import os 
import sys
import pylab as pl 

import validate_models
reload(validate_models)
import data
reload(data)

reps = int(sys.argv[1])
dir = str(sys.argv[2])

true_cf = data.csv2array('%s/truth_cf.csv' % (dir))
T, J = true_cf.shape

validate_models.combine_output(J, T, 'bad_model', dir, reps, True)
validate_models.combine_output(J, T, 'latent_simplex', dir, reps, True)

validate_models.clean_up('bad_model', dir, reps)
validate_models.clean_up('latent_simplex', dir, reps)
os.remove('%s/truth.csv' %dir)
Exemple #2
0
import os
import sys
import pylab as pl

import validate_models
reload(validate_models)
import data
reload(data)

i = int(sys.argv[1])
dir = str(sys.argv[2])

true_std = data.csv2array('%s/truth_std.csv' % (dir))
true_cf = data.csv2array('%s/truth_cf.csv' % (dir))
std_bias = data.csv2array('%s/truth_bias.csv' % (dir))[0]

validate_models.validate_once(true_cf, true_std, std_bias, True, dir, i)
Exemple #3
0
import os
import sys
import pylab as pl

import validate_models

reload(validate_models)
import data

reload(data)

reps = int(sys.argv[1])
dir = str(sys.argv[2])

true_cf = data.csv2array('%s/truth_cf.csv' % (dir))
T, J = true_cf.shape

validate_models.combine_output(J, T, 'bad_model', dir, reps, True)
validate_models.combine_output(J, T, 'latent_simplex', dir, reps, True)

validate_models.clean_up('bad_model', dir, reps)
validate_models.clean_up('latent_simplex', dir, reps)
os.remove('%s/truth.csv' % dir)
import os 
import sys
import pylab as pl 

import validate_models
reload(validate_models)
import data
reload(data)

i = int(sys.argv[1])
dir = str(sys.argv[2])

true_std = data.csv2array('%s/truth_std.csv' % (dir))
true_cf = data.csv2array('%s/truth_cf.csv' % (dir))
std_bias = data.csv2array('%s/truth_bias.csv' % (dir))[0]

validate_models.validate_once(true_cf, true_std, std_bias, True, dir, i)