if(op.Country__c=='India'){                   if((op.createddate &gt; 01/01/2015)||(op.createddate &lt; 31/03/2015))                     InProjectedQ1Count = InProjectedQ1Count + 1;               if((op.createddate &gt; 01/04/2015)||(op.createddate &lt; 30/06/2015))                 InProjectedQ2Count = InProjectedQ2Count + 1;               if((op.createddate &gt; 01/07/2015)||(op.createddate &lt; 30/09/2015))                 InProjectedQ3Count = InProjectedQ3Count + 1;               if((op.createddate &gt; 01/10/2015)||(op.createddate &lt; 31/12/2015))                 InProjectedQ4Count = InProjectedQ4Count + 1;
wanted to get end date for Current Fiscal Quarter in apex any ideas, how to get this information.
Due to this question and answer , I assume 1k = 1000, 2k= 2000...  I don't believe it. Or maybe I am wrong. Please check the below images.        You will see 2kviews. But when you navigate to it, the actual view count is 15xx.         What is going on? Is it a bug? Methods to work around the problem of missing data in machine learning Suppose $f$ and $g$ are entire functions with $|f(z)|\leq|g(z)|$ for all $z$.  How can we show that $f=cg$ for some complex constant $c$?  Thanks for any help :) Solve $x\equiv 1\pmod2$, $x\equiv 2\pmod3$, $x\equiv 3\pmod4$, $x\equiv 4\pmod5$, $x\equiv 5\pmod6$ and $x\equiv 0\pmod7$ $$\begin{align*} x&amp;\equiv 1\pmod2\\ x&amp;\equiv 2\pmod3\\ x&amp;\equiv 3\pmod4\\ x&amp; \equiv 4\pmod5\\ x&amp;\equiv 5\pmod6\\ x&amp;\equiv 0\pmod7\\ \end{align*}$$  So the solution says we can eliminate $x\equiv 5(\bmod6)$ because the first two cases cover it, but I don't really know how it does. How do we solve it in cases like this where the moduli are not mutually relatively prime. Add/Delete table rows dynamically using JavaScript $\displaystyle\sum_{n=1}^\infty \frac{1}{n^s}$ only converges to $\zeta(s)$ if $\text{Re}(s) &gt; 1$.  Why should analytically continuing to $\zeta(-1)$ give the right answer? I have been wondering about the differences between model paramters and model hyperparameters, as well as what their categorization means for a learning problem.  Is the distinction between model parameters and hyperparameters only about reducing the complexity of the problem or are there implications for the 'model quality' to be considered?  Let's consider an example from (1, Eq. 2.43), a linear expansion of basis functions   $$f_\theta(x) = \sum_{m=1}^{M} \theta_m h_m(x), $$  where $h_m\:\forall m=1,...,M$ is some function of $x$, $\theta=[\theta_1,...,\theta_M]^\intercal$ is a vector of the parameters of the model and $M$ is the number of basis functions.   In my understanding, we would consider $\theta$ to contain the parameters of the model, while $M$ is a .  To quote the Wikipedia article of hyperparameter that I linked above:     In machine learning, a hyperparameter is a parameter whose value is   set before the learning process begins. By contrast, the values of   other parameters are derived via training.   It makes sense treating the values in $\theta$ as parameters that should be learned from the data, similar to the coefficients in a linear model - it is surely far more reliable  and  specifying them manually would be quite annoying, although it's not impossible. But what about $M$? It surely is easier to pick some integer value for it. But would it really be terrible to determine $M$ based on the data, too? Probably not, as that is what we would do if we search a grid of possible values for the best performing one.   Let's consider the above basis expansion model and choose Gaussian kernel functions to serve as basis functions. As the Kernel itself has parameters too  this makes the problem a bit more complicated.  $$f_\theta(x) = \sum_{m=1}^{M}\theta_m K_{\lambda_m}(\mu_m,x), $$ with $\mu_1,...,\mu_m$ being the location of the kernels and $\lambda_1,...,\lambda_m$ their scales/bandwidths.  Friedman et al. write in (1, p. 36)        "In general we would like the data to dictate them as well. Including   these as parameters changes the regression problem from a   straightforward linear problem to a combinatorially hard nonlinear   problem. In practice, shortcuts such as greedy algorithms or two stage processes are used."   I see that if we were to treat $M$, $\theta_1 ... \theta_M$, $\mu_1 ... \mu_M$ and $\lambda_1 ... \lambda_M$, as model parameters, this problem would be really complex, especially with the choice of $M$ determining the number of the other parameters. But what if we simplify the model definition - let us for example use the same scale/bandwidth for each kernel, that gives us only one parameter $\lambda$. Furthermore, let's say we specify $\mu_1 ... \mu_M$ based on some heuristic, which would mean we treat it as a hyperparameter.  This gives us   $$f_{\theta,\lambda}(x) = \sum_{m=1}^{M}\theta_m K_\lambda(\mu_m,x), $$  Besides increased complexity of the involved optimization problem, does treating $\lambda$ as a parameter rather than a hyperparameter have other negative or undesired effects?      (1) Friedman, Jerome, Trevor Hastie, and Robert Tibshirani. The   elements of statistical learning. Vol. 1. No. 10. New York: Springer   series in statistics, 2001. How can I install Steam? How can I install and run  on Ubuntu? Is there an equivalent of 'which' on the Windows command line? As I sometimes have path problems, where one of my own cmd scripts is hidden (shadowed) by another program (earlier on the path), I would like to be able to find the full path to a program on the Windows command line, given just its name.  Is there an equivalent to the UNIX command 'which'?  On UNIX, which command prints the full path of the given command to easily find and repair these shadowing problems. The space $ C^1[0,1] $- the space of all continuously differentiable functions on $ [0,1]$ is not a Banach space with respect to the sup norm,$ \|.\|_{\infty} $ since the uniform limit of a continuously differentiable function need not be differentiable.  How can I illustate this statement using a counter example? Can I use $ f_{n}=\frac1 n \sin nx $ as a counter example?  Also, is $ C^1[0,1] $ is same as the space given by $ X=\{f\in C^1[0,1]:f(0)=0 \}$. Can I use the same example to show that this is not a Banach space?  More help is appreciated! Thanks! Sometimes it is convenient to use an absolute path to include a preamble. To do so, I use the following "hack":  \documentclass[a4paper, 12pt]{article} \newcommand{\folder}{/path/to/folder} \input{\folder/preamble}  \begin{document} \end{document}   Now my question is, whether there is a more elegant way to do this, especially a way which is compatible with one of the flatten scripts from  ? What are the strategies for getting feedback on articles? As an academic, one has to publish. Often, an article is drafted, and you need feedback of a colleague.  I see that quite often some perspective articles are often written by long-term collaborating pairs of authors.  What are some strategies people use to get feedback on their articles  (outside of immediate boss; and in the case that none of the friends in the field of work in the specific domain of the article, and one needs specific (not general) feedback)?   How do you approach a colleague to simply read your article. Or do you just mention it at a conference to the most suitable colleague - would you like to read an article and give me feedback? How do handle the co-authorship or acknowledgement? Do you establish the limits at the "approach time"?  What are some strategies to establish a "publishing" duos (buddies). How to prepare data for ListVectorPlot[]? Mixing different front and rear tires Are there areas of mathematics (current or future) that cannot be formalized in set theory? I often read that ZFC can formalize "most" of everyday mathematics, but I could never find an example which it cannot. The closest I got is differential geometry (DF), where some article mentions that the translation from concepts of DF into set theory would render it almost "meaningless", in the sense that any intuitions one could have would be lost in the translation. So, if I didn't misunderstand, at least in theory, the translation could be done, so that a future AI machine could prove theorems of differential geometry using set theory. On the other hand, the Stanford Encyclopedia of  Philosophy states that "all of mathematics can be reduced to a formal system of set theory"().   So, the questions are:    Is there any current area of mathematics that could be not formalized within set theory? If so, which ones? and  Is there any reason to think that every math of the future will be always formalizable in set theory? any references to read on the subject are welcomed!   Clarification: by set theory I am not restricting to ZFC, but to any flavor of it, including all additional axioms you want (about large cardinals or any kind) Install Headers on Kali apt-get install linux-headers-`uname -r` Reading package lists... Done Building dependency tree Reading state information... Done E: Unable to locate package linux-headers-4.9.0-kali3-amd64 E: Couldn't find any package by glob 'linux-headers-4.9.0-kali3-amd64' E: Couldn't find any package by regex 'linux-headers-4.9.0-kali3-amd64' Proving : $ \bigl(1+\frac{1}{n+1}\bigr)^{n+1} \gt (1+\frac{1}{n})^{n} $ Do I use ext4 for all partitions (such as / and /boot)? What are the other formats used for? I'm installing ubuntu onto a new laptop with 24gb SSD and a 500gb HDD. I wanted to put / and swap onto the SSD and /home onto the HDD. Is there anything I shouldn't use ext4 for?  I do not ever intend to dual boot. Ubuntu only. How can I add an empty directory to a Git repository? Is it feasible to compile Python to machine code?