I <3 OpenSource

print("Starting a game of Hangman.") from random import randint valid1 = 0 x = 0 hg = 0 valid2 = 0 valid27000 = [ "a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "k", "l", "m", "n", "o", "p", "q", "r", "s", "t", "u", "v", "w", "x", "y", "z" ] valid30 = [ "1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "14", "15", "16", "17", "18", "19", "20", "21", "22", "23", "24", "25" ] valid900 = ["3", "4", "5", "6", "7", "8", "9", "10"] allwords = [ "dog", "cat", "man", "woman", "drums", "cousin", "brother", "epidemic", "pencilcase" ] y = len(allwords) validwords = [] while valid1 == 0: attempts = input("How many incorrect attempts do you want? [1-25] ") if attempts not in valid30: print("Not a valid number from 1 to 25") else: valid1 = 1 attempts = int(attempts) while valid2 == 0: minlen = input("What's the minimum length of the word? [3-10] ") if minlen not in valid900: print("Not a valid number from 3 to 10") else: valid2 = 1 minlen = int(minlen) while x in range(-1, y - 1): x = x + 1 lenx = int(len(allwords[x])) valid3 = max(lenx, minlen) if valid3 == lenx: validwords.append(allwords[x]) wordnm = randint(0, len(validwords) - 1) word = validwords[wordnm] wordlen = len(word) guess = [] wordlist = [] for x in range(0, wordlen): x = x + 1 guess.append("*") wordlist.append(word[x - 1]) while attempts > 0: print(guess) guess1 = input("Enter your guess: ") if guess1 not in valid27000: print( "This is not a valid guess. Try inputting a lowercase English letter" ) else: if guess1 not in wordlist: if attempts != 1: print("This letter is not in the word. Try again") else: print( "This letter is not in the word. You have ran out of attempts." ) attempts = attempts - 1 else: print("This letter is in the word") while hg in range(0, len(wordlist)): if wordlist[hg] == guess1: guess[hg] = guess1 hg = hg + 1 if guess == wordlist: print("You won! Congrats. The word is " + word) while True: aohuni = 1 hg = 0

""" Copyright Adam Blumenfeld All rights reserved """ # These are the variable you have to set Email = "email@example.com" Password = "examplepassword" # Imports import requests from bs4 import BeautifulSoup import smtplib import time import datetime import keyboard as k import sys wait = True def wait_false(): global wait wait = False k.add_hotkey("ctrl+g", lambda: wait_false()) # URLS you want to check URLS = ["https://www.amazon.com/Lysol-Bathroom-Cleaner-Island-Breeze/dp/B06ZZQSH77/ref=sr_1_5?dchild=1&keywords=lysol+spray&qid=1588110272&sr=8-5", "https://www.amazon.com/Lysol-Disinfectant-Spray-Morning-Breeze/dp/B01IAIMYF2/ref=sr_1_1?dchild=1&keywords=lysol+disinfectant&qid=1588133936&sr=8-1", "https://www.amazon.com/Scott-67805-Recycled-Bathroom-Tissue/dp/B0040ZOIZO/ref=pd_ybh_a_5?_encoding=UTF8&psc=1&refRID=Y73XTT0K4CR1PQMFPV3V", "https://www.amazon.com/AmScope-120X-1200X-M30-ABS-KT2-W-Beginner-Microscope/dp/B00GGY85EC/ref=sr_1_1?dchild=1&keywords=microscope&qid=1588207972&s=industrial&sr=1-1"] # Time you want to space out already made emails (If you want to never relese emails on an item again, set it to float('inf')) cooldown_time = 7200 headers = { "User-Agent": 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/81.0.4044.113 Safari/537.36'} def check(URL, maillist): page = requests.get(URL, headers=headers) soup1 = BeautifulSoup(page.content, 'html.parser') soup = BeautifulSoup(soup1.prettify(), "html.parser") title = soup.find(id="productTitle").get_text().strip().split(',')[0] available = soup.find(id="availability") price = soup.find(id="priceblock_ourprice") if price != None: print("Product is available") send_mail(title, URL, maillist) return True if available == None: print("Product is available") send_mail(title, URL) return True available = available.findChild('span').get_text().strip()[0:15] if available == "Temporarily out": print("Product is available") send_mail(title, URL, maillist) return True print("Product isn't available") return False def send_mail(title, link, maillist): global email global password server = smtplib.SMTP('smtp.gmail.com', 587) server.ehlo() server.starttls() server.ehlo() server.login(email, password) subject = f'"{title}" is available!' body = f'Check the Amazon link to see availablility: {link}' msg = f"Subject: {subject}\n\n{body}" server.sendmail( email, maillist, msg ) print('Email Has Been Sent!') server.quit() wait = True cooldown = {} # The time you want between checks time_between_checks = 120 while True: print("Press CTRL+g to fast forward check") i = 0 while i < time_between_checks and wait: time.sleep(1) print("Time since last check:", i, "\\", time_between_checks, end="\r") i += 1 wait = True print("\n") for URL in URLS: time_ = datetime.datetime.now() print(f"Checking... <{time_}> <{URL}>\n") if cooldown.get(URL) != None: if time.time() - cooldown[URL] >= cooldown_time: del cooldown[URL] else: print(f"Cooldown time for URL <{URL}>: {cooldown_time-int((time.time() - cooldown[URL]) % 60)} seconds") continue availability = check(URL, sys.argv) if availability: cooldown[URL] = time.time() print("\n\n")

""" Copyright Adam Blumenfeld All rights reserved """ # These are the variable you have to set Email = "email@example.com" Password = "examplepassword" # Imports import requests from bs4 import BeautifulSoup import smtplib import time import datetime import keyboard as k import sys wait = True def wait_false(): global wait wait = False k.add_hotkey("ctrl+g", lambda: wait_false()) # URLS you want to check URLS = ["https://www.amazon.com/Lysol-Bathroom-Cleaner-Island-Breeze/dp/B06ZZQSH77/ref=sr_1_5?dchild=1&keywords=lysol+spray&qid=1588110272&sr=8-5", "https://www.amazon.com/Lysol-Disinfectant-Spray-Morning-Breeze/dp/B01IAIMYF2/ref=sr_1_1?dchild=1&keywords=lysol+disinfectant&qid=1588133936&sr=8-1", "https://www.amazon.com/Scott-67805-Recycled-Bathroom-Tissue/dp/B0040ZOIZO/ref=pd_ybh_a_5?_encoding=UTF8&psc=1&refRID=Y73XTT0K4CR1PQMFPV3V", "https://www.amazon.com/AmScope-120X-1200X-M30-ABS-KT2-W-Beginner-Microscope/dp/B00GGY85EC/ref=sr_1_1?dchild=1&keywords=microscope&qid=1588207972&s=industrial&sr=1-1"] # Time you want to space out already made emails (If you want to never relese emails on an item again, set it to float('inf')) cooldown_time = 7200 headers = { "User-Agent": 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/81.0.4044.113 Safari/537.36'} def check(URL, maillist): page = requests.get(URL, headers=headers) soup1 = BeautifulSoup(page.content, 'html.parser') soup = BeautifulSoup(soup1.prettify(), "html.parser") title = soup.find(id="productTitle").get_text().strip().split(',')[0] available = soup.find(id="availability") if available == None: print("Product is available") send_mail(title, URL) return True available = available.findChild('span').get_text().strip()[0:15] if available == "Temporarily out": print("Product is available") send_mail(title, URL, maillist) return True print("Product isn't available") return False def send_mail(title, link, maillist): global email global password server = smtplib.SMTP('smtp.gmail.com', 587) server.ehlo() server.starttls() server.ehlo() server.login(email, password) subject = f'"{title}" is available!' body = f'Check the Amazon link to see availablility: {link}' msg = f"Subject: {subject}\n\n{body}" server.sendmail( email, maillist, msg ) print('Email Has Been Sent!') server.quit() wait = True cooldown = {} # The time you want between checks time_between_checks = 120 while True: print("Press CTRL+g to fast forward check") i = 0 while i < time_between_checks and wait: time.sleep(1) print("Time since last check:", i, "\\", time_between_checks, end="\r") i += 1 wait = True print("\n") for URL in URLS: time_ = datetime.datetime.now() print(f"Checking... <{time_}> <{URL}>\n") if cooldown.get(URL) != None: if time.time() - cooldown[URL] >= cooldown_time: del cooldown[URL] else: print(f"Cooldown time for URL <{URL}>: {cooldown_time-int((time.time() - cooldown[URL]) % 60)} seconds") continue availability = check(URL, sys.argv) if availability: cooldown[URL] = time.time() print("\n\n")

WEBSITE URL: https://repl.it/@Snakebiking49/Unstable-Spiral

@AdamBlumenfeld The link to the website is https://unstable-spiral--snakebiking49.repl.co/ IN DEVELOPMENT

hm = int(input("Enter how many categories or inputs do you want: ")) boo = int(input("How many inputs per category do you want: ")) dataSet = [] for be in range(hm): print ("This is your " + str(be) + "th input set.") inputer = [] for i in range(boo): point = float(input("Enter value: ")) inputer.append(point) dataSet.append(inputer) outputs = [] for i in range(boo): print ("Enter you output.") output = float(input("Enter value: ")) outputs.append(output) dataSet.append(outputs) values = [] for i in range(hm): values.append(0) yIntercept = 0 def slopeDerivativeOne(dataSet, values, hm, boo, yIntercept, wanted): length = hm total = 0 wantedValues = [] a = dataSet[wanted] for i in range(len(a)): wantedValues.append(a[i]) for f in range(length): innerValues = [] for i in range(len(dataSet)): b = dataSet[i] val = b[f] innerValues.append(val) innerTotal = 0 for i in range(len(values)): bobby = values[i] * innerValues[i] innerTotal += bobby innerTotal += yIntercept jj = len(dataSet) - 1 nn = dataSet[jj] difference = (nn[f] - innerTotal) * wantedValues[f] total += difference returnValue = (-2/length) * total return returnValue def DerivativeOne(dataSet, values, hm, boo, yIntercept, wanted): length = hm total = 0 wantedValues = [] a = dataSet[wanted] for i in range(len(a)): wantedValues.append(a[i]) for f in range(length): innerValues = [] for i in range(len(dataSet)): z = dataSet[i] val = z[f] innerValues.append(val) innerTotal = 0 for i in range(len(values)): bobby = values[i] * innerValues[i] innerTotal += bobby innerTotal += yIntercept jj = len(dataSet) - 1 nn = dataSet[jj] difference = (nn[f] - innerTotal) total += difference print (total) returnValue = (-2/length) * total return returnValue def getCost(dataSet, values, hm, boo, yIntercept, wanted): total = 0 length = hm for f in range(length): innerValues = [] for i in range(len(dataSet)): z = dataSet[i] val = z[f] innerValues.append(val) innerTotal = 0 for i in range(len(values)): bobby = values[i] * innerValues[i] innerTotal += bobby innerTotal += yIntercept jj = len(dataSet) - 1 nn = dataSet[jj] difference = (nn[f] - innerTotal) total += difference print (total) return (abs(total)) l = 0.0001 iterations = 100000 previousValues = [] for x in range(len(values)): print (x) previousValues.append(5) previousYInt = 5 j = 0 while (True): j += 1 ds = [] for i in range(len(values)): d = slopeDerivativeOne(dataSet, values, hm, boo, yIntercept, i) word = values[i] - (l * d) values[i] = word d1 = DerivativeOne(dataSet, values, hm, boo, yIntercept, i) yIntercept = yIntercept - (l * d1) print (j) if j > 1000000: False break previousValues = (values) previousYInt = yIntercept for i in range(len(values)): bob = round(values[i], 2) print (bob) bobby = round(yIntercept, 2) print ("y-intercept" + str(bobby))

x = [15, 9, 12, 1, 10, 11, 4, 16, 2, 30, 4, 15, 18, 12, 14] y = [13 ,19, 16, 15, 24, 7, 18, 23, 28, 2, 26, 12, 18, 24, 17] def DerivativeSlope(x, y, slope, yInt): length = len(x) total = 0 for i in range(length): predicted = (slope * x[i]) + yInt difference = (y[i] - predicted) * x[i] total += difference returnValue = (-2/length) * total return returnValue def DerivativeIntercept(x, y, slope, yInt): length = len(x) total = 0 for i in range(length): predicted = (slope * x[i]) + yInt difference = (y[i] - predicted) total += difference returnValue = (-2/length) * total return returnValue m = 0 c = 0 l = 0.000001 iterations = 99999 for i in range(iterations): slope = DerivativeSlope(x, y, m ,c) intercept = DerivativeIntercept(x, y, m ,c) m = m - (l * slope) c = c - (l * intercept) print (m, c)

""" @nexclap/AdamBlumenfeld """ # Imports import numpy as np from random import randint from matplotlib import pyplot as plt # Define Style Of Matplotlib Graphs plt.style.use("ggplot") # Define data X = np.array([1, 3, 5, 6, 8, 10, 11, 18, 19, 20, 24, 26, 30, 32, 36, 38, 39, 40, 43, 46, 52, 55, 56, 58, 59]) y = np.array([3, 4, 5, 7, 8, 9, 10, 12, 14, 15, 21, 36, 37, 38, 39, 40, 43, 46, 49, 51, 54, 56, 58, 60, 69]) # Plot data plt.scatter(X, y) plt.show() #Regressor Class class Regressor: # Training Function def fit(self, X, y, learning_rate=0.00001, converge=0.001, cst=False): # Cst is weather or not to make a history of cost for further analysis self.cst_b = cst if cst: self.cst = [[], []] # Dataset self.X = X self.y = y # Learning rate, or "a" in the gradient decent formula self.learning_rate = learning_rate # The M and B values in the hypothysis function self.theta = [0, 0] # Cost, which initialtes at infinity self.cost = float('inf') # The iterator of the gradient decent algorithm, mine is recursive (Lol, I just had to add that flex) self.gradient_decent_step(converge) # isub for theta, basically saying theta -= (whatever), only for practical reasons, I had to make it a seprete function def theta_isub(self, i, other): self.theta[i] -= other return self.theta[i] # Calculate and update (or store if cst is True) cost def _cost(self, iteration=None): # Cost function self.cost = (1/(2*len(X))*sum([(self.h(X[index]) - y[index])*X[index] for index in range(len(X))])**2) if self.cst_b: # Update cst self.cst[0].append(self.cost) self.cst[1].append(iteration) # Hypothesis function def h(self, x): # h_θ(x) = θ₁ + θ₀x (Yes, I know that in my hypothysis function is switched around) return x*self.theta[0] + self.theta[1] # Gradient decent iterator def gradient_decent_step(self, converge, iteration=1): # Base case: if the cost is less than the set convergence point than accept current theata values if self.cost <= converge: return None # Do one iteration of gradient decent self._step() # Compute cost self._cost(iteration) return self.gradient_decent_step(converge, iteration+1) # All the math of gradient decent, (Now you know why I made the theta_isub function) def _step(self): return [self.theta_isub(0, self.learning_rate * (1/len(X)*sum([(self.h(X[index]) - y[index])*X[index] for index in range(len(X))]))),self.theta_isub(1, self.learning_rate * (1/len(X)*sum([self.h(X[index]) - y[index] for index in range(len(X))])))] # Define a model model = Regressor() # Train model (With cst = True for graphing) model.fit(X, y, cst=True) # Get the theta (M and B values) and the cst variable (or history of cost to iterations) theta = model.theta cst = model.cst # Nerd plot stuff (Plot linear regression graph) x = np.linspace(0,60,100) y1 = theta[0]*x+theta[1] plt.title("Linear Regression") plt.scatter(X, y, c='teal') plt.plot(x, y1) #plt.savefig("linear_regression.png") (Saves graph to file) plt.show() # More nerd plot stuf (Plot cost graph (cst)) plt.title("Cost") plt.plot(cst[1], cst[0]) #plt.savefig("cost.png") (Saves graph to file) plt.show()

import numpy as np import pandas as pd import matplotlib.pyplot as plt x = [1, 5, 3, 4, 7, 9, 12, 13, 15, 16, 17, 4, 5, 2, 10, 23, 25] y = [5, 12, 23, 14, 17, 8, 20, 21, 25, 38, 42, 10, 13, 7, 23, 50, 55] plt.plot(x, y) plt.show() def slopeDerivative(x, y, slope, yInt): length = len(x) total = 0 for i in range(length): predicted = (slope * x[i]) + yInt difference = (y[i] - predicted) * x[i] total += difference returnValue = (-2/length) * total return returnValue def interceptDerivative(x, y, slope, yInt): length = len(x) total = 0 for i in range(length): predicted = (slope * x[i]) + yInt difference = (y[i] - predicted) total += difference returnValue = (-2/length) * total return returnValue m = 0 c = 0 l = 0.0001 iterations = 1000000 for i in range(iterations): derivativeSlope = slopeDerivative(x, y, m ,c) derivativeIntercept = interceptDerivative(x, y, m ,c) m = m - (l * derivativeSlope) c = c - (l * derivativeIntercept) print (m, c)

import math #set up the predone data outlook = ['sunny','sunny','overcast','rainy','rainy','rainy','overcast','sunny', 'sunny','rainy','sunny','overcast','overcast','rainy'] temperature = ['hot','hot','hot','mild','cool','cool','cool','mild','cool', 'mild','mild','mild','hot','mild'] humidity = ['high','high','high','high','normal','normal','normal','high', 'normal','normal','normal','high','normal','high'] windy = ['false','true','false','false','false','true','true','false','false', 'false','true','true','false','true'] play = ['no','no','yes','yes','yes','no','yes','no','yes','yes','yes','yes', 'yes','no'] print("Hi welcome to the Naive Bayes Algorithm which will help calculate if it will be a good day to golf!") print("This program has some preset data that will be inputted into the algorithm""\n") #need sunny,cool,high humidity,strong wind, and play yes yescount = 0 nocount = 0 #finds the amount of days you can play golf and bad days to play golf out the the amount of days tested def findyes(): no = 0 yes = 0 for x in range(len(play)): if play[x] == 'no': no += 1 else: yes += 1 global yescount yescount += yes global nocount nocount += no print("There were", yes ,"/", len(play),"days you could golf on and","There were", no,"/",len(play),"days you couldn't golf on") findyes() yeshumid = 0 yessunny = 0 yescool = 0 yeswind = 0 ''' This is a function that helps determine the amount of humid, sunny, cool, and windy days out of the days that it was good to gold ''' def yescalculator(): highhumidity = 0 sunny = 0 cool = 0 wind = 0 for x in range(len(play)): if play[x] == 'yes': if outlook[x] == 'sunny': sunny += 1 if temperature[x] == 'cool': cool += 1 if humidity[x] == 'high': highhumidity += 1 if windy[x] == 'true': wind += 1 global yeshumid yeshumid += highhumidity global yessunny yessunny += sunny global yescool yescool += cool global yeswind yeswind += wind nohumid = 0 nosunny = 0 nocool = 0 nowind = 0 ''' This function helps determine the amount of sunny, humid, cool, a windy days bad golf days ''' def nocalculator(): sunny = 0 cool = 0 wind = 0 highhumidity = 0 for x in range(len(play)): if play[x] == 'no': if outlook[x] == 'sunny': sunny += 1 if temperature[x] == 'cool': cool += 1 if humidity[x] == 'high': highhumidity += 1 if windy[x] == 'true': wind += 1 global nohumid nohumid += highhumidity global nosunny nosunny += sunny global nocool nocool += cool global nowind nowind += wind yescalculator() nocalculator() print("\n""Probability we can play the game:") print("Probability of it being a good golf day and sunny is" ,yessunny,"/",yescount) print("Probability of it being a good golf day and cool is" ,yescool,"/",yescount) print("Probability of it being a good golf day and humid is" ,yeshumid,"/",yescount) print("Probability of it being a good golf day and windy is" ,yeswind,"/",yescount,"\n") print("Probability we cannot play a game:") print("Probability of it being a stay home day and sunny is",nosunny,"/",nocount) print("Probability of it being a stay home day and sunny is",nocool,"/",nocount) print("Probability of it being a stay home day and sunny is",nohumid,"/",nocount) print("Probability of it being a stay home day and sunny is",nowind,"/",nocount) #calculate evidence P(x) of the equation which is denominator evidences = 0 def findevidence(): evidence = (((yeshumid + nohumid)/14)*((yessunny + nosunny)/14)*((yeswind+nowind)/14)*((yescool + nocool)/14)) global evidences evidences += evidence findevidence() ''' this function multiplies all the no probabilities and yes probabilities and compares to see if it will be a good day to golf or a good day to stay home and watch a movie ''' def compare(): yesequation = (((yessunny/yescount)*(yeshumid/yescount)*(yeswind/yescount)*(yescool/yescount)*(yescount/len(play)))/evidences) noequation = (((nosunny/nocount)*(nohumid/nocount)*(nowind/nocount)*(nocool/nocount)*(nocount/len(play)))/evidences) print("\n""The probability it will be a good day is", yesequation) print("The probability it will be a bad day is", noequation) if yesequation > noequation: print("\n""There is higher probability it will be a good day for golf.","Let's go play some!") else: print("\n""There is a higher probability it will be a bad day for golf.","Lets go watch a movie!") compare()