```python
import numpy as np
import math
from random import random
import matplotlib.pyplot as plt
import sympy
from random import seed
import math
```

# Question 1 (2 points)
Use online, library and other resources to answer the following questions. You can use Internet references, library resources, your prior knowledge, and any other resource. For each question, provide references that you used.

Provide two applications in computer science for each of the following methodologies.

a. Monte Carlo Simulation\
b. Markov Decision Process\
c. Random Walks\
d. Resampling method

## Random Walks
The Random Walks theory applying in the investment. According to random walk theory, market price variations have the same distribution and are unrelated. As a result, it is concluded that a curent stock price and past movement cannot be used to forecast its future movement. In light of this, random walk theory states that stocks follow an unpredictable and random direction, rendering all methods of stock price prediction useless in the long run. Thus, we consider using random walks model to predict the stock price in the furture.

Reference: Phung, A. (2020, August 28). How can random walk theory be applied to investing https://www.investopedia.com/ask/answers/08/random-walk-theory.asp.

## Monte Carlo Simulation
According to this article, Monte Carlo simulation improves project management. Graves (2001) spoke about the various forms of probability distributions that can be used to estimate project durations. The merit of Monte Carlo Simulation model is that it can simplify time management and cost management, and that the PM team can know the consequences of the project without having a huge cost. They will be able to predict project schedules and budgets that are more practical.

Reference: Kwak, Y., Ingall, L. Exploring Monte Carlo Simulation Applications for Project Management. *Risk Manag 9, 44*–57 (2007). https://doi.org/10.1057/palgrave.rm.8250017

# Question 2 (4 points)
A gambler has the chance to enter a gamble using a fair coin. **The gambler needs to invest $100 to enter the game.** If the coin is tossed a head (p = 0.5), the gambler wins one dollar otherwise losses one dollar. The game stops after N=100 trials (100 times tossing the coin).

a) Model this problem and run a simulation model to determine if the gambler is better off getting into this game. Repeat the simulation when the number of trials N = 1000.

Because the gambler needs to pay \$100 to join the game at least, so we set the first round if the gambler win, the full balance will be \$101. Conversely, if the gambler lose, the full balance will be \$99.

According to the question, the minimum rounds to play this game is 100 trals. After we run 100 times, the full balance is \$106. If we run 1000 times, the full balance is \$154. Therefore, I highly suggest gambler join the game as soon as possible(p = 0.5).

```python
def gamblerCoin(N, p):
  if N < 100:
    print("You need to invest $100 to enter the game")
    return
  seed(1)
  res = []
  res.append(99 if random() < (1-p) else 101) # first time tossing a coin, and needs to invest $100 at the beginning
  for i in range(1, N):
    tossed = -1 if random() < (1-p) else 1
    tmp = res[i-1] + tossed # previous result + new result
    res.append(tmp)
  plt.plot(res)
  plt.xlabel("trials")
  plt.ylabel("profits")
  plt.show()
  return res[-1]

gamblerCoin(100, 0.5)
```

```python
gamblerCoin(1000, 0.5)
```

b) If the dealer uses an unfair coin (p = 0.49), does the gambler enters the game using N=100? Repeat the game when the number of trails N =1000.

- Compared to p = 0.5, the gambler will earn less money if p = 0.49
- N = 100 does not mean gambler will lose the money (the full balance is still 100)
- After the model run 1000 trials, the full balance is \$128. It's not caused a profunded effect for gambler

```python
gamblerCoin(100, 0.49)
```

```python
gamblerCoin(1000, 0.49)
```

Now, the gambler is given a chance to exit the game only if the remaining dollar in the middle of the game is \$100 (for example the gambler can exit when 25 tosses left and he owns \$100). Let us call this situation State100.

c) Model this problem and run a simulation model to estimate the number of times the gambler can exit the game, when N= 100 and N=1000.
 - N = 100, 7 times
 - N = 1000, 44 times
 - The default seed is `1`

```python
def stateOneHundred(N, p):
  State100 = 100
  seed(1)
  count = 0
  res = []
  res.append(99 if random() < (1-p) else 101) # first time tossing a coin, and needs to invest $100 at the beginning
  for i in range(1, N):
    tossed = -1 if random() < (1-p) else 1
    tmp = res[i-1] + tossed # previous result + new result
    res.append(tmp)
    if tmp == State100:
      count += 1
  return count

print(stateOneHundred(100, 0.5))
print(stateOneHundred(1000, 0.5))
```

    7
    44

d) Is there any difference between different State100s? For example, do you see the gambler makes different decision when there are 20 tossing coin left compared with 2 tossing coin left.

Yes. for example, if 20 tossing coins left maybe the gambler is able to earn \$20. However the gambler will also have a chance to lose \$20 correspondingly. On the other hand, if the situation is 2 tossing coins left, the gambler only has a chance to earn \$2 or lose \$2 at most. Thus, the expected values are different, meaning that the gambler has a different choice to get the final result.

# Question 3 (5 points)
We want to simulate a dollar slot machine that has three reels (spinning cylinders). To play the game, we first insert \$1 and pull the handle. All three reels then spin independently, and each reel will stop randomly on one of the five pictures it contains. Once the reels stop, we examine the three pictures and determine our winnings. If we have exactly 2 cherries, we win \$1 (so we break even). If we have 3 bells, we win \$2. If we have 3 cherries, we win \$3. For all other outcomes, we win nothing. The composition of each reel is summarized below.

a. Provide details for a strategy to determine what picture each reel stops on, given a U (0,1) number. Detailed strategy means that you need to divide the uniform variable range U(0,1) into sub-intervals and assign pictures. For example, if the random variable is between 0 and x (x is between 0 and 1) then it is a cherry, etc.

---
b. Complete the following table to simulate 5 replications of this system. What is the average profit over these five games?
- The average profit over these five games is negative 0.4.

![3b.png](data:image/png;base64,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

c. Assume that the gambler starts with \$100. Write a Python code to determine the expected value of the player after 50 rounds of the game.
- The `n` parameter specifies the number of rounds in the game, while the `profits` parameter specifies how much the gambler wants to start with. The result then shows your how much money do you have after game. The probability of a slot machine is used to create this model.
- please run the model to see the expected value below.

```python
def slotMachine(n, profits):
  if profits <= 0:
    print("You cannot play the game") # must have money to enter the game
    return
  reelOne, reelTwo, reelThree = [], [], [] # three reels
  first = np.random.uniform(0, 1, n) # random selected between 0 to 1
  # set the probability for each picture
  for i in first:
    if i <= 0.4:
      reelOne.append("Lemon")
    elif i > 0.4 and i <= 0.6:
      reelOne.append("Bell")
    else:
      reelOne.append("Cherry")
  second = np.random.uniform(0, 1, n) # random selected between 0 to 1
  for i in second:
    if i <= 0.6:
      reelTwo.append("Lemon")
    elif i > 0.6 and i <= 0.8:
      reelTwo.append("Bell")
    else:
      reelTwo.append("Cherry")
  third = np.random.uniform(0, 1, n) # random selected between 0 to 1
  for i in third:
    if i <= 0.4:
      reelThree.append("Lemon")
    elif i > 0.4 and i <= 0.8:
      reelThree.append("Bell")
    else:
      reelThree.append("Cherry")
  zipped = zip(reelOne, reelTwo, reelThree) # zipped the results
  rounds = list(zipped)

  values = 0 # earn or lose the money per time (insert $1 for play game so needs minus 1 each time)
  for i in range(len(rounds)):
    if rounds[i][0] and rounds[i][1] and rounds[i][2] == "Cherry":
      values += 3
      values -= 1
    elif rounds[i][0] and rounds[i][1] or rounds[i][0] and rounds[i][2] or rounds[i][1] and rounds[i][2] == "Cherry":
      values += 1
      values -= 1
    elif rounds[i][0] and rounds[i][1] and rounds[i][2] == "Bell":
      values += 2
      values -= 1
    else:
      values += 0
      values -= 1
  return values+profits # net profits + your money

slotMachine(50, 100)
```

    120

# Question 4 (5 points)
Consider the integral
a. What distribution function this probability distribution function represents? Use Normal distribution table to determine the “exact” value of I? (Show your work to receive credit. I’m not testing whether your calculator can integrate this numerically!)

b. Evaluate I using Monte Carlo integration using a Python code and U(0,1) random numbers. (Provide the details of the Python code.)

- The blue points that is under the curve.
- The yellow points that is above the function.

- Reference: https://stackoverflow.com/questions/30030659/in-python-what-is-the-difference-between-random-uniform-and-random-random

```python
import random
import numpy as np
import matplotlib.pyplot as plt

random.seed(2)
f = lambda x:(1/math.sqrt(2*np.pi))*np.exp(-x**2/2) # equation
a = 1
b = 1.5
NumSteps = 1000000
XIntegral=[]
YIntegral=[]
XRectangle=[]
YRectangle=[]

ymin = f(a)
ymax = ymin
# extract min and max without repeated min/max
for i in range(NumSteps):
    x = a + (b - a) * float(i) / NumSteps # calculate the rectangle area
    y = f(x)
    if y < ymin: ymin = y
    if y > ymax: ymax = y

# Monte Carlo method
A = (b - a) * (ymax - ymin)
N = 1000000
M = 0
for k in range(N):
    x = a + (b - a) * random.uniform(0, 1)
    y = ymin + (ymax - ymin) * random.uniform(0, 1)
    if y <= f(x):
            M += 1
            XIntegral.append(x)
            YIntegral.append(y)
    else:
            XRectangle.append(x)
            YRectangle.append(y)
NumericalIntegral = M / N * A
print ("Numerical integration I = " + str(NumericalIntegral))

XLin=np.linspace(a,b)
YLin=[]
for x in XLin:
    YLin.append(f(x))

# plt.axis   ([a, b, 0, f(b)])
plt.plot   (XLin,YLin, color="red" , linewidth="4")
plt.scatter(XIntegral, YIntegral, color="blue", marker   =".") # visual the points
plt.scatter(XRectangle, YRectangle, color="yellow", marker   =".") # visual the points
plt.title  ("Numerical Integration using Monte Carlo method")
plt.show()
```

# Question 5 (4 points)
Using the Gradient Descent algorithm and the Newton-Raphson method in Python to calculate the maximum/minimum value of

$$
𝑦 = 3𝑥^4 − 5𝑥+2
$$
Use Python codes to determine the possible local maximum/minimum of this function.

- According to the plot, we need to find the **minimum value** of the equation.

```python
def gradientDescent(Xvalue):
  x = np.linspace(0, 1.2, 100)
  y= 3*x**4-5*x+2

  # visualize the equation in order to confirm we need to find the maximum or the minimum value
  fig = plt.figure()
  axdef = fig.add_subplot(1, 1, 1)
  axdef.spines['left'].set_position('center')
  axdef.spines['bottom'].set_position('zero')
  axdef.spines['right'].set_color('none')
  axdef.spines['top'].set_color('none')
  axdef.xaxis.set_ticks_position('bottom')
  axdef.yaxis.set_ticks_position('left')
  plt.plot(x,y, 'r')

  # according to the plot, we need to find the minimum value of the equation.

  Gradf = lambda x: 12*x**3-5  # define the gradient function (The derivative of y equation)

  ActualX = Xvalue
  LearningRate =  0.01 # learn rate variable, 0.01 not too many iterations and can keep the optimal solution
  PrecisionValue = 0.000001 # defines the degree of precision
  PreviousStepSize = 1
  MaxIteration = 10000 # will be used to stop the procedure if it does not converge
  IterationCounter = 0 # counter

  xValues = []
  while PreviousStepSize > PrecisionValue and IterationCounter < MaxIteration: # Define the gradient descent
      PreviousX = ActualX
      ActualX = ActualX - LearningRate * Gradf(PreviousX) # find the max
      xValues.append(ActualX)
      PreviousStepSize = abs(ActualX - PreviousX)
      IterationCounter = IterationCounter+1
      # print("Number of iterations = ",IterationCounter,"\nActual value of x  is = ", ActualX)

  # draw the maximum point of the function
  plt.scatter(ActualX, 3*ActualX**4-5*ActualX+2)
  plt.title("The optimal coordinate")
  plt.show()

  print('The last one X values of f(x) minimum is', xValues[-1])
  xValues = np.array(xValues) # change the array type to numpt array in order to compute smoothly
  y = (3*xValues**4)-5*xValues+2
  print("The minimum value of y in the iterations is", y[-1])

gradientDescent(1.2) # based on the plot, the far-right point value is 1.2, so let we start with 1.2
```

```python
def NewtonRaphsonMethod(xValue):
  x = np.linspace(0, 1.2, num = 100) # creates numerical sequences(start, stop, num: Number of samples to generate. Default is 50. Must be non-negative.)
  y = 3*x**4-5*x+2

  fig = plt.figure()
  axdef = fig.add_subplot(1, 1, 1)
  axdef.spines['left'].set_position('center')
  axdef.spines['bottom'].set_position('zero')
  axdef.spines['right'].set_color('none')
  axdef.spines['top'].set_color('none')
  axdef.xaxis.set_ticks_position('bottom')
  axdef.yaxis.set_ticks_position('left')

  # print('Value of x at the minimum of the function', x[np.argmin(y)]) # np.argmin(): returns the indices of the minimum values along an axis

  plt.plot(x,y, 'r')

  FirstDerivative = lambda x: 12*x**3-5
  SecondDerivative = lambda x: 36*x**2

  ActualX = xValue
  PrecisionValue = 0.000001
  PreviousStepSize = 1
  MaxIteration = 10000
  IterationCounter = 0
  points = []
  while PreviousStepSize > PrecisionValue and IterationCounter < MaxIteration:
      PreviousX = ActualX
      ActualX = ActualX - FirstDerivative(PreviousX)/ SecondDerivative(PreviousX) # actual value - first derivative/ second derivative
      points.append(ActualX) # the iteration add in array
      PreviousStepSize = abs(ActualX - PreviousX) # diff between current and previous
      IterationCounter = IterationCounter+1
      # print("Number of iterations = ",IterationCounter,"\nActual value of x  is = ",ActualX)

  # draw the maximum point of the function
  plt.scatter(ActualX, 3*ActualX**4-5*ActualX+2)
  plt.title("The optimal coordinate")
  plt.show()
  print("X value of f(x) minimum is ", ActualX)
  print("The minimum value of y is", 3*ActualX**4-5*ActualX+2)

NewtonRaphsonMethod(1.2)

```

```python
!apt-get install texlive texlive-xetex texlive-latex-extra pandoc
!pip install pypandoc
from google.colab import drive
drive.mount('/content/drive')
!cp drive/My Drive/Colab Notebooks/test.ipynb ./
!jupyter nbconvert --to PDF "mynotebook.ipynb"
```

```python
"""
from google.colab import drive
drive.mount('/content/drive')
!cp drive/My Drive/Colab Notebooks/Untitled.ipynb ./
!jupyter nbconvert --to pdf fileName.ipynb
"""
```