def run_state_counts(self, i, out): c = None hidden.set_implementation(self.kernel) time1 = time.time() for k in range(self.nrep): c = hidden.state_counts(self.gamma[i], self.T[i]) # compare time2 = time.time() d = (time2-time1)/(1.0*self.nrep) return (c, d)
def run_state_counts(self, i, out): c = None hidden.set_implementation(self.kernel) time1 = time.time() for k in range(self.nrep): c = hidden.state_counts(self.gamma[i], self.T[i]) # compare time2 = time.time() d = (time2-time1)/(1.0*self.nrep) return c, d
def run_state_counts(self, i, kernel, out): nrep = max(1, int(10000/self.T[i])) c = None hidden.set_implementation(kernel) time1 = time.time() for k in range(nrep): c = hidden.state_counts(self.gamma[i], self.T[i]) # compare time2 = time.time() d = (time2-time1)/(1.0*nrep) return c, d
def run_state_counts(self, i, kernel, out): nrep = max(1, int(10000 / self.T[i])) c = None hidden.set_implementation(kernel) time1 = time.time() for k in range(nrep): c = hidden.state_counts(self.gamma[i], self.T[i]) # compare time2 = time.time() d = (time2 - time1) / (1.0 * nrep) return (c, d)
def run_all(self, A, pobs, pi): # forward logprob, alpha = hidden.forward(A, pobs, pi) # backward beta = hidden.backward(A, pobs) # gamma gamma = hidden.state_probabilities(alpha, beta) # state counts T = pobs.shape[0] statecount = hidden.state_counts(gamma, T) # transition counts C = hidden.transition_counts(alpha, beta, A, pobs) # viterbi path vpath = hidden.viterbi(A, pobs, pi) # return return logprob, alpha, beta, gamma, statecount, C, vpath
def run_all(self, A, pobs, pi): # forward logprob, alpha = hidden.forward(A, pobs, pi) # backward beta = hidden.backward(A, pobs) # gamma gamma = hidden.state_probabilities(alpha, beta) # state counts T = pobs.shape[0] statecount = hidden.state_counts(gamma, T) # transition counts C = hidden.transition_counts(alpha, beta, A, pobs) # viterbi path vpath = hidden.viterbi(A, pobs, pi) # return return (logprob, alpha, beta, gamma, statecount, C, vpath)
def run_all_mem(self, A, pobs, pi): T = pobs.shape[0] N = A.shape[0] alpha = np.zeros((T, N)) beta = np.zeros((T, N)) gamma = np.zeros((T, N)) C = np.zeros((N, N)) logprob, alpha = hidden.forward(A, pobs, pi, alpha_out=alpha) # backward hidden.backward(A, pobs, beta_out=beta) # gamma hidden.state_probabilities(alpha, beta, gamma_out=gamma) # state counts statecount = hidden.state_counts(gamma, T) # transition counts hidden.transition_counts(alpha, beta, A, pobs, out=self.C) # viterbi path vpath = hidden.viterbi(A, pobs, pi) # return return logprob, alpha, beta, gamma, statecount, C, vpath
def run_all_mem(self, A, pobs, pi): T = pobs.shape[0] N = A.shape[0] alpha = np.zeros((T, N)) beta = np.zeros((T, N)) gamma = np.zeros((T, N)) C = np.zeros((N, N)) logprob, alpha = hidden.forward(A, pobs, pi, alpha_out=alpha) # backward hidden.backward(A, pobs, beta_out=beta) # gamma hidden.state_probabilities(alpha, beta, gamma_out=gamma) # state counts statecount = hidden.state_counts(gamma, T) # transition counts hidden.transition_counts(alpha, beta, A, pobs, out=self.C) # viterbi path vpath = hidden.viterbi(A, pobs, pi) # return return (logprob, alpha, beta, gamma, statecount, C, vpath)