| package es.catmobil.tornofici.ui.views; | |
| import android.content.Context; | |
| import android.content.res.TypedArray; | |
| import android.os.Build; | |
| import android.text.Editable; | |
| import android.text.TextWatcher; | |
| import android.util.AttributeSet; | |
| import android.util.Log; | |
| import android.view.View; |
To demonstrate text classification with Scikit Learn, we'll build a simple spam filter. While the filters in production for services like Gmail will obviously be vastly more sophisticated, the model we'll have by the end of this chapter is effective and surprisingly accurate.
Spam filtering is the "hello world" of document classification, but something to be aware of is that we aren't limited to two classes. The classifier we will
| """ | |
| This is a batched LSTM forward and backward pass | |
| """ | |
| import numpy as np | |
| import code | |
| class LSTM: | |
| @staticmethod | |
| def init(input_size, hidden_size, fancy_forget_bias_init = 3): |
| """ | |
| This is a batched LSTM forward and backward pass | |
| """ | |
| import numpy as np | |
| import code | |
| class LSTM: | |
| @staticmethod | |
| def init(input_size, hidden_size, fancy_forget_bias_init = 3): |
| import sys | |
| import numpy as np | |
| import scipy.ndimage as nd | |
| from scipy.cluster.vq import vq | |
| from scipy.misc import imsave | |
| def crayola_9(): | |
| """ | |
| Palette of the first 8 crayola colors + white | |
| """ |
| # see also: http://code.activestate.com/recipes/578150-sending-non-ascii-emails-from-python-3/ | |
| import os | |
| import smtplib | |
| from email.utils import formataddr | |
| from email.utils import formatdate | |
| from email.utils import COMMASPACE | |
| from email.header import Header |
As websites become more JavaScript heavy, it's harder to automate things like screenshotting for archival purposes. I've seen examples and suggestions to use PhantomJS for visual testing/archiving of websites, but have run into issues such as the non-rendering of webfonts. I've never tried out Selenium until today...and while I'm not thinking about performance implications yet, Selenium seems far more accurate than PhantomJS...which makes sense since it actually opens a real browser. And it's not too hard to script to do complex interactions: here's an [example of how to log in to Twitter, write a tweet, upload an image, and send a tweet via Selenium and DOM element selection](https://gist.github.com/dannguyen/8a6fa49253c1d6a0eb92
| #!/usr/bin/env python | |
| # | |
| # Extracts email addresses from one or more plain text files. | |
| # | |
| # Notes: | |
| # - Does not save to file (pipe the output to a file if you want it saved). | |
| # - Does not check for duplicates (which can easily be done in the terminal). | |
| # | |
| # (c) 2013 Dennis Ideler <[email protected]> |
| #import necessary libraries | |
| import cv2 | |
| import numpy as np | |
| #capture video from the webcam | |
| cap = cv2.VideoCapture(0) | |
| #load the face finder | |
| face_cascade = cv2.CascadeClassifier('/home/sm/Desktop/haarcascade_frontalface_default.xml') |
This Gist is about how I use PyAudio, NumPy, and Matplotlib to plot freqency spectrum of system sound or microphone.
You can read this blog post for more detail or watch this video:
