def _generateFirstOrder0():
 numCategories = 5
 initProb = numpy.zeros(numCategories)
 initProb[0] = 1.0
 firstOrder = dict()
 firstOrder['0'] = numpy.array([0, 0.1, 0, 0, 0.9])
 firstOrder['1'] = numpy.array([0, 0, 0.75, 0.25, 0])
 firstOrder['2'] = numpy.array([1.0, 0, 0, 0, 0])
 firstOrder['3'] = numpy.array([1.0, 0, 0, 0, 0])
 firstOrder['4'] = numpy.array([0, 0, 0.5, 0.5, 0])
 secondOrder = None
 categoryList = [('%d' % x) for x in range(5)]
 return (initProb, firstOrder, secondOrder, 3, categoryList)

def _generateFirstOrder0 ( ) : numCategories = 5 initProb = numpy . zeros ( numCategories ) initProb [ 0 ] = 1.0 firstOrder = dict ( ) firstOrder [ '0' ] = numpy . array ( [ 0 , 0.1 , 0 , 0 , 0.9 ] ) firstOrder [ '1' ] = numpy . array ( [ 0 , 0 , 0.75 , 0.25 , 0 ] ) firstOrder [ '2' ] = numpy . array ( [ 1.0 , 0 , 0 , 0 , 0 ] ) firstOrder [ '3' ] = numpy . array ( [ 1.0 , 0 , 0 , 0 , 0 ] ) firstOrder [ '4' ] = numpy . array ( [ 0 , 0 , 0.5 , 0.5 , 0 ] ) secondOrder = None categoryList = [ ( '%d' % x ) for x in range ( 5 ) ] return ( initProb , firstOrder , secondOrder , 3 , categoryList )
generate the initial .