markov

问题 Hi I am trying to generate steady state probabilities for a transition probability matrix. Here is the code I am using: import numpy as np one_step_transition = array([[0.125 , 0.42857143, 0.75 ], [0.75 , 0.14285714, 0.25 ], [0.125 , 0.42857143, 0. ]]) def steady_state_prop(p): dim = p.shape[0] q = (p-np.eye(dim)) ones = np.ones(dim) q = np.c_[q,ones] QTQ = np.dot(q, q.T) bQT = np.ones(dim) return np.linalg.solve(QTQ,bQT) steady_state_matrix = steady_state_prop(one_step_transition.transpose()

来源： https://stackoverflow.com/questions/64412098/rcpp-and-the-operator-revisited

问题 I'm trying to generate a string of 140 Characters based on probabilities of repetition from an input text. I already have an array with each valid character and in a different array probability of each char. char[] array = [a, b, c, ...] double[] array2 = [.1, .3, .4, ...] I already read that I need to generate a random double from 0-1, but I don't get how to relate it to my arrays and generate a 140 char String. Just need help with that method, at least the explanation on how to do it.

问题 I'm trying to generate a string of 140 Characters based on probabilities of repetition from an input text. I already have an array with each valid character and in a different array probability of each char. char[] array = [a, b, c, ...] double[] array2 = [.1, .3, .4, ...] I already read that I need to generate a random double from 0-1, but I don't get how to relate it to my arrays and generate a 140 char String. Just need help with that method, at least the explanation on how to do it.

问题 I'm trying to generate a string of 140 Characters based on probabilities of repetition from an input text. I already have an array with each valid character and in a different array probability of each char. char[] array = [a, b, c, ...] double[] array2 = [.1, .3, .4, ...] I already read that I need to generate a random double from 0-1, but I don't get how to relate it to my arrays and generate a 140 char String. Just need help with that method, at least the explanation on how to do it.

问题 I'm working with R in some biology-behavioural problems, and I have a transition matrix which I want to plot in a certain way. I'm using the markovchain package, which makes easy the visualization. This is a test-code and it's output. > a<-array(0.25,dim = c(4,4)) > markov<-new("markovchain",transitionMatrix=a,states=c("a","b","c","d"), name="test") > markov test A 4 - dimensional discrete Markov Chain defined by the following states: a, b, c, d The transition matrix (by rows) is defined as

问题 tl;dr: I'm looking for methods to implement a weighted random choice based on the relative magnitude of values (or functions of values) in an array in golang. Are there standard algorithms or recommendable packages for this? Is so how do they scale? Goals I'm trying to write 2D and 3D markov process programs in golang. A simple 2D example of such is the following: Imagine one has a lattice, and on each site labeled by index (i,j) there are n(i,j) particles. At each time step, the program

问题 I am trying to fit a multi-state model using R package R2BayesX. How can I do so correctly? There is no example in the manual. Here is my attempt. activity is 1/0 ie the states time is time patient id is the random effect I want f <- activity ~ sx(time,bs="baseline")+sx(PatientId, bs="re") b <- bayesx(f, family = "multistate", method = "MCMC", data=df) Note: created new output directory Warning message: In run.bayesx(file.path(res$bayesx.prg$file.dir, prg.name = res$bayesx.prg$prg.name), : an

问题 I am trying to fit a multi-state model using R package R2BayesX. How can I do so correctly? There is no example in the manual. Here is my attempt. activity is 1/0 ie the states time is time patient id is the random effect I want f <- activity ~ sx(time,bs="baseline")+sx(PatientId, bs="re") b <- bayesx(f, family = "multistate", method = "MCMC", data=df) Note: created new output directory Warning message: In run.bayesx(file.path(res$bayesx.prg$file.dir, prg.name = res$bayesx.prg$prg.name), : an

问题 I am trying to calculate one-step, two-step transition probability matrices for a sequence as shown below : sample = [1,1,2,2,1,3,2,1,2,3,1,2,3,1,2,3,1,2,1,2] import numpy as np def onestep_transition_matrix(transitions): n = 3 #number of states M = [[0]*n for _ in range(n)] for (i,j) in zip(transitions,transitions[1:]): M[i-1][j-1] += 1 #now convert to probabilities: for row in M: s = sum(row) if s > 0: row[:] = [f/s for f in row] return M one_step_array = np.array(onestep_transition_matrix