In jmeter, how to calculate Standard deviation?
E.g.:
Samples: 1000
Average: 305
Then how to calculate Std.Dev?
Add a Listener of type "Summary Report". It calculates Standard Deviation for you and includes it as a column of the table.
Related
I'm creating an app to monitor water quality. The temperature data is updated every 2 min to firebase real-time database. App has two requirements
1) It should alert the user when temperature exceed 33 degree or drop below 23 degree - This part is done
2) It should alert user when it has big temperature fluctuation after analysing data every 30min - This part i'm confused.
I don't know what algorithm to use to detect big temperature fluctuation over a period of time and alert the user. Can someone help me on this?
For a period of 30 minutes, your app would give you 15 values.
If you want to figure out a big change in this data, then there is one way to do so.
You can use implement the following method:
Calculate the mean and the standard deviation of the values.
Subtract the data you have from the mean and then take the absolute value of the result.
Compare if the absolute value is greater than one standard deviation, if it is greater then you have a big data.
See this example for better understanding:
Lets suppose you have these values for 10 minutes:
25,27,24,35,28
First Step:
Mean = 27 (apprx)
One standard deviation = 3.8
Second Step: Absolute(Data - Mean)
abs(25-27) = 2
abs(27-27) = 0
abs(24-27) = 3
abs(35-27) = 8
abs(28-27) = 1
Third Step
Check if any of the subtraction is greater than standard deviation
abs(35-27) gives 8 which is greater than 3.8
So, there is a big fluctuation. If all the subtracted results are less than standard deviation, then there is no fluctuation.
You can still improvise the result by selecting two or three standard deviation instead of one standard deviation.
Start by defining what you mean by fluctuation.
You don't say what temperature scale you're using. Fahrenheit, Celsius, Rankine, or Kelvin?
Your sampling rate is a new data value every two minutes. Do you define fluctuation as the absolute value of the difference between the last point and current value? That's defensible.
If the max allowable absolute value is some multiple of your 33-23 = 10 degrees you're in business.
When I'm trying to execute my test plan in jmeter for 10,50,100... virtual users with ram up period 30 sec and Loop count is 1. I'm not getting Average response time exactly when I calculated with Average Time=(Min Time+ Max Time)/2.
Please check my attached image for differences in Average time
Can anyone suggest me please how we need to understand this.
Thanks in Advance.
Average: This is the Average elapsed time of a set of results. It is the arithmetic mean of all the samples response time.
The following equation show how the Average value (μ) is calculated:
μ = 1/n * Σi=1…n xi
An important thing to understand is that the mean value can be very misleading as it does not show you how close (or far) your values are from the average.The main thing you should focus on is "Standard Deviation".
The standard deviation (σ) measures the mean distance of the values to their average (μ). In other words, it gives us a good idea of the dispersion or variability of the measures to their mean value.
The following equation show how the standard deviation (σ) is calculated:
σ = 1/n * √ Σi=1…n (xi-μ)2
So interpreting the standard deviation is wise as mean value could be the same for the different response time of the samples! If the deviation value is low compared to the mean value, it will indicate you that your measures are not dispersed (or mostly close to the mean value) and that the mean value is significant.
Min - The lowest elapsed time(response time) for the samples with the same label.
Max - The longest elapsed time (response time) for the samples with the same label.
For further detail you could go through JMeter documentation and this blog. It will really help you to understand the concept.
I have come across few (Machine learning-classification problem) journal papers mentioned about evaluate accuracy with Top-N approach. Data was show that Top 1 accuracy = 42.5%, and Top-5 accuracy = 72.5% in the same training, testing condition.
I wonder how to calculate this percentage of top-1 and top-5?
Can some one show me example and steps to calculate this?
Thanks
Top-1 accuracy is the conventional accuracy: the model answer (the one with highest probability) must be exactly the expected answer.
Top-5 accuracy means that any of your model 5 highest probability answers must match the expected answer.
For instance, let's say you're applying machine learning to object recognition using a neural network. A picture of a cat is shown, and these are the outputs of your neural network:
Tiger: 0.4
Dog: 0.3
Cat: 0.1
Lynx: 0.09
Lion: 0.08
Bird: 0.02
Bear: 0.01
Using top-1 accuracy, you count this output as wrong, because it predicted a tiger.
Using top-5 accuracy, you count this output as correct, because cat is among the top-5 guesses.
The Complement of the accuracy is the error, The top-1 error is the percentage of time that the classifier did not give the correct class highest probability score.
The top-5 error:- The percentage of time that the classifier did not include the correct class among the top 5 probabilities or guesses.
How does JMeter calculate Std Dev. total value? What is it based on?
Please see the image below:
Even though the summary row is named TOTAL the values in the row aren't the sum of the values above it. Rather it is a calculated value for the entire data set.
If there are 100 samples in row1 and 100 samples in row2, the total number of samples is 200. "Total" average is the average of all 200 samples and "Total" Std Dev. is the standard deviation of all 200 samples.
OVERALL would probably be a better word to use in place of TOTAL for the summary row.
As for what standard deviation means, this isn't a JMeter measure per se, but a statistical measure of the data set. It represents how much variation or spread is present in the data set.
I am new in machine learning. In my work I require a machine learning algorithm to select some columns out of many columns in a 2D matrix depending on the spread of the data. Below is a sample of the 2D matrix:
400 700 4 1400
410 710 4 1500
416 716 4 1811
..............
410 710 4 1300
Previously I have used standard deviation method to select columns depending on some threshold values(as a measure of spread of data for a particular column). Observe that the 3rd column is constant and last column in varying tremendously. 1st and 2nd column in also varying but the spread of their data is small. By applying standard deviation on each of the columns I get (sigma) = 10, 10, 0, 200 respectively.
I have considered some experimental threshold values to discard some columns. If the (sigma) crosses the threshold value range then the corresponding column gets discarded. I calculated those threshold values manually. Though this method was very simple but dealing with the threshold values is a very tedious task as there are many existing columns.
For this reason I want to use a standard machine learning algorithm or somehow if I can make these threshold values adaptive. So that I don't require to hard-code the threshold values inside the code. Can anyone please suggest me an appropriate algorithm for this?