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import numpy as np
import matplotlib.pyplot as plt
def generate_points(initial_value, final_value, smoothness=0.1, max_num = 200):
x = np.array([_ for _ in range(max_num)])
y = initial_value + ( final_value-initial_value) * (x/200)**smoothness
noise = np.random.normal(0, 0.01, max_num)
y += noise
return x, y
def generate_line_plots(data, num_curves, legends, x_label, y_label, save_to = "fig.png" ):
plt.figure()
for i in range(num_curves):
x, y = data[i]
plt.plot(x , y, label=legends[i])
plt.xlabel(x_label)
plt.ylabel(y_label)
plt.legend()
plt.savefig(save_to)
def generate_random_figures(list_of_methods, save_to = "fig.png" ):
num_curves = len(list_of_methods) + 1
ini_value = [np.random.uniform(1, 2)] * num_curves
final_value = sorted([0.1 + np.random.normal(0,0.1) for _ in range(num_curves)])
legends = ["Ours"] + list_of_methods
x_label = "# of Epochs"
y_label = "Loss"
all_data = []
for i in range(num_curves):
all_data.append(generate_points(ini_value[i], final_value[i]))
generate_line_plots(all_data, num_curves, legends, x_label, y_label, save_to)
if __name__ == "__main__":
num_curves = 3
legends = ["method 1", "method 2"]
x_label = "# of epochs"
y_label = "loss"
ini_value = [1.5, 1.5, 1.5]
final_value = [0.01, 0.05, 0.10]
generate_random_figures(legends, save_to="fig1.png")
generate_random_figures(legends, save_to="fig2.png")
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