01 hotova

hod_1/data/numpy_entropy.py

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+#!/usr/bin/env python3
+import argparse
+
+import numpy as np
+
+parser = argparse.ArgumentParser()
+# These arguments will be set appropriately by ReCodEx, even if you change them.
+parser.add_argument("--data_path", default="numpy_entropy_data.txt", type=str, help="Data distribution path.")
+parser.add_argument("--model_path", default="numpy_entropy_model.txt", type=str, help="Model distribution path.")
+parser.add_argument("--recodex", default=False, action="store_true", help="Evaluation in ReCodEx.")
+# If you add more arguments, ReCodEx will keep them with your default values.
+
+
+def main(args: argparse.Namespace) -> tuple[float, float, float]:
+    # TODO: Load data distribution, each line containing a datapoint -- a string.
+    with open(args.data_path, "r") as data:
+        for line in data:
+            line = line.rstrip("\n")
+            # TODO: Process the line, aggregating data with built-in Python
+            # data structures (not NumPy, which is not suitable for incremental
+            # addition and string mapping).
+
+    # TODO: Create a NumPy array containing the data distribution. The
+    # NumPy array should contain only data, not any mapping. Alternatively,
+    # the NumPy array might be created after loading the model distribution.
+
+    # TODO: Load model distribution, each line `string \t probability`.
+    with open(args.model_path, "r") as model:
+        for line in model:
+            line = line.rstrip("\n")
+            # TODO: Process the line, aggregating using Python data structures.
+
+    # TODO: Create a NumPy array containing the model distribution.
+
+    # TODO: Compute the entropy H(data distribution). You should not use
+    # manual for/while cycles, but instead use the fact that most NumPy methods
+    # operate on all elements (for example `*` is vector element-wise multiplication).
+    entropy = ...
+
+    # TODO: Compute cross-entropy H(data distribution, model distribution).
+    # When some data distribution elements are missing in the model distribution,
+    # the resulting crossentropy should be `np.inf`.
+    crossentropy = ...
+
+    # TODO: Compute KL-divergence D_KL(data distribution, model_distribution),
+    # again using `np.inf` when needed.
+    kl_divergence = ...
+
+    # Return the computed values for ReCodEx to validate.
+    return entropy, crossentropy, kl_divergence
+
+
+if __name__ == "__main__":
+    main_args = parser.parse_args([] if "__file__" not in globals() else None)
+    entropy, crossentropy, kl_divergence = main(main_args)
+    print(f"Entropy: {entropy:.2f} nats")
+    print(f"Crossentropy: {crossentropy:.2f} nats")
+    print(f"KL divergence: {kl_divergence:.2f} nats")