def run_test2_2(l, IT, LAMBDA = 1,smalls=False, pens=False ):
#run_logistics(l = 0.17, IT = 500, LAMBDA = 0.01,smalls=False, pens=False):
res = logistic_regression_template.run_logistics(l,IT,LAMBDA,smalls, pens)
print "learnigrate:{}".format(l)
stat_msg = "TRAIN NLOGL:{:4.2f} TRAIN CE:{:.6f} "
stat_msg += "TRAIN FRAC:{:2.2f} VALID CE:{:.6f} VALID FRAC:{:2.2f}"
stat_msg += "iteration at max frac train:{} iteration at max frac valid:{} \n iteration at min CE valid:{}"
print stat_msg.format(res[0], res[1], res[2], res[3], res[4], res[5], res[6], res[7])
# res[8] is training CE, res[9] is valid CE
fig_train = plt.plot(np.arange(0, IT), res[8], 'g')
fig_valid = plt.plot(np.arange(0, IT), res[9], 'b')
plt.axis([0, IT, 0, 150])
plt.xlabel('Iteration')
plt.ylabel('CE')
gtitle = '''CE as function of the iter number on mnist_train, \n
Learning rate: {}, Number of iterations: IT \n green:=train, blue:=validation'''
plt.title(gtitle.format(l,IT))
plt.show()
def run_test2_3(l,IT,smalls):
final_results = [[],[],[],[],[]]
for lam in [0.001, 0.01, 0.1, 0.5, 0.8, 1.0]:
#for lam in [0.001, 0.01, 0.02]:
res = np.zeros(shape=(5, ))
res[0] = lam
for it in range(10):
tres = logistic_regression_template.run_logistics(l,IT,lam,smalls, True)
res[1] += tres[1]
res[2] += tres[2]
res[3] += tres[3]
res[4] += tres[4]
avg_res = res / 10.0
avg_res[0] *= 10.0
print "for Lambda = {} the average results are: \n train CE:{}, train class err:{}," \
" valid CE:{}, valid calss err:{}".format(lam, avg_res[1], avg_res[2], avg_res[3],avg_res[4])
for i in range(5):
final_results[i].append(avg_res[i])
plot2_3(l,IT,smalls,final_results)
