yvan

%matplotlib inline
import matplotlib.pyplot as plt

# calculate the cost at -5
def f(w1):
    return sum((w1*data['speed'] - data['dist'])**2)

w1 = -5
h = 0.1
x_tan = np.linspace(-10, 0, 15)

yvan

%matplotlib inline
import matplotlib.pyplot as plt
# calculate the cost for many different values
# of w1 using our cost function
costs = []
for i in range(-10,11,1):
    w1 = i
    y_actual = data['dist']
    y_predict = w1*data['speed']
    costs.append(np.mean((y_predict - y_actual)**2))

yvan

# performs the gradient descent for linear regression
def calc_regression_simple(w1, frames, x_data, y_data):
    learn_rate = 0.001
    ys = []
    for i in range(frames):
        # get the gradient and update the parameter
        w1_gradient = 2*np.mean(x_data*(w1*x_data - y_data))
        w1 = w1 - (w1_gradient*learn_rate)
        # calculate the predictions from this new function
        x = np.linspace(0,30)

yvan

import pandas as pd
# load the data 
data = pd.read_csv('cars.csv')
data.head()

yvan

%matplotlib inline
import matplotlib.pyplot as plt

# calculate the cost at -5
def f(w1):
    return np.mean((w1*data['speed'] - data['dist'])**2)

w1 = -5
h = 0.1
x_tan = np.linspace(-10, 0, 15)

yvan

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

from matplotlib import animation, rc
from IPython.display import HTML

# set ffmpeg path to installed path (for anmation)
plt.rcParams['animation.ffmpeg_path'] = '/usr/local/bin/ffmpeg'    

yvan

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

from matplotlib import animation, rc
from IPython.display import HTML

# set ffmpeg path to installed path (for anmation)
plt.rcParams['animation.ffmpeg_path'] = '/usr/local/bin/ffmpeg'    

yvan

import random
import numpy as np

def display_cave(matrix):
    for i in range(matrix.shape[0]):
        for j in range(matrix.shape[1]):
            char = "#" if matrix[i][j] == WALL else "."
            print(char, end='')
        print()
        

yvan

import random
import numpy as np

def display_cave(matrix):
    for i in range(matrix.shape[0]):
        for j in range(matrix.shape[1]):
            char = "#" if matrix[i][j] == WALL else "."
            print(char, end='')
        print()
        

yvan

shape = (42,42)
WALL = 0
FLOOR = 1

# set the probability of filling
# a wall at 40% not 50%
fill_prob = 0.4

new_map = np.ones(shape)
for i in range(shape[0]):