xinsblog

from typing import List
import json
import sys

import openai


class SimChatGPT:

    def __init__(self, api_key: str, messages: List = None):

ululh

# derived from http://scikit-learn.org/stable/auto_examples/applications/topics_extraction_with_nmf_lda.html
# explanations are located there : https://www.linkedin.com/pulse/dissociating-training-predicting-latent-dirichlet-lucien-tardres

from sklearn.feature_extraction.text import CountVectorizer
from sklearn.decomposition import LatentDirichletAllocation
import pickle

# create a blank model
lda = LatentDirichletAllocation()

asimshankar

# Mostly copied from https://keras.io/applications/#usage-examples-for-image-classification-models                                           
# Changing it to use InceptionV3 instead of ResNet50                                                                                         
from keras.applications.inception_v3 import InceptionV3, preprocess_input, decode_predictions                                                
from keras.preprocessing import image                                                                                                        
import numpy as np                                                                                                                           
                                                                                                                                             
model = InceptionV3()                                                                                                                        
img_path = 'elephant.jpg'     

aneesha

from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer
from sklearn.datasets import fetch_20newsgroups
from sklearn.decomposition import NMF, LatentDirichletAllocation
import numpy as np

def display_topics(H, W, feature_names, documents, no_top_words, no_top_documents):
    for topic_idx, topic in enumerate(H):
        print "Topic %d:" % (topic_idx)
        print " ".join([feature_names[i]
                        for i in topic.argsort()[:-no_top_words - 1:-1]])

aneesha

from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer
from sklearn.datasets import fetch_20newsgroups
from sklearn.decomposition import NMF, LatentDirichletAllocation

def display_topics(model, feature_names, no_top_words):
    for topic_idx, topic in enumerate(model.components_):
        print "Topic %d:" % (topic_idx)
        print " ".join([feature_names[i]
                        for i in topic.argsort()[:-no_top_words - 1:-1]])

danijar

# Working example for my blog post at:
# https://danijar.github.io/structuring-your-tensorflow-models
import functools
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data


def doublewrap(function):
    """
    A decorator decorator, allowing to use the decorator to be used without

colah

Understanding LSTMs

• Japanese: qiita
• Russian: Alex Sosn
• Chinese: jianshu, csdn, csdn, Yangyu Chen

songron

#!/usr/bin/env python
#coding=utf8


""" Simulate a user login to Sina Weibo with cookie.
You can use this method to visit any page that requires login.
"""


import urllib2

st-j

#include <ctime>
#include <iostream>

#include <boost/date_time/gregorian/gregorian.hpp>
#include <boost/date_time/posix_time/posix_time.hpp>


//==============================================================================
//! Convert date part of Unix timestamp (time_t) to boost date
//!

bsweger

Useful Pandas Snippets

A personal diary of DataFrame munging over the years.

Data Types and Conversion

Convert Series datatype to numeric (will error if column has non-numeric values)
(h/t @makmanalp)