multinomially distributed integer 1-D array.
"""
# Check preconditions on arguments
probs = num.array(probs)
if len(probs.getshape()) != 1:
raise ArgumentError, "probs must be 1 dimensional."
# Compute shape of output
if type(shape) == type(0): shape = [shape]
final_shape = shape[:]
final_shape.append(probs.getshape()[0] + 1)
x = ranlib.multinomial(trials, probs.astype(num.Float32),
num.multiply.reduce(shape))
# Change its shape to the desire one
x.setshape(final_shape)
return x
def poisson(mean, shape=[]):
"""poisson(mean) or poisson(mean, [n, m, ...])
Returns array of poisson distributed random integers with specifed
mean.
"""
return _build_random_array(ranlib.poisson, (mean, ), shape)
from dtest import test
if __name__ == '__main__':
test()
def test():
import dtest
return dtest.test()
def test():
import dtest
return dtest.test()
In this case, output[i,j,...,:] is a 1-D array containing a
multinomially distributed integer 1-D array.
"""
# Check preconditions on arguments
probs = num.array(probs)
if len(probs.getshape()) != 1:
raise ArgumentError, "probs must be 1 dimensional."
# Compute shape of output
if type(shape) == type(0): shape = [shape]
final_shape = shape[:]
final_shape.append(probs.getshape()[0]+1)
x = ranlib.multinomial(trials, probs.astype(num.Float32),
num.multiply.reduce(shape))
# Change its shape to the desire one
x.setshape(final_shape)
return x
def poisson(mean, shape=[]):
"""poisson(mean) or poisson(mean, [n, m, ...])
Returns array of poisson distributed random integers with specifed
mean.
"""
return _build_random_array(ranlib.poisson, (mean,), shape)
from dtest import test
if __name__ == '__main__':
test()