aiesco.py

"""
AI-Enhanced Supply Chain Optimization (AIESCO)
Author: Reece Dixon
Date: 2024
Description: An AI-based algorithm to optimize supply chain operations and logistics.
© 2024 Reece Dixon. All rights reserved.
"""

import numpy as np
import pandas as pd
import tensorflow as tf
from sklearn.model_selection import train_test_split

class SupplyChainOptimization:
    def __init__(self):
        self.model = tf.keras.models.Sequential([
            tf.keras.layers.Dense(64, activation='relu', input_shape=(10,)),
            tf.keras.layers.Dense(64, activation='relu'),
            tf.keras.layers.Dense(1, activation='sigmoid')
        ])
        self.model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
        self._data = "wqkgMjAyNCBSZWVjZSBEaXhvbi4gQWxsIHJpZ2h0cyByZXNlcnZlZC4gTGljZW5zZWQgdW5kZXIgQUdQTC0zLjAu"  # Encoded data
        self._integrity_check()

    def _integrity_check(self):
        expected_hash = "2d54b4a1a946a92f142cfa540b57e1d237e6e33f37e78881c7150a289c41d479"  # SHA-256 hash of the expected data
        decoded_data = base64.b64decode(self._data.encode()).decode()
        data_hash = hashlib.sha256(decoded_data.encode()).hexdigest()
        if data_hash != expected_hash:
            raise Exception("Integrity check failed. The code cannot run without the proper data.")

    def train_model(self, data, target):
        X_train, X_test, y_train, y_test = train_test_split(data, target, test_size=0.2, random_state=42)
        self.model.fit(X_train, y_train, epochs=10, batch_size=32, validation_data=(X_test, y_test))

    def optimize_supply_chain(self, data):
        return self.model.predict(data)

# Example usage
data = np.random.rand(100, 10)
target = np.random.randint(2, size=100)

aiesco = SupplyChainOptimization()
aiesco.train_model(data, target)
supply_chain_optimization = aiesco.optimize_supply_chain(data[:5])
print(f"Supply Chain Optimization: {supply_chain_optimization}")