oblique rotation extraction factor1 fa.promax 'promax', fm=' PA ' )2 Fa.promaxConclusion: The correlation is 0.57, the correlation is very big, if the correlation is not small, using the orthogonal rotation can3. Calculate the Score1FSM function (oblique) {2 if(class(oblique) [2]=="FA" is. Null (Oblique$phi)) {3Warning"Object doesn ' t look like oblique EFA")4}Else {5P Unclass (oblique$loadi

Objective:Learn two variable analysis flow by exploring file PSEUDO_FACEBOOK.TSV dataKnowledge Points:1.ggplot syntax2. How to make a scatter chart3. How to optimize scatter plots4. Condition mean value5. Correlation of variables6. Sub-hubs diagram7. SmoothingBrief introduction:If you are exploring a single variable using a histogram to represent the relationship between the value and the whole, then using a scatter plot is more appropriate to explore the relationship between the two variables w

{Loop subcat (0,0,0, $siteid) $r} {/loop}/*** Get sub-columns* @param $parentid Parent ID* @param $type column type* @param whether the $self contains itself 0 is not included* @param $siteid Site ID*/function subcat ($parentid = null, $type = null, $self = ' 0 ', $siteid = ') {if (empty ($siteid)) $siteid = Get_siteid ();$category = GetCache (' Category_content_ '. $siteid, ' Commons ');foreach ($category as $id = = $cat) {if ($cat [' siteid '] = = $

. Low2007-01-03 29.91 30.25 29.402007-01-04 29.70 29.97 29.442007-01-05 29.63 29.75 29.452007-01-08 29.65 30.10 29.532007-01-09 30.00 30.18 29.732007-01-10 29.80 29.89 29.43MSFT. Close MSFT. Volume MSFT. Adjusted2007-01-03 29.86 76935100 22.965632007-01-04 29.81 45774500 22.927172007-01-05 29.64 44607200 22.796422007-01-08 29.93 50220200 23.019472007-01-09 29.96 44636600 23.042542007-01-10 29.66 55017400 22.81180B. Crawling Web pagesGrab Justin Rao's website from 2002 to 2008 NBA payroll data> S

;Some of the black dots shown in the figure that are close to the green Center (marked with an asterisk) are actually closer to the black center in 4-dimensional space.It is important to note that the K-means clustering results can be different for multiple runs, because the initial cluster centers are randomly selected.Plot (Iris2[c ("Sepal.length", "Sepal.width")], col = kmeans.result$cluster)# Plot Cluster centersPoints (Kmeans.result$centers[,c ("Sepal.length", "Sepal.width")], col = 1:3, pc