Generate 100 synthetic data points (x,y) as follows: x is uniform over [0,1]10 and y = P10 i=1 i ∗ xi + 0.1 ∗ N(0,1) where N(0,1) is the standard normal distribution. Implement full gradient descent and stochastic gradient descent, and test them on linear regression over the synthetic data points. Subject: Python Programming
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Generate 100 synthetic data points (x,y) as follows: x is uniform over [0,1]10 and y = P10 i=1 i ∗ xi + 0.1 ∗ N(0,1) where N(0,1) is the standard normal distribution. Implement full gradient descent and stochastic gradient descent, and test them on linear regression over the synthetic data points.
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- In Python, write a function that produces plots of statistical power versus sample size for simple linear regression. The function should be of the form LinRegPower(N,B,A,sd,nrep), where N is a vector/list of sample sizes, B is the true slope, A is the true intercept, sd is the true standard deviation of the residuals, and nrep is the number of simulation replicates. The function should conduct simulations and then produce a plot of statistical power versus the sample sizes in N for the hypothesis test of whether the slope is different than zero. B and A can be vectors/lists of equal length. In this case, the plot should have separate lines for each pair of A and B values (A[1] with B[1], A[2] with B[2], etc). The function should produce an informative error message if A and B are not the same length. It should also give an informative error message if N only has a single value. Demonstrate your function with some sample plots. Find some cases where power varies from close to zero to…Computer Science Implement a function in python that takes in parameters X (a set of data points) and k (number of neighbors to use / smoothing amount) to execute kernel density estimation using k-nn regression, and plot the resultant estimation and real densities. You may use any libraries you want (eg. numpy, matplotlib, sklearn, etc)In python 3 We all know that when the temperature of a metal increases, it begins to expand. So,we experimented with exposing a metal rod to different temperatures and recorded itslength as follows:Temp 20 25 30 35 40 45 50 55 60 65Length 0.5 1.8 5 6 6.2 6.5 7.8 9.4 9.8 10.9 Now do these requirments: 1) Implement and plot a simple linear regression for the above data, where the temperature is “x”, and the length is “y” 2) Implement and plot a multiple linear regression "Polynomial regression" with different degrees.For example, Degree of 3:Y = w1x1 + w2x2 + w3x3 + w4Where w4 represents bias.*you can use a normal equation to calculate ‘W’ as follow:W = (XT.X)-1.(XT.Y)Then calculate Y, Where Y = X.WT 3) Try degrees of 2, 3, 5, and 8
- Write a python code that implements the Forward Euler method to solve thedifferential equation. The slope function depends on the unknown solution y(t). Define your slope function so that the model parameters, b, PM, h areinput variables in your function definition. Complete your code by writing a loop that calculates the solution foreach time point and can plot your final approximate solution.Consider the problem of making change for n cents using the fewest number of coins. Assume that we live in a country where coins come in k dierent denominations c1, c2, . . . , ck, such that the coin values are positive integers, k ≥ 1, and c1 = 1, i.e., there are pennies, so there is a solution for every value of n. For example, in case of the US coins, k = 4, c1 = 1, c2 = 5, c3 = 10, c4 = 25, i.e., there are pennies, nickels, dimes, and quarters. To give optimal change in the US for n cents, it is sufficient to pick as many quarters as possible, then as many dimes as possible, then as many nickels as possible, and nally give the rest in pennies. Design a bottom-up (non-recursive) O(nk)-time algorithm that makes change for any set of k different coin denominations. Write down the pseudocode and analyze its running time. Argue why your choice of the array and the order in which you fill in the values is the correct one. Notice how it is a lot easier to analyze the running time of…Consider the problem of making change for n cents using the fewest number of coins. Assume that we live in a country where coins come in k dierent denominations c1, c2, . . . , ck, such that the coin values are positive integers, k ≥ 1, and c1 = 1, i.e., there are pennies, so there is a solution for every value of n. For example, in case of the US coins, k = 4, c1 = 1, c2 = 5, c3 = 10, c4 = 25, i.e., there are pennies, nickels, dimes, and quarters. To give optimal change in the US for n cents, it is sufficient to pick as many quarters as possible, then as many dimes as possible, then as many nickels as possible, and nally give the rest in pennies. Design a bottom-up (non-recursive) O(nk)-time algorithm that makes change for any set of k different coin denominations. Write down the pseudocode and analyze its running time. Argue why your choice of the array and the order in which you ll in the values is the correct one.
- Consider the problem of making change for n cents using the fewest number of coins. Assume that we live in a country where coins come in k dierent denominations c1, c2, . . . , ck, such that the coin values are positive integers, k ≥ 1, and c1 = 1, i.e., there are pennies, so there is a solution for every value of n. For example, in case of the US coins, k = 4, c1 = 1, c2 = 5, c3 = 10, c4 = 25, i.e., there are pennies, nickels, dimes, and quarters. To give optimal change in the US for n cents, it is sufficient to pick as many quarters as possible, then as many dimes as possible, then as many nickels as possible, and nally give the rest in pennies. Prove that the coin changing problem exhibits optimal substructure. Design a recursive backtracking (brute-force) algorithm that returns the minimum number of coins needed to make change for n cents for any set of k different coin denominations. Write down the pseudocode and prove that your algorithm is correct.In R, write a function that produces plots of statistical power versus sample size for simple linear regression. The function should be of the form LinRegPower(N,B,A,sd,nrep), where N is a vector/list of sample sizes, B is the true slope, A is the true intercept, sd is the true standard deviation of the residuals, and nrep is the number of simulation replicates. The function should conduct simulations and then produce a plot of statistical power versus the sample sizes in N for the hypothesis test of whether the slope is different than zero. B and A can be vectors/lists of equal length. In this case, the plot should have separate lines for each pair of A and B values (A[1] with B[1], A[2] with B[2], etc). The function should produce an informative error message if A and B are not the same length. It should also give an informative error message if N only has a single value. Demonstrate your function with some sample plots. Find some cases where power varies from close to zero to near…For the Linear Congruential method, assume the following parameters:Xo = 23,947, a = 2,902, c = 15,853 and M = 491.Feeling free to state any assumptions you deem necessary, define the first 10 random numbers.
- Imagine there are N teams competing in a tournament, and that each team plays each of the other teams once. If a tournament were to take place, it should be demonstrated (using an example) that every team would lose to at least one other team in the tournament.Implement an algorithmic solution, indicating which states are valid and which are not, and model the space of the following problem: An interest group from a small town decided to sue a company for commercial abuse. For this, the people have organized themselves and decided to send 3 representatives, who will have to travel in a Van to the city where the lawsuit will be filed. The company to be sued, upon learning of these actions, has decided to send 3 lawyers to persuade the representatives, who will also travel in the same Van for that purpose. The community must file the class action suit under these conditions: - The three applicants must reach the destination city; - Only two people can travel per trip in the Van (small town - city, city - small town); - There can never be more lawyers than plaintiffs in any one place (either in the small town or city) because the lawyers can persuade the plaintiffs and as a consequence, the lawsuit would not be made; - The Van cannot be…(control variates) Reproduce the class example of estimating int 0 ^ 1 2 dz 1+x by the MC approach using 100 uniform random variables and after that by using a control variate with function g(U) = 1 + U as suggested in class. Compare the results.