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d347290 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 | import gradio as gr
import logging
from gradio_log import Log
from numpy import asarray
from numpy import exp
from numpy.random import randn
from numpy.random import rand
from numpy.random import seed
from matplotlib import pyplot
# setup logging
LOG_FILE = "sim-ann.log"
logging.basicConfig(
filename=LOG_FILE,
level=logging.INFO,
format="%(levelname)s: %(asctime)s %(message)s",
datefmt="%m/%d/%Y %I:%M:%S",
)
# clear the log file
with open(LOG_FILE, "w"):
pass
# Define the objective function
def objective(x):
return x[0] ** 2.0
# Simulated Annealing algorithm
def simulated_annealing(objective, bounds, n_iterations, step_size, temp):
# Generate an initial point
best = bounds[:, 0] + rand(len(bounds)) * (bounds[:, 1] - bounds[:, 0])
# Evaluate the initial point
best_eval = objective(best)
# Current working solution
curr, curr_eval = best, best_eval
scores = list()
# Run the algorithm
for i in range(n_iterations):
# Take a step
candidate = curr + randn(len(bounds))
candidate = curr + randn(len(bounds)) * step_size
# Evaluate the candidate point
candidate_eval = objective(candidate)
# Check for a new best solution
if candidate_eval < best_eval:
# Store the new best point
best, best_eval = candidate, candidate_eval
# Keep track of scores
scores.append(best_eval)
# Report progress
logging.info(">%d f(%s) = %.10f" % (i, best, best_eval))
# Difference between candidate and current point evaluation
diff = candidate_eval - curr_eval
# Calculate temperature for the current epoch
t = temp / float(i + 1)
# Calculate Metropolis acceptance criterion
metropolis = exp(-diff / t)
# Check if we should keep the new point
if diff < 0 or rand() < metropolis:
# Store the new current point
curr, curr_eval = candidate, candidate_eval
return [best, best_eval, scores]
def run(s=1, l_bound=-0.5, u_bound=0.5, n_iterations=1000, step_size=0.1, temp=10):
# Seed the pseudorandom number generator
seed(s)
# Define the range for input
bounds = asarray([[l_bound, u_bound]])
# Perform the simulated annealing search
best, score, scores = simulated_annealing(
objective, bounds, n_iterations, step_size, temp
)
logging.info("Done!")
logging.info("f(%s) = %.10f" % (best, score))
with gr.Blocks(title="Simulated Annealing") as demo:
title = gr.Markdown("## Simulated Annealing")
with gr.Row():
with gr.Column():
s = gr.Number(label="Seed", value=1)
l_bound = gr.Number(label="Lower Bound", value=-0.5)
u_bound = gr.Number(label="Upper Bound", value=0.5)
with gr.Column():
n_iterations = gr.Number(label="# Iterations", value=1000)
step_size = gr.Number(label="Step Size", value=0.1)
temp = gr.Number(label="Temperature", value=10)
# output = gr.Textbox()
with gr.Row():
btn = gr.Button("Run")
btn.click(
fn=run,
inputs=[s, l_bound, u_bound, n_iterations, step_size, temp],
# outputs=[output],
)
Log(LOG_FILE, dark=True, xterm_font_size=12)
if __name__ == "__main__":
demo.launch(share=True)
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