# 0 stress, 1 sleep, 2 diet, 3 exercise, 4 age, 5 gd.get_weight0 nd = 6 # data_mat = np.zeros( ( nn, nd ), float ) # for rr in xrange( nn ) : # stress, sleep, diet, exercise = 2 * nr.random( 4 ) - 1 # age = young + nr.random() * ( old - young ) # data_mat[ rr ] = stress, sleep, diet, exercise, age, gd.get_weight0( normal_w, stress, sleep, diet, exercise, w_var, age, young, old ) # nq_list = [ 3 for xx in xrange(nd) ] # int_mat = gd.discretize_mat( data_mat, nq_list ) print int_mat[ :10 ] # mat_file = "../data/dummy_A_discrete_data0.tab"i var_id_list = range( nd ) #
#!/usr/bin/python # -*- coding: utf-8 -*- import numpy as np import numpy.random as nr import generate_data as gp # DEBUG gender = "m" normal_w = 70 w_var = 5 age = 40 # for rr in xrange( 10 ) : stress, sleep, diet, exercise = 2 * nr.random( 4 ) - 1 # #stress = 0 #sleep = 0 #diet = 0 #exercise = 0 # print "stress %.2f, sleep %.2f, diet %.2f, exercise %.2f, weight %.2f" % ( stress, sleep, diet, exercise, gp.get_weight0( normal_w, stress, sleep, diet, exercise, w_var, age ) )
# for rr in xrange(n_data): # stress, sleep, diet, exercise = 2 * nr.random(4) - 1 # age = young + nr.random() * (old - young) # data_mat[rr] = ( stress, sleep, diet, exercise, age, gp.get_weight0(normal_w, stress, sleep, diet, exercise, w_var, age, young, old), ) # print data_mat[:10] # nq = 4 print gp.get_quantile_limits0(data_mat[:, 0], nq) # nn = 20 nd = 4