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""" | |
Code modified from https://jessesw.com/Data-Science-Skills/ | |
Jeremy Karnowski August 30, 2015 | |
""" | |
from bs4 import BeautifulSoup # For HTML parsing | |
import urllib2 # Website connections | |
import tldextract # Extracts domain information | |
import re # Regular expressions | |
from time import sleep # To prevent overwhelming the server between connections |
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""" | |
Solve OpenAI Gym Cartpole V0 with DQN. | |
TensorFlow code by TiehHung Chuang (imironhead), 2016-09-06 | |
tf-slim code by Jeremy Karnowski jkarnows, 2016-09-07 | |
""" | |
from __future__ import absolute_import | |
from __future__ import print_function | |
from __future__ import division |
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from pylab import * | |
import urllib2 | |
import twill | |
from twill.commands import * | |
import re | |
import os | |
import magic | |
import sys | |
from docx import * | |
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from matplotlib import pyplot as plt | |
def DETCurve(fps,fns): | |
""" | |
Given false positive and false negative rates, produce a DET Curve. | |
The false positive rate is assumed to be increasing while the false | |
negative rate is assumed to be decreasing. | |
""" | |
axis_min = min(fps[0],fns[-1]) | |
fig,ax = plt.subplots() | |
plot(fps,fns) |
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from RPIO import PWM | |
servoLeft = 18 # 1300 is backwards, 1700 forward | |
servoRight = 23 # 1700 is backwards, 1300 forward | |
servo = PWM.Servo() | |
servo.set_servo(servoLeft,1500) | |
servo.set_servo(servoRight,1500) |
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from cv2 import circle | |
# Input code here to determine the center of the circle, (x,y) and the radius. | |
color = (0,255,0) | |
thickness = 2 | |
cv2.circle(img,(x,y),radius,color,thickness) |
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import os | |
loss_files = sort(os.listdir('cv/')) | |
lfs = [] | |
for lf in loss_files: | |
lf = map(float,lf[13:-3].split('_')) | |
lfs.append(lf) | |
lfs = array(sorted(lfs, key = lambda x: x[0])) | |
plot(lfs[:,0],lfs[:,1]) | |
title('Training Lossn') | |
ylabel('Loss') |
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import numpy as np | |
# import cv2 | |
import Image | |
import gzip | |
import os | |
import struct | |
def _read32(bytestream): | |
dt = np.dtype(np.uint32).newbyteorder('>') | |
return np.frombuffer(bytestream.read(4), dtype=dt) |
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Using Mac OS X El Capitan 10.11.6 | |
sudo pip install virtualenv # Using Virtual Environment to contain dev environment | |
# [Navigate to folder rosetta-stone] | |
virtualenv rosetta # Create a virtual environment for this project | |
source rosetta/bin/activate # Start the virtual environment to install dev environment | |
pip install -U python # Install most recent Python | |
pip install numpy scipy matplotlib ipython pandas sympy nose # Install SciPy Stack | |
pip install sklearn # Install scikit-learn |