mean

I have a dataframe that looks like this: >>> df[['data','category']] Out[47]: data category 0 4610 2 15 4610 2 22 5307 7 23 5307 7 25 5307 7 ... ... ... Both data and category are numeric so I'm able to do this: >>> df[['data','category']].mean() Out[48]: data 5894.677985 category 13.805886 dtype: float64 And i'm trying to get the mean for each category. It looks straight forward but when I do this: >>> df[['data','category']].groupby('category').mean() or >>> df.groupby('category')['data'].mean() It returns an error like this: DataError: No numeric types to aggregate There's no error if I

I am trying to calculate Z-scores using PHP. Essentially, I am looking for the most efficient way to calculate the mean and standard deviation of a data set (PHP array). Any suggestions on how to do this in PHP? I am trying to do this in the smallest number of steps. to calculate the mean you can do: $mean = array_sum($array)/count($array) standard deviation is like so: // Function to calculate square of value - mean function sd_square($x, $mean) { return pow($x - $mean,2); } // Function to calculate standard deviation (uses sd_square) function sd($array) { // square root of sum of squares

目录 一. 抛砖引玉的Faster-RCNN 1.1 候选框的作用 1.2 下采样问题 二. SSD细节理解 2.1 六个LOSS 2.2 Anchor生成细节 2.3 Encode&&Decode 2.4 负样本挖掘 参考文献 之前感觉SSD很简单，这两天从头到尾把论文和源码都看了一下，发现之前很多细节都没掌握。 这篇文章只说一些之前遗漏的点，读者阅读有一定基础 @ 一. 抛砖引玉的Faster-RCNN 1.1 候选框的作用 之前看 Fast-RCNN 代码对 Selective Search 的操作一直有很大的疑惑？ 为什么一张图会分割成这样大大小小的区域？分割后有啥意义呢？ 第一个问题很简单，使用了 贪心算法 和 图论 方面的知识，区域合并等算法。 第二个问题到后来才明白，是深度学习的 学习成本 的问题。。。 看上图的 resnet 核心模块，就是降低了 学习成本 ，使得网络更容易学习 下面这张图预测区域通过两次平移到达目标区域 下面这张图预测区域先通过放大再做两次平移到达目标区域 下面这张图通过多个预测区域对不同的目标进行预测 通过上面的三幅图可以发现，回归的方式需要付出不同的 代价 当然代价越低越容易回归，可以看我之前的 文章EAST和改进的EAST ，就是通过回归的代价不同，最后效果提升挺大的。 最后一幅图，通过打不同的 回归点(Anchor)

So lets say I have a vector a <- rnorm(6000) I want to calculate the mean of the 1st value to the 60th, then again calculate the mean for the 61st value to the 120th and so fourth. So basically I want to calculate the mean for every 60th values giving me 100 means from that vector. I know I can do a for loop but I'd like to know if there is a better way to do this? I would use colMeans(matrix(a, 60)) .colMeans(a, 60, length(a) / 60) # more efficient (without reshaping to matrix) Enhancement on user adunaic 's request This only works if there are 60x100 data points. If you have an incomplete 60