Check the links on mathtools.net. The page for statistics libraries for C++ has links. Another page http://www.thefreecountry.com/sourcecode/mathematics.shtml lists few more. Have you checked the ‘R project’? I think you can call ‘R objects’ from C/C++.
it depends what sort of t-test you want to do (one sided or two sided dependent or independent) but it should be as simple as: from scipy.stats import ttest_ind cat1 = my_data[my_data[‘Category’]==’cat1′] cat2 = my_data[my_data[‘Category’]==’cat2′] ttest_ind(cat1[‘values’], cat2[‘values’]) >>> (1.4927289925706944, 0.16970867501294376) it returns a tuple with the t-statistic & the p-value see here for other t-tests … 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
Let’s assume that rand() generates a uniformly-distributed value I in the range [0..RAND_MAX], and you want to generate a uniformly-distributed value O in the range [L,H]. Suppose I in is the range [0..32767] and O is in the range [0..2]. According to your suggested method, O= I%3. Note that in the given range, there are … Read more
In a scipy.stats package there are few ttest_… functions. See example from here: >>> print ‘t-statistic = %6.3f pvalue = %6.4f’ % stats.ttest_1samp(x, m) t-statistic = 0.391 pvalue = 0.6955
If you know in advance that you’re going to want to multiple samples without overlaps, easiest is to do random.shuffle() on list(range(100)) (Python 3 – can skip the list() in Python 2), then peel off slices as needed. s = list(range(100)) random.shuffle(s) first_sample = s[-10:] del s[-10:] second_sample = s[-10:] del s[-10:] # etc Else … Read more
The distributions in scipy are coded in a generic way wrt two parameter location and scale so that location is the parameter (loc) which shifts the distribution to the left or right, while scale is the parameter which compresses or stretches the distribution. For the two parameter lognormal distribution, the “mean” and “std dev” correspond … Read more
Specifying bins=8 in the hist call means that the range between the minimum and maximum value is divided equally into 8 bins. What is equal on a linear scale is distorted on a log scale. What you could do is specify the bins of the histogram such that they are unequal in width in a … Read more