java. i have known "A float is generally subject to round-offs in its 8th significant digit",and using double will avoid that problem.  can anyone tell me  why it will round-offs in its 8th significant digit.
Consider the following code:   0.1 + 0.2 == 0.3  -&gt;  false   0.1 + 0.2         -&gt;  0.30000000000000004   Why do these inaccuracies happen?
How to align prices in columns when both 2 digit and 4 digit variations of a price exist? Im trying to craft a regex that only returns &lt;link&gt; tag hrefs  Why does this regex return all hrefs including &lt;a hrefs?       (?&lt;=&lt;link\s+.*?)href\s*=\s*[\'\"][^\'\"]+        &lt;link rel="stylesheet" rev="stylesheet"      href="idlecore-tidied.css?T_2_5_0_228" media="screen">     &lt;a href="anotherurl">Slash Boxes&lt;/a>   thank you Ubuntu 20.04 run ok on Lenovo ThinkPad E15 laptop? How to cleanup deadlinks I was reading some answers about ruby and there are a lot of links to _why's sites.  and took all his sites with him.  Almost every introduction question about ruby will include at least one or two links to one of his sites. Examples:          With a bit more effort I could probably find more questions with a lot of deadlinks.  My suggestion for discussion was, what would be a good way to solve deadlinks in the answers/comments? Not every site _why created has resurfaced somewhere else, but some could be replaced by new links.  But I wanted the discussion to go beyond this specific problem of deadlinks in the ruby answers, an automatic solution would be to let the links be scanned in a batch and when found dead be replaced by a google cache or webarchive version of the page, and perhaps mark it so that the community can replace the link with an alternative? Proving by induction that $1^3 + 2^3 + 3^3 + \ldots + n^3 = \left[\frac{n(n+1)}{2}\right]^2$ Need guidance on this proof by mathematical induction. I am new to this type of math and don't know how exactly to get started.   $$ 1^3  +  2^3  +  3^3  +  \ldots  +  n^3  =  \left[\frac{n(n+1)}{2}\right]^2 $$ In Harry Potter, Who is Merlin? In Harry Potter, who is Merlin and what is his role in wizarding history?  His contribution to the wizarding history is apparently known from the fact that some special honors are named after him, such as the Order of Merlin (First Class, Second Class and so on...). Did he live around the time of the four founders of Hogwarts? I'm currently working on building a predictive model for a binary outcome on a dataset with ~300 variables and 800 observations. I've read much on this site about the problems associated with stepwise regression and why not to use it.  I've been reading into LASSO regression and its ability for feature selection and have been successful in implementing it with the use of the "caret" package and "glmnet".   I am able to extract the coefficient of the model with the optimal lambda and alpha from "caret"; however, I'm unfamiliar with how to interpret the coefficients.    Are the LASSO coefficients interpreted in the same method as logistic regression? Would it be appropriate to use the features selected from LASSO in logistic regression?   EDIT  Interpretation of the coefficients, as in the exponentiated coefficients from the LASSO regression as the log odds for a 1 unit change in the coefficient while holding all other coefficients constant. Is there a statement whose undecidability is undecidable? We know there are statements that are undecidable/independent of ZFC. Can there be a statement S, such that (ZFC $\not\vdash$ S and ZFC $\not\vdash$ ~S) is undecidable? An item to break only SPECIFIED blocks? I know that I can make certain blocks only placeable on a specified block, but what is the command to give an item that can only BREAK certain blocks? Custom unique constraint, only enforced if one column has a specific value What is a word for getting an award in a competition or being one of the top three? How to calculate time difference in java? Rule of thumb for number of bootstrap samples I wonder if someone knows any general rules of thumb regarding the number of bootstrap samples one should use, based on characteristics of the data (number of observations, etc.) and/or the variables included? Show that the sequence $a_0 = 1$, $a_{n+1 }= \sqrt{2+a_n}$ is monotonically increasing Given the sequence $a_0 = 1$, $a_{n+1}= \sqrt{2+a_n}$, how can I show that it is monotonically increasing?  I need it to show, that the sequence converges. I already proved boundedness but I can't figure out this last part. Proving a trig identity: $\frac{\sin(A + B)}{\sin(A - B)}=\frac{\tan A + \tan B}{\tan A - \tan B}$ How to show (raise) all windows of an application? Proof of a combinatorial identity: $\sum\limits_{i=0}^n {2i \choose i}{2(n-i)\choose n-i} = 4^n$ Possible Duplicate:          This was part of a homework assignment that I had, and I couldn't figure it out. Now it is bugging me. Can anyone help me? Although a proof would be nice, I wouldn't mind a push in the right direction.   $\displaystyle\sum_{i=0}^n {2i \choose i}{2(n-i)\choose n-i} = 4^n$ I had a heck of time while editing my  question. But, I guess, I discovered a new weird problem in the editor while formatting hyperlink text next to  tag.  To illustrate this problem better, I would like to give the following examples:  Has problem with one or none white-space in between:  [meta-tag:status-planned] [details page reads:][2] [meta-tag:status-planned][details page reads:][2]     Gives weird output as:   [details page reads:]   [details page reads:]   But, its okay with two white-spaces in between:  [meta-tag:status-planned]  [details page reads:][2]   Gives the correct output as:      Something is strange here. I don't understand why do I have to insert two white-spaces instead of just one? Are the two white-spaces are mandatory when using [meta-tag:some-tag] with hyperlink text adjacent to it? Or, is it one of those  thing?  But wait, it's get even further complicated if I do like:   1. No white-spaces in between:  [meta-tag:status-planned][meta-tag:status-completed][Moderator-only Tags][3]     2. One white-space in between:  [meta-tag:status-planned] [meta-tag:status-completed] [Moderator-only Tags][3]       3. Two white-spaces in between:  [meta-tag:status-planned]  [meta-tag:status-completed]  [Moderator-only Tags][3]         They all seems to work perfectly fine. Now, thats very strange! Why would the two [meta-tag:some-tag] works like a charm with or without requiring to insert any white-spaces? Can anyone explain this in a better way. I don't want to jump on the conclusion by calling this as a bug yet, so I used  tag instead of  tag. Please share your thoughts. Counting area percentage of overlapping polygons I have a set of points (meteorological stations) with 5km buffers around them. Additionally, I have a set of polygons (municipalities) in which those stations are located.  I would like to have data on how much of the buffer is filled by each polygon but I was not able to find a function or a way to do it. So for example for point A I would have that in the buffer of 5km there is 80% of polygon B and 20% of polygon C as the output.  Edit:  The anwsers provided do anwser this question in a setting in which I know what polygons and buffers I need to have in the code or when I can do it manualy. Problem is that in my setting with 1 shapefile having 500 polygons and one vector layer having 13000 buffers I am unable to do this manually and I am asking if there is an easier way to do it. I tried implementing the approaches in the questions but I was unable to use them on my example in big scale. Moreover I had problems with coding that I also noted in comments down below.   Main difference to the other similar question provided is that I have 2 vector layers (Buffer with 500 buffers and Municipalities a shape file with 500 different polygons). Previous questions were analyzing this question in situations in which I know precisely the polygons and buffers analyzed, at least that is how I understood them, so after trying to use those approaches on my datasets I was not able to replicate them.  Another way of approaching this problem for me is to use join attributes by location -one-to-many and then getting averages of the duplicate results, only problem is that then I take average of polygons instead of their weights corresponding to % of intersection with the buffer. In my case I do not know what polygons of the shapefile are going to intersect what buffers, or even if the buffer is going to be intersected by 1,2 or more polygons How can I get a significant overall ANOVA but no significant pairwise differences with Tukey's procedure? I performed with R an ANOVA and I got significant differences. However when checking which pairs were significantly different using the Tukey's procedure I did not get any of them. How can this be possible?  Here is the code:  fit5_snow&lt;- lm(Response ~ Stimulus, data=audio_snow) anova(fit5_snow)  &gt; anova(fit5_snow) Analysis of Variance Table  Response: Response           Df Sum Sq Mean Sq F value  Pr(&gt;F)   Stimulus   5  73.79 14.7578  2.6308 0.02929 * Residuals 84 471.20  5.6095                   --- Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1   df&lt;-df.residual(fit5_snow) MSerror&lt;-deviance(fit5_snow)/df  comparison &lt;-  HSD.test(audio_snow$Response, audio_snow$Stimulus, df, MSerror, group=FALSE)  &gt; comparison &lt;-  HSD.test(audio_snow$Response, audio_snow$Stimulus, df, MSerror, group=FALSE)  Study:  HSD Test for audio_snow$Response   Mean Square Error:  5.609524   audio_snow$Stimulus,  means                  audio_snow.Response   std.err replication snow_dry_leaves            4.933333 0.6208034          15 snow_gravel                6.866667 0.5679258          15 snow_metal                 6.333333 0.5662463          15 snow_sand                  6.733333 0.5114561          15 snow_snow                  7.333333 0.5989409          15 snow_wood                  5.066667 0.7713110          15  alpha: 0.05 ; Df Error: 84  Critical Value of Studentized Range: 4.124617   Comparison between treatments means                                Difference   pvalue sig        LCL      UCL snow_gravel - snow_dry_leaves  1.9333333 0.232848     -0.5889913 4.455658 snow_metal - snow_dry_leaves   1.4000000 0.588616     -1.1223246 3.922325 snow_sand - snow_dry_leaves    1.8000000 0.307012     -0.7223246 4.322325 snow_snow - snow_dry_leaves    2.4000000 0.071587   . -0.1223246 4.922325 snow_wood - snow_dry_leaves    0.1333333 0.999987     -2.3889913 2.655658 snow_gravel - snow_metal       0.5333333 0.989528     -1.9889913 3.055658 snow_gravel - snow_sand        0.1333333 0.999987     -2.3889913 2.655658 snow_snow - snow_gravel        0.4666667 0.994348     -2.0556579 2.988991 snow_gravel - snow_wood        1.8000000 0.307012     -0.7223246 4.322325 snow_sand - snow_metal         0.4000000 0.997266     -2.1223246 2.922325 snow_snow - snow_metal         1.0000000 0.855987     -1.5223246 3.522325 snow_metal - snow_wood         1.2666667 0.687424     -1.2556579 3.788991 snow_snow - snow_sand          0.6000000 0.982179     -1.9223246 3.122325 snow_sand - snow_wood          1.6666667 0.393171     -0.8556579 4.188991 snow_snow - snow_wood          2.2666667 0.103505     -0.2556579 4.788991