def main():
# MH algorithm sampling IDs in the complete space
(complete, Hs, Ms)=complete_dataset(16)
prob=compute_boltz_weights(Hs)
print prob
train_res=mh.get_MH_samples(complete)
#train_labels=mh.gen_train_labels(complete, Hs, Ms)
#train=[]
#for i in range(len(complete)):
# row2=complete[i]
# train.append(np.hstack((row2,train_labels[i])))
#np.reshape(train,(len(train),17))
#train2=sorted(train,key=itemgetter(-1))
#train_classes=np.arange(0,len(levels),1)
#temp=40.0
#train_res=get_MH_sampled_IDs(train2,train_classes,temp)
H=[]
for i in range(len(train_res)):
H.append(train_res[i][-2])
histogr(H)
from sklearn.decomposition import PCA
from sklearn.preprocessing import scale
nr=input('Enter the dimension N of the square lattice -->')
reps=nr*nr # number of lattice sites
# generate complete dataset
(training_data,training_H,training_M)=mh.complete_dataset(reps)
train_labels=mh.gen_train_labels(training_data, training_H, training_M)
[train,test]=mh.partition(training_data,train_labels)
train_set=train[0]
train_y=train[1]
test_set=test[0]
test_y=test[1]
train_data=mh.get_MH_samples(train_set)
print "... Computed training dataset"
test_data=mh.get_MH_samples(test_set)
print "... Computed testing dataset"
expr=input('Enter "H" for Hamiltonian energy estimation and "M" for magnetization -->')
if expr=="H":
train_labels=train_data[:,-2] # Hamiltonian
test_labels=test_data[:,-2]
else:
train_labels=train_data[:,-1]
test_labels=test_data[:,-1]
# map Hamiltonian data into classes
train_dat=train_data[:,:-2]
