python - How can I avoid value errors when using numpy.random.multinomial? -

when use random generator: numpy.random.multinomial, keep getting:

valueerror: sum(pvals[:-1]) > 1.0 

i passing output of softmax function:

def softmax(w, t = 1.0):     e = numpy.exp(numpy.array(w) / t)     dist = e / np.sum(e)     return dist 

except getting error, added parameter (pvals):

while numpy.sum(pvals) > 1:     pvals /= (1+1e-5) 

but didn't solve it. right way make sure avoid error?

edit: here function includes code

def get_mdn_prediction(vec):     coeffs = vec[::3]     means = vec[1::3]     stds = np.log(1+np.exp(vec[2::3]))     stds = np.maximum(stds, min_std)     coe = softmax(coeffs)     while np.sum(coe) > 1-1e-9:         coe /= (1+1e-5)     coeff = unhot(np.random.multinomial(1, coe))     return np.random.normal(means[coeff], stds[coeff]) 

something few people noticed: robust version of softmax can obtained removing logsumexp values:

from scipy.misc import logsumexp  def log_softmax(vec):     return vec - logsumexp(vec)  def softmax(vec):     return np.exp(log_softmax(vec)) 

just check it:

print(softmax(np.array([1.0, 0.0, -1.0, 1.1]))) 

simple, isn't it?