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Plotting • Plot the generated data points, the true line y = ax + ẞ, and the fitted line y [17]: def plot_fit(n): alpha beta = = np.random. uniform (1.0,5.0) np.random.uniform (0.0, 10.0) x, y = generate_data(n) slope = find_optimal_slope (x, y) intercept = find_optimal_intercept(x,y) fitted line = slope x + intercept true_line = alphax beta plot_fit (50) = wxwo on the same plot. ← 古早 G

Plotting • Plot the generated data points, the true line y = ax + ẞ, and the fitted line y [17]: def plot_fit(n): alpha beta = = np.random. uniform (1.0,5.0) np.random.uniform (0.0, 10.0) x, y = generate_data(n) slope = find_optimal_slope (x, y) intercept = find_optimal_intercept(x,y) fitted line = slope x + intercept true_line = alphax beta plot_fit (50) = wxwo on the same plot. ← 古早 G

Operations Research : Applications and Algorithms
Operations Research : Applications and Algorithms
4th Edition
ISBN:9780534380588
Author:Wayne L. Winston
Publisher:Wayne L. Winston
Chapter20: Queuing Theory
Section20.2: Modeling Arrival And Service Processes
Problem 3P
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alpha = np.random.uniform(1.0,5.0)
beta = np.random.uniform(0.0,10.0)

def generate_data(n):
    x = np.random.uniform(-10, 10) 
    epsilon = np.random.uniform(-n,n)
    y = alpha * x + beta + epsilon
    return x,y

def find_optimal_slope(x,y):
    x_mean = np.mean(x)
    y_mean = np.mean(y)
    numerator = np.sum((x - x_mean) * (y - y_mean))
    denominator = np.sum((x - x_mean)**2)
    if denominator == 0:
        slope = 9999999  
    else:
        slope = numerator / denominator
    return slope
def find_optimal_intercept(x,y):
    slope = find_optimal_slope(x, y)  
    x_mean = np.mean(x)
    y_mean = np.mean(y)
    intercept = y_mean - slope * x_mean
    return intercept

Plotting • Plot the generated data points, the true line y = ax + ẞ, and the fitted line y [17]: def plot_fit(n): alpha beta = = np.random. uniform (1.0,5.0) np.random.uniform (0.0, 10.0) x, y = generate_data(n) slope = find_optimal_slope (x, y) intercept = find_optimal_intercept(x,y) fitted line = slope x + intercept true_line = alphax beta plot_fit (50) = wxwo on the same plot. ← 古早 G
Transcribed Image Text:Plotting • Plot the generated data points, the true line y = ax + ẞ, and the fitted line y [17]: def plot_fit(n): alpha beta = = np.random. uniform (1.0,5.0) np.random.uniform (0.0, 10.0) x, y = generate_data(n) slope = find_optimal_slope (x, y) intercept = find_optimal_intercept(x,y) fitted line = slope x + intercept true_line = alphax beta plot_fit (50) = wxwo on the same plot. ← 古早 G
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