有一个API谷歌保持?我想为谷歌Keep做一个windows 8的应用程序，这样它就可以与你的手机同步。

我查看了Drive SDK，因为谷歌Keep是谷歌Drive的扩展，但我找不到它。

我注意到一些较新的TensorFlow版本与较旧的CUDA和cuDNN版本不兼容。是否存在兼容版本的概述，甚至官方测试的组合列表?我在TensorFlow文档中找不到它。

我试图在JAGS中建立一个生存模型，允许时变协变量。我希望它是一个参数模型——例如，假设生存遵循威布尔分布(但我希望允许风险变化，所以指数太简单了)。因此，这本质上是flexsurv包中可以做的事情的贝叶斯版本，它允许参数模型中的时变协变量。

因此，我希望能够以“计数过程”的形式输入数据，其中每个主题都有多行，每一行对应于一个时间间隔，其中协变量保持不变(如此pdf或此处所述)。这是survival或flexurv包所允许的(开始，停止)公式。

不幸的是，每一个关于如何在JAGS中进行生存分析的解释似乎都假设每个受试者有一行。

我试图采用这种更简单的方法，并将其扩展到计数过程格式，但该模型不能正确地估计分布。

失败的尝试:

举个例子。首先我们生成一些数据:

library('dplyr')
library('survival')

## Make the Data: -----
set.seed(3)
n_sub <- 1000
current_date <- 365*2

true_shape <- 2
true_scale <- 365

dat <- data_frame(person = 1:n_sub,
                  true_duration = rweibull(n = n_sub, shape = true_shape, scale = true_scale),
                  person_start_time = runif(n_sub, min= 0, max= true_scale*2),
                  person_censored = (person_start_time + true_duration) > current_date,
                  person_duration = ifelse(person_censored, current_date - person_start_time, true_duration)
)

  person person_start_time person_censored person_duration
   (int)             (dbl)           (lgl)           (dbl)
1      1          11.81416           FALSE        487.4553
2      2         114.20900           FALSE        168.7674
3      3          75.34220           FALSE        356.6298
4      4         339.98225           FALSE        385.5119
5      5         389.23357           FALSE        259.9791
6      6         253.71067           FALSE        259.0032
7      7         419.52305            TRUE        310.4770

然后我们将每个受试者的数据分成2个观测值。我只是在时间=300时将每个受试者分开(除非他们没有赶上时间=300，他们只有一次观察)。

## Split into multiple observations per person: --------
cens_point <- 300 # <----- try changing to 0 for no split; if so, model correctly estimates
dat_split <- dat %>%
  group_by(person) %>%
  do(data_frame(
    split = ifelse(.$person_duration > cens_point, cens_point, .$person_duration),
    START = c(0, split[1]),
    END = c(split[1], .$person_duration),
    TINTERVAL = c(split[1], .$person_duration - split[1]), 
    CENS = c(ifelse(.$person_duration > cens_point, 1, .$person_censored), .$person_censored), # <— edited original post here due to bug; but problem still present when fixing bug
    TINTERVAL_CENS = ifelse(CENS, NA, TINTERVAL),
    END_CENS = ifelse(CENS, NA, END)
  )) %>%
  filter(TINTERVAL != 0)

  person    split START      END TINTERVAL CENS TINTERVAL_CENS
   (int)    (dbl) (dbl)    (dbl)     (dbl) (dbl)        (dbl)
1      1 300.0000     0 300.0000 300.00000     1           NA
2      1 300.0000   300 487.4553 187.45530     0    187.45530
3      2 168.7674     0 168.7674 168.76738     1           NA
4      3 300.0000     0 300.0000 300.00000     1           NA
5      3 300.0000   300 356.6298  56.62979     0     56.62979
6      4 300.0000     0 300.0000 300.00000     1           NA

现在我们可以建立JAGS模型了。

## Set-Up JAGS Model -------
dat_jags <- as.list(dat_split)
dat_jags$N <- length(dat_jags$TINTERVAL)
inits <- replicate(n = 2, simplify = FALSE, expr = {
       list(TINTERVAL_CENS = with(dat_jags, ifelse(CENS, TINTERVAL + 1, NA)),
            END_CENS = with(dat_jags, ifelse(CENS, END + 1, NA)) )
})

model_string <- 
"
  model {
    # set priors on reparameterized version, as suggested
    # here: https://sourceforge.net/p/mcmc-jags/discussion/610036/thread/d5249e71/?limit=25#8c3b
    log_a ~ dnorm(0, .001) 
    log(a) <- log_a
    log_b ~ dnorm(0, .001)
    log(b) <- log_b
    nu <- a
    lambda <- (1/b)^a
    
    for (i in 1:N) {
      # Estimate Subject-Durations:
      CENS[i] ~ dinterval(TINTERVAL_CENS[i], TINTERVAL[i])
      TINTERVAL_CENS[i] ~ dweibull( nu, lambda )
    }
  }
"

library('runjags')
param_monitors <- c('a', 'b', 'nu', 'lambda') 
fit_jags <- run.jags(model = model_string,
                     burnin = 1000, sample = 1000, 
                     monitor = param_monitors,
                     n.chains = 2, data = dat_jags, inits = inits)
# estimates:
fit_jags
# actual:
c(a=true_shape, b=true_scale)

根据分裂点的位置，该模型为底层分布估计了非常不同的参数。只有当数据没有被分割成计数过程形式时，它才能得到正确的参数。对于这类问题，这似乎不是格式化数据的方法。

If I am missing an assumption and my problem is less related to JAGS and more related to how I'm formulating the problem, suggestions are very welcome. I might be despairing that time-varying covariates can't be used in parametric survival models (and can only be used in models like the Cox model, which assumes constant hazards and which doesn't actually estimate the underlying distribution)— however, as I mentioned above, the flexsurvreg package in R does accommodate the (start, stop] formulation in parametric models.

如果有人知道如何用另一种语言(例如STAN而不是JAGS)构建这样的模型，那也会很感激。

编辑:

克里斯·杰克逊通过电子邮件提供了一些有用的建议:

我认为这里需要JAGS中用于截断的T()构造。本质上，对于每个时期(t[i]， t[i+1])，如果一个人活着，但协变量是常数，那么生存时间在时期开始时左截短，在结束时也可能右截短。你可以这样写y[i] ~ dweib(shape, scale[i])T(T [i]，)

我试着这样执行这个建议:

model {
  # same as before
  log_a ~ dnorm(0, .01) 
  log(a) <- log_a
  log_b ~ dnorm(0, .01)
  log(b) <- log_b
  nu <- a
  lambda <- (1/b)^a
  
  for (i in 1:N) {
    # modified to include left-truncation
    CENS[i] ~ dinterval(END_CENS[i], END[i])
    END_CENS[i] ~ dweibull( nu, lambda )T(START[i],)
  }
}

Unfortunately this doesn't quite do the trick. With the old code, the model was mostly getting the scale parameter right, but doing a very bad job on the shape parameter. With this new code, it gets very close to the correct shape parameter, but consistently over-estimates the scale parameter. I have noticed that the degree of over-estimation is correlated with how late the split point comes. If the split-point is early (cens_point = 50), there's not really any over-estimation; if it's late (cens_point = 350), there is a lot.

I thought maybe the problem could be related to 'double-counting' the observations: if we see a censored observation at t=300, then from that same person, an uncensored observation at t=400, it seems intuitive to me that this person is contributing two data-points to our inference about the Weibull parameters when really they should just be contributing one point. I, therefore, tried incorporating a random-effect for each person; however, this completely failed, with huge estimates (in the 50-90 range) for the nu parameter. I'm not sure why that is, but perhaps that's a question for a separate post. Since I'm not whether the problems are related, you can find the code for this whole post, including the JAGS code for that model, here.

我的流浪汉昨晚工作得很好。我刚打开电脑，点击《流浪者》，这就是我得到的:

==> default: Clearing any previously set network interfaces...
==> default: Preparing network interfaces based on configuration...
    default: Adapter 1: nat
    default: Adapter 2: hostonly
==> default: Forwarding ports...
    default: 22 => 2222 (adapter 1)
==> default: Booting VM...
==> default: Waiting for machine to boot. This may take a few minutes...
    default: SSH address: 127.0.0.1:2222
    default: SSH username: vagrant
    default: SSH auth method: private key
    default: Error: Connection timeout. Retrying...
    default: Error: Connection timeout. Retrying...
    default: Error: Connection timeout. Retrying...
    default: Error: Connection timeout. Retrying...
    default: Error: Connection timeout. Retrying...
    default: Error: Connection timeout. Retrying...
    default: Error: Connection timeout. Retrying...
    default: Error: Connection timeout. Retrying...
    default: Error: Connection timeout. Retrying...
    default: Error: Connection timeout. Retrying...
    default: Error: Connection timeout. Retrying...
    default: Error: Connection timeout. Retrying...
    default: Error: Connection timeout. Retrying...

有人吃过这个吗?vagrant在网络上还没有被广泛报道，我也找不到发生这种情况的原因。

如何检查Python对象是否为字符串(常规或Unicode)?

是否可以在ES6类中创建私有属性?

举个例子。 如何阻止访问instance.property?

class Something {
  constructor(){
    this.property = "test";
  }
}

var instance = new Something();
console.log(instance.property); //=> "test"

我已经用完了虚拟机磁盘(vmdk)上的空间，需要调整虚拟映像的大小。使用命令调整大小

vboxmanage modifyhd Machine-disk1.vmdk --resize 30720

给出错误

Progress state: VBOX_E_NOT_SUPPORTED
VBoxManage: error: Resize hard disk operation for this format is not implemented yet!

如何调整vmdk映像的大小?

我在RedHat上使用Windows和Virtualbox，简单来说:我已经创建了一个共享文件夹，这样我就可以在Windows操作系统上使用Eclipse，并在Linux上做一些测试。

但是，我无法使用我的用户访问共享文件夹，我已经使用root登录并使用chmod 777，甚至将我的用户移动到文件夹的组中。

无论我做什么结果都是一样的

/media/sf_sharedFolder/:拒绝权限

我该怎么办?如何使用我的用户访问共享文件夹?

我有一个Ubuntu虚拟机运行在我的Windows 7机器上。我如何设置它，以便我可以通过SSH从外部访问web服务器?

我发现步骤(在VirtualBox主机和客户虚拟机之间设置SSH访问)能够从我的主机SSH到我的客户，但这仍然留给我通过路由器访问它的问题。

我想我可以在我的Windows机器上安装一个SSH服务器，然后隧道几次(虽然我不是100%确定在本地，动态等方面使用什么，或者如何设置多个隧道?)，但是有没有一种方法可以让虚拟机直接访问我的路由器，这样我就可以直接端口转发到它?

Windows 7/10主机下Oracle VM VirtualBox退出缩放模式的快捷方式是什么?