jkarnows

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

jkarnows

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)

jkarnows

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')

jkarnows

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)

jkarnows

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)

jkarnows

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)

jkarnows

from pylab import *
import urllib2
import twill
from twill.commands import *
import re
import os
import magic
import sys
from docx import *
 

jkarnows

"""
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

jkarnows

"""
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