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Lincoln Wright
Lincoln Wright

Permute 3.4.2



Installing RIf you don't have R on your system, installing R (and uninstalling) is very easy. Do an Internet search for "Install R" and you'll find a page on the cran.r-project.org Web site with a link labeled "Download R 3.x.y for Windows." Click that link and you'll get an option to run a self-extracting installer file named something like R-3.x.y-win.exe. Click on the Run button presented to you by your browser to launch the installer. You can accept all the configuration defaults and installation is very quick. The demo program (technically a script because R is interpreted) uses R version 3.4.2, but any 3.x version will work.




Permute 3.4.2



matplotlib,pyplot.imshow expects a numpy array in a valid image shape (e.g. [height, width, channels] or [height, width]), while you are passing tensors in the shape [batch_size, channels, height, width] to it.You would thus have to index each image (via image = images[0]) and permute the axes to match the expected shape:


SAMPLE RANDOM PERMUTATION Name: SAMPLE RANDOM PERMUTATION (LET) Type: Let Subcommand Purpose: Generate a series of random permutations where possibly only a subset of the random permutations will be retained and where a specified proportion of the permutation values are allowed. Description: For a given size N, to generate a random permutation the integers from 1 to N are randomly sampled (without replacement) until all elements have been selected. This is implemented with the command LET Y = RANDOM PERMUTATION FOR I = 1 1 N This SAMPLE RANDOM PERMUTATION command was motivated by the desire to simulate the following problem. Given a worm that has infected a single computer, simulate the following: The infected machine will propogate to NKEEP IP addresses from a master list of NPOP IP addresses. Of the NPOP addresses, only a proportion, p, denote addresses corresponding to an actual machine. That is, there will be p*NPOP valid addresses. Each newly infected machine will likewise attempt to propogate to NKEEP addresses. This process will be repated until all IP addresses corresponding to valid machines are infected. The Program 1 example demonstrates how this simulation can be implemented using the SAMPLE RANDOM PERMUTATION command. Syntax: LET = SAMPLE RANDOM PERMUTATION where is a number or parameter that specifies the size of the random permutation; is a number of parameter that specifies how many permutated values are kept at each step; is a parameter or number (greater than 0 and less than or equal to 1) that specifies the proportion of valid permutation values; is a number of parameter (less than or equal to ) that specifies how many sets of randon permutations are generated; is a variable where the permutated values are saved; is a variable that identifies which iteration generated the permuted values. Examples: LET Y TAG = SAMPLE RANDOM PERMUTATION NPOP NKEEP P NITER Note: The SEED command can be used to specify a seed for the random number generation. The SET RANDOM NUMBER GENERATOR command can be used to specify which random number generator to use. Note: By default, only the unique permutation values in the output variable are saved. If you want all permutation values to be saved, then enter the command SET SAMPLE RANDOM PERMUTATION DISTINCT OFF This is demonstrated in the Program 2 and Program 3 examples below. Note: Although this command was implemented with a specific simulation scenario in mind, it does have wider applicability. Specifically, it can be used to efficiently implement two sample permutation tests. The Program 2 and Program 3 examples below demonstrate a two sample permutation test for the difference between the means of two samples. These examples can be easily adapted to test for the difference between other location statistics such as the median or the trimmed mean. Based on extensive simulation studies, Higgins recommends that 1,600 random permutations should be sufficient for most purposes. Note: This routine uses a random permutation algorithm suggested by Knuth. Specifically, it adapts the RANDPERM routine of Knoble. Default: None Synonyms: None Related Commands: LET = Generate data transformations. RANDOM PERMUTATION = Generate a random permutation. SEED = Specify the seed for random number generation. RANDOM NUMBER GENERATOR = Specify the random number generator to use. RANDOM NUMBER = Generate random numbers from a specified distribution. BOOTSTRAP SAMPLE = Generate a bootstrap sample. References: Knuth (1998), "The Art of Computer Programming: Volume 2 Seminumerical Algorithms, Third Edition", Section 3.4.2, Addison-Wesley. Knoble RANDPERM algorithm downloaded from: -03/msg00748.html. Higgins (2004), "Introduction to Modern Nonparametric Statistics", Duxbury Press, Chapter 2. Applications: Simulation, Permutation Tests Implementation Date: 2017/08 Program 1: set random number generator fibbonacci congruentialseed 56791.let npop = 64000let nkeep = 10let p = 0.25let maxiter = 20let maxit2 = 19let nmax = p*npop.let y(1) = 1let nyv(1) = 1let y = 0 for i = 2 1 maxiterlet iterv = sequence 0 1 maxit2.loop for k = 1 1 maxiter let niter = size y let ynew tag = sample random permutation npop nkeep p niter let ny = size y let nyv(k) = ny let y = combine y ynew let y = distinct y if ny >= nmax break loop end of ifend of loop.set write decimals 0print iterv nyv The following output is generated ------------------------------ ITERV NYV------------------------------ 0 20 1 50 2 181 3 623 4 2040 5 5860 6 11932 7 15351 8 15941 9 15995 10 16000 11 0 12 0 13 0 14 0 15 0 16 0 17 0 18 0 19 0 Program 2: set random number generator fibbonacci congruentialseed 32119.. Read the data.dimension 15 columns.skip 25read auto83b.dat y1 y2retain y2 subset y2 >= 0let y = comb y1 y2let n1 = size y1let n2 = size y2skip 0.. Compute the statistic for the original sample.let mean1 = mean y1let mean2 = mean y2let statval = mean1 - mean2.. Generate the random permutations.let npop = n1 + n2let nkeep = npoplet p = 1let niter = 1600let ntot = npop*niterlet repeat = data n1 n2let val = data 1 2let tag2 = sequence val repeat for i = 1 1 ntotset sample random permutation distinct off.let yindx tag1 = sample random permutation npop nkeep p niterlet ynew = gather y yindx.. Now compute the statistic for the permutations.set let cross tabulate collapselet ynew2 = ynewlet tag12 = tag1retain ynew2 tag12 subset tag2 = 1let ym1 = cross tabulate mean ynew2 tag12let ynew2 = ynewlet tag12 = tag1retain ynew2 tag12 subset tag2 = 2let ym2 = cross tabulate mean ynew2 tag12let ydiff = ym1 - ym2.. Now plot the results of the permutation test.let xq = 0.025let low025 = quantile ydifflet low025 = round(low025,2)let xq = 0.975let upp975 = quantile ydifflet upp975 = round(upp975,2).let xq = 0.025let low025 = quantile ydifflet low025 = round(low025,2)let xq = 0.975let upp975 = quantile ydifflet upp975 = round(upp975,2).title offset 2title case asislabel case asisy1label Countx1label Difference of Means for Permutationslet statval = round(statval,2)x2label color redx2label Difference of Means for Original Sample: ^statvalx3label color bluex3label 2.5 Percentile: ^low025, 97.5 Percentile: ^upp975xlimits -15 15title Histogram of Difference of Means for ^niter Permutations (AUTO83B.DAT).histogram ydiff.line color redline dashdrawdsds statval 20 statval 90line color blueline dashdrawdsds low025 20 low025 90drawdsds upp975 20 upp975 90 Program 3: set random number generator fibbonacci congruentialseed 32119.. Read the data.dimension 15 columns.let y1 = norm rand numb for i = 1 1 200let y2 = norm rand numb for i = 1 1 80let y2 = y2 + 0.1let y = comb y1 y2let n1 = size y1let n2 = size y2skip 0.. Generate the random permutations.let mean1 = mean y1let mean2 = mean y2let statval = mean1 - mean2.let npop = n1 + n2let nkeep = npoplet p = 1let niter = 1600let ntot = npop*niterlet repeat = data n1 n2let val = data 1 2let tag2 = sequence val repeat for i = 1 1 ntotset sample random permutation distinct off.let yindx tag1 = sample random permutation npop nkeep p niterlet ynew = gather y yindx.. Now compute the statistic for the permutations.set let cross tabulate collapselet ynew2 = ynewlet tag12 = tag1retain ynew2 tag12 subset tag2 = 1let ym1 = cross tabulate mean ynew2 tag12let ynew2 = ynewlet tag12 = tag1retain ynew2 tag12 subset tag2 = 2let ym2 = cross tabulate mean ynew2 tag12let ydiff = ym1 - ym2.. Now plot the results of the permutation test.let xq = 0.025let low025 = quantile ydifflet low025 = round(low025,2)let xq = 0.975let upp975 = quantile ydifflet upp975 = round(upp975,2).title offset 2title case asislabel case asisy1label Countx1label Difference of Means for Permutationslet statval = round(statval,2)x2label color redx2label Difference of Means for Original Sample: ^statvalx3label color bluex3label 2.5 Percentile: ^low025, 97.5 Percentile: ^upp975xlimits -0.5 0.5title Histogram of Difference of Means for ^niter Permutations.histogram ydiff.line color redline dashdrawdsds statval 20 statval 90line color blueline dashdrawdsds low025 20 low025 90drawdsds upp975 20 upp975 90 Privacy Policy/Security Notice Disclaimer FOIA


When cufftXtMemcpy() is used to copy data from GPU memory back to host memory, the results are in natural order regardless of whether the data on the GPUs is in natural order or permuted. Using CUFFT_COPY_DEVICE_TO_DEVICE allows users to copy data from the permuted data format produced after a single transform to the natural order on GPUs.


For single 2D or 3D transforms on multiple GPUs, when cufftXtMemcpy() distributes the data to the GPUs, the array is divided on the X axis. E.G. for two GPUs half of the X dimenson points, for all Y (and Z) values, are copied to each of the GPUs. When the transform is computed, the data are permuted such that they are divided on the Y axis. I.E. half of the Y dimension points, for all X (and Z) values are on each of the GPUs.


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