Cara menggunakan time decay python

The following program plots the exponential decay described by $y = Ne^{-t/\tau}$ labeled by lifetimes, ($n\tau$ for $n = 0, 1, \cdots$) such that after each lifetime the value of $y$ falls by a factor of $e$.

import numpy as np import matplotlib.pyplot as plt # Initial value of y at t=0, lifetime in s N, tau = 10000, 28 # Maximum time to consider (s) tmax = 100 # A suitable grid of time points, and the exponential decay itself t = np.linspace(0, tmax, 1000) y = N * np.exp(-t/tau) fig = plt.figure() ax = fig.add_subplot(111) ax.plot(t, y) # The number of lifetimes that fall within the plotted time interval ntau = tmax // tau + 1 # xticks at 0, tau, 2*tau, ..., ntau*tau; yticks at the corresponding y-values xticks = [i*tau for i in range(ntau)] yticks = [N * np.exp(-i) for i in range(ntau)] ax.set_xticks(xticks) ax.set_yticks(yticks) # xtick labels: 0, tau, 2tau, ... xtick_labels = [r'$0$', r'$\tau$'] + [r'${}\tau$'.format(k) for k in range(2,ntau)] ax.set_xticklabels(xtick_labels) # corresponding ytick labels: N, N/e, N/2e, ... ytick_labels = [r'$N$',r'$N/e$'] + [r'$N/{}e$'.format(k) for k in range(2,ntau)] ax.set_yticklabels(ytick_labels) ax.set_xlabel(r'$t\;/\mathrm{s}$') ax.set_ylabel(r'$y$') ax.grid() plt.show()

The $x$-axis tick labels have been set to $1, \tau, 2\tau, ...$ and the $y$-axis tick labels to $N, N/e, N/2e, ...$

Note that the length of the sequence of tick labels must correspond to that of the list of tick values required.

How Time Matters: Learning Time-Decay Attention for Contextual Spoken Language Understanding in Dialogue

Reference

Main paper to be cited

@inproceedings{su2018how, title={How time matters: Learning Time-Decay Attention for Contextual Spoken Language Understanding in Dialogues}, author={Shang-Yu Su, Pei-Chieh Yuan, and Yun-Nung Chen}, booktitle={Proceedings of The 16th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies}, year={2018} }

Usage

1. Put the DSTC4 data into some directory (e.g. /home/workspace/dstc4). Run the code parse_history.py to preprocess the data.

2. Put the embedding files into some directory (e.g. /home/workspace/glove) Modify line 29 in the code slu_preprocess.py