starhopp3r

def step_gradient(c_current, m_current, points, learning_rate):
    # Gradient descent
    c_gradient = 0
    m_gradient = 0
    N = float(len(points))
    # Iterate
    for i in range(0, len(points)):
        x = points[i, 0]
        y = points[i, 1]
        c_gradient += -(2 / N) * (y - ((m_current * x) + c_current))

starhopp3r

def compute_error_for_points(c, m, points):
    totalError = 0
    # Iterate
    for i in range(0, len(points)):
        x = points[i, 0]
        y = points[i, 1]
        totalError += (y - (m * x + c)) ** 2
    return totalError / float(len(points))

starhopp3r

from numpy import *


def compute_error_for_points(c, m, points):
    totalError = 0
    # Iterate
    for i in range(0, len(points)):
        x = points[i, 0]
        y = points[i, 1]
        totalError += (y - (m * x + c)) ** 2

starhopp3r

// BlinkLeds.c
// Program to use 1 switch to control 8 leds on General I/O Board

#include <xc.h>
#include "delays.h"

void main(void) {
    ADCON1 = 0x0F; //make Port A digital

    TRISAbits.TRISA5 = 1; // RA5 is input

starhopp3r

// BlinkLeds.c
// Program to use 1 switch to control 8 leds on General I/O Board

#include <xc.h>
#include "delays.h"

void main(void) {
    ADCON1 = 0x0F; //make Port A digital

    TRISAbits.TRISA5 = 1; // RA5 is input

starhopp3r

/*
 * File:   Four7seg.c
 *
 * Created on 13 January, 2016, 1:52 PM
 */


#include <xc.h>
#include "delays.h"
void main(void)

starhopp3r

// LCDKeypad.c
// Program to test LCD and keypad.
// For project using USB interface with Bootloader


#include <xc.h>
#include "keypad.h"
#include "delays.h"
#include "lcd.h"	
unsigned char key,outchar,outcharTwo;

starhopp3r

import numpy as np
import gym

# Pong env
env = gym.make('Pong-ram-v0')
# Reset env
env.reset()
# Initialize previous obs
prev_obs = np.zeros((128,))
# Render and test

starhopp3r

/*
 * File:   main.c
 * Author: ColdPro Project Authors
 * 
 * Copyright (C) 2018 ColdPro Project Authors. All rights reserved.
 * 
 * This document is the property of ColdPro Authors.
 * It is considered confidential and proprietary.
 * 
 * This document shall not be reproduced or transmitted in any form,

starhopp3r

from keras.models import Sequential      # One layer after the other
from keras.layers import Dense, Flatten  # Dense layers are fully connected layers, Flatten layers flatten out multidimensional inputs
from collections import deque            # For storing moves 
import numpy as np
import gym                                # To train our network
import random     # For sampling batches from the observations

env = gym.make('SuperMarioBros-1-1-v0')          # Choose game (any in the gym should work)
action_space = 6
# Create network. Input is two consecutive game states, output is Q-values of the possible moves.