python - How to efficiently compute function for every cell in numpy array? -

i guess iterating on numpy array not efficient way, , can see program slow have bit larger dataset.

1) go way iterate on matrix , apply function each cell?

this part of code:

# states , data 2 lists few appended items ~100 rows = len(self.states) cols = len(self.data) self.trellis = np.zeros((rows, cols))     i, state in enumerate(self.states):         j, vector in enumerate(self.data):             self.trellis[i][j] = mvnun_wrapper(vector, state.mu, state.sigma, vector_length) 

it seems classic numpy problem. states sound list of state 2 attributes, mu , sigma.

i don't think vector_length requisite here, , suppose mvnun function of 3 scalars.

then try:

mu = [state.mu state in states] sigma = [state.sigma state in states] v=np.asarray(vector).reshape(-1,1) # "column" vector result = mvnun(v,mu,sigma) 

as example :

class state():     def __init__(self):         self.mu=np.random.random()         self.sigma=np.random.random()   states=[state() _ in range(10)]  # 10 states vector=list(range(5))  # 5-vector def mvnun(x,m,s) : return x*m+3*x*s # scalar function  mu=[state.mu state in states] sigma = [state.sigma state in states] v=np.asarray(vector).reshape(-1,1) # "column" vector result = mvnun(v,mu,sigma) 

result.shapeis (5,10).