similar to: PPCOR: Semipartial Correlation & Regression weights

Hello Everyone, Trying to learn a little bit about data mining. I'm working on a text mining project that will attempt to predict whether cancer patients got a particular type of genetic testing. A subsequent stage then will be aimed at predicting what the results of that testing were. ? I've used the tm package to prepare my data and am planning to use rattle to do the actual data

Hi Linfeng, On 06/02/17 02:51 PM, Linfeng Zhang wrote: > However, the critical thing is that all the states in each stage when > processing input[i] are reused by the next input[i+1]. That is > input[i+1] must wait input[i] for 1 stage, and input[i+2] must wait > input[i+1] for 1 stage, etc. That is indeed the tricky part... and the one I think you could do slightly differently. If

Hi Linfeng, On 06/02/17 07:18 PM, Linfeng Zhang wrote: > This is a great idea. But the order (psEncC->shapingLPCOrder) can be > configured to 12, 14, 16, 20 and 24 according to complexity parameter. > > It's hard to get a universal function to handle all these orders > efficiently. Any suggestions? I can think of two ways of handling larger orders. The obvious one is

Hi Jean-Marc, Attached is the silk_warped_autocorrelation_FIX_neon() which implements your idea. Speed improvement vs the previous optimization: Complexity 0-4: Doesn't call this function. Complexity 5: 2.1% (order = 16) Complexity 6: 1.0% (order = 20) Complexity 8: 0.1% (order = 24) Complexity 10: 0.1% (order = 24) Code size of silk_warped_autocorrelation_FIX_neon() changes from 2,644

Hi Linfeng, Thanks for the updated patch. I'll have a look and get back to you. When you report speedup percentages, is that relative to the entire encoder or relative to just that function in C? Also, what's the speedup compared to master? Cheers, Jean-Marc On 05/04/17 12:14 PM, Linfeng Zhang wrote: > I attached a new patch with small cleanup (disassembly is identical as > the

Hi Jean-Marc, Thanks a lot for reviewing this huge assembly function! silk_warped_autocorrelation_FIX_c()'s kernel part is for( n = 0; n < length; n++ ) { tmp1_QS = silk_LSHIFT32( (opus_int32)input[ n ], QS ); /* Loop over allpass sections */ for( i = 0; i < order; i++ ) { /* Output of allpass section */ tmp2_QS = silk_SMLAWB(

This is a great idea. But the order (psEncC->shapingLPCOrder) can be configured to 12, 14, 16, 20 and 24 according to complexity parameter. It's hard to get a universal function to handle all these orders efficiently. Any suggestions? Thanks, Linfeng On Mon, Feb 6, 2017 at 12:40 PM, Jean-Marc Valin <jmvalin at jmvalin.ca> wrote: > Hi Linfeng, > > On 06/02/17 02:51 PM,

Hi Linfeng, I had a closer look at your patch and the code looks good -- and slightly simpler than I had anticipated, so that's good. I did some profiling on a Cortex A57 and I've been seeing slightly less improvement than you're reporting, more like 3.5% at complexity 8. It appears that the warped autocorrelation function itself is only faster by a factor of about 1.35. That's a

Hello R-friends, i have a question to the "text"-function. a little test-dataset for better understanding: -the dataset was imported with read.table(....,header=TRUE) s1-s10 are the samplenames var1 var2 var3 s1 1 1 2 s2 2 3 1 s3 2 2 3 s4 5 4 3 s5 4 2 3 s6 6 3 2 s7 8 5 4 s8 7 2 1 s9 9 3 2

I attached a new patch with small cleanup (disassembly is identical as the last patch). We have done the same internal testing as usual. Also, attached 2 failed temporary versions which try to reduce code size (just for code review reference purpose). The new patch of silk_warped_autocorrelation_FIX_neon() has a code size of 3,228 bytes (with gcc). smaller_slower.c has a code size of 2,304

Hi Jean-Marc, Thanks for your suggestions. Will get back to you once we have some updates. Linfeng On Mon, Feb 6, 2017 at 5:47 PM, Jean-Marc Valin <jmvalin at jmvalin.ca> wrote: > Hi Linfeng, > > On 06/02/17 07:18 PM, Linfeng Zhang wrote: > > This is a great idea. But the order (psEncC->shapingLPCOrder) can be > > configured to 12, 14, 16, 20 and 24 according to

Thank Jean-Marc! The speedup percentages are all relative to the entire encoder. Comparing to master, this optimization patch speeds up fixed-point SILK encoder on NEON as following: Complexity 5: 6.1% Complexity 6: 5.8% Complexity 8: 5.5% Complexity 10: 4.0% when testing on an Acer Chromebook, ARMv7 Processor rev 3 (v7l), CPU max MHz: 2116.5 Thanks, Linfeng On Wed, Apr 5, 2017 at 11:02 AM,

Hi, ABCRegister.td : def SGPR32 : RegisterClass<"ABC", [i32], 16, (add S0, S1, S2, S3, S4, S5, S6, S7, S8, S9, S10, S11, S12, S13, S14, S15 )>; def SFGPR32 : RegisterClass<"ABC", [f32], 16, (add S0, S1, S2, S3, S4, S5, S6, S7, S8, S9, S10, S11, S12, S13, S14, S15 )>; ===== Instruction selection ends: ... t8: i32 = ADDrr t37, t32

Hello! I am trying test my observed data against the null-hypothesis that different items from a psychological questionnaire contribute equally to the metric dependent variable that measures problems (sum score of a questionnaire). That is, I am interested in relative strength of the predictors. Predictor items of the questionnaire are on a scale from 0-3, and technically ordinal, although most

Hi R-users, I have a question relating to the HoltWinters() function. I am trying to forecast a series using the Holt Winters methodology but I am getting some unusual results. I had previously been using R for Windows version 2.7.2 and have just started using R 2.9.1. While using version 2.7.2 I was getting reasonable results however upon changing versions I found I started to see unusual

Good Evening- I have a dataframe that has 10 columns that has a header and 7306 rows in each column, I want to combine these columns into one. I utilized the stack function but it only returned 3/4 of the data...my code is: where nfcuy_bw is the dataframe with 7305 obs. and 10 variables Once I apply this code I only receive a data frame with 58440 obs. of 2 variables, of which there should be

Dear R-Experts, Thanks to Prof. Bonnett, I have got an R script working to calculate confidence intervals around the semipartial correlation coefficients. Now, I would like to calculate BCa bootstrap CIs using the boot library and the boot.ci(results, type="all") function. How could I modify my R script (here below reproducible example) to get the BCa bootstrap CIs ? CIsemipartcorr

Commit-ID: f1c1f4f99e60ac0f855a0582b4aebebfbb0804dc Gitweb: http://git.kernel.org/?p=libs/klibc/klibc.git;a=commit;h=f1c1f4f99e60ac0f855a0582b4aebebfbb0804dc Author: Ben Hutchings <ben at decadent.org.uk> AuthorDate: Tue, 17 Jul 2018 02:55:19 +0100 Committer: Ben Hutchings <ben at decadent.org.uk> CommitDate: Fri, 18 Jan 2019 03:10:14 +0000 [klibc] Add RISC-V (RV64) port

Dear fellow R-users I've always used Prof Harrell's rms package to calculate/visualize partial R2 values from a standard regression analysis (below is a quick example on how I've done so). Recently, I calcuated partial R2 values using the ppcor and gRbase packages. As it turns out, ppcor and gRbase agree with each other but the values generated from these packages tended to be greater

Hi R Users, I do have very big data sets and wanted to run some of the analyses many times with randomization (1000 times). I have done the analysis using an example data but it need to be done with randomized data (1000 times). I am doing manually for 10000 times but taking so much time, I wonder whether it is possible to perform the analysis with creating a loop for many replicated datasets?