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Hi Soham, You're right that in LLVM IR arithmetic (with the current definition of `undef`) is not distributive. You can't replace `A * (B + C)` with `A * B + A * C` in general, since (exactly as you said) for A = `undef`, B = `1`, C = `-1` the former always computes `0` while the latter computes `undef`. This is fundamentally because replacing `A * (B + C)` with `A * B + A * C`

...not only the regression coefficients for each "interval" but also the beginning and ends of the intervals. To simplify it to the one dimensional case and two intervals, the problem is to find A_0, A_1, ... A_p and "C" from the given sample, assuming the curve is like this: A_0*f_0(x)+A_1*f_1(x)+ ... + A_p*f_p(x) with x < C A_0'*f_0(x)+A_1'*f_1(x)+ ... + A_p'*f_p(x) with x >= C Functions f_1, f_2, ... f_p are known. Is there anything in R for that? I have tried to use nonlinear (nls package) regression, "forcing" with the "nls" functi...

This came up in D20764, this IR verifies today: %X = type opaque define void @f_0(%X* %ptr) { %t = load %X, %X* %ptr ret void } define void @f_1(%X %val, %X* %ptr) { store %X %val, %X* %ptr ret void } which I found surprising -- what does it mean to load / store values of unknown sizes? Passing it to llc fails an assertion. Are there legitimate cases where we'd w...

...id on IRC, I agree with David and would expect the verifier to reject unsized loads and stores. On Mon, May 30, 2016 at 4:05 PM Sanjoy Das via llvm-dev < llvm-dev at lists.llvm.org> wrote: > This came up in D20764, this IR verifies today: > > %X = type opaque > > define void @f_0(%X* %ptr) { > %t = load %X, %X* %ptr > ret void > } > > define void @f_1(%X %val, %X* %ptr) { > store %X %val, %X* %ptr > ret void > } > > which I found surprising -- what does it mean to load / store values > of unknown sizes? Passing it to llc fails an as...

1. There is, I believe, some redundant code in the calculation of the test statistic in ks.test in the package ctest. Lines 34-37 of the code read x <- y(sort(x), ...) - (0:(n - 1))/n STATISTIC <- switch(alternative, two.sided = max(abs(c(x, x - 1/n))), greater = max(c(x, x - 1/n)), less = -min(c(x, x - 1/n))) Lines 35-37 could read

...X - X` is not > > equivalent `0`, in that `0` cannot be replaced with `X - X`, even > > though `X - X` can be folded to `0`. > > I forgot to state that `X` above is some unknown value which can > potentially be `undef`. A full example would be: > > ``` > define i32 @f_0(i32 %x) { > ret i32 0 > } > > define i32 @f_1(i32 %x) { > %v = sub i32 %x, %x > ret i32 %v > } > ``` > > `@f_1` can be optimized to `@f_0` but not vice versa, unless you > somehow know that `%x` cannot be `undef`. > > -- Sanjoy >

...*s^2*(1-s)^2 + 6*(1-p)*t^2*(1-t)^2; f3 = 4*p*s^3*(1-s) + 4*(1-p)*t^3*(1-t); f4 = p*s^4 + (1-p)*t^4; The polynomial f_i represents the probability of seeing i successes. Suppose we repeat this experiment 1000 times, and u_i is the number of times we saw i successes. The likelihood of this event is f_0^u_0*f_1^u_1*f_2^u_2*f_3^u_3*f_4^u_4, and we seek to find those parameter values for s,t,p which maximize the likelihood. My Singular package has as input the 5 polynomials and a data vector u. For the particular example of u = (3,5,7,11,13). The output are the following four roots (p,s,t) and the...

...l in the study given by h_i(t) = > exp(\beta'x_i)*\lambda*\gamma*t^{\gamma-1}, it doesn't look like to me that > predict(mwa, type='linear') is \beta'x_i. No, it's beta'x_i for the accelerated failure parametrization of the Weibull. In terms of the CDF F_i(t) = F_0( exp((t+beta'x_i)/scale) ) So you need to multiply by the scale parameter and change sign to get the log hazard ratios. > b) since I need the coefficient intercept from the model to obtain the scale > parameter to obtain the base hazard function as defined in Collett > (h_0(t)=\lam...

Hal, To simplify this discussion, lets first just focus on code without asserts and assumes, I don’t follow your logic, you seem to be implying we don’t optimize property-propagation through “if-then” and “while-do” well ? --Peter. From: Hal Finkel [mailto:hfinkel at anl.gov] Sent: Tuesday, June 14, 2016 11:12 AM To: Lawrence, Peter <c_plawre at qca.qualcomm.com> Cc: llvm-dev

Dear R help list, I am modeling some survival data with coxph and survreg (dist='weibull') using package survival. I have 2 problems: 1) I do not understand how to interpret the regression coefficients in the survreg output and it is not clear, for me, from ?survreg.objects how to. Here is an example of the codes that points out my problem: - data is stc1 - the factor is dichotomous