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I am attempting to use optim to solve a neural network problem. I would like to optimize coefficients that are currently stored in a matrix Y=270 x 1 X= 27- x 14 b1= 10x14 b2= 11x1 V= 10 x 14 set of prior variances. I have the following function: posterior.mode1=function(y,X,b_0,b2,V) { log.like=function(b1) { a_g=compute(b1) z_g=tanh(a_g); z_g=cbind(1,z_g) p=softmax(z_g%*%b2); a=sum(y*log(p)+(1-y)*log(1-p)); return(a); } compute=function(b1) { a_g=NULL; for(i in 1:nrow(b1)){ a_g=cbind(a_g,X%*%b1[i,]) } return(a_g); }...

...124, 0.096, 0, 0, 0, 0.012, 0, 0, 0.002, 0, 0, 0, 0.053, 0.178, 0.068, 0.126, 0, 0.001, 0.001, 0, 0.001, 0, 0, 0) matplot(cbind(flow,rain), type="lh") # Note that 'u' is highly skewed. # The system can be modelled by this transfer function: # x[i] <- a_1*x[i-1] + a_2*x[i-2] + b_0*u[i] + b_1*u[i-1] # I happen to know that a good model is a_1 <- 1.6545 a_2 <- -0.6580 b_0 <- 0.1149 b_1 <- -0.1115 # (This was estimated by a proprietary program using # "Simple Refined Instrumental Variable" algorithm). # The transfer function differs from the model used...

Hi, I am conducting an association analysis of genotype and a phenotype such as cholesterol level as an outcome and the genotype as a regressor using multiple linear regression. There are 3 possibilities for the genotype AA, AG, GG. There are 5 people with the AA genotype, 100 with the AG genotype and 900 with the GG genotype. I coded GG genotype as 1, AG as 2 and AA as 3 and the p-value for the

..., ..., J}* *(level 1)* *i in {1, ..., n_j}* *(level 2)* *y_{ij} ~ Beta(mu_{ij} * phi_{ij}; (1 - mu_{ij}) * phi_{ij}) y_{ij} = mu_{ij} + w_{ij} * *with* *logit(mu_{ij}) = Beta_{0i} + Beta_{1i} * x1_{ij} + b2 * x2_{ij} log(phi_{ij}) = Gamma_{0i} + Gamma_{1i} * z1_{ij} + c2 * z2_{ij} * *Beta_{0i} = b_0 + u_{0i} Beta_{1i} = b_1 + u_{1i} Gamma_{0i} = c_0 + v_{0i} Gamma_{1i} = c_1 + v_{1i} * *The vector* *(u_{0i}, u_{1i})'* *has normal distribution with mean* *(0, 0)'* *and covariance matrix* *sigma_{00} sigma_{01} sigma_{10} sigma_{11} * *The vector* *(v_{0i}, v_{1i})'* *has normal dis...

......, J}* *(level 1)* *i in {1, ..., n_j}* *(level 2)* *y_{ij} ~ Beta(mu_{ij} * phi_{ij}; (1 - mu_{ij}) * phi_{ij}) y_{ij} = mu_{ij} + w_{ij} * *with* *logit(mu_{ij}) = Beta_{0i} + Beta_{1i} * x1_{ij} + b_2 * x2_{ij} log(phi_{ij}) = Gamma_{0i} + Gamma_{1i} * z1_{ij} + c_2 * z2_{ij} * *Beta_{0i} = b_0 + u_{0i} Beta_{1i} = b_1 + u_{1i} Gamma_{0i} = c_0 + v_{0i} Gamma_{1i} = c_1 + v_{1i} * *The vector* *(u_{0i}, u_{1i})'* *has normal distribution with mean* *(0, 0)'* *and covariance matrix* *sigma_{00} sigma_{01} sigma_{10} sigma_{11} * *The vector* *(v_{0i}, v_{1i})'* *has normal dis...

...roblems, so I decided to do a partial regression to predict y. I did it this way: - I regressed y to A, and calculated the residuals (e_y) (reg1) - I regressed x to A, and calculated the residuals (e_x) (reg2) - I regressed e_y to e_x (reg5) It looks like this: y = a_0 + a_1 A (reg1) x = b_0 + b_1 A (reg2) e_y = y - (a_0 + a_1 A) (3) e_x = x - (b_0 + b_1 A) (4) e_y = beta_0 + beta_1 e_x (reg5) Then, to predict a y_0 from a new x_0 and A_0, we would: Calculate e_x0 with the equation (4). Calculate e_y0 with the equation (reg5) and then: y_0 = e_y0 + (a_0 + a_1 A_0) Now, I would like to...

Dear R people: Is there a way to perform simple polynomial multiplication; that is, something like (x - 3) * (x + 3) = x^2 - 9, please? I looked in poly and polyroot and expression. There used to be a package that had this, maybe? thanks, Erin -- Erin Hodgess Associate Professor Department of Computer and Mathematical Sciences University of Houston - Downtown mailto: erinm.hodgess at

...er explain the calculation of the standard errors or provide code that explains the inner workings of this function. Thanks! Example of the model I am running: model1<- gls(Y~ X1I + X2 + X3 + X4, data=Dat1, correlation = corAR1(), method = "ML") My understanding of model errors: Y = b_0 + X1 b_1+ ...Xk b_k + Z Z_t =phi Z_{t-1) + e_t The residuals reported by GLS are the Z's, while the white noise terms are the e's. I cannot replicate the reported residuals using this approach. I also do not know how Z_0 should be calculated, i.e. what does the first step of this recursive...

I'm getting lots of grief from reviewers about figures generated with the envfit function in the Vegan package. Has anyone else struggled to effectively explain this analysis? If so, can you share any helpful tips? The most recent comment I've gotten back: "What this shows is which NMDS axis separates the communities, not the relationship between the edaphic factor and the

...- b_k|) where a : is a *positive* real scalar x : is a real scalar n : is an integer b_k: are negative and positive scalars which is a "median with a shift towards zero". This can be rewritten as x* = arg_x min (sum_{k=0}^n w_k|x - b_k|) where w = (a, 1, 1, ..., 1) and b_0 = 0, which is a problem that could be solved by a weighted median function. If someone has a better solution (note that 'a' does *not* have to be an integer) to this specific optimization problem I would be very happy to hear about it. Thanks Henrik Bengtsson Dept. of Mathematical Stati...

I am trying to use optim() to minimize a sum-of-squared deviations function based upon four parameters. The basic function is defined as ... SPsse <- function(par,B,CPE,SSE.only=TRUE) { n <- length(B) # get number of years of data B0 <- par["B0"] # isolate B0 parameter K <- par["K"]

..._sensitive(ctx); } void tcp_fastopen_destroy_cipher(struct sock *sk) diff --git a/net/mac80211/aead_api.c b/net/mac80211/aead_api.c index c5fe95e49c68..d7b3d905d535 100644 --- a/net/mac80211/aead_api.c +++ b/net/mac80211/aead_api.c @@ -41,7 +41,7 @@ int aead_encrypt(struct crypto_aead *tfm, u8 *b_0, u8 *aad, size_t aad_len, aead_request_set_ad(aead_req, sg[0].length); crypto_aead_encrypt(aead_req); - kzfree(aead_req); + kfree_sensitive(aead_req); return 0; } @@ -76,7 +76,7 @@ int aead_decrypt(struct crypto_aead *tfm, u8 *b_0, u8 *aad, size_t aad_len, aead_request_set_ad(aead_req,...

..._sensitive(ctx); } void tcp_fastopen_destroy_cipher(struct sock *sk) diff --git a/net/mac80211/aead_api.c b/net/mac80211/aead_api.c index c5fe95e49c68..d7b3d905d535 100644 --- a/net/mac80211/aead_api.c +++ b/net/mac80211/aead_api.c @@ -41,7 +41,7 @@ int aead_encrypt(struct crypto_aead *tfm, u8 *b_0, u8 *aad, size_t aad_len, aead_request_set_ad(aead_req, sg[0].length); crypto_aead_encrypt(aead_req); - kzfree(aead_req); + kfree_sensitive(aead_req); return 0; } @@ -76,7 +76,7 @@ int aead_decrypt(struct crypto_aead *tfm, u8 *b_0, u8 *aad, size_t aad_len, aead_request_set_ad(aead_req,...

..._sensitive(ctx); } void tcp_fastopen_destroy_cipher(struct sock *sk) diff --git a/net/mac80211/aead_api.c b/net/mac80211/aead_api.c index c5fe95e49c68..d7b3d905d535 100644 --- a/net/mac80211/aead_api.c +++ b/net/mac80211/aead_api.c @@ -41,7 +41,7 @@ int aead_encrypt(struct crypto_aead *tfm, u8 *b_0, u8 *aad, size_t aad_len, aead_request_set_ad(aead_req, sg[0].length); crypto_aead_encrypt(aead_req); - kzfree(aead_req); + kfree_sensitive(aead_req); return 0; } @@ -76,7 +76,7 @@ int aead_decrypt(struct crypto_aead *tfm, u8 *b_0, u8 *aad, size_t aad_len, aead_request_set_ad(aead_req,...

v5: - Break the btrfs patch out as a separate patch to be processed independently. - Update the commit log of patch 1 to make it less scary. - Add a kzfree backward compatibility macro in patch 2. v4: - Break out the memzero_explicit() change as suggested by Dan Carpenter so that it can be backported to stable. - Drop the "crypto: Remove unnecessary

v4: - Break out the memzero_explicit() change as suggested by Dan Carpenter so that it can be backported to stable. - Drop the "crypto: Remove unnecessary memzero_explicit()" patch for now as there can be a bit more discussion on what is best. It will be introduced as a separate patch later on after this one is merged. This patchset makes a global rename of the kzfree()

v4: - Break out the memzero_explicit() change as suggested by Dan Carpenter so that it can be backported to stable. - Drop the "crypto: Remove unnecessary memzero_explicit()" patch for now as there can be a bit more discussion on what is best. It will be introduced as a separate patch later on after this one is merged. This patchset makes a global rename of the kzfree()

This patchset makes a global rename of the kzfree() to kfree_sensitive() to highlight the fact buffer clearing is only needed if the data objects contain sensitive information like encrpytion key. The fact that kzfree() uses memset() to do the clearing isn't totally safe either as compiler may compile out the clearing in their optimizer. Instead, the new kfree_sensitive() uses

Hello list. I'm trying to rebuild the 2.6.9.67.0.20.EL kernel, but it fails even without modifications. How did I try it? Created a (non-root) build environment (not a mock ) Installed the kernel.scr.rpm and did a rpmbuild -ba --target=`uname -m` kernel-2.6.spec 2> prep-err.log | tee prep-out.log The build failed at the end: Processing files: kernel-xenU-devel-2.6.9-67.0.20.EL Checking