def part_b(file_n):
f = open(file_n, "r")
ip = int(f.readline())
op = int(f.readline())
batch = int(f.readline())
n = int(f.readline())
h = (f.readline()).rstrip().split(" ")
h = map(int, h)
h = [ip] + h + [op]
if f.readline() == "relu\n":
non_lin = 1
else:
non_lin = 0
if f.readline() == "fixed\n":
eta = 0
else:
eta = 1
print ip, op, batch, n
print h
print non_lin, eta
start = timeit.default_timer()
net = NeuralNet(h, bool(non_lin))
net.grad_des(x[:, 0:-1], x[:, -1], batch, bool(eta))
stop = timeit.default_timer()
t_acc = 100 * net.score(x[:, 0:-1], x[:, -1])
ts_acc = 100 * net.score(tests[:, 0:-1], tests[:, -1])
print "Train accuracy ", t_acc
print "Test accuracy ", ts_acc
print "Training time ", (stop - start)
conf = confusion_matrix(tests[:, -1].tolist(), net.pred(tests[:, 0:-1]))
plot_confusion(conf, list(set(tests[:, -1].flatten().tolist())),
"For layers " + str(h))
def part_d(eta_a=False, rlu=False):
tt = np.zeros((5, 2))
m = 0
h = [85, 0, 0, 10]
l = [5, 10, 15, 20, 25]
for i in [5, 10, 15, 20, 25]:
print "For 2 layer ", i, eta_a, rlu
h[1] = i
h[2] = i
start = timeit.default_timer()
net = NeuralNet(h, rlu)
net.grad_des(x[:, 0:-1], x[:, -1], 100, eta_a)
stop = timeit.default_timer()
t_acc = 100 * net.score(x[:, 0:-1], x[:, -1])
ts_acc = 100 * net.score(tests[:, 0:-1], tests[:, -1])
f_ptr.write("\nFor double layer " + str(eta_a) + str(rlu))
f_ptr.write(str(i))
f_ptr.write("\nTraining acc ")
f_ptr.write(str(t_acc))
f_ptr.write("\nTesting acc ")
f_ptr.write(str(ts_acc))
f_ptr.write("\nTrainig time ")
f_ptr.write(str(stop - start))
print "Train accuracy ", t_acc
print "Test accuracy ", ts_acc
print "Training time ", (stop - start)
tt[m, 0] = t_acc
tt[m, 1] = ts_acc
m = m + 1
conf = confusion_matrix(tests[:, -1].tolist(), net.pred(tests[:,
0:-1]))
plot_confusion(conf, list(set(tests[:, -1].flatten().tolist())),
"For 2 layers " + str(h) + str(eta_a) + str(rlu))
print tt
plot_metric(tt, l, "For two hidden layers " + str(eta_a) + str(rlu))
def libsvm(prob,param,par_s,s,flag=False,flag_x=False,flag_v=False,conf=False):
global acc,acc_v
param.parse_options(par_s)
print "c ",param.C
start = timeit.default_timer()
model = svm_train(prob,param)
stop = timeit.default_timer()
print "Training time with libsvm "+s+" kernel",stop-start,"seconds"
l = model.l
print "Libsvm weight vector w is "
alph = np.zeros((m,1))
if flag==False:
for i in range(l):
ind = model.sv_indices[i]
alph[ind,0] = model.sv_coef[0][i]
w = np.matmul(np.transpose(alph*y),x)
print "Weight vector w is ",w
print "Intercept form b is ",b
if flag_v==True:
_, acc_v, _ = svm_predict(v_set_y, v_set_x.tolist(), model)
print("Accuracy over validation test set using libsvm with "+s+" kernel: ", acc_v)
if flag_x==True:
_, acc_x, _ = svm_predict(y, x.tolist(), model)
print("Accuracy over training set using libsvm with "+s+" kernel: ", acc_x)
pred_val, acc, _ = svm_predict(test_y, test_x.tolist(), model)
print("Accuracy over test set using libsvm with "+s+" kernel: ", acc)
if conf==True:
conf = confusion_matrix(test_y.tolist(),pred_val)
confmat.plot_confusion(conf,list(set(test_y.flatten().tolist())),"SVM (Multi-Classification)")
def conf_matrix(p):
cnt = len(test_data)
actual = [int(test_data[i][0]) for i in range(cnt)]
pred = [int(p[i] + 1) for i in range(cnt)]
con_mat = confusion_matrix(actual, pred)
print("Confusion matrix on test set is: ")
print(con_mat)
confmat.plot_confusion(con_mat, list(set(actual)),
"Naive Bayes Prediction on Test Data")