average

I have a range of dates and a measurement on each of those dates. I'd like to calculate an exponential moving average for each of the dates. Does anybody know how to do this? I'm new to python. It doesn't appear that averages are built into the standard python library, which strikes me as a little odd. Maybe I'm not looking in the right place. So, given the following code, how could I calculate the moving weighted average of IQ points for calendar dates? from datetime import date days = [date(2008,1,1), date(2008,1,2), date(2008,1,7)] IQ = [110, 105, 90] (there's probably a better way to

Example: $a[] = '56'; $a[] = '66'; $a[] = ''; $a[] = '58'; $a[] = '85'; $a[] = ''; $a[] = ''; $a[] = '76'; $a[] = ''; $a[] = '57'; Actually how to find average value from this array excluding empty. please help to resolve this problem. first you need to remove empty values, otherwise average will be not accurate. so $a = array_filter($a); $average = array_sum($a)/count($a); echo $average; DEMO More concise and recommended way if(count($a)) { $a = array_filter($a); echo $average = array_sum($a)/count($a); } See here The accepted answer works for the example values, but in general simply using

I have a homework assignment and I was wondering if anyone could help me as I am new to Java and programming and am stuck on a question. The question is: The first method finds the average of the elements of an integer array: public double average(int[] data) That is, given an integer array, data, calculate the average of its elements are return the average value. For example, the average of {1, 3, 2, 5, 8} is 3.8. Here is what I have done so far: public double average(int[] data) { int sum = 0; while(int i=0; i < data.length; i++) sum = sum + data[i]; double average = sum / data.length;;

I want to share this particular Apache Spark with Python solution because documentation for it is quite poor. I wanted to calculate the average value of K/V pairs (stored in a Pairwise RDD), by KEY. Here is what the sample data looks like: >>> rdd1.take(10) # Show a small sample. [(u'2013-10-09', 7.60117302052786), (u'2013-10-10', 9.322709163346612), (u'2013-10-10', 28.264462809917358), (u'2013-10-07', 9.664429530201343), (u'2013-10-07', 12.461538461538463), (u'2013-10-09', 20.76923076923077), (u'2013-10-08', 11.842105263157894), (u'2013-10-13', 32.32514177693762), (u'2013-10-13', 26

I have a continuous value for which I'd like to calculate an exponential moving average . Normally I'd just use the standard formula for this: S n = αY + (1-α)S n-1 where S n is the new average, α is the alpha, Y is the sample, and S n-1 is the previous average. Unfortunately, due to various issues I don't have a consistent sample time. I may know I can sample at the most, say, once per millisecond, but due to factors out of my control, I may not be able to take a sample for several milliseconds at a time. A likely more common case, however, is that I simple sample a bit early or late: instead