sampling

I want to sample 140 numbers between 1000 to 100000 such that the sum of these 140 numbers is around 2 million (2000000): sample(1000:100000,140) such that: sum(sample(1000:100000,140)) = 2000000 Any pointers how I can achieve this? There exist an algorithm for generating such random numbers. Originally created for MATLAB , there is an R implementation of it: Surrogate::RandVec Citation from MATLAB script comment: % This generates an n by m array x, each of whose m columns % contains n random values lying in the interval [a,b], but % subject to the condition that their sum be equal to s. The %

I want to sample values I get from a gpio 4000 times per second, currently I do something like that: std::vector<int> sample_a_chunk(unsigned int rate, unsigned int block_size_in_seconds) { std::vector<std::int> data; constexpr unsigned int times = rate * block_size_in_seconds; constexpr unsigned int delay = 1000000 / rate; // microseconds for (int j=0; j<times; j++) { data.emplace_back(/* read the value from the gpio */); std::this_thread::sleep_for(std::chrono::microseconds(delay)); } return data; } yet according to the reference sleep_for is guaranteed to wait for at least the specified

I am new to C++ and extremely surprised by the lack of accessible, common probability manipulation tools (i.e. the lack of things in Boost and the standard library). I've done a lot of scientific programming in other languages, but the standard and/or ubiquitious third party add-ons always include a full range of probability tools. A friend billed up Boost to be the equivalent ubiquitous add-on for C++, but as I read the Boost documentation, even it seems to have a dearth of what I would consider extremely elementary built-ins. I cannot find a built in that takes some sort of array of discrete

I would like to ask if current API of datasets allows for implementation of oversampling algorithm? I deal with highly imbalanced class problem. I was thinking that it would be nice to oversample specific classes during dataset parsing i.e. online generation. I've seen the implementation for rejection_resample function, however this removes samples instead of duplicating them and its slows down batch generation (when target distribution is much different then initial one). The thing I would like to achieve is: to take an example, look at its class probability decide if duplicate it or not.

I'm using the sensors to balance a robot, but SENSOR_DELAY_FASTEST only provides a 10Hz sampling rate, which is simply not fast enough. Is there a way of sampling even faster? Is there a way of polling the sensors rather than waiting for a SensorEvent ? Is there a way of sampling even faster? SENSOR_DELAY_FASTEST is a 0ms delay. You cannot have negative time, at least as far as physicists have determined. Hence, there is no "faster" delay than SENSOR_DELAY_FASTEST . Use Traceview to determine where your time is being taken up. It is possible that it is your code that is slowing down your