def get_CRBM(how_long=4000):
print "Creating data...."
mean1 = np.array([-0.2, 0.3])
mean2 = np.array([0.5, -0.5])
cov1 = np.array([[0.02, 0.005], [0.001, 0.01]])
cov2 = np.array([[0.02, 0.0], [0.0, 0.02]])
dataset = np.random.multivariate_normal(mean1, cov1, 200).tolist()
dataset += np.random.multivariate_normal(mean2, cov2, 200).tolist()
r = CRBM(2, 4)
# Train at the following learning rates:
print "Training..."
for i in range(how_long):
print ' ' + str(i) + '...',
r.train_epoch(dataset, (0.9, 0.9), 20)
E = np.sum([r.energy(data) for data in dataset])
print 'Energy = ' + str(E) + '...',
print 'Done'
return r, dataset
return self.pi, self.A, self.O, self.A_ijk, self.O_jl, self.O_jf
if __name__ == "__main__":
num_str = '25'
itr = 5
feedback_tperiod = 5
session_len = 250
it_str = str(itr)
f_str = str(feedback_tperiod)
# import inital transition and emissions probability matrices
prob_transition = CRBM.CRBM('transition')
prob_emission = CRBM.CRBM('emission')
prob_emission_f = CRBM.CRBM('emission_f')
a_matrix = prob_transition.total_transition_probs()
o_matrix = prob_emission._o_jk()
o_f_matrix = prob_emission_f._o_jf()
# Training Data Import
TrainingD = trainingData.TrainingData()
[training_input_seq, training_output_seq, feedback_seq_all] = TrainingD.io_sequence_generator()
training_total_len = len(training_input_seq)
training_output_f_seq = np.zeros((int(len(training_input_seq)//feedback_tperiod)+1,))
output_f_time_stamp = np.zeros_like(training_output_f_seq) # feedback time stamps
from scipy import *
import pickle
sys.path.insert(0, '../Categorical_Boltzmann_Machines')
np.set_printoptions(linewidth=700)
np.set_printoptions(precision=3, edgeitems=25)
# prob_transition = CRBM.CRBM('transition')
# A_average = prob_transition.total_transition_probs()
# for i in arange(0, 1000, 5):
# barchart(A_average[i])
# view(azimuth=180, elevation=180)
# show()
#
prob_emission = CRBM.CRBM('emission')
# O_average = prob_emission._o_jk()
#
# prob_emission_f = CRBM.CRBM('emission_f')
# O_f_average = prob_emission_f._o_jf()
# TestD = testdata.TrainingData()
# [test_input_seq, test_output_seq, test_output_f_seq] = TestD.io_sequence_generator()
TrainingD = trainingData.TrainingData()
[test_input_seq, test_output_seq,
test_output_f_seq] = TrainingD.io_sequence_generator()
with open('data_info.pickle', 'rb') as d_i:
data_info = pickle.load(d_i)
#dataset = [[-1.0 + np.random.normal(0,0.15) + 0.02*n, 0.5 + np.random.normal(0,0.2)] for n in range(100)]
XY = np.array(dataset)
# And show it
f = plt.figure(1)
plt.scatter(XY[:,0], XY[:,1], 20, 'b', 'o')
plt.title('Distribution')
plt.draw()
f.show()
# Make a CRBM
print "Creating RBM"
rbm = CRBM(2,20)
rbm.lo = -2
rbm.hi = 2
# Train it
k=20
for i in range(10000):
#for i in range(0):
err = rbm.train_epoch(dataset, 0.1, k)
if i%10 == 0:
print "Epoch", i, ": Error =", err
# Now reconstruct some stuff
XY_rec = np.random.uniform(-2,2,(100,2))
