def record_trial_error(self):
#throw out samples at the beginning of the trial
self.trial_error = self.trial_error[20:]
squared_error = [np.sum(te**2) for te in self.trial_error]
if len(squared_error) != 0:
self.logger.info("RMSE for trial: %g" % (np.sqrt(np.mean(squared_error))))
print "RMSE for trial: %g" % np.sqrt(np.mean(squared_error))
if not self.is_learning:
self.error_history.extend(squared_error)
self.trial_error = []
def record_trial_error(self):
#throw out samples at the beginning of the trial
self.trial_error = self.trial_error[20:]
squared_error = [np.sum(te**2) for te in self.trial_error]
if len(squared_error) != 0:
self.logger.info("RMSE for trial: %g" %
(np.sqrt(np.mean(squared_error))))
print "RMSE for trial: %g" % np.sqrt(np.mean(squared_error))
if not self.is_learning:
self.error_history.extend(squared_error)
self.trial_error = []
def make_unitary(self):
fft_val = numeric.fft(self.v)
fft_imag = fft_val.imag
fft_real = fft_val.real
fft_norms = [sqrt(fft_imag[n]**2 + fft_real[n]**2) for n in range(len(self.v))]
fft_unit = fft_val / fft_norms
self.v = (numeric.ifft(fft_unit)).real
def calc_stats(self):
if len(self.error_history) != 0:
self.latest_rmse = np.sqrt(np.mean(self.error_history))
self.logger.info("Latest RMSE: %g" % (self.latest_rmse))
self.error_history = []
self.trial_error = []
def calc_stats(self):
if len(self.error_history) != 0:
self.latest_rmse = np.sqrt(np.mean(self.error_history))
self.logger.info("Latest RMSE: %g" % (self.latest_rmse))
self.error_history = []
self.trial_error = []
def f(input_vec):
v = input_vec + noise * hrr.HRR(D).v
v = v / np.sqrt(sum(v**2))
return v
def f(input_vec):
v = input_vec + noise * hrr.HRR(D).v
v = v / np.sqrt(sum(v**2))
return v
def add(self,lhs,rhs,lhs_scale=1,lhs_terms=1):
p=Production(lhs,rhs,lhs_scale*1.0/numeric.sqrt(lhs_terms))
self.productions.append(p)
