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..., I am having a question about efficiently finding the integrals of a list of functions. To be specific, here is a simple example showing my question. Suppose we have a function f defined by f<-function(x,y,z) c(x,y^2,z^3) Thus, f is actually corresponding to three uni-dimensional functions f_1(x)=x, f_2(y)=y^2 and f_3(z)=z^3. What I am looking for are the integrals of these three functions f_1,f_2,f_3 over some interval, say, (0,1). More specifically, the integrals \int_0^1 f_1(x) dx, \int_0^1 f_2(y) dy and \int_0^1 f_3(z) dz. For this simple example, of course we can do these three int...

...e 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" function the shap...

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`

Hello all. I am trying to use app_mysql. It works for selects and functions, but does not want to work with procedures. Pls have a look: Calling function: CREATE FUNCTION f_1(a VARCHAR(20)) RETURNS INTEGER RETURN (SELECT count(*) from peer where name = a); Result: -- Executing Macro("IAX2/100-3", "local|100") in new stack -- Executing MYSQL("IAX2/100-3", "Connect connid localhost asterisk asterisk pbx_manager_new") in new...

...,size=1,prob=p_0) X_1=rmultinom(n-q+1,size=1,prob=cbind(p_11,p_12,(1-p_11-p_12))) N_0=apply(X_0[,(n-2*k-L+1):(n-k-L)],1,sum) N_1=apply(X_1[,(n-q-k+2):(n-q+1)],1,sum) S_vek[i]=((sum(((N_1-k*cbind(p_11,p_12,(1-p_11-p_12)))^2)/k*cbind(p_11,p_12,(1-p_11-p_12))))/(sum(((N_0-k*p_0)^2)/k*p_0)))-1 } 1-mean(f_1<=S_vek & S_vek <=f_2) } An other problem i have is when i define p_11=seq(0,1,0.1) p_12=seq(0,1,0.1) then i get also this error message: Error in rmultinom(n - q + 1, size = 1, prob = rbind(p_11, p_12, (1 - : non-positive probability i cannot solve this problem too. Thank you for...

Hi all, Is there any other packages to do numerical integration other than the default 'integrate'? Basically, I am integrating: integrate(function(x) dnorm(x,mu,sigma)/(1+exp(-x)),-Inf,Inf)$value The integration is ok provided sigma is >0. However, when mu=-1.645074 and sigma=17535.26 It stopped working. On the other hand, Maple gives me a value of 0.5005299403. It is an

Dear users, I am trying to show the equation (including coefficients from the model estimates) for a gam model but do not understand how to. Slide 7 from one of the authors presentations (gam-theory.pdf URL: http://people.bath.ac.uk/sw283/mgcv/) shows a general equation log{E(yi )} = ?+ ?xi + f (zi ) . What I would like to do is put my model coefficients and present the equation used. I am an

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 want to generate such IR? -- Sanjoy

Hi all, this is OT, but maybe someone can give me a clue. I've got data from eye tracker experiments (750 data points). These figures show how the data lock like |** * ***** |*** ****** | ** **** | * * | | * * | | * |-------------- |--------------- Y axes display velocity, x axes display time, * are the data points.

...es. 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 assertion. Are there > legitimate cases where we'd want to generate such IR? > >...

Hi, Let f(rho) = E[F_1(x) F_2(y)], i.e f(rho) is the expectation of F(x) * F(y) with respect to the bivariate Gaussian density with mean 0 and covariance matrix [1 rho; rho 1]. Moreover, assume F_1(x) and F_2(y) to be increasing functions of x and y respectively. I was wondering if it was true that f(rho) is an increasi...

...SE, init) # init: for recursive filters only. Specifies the initial values of # the time series just prior to the start value, in reverse # time order. The default is a set of zeros. but looks as if it should be in usual order as x is e.g. init y_0 y_-1 y_-2: 3, 2, 1 filter f_1 f_2 f_3: 1, .5, .25 x: 4, 5, 6, 7, 8 y_1 = 4 + 1*3 + .5*2 + .25*1 = 8.25 y_2 = 5 + 1*8.25 + .5*3 + .25*2 = 15.25 ... but > filter(4:8,c(1,.5,.25),method="recursive", init=3:1) Time Series: Start = 1 End = 5 Frequency = 1 [1] 6.7500 12.7500 22.3750 37.4375 59.8125 whereas > f...

Saludo cordial para cada uno. Les pido ayuda para generar números aleatorios de una mixtura k-puntos. Sabemos que la función de distribución F es una mixtura k-puntos si es de la forma F(x) = p_1 F_1(x) + p_2 F_2(x) + … + p_k F_k(x), donde F_j es una función de distribución de probabilidad, p_j > 0 y suma(p_j) = 1, para j = 1, 2, …, k. En mi caso particular F es la suavización de la función de probabilidad discreta {p_j} mediante kernel gamma. En el siguiente código F es la función Keg:...

...e 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 >

Hi there. Thanks for your time in advance. I am using R 2.2.0 and OS: Windows XP. My final goal is to calculate 1/2*integral of (f1(x)^1/2-f2(x)^(1/2))^2dx (Latex codes: $\frac{1}{2}\int^{{\infty}}_{\infty} (\sqrt{f_1(x)}-\sqrt{f_2(x)})^2dx $.) where f1(x) and f2(x) are two marginal densities. My problem: I have the following R codes using "adapt" package. Although "adapt" function is mainly designed for more than 2 dimensions, the manual says it will also call up "integrate" if t...

...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 correspo...

Hi there. Thanks for your time in advance. My final goal is to calculate 1/2*integral of (f1(x)^1/2-f2(x)^(1/2))^2dx (Latex codes: $\frac{1}{2}\int^{{\infty}}_{\infty} (\sqrt{f_1(x)}-\sqrt{f_2(x)})^2dx $.) where f1(x) and f2(x) are two estimated marginal densities. My problem: I have the following R codes using "adapt" package. Although "adapt" function is mainly designed for more than 2 dimensions, the manual says it will also call up "integrate&...

Hi there. Thanks for your time in advance. My final goal is to calculate 1/2*integral of (f1(x)^1/2-f2(x)^(1/2))^2dx (Latex codes: $\frac{1}{2}\int^{{\infty}}_{\infty} (\sqrt{f_1(x)}-\sqrt{f_2(x)})^2dx $.) where f1(x) and f2(x) are two marginal densities. My problem: I have the following R codes using "adapt" package. Although "adapt" function is mainly designed for more than 2 dimensions, the manual says it will also call up "integrate" if t...

Hi, I am trying to fit a smoothing model where there are three dimensions over which I can smooth (x,y,z). I expect interactions between some, or all, of these terms, and so I have set up my model as mdl <- gam(PA ~ s(x) + s(y) + s(z) + te(x,y) + te(x,z) + te(y,z) + te(x,y,z),...) I have recently read about the ti(), "tensor product interaction smoother", which takes care of these

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