def _lowvariance_sources(self, w, num):
r = np.random.rand() / num
bins = np.concatenate(((0, ), np.cumsum(w)))
return ibincount(
np.histogram(r + np.linspace(0, 1, num, endpoint=False), bins)[0])
def _lowvariance_sources(self, w, num):
r = np.random.rand()/num
bins = np.concatenate(((0,),np.cumsum(w)))
return ibincount(np.histogram(r+np.linspace(0,1,num,endpoint=False),bins)[0])
def _independent_sources(self, w, num):
# Return an ordered array of source indices from source counts
# e.g. if the sources are 3x'0', 2x'1', 0x'2', and 1x'3', as specified
# by the vector [3,2,0,1], then the output will be
# [0, 0, 0, 1, 1, 3]
return ibincount(np.random.multinomial(num, w))
def _independent_sources(self, w, num):
# Return an ordered array of source indices from source counts
# e.g. if the sources are 3x'0', 2x'1', 0x'2', and 1x'3', as specified
# by the vector [3,2,0,1], then the output will be
# [0, 0, 0, 1, 1, 3]
return ibincount(np.random.multinomial(num,w))
