tanvipenumudy

MSSubClass: Identifies the type of dwelling involved in the sale.	

        20	1-STORY 1946 & NEWER ALL STYLES
        30	1-STORY 1945 & OLDER
        40	1-STORY W/FINISHED ATTIC ALL AGES
        45	1-1/2 STORY - UNFINISHED ALL AGES
        50	1-1/2 STORY FINISHED ALL AGES
        60	2-STORY 1946 & NEWER
        70	2-STORY 1945 & OLDER
        75	2-1/2 STORY ALL AGES

tanvipenumudy

import numpy as np
import cv2

cap = cv2.VideoCapture(0)

# Define the codec and create VideoWriter object
fourcc = cv2.VideoWriter_fourcc(*'XVID')
out = cv2.VideoWriter('output.avi',fourcc, 20.0, (640,480))

while(cap.isOpened()):

tanvipenumudy

import numpy as np
import cv2

cap = cv2.VideoCapture('vtest.avi')

while(cap.isOpened()):
    ret, frame = cap.read()

    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

tanvipenumudy

import numpy as np
import cv2

cap = cv2.VideoCapture(0)

while(True):
    # Capture frame-by-frame
    ret, frame = cap.read()

    # Our operations on the frame come here

tanvipenumudy

import numpy as np
from math import sin as sn
import matplotlib.pyplot as plt
import time

# Number of test points
N_point  = 1000

# Define a custom function with some if-else loops
def myfunc(x,y):

tanvipenumudy

from IPython.display import clear_output
from time import sleep
def print_frames(frames):
    for i, frame in enumerate(frames):
        clear_output(wait=True)
        print(frame['frame'].getvalue())
        print(f"Timestep: {i + 1}")
        print(f"State: {frame['state']}")
        print(f"Action: {frame['action']}")
        print(f"Reward: {frame['reward']}")

tanvipenumudy

env.s = 328  # Set environment to illustration's state
epochs = 0
penalties, reward = 0, 0
frames = [] #for animation
done = False
while not done:
    action = env.action_space.sample()
    state, reward, done, info = env.step(action)
if reward == -10:
        penalties += 1

tanvipenumudy

"""Evaluate agent's performance after Q-learning"""
total_epochs, total_penalties = 0, 0
episodes = 100
for _ in range(episodes):
    state = env.reset()
    epochs, penalties, reward = 0, 0, 0
    
    done = False
    
    while not done:

tanvipenumudy

%%time
"""Training the agent"""
import random
from IPython.display import clear_output
# Hyperparameters
alpha = 0.1
gamma = 0.6
epsilon = 0.1
# For plotting metrics
all_epochs = []

tanvipenumudy