AtomV0-fullsample code 9.txt

#9

# atomv0-fullsample: Continuous Learning and Adaptation

import numpy as np
from sklearn.linear_model import SGDRegressor
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler

class ContinuousLearning:
    def __init__(self):
        self.scaler = StandardScaler()
        self.model = SGDRegressor()

    def prepare_data(self, task_data):
        features = []
        target = []

        for task in task_data:
            # Extract task features and priority
            features.append([task["deadline"], task["dependencies"], task["status"]])
            target.append(task["priority"])

        # Normalize the features
        features = self.scaler.fit_transform(features)

        return features, target

    def train(self, task_data):
        features, target = self.prepare_data(task_data)
        X_train, X_test, y_train, y_test = train_test_split(features, target, test_size=0.2)

        # Train the model and check the score on test data
        self.model.fit(X_train, y_train)
        score = self.model.score(X_test, y_test)
        print("Model score:", score)

    def predict_priority(self, task):
        features = [task["deadline"], task["dependencies"], task["status"]]
        scaled_features = self.scaler.transform([features])
        predicted_priority = self.model.predict(scaled_features)[0]
        return predicted_priority

def main():
    task_data = [
        {"deadline": 5, "dependencies": 1, "status": 0, "priority": 3},
        {"deadline": 3, "dependencies": 2, "status": 1, "priority": 4},
        {"deadline": 1, "dependencies": 0, "status": 2, "priority": 2},
        # ...
    ]

    continuous_learning = ContinuousLearning()
    continuous_learning.train(task_data)

    new_task = {"deadline": 4, "dependencies": 1, "status": 0}
    predicted_priority = continuous_learning.predict_priority(new_task)
    print("Predicted priority:", predicted_priority)

if __name__ == '__main__':
    main()

In this sample code, I've implemented a ContinuousLearning class that uses machine learning techniques to improve task prioritization over time. The class uses a stochastic gradient descent (SGD) regressor from the scikit-learn library to predict task priorities based on features such as deadline, dependencies, and status.

The prepare_data method extracts features and targets from the task data and normalizes the features using a StandardScaler. The train method trains the SGD regressor on a split of the task data, testing its performance on a separate test set.

The predict_priority method takes a new task and predicts its priority based on the trained model. This can be used to refine task prioritization, resource allocation, and decision-making strategies over time, as more task data becomes available and the model learns from the data.

The main function demonstrates how to use the ContinuousLearning class to train the model on task data and predict the priority of a new task. This demonstrates the integration of continuous learning and adaptation mechanisms into the ATOM Framework.