Yes, there are some: Scalalab The ScalaLab project aims to provide an efficient scientific programming environment for the Java Virtual Machine. The scripting language is based on the Scala programming language enhanced with high level scientific operators and with an integrated environment that provides a Matlab-like working style. The scripting code is extremely fast, close … Read more
Markov chains can be represented by finite state machines. The idea is that a Markov chain describes a process in which the transition to a state at time t+1 depends only on the state at time t. The main thing to keep in mind is that the transitions in a Markov chain are probabilistic rather … Read more
The Fibonacci series is just one example of an exponential estimation scale. The reason an exponential scale is used comes from Information Theory. The information that we obtain out of estimation grows much slower than the precision of estimation. In fact it grows as a logarithmic function. This is the reason for the higher uncertainty … Read more
>>> import scipy.stats as st >>> st.norm.ppf(.95) 1.6448536269514722 >>> st.norm.cdf(1.64) 0.94949741652589625 As other users noted, Python calculates left/lower-tail probabilities by default. If you want to determine the density points where 95% of the distribution is included, you have to take another approach: >>>st.norm.ppf(.975) 1.959963984540054 >>>st.norm.ppf(.025) -1.960063984540054
The answer is to use Welford’s algorithm, which is very clearly defined after the “naive methods” in: Wikipedia: Algorithms for calculating variance It’s more numerically stable than either the two-pass or online simple sum of squares collectors suggested in other responses. The stability only really matters when you have lots of values that are close … Read more
Actually, git already has a command for this: git shortlog in your case, it sounds like you’re interested in this form: git shortlog -sne See the –help for various options. You may also be interested in the GitStats project. They have a few examples, including the stats for the Git project. From the GitStat main … Read more
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
The standard error is just the standard deviation divided by the square root of the sample size. So you can easily make your own function: > std <- function(x) sd(x)/sqrt(length(x)) > std(c(1,2,3,4)) [1] 0.6454972
I don’t know if Boost has more specific functions, but you can do it with the standard library. Given std::vector<double> v, this is the naive way: #include <numeric> double sum = std::accumulate(v.begin(), v.end(), 0.0); double mean = sum / v.size(); double sq_sum = std::inner_product(v.begin(), v.end(), v.begin(), 0.0); double stdev = std::sqrt(sq_sum / v.size() – mean … Read more
You are using the one-sample KS test. You probably want the two-sample test ks_2samp: >>> from scipy.stats import ks_2samp >>> import numpy as np >>> >>> np.random.seed(12345678) >>> x = np.random.normal(0, 1, 1000) >>> y = np.random.normal(0, 1, 1000) >>> z = np.random.normal(1.1, 0.9, 1000) >>> >>> ks_2samp(x, y) Ks_2sampResult(statistic=0.022999999999999909, pvalue=0.95189016804849647) >>> ks_2samp(x, z) Ks_2sampResult(statistic=0.41800000000000004, … Read more