hlzz

#!/usr/bin/python

from PIL import Image
import math

test_image_list = '/home/tianwei/Data/sfm_data/test_image_list' # path to the image list
with open(test_image_list, 'r') as f:
    test_images = f.readlines()
    test_images = map(str.strip, test_images)

philmerrell

import { Injectable } from '@angular/core';
import { Observable, BehaviorSubject } from 'rxjs/Rx';

@Injectable()
export class AudioService {

    public audio: HTMLAudioElement;
    public timeElapsed: BehaviorSubject<string> = new BehaviorSubject('00:00');
    public timeRemaining: BehaviorSubject<string> = new BehaviorSubject('-00:00');
    public percentElapsed: BehaviorSubject<number> = new BehaviorSubject(0);

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()

ilblackdragon

import logging

import numpy as np
import tensorflow as tf
from tensorflow.contrib import layers


GO_TOKEN = 0
END_TOKEN = 1
UNK_TOKEN = 2

joshuatvernon

from random import randint

# Quicksort with starting index as pivot
def qsort(items):
  if len(items) < 2:
    return items
  pivot = items[0]
  left = list(filter(lambda x: x <= pivot, items[1:]))
  right = list(filter(lambda x: x > pivot, items[1:]))
  return qsort(left) + [pivot] + qsort(right)

benjamintd

# coding: utf-8

# Imports

import os
import cPickle
import numpy as np
import theano
import theano.tensor as T

hofmannsven

MySQL

Getting started

• http://www.sqlteaching.com/
• https://www.codecademy.com/learn/learn-sql
• https://www.codecademy.com/catalog/language/sql

Related tutorials

• MySQL-CLI
• Analyzing Business Metrics

aronwc

""" Example using GenSim's LDA and sklearn. """

import numpy as np

from gensim import matutils
from gensim.models.ldamodel import LdaModel
from sklearn import linear_model
from sklearn.datasets import fetch_20newsgroups
from sklearn.feature_extraction.text import CountVectorizer