I think you’re reading those stats incorrectly. They show that Python is up to about 400 times slower than C++ and with the exception of a single case, Python is more of a memory hog. When it comes to source size though, Python wins flat out. My experiences with Python show the same definite trend … Read more
(It is possible that my interpretation of the question is wrong. If the question is how to get from a discrete PDF into a discrete CDF, then np.cumsum divided by a suitable constant will do if the samples are equispaced. If the array is not equispaced, then np.cumsum of the array multiplied by the distances between the points will … Read more
In MariaDB / MySQL: Steve Cohen points out, that after the first pass, @rownum will contain the total number of rows. This can be used to determine the median, so no second pass or join is needed. Also AVG(dd.val) and dd.row_number IN(…) is used to correctly produce a median when there are an even number of records. Reasoning: Finally, MariaDB 10.3.3+ … Read more
I am asking on how the algorithm works to produce the numbers. – WhiteSolstice 35 mins ago Non-technical explanation If you pass n=1 to the Binomial distribution it is equivalent to the Bernoulli distribution. In this case the function could be thought of simulating coin flips. size=3 tells it to flip the coin three times and p=0.5 makes it a fair … Read more
You might be interested in the SciPy Stats package. It has the percentile function you’re after and many other statistical goodies. percentile() is available in numpy too. This ticket leads me to believe they won’t be integrating percentile() into numpy anytime soon
I don’t know why I am getting an error of index. I am quite new to python and therefore am not able to figure out what to do. I think I am initialzing some wrong dimensions but I am not able to break it.
If you’re using scikit-learn you can use sklearn.preprocessing.normalize:
I am trying to plot a ROC curve to evaluate the accuracy of a prediction model I developed in Python using logistic regression packages. I have computed the true positive rate as well as the false positive rate; however, I am unable to figure out how to plot these correctly using matplotlib and calculate the AUC value. … Read more
If you’re using scikit-learn you can use sklearn.preprocessing.normalize:
The F-statistic and p-value will not conflict with each other. The p-value is a measure of how extreme the F-statistic is – it’s a tail probability from the F distribution. So, for example, suppose your criterion of choosing the alternative hypothesis is a p-value less than or equal to 0.05 (a commonly used value) and … Read more