help would be appreciated on this coding program. print('==> Bull Kelp and Purple Urchin Population Simulator <==')
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any help would be appreciated on this coding
print('==> Bull Kelp and Purple Urchin Population Simulator <==')
print('--- Model Parameters ---')
# Get parameters from user and error check each
print('--- Initial Population ---')
# Get populations at time t=0 from user
print('--- Simulation ---')
# Do simulation to get population at time t=1
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- How make simulation Aquarium Automatic Care (AOC) system (incl. coding) with Arduino. AOC is simulated using Tinkercad based on below conditions? Conditions: 1. Water must be replaced when -Water level (T) < 41 mm -Discard water and fill 30% of Aquarium water volume when high Acidic pH (pH> 9)2. Fish food (pellets): 1 x 4.5 mL/day at 6am in the morning, the machine only contains 100 mL (warning on D-day is over)3. To save electricity, the aquarium lights are on at night (8 hours)The world queen in the new post-apocalyptic world is extremely worried about the birth rate. She therefore orders that every family must have at least one daughter or else pay hefty fines. What will the gender ratio of the next generation be if all families adhere to this policy, which requires them to keep having children until they have one girl, at which point they immediately stop? (Presume that all pregnancies have an equal chance of producing a boy or a girl.) Write a computer simulation of this problem when you have logically resolved it.The language for coding must be in python Neural Network Units Implement a single sigmoid neural network unit with weights of [-1.2, -1.1, 3.3, -2.1] Calculate the outputs for two training examples:Example 1: [0.9, 10.0, 3.1, 1]Example 2: [0.9, 2.1, 3.7, 1] Note that you don't have to explicitly include a threshold or bias since the examples include a last element of 1 which means that the last weight effectively operates as a threshold. Assuming that a sigmoid unit response >0.5 denotes a positive class and <0.5 is negative class, is example 1 positive or negative? is example 2 positive or negative? Create a single ReLU unit and provide the outputs for those examples. Calculate the derivative of the sigmoid with respect to net input for both examples Calculate the derivative of the ReLU with respect to net input for both examples
- The language must be in python. Neural Network Units weights of [-1.2, -1.1, 3.3, -2.1] two training examples:Example 1: [0.9, 10.0, 3.1, 1]Example 2: [0.9, 2.1, 3.7, 1] Note that you don't have to explicitly include a threshold or bias since the examples include a last element of 1 which means that the last weight effectively operates as a threshold. Create a single ReLU unit and provide the outputs for those examples. Calculate the derivative of the sigmoid with respect to net input for both examples Calculate the derivative of the ReLU with respect to net input for both examplesIn the context of evolutionary computing the goal function is known as the fitnessfunction and the problem is to maximize it. The typical formulation has to be changedin a simple way.min f (x) = − max[− f (x)] (4.9)Another requirement is that the goal function is positive.Phenotype evolution treats x as a phenotype and the goal function as the fitnessfunction. The typical framework for the method is as follows:Write a MATLAB program that will generate a surface plot of the mass of a conic surface. Theconic surface is defined by ? = 2√?2 + ?2 for 0.5 ≤ z ≤ 4 and the mass is related to z by ? =6 − ? . The plot should have an appropriate title and axis labels.This problem is best solved using cylindrical polar coordinates (r, θ, z) rather than rectangularcoordinates (x, y, z) for the underlying grid. Use z and θ for creating the initial grid, a grid for rcan then be computed from the z and θ grids, grids for x and y can be computed from the r and θgrids, and m can be computed from z grid. Plot m versus and x and y for the surface plot.
- Although the plot function is designed primarily for plotting standard xy graphs, it can be adapted for other kinds of plotting as well. b. Make a plot of the curve, which is defined parametrically by the equations x = 2cosθ + cos2θ, y = 2sinθ - sin2θ, where 0 < θ < 2π. Take a set of values of θ between zero and 2π and calculate x and y for each from the equations above, then plot y as a function of x. b. Taking this approach a step further, one can make a polar plot r = f(θ) for some function f by calculating r for a range of values of θ and then converting r and θ to Cartesian coordinates using the standard equations x = r cosθ, y = r sinθ. Use this method to make a plot of the function r = ecosθ – 2 cos(4θ) + sin5 (θ/12) in the range 0 <= θ <= 24π. use python code to answer the highlight oneModeling and simulation question Read the document "Monte Carlo Example" and watch the two videos on Monte Carlo Simulation and solve the following problem: Write a program to implement the theoretical logic of Monte Carlo simulation to (1) estimate the value of π based on the area under the curve of the COS(x) defined on the interval [0, π/2]. (2) Test your program using: N = your ID ( for example, if your student's ID = 0564321, the Input will be N = 564321). Submit your program with the output after running the program. MY ID 0161231The language must be in python. The code for parts 1-4 is below. Just do parts 5-7 Neural Network Units Implement a single sigmoid neural network unit with weights of [-1.2, -1.1, 3.3, -2.1] Calculate the outputs for two training examples:Example 1: [0.9, 10.0, 3.1, 1]Example 2: [0.9, 2.1, 3.7, 1] Note that you don't have to explicitly include a threshold or bias since the examples include a last element of 1 which means that the last weight effectively operates as a threshold. Assuming that a sigmoid unit response >0.5 denotes a positive class and <0.5 is negative class, is example 1 positive or negative? is example 2 positive or negative? Create a single ReLU unit and provide the outputs for those examples. Calculate the derivative of the sigmoid with respect to net input for both examples Calculate the derivative of the ReLU with respect to net input for both examples import numpy as np import math def forward(x,w): t = np.dot(x.T,w) t = 1/(1+math.e**(-t))…
- The language must be in python. Neural Network Units two training examples:Example 1: [0.9, 10.0, 3.1, 1]Example 2: [0.9, 2.1, 3.7, 1] Note that you don't have to explicitly include a threshold or bias since the examples include a last element of 1 which means that the last weight effectively operates as a threshold. Create a single ReLU unit and provide the outputs for those examples. Calculate the derivative of the ReLU with respect to net input for both examplesAssume a very good NBA team has a 70% chance of winning in each game it plays. Use simulation to answer these questions, where each iteration of the simulation generates the outcomes of all 82 games. Use simulation with 10,000 iterations to answer the questions. For part a, write your numerical answer with no decimal or commas (e.g., if your computed answer is 101,021.128, write your answer as 101021). For part b, write your probability answer to two decimal places with a leading 0 (e.g., write a probability of 90.02% as 0.90). a. During an 82-game season what is the average length of the team's longest winning streak? The average length of the winning streak is . b. What is the probability that the team has a winning streak of at least 16 games? The probability isH1. Compute the value of an European Call option at time 0 for the following parameters: strike, K = 120, maturity 1 year, σ = 0.15, S0 = 120 and interest rate, r = 0.02: (a) by implementing, using Python, R, C++ or Matlab, a binomial model where n = 100, 1000, or 10000. For each value of n compute the error (difference between option value obtained using the B-S model and the one from the binomial model). Compute ∆0.