def scoring(fname, n, test):
# load test data
a_test, age_err, age_errp, a_errm, p_test, p_err, bv_test, bv_err, g, g_err, \
g_errp, g_errm, flag = load_dat(fname, test, cv=True)
# load parameters from training data
data = np.genfromtxt('/Users/angusr/Python/noisy-plane/parameters%s%s.txt'%(n,fname)).T
pars = data[0][:3]
# calculate score
p_pred = period_model(pars, a_test, bv_test, .45)
l = np.isfinite(p_pred)
n = len(p_pred[l])
# root mean squared error
return np.sqrt((np.sum((p_test[l] - p_pred[l])**2))/n)
trains = ['CF45', 'HF45', 'PF45', 'PF5']
tests = ['AHP', 'ACP', 'ACH', 'ACH']
RMS = []
for i in range(len(tests)):
# load test data
a_test, age_err, age_errp, a_errm, p_test, p_err, bv_test, bv_err, g, g_err, \
g_errp, g_errm, flag = load_dat(tests[i], False, False)
print len(p_test)
# load parameters from training data
data = np.genfromtxt('/Users/angusr/Python/noisy-plane/parameters%s.txt'%trains[i]).T
pars = data[0][:3]
# calculate score
p_pred = period_model(pars, a_test, bv_test, .45)
l = np.isfinite(p_pred)
n = len(p_pred[l])
# root mean squared error
RMS.append(np.sqrt((np.sum(((p_test[l] - p_pred[l])**2)))/n))
# # reduced chi squared
# print 'chi', sum(((p_test[l] - p_pred[l])**2)/p_err[l]**2)/n
#
# # log likelihood
L = (.5*np.sum(((p_pred[l] - p_test[l]) / p_err[l])**2)) #- np.log(2*np.pi*n)
# print p_err[l]
# raw_input('enter')
# print 'likelihood = ', L