Goddard

#!/bin/python3

import math
import os
import random
import re
import sys

# Complete the staircase function below.
def staircase(n):

Goddard

#!/bin/python3

import math
import os
import random
import re
import sys

# Complete the diagonalDifference function below.
def diagonalDifference(arr):

Goddard

#define F_CPU 1000000  // by default we have the 1MHz internal oscillator

#include <avr/io.h>      // this contains all the IO port definitions
#include <util/delay.h>

int main(void) {
  DDRB = (1 << PB5);  // set PORTB5 to an output
  DDRC = (1 << PC1);  // set PORTC1 to an output

  while (1) {

Goddard

def draw_lines(img, lines, color=[255, 0, 0], thickness=10):
    """
    NOTE: this is the function you might want to use as a starting point once you want to 
    average/extrapolate the line segments you detect to map out the full
    extent of the lane (going from the result shown in raw-lines-example.mp4
    to that shown in P1_example.mp4).  
    
    Think about things like separating line segments by their 
    slope ((y2-y1)/(x2-x1)) to decide which segments are part of the left
    line vs. the right line.  Then, you can average the position of each of 

Goddard

sudo apt-get install build-essential libssl-dev curl

# The install script
# Adapted from https://gist.github.com/579814

echo '# Added by install script for node.js and npm in 30s' >> ~/.bashrc
echo 'export PATH=$HOME/local/bin:$PATH' >> ~/.bashrc
echo 'export NODE_PATH=$HOME/local/lib/node_modules' >> ~/.bashrc
. ~/.bashrc

Goddard

def gen_test_video(sess, logits, keep_prob, input_image, clip, image_shape): #test_image
    # image = scipy.misc.imresize(scipy.misc.imread(test_image), image_shape)
    image = clip

    im_softmax = sess.run(
        [tf.nn.softmax(logits)],
        {keep_prob: 1.0, input_image: [image]})
    im_softmax = im_softmax[0][:, 1].reshape(image_shape[0], image_shape[1])
    segmentation = (im_softmax > 0.5).reshape(image_shape[0], image_shape[1], 1)
    mask = np.dot(segmentation, np.array([[0, 255, 0, 127]]))

Goddard

import os.path
import tensorflow as tf

import helper
import freeze

import warnings
from distutils.version import LooseVersion
import project_tests as tests

Goddard

import os.path
import tensorflow as tf
import helper
import cv2

from distutils.version import LooseVersion

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

Goddard

import numpy as np  
import matplotlib.pyplot as plt  

def graph(formula, x_range):  
    x = np.array(x_range)  
    y = formula(x)
    plt.plot(x, y)  
    plt.show()  

def my_formula(x):

Goddard

#!/bin/bash
##
## This is bash script for saving and loading gnome-terminal session. Special for Ubuntu.
##
## Usage: ./terminal-session -s <name_profile> - for save session
##        ./terminal-session -l <name_profile> - for load existing session
##        ./terminal-session --profiles - for show existing session files
##
## Instructions:
##  - Run gnome-terminal;