def classify_experiment(filename_train,
filename_test,
save_path,
range_start,
range_end,
range_step,
ap_N,
num_inter_samples):
"""
Supervised Classification Task
@param filename_train: train data file
@param filename_test: test data file
@param save_path: result save path
@param range_start: number, start number of neuron range
@param range_end: number, end number of neuron range
@param range_step: number, neuron step
"""
# Load data
train_data, test_data=load_arlab_feature(filename_train, filename_test);
# Global paramete settings
num_classes=int(np.max(train_data[:,0])+1);
num_train=train_data.shape[0];
num_test=test_data.shape[0];
neuron_range=np.arange(range_start, range_end+range_step, range_step);
train_input, train_label, test_input, test_label=util.parse_arlab_feature(train_data, test_data);
print test_label[0:100];
train_input, test_input=util.normalize_arlab_feature(train_input, test_input);
train_input=train_input.T;
test_input=test_input.T;
_, tr_start_idx=np.unique(train_label, return_index=True);
#_, te_start_idx=np.unique(test_label, return_index=True);
print "[MESSAGE] Data is prepared.";
for trail in xrange(len(neuron_range)):
start_time=time.clock();
## parameter settings
save_file=open(save_path, "a+");
num_in=train_input.shape[0];
num_neuron=neuron_range[trail];
print "[MESSAGE] Trail %d --- Number of Neuron: %d" % ((trail+1), num_neuron);
## create network
network=net.ConceptorNetwork(num_in=num_in,
num_neuron=num_neuron,
sr=1.5,
in_scale=1.5,
bias_scale=0.2,
washout_length=0,
learn_length=1,
signal_plot_length=0,
tychonov_alpha_readout=0.01,
tychonov_alpha_readout_w=0.0001);
def calculate_block(block, num_classes):
if block==num_classes-1:
start_idx=tr_start_idx[block];
end_idx=train_input.shape[1];
else:
start_idx=tr_start_idx[block];
end_idx=tr_start_idx[block+1];
return start_idx, end_idx;
all_train_states=np.array([]);
for block in xrange(num_classes):
start_idx, end_idx=calculate_block(block, num_classes);
temp_train_states=network.drive_class(train_input[:, start_idx:end_idx]);
if not all_train_states.size:
all_train_states=temp_train_states;
else:
all_train_states=np.hstack((all_train_states,temp_train_states));
print "[MESSAGE] Train data driven"
R_all=all_train_states.dot(all_train_states.T);
C_poss=[]; C_negs=[]; R_poss=[]; R_others=[];
for block in xrange(num_classes):
start_idx, end_idx=calculate_block(block, num_classes);
C_pos_class, C_neg_class, R, R_other=network.compute_conceptor(all_train_states[:, start_idx:end_idx],
ap_N,
R_all,
(num_train-(end_idx-start_idx)));
C_poss.append(C_pos_class);
C_negs.append(C_neg_class);
R_poss.append(R);
R_others.append(R_other);
print "[MESSAGE] Conceptors Computed"
best_aps_poss=np.zeros(num_classes);
best_aps_negs=np.zeros(num_classes);
for i in xrange(num_classes):
best_aps_poss[i], best_aps_negs[i]=network.compute_aperture(C_poss[i],
C_negs[i],
ap_N,
num_inter_samples);
best_ap_pos=np.mean(best_aps_poss);
best_ap_neg=np.mean(best_aps_negs);
print "[MESSAGE] Best Positive Aperture: %.2f, Best Negative Aperture: %.2f" % (best_ap_pos, best_ap_neg);
C_pos_best=[]; C_neg_best=[];
for block in xrange(num_classes):
start_idx, end_idx=calculate_block(block, num_classes);
c_pos_best, c_neg_best=network.compute_best_conceptor(R_poss[block],
R_others[block],
best_ap_pos,
best_ap_neg,
end_idx-start_idx,
num_train-(end_idx-start_idx));
C_pos_best.append(c_pos_best);
C_neg_best.append(c_neg_best);
print "[MESSAGE] Best conceptors computed"
x_test=network.drive_class(test_input);
xTx=x_test.T.dot(x_test).diagonal();
pos_ev=np.zeros((num_classes, num_test));
neg_ev=np.zeros((num_classes, num_test));
comb_ev=np.zeros((num_classes, num_test));
for i in xrange(num_classes):
for j in xrange(num_test):
pos_ev[i,j]=x_test[:,j].dot(C_pos_best[i]).dot(x_test[:,j][None].T)/xTx[j];
neg_ev[i,j]=x_test[:,j].dot(C_neg_best[i]).dot(x_test[:,j][None].T)/xTx[j];
comb_ev[i,j]=pos_ev[i,j]+neg_ev[i,j];
print "[MESSAGE] %i class evidence is calculated" % (i+1);
output_label=np.argmax(comb_ev, axis=0);
pos_out_label=np.argmax(pos_ev, axis=0);
neg_out_label=np.argmax(neg_ev, axis=0);
accuracy=float(np.sum(output_label==test_label))/float(num_test);
pos_accuracy=float(np.sum(pos_out_label==test_label))/float(num_test);
neg_accuracy=float(np.sum(neg_out_label==test_label))/float(num_test);
print "[MESSAGE] Accuracy %.2f %%" % (accuracy*100);
end_time=time.clock();
print "[MESSAGE] Total for %.2fm" % ((end_time-start_time)/60);
info=np.column_stack((num_neuron, best_ap_pos, best_ap_pos, accuracy, pos_accuracy, neg_accuracy, ((end_time-start_time)/60)));
np.savetxt(save_file, info, delimiter=',',newline='\n');
save_file.close();
## remove variable
del output_label; del pos_out_label; del neg_out_label;
del pos_ev; del neg_ev; del comb_ev;
del xTx;
del network;
def inc_learn_exp(filename_train, filename_test, save_path, num_neuron,
ap_pos):
"""
Setup incremental learning task
@param filename_train: train data file
@param filename_test: test data file
@param save_path: result save path
@param num_neuron: numbe of hidden neurons
"""
# Load data
train_data, test_data = load_arlab_feature(filename_train, filename_test)
# Global paramete settings
num_classes = int(np.max(train_data[:, 0]) + 1)
num_train = train_data.shape[0]
num_test = test_data.shape[0]
train_input, train_label, test_input, test_label = util.parse_arlab_feature(
train_data, test_data)
train_input, test_input = util.normalize_arlab_feature(
train_input, test_input)
train_input = train_input.T
test_input = test_input.T
_, tr_start_idx = np.unique(train_label, return_index=True)
_, te_start_idx = np.unique(test_label, return_index=True)
print "[MESSAGE] Data is prepared."
# setup network
save_file = open(save_path, "a+")
num_in = train_input.shape[0]
network = net.ConceptorNetwork(num_in=num_in,
num_neuron=num_neuron,
sr=1.5,
in_scale=1.5,
bias_scale=0.2,
washout_length=0,
learn_length=1,
signal_plot_length=0,
tychonov_alpha_readout=0.01,
tychonov_alpha_readout_w=0.0001)
def calculate_block(set_idx, block, num_classes, dataset="train"):
if block == num_classes - 1:
start_idx = set_idx[block]
if dataset == "train":
end_idx = train_input.shape[1]
elif dataset == "test":
end_idx = test_input.shape[1]
else:
start_idx = set_idx[block]
end_idx = set_idx[block + 1]
return start_idx, end_idx
all_train_states = np.array([])
for block in xrange(num_classes):
start_idx, end_idx = calculate_block(tr_start_idx, block, num_classes)
temp_train_states = network.drive_class(train_input[:,
start_idx:end_idx])
if not all_train_states.size:
all_train_states = temp_train_states
else:
all_train_states = np.hstack((all_train_states, temp_train_states))
print "[MESSAGE] Train data driven"
x_test = network.drive_class(test_input)
xTx = x_test.T.dot(x_test).diagonal()
print "[MESSAGE] Test data driven"
# setup for classification 2 classes
curr_num_classes = 2
_, curr_num_train = calculate_block(tr_start_idx, curr_num_classes - 1,
num_classes)
_, curr_num_test = calculate_block(te_start_idx, curr_num_classes - 1,
num_classes, "test")
print "[MESSAGE] Current number of classes: %d" % (curr_num_classes)
## init for correlation matrix list
R_poss = []
for block in xrange(curr_num_classes):
start_idx, end_idx = calculate_block(tr_start_idx, block, num_classes)
states_c = all_train_states[:, start_idx:end_idx]
R_poss.append(states_c.dot(states_c.T))
## compute positive conceptors for each class
C_pos = []
for block in xrange(curr_num_classes):
start_idx, end_idx = calculate_block(tr_start_idx, block, num_classes)
c_pos = network.compute_pos_conceptor(R_poss[block], ap_pos,
end_idx - start_idx)
C_pos.append(c_pos)
print "[MESSAGE] Best conceptors computed"
pos_ev = np.zeros((curr_num_classes, curr_num_test))
for i in xrange(curr_num_classes):
for j in xrange(curr_num_test):
pos_ev[i, j] = x_test[:, j].dot(C_pos[i]).dot(
x_test[:, j][None].T) / xTx[j]
print "[MESSAGE] %i class evidence is calculated" % (i + 1)
pos_out_label = np.argmax(pos_ev, axis=0)
pos_accuracy = float(np.sum(
pos_out_label == test_label[0:curr_num_test])) / float(curr_num_test)
print "[MESSAGE] Accuracy %.2f %%" % (pos_accuracy * 100)
info = np.column_stack((curr_num_classes, pos_accuracy))
np.savetxt(save_file, info, delimiter=',', newline='\n')
while curr_num_classes < num_classes:
curr_num_classes += 1
_, curr_num_train = calculate_block(tr_start_idx, curr_num_classes - 1,
num_classes)
_, curr_num_test = calculate_block(te_start_idx, curr_num_classes - 1,
num_classes, "test")
print "[MESSAGE] Current number of classes: %d" % (curr_num_classes)
# get new R
start_idx, end_idx = calculate_block(tr_start_idx,
curr_num_classes - 1, num_classes)
states_c = all_train_states[:, start_idx:end_idx]
R_poss.append(states_c.dot(states_c.T))
# get new Conceptor
c_pos = network.compute_pos_conceptor(R_poss[curr_num_classes - 1],
ap_pos, end_idx - start_idx)
C_pos.append(c_pos)
print "[MESSAGE] New conceptor is computed"
pos_ev = np.zeros((curr_num_classes, curr_num_test))
for i in xrange(curr_num_classes):
for j in xrange(curr_num_test):
pos_ev[i, j] = x_test[:, j].dot(C_pos[i]).dot(
x_test[:, j][None].T) / xTx[j]
print "[MESSAGE] %i class evidence is calculated" % (i + 1)
pos_out_label = np.argmax(pos_ev, axis=0)
#print pos_out_label;
#print test_label[0:curr_num_test];
pos_accuracy = float(
np.sum(pos_out_label == test_label[0:curr_num_test])) / float(
curr_num_test)
print "[MESSAGE] Accuracy %.2f %%" % (pos_accuracy * 100)
info = np.column_stack((curr_num_classes, pos_accuracy))
np.savetxt(save_file, info, delimiter=',', newline='\n')
del pos_ev
save_file.close()
def classify_experiment(filename_train, filename_test, save_path, range_start,
range_end, range_step, ap_N, num_inter_samples):
"""
Supervised Classification Task
@param filename_train: train data file
@param filename_test: test data file
@param save_path: result save path
@param range_start: number, start number of neuron range
@param range_end: number, end number of neuron range
@param range_step: number, neuron step
"""
# Load data
train_data, test_data = load_arlab_feature(filename_train, filename_test)
# Global paramete settings
num_classes = int(np.max(train_data[:, 0]) + 1)
num_train = train_data.shape[0]
num_test = test_data.shape[0]
neuron_range = np.arange(range_start, range_end + range_step, range_step)
train_input, train_label, test_input, test_label = util.parse_arlab_feature(
train_data, test_data)
print test_label[0:100]
train_input, test_input = util.normalize_arlab_feature(
train_input, test_input)
train_input = train_input.T
test_input = test_input.T
_, tr_start_idx = np.unique(train_label, return_index=True)
#_, te_start_idx=np.unique(test_label, return_index=True);
print "[MESSAGE] Data is prepared."
for trail in xrange(len(neuron_range)):
start_time = time.clock()
## parameter settings
save_file = open(save_path, "a+")
num_in = train_input.shape[0]
num_neuron = neuron_range[trail]
print "[MESSAGE] Trail %d --- Number of Neuron: %d" % (
(trail + 1), num_neuron)
## create network
network = net.ConceptorNetwork(num_in=num_in,
num_neuron=num_neuron,
sr=1.5,
in_scale=1.5,
bias_scale=0.2,
washout_length=0,
learn_length=1,
signal_plot_length=0,
tychonov_alpha_readout=0.01,
tychonov_alpha_readout_w=0.0001)
def calculate_block(block, num_classes):
if block == num_classes - 1:
start_idx = tr_start_idx[block]
end_idx = train_input.shape[1]
else:
start_idx = tr_start_idx[block]
end_idx = tr_start_idx[block + 1]
return start_idx, end_idx
all_train_states = np.array([])
for block in xrange(num_classes):
start_idx, end_idx = calculate_block(block, num_classes)
temp_train_states = network.drive_class(
train_input[:, start_idx:end_idx])
if not all_train_states.size:
all_train_states = temp_train_states
else:
all_train_states = np.hstack(
(all_train_states, temp_train_states))
print "[MESSAGE] Train data driven"
R_all = all_train_states.dot(all_train_states.T)
C_poss = []
C_negs = []
R_poss = []
R_others = []
for block in xrange(num_classes):
start_idx, end_idx = calculate_block(block, num_classes)
C_pos_class, C_neg_class, R, R_other = network.compute_conceptor(
all_train_states[:, start_idx:end_idx], ap_N, R_all,
(num_train - (end_idx - start_idx)))
C_poss.append(C_pos_class)
C_negs.append(C_neg_class)
R_poss.append(R)
R_others.append(R_other)
print "[MESSAGE] Conceptors Computed"
best_aps_poss = np.zeros(num_classes)
best_aps_negs = np.zeros(num_classes)
for i in xrange(num_classes):
best_aps_poss[i], best_aps_negs[i] = network.compute_aperture(
C_poss[i], C_negs[i], ap_N, num_inter_samples)
best_ap_pos = np.mean(best_aps_poss)
best_ap_neg = np.mean(best_aps_negs)
print "[MESSAGE] Best Positive Aperture: %.2f, Best Negative Aperture: %.2f" % (
best_ap_pos, best_ap_neg)
C_pos_best = []
C_neg_best = []
for block in xrange(num_classes):
start_idx, end_idx = calculate_block(block, num_classes)
c_pos_best, c_neg_best = network.compute_best_conceptor(
R_poss[block], R_others[block], best_ap_pos, best_ap_neg,
end_idx - start_idx, num_train - (end_idx - start_idx))
C_pos_best.append(c_pos_best)
C_neg_best.append(c_neg_best)
print "[MESSAGE] Best conceptors computed"
x_test = network.drive_class(test_input)
xTx = x_test.T.dot(x_test).diagonal()
pos_ev = np.zeros((num_classes, num_test))
neg_ev = np.zeros((num_classes, num_test))
comb_ev = np.zeros((num_classes, num_test))
for i in xrange(num_classes):
for j in xrange(num_test):
pos_ev[i, j] = x_test[:, j].dot(C_pos_best[i]).dot(
x_test[:, j][None].T) / xTx[j]
neg_ev[i, j] = x_test[:, j].dot(C_neg_best[i]).dot(
x_test[:, j][None].T) / xTx[j]
comb_ev[i, j] = pos_ev[i, j] + neg_ev[i, j]
print "[MESSAGE] %i class evidence is calculated" % (i + 1)
output_label = np.argmax(comb_ev, axis=0)
pos_out_label = np.argmax(pos_ev, axis=0)
neg_out_label = np.argmax(neg_ev, axis=0)
accuracy = float(np.sum(output_label == test_label)) / float(num_test)
pos_accuracy = float(
np.sum(pos_out_label == test_label)) / float(num_test)
neg_accuracy = float(
np.sum(neg_out_label == test_label)) / float(num_test)
print "[MESSAGE] Accuracy %.2f %%" % (accuracy * 100)
end_time = time.clock()
print "[MESSAGE] Total for %.2fm" % ((end_time - start_time) / 60)
info = np.column_stack(
(num_neuron, best_ap_pos, best_ap_pos, accuracy, pos_accuracy,
neg_accuracy, ((end_time - start_time) / 60)))
np.savetxt(save_file, info, delimiter=',', newline='\n')
save_file.close()
## remove variable
del output_label
del pos_out_label
del neg_out_label
del pos_ev
del neg_ev
del comb_ev
del xTx
del network
def super_sub_exp(filename_train,
filename_test,
save_path,
num_neuron,
ap_pos):
"""
Setup incremental learning task
@param filename_train: train data file
@param filename_test: test data file
@param save_path: result save path
@param num_neuron: numbe of hidden neurons
"""
# Load data
train_data, test_data=load_arlab_feature(filename_train, filename_test);
# Global paramete settings
num_classes=int(np.max(train_data[:,0])+1);
num_train=train_data.shape[0];
num_test=test_data.shape[0];
train_input, train_label, test_input, test_label=util.parse_arlab_feature(train_data, test_data);
train_input, test_input=util.normalize_arlab_feature(train_input, test_input);
train_input=train_input.T;
test_input=test_input.T;
_, tr_start_idx=np.unique(train_label, return_index=True);
_, te_start_idx=np.unique(test_label, return_index=True);
print "[MESSAGE] Data is prepared.";
# setup network
save_file=open(save_path, "a+");
num_in=train_input.shape[0];
network=net.ConceptorNetwork(num_in=num_in,
num_neuron=num_neuron,
sr=1.5,
in_scale=1.5,
bias_scale=0.2,
washout_length=0,
learn_length=1,
signal_plot_length=0,
tychonov_alpha_readout=0.01,
tychonov_alpha_readout_w=0.0001);
def calculate_block(set_idx, block, num_classes, dataset="train"):
if block==num_classes-1:
start_idx=set_idx[block];
if dataset=="train":
end_idx=train_input.shape[1];
elif dataset=="test":
end_idx=test_input.shape[1];
else:
start_idx=set_idx[block];
end_idx=set_idx[block+1];
return start_idx, end_idx;
all_train_states=np.array([]);
for block in xrange(num_classes):
start_idx, end_idx=calculate_block(tr_start_idx, block, num_classes);
temp_train_states=network.drive_class(train_input[:, start_idx:end_idx]);
if not all_train_states.size:
all_train_states=temp_train_states;
else:
all_train_states=np.hstack((all_train_states,temp_train_states));
print "[MESSAGE] Train data driven"
x_test=network.drive_class(test_input);
xTx=x_test.T.dot(x_test).diagonal();
print "[MESSAGE] Test data driven"
## init for correlation matrix list
R_poss=[];
for block in xrange(num_classes):
start_idx, end_idx=calculate_block(tr_start_idx, block, num_classes);
states_c=all_train_states[:, start_idx:end_idx];
R_poss.append(states_c.dot(states_c.T));
## compute positive conceptors for each class
C_pos=[];
for block in xrange(num_classes):
start_idx, end_idx=calculate_block(tr_start_idx, block, num_classes);
c_pos=network.compute_pos_conceptor(R_poss[block],
ap_pos,
end_idx-start_idx);
C_pos.append(c_pos);
print "[MESSAGE] Conceptor %i is computed" % (block+1);
print "[MESSAGE] Best conceptors computed"
## combine conceptors
### use CIFAR-100;
C_pos_super=[];
num_super_classes=20;
for idx in xrange(num_super_classes):
c_temp=logic.OR(C_pos[idx*5], C_pos[idx*5+1]);
c_temp=logic.OR(c_temp, C_pos[idx*5+2]);
c_temp=logic.OR(c_temp, C_pos[idx*5+3]);
c_temp=logic.OR(c_temp, C_pos[idx*5+4]);
C_pos_super.append(c_temp);
print "[MESSAGE] Super conceptor %i is computed" % (idx+1);
pos_ev=np.zeros((num_super_classes, num_test));
for i in xrange(num_super_classes):
for j in xrange(num_test):
pos_ev[i,j]=x_test[:,j].dot(C_pos[i]).dot(x_test[:,j][None].T)/xTx[j];
print "[MESSAGE] %i class evidence is calculated" % (i+1);
pos_out_label=np.argmax(pos_ev, axis=0);
pos_accuracy=float(np.sum(pos_out_label==test_label))/float(num_test);
print "[MESSAGE] Accuracy %.2f %%" % (pos_accuracy*100);
def classify_experiment(filename_train,
filename_test,
save_path,
ap_N,
num_inter_samples):
"""
Supervised Classification Task
@param filename_train: train data file
@param filename_test: test data file
@param save_path: result save path
"""
# Load data
train_data, test_data=load_arlab_feature(filename_train, filename_test);
# Global paramete settings
num_classes=int(np.max(train_data[:,0])+1);
num_train=train_data.shape[0];
num_test=test_data.shape[0];
train_input, train_label, test_input, test_label=util.parse_arlab_feature(train_data, test_data);
train_input, test_input=util.normalize_arlab_feature(train_input, test_input);
train_input=train_input.T;
test_input=test_input.T;
_, tr_start_idx=np.unique(train_label, return_index=True);
print "[MESSAGE] Data is prepared.";
start_time=time.clock();
save_file=open(save_path, "a+");
feature_size=train_input.shape[0];
network=FeatureNet(feature_size=feature_size);
def calculate_block(block, num_classes):
if block==num_classes-1:
start_idx=tr_start_idx[block];
end_idx=train_input.shape[1];
else:
start_idx=tr_start_idx[block];
end_idx=tr_start_idx[block+1];
return start_idx, end_idx;
C_poss=[]; R_poss=[];
for block in xrange(num_classes):
start_idx, end_idx=calculate_block(block, num_classes);
C_pos_class, R=network.compute_conceptor(train_input[:, start_idx:end_idx],
ap_N,
(num_train-(end_idx-start_idx)));
C_poss.append(C_pos_class);
R_poss.append(R);
print "[MESSAGE] Conceptors Computed"
best_aps_poss=np.zeros(num_classes);
for i in xrange(num_classes):
best_aps_poss[i]=network.compute_pos_aperture(C_poss[i],
ap_N,
num_inter_samples);
best_ap_pos=np.mean(best_aps_poss);
print "[MESSAGE] Best Positive Aperture: %.2f" % (best_ap_pos);
C_pos_best=[];
for block in xrange(num_classes):
start_idx, end_idx=calculate_block(block, num_classes);
c_pos_best=network.compute_pos_conceptor(R_poss[block],
best_ap_pos,
end_idx-start_idx);
C_pos_best.append(c_pos_best);
print "[MESSAGE] Best conceptors computed"
xTx=test_input.T.dot(test_input).diagonal();
pos_ev=np.zeros((num_classes, num_test));
for i in xrange(num_classes):
for j in xrange(num_test):
pos_ev[i,j]=test_input[:,j].dot(C_pos_best[i]).dot(test_input[:,j][None].T)/xTx[j];
print "[MESSAGE] %i class evidence is calculated" % (i+1);
pos_out_label=np.argmax(pos_ev, axis=0);
pos_accuracy=float(np.sum(pos_out_label==test_label))/float(num_test);
print "[MESSAGE] Pos Accuracy %.2f %%" % (pos_accuracy*100);
end_time=time.clock();
print "[MESSAGE] Total for %.2fm" % ((end_time-start_time)/60);
info=np.column_stack((best_ap_pos, best_ap_pos, pos_accuracy, ((end_time-start_time)/60)));
np.savetxt(save_file, info, delimiter=',',newline='\n');
save_file.close();
def super_sub_exp(filename_train, filename_test, save_path, num_neuron,
ap_pos):
"""
Setup incremental learning task
@param filename_train: train data file
@param filename_test: test data file
@param save_path: result save path
@param num_neuron: numbe of hidden neurons
"""
# Load data
train_data, test_data = load_arlab_feature(filename_train, filename_test)
# Global paramete settings
num_classes = int(np.max(train_data[:, 0]) + 1)
num_train = train_data.shape[0]
num_test = test_data.shape[0]
train_input, train_label, test_input, test_label = util.parse_arlab_feature(
train_data, test_data)
train_input, test_input = util.normalize_arlab_feature(
train_input, test_input)
train_input = train_input.T
test_input = test_input.T
_, tr_start_idx = np.unique(train_label, return_index=True)
_, te_start_idx = np.unique(test_label, return_index=True)
print "[MESSAGE] Data is prepared."
# setup network
save_file = open(save_path, "a+")
num_in = train_input.shape[0]
network = net.ConceptorNetwork(num_in=num_in,
num_neuron=num_neuron,
sr=1.5,
in_scale=1.5,
bias_scale=0.2,
washout_length=0,
learn_length=1,
signal_plot_length=0,
tychonov_alpha_readout=0.01,
tychonov_alpha_readout_w=0.0001)
def calculate_block(set_idx, block, num_classes, dataset="train"):
if block == num_classes - 1:
start_idx = set_idx[block]
if dataset == "train":
end_idx = train_input.shape[1]
elif dataset == "test":
end_idx = test_input.shape[1]
else:
start_idx = set_idx[block]
end_idx = set_idx[block + 1]
return start_idx, end_idx
all_train_states = np.array([])
for block in xrange(num_classes):
start_idx, end_idx = calculate_block(tr_start_idx, block, num_classes)
temp_train_states = network.drive_class(train_input[:,
start_idx:end_idx])
if not all_train_states.size:
all_train_states = temp_train_states
else:
all_train_states = np.hstack((all_train_states, temp_train_states))
print "[MESSAGE] Train data driven"
x_test = network.drive_class(test_input)
xTx = x_test.T.dot(x_test).diagonal()
print "[MESSAGE] Test data driven"
## init for correlation matrix list
R_poss = []
for block in xrange(num_classes):
start_idx, end_idx = calculate_block(tr_start_idx, block, num_classes)
states_c = all_train_states[:, start_idx:end_idx]
R_poss.append(states_c.dot(states_c.T))
## compute positive conceptors for each class
C_pos = []
for block in xrange(num_classes):
start_idx, end_idx = calculate_block(tr_start_idx, block, num_classes)
c_pos = network.compute_pos_conceptor(R_poss[block], ap_pos,
end_idx - start_idx)
C_pos.append(c_pos)
print "[MESSAGE] Conceptor %i is computed" % (block + 1)
print "[MESSAGE] Best conceptors computed"
## combine conceptors
### use CIFAR-100;
C_pos_super = []
num_super_classes = 20
for idx in xrange(num_super_classes):
c_temp = logic.OR(C_pos[idx * 5], C_pos[idx * 5 + 1])
c_temp = logic.OR(c_temp, C_pos[idx * 5 + 2])
c_temp = logic.OR(c_temp, C_pos[idx * 5 + 3])
c_temp = logic.OR(c_temp, C_pos[idx * 5 + 4])
C_pos_super.append(c_temp)
print "[MESSAGE] Super conceptor %i is computed" % (idx + 1)
pos_ev = np.zeros((num_super_classes, num_test))
for i in xrange(num_super_classes):
for j in xrange(num_test):
pos_ev[i, j] = x_test[:, j].dot(C_pos[i]).dot(
x_test[:, j][None].T) / xTx[j]
print "[MESSAGE] %i class evidence is calculated" % (i + 1)
pos_out_label = np.argmax(pos_ev, axis=0)
pos_accuracy = float(np.sum(pos_out_label == test_label)) / float(num_test)
print "[MESSAGE] Accuracy %.2f %%" % (pos_accuracy * 100)
def inc_learn_exp(filename_train,
filename_test,
save_path,
num_neuron,
ap_pos):
"""
Setup incremental learning task
@param filename_train: train data file
@param filename_test: test data file
@param save_path: result save path
@param num_neuron: numbe of hidden neurons
"""
# Load data
train_data, test_data=load_arlab_feature(filename_train, filename_test);
# Global paramete settings
num_classes=int(np.max(train_data[:,0])+1);
num_train=train_data.shape[0];
num_test=test_data.shape[0];
train_input, train_label, test_input, test_label=util.parse_arlab_feature(train_data, test_data);
train_input, test_input=util.normalize_arlab_feature(train_input, test_input);
train_input=train_input.T;
test_input=test_input.T;
_, tr_start_idx=np.unique(train_label, return_index=True);
_, te_start_idx=np.unique(test_label, return_index=True);
print "[MESSAGE] Data is prepared.";
# setup network
save_file=open(save_path, "a+");
num_in=train_input.shape[0];
network=net.ConceptorNetwork(num_in=num_in,
num_neuron=num_neuron,
sr=1.5,
in_scale=1.5,
bias_scale=0.2,
washout_length=0,
learn_length=1,
signal_plot_length=0,
tychonov_alpha_readout=0.01,
tychonov_alpha_readout_w=0.0001);
def calculate_block(set_idx, block, num_classes, dataset="train"):
if block==num_classes-1:
start_idx=set_idx[block];
if dataset=="train":
end_idx=train_input.shape[1];
elif dataset=="test":
end_idx=test_input.shape[1];
else:
start_idx=set_idx[block];
end_idx=set_idx[block+1];
return start_idx, end_idx;
all_train_states=np.array([]);
for block in xrange(num_classes):
start_idx, end_idx=calculate_block(tr_start_idx, block, num_classes);
temp_train_states=network.drive_class(train_input[:, start_idx:end_idx]);
if not all_train_states.size:
all_train_states=temp_train_states;
else:
all_train_states=np.hstack((all_train_states,temp_train_states));
print "[MESSAGE] Train data driven"
x_test=network.drive_class(test_input);
xTx=x_test.T.dot(x_test).diagonal();
print "[MESSAGE] Test data driven"
# setup for classification 2 classes
curr_num_classes=2;
_, curr_num_train=calculate_block(tr_start_idx, curr_num_classes-1, num_classes);
_, curr_num_test=calculate_block(te_start_idx, curr_num_classes-1, num_classes, "test");
print "[MESSAGE] Current number of classes: %d" % (curr_num_classes);
## init for correlation matrix list
R_poss=[];
for block in xrange(curr_num_classes):
start_idx, end_idx=calculate_block(tr_start_idx, block, num_classes);
states_c=all_train_states[:, start_idx:end_idx];
R_poss.append(states_c.dot(states_c.T));
## compute positive conceptors for each class
C_pos=[];
for block in xrange(curr_num_classes):
start_idx, end_idx=calculate_block(tr_start_idx, block, num_classes);
c_pos=network.compute_pos_conceptor(R_poss[block],
ap_pos,
end_idx-start_idx);
C_pos.append(c_pos);
print "[MESSAGE] Best conceptors computed"
pos_ev=np.zeros((curr_num_classes, curr_num_test));
for i in xrange(curr_num_classes):
for j in xrange(curr_num_test):
pos_ev[i,j]=x_test[:,j].dot(C_pos[i]).dot(x_test[:,j][None].T)/xTx[j];
print "[MESSAGE] %i class evidence is calculated" % (i+1);
pos_out_label=np.argmax(pos_ev, axis=0);
pos_accuracy=float(np.sum(pos_out_label==test_label[0:curr_num_test]))/float(curr_num_test);
print "[MESSAGE] Accuracy %.2f %%" % (pos_accuracy*100);
info=np.column_stack((curr_num_classes, pos_accuracy));
np.savetxt(save_file, info, delimiter=',',newline='\n');
while curr_num_classes<num_classes:
curr_num_classes+=1;
_, curr_num_train=calculate_block(tr_start_idx, curr_num_classes-1, num_classes);
_, curr_num_test=calculate_block(te_start_idx, curr_num_classes-1, num_classes, "test");
print "[MESSAGE] Current number of classes: %d" % (curr_num_classes);
# get new R
start_idx, end_idx=calculate_block(tr_start_idx, curr_num_classes-1, num_classes);
states_c=all_train_states[:, start_idx:end_idx];
R_poss.append(states_c.dot(states_c.T));
# get new Conceptor
c_pos=network.compute_pos_conceptor(R_poss[curr_num_classes-1],
ap_pos,
end_idx-start_idx);
C_pos.append(c_pos);
print "[MESSAGE] New conceptor is computed"
pos_ev=np.zeros((curr_num_classes, curr_num_test));
for i in xrange(curr_num_classes):
for j in xrange(curr_num_test):
pos_ev[i,j]=x_test[:,j].dot(C_pos[i]).dot(x_test[:,j][None].T)/xTx[j];
print "[MESSAGE] %i class evidence is calculated" % (i+1);
pos_out_label=np.argmax(pos_ev, axis=0);
#print pos_out_label;
#print test_label[0:curr_num_test];
pos_accuracy=float(np.sum(pos_out_label==test_label[0:curr_num_test]))/float(curr_num_test);
print "[MESSAGE] Accuracy %.2f %%" % (pos_accuracy*100);
info=np.column_stack((curr_num_classes, pos_accuracy));
np.savetxt(save_file, info, delimiter=',',newline='\n');
del pos_ev;
save_file.close();
