initial commit

app.py

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+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)