| <!DOCTYPE html> | |
| <html lang="en"> | |
| <head> | |
| <meta charset="utf-8"/> | |
| <meta http-equiv="X-UA-Compatible" content="IE=edge"/> | |
| <meta name="viewport" content="width=device-width, initial-scale=1"/> | |
| <meta name="description" content=""/> | |
| <meta name="author" content=""/> |
| #!/usr/bin/env python | |
| # -*- coding: utf-8 -*- | |
| """ Caluculates symmetric Kullback-Leibler divergence. | |
| @author Shingo OKAWA | |
| """ | |
| import numpy | |
| import sys | |
| import scipy.stats as stats | |
| import matplotlib.pyplot as plotter | |
| from gensim import corpora, models, similarities, matutils |
| """Information Retrieval metrics | |
| Useful Resources: | |
| http://www.cs.utexas.edu/~mooney/ir-course/slides/Evaluation.ppt | |
| http://www.nii.ac.jp/TechReports/05-014E.pdf | |
| http://www.stanford.edu/class/cs276/handouts/EvaluationNew-handout-6-per.pdf | |
| http://hal.archives-ouvertes.fr/docs/00/72/67/60/PDF/07-busa-fekete.pdf | |
| Learning to Rank for Information Retrieval (Tie-Yan Liu) | |
| """ | |
| import numpy as np |
| import tensorflow as tf | |
| import numpy as np | |
| class ConvolutionalAttentionNLI(object): | |
| def __init__(self, embeddings_shape, target_classes=2, conv_filter_size=3, conv_projection_size=300, attention_output_size=200, comparison_output_size=100, learning_rate=0.05): | |
| self._embeddings_shape = embeddings_shape | |
| self._target_classes = target_classes | |
| self._conv_filter_size = conv_filter_size | |
| self._conv_projection_size = conv_projection_size |
| class AttentionWithContext(Layer): | |
| """ | |
| Attention operation, with a context/query vector, for temporal data. | |
| Supports Masking. | |
| Follows the work of Yang et al. [https://www.cs.cmu.edu/~diyiy/docs/naacl16.pdf] | |
| "Hierarchical Attention Networks for Document Classification" | |
| by using a context vector to assist the attention | |
| # Input shape | |
| 3D tensor with shape: `(samples, steps, features)`. | |
| # Output shape |
It's a common pattern in React to wrap a component in an abstraction. The outer component exposes a simple property to do something that might have more complex implementation details.
We used to have a helper function called transferPropsTo. We no longer support this method. Instead you're expected to use a generic object helper to merge props.
render() {
return Component(Object.assign({}, this.props, { more: 'values' }));
| """ | |
| Demo of json_required decorator for API input validation/error handling | |
| """ | |
| import inspect | |
| import functools | |
| import json | |
| from traceback import format_exception | |
| from flask import jsonify, request | |
| import sys |
| git branch -m old_branch new_branch # Rename branch locally | |
| git push origin :old_branch # Delete the old branch | |
| git push --set-upstream origin new_branch # Push the new branch, set local branch to track the new remote |
| import numpy as np | |
| __author__ = 'Fariz Rahman' | |
| def eq(x, y): | |
| return x.lower().replace(" ", "") == y.lower().replace(" ", "") | |
| def get_words(x): | |
| x = x.replace(" ", " ") |
| { | |
| "metadata": { | |
| "name": "" | |
| }, | |
| "nbformat": 3, | |
| "nbformat_minor": 0, | |
| "worksheets": [ | |
| { | |
| "cells": [ | |
| { |